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Multi-Path Transmission Control Protocol (MPTCP) is an extension of TCP that enables the concurrent transmission of information through different network interfaces (e.g., Cellular, Wi-Fi, 802.11p, and so on) available at terminal side. It is well known that MPTCP can provide significant advantages in bandwidth aggregation and transmission stability. Unfortunately, path diversity can limit bandwidth aggregation efficiency and incur higher delays. These issues become critical when in presence of emerging mobile AR and VR applications, which are bandwidth hungry, time-sensitive and exhibit abrupt variations of the bitrate. To address these issues, we propose the Reinforcement Learning-based mobile AR/VR multipath transmission with streaming Power Spectrum Density analysis (RL-PSD). RL-PSD analyses the Power Spectrum Density (PSD) of the AR/VR input stream to extract its features. Then, both the input stream and network features are considered to model the MPTCP congestion control as an reinforcement learning process. Finally, a two-stage reinforcement algorithm is proposed to optimize transmission performance. RL-PSD has been tested in both single-terminal and multi-terminal scenarios: results show that it outperforms the other advanced solutions conceived to support the multipath transmission of AR/VR streams.

Unmanned Aerial Vehicles (UAVs) are key enablers in many emerging verticals, thanks to their versatility in fulfilling sensing, actuation, and communications tasks. At the same time, their resources are quite constrained so that sophisticated optimization approaches are required to prolong mission lifetime. Current literature mostly focuses on energy constraints, but when UAVs gather high-resolution multimedia signals, memory constraints become critical too. To this end, a lean convex optimization framework is proposed hereby to maximize the acquired/uploaded data, subject to joint energy and memory constraints. Simulation results validate the proposed approach in a realistic 5G scenario.

Quantum computing is a promising technology which is able to speed up computation, by several orders of magnitude, with respect to classical systems. In particular, quantum optimization can be employed to solve complex problems in many engineering fields. In this work, it is proposed to use Quantum Annealing (QA) to solve a binary optimization problem to derive an optimal scheduling plan in an Unmanned Aerial Vehicle (UAV)-enabled Internet of Things (IoT) network. Based on specific design criteria, specific problem formulation can be cast, thus providing real-time solutions.

This chapter aims at shedding light on low power technologies as the key enablers for industrial automation. It discusses the different types of costs involved in harsh industrial environments, with a focus on the damage penalty and battery replenishment costs for the sensor motes deployed across the production line. It analyses the reciprocal relationship and trade‐off analysis for these costs to suggest some optimization strategies for ultimate production efficiency. The chapter presents an overview of the aforementioned low energy networking protocols and highlights the key technologies and the results of long range wide area networks evaluation. Energy harvested from renewable energy sources, available in the industrial environment, can dramatically play its role to improve both performance and production efficiency in industry 4.0. Harvesting sources available in the industry are considered to demonstrate the huge cost savings and the great impact of the availability of this surplus energy on the industrial cost optimization process.

The manyfold capacity magnification promised by dense 5G networks will make possible the provisioning of broadband multimedia services, including virtual reality, augmented reality, and mobile immersive video, to name a few. These new applications will coexist with classic ones and contribute to the exponential growth of multimedia services in mobile networks. At the same time, the different requirements of past and old services pose new challenges to the effective usage of 5G resources. In response to these challenges, a novel Stochastic Optimization framework for Green Multimedia Services named SOGMS is proposed herein that targets the maximization of system throughput and the minimization of energy consumption in data delivery. In particular, Lyapunov optimization is leveraged to face this optimization objective, which is formulated and decomposed into three tractable subproblems. For each subproblem, a distinct algorithm is conceived, namely quality of experience--based admission control, cooperative resource allocation, and multimedia services scheduling. Finally, extensive simulations are carried out to evaluate the proposed method against state-of-art solutions in dense 5G networks.

Container networking is emerging as a game‐changer paradigm for the deployment of virtualized service infrastructures in a faster and reliable way. Nevertheless, small and medium enterpris...

Drones are increasingly employed in several application domains thanks to their inherent versatility. This work envisions a scenario in which a swarm of Unmanned Aerial Vehicles (UAVs) enables the communication between a set of Sensor Nodes (SNs) and a control center. Considering a general fading channel model, a Mixed-Integer Non-Linear Programming (MINLP) problem is formulated to maximize the overall amount of relayed data by jointly optimizing trajectory and scheduling plan of each drone. Combining convex optimization and Ant Colony Optimization (ACO) algorithm, a quasi-optimal solution is obtained. Finally, numerical results demonstrate the effectiveness of the proposed solution in different parameter configurations and with respect to a benchmark algorithm.

The Multiple-Input-Multiple-Output (MIMO) capabilities of an Intelligent Reflective Surface (IRS) combined with the inherent versatility of an Unmanned Aerial Vehicle (UAV) can significantly enhance the communication quality between a Ground User (GU) and a Base Station (BS). This work proposes a composite channel gain expression for performance analysis in IRS-assisted UAV-aided networks under Rician fading conditions. Differently from the present literature, the proposed formulation takes into account wave interference among surface elements and with the receiver. Moreover, Orthogonal Frequency Multiple Access (OFDMA) is employed to cope with spatial- and frequency-selective fading, enabling direct application in multi-user multi-drone scenarios. Numerical results demonstrate that the proposed model provides an underestimate, as expected from theoretical analysis, from which a lower bound for the data rate can be obtained, to be practically employed for system design and assessment.

Nowadays, amateur broadcasters can massively generate video contents and stream them across the Internet. For this reason, crowdsourced livecast services (CLS) are attracting millions of users around the world. To provide a smooth and high-quality playback experience to viewers with diversified device configurations in dynamic network conditions, CLS providers have to find a way to deploy cost-effective transcoding operations by distributing the computation-intensive workload among Cloud, Edge, and Crowd. In addition, it is necessary to control transcoded streams from million broadcasters to worldwide viewers. To address these challenges, we propose a novel stochastic approach that jointly optimizes the usage of transmission resources ( e.g. , bandwidth), and transcoding resources ( e.g. , CPU) in CLS systems that leverage the cooperation of Cloud, Edge, and Crowd technologies. In particular, we first design an augmented queue structure that can jointly capture the dynamic features of data transmission and online transcoding, based on the virtual queue technology. Then, we formulate a joint resource allocation problem, using stochastic optimization arguments, and devise an Accelerated Gradient Optimization (AGO) algorithm to solve the optimization problem in a scalable way. Moreover, we provide four main theoretical results that characterize the algorithm’s steady-state queue-length, optimality, and fast-convergence. By conducting both numerical simulations and system-level evaluations based on our prototype, we demonstrate that our solution provides lower system costs and higher QoE performance against state-of-the-art solutions.

Named-Data Networking is a prominent solution for offering flexible communications in Metropolitan Area Networks. Its features were recently extended with SDN and MEC capabilities for better supporting consumer mobility and improving packet forwarding functionalities. However, the scientific literature lacks a thorough investigation on the impact on the energy consumption of these protocols. Focusing the attention on the novel approach presented by the same authors of this paper, this contribution analyzes the energy saving in Information-Centric Metropolitan Area Networks. Results demonstrate the efficacy of the investigated approach by evaluating the different components of energy consumption in the considered scenario.

Information-Centric Networking emerged as a powerful enabling technology for the provisioning of scalable and efficient real-time services in Future Internet, mobile architectures, and multi-hop wireless mesh networks. To serve mobile consumers, most of scientific contributions suggest to extend the information-centric communication primitives by means of a pull-based methodology, according to which the mobile consumer issues pending requests every time it reaches a new network attachment point. This approach, however, generates two important shortcomings. First, the requests delivered before the handover will generate stale paths with wrong forwarding information in their network routers. As a consequence, some new contents will be delivered also to previous locations, thus wasting bandwidth. Second, during handovers, mobile consumers may miss some contents released in real-time and lose the synchronization with the remote producer. In order to solve these issues, this work conceives a novel protocol architecture that successfully integrates and properly customizes the key functionalities of Information-Centric Networking, Multi-access Edge Computing, and Software Defined Networking paradigms. Specifically, the designed approach envisages that (1) Multi-access Edge Computing assists mobile consumers in retrieving data, while transparently managing the information-centric communication primitives and recovering the synchronization with the remote producer after the handover, (2) Software-Defined Controllers dynamically configure forwarding functionalities, and (3) Information-Centric Networking enables efficient data dissemination and delivers network control instructions. The impact of the devised protocol architecture on the communication overhead is analytically formulated and evaluated in scenarios with different topology, mobility, application settings, and number of mobile consumers. The comparison with respect to the pure Information-Centric Networking deployment demonstrates that the proposed solution ensures a reduction of the communication overhead up to 99.99% on the data plane, an overall bandwidth saving up to 99.93%, and a not negligible memory saving in intermediary routers. At the same time, the adoption of information-centric communication primitives for the control plane achieves an overhead reduction ranging from 29.36% to 51.13% with respect to an implementation based on the conventional OpenFlow protocol.

The Internet of Drones (IoD) architecture is quickly gaining momentum thanks to its inherent ability to capitalize the pros of both Unmanned Aerial Vehicles (UAVs) and networking technologies. This work discusses a scenario involving a drone carrying out a surveying mission along a predefined path. With the aim of maximizing the gathered and transmitted high resolution video signals, a comparative study is proposed, encompassing two different radio coverage conditions. The resulting optimization problems are solved with different techniques: one of them is solved in closed-form expression, while the other one is approached using Linear Programming (LP). Simulation results are also presented to demonstrate the effectiveness of the proposed solutions.

A new Domain Name System (DNS) cache poisoning attack aiming at clients has emerged recently. It induced cloud users to visit fake web sites and thus reveal information such as account passwords. However, the design of current DNS defense architecture does not formally consider the protection of clients. Although the DNS traffic encryption technology can alleviate this new attack, its deployment is as slow as the new DNS architecture. Thus we propose a lightweight adaptive intelligent defense strategy, which only needs to be deployed on the client without any configuration support of DNS. Firstly, we model the attack and defense process as a static stochastic game with incomplete information under bounded rationality conditions. Secondly, to solve the problem caused by uncertain attack strategies and large quantities of game states, we adopt a deep reinforcement learning (DRL) with guaranteed monotonic improvement. Finally, through the prototype system experiment in Alibaba Cloud, the effectiveness of our method is proved against multiple attack modes with a success rate of 97.5% approximately.

The Internet of Drones paradigm is considered as a key enabler for several cutting edge verticals, including surveillance, planetary exploration, protection, loads transportation, and aerology. The main limitations to its wide-scale adoption arise from the constraints on the resources available onboard of drones: this concerns energy, computational and storage capabilities. Unfortunately, current literature mainly focuses on energy limitations, leaving unexplored the interplay with other constraints. To bridge this gap, the present contribution also encompasses the limitations on the memory onboard, which can be critical when drones have to acquire high resolution multimedia signals for ambient awareness services. In particular, an iterative stochastic approach is conceived hereby to tune data flows from/to drones subject to energy and memory constraints in order to fulfill an Out-of-Service probability below a given threshold. Stemming from the proposed approach, two algorithms have been also designed that seek a different complexity-performance tradeoff. The first one is less complex and more conservative, since it plans the mission once at the beginning. The second, instead, is slightly more complex and aggressive but it allows the drone to gather and upload a higher volume of data and shorten the gap with respect to the ideal case.

In a quite short time after its envisioning, Information Centric Networking paradigm seems to be a standing reality: name-driven primitives have been defined to support networking functionalities and a few architectures have been implemented. In fact, a content consumer can now search for a data item by name and completely disregard its IP address. But the question to raise is: is this searching mechanism really information-centric or is it rather name-centric only? This paper defines the requirements enabling the transition to a paradigm for content exchange which is exclusively based on information. A running example, based on the Web of Data, has been illustrated to describe the key functionalities of the proposed approach. The envisioned solution grounds on a mechanism to embed content in names which, notably, is architecture-agnostic and can run on top of all existing ICN flavors designed so far. This mechanism supports a semantic-based retrieval of content, that returns a list of contents potentially satisfying the consumer, incrementing retrieval precision and recall. The enhancement in terms of recall comes at the cost of a communication overhead, which has been analytically modeled and estimated in this paper.

Complex and interconnected systems belonging to biological, social, economic, and technology application fields are generally described through scale-free topology models. In this context, it is essential to characterize the distribution of shortest paths in order to obtain precious insights on the network behavior. Unfortunately, the few contributions available in the current scientific literature require a case-by-case tuning of model parameters. To bridge this gap, novel Gaussian-based models are proposed hereby, whose parameters can be immediately tuned based on the number of nodes ( $N$ ) composing the network, only. In this way, given $N$ , it becomes possible to predict the distribution of shortest paths without retuning the model for each scenario of interest. The outcomes of the proposed models have been successfully validated and compared with respect to state-of-the-art approaches in a wide set of network topologies. To provide a further insight, the conceived Gaussian-based models have been also evaluated for real Internet topologies, learned from reference data sets. Obtained results highlight that the proposed models are able to reach a good tradeoff between the level of accuracy and complexity, even for real network configurations.

Information-Centric Networking (ICN) has recently emerged as a result of the increased demand to access contents regardless of their location in the network services. This new approach facilitates content distribution as a service of the network with lower delay and higher security in comparison with the current IP network. Applying ICN in current IP infrastructure leads to major complexities. One approach to deploy ICN with less complexity is to integrate ICN with Software Defined Networking (SDN). The SDN controller manages the content distribution, caching, and routing based on the users' requests. In this paper, we extend these context by addressing the ICN topology management problem over the SDN network to achieve an improved user experience as well as network performance. In particular, a centralized controller is designed to construct and manage the ICN overlay. Experimental results indicate that this adopted topology management strategy achieves high performance, in terms of low failure in interest satisfaction and reduced download time compared to a plain ICN.

Information-centric networking (ICN), as a novel network paradigm, is expected to natively support mobility, multicast, and multihoming in vehicular ad hoc networks (VANETs). In this paper, the adoption of ICN principles for multimedia streaming in multihomed VANETs is investigated, with a major emphasis on the tradeoff between the quality of experience and energy efficiency (EnE). To formalize this problem, a cost optimization model is first proposed, based on queueing theory arguments. Then, a novel green information-centric multimedia streaming (GrIMS) framework is designed to drive the system toward optimal working points in practical settings. GrIMS consists of three enhanced mechanisms for on-demand cloud-based processing, adaptive multipath transmission, and cooperative in-network caching. Finally, a massive simulation campaign has been carried out, demonstrating that, thanks to its core components, the GrIMS enables flexible multimedia service provisioning and achieves an improved performance in terms of start-up delay, playbacks continuity, and EnE with respect to state-of-the-art solutions.

The Internet of Drones (IoD) recently gained momentum due to its high adaptability to a wide variety of complex scenarios. Indeed, Unmanned Aerial Vehicles (UAVs) can successfully be employed in different applications, such as agriculture, search and rescue missions, surveillance systems, mission-critical services, among others, thanks to some technological and practical advantages: high mobility, capability to extend wireless coverage areas, or ability to reach places inaccessible to humans. Moreover, the employment of drones promisingly improves the performance parameters of different network architectures, e.g., reliability, connectivity, throughput, and delay. Nevertheless, the adoption of networks of drones gives rise to several issues related to the intrinsic unreliability of the wireless medium, the duration of batteries, and the high mobility degree, which may cause frequent topology changes. Also security and privacy issues need to be properly investigated. This explains the very large number of works produced in the recent literature on IoD-related topics. With respect to other surveys on IoD-related topics, the goal of the present work is to categorize the multifaceted aspects of IoD, proposing a classification approach of the IoD environment that develops along two main directions. The IoD is initially introduced according to the possible applications and reference operating scenarios. On top of that, this work discussed with its applicability with a dedicated focus on the economical/social implications that the adoption of drones at scale may have. At a macroscopic level, it follows the structure of the Internet protocol stack, starting from the physical layer and extending to the upper layers, without neglecting cross-layer and optimization approaches. At a finer level, all the most relevant works belonging to each layer of the stack are further classified, according to the different issues that propose a peculiar challenges for each layer, and highlighting the most relevant differences with the other surveys present in literature. To provide a deeper insight, the present work embraces many facets of the IoD, with a thorough examination of privacy and security considerations. Finally, a discussion on the main research challenges and possible future directions is carried out, focusing on both open issues and the most promising technologies that deserve to be further developed in the IoD field.

Latest research in the integration of robotics and Internet of Things (IoT) systems paved the way for the development of new and potentially disruptive technologies, with applications in critical domains such as surveillance, environmental monitoring, smart agriculture and ambient intelligence in general. In this work, a robotic-aided IoT system for the automatic deployment of sensor networks is developed. Specifically, an Unmanned Ground Vehicle (UGV) is used to look for the best suitable deploying positions of the IoT nodes in extensive environments, such as in outdoor settings, based on the Received Signal Strength Indicator (RSSI) value measured between IoT devices. The system exploits accurate robot localization information obtained by integrating odometry, inertial and Global Position System (GPS) measurements. IoT nodes implement an IPv6 protocol, with IEEE 802.15.4 (6TiSCH) technology and make use of multi-hop communications, so as to guarantee an IoT network topology that provides wide coverage. Experimental results carried out in a real outdoor context are presented to demonstrate the feasibility of the system. It is shown that compared to a manual deployment, the proposed robotic-aided approach leads to higher performance, thus increasing communication efficiency between nodes and attaining stable IoT network topology.

International audience; The information-centric networking (ICN) is an emerging paradigm that grounds networking primitives on content names rather than node locators (as in the current Internet). ICN targets seamless mobility, native muticast/multipath support, and content oriented security to better reflect the needs of today users. ICN could greatly improve the efficiency of content delivery also in wireless sensor networks (WSNs). A WSN typically provides information centric services: in fact, whenever a mote is queried, the asking user is interested to the information acquired by the sensors on top of that mote rather than establishing a point-to-point remote communication. In this manuscript, without lack of generality, we will focus on a particular type of ICN architecture, known as content centric networking (CCN). In such a context, we place our attention on the energy efficiency of forwarding, which is achieved via costly broadcasting. Our objective is to save energy while achieving a high user satisfaction rate. In CCN, when a node requests a content, it sends an interest message and the node with the corresponding content replies with a Content Object message. To enable CCN features, each node maintains three tables: a Content Store to cache contents; a Forwarding Interest Base to store forwarded interests and a Pending Interest Table (PIT) to record unsatisfied interests. In this work, we start by introducing the features of CCN in WSNs and the advantages that it brings. For the forwarding optimization, we come up with an ‘Adaptive and fully Distributed Duty-Cycle for Content-Centric Wireless Sensor Network’ (ADDC-CCWSN) mechanism. ADDC-CCWSN aims to reduce the activity of nodes with a high percentage of unsatisfied interests in their PIT. We argue that the approach can be applied (with some modifications) to any ICN architecture that works as a network of caches in pull mode. We also propose an analytical model for CCN-WSNs to examine the energy consumption of content delivery. In addition, we implement the proposed mechanism on Contiki and, through extensive simulations with Cooja, we demonstrate that our approach achieves a significant gain of energy efficiency compared to a CCN approach with mostly-on sensor nodes while ensuring a high interest satisfaction rate and keeping nearly the same delay.

This letter focuses on a diffusion-based molecular communication system, fed by a nanoscale energy-harvesting mechanism, and conceives a power control mechanism based on feedback control theory. Specifically, the load current drained by the transmitter interface is set proportionally to the available energy harvested from the environment by using a closed-loop control scheme. The resulting system is analytically described through a nonlinear state equation, that jointly considers harvesting and discharging processes. Its asymptotic stability is evaluated around the equilibrium point, while considering some technological constraints. Finally, a numerical example is discussed to clearly show the behavior of the proposed approach in conceivable scenarios.

The Internet of Drones (IoD) is an emerging network architecture that leverages bidirectional communications between Unmanned Aerial Vehicles (UAVs) and a ground infrastructures. Given its versatility, the IoD is a key enabler for the evolution of sophisticated verticals, thus including Smart Cities, Industry 4.0 and Smart Transportation, to name a few. As a matter of fact, a swarm of drones can carry out real-time data gathering, industrial plants monitoring and wide area surveying. Since drones are battery-supplied systems, energy efficiency, communication reliability, and flight formation management routines become critical aspects of the IoD design. This work discusses the communication facets in a swarm of drones, leveraging the joint adoption of Bluetooth and Millimeter-Wave technologies. The proposal enables the exchange of heterogeneous data with a ground infrastructure while facilitating coordinated flight of the swarm. The resulting dual mode system optimizes energy resources, while maximizing reliability and effectiveness of drones’ communications.

The continuous spreading of innovative applications and services, based on the emerging Internet of Things (IoT) paradigm, leads to the need of even more efficient network architectures/infrastructures, in order to support the huge amount of information to be transmitted in real-time. Hence, new protocols and mechanisms must be conceived to allow the IoT network to be more reactive towards environmental changes and in promptly satisfying the IoT users’ requests. Aiming at dealing with the emerged issues, the paper presents an efficient IoT platform, which, thanks to fog computing principles, acts as a middleware layer between data producers and consumers; it adopts a security-aware publish&subscribe protocol, based on MQTT, coupled with a network of brokers, for efficiently sharing the processed information with end-users. Transmitted data are kept secure under an enforcement framework based on sticky policies. A test campaign is conducted on a prototypical implementation of the just mentioned platform, for preliminary evaluating its efficiency, in terms of computing effort and latency.

During the last decade, the research on nanotechnology and wireless communications in the terahertz band supported the design of pioneering biomedical applications. To counteract the very scarce amount of energy available for nano-devices, a current challenge is to develop energy-aware and energy harvesting mechanisms enabling long-lasting communications at the nanoscale. Many contributions in this direction envisage exploiting piezoelectric nanogenerators to retrieve energy from external vibrations (i.e., the human heartbeat) and use it for transmission purposes. Indeed, in line with the recent scientific achievements in this context, this paper investigates a power control mechanism based on the feedback control theory. The control law is conceived for managing the communication in human tissues, where nano-devices are equipped with a piezoelectric nanogenerator and transmit information messages through electromagnetic waves in the terahertz band. The amount of energy spent to transmit an information message is dynamically tuned by a proportional controller in a closed-loop control scheme which simultaneously considers harvesting and discharging processes. The whole system is analytically described with a nonlinear state equation. As well, it is presented the acceptable range of values of the proportional gain guaranteeing technological constraints and its asymptotic stability. Finally, a numerical evaluation shows the behavior of the proposed approach in a conceivable biomedical scenario.

A narrow-band system introduced in Release 13 of 3GPP has recently gain momentum to support a range of IoT use-cases. Narrowband-Internet of Things (NB-IoT) comes with low-cost devices characterized by extremely low power consumption, offering a battery life of more than 10 years, and broad radio coverage to target tens of kilometers, but on the cost of low data rate and higher end-to-end latency. NB-IoT can be deployed in three different modes of operation; stand-alone, in-band, and within the guard-band of existing LTE carrier. In this paper, a hybrid network model embracing both NB-IoT and D2D technologies has been introduced. we first present an analytical framework to derive analytical rate expressions for D2D in NB-IoT networks. Then, the performance gains of network model are investigated through numerical evaluations that demonstrate the superiority of proposed model over the traditional NB-IoT network.

Software-defined networking (SDN) is an innovative technology that aims to improve the network control, while providing an improved usage of virtual resources deployed across modern networks. Recently, telco operators started to adapt the SDN technology in their transport networks, thus boosting the research activities around the Transport-SDN (T-SDN) paradigm. The integration of SDN in transportation networks, however, poses numerous challenges related to the design of efficient routing approaches, working in large-scale and distributed networks. In this context, this paper investigates the design issues addressed by the current scientific literature and provides a cross-comparison of routing techniques published during recent years. The analysis of key features and drawbacks characterizing the reviewed methodologies reveals the lack of routing strategies that could ensure both energy effectiveness and better bandwidth requirements also known as quality of service in a real-time scenario, which is the primary requirement of the modern large-scale telecommunication networks.

Telecom operators recently started to integrate Software-Defined Networking facilities for controlling and managing their optical transport networks. Here, the management of forwarding rules into the resulting Transport Software-Defined Networking (T-SDN) architecture has to be addressed by taking into account the energy and quality of service requirements. While the most of works in the literature studied these aspects separately, the few contributions that simultaneously take care of energy and quality of service requirements present latency, scalability, or control communication issues. Starting from these considerations, this paper formulates a novel methodology for the dynamic and reactive management of forwarding rules in a (potentially large-scale) T-SDN network, based on the knowledge of network topology, the power consumption of optical switches, the expected volume of traffic, and the variability of the actual traffic load. First, the expected volume of traffic and the estimated power consumption of optical switches are exploited to select the minimum number of nodes and transport links to activate, which enable the communication among any source and destination pairs declared within a given traffic matrix. Then, the bandwidth consumption of activated transport links is periodically monitored by a centralized controller and, in case of congestion, a new set of optical switches and transport links are quickly turned on for addressing the growth of the traffic load. The effectiveness of the proposed approach has been investigated through experimental tests and compared against another reference scheme which considers the energy issue only. Obtained results demonstrate its ability to offer higher levels of quality of service to end-users, at the expense of a limited decrease of the registered energy-saving.

Smart mobility embraces many services that require the exchange of huge amount of data between mobile consumers (e.g., vehicles, drones, smartphones) and fixed producers (e.g., cloud servers). Information Centric Networking emerged more than one decade ago as a suitable approach to build the Future Internet, offering flexible communication primitives for data exchange and native support to mobility. Unfortunately, baseline schemes do not take care of stale forwarding information generated into the core network because of consumer mobility and, as a consequence, it leads to an important waste of bandwidth and a higher energy consumption. An SDN-aided Information-Centric Networking approach has been recently formulated by the same authors of this contribution as a valid solution for the aforementioned issue. This paper further extends these promising research results by evaluating the impact of the amount of energy consumed by intermediary routers. The conducted tests demonstrate the proposed solution effectiveness through computer simulations, by considering different network, mobility, and application scenarios.

Information Centric Networking emerged more than one decade ago as an alternative approach to the Future Internet, based on name driven networking primitives. Since most of IoT services are information centric by design, the scientific community is actively investigating novel ICN-IoT architectures to circumvent the limitations of host-centric approaches. In this context, it is imperative to evaluate how different ICN architectures cope with Publish-Subscribe approaches with different degree of mobility. Starting from this premise, the keynote will: (i) briefly summarize the key features of the ICN rationale; (ii) introduce the approaches proposed so far to support publish-subscribe in ICN-IoT architectures; (iii) highlight the technical issues that can arise in presence of mobility; (iv) illustrate possible solutions to those issues; and (v) draw future research directions.

The absence of proper infrastructure and rapid change in topological formation makes it challenging to adapt the appropriate clustering scheme for choosing a stable cluster head. In this paper, we propose an Adaptive REsilience Algorithm (AREA) for cluster reformation in VANET that takes into account multiple interrelated metrics including the direction, relative velocity, and inter-nodal distance of the moving vehicles yielding a stable clustering with better life time. Simulation results clearly exhibit the robustness and stability of the proposed algorithm.

Software-Defined Systems are offering great opportunities for deploying programmable networks, as well as large-scale services distributed across clouds. Starting from the baseline principles of both Software-Defined Networking and Network Function Virtualization, they embrace a number of novel enabling technologies (like containers, container orchestrators, and many other supporting tools) that significantly simplify the integration and the management of virtual components, while promising high level of flexibility, isolation, and performance. The major tech giants are drastically building their business on these technologies. However, many other companies are struggling in the selection of suitable platforms, tools, and any other software instruments allowing them to move forwards in this direction. Based on these premises, this paper provides a three-folded contribution. First, it explores the state of the art of container engines and container orchestrators. Second, it analyzes the main supporting tools that offer advanced and additional features to the resulting container networking. Third, it defines a set of qualitative Key Performance Indicators to carry out a preliminary comparison of the reviewed technologies. The proposed study aims at providing high-level guidelines and constructive comments to foster the widespread usage of Software-Defined Systems.

Information Centric Networking represents a fundamental technology for the Future Internet. Its baseline functionalities can be extended to support publish-subscribe communication schema. But, in case of consumer mobility, its benefits slam against two main drawbacks. On one hand, available handover management solutions temporarily leave wrong forwarding information within network routers and do not take care of data dissemination across stale paths. On the other hand, mobile consumers inevitably lose content updates during the handover and definitively become unaware about the latest version of the content to request. By leveraging the potentials of the Software-Defined Network paradigm, this paper formulates new methodologies willing to solve these problems. Specifically, it proposes protocols for (1) dynamically updating forwarding functionalities through the control plane when the consumer detaches from the network and (2) restoring the synchronization between consumer and producer when the former one attaches to a new network attachment point. It addition, it develops preliminary analytical models for evaluating the average number of control information these protocols require to exchange per unit of time. The resulting analysis illustrates the pros and cons they achieve in different network configurations.

Thanks to the availability of miniaturized camera and microphones, nowadays Wireless Multimedia Sensor Networks (WMSNs) can sense and deliver audio/video signals from a target environment to remote analysis sites. Hence, new opportunity are disclosed for advanced applications in health care, surveillance, military, and traffic monitoring domains, to name a few. But, at the same time, due to the high volume of multimedia streams and the richness of information they bring, WMSNs incur critical issues in terms of congestion control, privacy, and security. These problems can be solved separately by adopting consolidated solutions conceived to address each of them. But one of the pivotal point of optimization in a Wireless Sensor Network is the possibility of exploiting a cross layer design. To bridge this gap, an integrated solution is proposed hereby, namely Secure Selective Dropping Congestion Control $$(\hbox {S}^{2}\hbox {DCC})$$ , based on end-to-end ciphering, in-network selective data dropping, scalable multimedia encoding, and hierarchical and hybrid network design. Moreover, an open source implementation of $$\hbox {S}^{2}\hbox {DCC}$$ has been developed in the Castalia simulator. The main outcomes of the performance evaluation show that $$\hbox {S}^{2}\hbox {DCC}$$ is able to meet data security and privacy requirements and to improve the quality of the received images at the sink with respect to state of the art solutions.

Research interest on Information Centric Networking (ICN) has been sharply growing. Although new architectures, algorithms, and analytical models have been proposed, their evaluation remains often isolated and not rigorously verified by the research community. This paper initially portrays the composition of open source software tools available for ICN, certifying the predominance of Content Centric Networking (CCN)/Named Data Networking (NDN) simulators. Then, inspired by similar works related to the P2P field, it surveys related research papers to qualify the ICN literature produced so far, finding that a large fraction of contributions either uses custom, proprietary, and unavailable software, or even plainly fails to mention any information in this regard. By adopting a rigorous methodology, in the second part of the paper four simulators, namely ndnSIM, ccnSim, CCNPL-Sim, and Icarus, are cross-compared under several key aspects. Our findings confirm both their accuracy with respect to reference theoretical models in simple settings, and their consistency in more complex scenario. Additionally, our analysis can both assist researchers in the choice of the tool that best fits their needs, and provide guidelines to avoid common pitfalls in the ICN performance evaluation.

The relentless growth of the human population over the time is driving an exceptional rise in food demand. Improving the efficiency of farming processes is the only way to face the so called Malthusian catastrophe. This objective could be pursued by automating production processes in farms. Robots can play a key role in this context, especially when they can execute tasks on collaborative basis. At the same time, low latency communication capabilities are required to translate in reality the robotic-aided smart agriculture vision. This contribution explores the interplay of 5G, Internet of Things (IoT), and Mobile Edge Computing (MEC) as enabling drivers for technology spread in the agriculture domain, based on Industry 4.0 principles. In particular, some key performance indicators have been investigated for a rural-area scenario, exploring different technological configurations.

In Industrial Internet of Things (IIoT) networks, end-nodes may disconnect or become no longer reachable, due to manifold causes, such as battery discharge, network interference and/or packet collisions. Overall, this could lead to low Quality of Service (QoS) levels. To satisfy application requirements, i.e., reliability, it is mandatory to continuously monitor connectivity and properly managing disconnections, without causing signaling overhead. The optimization of the exchange of messages can be pursued thanks to dedicated communication slots, eventually rescheduled in cases of deletion, replacement or dynamic reassignments. This solution lowers energy consumption while accounting for a number of improvements in network operative conditions. In this work, network formation optimization mechanisms for IPv6 over the TSCH mode of IEEE 802.15.4 (6TiSCH) networks are analyzed in real use cases in which the number of nodes in the network varies over time. Once the network was fully connected, a performance analysis has been carried out in order to measure typical networks parameters, such as latencies and Packet Loss Ratio (PLR). The study has proven the effectiveness of dedicated slots dynamic assignment. In particular, it has been measured a sensible reduction of latencies and losses of information. The obtained results pave the way towards robotics-aided set up of Internet of Things (IoT) networks in industrial contexts.

The Internet of Drones (IoD) represents a key enabling paradigm for the Internet of Mobile Things. By offering smart interactions among unmanned aerial vehicles, i.e., drones, it promises to support many advanced services, spanning from control signaling and traffic management to real-time applications and multimedia streaming. From a networking perspective, IoD should face heterogeneous architectures and extremely dynamics topologies, while ensuring the different levels of quality of service expected for the enabled applications. In this context, the definition of novel communication protocols and algorithms must be properly supported by valid instruments able to model the overall network architecture, as well as to evaluate and improve its performance. To meet these goals, this work proposes an open source and system-level simulator for the IoD. Developed on top of the well-known Network Simulator 3, it implements the key networking elements (drones, network access points, and Zone Service Provider), a standard-compliant communication stack based on the IEEE 802.11 technology, and scenarios with various mobility model. Thanks to its native modularity, the simulator is ready to be used for investigating the performance of customizable scenarios with a variable number of drones, and can be easily extended with new functionalities.

The many fold capacity magnification promised by 5G vehicular networks will likely provide massive multimedia services, including infotainment, augmented reality, location services, etc. However, the large-scale and stochastic characteristic of these burgeoning multimedia applications will lead to an exponential increase of traffic in vehicular networks. Meanwhile, the diversified requirements introduced by the coexistence with traditional services will also bring new challenges to the efficient usage of resources. To cope with the above challenges, we propose a novel Stochastic Optimization framework for Pricing-aware Multimedia Services (SOPMS) in this paper, which targets the maximization of utility with the constraints of system stability and traffic pricing policy. Specifically, we leverage the Lyapunov function to address this optimization objective, which is decomposed into three tractable subproblems. For each problem, a distinct algorithm is conceived, i.e. Quality of Experience (QoE) based utility maximization, cooperative resource allocation and pricing-based transmission control. Finally, validated by the simulations, our proposed SOPMS preserves the optimality and significantly improves the queue stability and service utility, in comparison with other state-of-the-art solutions.

Detecting disruptive events, such as earthquakes, using environmental monitoring systems is a particularly promising, but rather challenging, opportunity. The Internet of Things (IoT) can play a significant role in characterizing and predicting seismic events. The present contribution introduces QuakeSense, an open-source earthquake and weather monitoring system. The implemented IoT system is configured as a Long Range (LoRa)-based star topology with a fully energy-autonomous sensor node. The system leverages some of the most useful features of two emerging IoT technologies, e.g., LoRa and Message Queue Telemetry Transport (MQTT), and enables the near real-time monitoring of seismic events through a web-based interface. An experimental campaign has been carried out to verify the current consumption and, therefore, the battery lifetime of the sensor node. Moreover, LoRa parameters have been extensively tested as to evaluate performances in several configurations. The obtained results in terms of latency and Packet Delivery Ratio (PDR) demonstrated the reliability of the proposal.

The automated setup of Internet of things (IoT) systems in industrial environments is an open challenge at the forefront of networking and robotics domains. In this study, a robotic-aided deployment system is proposed and experimentally tested with reference to the Internet protocol version 6 technology. To this end, a design methodology has been conceived to attain the setup of a fully connected time-slotted channel hopping network in a target area, based on the measured received signal strength indication of wireless links, required spatial sensing resolution, and number of available IoT nodes. To demonstrate the effectiveness of the proposed methodology, an experimental test bed has been set up, consisting of an unmanned ground vehicle (UGV) that automatically deploys an IoT network in a laboratory environment. Experimental results clearly show that the UGV is able to deploy a fully connected 6TiSCH network that match spatial resolution requirements, highlighting how the proposed policy affects the position of IoT nodes release points.

Smart agriculture is a promising Internet of Things (IoT) application domain in the Industry 4.0 framework. Climate changes affect the natural resources exploitation policies becoming a serious matter in the agricultural and food production context. Continuous monitoring of environmental parameters and agricultural processes automation can lead to resources optimization and production maximization. This work proposes both the conceptual model and the design of Solarfertigation, an IoT system, specifically designed for smart agriculture. In particular, the envisioned solution is able to detect some of the most meaningful terrain parameters to feed a decision-making process that drives automated fertilization and irrigation subsystems. In addition, to attain energy self-sustainability, Solarfertigation is powered by a photovoltaic plant. The key features of Solarfertigation are illustrated throughout this contribution together with its preliminary prototype implementation.

Exploiting the advantages brought by long-range radio communications and extremely low power consumptions, LoRaWAN is capable to support low rate industry 4.0 services. Despite being energy efficient, LoRa motes can still undergo frequent battery replenishments caused by the monitoring requirements of industrial applications. Duty-cycle constrained operations can partially face this issue at the expense of increased communication delays, which, in turn, inflate higher costs due to damaged products on the production line. This letter proposes a model to analyze this cost tradeoff against different sensing intervals. It further highlights the impact of energy harvesting sources on this cost relationship mapping a way toward improved production efficiency.

This work presents a novel information-centric communication bus for Intelligent Transportation Systems, namely BONVOYAGE Communication System. Based on the Internames principle (i.e., an evolution of the well-known Information-Centric Networking paradigm), it offers a scalable and distributed dissemination of real-time contents through standardized interfaces, request-response and publish-subscribe schema, while ensuring interoperability across borders. To prove its suitability in real scenarios, the main functionalities of the BONVOYAGE Communication System (including namespace, data processing, and caching) have been properly designed and characterized in order to effectively disseminate travel-centric data (i.e. contents of interest of Intelligent Transportation Systems) formatted according to DATEX II specifications. Experimental tests demonstrate that the proposed solution outperforms baseline dissemination mechanisms in terms of processing complexity, communication latencies, and bandwidth consumption.

Time Slotted Channel Hopping (TSCH) is a recent enhancement of the IEEE 802.15.4 standard aimed at the industrial sector. To complement its unique features, the IPv6 over the TSCH mode of IEEE 802.15.4e (6tisch) Working Group released a set of specifications that define the way Time Slotted Channel Hopping could be integrated in embedded IPv6 networks based on the IPv6 over Low Power WPAN. TheI 6tisch standard is now available under several hardware/software platforms so that a cross-comparison of the different implementations deserves particular attention in order to properly identify performance bottlenecks and scavenge pros and cons of the 6tisch technology. The present contribution provides an experimental cross-comparison of protocol stack implementations running on top of different devices. In particular, experimental tests were conducted to characterize the behavior of OpenWSN and Contiki in Time Slotted Channel Hopping- based scenarios made up by TelosB, Zolertia Z1, and OpenMote motes. By considering star and chain topologies and different traffic load conditions, many performance indices were measured, including memory footprint, energy consumption, synchronization time, duty- cycle, end-to-end packet delays, and Packet Loss Ratio. Obtained results highlight strengths and weaknesses of each hardware/software platform, while providing trade-off considerations about flexibility, accuracy, energy-efficiency, and Quality of Service.

International audience; Wireless Sensor Networks (WSNs) have been recognized as a promising communication technology for smart grid monitoring and control applications. However, the deployment of WSNs in smart grid brought new challenges that pertain to the harsh electrical grid nature, and the different and often contradicting communication requirements of smart grid monitoring applications. MAC protocols play a crucial role to meet the reliability and latency requirements of WSN-based smart grid communications. In particular, the IEEE 802.15.4 TSCH (Time Slotted Channel Hopping), the latest generation of low-power and highly reliable MAC protocols, orchestrates the medium access according to a time-frequency communication schedule. However, TSCH specification does not provide any practical solution for the establishment of the schedule. Orchestra is a recent scheduling solution for TSCH that brings significant advantages such as, the use of simple scheduling rules, the low signaling overhead, and the high delivery ratio. Despite its unique features, Orchestra has the limitation of computing the TSCH schedule at each node independently from its traffic load, which can drastically affect the communication delay. This limitation makes Orchestra not sufficiently convenient for several delay-sensitive smart grid applications. Further, the current TSCH specification does not support traffic differentiation (i.e. handle all packets equally regardless of their criticality levels). In this paper, we propose an enhanced Orchestra-based TSCH protocol, called e-TSCH-Orch, that dynamically adjusts time slots assignment according to traffic load and criticality level. The performance analysis of e-TSCH-Orch shows that it significantly reduces the communication delay compared to the original Orchestra-based TSCH, while preserving the low signaling overhead and the high packet delivery ratio.

The IoT paradigm holds the promise to revolutionize the way we live and work by means of a wealth of new services, based on seamless interactions between a large amount of heterogeneous devices. After decades of conceptual inception of the IoT, in recent years a large variety of communication technologies has gradually emerged, reflecting a large diversity of application domains and of communication requirements. Such heterogeneity and fragmentation of the connectivity landscape is currently hampering the full realization of the IoT vision, by posing several complex integration challenges. In this context, the advent of 5G cellular systems, with the availability of a connectivity technology, which is at once truly ubiquitous, reliable, scalable, and cost-efficient, is considered as a potentially key driver for the yet-to emerge global IoT. In the present paper, we analyze in detail the potential of 5G technologies for the IoT, by considering both the technological and standardization aspects. We review the present-day IoT connectivity landscape, as well as the main 5G enablers for the IoT. Last but not least, we illustrate the massive business shifts that a tight link between IoT and 5G may cause in the operator and vendors ecosystem.

Device-to-Device (D2D) communications are a prominent feature of 5G systems, introduced to provide a native support to distributed services in mobile environments. D2D technologies enable straight interactions between mobile terminals without a compulsory involvement of base stations. In this manuscript, we study and propose an optimized caching strategy to content distribution on top of D2D technology, based on Information Centric Networking (ICN) principles. The rationale is that ICN architectures can provide seamless support to mobile services and decouple contents from node identifiers, thus providing a promising match with D2D requirements. To this end, a novel fluid-based model in proposed hereby that catches the interplay between ICN functionalities, D2D requirements, and 5G specifications. Then, based on this model, an optimal content replication problem is formulated, encompassing caching overhead and system load. Additionally, this problem is thoroughly analyzed to prove that it has an optimal solution with time threshold form. A practical algorithm $\varsigma ^*$ -OCP is further proposed in order to implement the optimal caching control in realistic environments. Finally, a massive simulation campaign is carried out to test the proposed algorithm in comparison to state-of-the-art solutions.

Low-power wide area networks (LPWANs) are gaining momentum within Internet of Things circles, thanks to their inherent capabilities to simplify network topologies and increase coverage in low-rate and low-power communications. This study focuses on a specific LPWAN technology, namely Long Range Wireless Area Network (LoRaWAN), which is being widely investigated by the research community worldwide. LoRaWAN offers many noticeable features, including wide coverage areas, security by design, power consumption and bandwidth optimisation, and a high degree of flexibility and reconfigurability. Unfortunately, in spite of these valuable characteristics, LoRaWAN appliances are often closed source and proprietary. To bridge the gap, this study proposes a prototype, which has been designed and set up using open low-cost experimental boards and programming tools, only. Moreover, an experimental campaign has been carried out in order to demonstrate the effectiveness of the developed platform.