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Multipath communication has recently arisen as a promising tool to address reliable communication in vehicular networks. The architecture of Multipath TCP (MPTCP) is designed to facilitate concurrent utilization of multiple network interfaces, thereby enabling the system to optimize network throughput. In the context of vehicular environments, MPTCP offers a promising solution for seamless roaming, as it enables the system to maintain a stable connection by switching between available network interfaces. This paper investigates the suitability of MPTCP to support resilient and efficient Vehicleto-Infrastructure (V2I) communication over heterogeneous networks. First, we identify and discuss several challenges that arise in heterogeneous vehicular networks, including issues such as Head-of-Line (HoL) blocking and service interruptions during handover events. Then, we propose a cross-layer path management scheme for MPTCP, that leverages real-time network information to improve the reliability and efficiency of multipath vehicular communication. Our emulation results demonstrate that the proposed scheme not only achieves seamless mobility across heterogeneous networks but also significantly reduces handover latency, packet loss, and out-of-order packet delivery. These improvements have a direct impact on the quality of experience for vehicular users, as they lead to lower application layer delay and higher throughput.

5G standard by 3GGP has provision for Device-to-Device (D2D) communication over a Wireless Mobile Ad-hoc Network (MANET) framework. Recent research and development in 6G Networks and beyond has support for integration of MANET to the Mobile communication infrastructure to cater services to many potential future-proof applications like Smart Vehicular Networks using Internet of Vehicles (IoV) and Flying Ad-hoc Networks (FANET) for drone networks and military Ad-hoc networks. MANET being an Ad-hoc and infrastructure-less network poses major deployment challenge in resource allocation and management to facilitate strict Quality of Service (QoS) requirements for future Internet of Things (IoT) based Next-generation Cyber Physical System (NG-CPS). Most of MANETs use Contention-based Distributed Coordination Function (DCF) random Access Media Access Control (MAC) and a generic TCP/IP stack implementation. It is a daunting task to have deterministic model of network parameters for specific QoS requirements of IoT applications hence a soft-computing approach would be most viable solution considering network parameters across all five layers of TCP/IP stack that is a Cross-Layer design approach to improve QoS. In this paper, we have proposed a Fuzzy Logic system based Cross-Layer (FLS-CL) design for MANET to improve Network QoS parameters like Throughput, Packet Delivery ration (PDR) and End-to-End (E2E) delay. The proposed system has been implemented using Fuzzy Logic based parameters modeling using MATLAB and the Network simulation study using QualNet® simulator. The results are promising with average 27.64 % improvement in PDR and 76.04 % less E2E delay for connection-less UDP-based CBR applications and 3.28 % improvement in PDR and 58.81 % less E2E delay for connection-oriented TCP-based FTP applications. The QoS improvements are achieved with proposed FLS-CL as compared to state-of-the-art IEEE 802.11 based MANET without any FLS cross-layer optimization.

This document provides an overview of the iNGENIOUS cross-layer architecture. The focus is on component groups within the overall architecture and how their integration and cooperation enable the related iNGENIOUS innovations.

To provide a new level of reliability, latency, and support a massive number of users and smart objects, a new 5G multi-services air interface needs to be addressed for the factory of the future (FoF). However, there are limitations in providing connectivity to a dynamic machine in a factory due to several strict industrial automation requirements. In particular, the strict wireless communication latency and reliability requirements are the major challenges to enable the Industry 4.0 vision. In this paper, a PHY-MAC layer cross-layer model that combines a semi-persistent scheduling at the medium access control layer and NOMA at the physical layer has been proposed to address the limitations. The work extensively investigates the performance of the factory of the future with various considerations of 5G spectrums (in this case 3.5 GHz and 28 GHz), speeds and frequency diversity. In addition, the packet error rate (PER), outage probability and throughput in MAC are evaluated in terms of network density deployment (sparse, moderate, dense), different kinds of speed; 0 km/h, 3 km/h, 7 km/h and 10 km/h, under two 5G frequency spectrums. Through extensive simulations, the considered 5G system parameters produced better results in terms of reliability, where the results showed that the frequency diversity outperformed non-diversity by 2 dB. In a sparse network, the PER results showed better results compared to the dense network density by 2 dB (MMSE), 8 dB (LS-Linear) and 2 dB (LS-Spline). Besides that, robotics in sparse network density and stationary exhibited the best PER results, which is as low as 10−7. Moreover, the performance of mid-band frequency outperformed the high-band frequency by 1.8dB (MMSE) in dense condition and 1.5 dB (MMSE) in sparse deployment at PER = 10−6. Hence, this study could be a useful insight for the factory of the future services that are utilizing a 5G mid-band spectrum as well as a high-band spectrum.

In this paper, we propose a new deep Q-network (DQN) design for quality-of-service (QoS) multicast routing (DQMR) protocol to establish efficient QoS multicast (EQM) trees in cognitive radio mobile ad hoc networks (CR-MANETs). An EQM tree is a shortest-path multicast tree with minimum end-to-end (E2E) cost (a combination of queuing size ratio and link stability) subject to QoS constraints such as queuing size ratio, link stability, number of hops, number of time slots and avoiding the licensed channel of primary users. Particularly, we propose an NP-complete optimization problem such that its feasible solution is an EQM tree. To address this problem, we design a new DQN model and a new game-based model to form EQM trees in real-time by offline training instead of online training as done in previous papers. Moreover, the DQMR protocol is also guaranteed to have high stability, low routing delay, low control overhead, and high packet delivery ratio (PDR). Furthermore, one more new contribution of the paper is that exact closed-form expressions for the E2E queuing delay of a multicast routing tree are also derived assuming random waypoint mobility and the reference point group mobility models to compare with simulation results of routing delay. Simulation results show that the DQMR protocol outperforms multicast ad hoc on-demand distance vector routing protocol in terms of routing delay, control overhead, and PDR.

La tecnología wireless en los últimos años ha experimentado una expansión sin precedentes, alcanzando hasta el lugar más recóndito de la tierra. La tecnología inalámbrica permite la creación de redes sin la necesidad de cables, dando al usuario de este tipo de redes la libertad de moverse por su entorno sin la restricción de movilidad asociada a tener una conexión cableada. El mejor exponente del salto tecnológico que experimentan las redes wireless son las MANET (Mobile Ad hoc Networks, Redes móviles ad hoc) que se presentan como la arquitectura de comunicación más flexible que existe actualmente. Este tipo de redes esta integrado por sistemas autónomos móviles con capacidades de enrutamiento y conectados a través de enlaces inalámbricos formando un grafo de comunicación arbitrario. Utilizar las redes móviles ad hoc para desarrollar un esquema de computación distribuida sobre dispositivos heterogéneos que pueda explotar las crecientes capacidades de cómputo de estos, es la motivación principal del estudio plasmado en este documento. La mayor dificultad que se debe superar es encontrar un mecanismo para manejar la indeterminación de las unidades de red disponibles para balancear la carga en el procesamiento distribuido. Esta indeterminación es provocada mayoritariamente por la movilidad de los nodos que conforman estas redes. En este trabajo se propone el diseño de un protocolo que combina un enfoque para lidiar con la indeterminación de clientes al momento de comenzar un cálculo científico distribuido basado en un diseño cross layer con un mecanismo que agrupa los dispositivos presentes en una MANET en clusters, sacando rédito de la heterogeneidad inherente a este tipo de redes a fin de determinar que dispositivos serán los lideres de los agrupamientos conformados. La propuesta de solución implementada se basa en el protocolo de enrutamiento reactivo AODV (Ad hoc On-demand Distance Vector), la cual modifica los formatos de mensajes para dar soporte a toda la nueva funcionalidad. El método de validación de la propuesta presentada en esta investigación se delinea a través de la definición de diversas pruebas de rendimiento bajo diferentes valores en los parámetros que fijan: el número de dispositivos, la movilidad, el tamaño del área de circulación, entre otros; que al dimensionar los valores registrados vía métricas estándar permitirán arribar a los resultados de su desempeño., Facultad de Informática

Detecting the frequency of the pest occurrence is always a time consuming and laborious task for agriculture. This paper attempts to solve the problem through the combination of deep learning and pest detection. We propose an entire-and-partial feature transfer learning architecture to perform pest detection, classification and counting tasks which can reach the final goal for detecting the frequency of pest occurrence. In the partial-feature transfer learning, different fine-grained feature map are strengthened by using the weighting scheme of the entire-feature transfer learning. Finally, the cross-layer of the entire-feature network is combined with the multi-scale feature map. The entire-feature transfer learning approach enhances the feature map by creating a shortcut topology for the input and output layers to reduce the gradient disappearance problem which is common to deep networks. The experimental results show that the detection accuracy can be significantly improved and the accuracy can reach 90.2%.

Internet-connected Internet of Things (IoT) devices are exploding, which pose a significant threat for their management and security protection. IoT device identification is a prerequisite for discovering, monitoring, and protecting these devices. Although the existing proactive identification methods based on protocol fingerprint can discover and identify large-scale IoT devices, the fingerprint granularity is difficult to meet the requirements of security risk assessment for large-scale IoT devices. Since IoT devices usually support multiple network protocols for specific collection and control tasks, we propose a cross-layer protocol fingerprint to achieve large-scale fine-grained devices identification instead of traditional single protocol fingerprint. We first design a probing scheme for gathering HTTP and TCP cross-layer packets. Then we select the specific field of the HTTP and TCP protocols based on the diversity and consistence of field value. Finally, we utilize convolutional neural network (CNN) and long-term memory network (LSTM) to extract and construct feature fingerprint of these specific fields, and achieve a fine-grain IoT devices identification with high accuracy. The experimental results show that our identification accuracy of devices model reaches 96.6%, the recall rate reaches 97.4%.

As a promising communication paradigm in the 5G era, IEEE 802.11ad 60GHz mmWave communication has many desirable properties, including high bandwidth, narrow beam, high transmission quality, and low mutual-interference. However, recently revealed Man-in-the-middle (MITM) attack can endanger the security and privacy of mmWave communication systems. Existing MITM attack detection solutions, established on ideal assumptions, neglect the fact that a MITM attacker can change its transmitting power and make it hard to be detected. In this backdrop, a cross-layer location consistency-based MITM detection and localization algorithm is introduced in this paper. Firstly, we propose a positioning algorithm based on the information of the physical layer with a directional antenna sector propagation model; secondly, based on the information at the MAC layer, we propose a MITM attack detection scheme and a sector consistency check algorithm; thirdly, a MITM attack detection and localization algorithm using cross-layer information is proposed. Simulation results have shown that the proposed algorithm can effectively detect the MITM attack and its positioning error is less than 0.53m in typical parameter settings.

Wireless Sensor Networks (WSNs) are emerging networks that are being utilized in a variety of applications, such as remote sensing images, military, healthcare, and traffic monitoring. Those critical applications require different levels of security; however, due to the limitation of the sensor networks, security is a challenge where traditional algorithms cannot be used. In addition, sensor networks are considered as the core of the Internet of Things (IoT) and smart cities, where security became one of the most significant problems with IoT and smart cities applications. Therefore, this paper proposes a novel and light trust algorithm to satisfy the security requirements of WSNs. It considers sensor nodes’ limitations and cross-layer information for efficient secure routing in WSNs. It proposes a Tow-ACKs Trust (TAT) Routing protocol for secure routing in WSNs. TAT computes the trust values based on direct and indirect observation of the nodes. TAT uses the first-hand and second-hand information from the Data Link and the Transmission Control Protocol layers to modify the trust’s value. The suggested TATs’ protocols performance is compared to BTRM and Peertrust models in terms of malicious detection ratio, accuracy, average path length, and average energy consumption. The proposed algorithm is compared to BTRM and Peertrust models, the most recent algorithms that proved their efficiency in WSNs. The simulation results indicate that TAT is scalable and provides excellent performance over both BTRM and Peertrust models, even when the number of malicious nodes is high.

Data packet reaches from the end node to sink in a multihop fashion in the internet of things (IoTs) and sensor networks. Usually, a head node (among neighboring or special purpose nodes) can collect data packets from the nodes and forward them further to sink or other head nodes. In Time-division multiple access (TDMA) driven scheduling, nodes often own slots in a time frame and are scheduled for data forwarding in the allotted time slot (owner node) in each time frame. A time frame in which the owner node does not have data to forward goes into sleep mode. Though the supposed owner node is in sleep mode, the corresponding head node is active throughout the time frame. This active period of a head node can cause an increase in energy consumption. Besides, because the head node in an active state does not receive a data packet, it is causing significantly to the throughput, ultimately leading to low channel utilization. We propose the Markov design policy (MDP) for such head nodes to reduce the number of time slots wasted in the time frame in our work. The proposal is the first such kind of MDP-based modeling for node scheduling in TDMA. The simulation results show that the proposed method outperforms existing adaptive scheduling algorithms for channel utilization, end-to-end delay, system utilization, and balance factor. WOS:000859413200011 Q1 - Q2

The mobile Internet is becoming increasingly complex with a wide diversity of end systems (including wearables and automobiles), the co-presence of multiple devices with collaboration potential, and the growth of user-generated application traffic fueled by improved mobile sensors and wireless access. As the Internet evolves along with these trends, the increased complexity of different components and protocol layers makes it more challenging to achieve high network utilization and meet the diverse QoE requirements for mobile applications. As a result, despite the richness of various network resources, the performance of today’s mobile applications still falls behind expectations. To address this challenge, in this dissertation, I demonstrate that with a better understanding of the various components and different protocol layers of the increasingly complex mobile Internet, we can identify unique performance problems and leverage such knowledge to develop network transport protocols with cross-device awareness and application adaptation strategies with cross-layer considerations for better mobile app performance. Specifically, to understand the interaction between multiple mobile devices and its impact on end-to-end performance, we conduct an empirical study on wearable networking, where wearables oftentimes rely on their paired smartphones for Internet access. Based on our measurement findings, we develop cross-device network transport management solutions for improving wearable networking performance. To better support multi-device collaboration in a more general setting, we develop MPBond, a distributed multipath transport system for efficient network-level collaboration among personal mobile devices, with cross-device connection management and packet scheduling. To explore opportunities on the mobile application design, we build Livelyzer, a generalized measurement tool for characterizing commercial live video streaming upstream ingest performance under mobile networks. Based on our measurements, we identify deficiencies in broadcasting app rate adaptation, and propose network-aware adaptation strategies to improve the same. We also investigate another emerging mobile application, vehicular sensing for autonomous driving, where we build Harbor, a cross-layer system architecture for collaborative vehicular sensor data sharing, with adaptive usage of V2V and V2I network resources.

Real-life implementation of the Internet of Things (IoT) in healthcare requires sufficient quality of service (QoS) to transmit the collected data successfully. However, unsolved challenges in prioritization and congestion issues limit the functionality of IoT networks by increasing the likelihood of packet loss, latency, and high-power consumption in healthcare systems. This study proposes a priority-based cross-layer congestion control protocol called QCCP, which is managed by communication devices’ transport and medium access control (MAC) layers. Unlike existing methods, the novelty of QCCP is how it estimates and resolves wireless channel congestion because it does not generate control packets, operates in a distributed manner, and only has a one-bit overhead. Furthermore, at the same time, QCCP offers packet scheduling considering each packet’s network load and QoS. The results of the experiments demonstrated that with a 95% confidence level, QCCP achieves sufficient performance to support the QoS requirements for the transmission of health signals. Finally, the comparison study shows that QCCP outperforms other TCP protocols, with 64.31% higher throughput, 18.66% less packet loss, and 47.87% less latency.

This document provides an overview of the iNGENIOUS cross-layer architecture. The main purpose of this deliverable is to reach a common understanding of the project at a technical level among all consortium partners and those supporting the project from outside.

Internet-of-things (IoT) is one of the proliferated technologies that result in a larger scale of connection among different computational devices. However, establishing such a connection requires a fault-tolerant routing scheme. The existing routing scheme results in communication but does not address various problems directly linked with energy consumption. Cross layer-based scheme and optimization schemes are frequently used scheme for improving the energy efficiency performance in IoT. Therefore, this paper investigates the approaches where cross-layer-based schemes are used to retain energy efficiencies among resource-constrained devices. The paper discusses the effectivity of the approaches used to optimize network performance in IoT applications. The study outcome of this paper showcase that there are various open-end issues, which is required to be addressed effectively in order to improve the performance of application associated with the IoT system.

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Cross-layer reliability is becoming the preferred solution when reliability is a concern in the design of a microprocessor-based system. Nevertheless, deciding how to distribute the error management across the different layers of the system is a very complex task that requires the support of dedicated frameworks for cross-layer reliability analysis. This paper proposes SyRA, a system-level cross-layer early reliability analysis framework for radiation induced soft errors in memory arrays of microprocessor-based systems. The framework exploits a multi-level hybrid Bayesian model to describe the target system and takes advantage of Bayesian inference to estimate different reliability metrics. SyRA implements several mechanisms and features to deal with the complexity of realistic models and implements a complete tool-chain that scales efficiently with the complexity of the system. The simulation time is significantly lower than micro-architecture level or RTL fault-injection experiments with an accuracy high enough to take effective design decisions. To demonstrate the capability of SyRA, we analyzed the reliability of a set of microprocessor-based systems characterized by different microprocessor architectures (i.e., Intel x86, ARM Cortex-A15, ARM Cortex-A9) running both the Linux operating system or bare metal. Each system under analysis executes different software workloads both from benchmark suites and from real applications.

Reducing collisions in UHF RFID systems is a crucial problem. In most anti-collision designs, the reader has to estimate the number of tags within its reading range during the interrogation process. Most of the approaches estimate the number of tags with observations in multiple time slots. Since estimation error varies along with the initial frame length and the number of tags, system efficiency is affected by both of them. To reduce the sensitiveness to them and improve system efficiency, we propose a novel MAC-PHY cross-layer anti-collision algorithm based on Bayesian inference. Specifically, each frame is allowed to end at an early stage and a slot-by-slot estimator is proposed. Benefit from its slot-by-slot nature, system efficiency is more stable and independent on the initial frame length and very close to the upper bound of dynamic framed slotted ALOHA (DFSA) algorithm. The performance of the proposed method is compared with the state-of-the-art algorithms through numeric simulations. The results show that the proposed solution significantly outperforms the compared ones.

Due to rapid growth of multimedia traffic used over the mobile ad-hoc networks (MANETs), to keep up with the progress of this constraints MANETs protocols becoming increasingly concerned with the quality of service. In view of the random mobility nodes in MANET, TCP becomes more unreliability in case of higher energy consumption and packet loss. In this paper we proposed a new optimization approach to enhance decision making of TCP based on some changes of IEEE 802.11 MAC uses cross layer approach. The aim is to minimize the impact of retransmissions of packet lost and energy consumption in order to analysed and chose the appropriate routing protocol for TCP that can be enhance QoS MANET. Our simulation results based QoS study using NS3 show that, our proposed achieves better performance of TCP in MANETs significantly, and also improved the throughput, energy consumption and facilitates the traffic transmission over routing protocol.

Future Internet of Things (IoT) applications, such as connected industry 4.0, become more challenging with the strict Quality of Service (QoS) requirements, including reliability and delay guarantees. Several mechanisms in the communication stack to match the expected QoS are already discussed and specified at different layers, with the goal to make the communication more reliable. They focus on the layer-specific enhancements. For example, Time-Slotted Channel Hopping (TSCH) is a link layer mechanism to avoid narrowband interference. On the network layer, several multi-path routing schemes are proposed to distribute the traffic load or to have backup paths with the purpose of making data transmissions more robust to link failures. In addition to the layer-specific improvements, an integration of the cross-layer information can guarantee an end-to-end QoS for communication in dynamic environments. In this work we propose and evaluate a cross-layer framework for cell- disjoint routing, which eliminates overlapping resource scheduling in both time and frequency. It enables the end-to-end QoS for wireless sensor networks under the IPv6 Over the TSCH Mode of IEEE 802.15.4 (6TiSCH). The proposed framework, called 6TiSCH stack with cross-layer information exchange (6TiSCH-CLX), is validated on a selected set of aviation industry applications using both simulations and analytical model., Electronic Communications of the EASST, Volume 80: Conference on Networked Systems 2021 (NetSys 2021)

Peer-to-Peer (P2P) energy trading platforms are being actively designed, tested and operated by engineers, power distribution companies and prosumers. The assurance of the accountability of the conduct of different stakeholders through a robust trust management mechanism is imperative in such platforms. The usage of blockchain, as an underlying technology, can ensure numerous properties such as immutability, transparency and traceable execution of transactions, in addition to ensuring trust establishment among different entities of the system. Few blockchain-based decentralized energy trading platforms have been designed in the literature to build trust about the platform and among prosumers. However, none of these proposals have considered human-in-the-loop in the trust establishment process. Moreover, these solutions have considered trust only at a particular layer of blockchain, such as at the application or consensus layer. To bridge this gap, this paper presents a novel cross-layer trust-based consensus protocol that considers human-in-the-loop and employs fuzzy logic to address the issue of vagueness of trust values by offering human interpretable trust level. The experiment results demonstrate the efficiency and effectiveness of our proposed protocol in comparison to established consensus mechanisms. The analysis also shows the protocol is immune against selfish mining, 51% and Sybil attacks.

Considering that networks based on New Radio (NR) technology are oriented to provide services of desired quality (QoS), it becomes questionable how to model and predict targeted QoS values, especially if the physical channel is dynamically changing. In order to overcome mobility issues, we aim to support the evaluation of second-order statistics of signal, namely level-crossing rate (LCR) and average fade duration (AFD) that is missing in general channel 5G models. Presenting results from our symbolic encapsulation point 5G (SEP5G) additional tool, we fill this gap and motivate further extensions on current general channel 5G. As a matter of contribution, we clearly propose: (i) anadditional tool for encapsulating different mobile 5G modeling approaches, (ii) extended, wideband, LCR, and AFD evaluation for optimal radio resource allocation modeling, and (iii) lower computational complexity and simulation time regarding analytical expression simulations in related scenario-specific 5G channel models. Using our deterministic channel model for selected scenarios and comparing it with stochastic models, we show steps towards higherlevel finite state Markov chain (FSMC) modeling, where mentioned QoS parameters become more feasible, placing symbolic encapsulation at the center of cross-layer design. Furthermore, we generate values within a specified 5G passband, indicating how it can be used for provisioningoptimal radio resource allocation.

Inerter-based vibration control systems have been developed rapidly in recent years. However, previous studies mainly focus on the development of new devices and parameter optimization strategies, while ignoring the improvements in the utilization efficiency of the inerter system that the bracing system and novel installation methods may bring. In this paper, a cross-layer installed cable-bracing inerter system (CICBIS) is proposed to improve the utilization efficiency of the inerter system, which can cross more layers and is suitable for shear-type multi-degree-of-freedom (MDOF) structures. A demand-based cable-bracing inerter system (CBIS) design method is developed. The mass enhancement and utilization efficiency improvement of the inerter system caused by the cross-layer installation are quantified through calculating the effective inerter-mass ratio of the CBIS-equipped MDOF structure. A 10-story benchmark structure is used to verify the control performance of the CICBIS and the design method. The analysis results show that the proposed design method can exert the cable-bracing system&rsquo, s adjustability and the damping enhancement of the inerter system. The CICBIS can reduce the total apparent mass and damping coefficient requirements of the inerter systems without increasing the control force. It means that the proposed design method is effective, and the CICBIS has a high efficiency.

Due to their proven efficiency, machine-learning systems are deployed in a wide range of complex real-life problems. More specifically, Spiking Neural Networks (SNNs) emerged as a promising solution to the accuracy, resource-utilization, and energy-efficiency challenges in machine-learning systems. While these systems are going mainstream, they have inherent security and reliability issues. In this paper, we propose NeuroAttack, a cross-layer attack that threatens the SNNs integrity by exploiting low-level reliability issues through a high-level attack. Particularly, we trigger a fault-injection based sneaky hardware backdoor through a carefully crafted adversarial input noise. Our results on Deep Neural Networks (DNNs) and SNNs show a serious integrity threat to state-of-the art machine-learning techniques., Accepted for publication at the 2020 International Joint Conference on Neural Networks (IJCNN)

The recent advancements of wireless technology and applications make downlink scheduling and resource allocations an important research topic. In this paper, we consider the problem of downlink scheduling for multi-user scalable video streaming over orthogonal frequency division multiple access (OFDMA) channels. The video streams are precoded using a scalable video coding (SVC) scheme. We propose a fuzzy logic-based scheduling algorithm, which prioritises the transmission to different users by considering video content, and channel conditions. Furthermore, a novel analytical model and a new performance metric have been developed for the performance analysis of the proposed scheduling algorithm. The obtained results show that the proposed algorithm outperforms the content-blind/channel aware scheduling algorithms with a gain of as much as 19% in terms of the number of supported users. The proposed algorithm allows for a fairer allocation of resources among users across the entire sector coverage, allowing for the enhancement of video quality at edges of the cell while minimising the degradation of users closer to the base station.

TCP and TLS are among the most essential protocols in today's Internet. TCP ensures reliable delivery of data while TLS secures the data transfer. Following the layered model, TLS was designed to be as independent as possible from the underlying transport protocol. This paper revisits this assumption and demonstrates the various benefits that a closer integration between TCP and TLS brings. We implement a first TCPLS prototype that demonstrates the feasibility of this integration. We show its usefulness on different use cases such as the benefit of bandwidth aggregation during a connection migration, and discuss several open research directions.

IoT concept took a part in everyday lives. Environments became smart, suggestive, recognizing, and adopting to its user needs. However, energy efficiency, interoperability, security, and other issues are restricting the number of use-cases and ways the technology can be used. To cope with these issues, cross-layer approach in standard architectures is necessary. This way, information propagating from one layer to the another can significantly influence overall performances—it is necessary to understand and implement all the significant details that may enable and improve specific applications. Moreover, tremendous evolution in data acquisition and transfer is needed to consider efficient hardware and innovative software architectures that still need to be developed to make it a reality. This mostly introduces distributed intelligence for a reliable and effective processing of the information about the physical world that should provide a complete knowledge to the top running applications. These are related to the efficient usage of radio technologies usual referred as in outdoor, indoor, urban, rural environments, its ranges, and battery/battery-less usage. In addition, communication protocols should handle IoT requirements for multiple device, low-power, real-time operations, or satisfying timely consumer needs. Some further challenges involve efficient device discovery schemes, sleep-mode management, application requirements, security issues, or other power constraints.

In recent years, modern technology has been increasing, and this has grown a derivate in big challenges related to the network and application infrastructures. New devices have been providing more high functionalities to users than ever before; however, these devices depend on a high functionality of network in order to ensure a correct functioning ability over applications. This is essential for mobile networking systems to evolve in order to meet the future requirements of capacity, coverage, and data rate. In addition, when a network problem happens, it could be converted into somethingmore disastrous and difficult to solve. A crucial point is the network physical change and the difficulties, such as loss continuity of services and the decision to select the future network to be connected. In this article, a new framework is proposed to forecast a future network to be connected through a mobile node in WLAN environments. The proposed framework considers a decision-making process based on five classifiers and the user’s position and acceleration data in order to anticipate the network change, reaching up to 96.75% accuracy in predicting the connection of this future network. In this way, an early change of network is obtained without packet and time loss during the network change.

The latest 6TiSCH standard enables highly reliable industrial monitoring and control applications through deterministic wireless communications and efficient allocation of radio resources among wireless nodes. However, much of these benefits are tied to the ability of the scheduler in orchestrating communication over individual links in an efficient and a non-conflicting manner. The state-of-the-art 6TiSCH scheduling algorithms only tackle the scheduling problem at the MAC layer with an assumption of having an optimal routing layer. Moreover, to the best of our knowledge, no solution so far has taken content-specific scheduling into account. This paper proposes CONCISE, which is a content-centric cross-layer scheduling solution. CONCISE creates content-independent routing topologies and schedulers, resulting in an overlaid routing structure and multiple content-based schedules. Further, CONCISE addresses in-network processing and data aggregation with the objective of reducing network traffic. Performance evaluation demonstrates that CONCISE can effectively reduce inner layer network traffic by up to 65%. As a result, it can provide up to 50% delay reduction and achieve significantly higher packet delivery ratio, compared with existing scheduling solutions. Initial proof of concept is implemented and evaluated in Contiki OS.

This paper investigates the cross-layer optimization for anti-jamming in the network and MAC layers, in which the jammer can adjust the jamming policies to maximize the jamming effectiveness. The joint problem of routing selection, channel allocation, and power control is formulated as a Stackelberg game. The jammer leads the game by choosing the optimal jamming power and channels. The user follows by selecting the optimal nodes and corresponding channels, and adjusts its transmitting power to meet the communication requirement. Then, based on Q-learning, a cross-layer anti-jamming learning algorithm is proposed to obtain the Stackelberg equilibrium. Finally, simulation results are presented to verify the effectiveness of the proposed algorithm.

Mobile ad hoc network (MANET) is among the networks which do not require any infrastructure to put nodes in communication. Due to its own nature, it is used by several applications. Even though it's a network that is extremely challenging and mostly when TCP is applied. In this paper, we have proposed a new improvement in the TCP algorithm that employed fuzzy logic to predict packet loss and avoid congestion. Specifically, we have used tree metrics such as stability, energy, and signal strength to use in fuzzy logic systems. To accomplish our approach, we have established some modifications based on a cross-layer. The results of the relevant simulation performed by NS3 demonstrated that our approach globally improves the performance of TCP in MANET. Precisely reduce the packet overhead and energy consumption also enhance throughput.

In this paper, a resource allocation algorithm in two-way orthogonal frequency division multiplexing (OFDM) based cognitive radio networks with quality of experience (QoE) and power consumption guarantees is proposed. We define the overall QoE perceived by secondary users (SUs) per power consumption as QoEW. The power consumption model consists of fixed circuit power, dynamic circuit power, and transmit power which depends on the efficiency of the power amplifiers at different terminals. Under the constraint of total maximum transmit power, the optimization objective is to maximize QoEW while meeting the minimum QoE demands of SUs and maintaining interference threshold limitations of multiple primary users. The resource allocation problem is formulated into a nonlinear fractional programming and transformed into an equivalent convex optimization problem via its hypograph form. Based on the Lagrange dual decomposition method and cross-layer (CL) optimization architecture, this convex optimization problem is separately solved in the physical layer and the application layer. The optimal QoEW is achieved through the proposed CL alternate iteration algorithm. Numerical simulation results demonstrate the impacts of system parameters on QoEW and the effectiveness and superiority of the proposed algorithm.

The demand for pervasive wireless access and high data rate services are expected to grow significantly in the near future. In this context, the deployment of Heterogeneous Networks (HetNets) will enable important capabilities, such as high data rates and traffic offloading, providing dedicated capacity to homes, enterprises, and urban hotspots. Despite HetNet technology will be beneficial for future wireless systems in many ways, the massive cells diffusion has as a consequence an exponential increase of the backhaul traffic that can create congestion and collapse the backhaul network. Virtualization of networks and radio access allows the implementation of complex and efficient decisional processes for radio and network resource optimization, but the interaction between lower and upper layers during resource allocation decisions is still mostly unexplored. In this paper we propose an artificial intelligence based approach, with two interdependent decisional cores exchanging information, one aware of physical layer aspects and the other controlling pure network resources. The two iterative procedures aim at jointly optimizing the distribution of the traffic in the backhaul network and the users cell association, with the goals of minimizing the unsatisfied users data rate requests and minimizing the energy consumption reducing the number of activated cells, respectively.

Large content providers, known as hyper-giants, are responsible for sending the majority of the content traffic to consumers. These hyper-giants operate highly distributed infrastructures to cope with the ever-increasing demand for online content. To achieve 40 commercial-grade performance of Web applications, enhanced end-user experience, improved reliability, and scaled network capacity, hyper-giants are increasingly interconnecting with eyeball networks at multiple locations. This poses new challenges for both (1) the eyeball networks having to perform complex inbound traffic engineering, and (2) hyper-giants having to map end-user requests to appropriate servers. We report on our multi-year experience in designing, building, rolling-out, and operating the first-ever large scale system, the Flow Director, which enables automated cooperation between one of the largest eyeball networks and a leading hyper-giant. We use empirical data collected at the eyeball network to evaluate its impact over two years of operation. We find very high compliance of the hyper-giant to the Flow Director’s recommendations, resulting in (1) close to optimal user-server mapping, and (2) 15% reduction of the hyper-giant’s traffic overhead on the ISP’s long-haul links, i.e., benefits for both parties and end-users alike.

CNPq - Conselho Nacional de Desenvolvimento Científico e Tecnológico FAPEMIG - Fundação de Amparo à Pesquisa do Estado de Minas Gerais CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior A pesquisa em redes corporais sem fio (Wireless Body Area Networks - WBAN) ganhou interesse e tornou-se um tema de serviços de saúde emergente devido ao seu potencial uso no monitoramento contínuo do bem-estar humano. Os movimentos naturais do corpo humano e a interferência ambiental impõem dificuldades ao desempenho de uma WBAN, como o tempo de vida e a confiabilidade na entrega de dados. Devido à complexidade das características das WBANS, um desafio é manter uma boa qualidade dos enlaces entre os nós sensores da rede enquanto o uso de suas baterias é prolongado. Diferentes trabalhos experimentais na literatura mostram que o uso de uma potência de transmissão fixa e com valor elevado pode não ser adequado, embora possibilite uma comunicação mais confiável, pode levar ao consumo de energia desnecessário. Por outro lado, a transmissão com potência baixa oferece maior economia de energia, mas pode reduzir a confiabilidade na entrega de pacotes. A pesquisa envolvendo a transmissão de dados nas WBANs tem importância como estratégia para economia de energia, uma vez que as técnicas de controle de potência permitem selecionar níveis de potência suficientes para obter bom desempenho e reduzir adicionalmente problemas de interferência. Esse trabalho propõe uma abordagem cross-layer, denominada CL, baseada no padrão IEEE 802.15.4 para garantir a qualidade do serviço em termos de entrega de pacotes e de tempo de vida da rede. A abordagem CL propõe compartilhar os valores de parâmetros entre as camadas da pilha de protocolos com o objetivo de melhorar a eficiência na entrega de pacotes ao sink e no consumo de energia. Os resultados desse trabalho foram obtidos através da implementação de WBANs no simulador Castalia. Três cenários foram usados, com diferentes taxas de dados e dois tipos de canal de comunicação, com e sem interferência. Os cenários foram comparados em termos da entrega de pacotes, consumo de energia e adaptabilidade ao meio de comunicação. A proposta é comprovada através de três abordagens cross-layer: CL-2, CL-4 e CL-Bin. CL-2 usa dois níveis de potência de Tx, CL-4 quatro níveis, onde a escolha é pela quantidade de pacotes recebidos pelo sink. CL-Bin é uma abordagem preditiva usando um modelo probabilístico para determinar a potência de Tx. Essas abordagens foram comparadas a uma WBAN convencional de camadas e os resultados mostram que a abordagem CL é mais eficiente ao considerar os cenários propostos. Research on wireless body area networks (WBAN) has gained interest and has become an emerging theme of health services due to its potential use in the continuous monitoring of human well-being. The natural movements of the human body and environmental interference impose difficulties on the performance of a WBAN, such as a lifetime and reliability of data delivery. Due to the complexity of the WBANS characteristics, it is a challenge to maintain the quality of the links among the network sensor nodes while extending the battery life. Different experimental studies in the literature show that the use of fixed and high power transmission may not be adequate, although it allows a more reliable communication, it can lead to unnecessary energy consumption. On the other hand, low power transmission offers more energy savings but may reduce the reliability of packet delivery. Research on data transmission in WBANs is essential as an energy-saving strategy, as power control techniques allow sufficient power levels to be selected to achieve excellent performance and further reduce interference problems. This work proposes a cross-layer approach, called CL, based on the IEEE\-802.15.4 standard to ensure the quality of service (QoS) in terms of packet delivery and network lifetime. The CL approach proposes to share parameter values between protocol stack layers to improve efficiency in sink packet delivery and power consumption. The results from this work were obtained through the implementation of WBANs with the Castalia simulator. It was used three scenarios, with different data rates and two types of communication channels (with and without interference). The results comparison of the scenarios concerned packet delivery, power consumption, and communication media adaptability. Three cross-layer approaches proved this proposition: CL-2, CL-4, and CL-Bin. CL-2 uses two levels of Tx power, CL-4 four levels, where the choice is by the number of packets received by the sink. CL-Bin is a predictive approach using a probabilistic model to determine the power of Tx. The results of these approaches, compared to a conventional layered WBAN, show that the CL approach is more efficient when considering the proposed scenarios.

International audience; Lack of coordination between network layers limits the performance of most proposed solution for new challenges posed by wireless networks. To overcome such limitations, cross-layer physical and medium access (PHY-MAC) design for multi-input-multi-output orthogonal frequency division multiple access system in heterogeneous networks (HetNETs) is proposed. In this paper, we formulate an optimization problem for hybrid beamforming, in a multiuser HetNET scenario aiming to maximize the total system throughput. Furthermore, analog beamforming is selected from a codebook containing a limited number of candidates for steering vectors. The proposed problem is non-convex and hard to solve. Thus it is relaxed by transforming it into a subtraction form of two convex funcions. Afterward we apply a group of well-known metaheuristic algorithms to calculate the normalized hybrid beamforming vectors. The optimal solution is obtained using an exhaustive search (ES) algorithm that provides an ideal solution, but with high complexity. In addition, zero-forcing-based approach (ZFA), matched filter (MF), and QR-based approach (QR) are applied to get quick sub-optimal solutions. Hence, we analyze the performance of our systems using the throughput metric. The simulation results show that QR algorithm outperforms ZFA and MF in low and middle signal-to-noise ratio (SNR) regime, while ZFA outperforms QR and MF at higher SNRs. Moreover, QR is close to the optimal solution ES.

At present, vehicular communications have become a reality guided by various applications. In particular, high-quality video delivery with low latency constraints is required for real-time applications. The new state-of-the-art high-effciency video coding (HEVC) standard is very promising for real-time video streaming in vehicular ad hoc networks (VANET). Nevertheless, these networks have variable channel quality and a limited bandwidth that penalizes the overall performances of end-to-end video transmission. In order to meet these constraints, we proposed in this work to consider both transmission and video encoding parameters through a joint source-channel-protocol coding approach to provide an improvement in video transmission. First, we have shown the interest and the gain brought by the "cross-layer" solutions. Then, we developed two approaches. The first one exploits a "crosslayer" solution between the application layer and the Medium Access Control (MAC) layer while the second exploits the transport layer protocols in the adaptation of the video stream. Regarding the first approach, we have proposed solutions to allocate the frames to the most appropriate Access Category (AC) queue on the MAC layer based on the image importance in the video stream. In another solution, we chose multiple descriptions source coding as an error resilient solution. Thus, the simluation results obtained for different realistic vehicular scenarios demonstrate that the proposed transmission schemes offer significant video quality improvements and end-to-end delay reduction compared to conventional transmission schemes.; Actuellement les communications véhiculaires sont devenues une réalité guidée par diverses applications. Notamment, la diffusion de vidéo de qualité élevée avec des contraintes de faible latence requises par les applications temps réel. Grâce au niveau de compression jamais atteint auparavant, l’encodeur H.265/HEVC est très prometteur pour la diffusion de vidéos en temps réel dans les réseaux ad hoc véhiculaire (VANET). Néanmoins, la qualité de la vidéo reçue est pénalisée par les mauvaises caractéristiques du canal de transmission (disponibilité, non stationnarité, rapport signal à bruit, etc.). Afin d’améliorer et d’assurer une qualité vidéo minimale à la réception nous proposons dans ce travail une optimisation conjointe source-canal-protocole de la transmission en tenant compte à la fois des paramètres de transmission et d’encodage vidéo. Dans un premier temps, nous montrons l’intérêt et le gain apporté par les solutions dites inter-couches « cross-layer ». Par la suite, nous développons deux approches l’une exploitant un « cross-layer » entre la couche application et la couche MAC et une seconde exploitant les protocoles de transports dans l’adaptation du flux vidéo. En ce qui concerne la première approche nous proposons une solution utilisant une gestion hiérarchique des trames au niveau des files d’attentes de la couche MAC, basée sur l’importance des images du flux vidéo. Dans une seconde solution, nous retenons le codage par descriptions multiples comme solution de protection à la source. Les résultats de simulations obtenus pour plusieurs types de scénarios véhiculaires réalistes montrent que les différents schémas de transmission véhiculaire proposés offrent des améliorations significatives en termes de qualité vidéo à la réception et de retard de bout en bout par rapport aux schémas classiques.

At present, vehicular communications have become a reality guided by various applications. In particular, high-quality video delivery with low latency constraints is required for real-time applications. The new state-of-the-art high-effciency video coding (HEVC) standard is very promising for real-time video streaming in vehicular ad hoc networks (VANET). Nevertheless, these networks have variable channel quality and a limited bandwidth that penalizes the overall performances of end-to-end video transmission. In order to meet these constraints, we proposed in this work to consider both transmission and video encoding parameters through a joint source-channel-protocol coding approach to provide an improvement in video transmission. First, we have shown the interest and the gain brought by the "cross-layer" solutions. Then, we developed two approaches. The first one exploits a "crosslayer" solution between the application layer and the Medium Access Control (MAC) layer while the second exploits the transport layer protocols in the adaptation of the video stream. Regarding the first approach, we have proposed solutions to allocate the frames to the most appropriate Access Category (AC) queue on the MAC layer based on the image importance in the video stream. In another solution, we chose multiple descriptions source coding as an error resilient solution. Thus, the simluation results obtained for different realistic vehicular scenarios demonstrate that the proposed transmission schemes offer significant video quality improvements and end-to-end delay reduction compared to conventional transmission schemes.; Actuellement les communications véhiculaires sont devenues une réalité guidée par diverses applications. Notamment, la diffusion de vidéo de qualité élevée avec des contraintes de faible latence requises par les applications temps réel. Grâce au niveau de compression jamais atteint auparavant, l’encodeur H.265/HEVC est très prometteur pour la diffusion de vidéos en temps réel dans les réseaux ad hoc véhiculaire (VANET). Néanmoins, la qualité de la vidéo reçue est pénalisée par les mauvaises caractéristiques du canal de transmission (disponibilité, non stationnarité, rapport signal à bruit, etc.). Afin d’améliorer et d’assurer une qualité vidéo minimale à la réception nous proposons dans ce travail une optimisation conjointe source-canal-protocole de la transmission en tenant compte à la fois des paramètres de transmission et d’encodage vidéo. Dans un premier temps, nous montrons l’intérêt et le gain apporté par les solutions dites inter-couches « cross-layer ». Par la suite, nous développons deux approches l’une exploitant un « cross-layer » entre la couche application et la couche MAC et une seconde exploitant les protocoles de transports dans l’adaptation du flux vidéo. En ce qui concerne la première approche nous proposons une solution utilisant une gestion hiérarchique des trames au niveau des files d’attentes de la couche MAC, basée sur l’importance des images du flux vidéo. Dans une seconde solution, nous retenons le codage par descriptions multiples comme solution de protection à la source. Les résultats de simulations obtenus pour plusieurs types de scénarios véhiculaires réalistes montrent que les différents schémas de transmission véhiculaire proposés offrent des améliorations significatives en termes de qualité vidéo à la réception et de retard de bout en bout par rapport aux schémas classiques.

National audience; Un MANET (mobile ad hoc network) est un réseau dynamique décentralisé qui s’autocon-figure continuellement, composé de nœuds pouvant se déplacer librement, rejoindre et quitter le système à tout moment. Les nœuds ne connaissent initialement que leur identifiant. Seuls les nœuds situés dans le rayon de transmission l’un de l’autre communiquent directement. En revanche, toute paire de nœuds peut communiquer via un ensemble de nœuds relais intermé-diaires.L’élection de leader est un composant essentiel des systèmes répartis permettant par exemple, de résoudre le problème du consensus. L’élection consiste à trouver une entente sur l’identité d’un unique nœud, considéré comme leader par l’ensemble des nœuds du réseau. Lors d’un départ ou d’une défaillance du leader, les autres nœuds détectent son indisponibilité et déclenchent une nouvelle élection.De nombreux travaux portent sur l’élection de leader dans les systèmes statiques [6, 8]. Dans un système dynamique, [1] proposent un algorithme adapté aux topologies mobiles et reposant sur des TVG [3]. D’autres algorithmes spécifiques aux MANET utilisent une vision partielle du réseau, le plus souvent pour élire le nœud ayant le plus faible identifiant [7, 5, 4, 2, 9].Notre algorithme obtient une vision globale à partir de la liste des voisins que chaque nœud maintient et échange. Périodiquement, les nœuds envoient un checksum de leur vision pour détecter les incohérences. Les communications sont en broadcast uniquement. Différents critères permettent de choisir le leader comme son ancienneté dans le réseau ou sa centralité.L’algorithme est conçu pour les réseaux mobiles génériques et possède une implémen-tation cross-layer adaptée aux MANET. Notre algorithme exploite la connexion sans fil et les informations maintenues par la couche MAC pour améliorer les performances des échanges.L’algorithme a été implémenté sur une plateforme de dix Raspberry Pi mobiles et de pre-miers résultats sont prometteurs. Dans le futur, le choix du leader sera amélioré en prenant en compte la stabilité des nœuds. Des expérimentations dans le simulateur OMNeT++ permet-tront de tester le passage à l’échelle avec différents patterns de mobilité.

Wireless sensor networks with mobile collectors or sinks face some challenges regarding the data collection process and the continuous connectivity and delivering of data while the mobile sink is moving throughout the network. These challenges increase as the network grows. For this aim, we propose in this paper a cross-layer routing protocol which supports mobility for large-scale wireless sensor networks, which we name CLR-MSPH. We adapt CLR-MSPH for the hierarchical architecture of the network, and it performs on cluster-based wireless sensor networks where the network is organized in clusters. Our proposed protocol deals with the problem of handover data after the mobile sink leaves the radio range of cluster head without sending all data stored in the cluster head&rsquo, s buffer. We also introduce a mobility model for the mobile sink for a better data collection process. CLR-MSPH is considered as an extending implementation of BMAC protocol with handover mechanism (BMAC-H). In order to prove the efficiency of the proposed protocol, we compare CLR-MSPH to BMAC-H, where we adapted BMAC-H to perform in cluster-based wireless sensor networks. The simulation results show that CLR-MSPH performs better than BMAC-H in terms of packets reception rate, energy, and latency.

JIF=2.924; International audience; There is nowadays a growing demand in vehicular communications for real-time applications requiring video assistance. The new state-of-the-art high-efficiency video coding (HEVC) standard is very promising for real-time video streaming. It offers high coding efficiency, as well as dedicated low delay coding structures. Among these, the all intra (Al) coding structure guarantees minimal coding time at the expense of higher video bitrates, which therefore penalizes transmission performances. In this work, we propose an original cross-layer system in order to enhance received video quality in vehicular communications. The system is low complex and relies on a multiple description coding (MDC) approach. It is based on an adaptive mapping mechanism applied at the IEEE 802.11p standard medium access control (MAC) layer. Simulation results in a realistic vehicular environment demonstrate that for low delay video communications, the proposed method provides significant video quality improvements on the receiver side. (C) 2019 Elsevier GmbH. All rights reserved.

Intelligent Transportation Systems (ITS) have been one of the promising technology that has a great interest attention from many researchers over the world. Vehicular Ad-hoc Network (VANET) communications environment as a part of ITS opens the way for a wide range of applications such as safety applications, mobility and connectivity for both driver and passengers to exploit the transport systems in a smoothly, efficiently and safer way. Several challenging tasks facing adopting VANET functionality for ITS such as modelling of wireless transmission and routing issues. These research issues have become more critical due to the high mobility of vehicles nodes (transmitters and receivers) and unexpected network topology due to the high speed of nodes. In fact, modelling radio propagation channel in VANET environment which considers as one of a stringent communications environment is a challenging task. The selection of a suitable transmission model plays a key role in the routing decisions for VANET. Different propagation models allow calculating the Received Signal Strength (RSS) based on key environmental properties such as the distance between transmitter vehicle and a receiver vehicle, the gain and antenna height of transmitter and a receiver vehicles. Hence, it is useful to calculate RSS and SNR values for a specific propagation model and then these values can be used later for routing decision in order to find the best path with high SNR. This paper evaluates the performance of different transmission models (freespace, two-ray and log-normal) in terms of Receive Signal Strength (RSS). In addition, the performance of such wireless transmission models for vehicular communication in terms of PDR, throughput and delay is evaluated by applying the proposed cross layer routing approach based on IEEE 802.11p. By using MATLAB, the obtained results confirm the best packet delivery ratio for our proposed approach, where it indicates poor quality of DSSS PHY with high number vehicles. The minimum delay achieved when traffic density is decreased.

A significant fraction of data traffic is transmitted via cellular networks. When introducing fifth-generation (5G) radio access technology, the maximum bitrate of the radio link increases significantly, and the delay is lowered. Network congestion occurs when the sender attempts to send data at a higher rate than the network link or nodes can handle. In order to improve the performance of the mobile networks, many congestion control techniques and approaches have been developed over the years. Varying radio conditions in mobile networks make it challenging to indicate the occurrence of the congestion using packet loss as congestion indicator. This master thesis develops a congestion control algorithm based on Artificial Intelligence (AI) technologies, evaluates and compares it with existing state-of-the-art congestion control algorithms that are used with TCP today.In this study, we use the abundant readable physical layer information exchanged between the base stations and the user equipment to predict the available bandwidth. Two neural network models, Multi-Layer Perceptron (MLP) and Long Short-Term Memory (LSTM), are introduced as congestion control algorithms based on cross-layer information in order to improve user throughput and utilize the available capacity as much as possible.Evaluation in a Long-Term Evolution (LTE) network system simulator confirms that the estimation of LSTM model is able to track the varying link capacity, while MLP is less accurate and induces higher delay. The sender uses the estimated link capacity to adjust its packet sending behavior. Our evaluation reveals that for large flows, the LSTM model can attain higher throughput than state-of-the-art congestion control algorithms, which are the Google Bottleneck Bandwidth and Round-trip propagation time (BBR) algorithm and the Data Center TCP (DCTCP) algorithm. However, it has higher latency than that of these two algorithms. The MLP based model provides unstable performance compared to LSTM; its prediction is not accurate enough and has the highest latency among the algorithms.In conclusion, the LSTM does not underperform the state-of-the-art congestion control algorithms. However, it does not provide additional performance gains in current settings. The MLP model underperforms BBR and DCTCP with L4S and it is not stable enough to be used as a congestion control algorithms. En betydande del av datatrafiken överförs via mobilnät. Vid introduktion av femte generationens (5G) radioåtkomstteknik ökar den maximala bithastigheten för radiolänken betydligt och förseningen sänks. Nätstockning uppstår när avsändaren försöker skicka data med högre hastighet än nätverkslänken eller noderna kan hantera. För att förbättra prestandan i mobilnät har många tekniker för trängselkontroll utvecklats under åren. Varierande radioförhållanden i mobilnätet gör det utmanande att indikera förekomsten av trängseln med hjälp av paketförlust som trängselindikator. Detta examensarbete utvecklar en trängselkontrollalgoritm baserad på AI-teknik (Artificial Intelligence), utvärderar och jämför den med befintliga toppmoderna trängselkontrollalgoritmer som används med TCP idag.I denna studie använder vi den rikliga läsbara informationen om fysiskt lager som utbyts mellan basstationerna och användarutrustningen för att förutsäga den tillgängliga bandbredden. Två neurala nätverksmodeller, Multi-Layer Perceptron (MLP) och Long Short-Term Memory (LSTM), introduceras som trängselkontrollalgoritmer baserade på tvärskiktsinformation för att förbättra användarens genomströmning och utnyttja den tillgängliga kapaciteten så mycket som möjligt.Utvärdering i en LTE-nätverkssystemsimulator (Long Term Evolution) bekräftar att uppskattningen av LSTM-modellen kan spåra den varierande länkkapaciteten, medan MLP är mindre exakt och inducerar högre fördröjning. Avsändaren använder den uppskattade länkkapaciteten för att justera sitt paketets sändningsbeteende. Vår utvärdering avslöjar att för stora flöden kan LSTM-modellen uppnå högre genomströmning än modernaste trängselkontrollalgoritmer, som är Google Bottleneck Bandbredd och BBR-algoritm och Data Center TCP (DCTCP) ) algoritm. Men det har högre latens än för dessa två algoritmer. Den MLP-baserade modellen ger instabil prestanda jämfört med LSTM; dess förutsägelse är inte nog noggrann och har den högsta latensen bland algoritmerna.Sammanfattningsvis underpresterar LSTM inte de senaste toppkontrollalgoritmerna. Det ger emellertid inte ytterligare prestationsvinster i de aktuella inställningarna. MLP-modellen underpresterar BBR och DCTCP med L4S och den är inte tillräckligt stabil för att användas som en överbelastningskontrollalgoritm.

This thesis studies the problems of bi-objective optimization of communications in heterogeneous wireless sensor networks from the point of view of the transmission range. The first criterion considered is necessarily the energy consumption that needs to be reduced in order to maximize the network lifetime. The second criterion is the data transmission delay, which is essential for real-time applications.To solve the optimality problem of the broadcast, we propose an integer linear program, an approximation algorithm, a meta-heuristic and a heuristic. All these techniques allow us to find or to approximate the optimal solutions for the considered objectives.For the routing case, we propose a cross-layer approach via an integer linear program between the MAC and Data Link layers. Our method calculates the optimal solution, which represents a point of comparison for future heuristics having the same goals.; Cette thèse étudie les problèmes d’optimisation bi-objectifs des communications dans les réseaux de capteurs hétérogènes du point de vue de la portée de transmission. Le premier critère considéré est nécessairement l'énergie qu’il s’agit d'économiser pour maximiser la durée de vie du réseau. Le second critère, primordial dans le cas des applications en temps réel, est le délai d'acheminement des données. Pour résoudre le problème d’optimalité de la diffusion, nous proposons une formulation en nombres entiers, un algorithme d’approximation, une méta-heuristique et une heuristique permettant de trouver ou s’approcher de l’optimal pour les objectifs considérés.Pour le cas du routage, nous proposons une approche cross-layer via un programme linéaire en nombre entiers entre les couches MAC et Liaison de données permettant de calculer l'optimal et ainsi d'avoir un point de comparaison pour les futures heuristiques ayant les mêmes objectifs.

This thesis studies the problems of bi-objective optimization of communications in heterogeneous wireless sensor networks from the point of view of the transmission range. The first criterion considered is necessarily the energy consumption that needs to be reduced in order to maximize the network lifetime. The second criterion is the data transmission delay, which is essential for real-time applications.To solve the optimality problem of the broadcast, we propose an integer linear program, an approximation algorithm, a meta-heuristic and a heuristic. All these techniques allow us to find or to approximate the optimal solutions for the considered objectives.For the routing case, we propose a cross-layer approach via an integer linear program between the MAC and Data Link layers. Our method calculates the optimal solution, which represents a point of comparison for future heuristics having the same goals.; Cette thèse étudie les problèmes d’optimisation bi-objectifs des communications dans les réseaux de capteurs hétérogènes du point de vue de la portée de transmission. Le premier critère considéré est nécessairement l'énergie qu’il s’agit d'économiser pour maximiser la durée de vie du réseau. Le second critère, primordial dans le cas des applications en temps réel, est le délai d'acheminement des données. Pour résoudre le problème d’optimalité de la diffusion, nous proposons une formulation en nombres entiers, un algorithme d’approximation, une méta-heuristique et une heuristique permettant de trouver ou s’approcher de l’optimal pour les objectifs considérés.Pour le cas du routage, nous proposons une approche cross-layer via un programme linéaire en nombre entiers entre les couches MAC et Liaison de données permettant de calculer l'optimal et ainsi d'avoir un point de comparaison pour les futures heuristiques ayant les mêmes objectifs.

A new phase of evolution of Machine-to-Machine (M2M) communication has started where vertical Internet of Things (IoT) deployments dedicated to a single application domain gradually change to multi-purpose IoT infrastructures that service different applications across multiple industries. New networking technologies are being deployed operating over sub-GHz frequency bands that enable multi-tenant connectivity over long distances and increase network capacity by enforcing low transmission rates to increase network capacity. Such networking technologies allow cloud-based platforms to be connected with large numbers of IoT devices deployed several kilometres from the edges of the network. Despite the rapid uptake of Long-power Wide-area Networks (LPWANs), it remains unclear how to organize the wireless sensor network in a scaleable and adaptive way. This paper introduces a hierarchical communication scheme that utilizes the new capabilities of Long-Range Wireless Sensor Networking technologies by combining them with broadly used 802.11.4-based low-range low-power technologies. The design of the hierarchical scheme is presented in detail along with the technical details on the implementation in real-world hardware platforms. A platform-agnostic software firmware is produced that is evaluated in real-world large-scale testbeds. The performance of the networking scheme is evaluated through a series of experimental scenarios that generate environments with varying channel quality, failing nodes, and mobile nodes. The performance is evaluated in terms of the overall time required to organize the network and setup a hierarchy, the energy consumption and the overall lifetime of the network, as well as the ability to adapt to channel failures. The experimental analysis indicate that the combination of long-range and short-range networking technologies can lead to scalable solutions that can service concurrently multiple applications.

A reliable energy-efficient routing protocol plays a key role in underwater data transmission. In the face of acoustic communication challenges in underwater wireless sensor networks (UWSNs), including long propagation delay, topology change, limited energy, and communication voids, we propose RECRP, a Reliable Energy-efficient Cross-layer Routing Protocol to achieve high data delivery rate. RECRP is a location-free single-copy protocol. The information of the physical layer such as Doppler scale shift measurement, Received Signal Strength Indication (RSSI), etc. are adopted to estimate the distance, thus no extra hardware is needed for localization. Moreover, the overhead introduced by redundant packets is avoided with the single-copy mechanism. To improve the two-hop packet delivery rate and balance energy consumption among adjacent nodes, an optimal max–min method is proposed that dynamically controls transmission power and channel frequency. Furthermore, a surface to bottom routing establishment method is also adopted to handle communication voids. Compared with depth-based routing (DBR) and hop-by-hop vector-based forwarding (HH-VBF) , RECRP is more energy-efficient with a higher delivery rate.

The down-scaling of CMOS technologies has continuously offered better performance, lower power and higher level of integration. However, in advanced nodes with smaller feature sizes, on-chip components such as transistors and interconnects are experiencing more aggressive degradations due to wearout (aging) effects, which are dominated by Bias temperature instability (BTI) and Electromigration (EM). Transistors become more susceptible to voltage stress due to the increased effective field with the scaling of the thin oxide. Similarly, the shrinking geometries of metal interconnects render higher current densities, and the tremendous number of transistors within a compact area has resulted in higher power densities as well. Together, these lead to increased on-chip temperatures which potentially accelerate the wearout effects. In the meanwhile, with the ubiquity of electronics (e.g. IoT) in our daily lives, there have been increasing demands for reliable system design. Many of such applications require very long lifetime, higher utilization rate and tighter hard error tolerances. They are possibly deployed in extreme environmental conditions, such as high temperatures, which, unfortunately, further accelerate wearout. Conventional techniques of coping with wearout by “tolerating”, “slowing down” or “compensating” still leave the wearout themselves unchecked since they keep accumulating fundamentally as the system operates. Moreover, the continuous increase of irreversible components of wearout over the entire lifetime will cause permanent errors and failures potentially. This thesis proposes and demonstrates a new category of techniques that “repair” wearout in a physical sense through accelerated and active recovery, by which wearout (both BTI and EM) can be reversed by actively applying several techniques, such as high temperature, negative voltages (for BTI) and reverse currents (for EM), thus leading to effective accelerated self-healing. By studying the frequency dependent behaviors of wearout and recovery experimentally, we demonstrate that the permanent portion of wearout can be almost fully eliminated and avoided by using in-time scheduled recovery. Our experiments on hardware demonstrate that the accelerated self-healing techniques for both BTI and EM wearout are fast, effective and feasible to implement. To enable the on-chip implementations of the accelerated self-healing techniques and fully utilize the explored frequency dependent behaviors, we propose a cross-layer accelerated self-healing (CLASH) system which instruments the notion of recovery across multiple layers (from circuit to the system level) of the system stack. A full set of circuit IP blocks, including recovery boost components, novel BTI and EM sensors, multi-mode recovery assist scheme and novel power gating structures, is designed and implemented. As wearout-induced failures become more visible at the system level, we also explore several potential architecture and system level solutions that are able to take advantages of intrinsic sleep behaviors for full recovery. Overall, these techniques work together to guarantee that the entire system performs for more of the time at higher levels of performance and power efficiency by fully exploiting the extra opportunities enabled by the accelerated self-healing. Leading-edge nodes such as FinFETs endeavor to offer advantages of future scaled devices while offsetting the problems introduced by many generations of planar CMOS scaling. Adapting to the new challenges and fully benefiting from FinFETs require the growing knowledge and design experiences. To contribute to this knowledge base, in this thesis, we perform a comprehensive study based on circuit simulations across multiple technology nodes ranging from conventional bulk to advanced planar technology nodes such as Fully Depleted Silicon-on-Insulator (FDSOI), to FinFETs. As challenges such as wearout appear to be more pronounced in these advanced nodes, and this grow to be critical especially in IoT applications in which industry is in the process of updating technologies. Each of those end markets has unique needs and characteristics, which affects how chips are used and under what conditions. We investigate how wearout can impact different categories of future IoT applications with foundry-provided wearout models. We conclude that wearout needs to be considered in the full design cycle and the IoT lifetime estimation requires to incorporate wearout as an important factor. IoT-specific design solutions for mitigating wearout are also presented in this thesis.

International audience; For future satellite-to-ground communications link, very high throughput might be achievable at a reasonable cost assuming the use of existing single mode components developed for fiber technologies (optical detectors and amplifiers, MUX/DEMUX...). The influence of atmospheric turbulence degrades the injection efficiency of the incoming wave into single mode components. This leads to signal fading and channel impairments. Several mitigation strategies are considered to prevent them. The use of adaptive optics (AO) should contribute to reduce substantially the criticality of the fading at the expense of potentially complex and expensive systems if very high stability of the injection is requested. The use of appropriate numerical mitigation techniques (coding+interleaving) can help to relax the specifications and cost of AO systems but could lead among others to unmanageable buffer size. Thus the specification of AO correction and interleavers/forward error codes should be addressed jointly. A model to evaluate the channel capacity in terms of outage probability and packet error rate has been developed that jointly takes into account partial correction by AO and channel interleaving. It includes the capability to evaluate coded transmission performance over the correlated FSO channel in satellite-to-ground scenarios. This model is presented here and confronted to numerical simulations for several distinct correction cases. The interdependence of AO correction with numerical mitigation techniques is investigated.

International audience; Nowadays, multimedia contents are widely used for various applications to ensure personal and public services. These contents are transferred through different kind of networks, from classical Adhoc networks to the recent Internet of Things (IoT). However, regardless the deployed network, the main goal remains the same: ensuring a better QoE and lower latency especially for emergency traffic. This domain attracts many research activities. In this paper, we tackle this challenging issue and we propose a cross-layer scheme to handle video streaming over wireless sensor networks. Our starting point is to provide a carrier sense aware disjoint multi-path routing protocol able to transmit efficiently video and regular data from many sources to a unique base station (BS). This protocol will cooperate with the application layer in order to provide a frame-aware solution to assign high priority to the most important frames and low priority for the least important ones. This priority concerns mainly the path that will be used by the frames and the queuing policy that will be applied. Our queuing policy involves enqueuing, de-queuing and congestion solving function in order to reduce video latency and enhance the reliability. Our scheme is explained through a theoretical study with illustrative examples. Many simulations have been conducted, they demonstrate improvements of some performance indicators such as packet data rate, delay, loss packet rate and user experience.