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Summary: Delivering health care services in isolated rural regions through vehicular communications has recently attracted more attention. Real‐time video streaming is one of the most commonly used applications in such services. Escorting this increasing interest, there is a high demand for high‐quality streaming video in vehicular environments. However, these services face challenging network characteristics such as high dynamic topology and mobility, leading to high packet loss and degraded visual quality. This paper presents a smart approach to multi‐access terrestrial and non‐terrestrial networks (NTNs) for high‐quality and real‐time video streaming in vehicular environments to enhance healthcare services in remote areas. The proposed approach integrates a mobile telemedicine unit connected to an expert site through different channels, including satellite communication, 5 Generation (5G), and long‐term evolution (LTE) networks. A modified multipath QUIC extension (MPQUIC) was introduced to stream data hybridly through the network. Furthermore, multiple description coding (MDC) was applied to split data into two different descriptions and prioritize one over the other. Important data would be transmitted through cellular networks, and non‐priority data would be transmitted through satellite links, enhancing the reliability of video transmission. We demonstrate the effectiveness of our approach through the use of a connected ambulance as a case study. The Mininet‐Wifi was used to emulate network conditions. The peak signal‐to‐noise ratio (PSNR), structural SIMilarity (SSIM), and video multimethod assessment fusion (VMAF) were measured to evaluate the received video quality; the received data rate and packet loss were also calculated. The obtained results show our proposed method's efficiency by reducing latency by up to 60% and improving the receiving data rate by up to 143% compared with the classical MPQUIC. The proposed system enhances video quality by up to 70%. It minimizes packet loss by up to 50% compared with the unreliable QUIC, showcasing the potential to improve m‐health services' efficiency and mobility in isolated rural regions. [ABSTRACT FROM AUTHOR]

Summary: Wireless sensor networks (WSNs) are becoming increasingly important and well liked for delivering pervasive computing environments for a range of applications. Extending the networking life lifetime in WSNs is an important issue that must be addressed. Effective techniques for conserving the WSN's limited energy resources must be developed. Cross‐layer protocols are employed in WSNs to solve network lifespan difficulties. This paper proposes a new cross‐layer–cross‐domain routing scheme with stages such as "(1) network association stage, (2) nearer node detection phase, and (3) consistent state phase." In the consistent stage, the optimal cluster head selection (CHS) is carried out by taking into account risk, delay, energy, trust, and distance. A new model called manta ray collided dwarf mongoose optimization (MRC‐DMO) is introduced to help with this. Furthermore, the routing is accomplished by dependable data communication. The results obtained establish the effectiveness of the MRC‐DMO scheme for SEACRCLCD in WSN over varied methods. [ABSTRACT FROM AUTHOR]

A minimal interrupted communication link set-up is the primary objective of the medium access control (MAC) layer. The MAC layer is responsible for accessing the communication channel. At MAC, the control information is shared through the control channel to select a collision-free data channel. Therefore, the sharing of control information prior to data channel selection plays a pivotal role in achieving desired QoS in cognitive radio (CR) technology. Random data channel selection is used in conventional CR scenarios. The random selection lacks in performance due to the unpredictability of primary user (PU) appearances in the data channels. This unpredictability in PU appearances will increase the number of handoffs in the scenario and sometimes may cause communication link failure. These adverse effects can be avoided if a common data channel selection scheme is implemented. The common data channel selection scheme selects a channel that has fewer chances of PU appearance. In the reported research paper, the work has been carried out in two parts. In the first part, a primary user free channel list (PCL) is formed at each node. Each node shares its PCL with all neighbour nodes. The sharing of PCL helps in implementing a common data channel selection scheme among nodes. The selected common channel is used for data transmission. The conventional OSI layered structure is used to observe the performances. In the second part, a cross-layer framework is proposed to improve the performance. The cross-layering has been done between the network and MAC layer. The proposed cross-layer framework helps in sharing PCL in an efficient manner. The need for an extra beacon to transfer PCL is avoided in the proposed cross-layer design. The performance of control overhead and average delay becomes better. The simulation results show that the proposed cross-layer design approach performs satisfactorily in terms of packet delivery ratio, average throughput, average delay, average energy consumption and control overhead. The observed results are also tested in the worst scenarios. The proposed scheme has applications in small throughput scenarios like cognitive radio aided sensor networks. [ABSTRACT FROM AUTHOR]

The continental shale reservoirs of Jurassic Lianggaoshan Formation in Sichuan Basin contain thin lamina, which is characterized by strong plasticity and developed longitudinal shell limestone interlayer. To improve the production efficiency of reservoirs by multi-cluster fracturing, it is necessary to consider the unbalanced propagation of hydraulic fractures and the penetration effect of fractures. This paper constructed a numerical model of multi-fracture propagation and penetration based on the finite element coupling cohesive zone method; considering the construction cluster spacing, pump rate, lamina strength and other parameters studied the influencing factors of multi-cluster fracture interaction propagation; combined with AE energy data and fracture mode reconstruction method, quantitatively characterized the comprehensive impact of the strength of thin interlayer rock interfaces on the initiation and propagation of fractures that penetrate layers, and accurately predicted the propagation pattern of hydraulic fractures through laminated shale oil reservoirs. Simulation results revealed that in the process of multi-cluster fracturing, the proportion of shear damage is low, and mainly occurs in bedding fractures activated by outer fractures. Reducing the cluster spacing enhances the fracture system's penetration ability, though it lowers the activation efficiency of lamina. The high plasticity of the limestone interlayer may impact the vertical propagation distance of the main fracture. Improving the interface strength is beneficial to the reconstruction of the fracture height, but the interface communication effect is limited. Reasonable selection of layers with moderate lamina strength for fracturing stimulation, increasing the pump rate during fracturing and setting the cluster spacing reasonably are beneficial to improve the effect of reservoir stimulation. [ABSTRACT FROM AUTHOR]

Change detection aims to identify the difference between dual-temporal images and has garnered considerable attention over the past decade. Recently, deep learning methods have shown robust feature extraction capabilities and have achieved improved detection results; however, they exhibit limitations in preserving clear boundaries for the identified regions, which is attributed to the inadequate contextual information aggregation capabilities of feature extraction, and fail to adequately constrain the delineation of boundaries. To address this issue, a novel dual-branch feature interaction backbone network integrating the CNN and Transformer architectures to extract pixel-level change information was developed. With our method, contextual feature aggregation can be achieved by using a cross-layer feature fusion module, and a dual-branch upsampling module is employed to incorporate both spatial and channel information, enhancing the precision of the identified change areas. In addition, a boundary constraint is incorporated, leveraging an MLP module to consolidate fragmented edge information, which increases the boundary constraints within the change areas and minimizes boundary blurring effectively. Quantitative and qualitative experiments were conducted on three benchmarks, including LEVIR-CD, WHU Building, and the xBD natural disaster dataset. The comprehensive results show the superiority of the proposed method compared with previous approaches. [ABSTRACT FROM AUTHOR]

Opportunistic networks, an evolution of mobile Ad Hoc networks (MANETs), offer decentralized communication without relying on preinstalled infrastructure, enabling nodes to route packets through different mobile nodes dynamically. However, due to the absence of complete paths and rapidly changing connectivity, routing in opportunistic networks presents unique challenges. This paper proposes a novel probabilistic routing model for opportunistic networks, leveraging nodes' meeting probabilities to route packets towards their destinations. Thismodel dynamically builds routes based on the likelihood of encountering the destination node, considering factors such as the last meeting time and acknowledgment tables to manage network overload. Additionally, an efficient message detection scheme is introduced to alleviate high overhead by selectively deleting messages from buffers during congestion. Furthermore, the proposed model incorporates cross-layer optimization techniques, integrating optimization strategies across multiple protocol layers to maximize energy efficiency, adaptability, and message delivery reliability. Through extensive simulations, the effectiveness of the proposed model is demonstrated, showing improved message delivery probability while maintaining reasonable overhead and latency. This research contributes to the advancement of opportunistic networks, particularly in enhancing connectivity and efficiency for Internet of Things (IoT) applications deployed in challenging environments. [ABSTRACT FROM AUTHOR]

With the recent advancement in mobile adhoc networks (MANET's) and technology, applicability, and integration of wireless multimedia sensor networks (WMSN) in MANET's has led to creation of smart distributed system for high-speed mobile multimedia streaming and real time multimedia traffic transmissions. In this paper, we propose cross-layer multipath routing approach with link quality indicator (CLMRLQI) to compute stable link between two nodes. CLMRLQI discovers stable multipath routes by considering cross-layer routing metrics such as energy and bandwidth to support quality of service (QoS). The simulation scenarios are carried on network simulation tool and QoS parameters such as throughput, PDR, delay, overhead and energy consumption are analysed. [ABSTRACT FROM AUTHOR]

Recently, security has been necessary in this computer world due to the fast development of technology and enormous user strength. The different kinds of security mechanisms including the Intrusion Detection System (IDS) were developed by many researchers to confirm the security of the data in the communication process. In general, the IDS are used to detect anomalous nodes, and attacks and increase the security level. Even though, the various disadvantages are available to ensure the data reliability on different kinds of applications. For this purpose, this work proposes a cross-layer IDS that is a combination of the trust-based secure routing method, attribute selection and classification algorithms. This study introduces a novel attribute selection approach known as the Weighted Genetic Feature Selection Algorithm (WGFSA). This method is designed to identify and prioritize valuable attributes within the context of network, physical, and data link layers. And introduce a deep classifier called the Hyperparameter-Tuned Fuzzy Temporal Convolutional Neural Network (HFT-CNN) for efficient categorization. Additionally, we propose a pioneering secure routing algorithm known as the Fuzzy Logic and Time-Constrained Dynamic Trusted Cross-Layer-Based Secure Routing Algorithm (FCSRA) to ensure the secure transmission of data packets. The effectiveness of the newly developed system is proved by conducting experiments with the network, standard Aegean Wi-Fi intrusion dataset (AWID) and proved superior to other systems in delay, energy consumption, packet delivery rate, and prediction accuracy. [ABSTRACT FROM AUTHOR]

In high-speed mobile scenarios characteristic of the Fifth Generation Mobile Networks (5G) environment, the user video experience may be compromised due to concurrent access by numerous users and frequent base station transitions. Addressing this issue, this study introduces a cross-layer resource allocation model that integrates environmental awareness and is tailored for the exigencies of high-speed mobile networks. The paper delves into the challenges engendered by rapid mobility and extensive user access within the 5G environment and critiques the constraints of prevalent resource allocation methodologies. The model delineated herein is conceptualized as an optimization challenge and characterized as a nonlinear, NP-hard problem. In response to this challenge, this study advocates a novel streaming media transmission algorithm underpinned by edge computing, in tandem with an environment-aware wireless resource allocation algorithm. The article articulates the foundational principles and operational modalities of these algorithms, underscoring the significance of environmental cognizance in resource distribution and the efficacy of edge computing in increasing video transmission efficiency. Empirical validation, achieved through simulation experiments, corroborates the efficacy of the proposed approach. Comparative analysis reveals that, relative to conventional methodologies, the proposed framework significantly improves video transmission quality and user experience, particularly in contexts characterized by frequent network fluctuations and high user densities. This research contributes novel insights and pragmatic solutions to optimize video transmission in existing 5G and prospective network paradigms. [ABSTRACT FROM AUTHOR]

Change detection aims to identify the difference between dual-temporal images and has garnered considerable attention over the past decade. Recently, deep learning methods have shown robust feature extraction capabilities and have achieved improved detection results; however, they exhibit limitations in preserving clear boundaries for the identified regions, which is attributed to the inadequate contextual information aggregation capabilities of feature extraction, and fail to adequately constrain the delineation of boundaries. To address this issue, a novel dual-branch feature interaction backbone network integrating the CNN and Transformer architectures to extract pixel-level change information was developed. With our method, contextual feature aggregation can be achieved by using a cross-layer feature fusion module, and a dual-branch upsampling module is employed to incorporate both spatial and channel information, enhancing the precision of the identified change areas. In addition, a boundary constraint is incorporated, leveraging an MLP module to consolidate fragmented edge information, which increases the boundary constraints within the change areas and minimizes boundary blurring effectively. Quantitative and qualitative experiments were conducted on three benchmarks, including LEVIR-CD, WHU Building, and the xBD natural disaster dataset. The comprehensive results show the superiority of the proposed method compared with previous approaches.

Internet of Vehicles (IoV), a vital component in intelligent transportation systems, supports diverse applications ranging from periodic safety message exchanges to on-demand multimedia streaming, gaming, and infotainment. Unicast routing is one of the primary techniques that support many such applications. Due to its highly dynamic nature and variable channel conditions, routing is extremely challenging in IoVs. Multiple contrasting metrics comprising cross-layer information need to be jointly examined to achieve an effective and reliable routing decision. In this paper, we propose a new unicast V2V routing protocol that consolidates and harmonizes the conflicting cross-layer and positional metrics such as distance, direction, link quality, link lifetime, available bandwidth, and queue information to select the most suitable next-hop nodes while forwarding packets. We combine these contrasting metrics using Fuzzy Logic to present a new intelligent routing protocol. Extensive simulation shows that our proposed protocol outperforms the standard existing routing techniques in terms of packet delivery ratio and average end-to-end delay across diverse vehicular environments. [ABSTRACT FROM AUTHOR]

Opportunistic networks, an evolution of mobile Ad Hoc networks (MANETs), offer decentralized communication without relying on preinstalled infrastructure, enabling nodes to route packets through different mobile nodes dynamically. However, due to the absence of complete paths and rapidly changing connectivity, routing in opportunistic networks presents unique challenges. This paper proposes a novel probabilistic routing model for opportunistic networks, leveraging nodes’ meeting probabilities to route packets towards their destinations. Thismodel dynamically builds routes based on the likelihood of encountering the destination node, considering factors such as the last meeting time and acknowledgment tables to manage network overload. Additionally, an efficient message detection scheme is introduced to alleviate high overhead by selectively deleting messages from buffers during congestion. Furthermore, the proposed model incorporates cross-layer optimization techniques, integrating optimization strategies across multiple protocol layers to maximize energy efficiency, adaptability, and message delivery reliability. Through extensive simulations, the effectiveness of the proposed model is demonstrated, showing improved message delivery probability while maintaining reasonable overhead and latency. This research contributes to the advancement of opportunistic networks, particularly in enhancing connectivity and efficiency for Internet of Things (IoT) applications deployed in challenging environments.

Summary: The Internet of Things (IoT) has recently attained a prominent role in enabling smooth and effective communication among various networks. Wireless sensor network (WSN) is utilized in IoT to collect peculiar data without interacting with humans in specific applications. Energy is a major problem in WSN‐assisted IoT applications, even though better data communication is achieved through cross‐layer models. This paper proposes a new cross‐layer‐based clustering and routing model to provide a scalable and energy‐efficient long data communication in WSN‐assisted IoT systems for smart agriculture. Initially, the fuzzy k‐medoids clustering approach is used to split the network into various clusters since the formation of clusters plays an important role in energy consumption. Then, a new swarm optimization known as enhanced sparrow search algorithm (ESSA), which is the combination of SSA and chameleon swarm algorithm (CSA), has been introduced for optimal cluster head (CH) selection to solve the energy‐hole problems in WSN. A cross‐layer strategy has been preferred to provide efficient data transmission. Each sensor node parameter of the physical layer, network layer and medium access control (MAC) is considered for processing routing. Finally, a new bio‐inspired algorithm is known as the sandpiper optimization algorithm (SOA), and cosine similarity (CS) has been employed to determine the optimal route for efficient data transmission and retransmission. The simulation of the proposed protocol is implemented by network simulator (NS2), and the simulation results are taken in terms of end‐to‐end delay, PDR, communication overhead, communication cost, average consumed energy, and network lifetime. [ABSTRACT FROM AUTHOR]

Recently, unmanned aerial vehicle (UAV) networks have been widely used in military and civilian scenarios; however, they suffer various attacks. Time-delay attacks maliciously delay the transmission of packets without tampering with the contents or significantly affecting the transmission pattern, making detection difficult. In this paper, a holistic cross-layer time-delay attack detection framework (HOTD) is proposed for UAV networks. A holistic selection of the delay-related features available at all layers is performed, before adopting supervised learning to build a consistency model between these features and the corresponding forwarding delay to calculate the degree of consistency of each node. Finally, the clustering method is used to distinguish malicious from benign nodes according to their degree of consistency. Experimental results show that the performance of HOTD is superior to that of state-of-the-art detection methods, and it achieves a detection accuracy higher than 85% with less than 2.5% additional overhead. • Time-delay attacks are modelled in UAV networks for the first time. • HOTD collects delay-related features at all layers in the UAV network protocol. • The relationship between the features and delay is used to identify malicious nodes. • Simulations show the method achieves over 85% detection accuracy. • HOTD is better than other available methods in detecting time-delay attack nodes. [ABSTRACT FROM AUTHOR]

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.

Intrusion is any unwanted activity that can disrupt the normal functions of wired or wireless networks. Wireless mesh networking technology has been pivotal in providing an affordable means to deploy a network and allow omnipresent access to users on the Internet. A multitude of emerging public services rely on the widespread, high-speed, and inexpensive connectivity provided by such networks. The absence of a centralized network infrastructure and open shared medium makes WMNs particularly susceptible to malevolent attacks, especially in multihop networks. Hence, it is becoming increasingly important to ensure privacy, security, and resilience when designing such networks. An effective method to detect possible internal and external attack vectors is to use an intrusion detection system. Although many Intrusion Detection Systems (IDSs) were proposed for Wireless Mesh Networks (WMNs), they can only detect intrusions in a particular layer. Because WMNs are vulnerable to multilayer security attacks, a cross-layer IDS are required to detect and respond to such attacks. In this study, we analyzed cross-layer IDS options in WMN environments. The main objective was to understand how such schemes detect security attacks at several OSI layers. The suggested IDS is verified in many scenarios, and the experimental results show its efficiency.

Vehicular Ad-hoc Network (VANET) is a set of intelligent vehicles that communicate without a fixed infrastructure. The communication between each source/destination pair is done by using routing protocols. On-demand multipath distance vector (AOMDV) is one of the most known ad-hoc multipath routing protocols used in VANETs. The decentralized nature of VANET makes this type of network vulnerable to various attacks, such as blackhole attack. In such attack, the malicious vehicle aims to make the communication unavailable. To achieve this goal, the malicious vehicle persuades the source to send its data packets through it because it has the fresher route toward the destination. This is done by forging routing information. After receiving the data packets, the malicious vehicle deletes them instead of forwarding them to their intended destinations. This paper introduces a new Cross-Layer method (CRAOMDV) where information is shared between MAC and network layers to detect and ignore the malicious vehicles in VANETs. Our experiments used the simulator NS2 and SUMO for the generation and simulation of real mobility scenarios. The evaluation results demonstrate the efficiency of CRAOMDV compared to AOMDV under blackhole attack in terms of improving the packet delivery and reducing the average end-to-delay and the routing overhead. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The Internet of Healthcare Things has significantly altered traditional patient-doctor relationships by allowing essential healthcare treatment from the comfort of one’s home. The wireless body area network is an IEEE 802.15.6 standard that focuses on healthcare data, necessitating various cross-layer and thermal-aware protocols. However, most cross-layer protocols have long convergence delays and a single failure point. Moreover, these protocols exploit excessive broadcasts and handshake acknowledgments, causing communication and processing overheads. Furthermore, thermal-aware protocols focus on thermal variations, disperse data collection, and do not support cross-layer techniques. To address these limitations, this study proposes an optimal distributive cross-layer and thermal-aware convergecast protocol. The proposed protocol enforces a novel hybrid convergecast using probability and both minimum attenuation strategies to collect data from leaf nodes to the root to improve data flow and adaptability in the network. In addition, it accelerates the convergence process by reducing recurrent broadcasts and unnecessary acknowledgments, resulting in improved energy efficiency and thermal control. The proposed protocol supports a distributive hierarchy by establishing multiple parent-child relationships to avoid a single root point failure. A multi-parameter maximum benefit-cost function is used to calculate the next hop according to the extracted weights. Packet loss probability validates the number and sequence of packets received at the sink node. The simulation results demonstrate that cross-layer and thermal-aware protocols can coexist effectively. The proposed protocol reduces delays to 19.4%, improves throughput from 8% to 13.75%, and retains a packet loss probability of 0.3% by keeping the thermal rise within bounds.

Secure communication with efficient energy consumption in Wireless Sensor Networks (WSN) is a challenging process. The existing security schemes compromises energy consumption due to their design and computational complexity to achieve reliable data transmission. In this paper, a novel energy efficient adaptive security aware routing mechanism "Secure Cross layer Energy Supplementing Adhoc on-demand Multipath Distance Vector (SCES-AOMDV) routing protocol for energy efficient design of WSN" is proposed. Security attacks arise at any layer in the protocol stack during transmission. The limitations of traditional layered approaches can be overcome by developing a cross layering technique for direct communication between non adjacent layers to arrive at an optimum solution. The proposed cross layer approach formulates a novel One Time Password (OTP) verification mechanism to secure the network against Denial-of-Service (DoS) attacks for achieving maximized Network Lifetime (NL). The proposed SCES-AOMDV routing protocol localize the nodes with novel swarm intelligence algorithm which is named as "Hybrid Tunicate Weighted Salp Swarm algorithm (HyTWSSA)" for obtaining the best position of the sensor nodes. Simulations are performed and compared with Crosslayer-RSA, BiHCLR and CL-NDRECT. The empirical results show that the proposed SCES-AOMDV outperforms the conventional methods and reduce the harm of DoS attacks and maximize the network lifetime. [ABSTRACT FROM AUTHOR]

In this paper, GSA-based Exigency Routing (GSA-ER) and CSA-based Near Exigency Relaying (CSA-NER) models have been proposed that support Multiple SS-based Optimized Cross-layer Routing (MB-OCR) framework. The GSA-ER model uses channel statistics, delivery estimates, cluster population, and energy parameters to shortlist an optimized multi-hop path to the static Base-Station (SS) held responsible for critical data-delivery within WSN. The CSA-NER model defines an optimized route for the mobile-sink (MS) to gather near-exigency support data within the network. With GSA-ER and CSA-NER models employed, the MB-OCR-configured WSN can adhere to the critical data deadlines while also achieve reliability under any unanticipated situation. MB-OCR framework further alleviates the hot-spot problem. As evident from the simulation analysis, MB-OCR outperforms the MS sojourn path based existing frameworks. Furthermore, MB-OCR performance has been investigated for varying critical scenarios to limelight the essence of employing MS and SS in WSN. [ABSTRACT FROM AUTHOR]

Growing interest in emerging IoT applications provides a strong drive to release a plethora of communication radios from different standards, which are largely classified into short-range (IEEE 802.15.4) and long-range radios (IEEE 802.15.4g). In this paper, we propose a joint, self-configuring MAC and routing protocol, SEDA-Net, which aims at adaptively choosing the best configuration for communication coordination and data delivery, depending on different deployed topologies and external conditions. SEDA-Net is a combination of SEDA-MAC, SEDA-Routing, and Cross-Opt. SEDA-MAC and SEDA-Routing adaptively determine the best radio configuration for communication coordination under duty-cycling and each node's next-hop over which radio and Cross-Opt jointly optimizes inter-coupled MAC and routing in an iterative manner. SEDA-Net differs from prior approaches which are designed with static configurations of radios and/or mainly with the goal of throughput maximization for dual Wi-Fi or Wi-Fi/LTE setups. We implement SEDA-Net on Contiki OS and perform extensive simulations and experiments using a testbed in an office building. This testbed consists of 45 nodes equipped with a commercial platform, Firefly, having 2.4 GHz short-range and 920 MHz long-range radios. We demonstrate that energy efficiency quantified by the network lifetime increases by up to 2.1 times, compared to that of existing approaches. [ABSTRACT FROM AUTHOR]

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.

Smart metasurfaces have recently emerged as a promising paradigm for next-generation wireless communication systems, providing a reconfigurable radio propagation environment for a wide range of services and applications. In this paper, we discuss how to include and exploit smart metasurfaces into a 6G environment employing non-orthogonal multiple access (NOMA) communications. We identify the key challenges in the characterization of NOMA systems assisted by metasurfaces, which encompass the mutual coupling between electromagnetic fields in the scattering surfaces and its impact on the signal-to-interference-plus-noise ratio at the receiver. Furthermore, we describe opportunities and limitations for fully integrated systems: in this frame we envisage and outline a “deep” cross-layer approach, that encompasses full-wave electromagnetic analysis, circuit analysis, signal processing, and communication, then providing a model with affordable computational complexity for solving challenging configuration tasks in 6G wireless scenarios.

The Internet of Things (IoT) is regarded as one of the most promising fields for ubiquitous access, information exchange, and real-time analysis. The enormous device population and direct interactions among devices help form social attributes of devices, enabling Social IoT (SIoT) that integrates an IoT system and Social Networks. Despite advantages including network navigability, service scalability, and enhanced trustworthiness of data, there are more severe security issues to be solved urgently in SIoT. We first depict a powerful SIoT and abstract primary social attributes, then present key security issues in SIoT and investigate specific solutions via cross-layer. The core of cross-layer designs is the tradeoff between security and other aspects (e.g., energy or complexity) in essence. Moreover, graph-powered learning has shown its superiority in Social Networks, leading to our discussion about applying graph learning in SIoT cross-layer security schemes for promoting future SIoT security researches. [ABSTRACT FROM AUTHOR]

Summary: Vehicular ad hoc networks (VANETs) are receiving increased attention, and several routing protocols have been proposed already by the research community. In such a framework, social aspects and human mobility are important performance enablers, which however are mostly overlooked. In this work, we propose the socially aware CLWPR (SCALE) protocol, significantly enhancing cross‐layer weighted position‐based routing (CLWPR), a routing protocol for urban VANET environments, combining social properties such as trust, influence, and users' individual mobility patterns to its core design in order to support an efficient content dissemination scheme. The design principles of SCALE that rely on both online and offline social metrics quantifying relationships on social networking platforms and opportunistic contacts of nodes due to physical proximity, respectively, are presented, while nodes with close online and offline social relationship are favored as next forwarder nodes. Subsequently we provide the performance evaluation of SCALE against other routing protocols in distributed vehicular networks, employing representative urban scenarios with synthetic and real traffic. It is shown that the proposed approach presents improved performance in terms of Packet Delivery Ratio (PDR) and throughput when compared with other similar protocols by an average of 37% in scenarios with synthetic dataset and 58% with real dataset for PDR and by an average of 45% in scenarios with synthetic dataset and 61% with real dataset for throughput. [ABSTRACT FROM AUTHOR]

In recent years, vehicular communication systems have taken an important place in Intelligent Transport Systems (ITS). These systems are designed on self-organizing networks, known as Vehicular Ad hoc NETworks (VANETs). Their objective is to struggle against road safety problems and road traffic congestion by integrating information and communication technologies in road infrastructures and vehicles. VANET differs from other types of ad hoc networks by high mobility and very dynamic topology. Consequently, the design of an efficient VANET routing protocol becomes a challenging task. Many routing schemes with QoS (Quality of Service) have been proposed in the literature for VANET, to satisfy the performances required by the different applications running upon such networks. Cross-layer designs, which make it possible to exchange and share information between the different layers of the network, have shown to be efficient in VANETs. This paper proposes an improved version of the geographic Greedy algorithm as a cross-layer routing protocol for VANETs. The cooperation between the network layer and the lower layers involves three cyber-physical parameters: Speed, position and Signal-to-Noise Ratio (SNR). The simulation results over the Network Simulator-3 (NS3) demonstrated the effectiveness of the proposed solution in terms of end-to-end delay, throughput and packet delivery ratio. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicles (UAVs), which connect to one another over wireless networks, are being used in warfare more frequently. Nevertheless, adversarial interference has the potential to disrupt wireless communication, and the UAV routing methods in use today struggle to handle interference. In this paper, we propose a Cross-Layer UAV Link State Routing protocol, CLUN-LSR, to combat against jamming attacks. CLUN-LSR features three designs. First, it obtains real-time spectrum status from the link layer. Such capabilities are provided by many existing radios, especially the ones in military applications, but are ignored by traditional routing protocols. Second, based on the cross-layer information, CLUN-LSR adds efficient routing functions during routing, including the use of the number of two-hop neighbor nodes as a metric for route selection. Third, CLUN-LSR selects nodes that are not in the interference area, thereby reducing network interruptions and improving data transmission efficiency. All table-driven routing protocols can apply CLUN-LSR for better performance. We apply CLUN-LSR to the existing routing protocol MP-OLSR and simulate it using a commercial network simulator. Simulation results show that our innovative routing protocol demonstrates superior performance compared to existing table-driven routing methods, particularly in terms of packet transmission rate and overall throughput. [ABSTRACT FROM AUTHOR]

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.

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. [ABSTRACT FROM AUTHOR]

This paper proposes a new cross-layer communication system for the provision of Internet services and applications to mitigate the negative impacts of COVID-19, due to which the massive online demands are affecting the current communication systems’ infrastructures and capabilities. The system requirements and model are investigated where it utilizes high-altitude platform (HAP) for fast and efficient connectivity provision to bridge the communication infrastructure gap in the current pandemic. The HAP is linked to the main server or gateway station located on ground and can provide communication narrow beams towards isolated areas which suffer from poor terrestrial radio coverage or lack of communication infrastructure. The vital e-learning applications using Internet services provision from the proposed HAP system are described and modelled including system adaptation parameters such as the application and physical layers to control the data rates of different e-learning applications and the overall cell data rate. On the other hand, the provision of high-speed Internet services from the proposed system is supported by using adaptive antenna arrays onboard HAP which provides high-gain beams to achieve the required high-quality transmission data rates at the student premises and provides the capability of coverage cell area adaptation for load balancing. The concentric circular antenna arrays with tapered feeding are proposed in this adaptive antenna system to control the cell mainlobe gain and reduce the out-of-coverage radiation as well. In addition, the system feasibility has been proved in two coverage scenarios including single-beam and multibeam HAP communications. [ABSTRACT FROM AUTHOR]

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%.

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.

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%.