International audience

In Industrial Internet of Things (IIoT) environments, trust management plays a vital role in securing systems, especially when dealing with resource-constrained devices. Traditional trust models often overlook the impact of fluctuating network quality, leading to slower trust convergence and inaccurate assessments. In this paper, we propose a dynamic trust management solution, known as the Trust Convergence Acceleration (TCA) approach, which integrates Machine Learning (ML) to accelerate trust convergence under poor network conditions. Our model predicts the number of time units needed for trust convergence based on key network metrics and dynamically adapts transition probabilities in the trust model to enhance convergence speed. Using a simulation framework that incorporates realistic Wi-Fi channel conditions based on the IEEE 802.11 standard, we demonstrate the effectiveness of the TCA-based approach, achieving up to a 28.6% reduction in trust convergence time under challenging conditions. Furthermore, the proposed solution exhibits resilience in scenarios involving malicious nodes, improving trust evaluation accuracy. This work provides a scalable and adaptive trust framework for IIoT systems in dynamic industrial environments, ensuring robust performance under varying network conditions.

The evolution toward Beyond 5G (B5G) and Sixth-Generation (6G) networks demands innovative solutions to enhance connectivity and network efficiency. Cell-free massive multiple-input multiple-output (CF-mMIMO) systems address these challenges by deploying distributed access points (APs) to provide consistent coverage and mitigate interference, making them particularly valuable in high-mobility scenarios like vehicular communications. This paper presents novel algorithms for efficient frequency resource allocation in CF-mMIMO systems, focusing on user pairing and subband sharing. By enabling multiple users to share a single subband while minimizing condition number (CN) and channel correlation (CC), these strategies significantly improve resource utilization. Simulation results using the WINNER II channel model demonstrate notable gains in efficiency and sustained user spectral efficiency (SE). Comparisons with a Best-Fit Subband (BFS) allocation strategy further highlight the importance of effective resource allocation in advancing the performance and scalability of CF-mMIMO for future networks.

Jamming attacks present a significant security threat to wireless networks by exploiting the open wireless medium, causing not only a denial-of-service, but also overloading the network and interfering with signals. The jamming attack can enable more sophisticated disruptions, such as protocol-aware and learning-based jamming, where adversaries transmit selectively upon detecting legitimate network activity, and this selective transmission conserves attacker resources and complicates detection. Channel hopping is widely adopted as a mitigation strategy, allowing networks to move away from interfered channels dynamically. However, recent studies have demonstrated that intelligent jammers, such as the Multi-Armed Bandit (MAB)-based attack, can effectively learn and predict channel hopping patterns, thereby continuously jamming communications and severely degrading network performance. Designing robust countermeasures against such intelligent jamming remains an open and critical challenge. In this paper, we analyze a kind of MAB-based attack methodology and propose two refined variants employing online and offline paradigms. To counteract these advanced threats, we introduce three defense strategies: (i) speeding up, which modifies the frequency of channel-hopping decisions to avoid the jammer’s attack, (ii) deploying a helper node, diversify the communication patterns to affect the attacker’s learning process, and (iii) employing a mirror Multi-Armed Bandit (mirror MAB) approach to predict and bypass channels of highest jamming probability. Comprehensive evaluations demonstrate the effectiveness of our proposed defenses, significantly mitigating MAB-based attacks. Specifically, our strategies achieve a PDR exceeding 90% under persistent attack conditions, while effectively preserving robust and dynamic channel hopping behaviors.

This paper introduces a novel hybrid physical layer authentication (PLA) method designed to enhance security in multi-node networks by leveraging inherent hardware impairments. The approach specifically exploits carrier frequency offset (CFO), direct current offset (DCO), and phase offset (PO) as multi-attribute features, improving the verification process for authorized users and enhancing the detection of unauthorized devices. Machine learning (ML) models are developed to authenticate devices without prior knowledge of malicious characteristics, resulting in robust and reliable device authentication capabilities. Experimental evaluations conducted on a commercial software-defined radio (SDR) platform demonstrate the effectiveness of the proposed approach under varying signal-to-noise ratio (SNR) conditions. The hybrid PLA scheme integrates advanced feature extraction methods with finely-tuned ML models, optimized through controlled experiments to ensure high performance across diverse network conditions and attack scenarios. Real experimental tests validate the efficacy of the proposed scheme, achieving high authentication rates exceeding 96% and reliable detection rates for malicious device attacks surpassing 95%. Additionally, the approach is highly efficient, with a mean inference time of less than 3.75 milliseconds (ms) and power consumption below 25.5 millijoules (mJ), confirming its suitability for real-time applications in energy-constrained environments.

Recently the use of Unmanned Aerial Vehicles (UAVs), commonly referred to as drones, has exploded in several domains, leading to potential security issues. As such, estimating the exact position of those eventual malicious drones has become of crucial interest. However, computing an accurate and precise geolocation of these drones, especially in outdoor environments, remains challenging. This paper focuses on outdoor 3-dimensional (3D) drone geolocation techniques based on Radio Frequency (RF) signals. We first present a RF-based 3D drone geolocation dataset, and then apply and compare various geolocation techniques, ranging from geometrical-based to machine learning-based methods. We further propose a new hybrid method blending the two above categories of geolocation techniques, that achieves an average 3D error of the order of 11.7 meters within a search volume of about 520×560×115 m3, significantly below the one achieved with geometrical-based techniques, and with a reduced computational complexity compared to the regular machine-learning based techniques.

In the context of 6G next generation networks, the Internet of vehicles (IoV) paradigm will be supported by overlapping network layers, like the aerial and space ones, to guarantee enhanced performance and 3D connectivity. Specifically, the integration of aerial devices, such as unmanned aerial vehicles (UAVs), into IoV is expected to enhance network performance, especially for vehicular safety applications, where packet delivery requires low latency and reduced connectivity loss, as compared to existing communication modes. In this paper, the technique ALI, an aggregate LiDAR images-based approach for road safety applications in a 6G context is introduced. It considers multiple LiDAR images that are shared and collected among vehicles to improve the accuracy in vulnerable road users detection. As compared to a single LiDAR image, ALI approach shows an enhanced detection accuracy, as well as allows alert message forwarding to other approaching vehicles in case of obstacle detection, via multiple interface cards and transmission modes. Results show that the adoption of UAVs as communication nodes allows to extend the coverage between 80 m and 130 m for an outage probability of 1%, overcoming vehicle-to-vehicle and vehicle-to-network communication modes and to provide a communication latency lower than 30 ms achieved up to 150 m.

Unmanned aerial vehicles (UAVs) offer a promising solution for enhancing network coverage, reliability, and data speed in future wireless network generations. However, deploying UAVs as aerial base stations requires careful consideration of crucial design factors, including three-dimensional (3D) placement and performance optimization tailored to specific applications. In this paper, the 3D placement of multiple UAVs, acting as aerial base stations, is investigated in a dynamic user scenario. First, a closed-form expression for the coverage probability is derived. Then, to maximize the network coverage and sum rate while ensuring reliable and energy efficient system, a joint multi-objective optimization problem is formulated considering the real-time user movements. To solve the problem, an improved Chaos-based Bonobo Optimizer (CBO) scheme is proposed which combines chaotic maps with the Bonobo Optimizer (BO) algorithm. The obtained results demonstrate the superior performance of the proposed approach compared with different benchmark algorithms. The results reveal that the proposed CBO algorithm offers a minimum of $\mathbf{1 5 \%}$ and $\mathbf{9 0} \mathbf{~ M b i t / s ~ i m p r o v e m e n t s ~}$ in coverage and sum rate, respectively.

The protocols for vehicular networks are mostly developed, tested and evaluated through simulations in network simulators first before implementing and experimenting them in the real-world. For simulating vehicular networks, various mobility datasets are made publicly available by SUMO that can be imported into network simulators (e.g., OMNeT++, NS-3). The development of protocols for vehicular networks requires analyzing some common metrics (such as distribution of vehicles, contact duration between vehicles and time to meet new neighbors). Therefore, researchers are high likely to implement and analyse these metrics as a first step. In this paper, we analyse the common required metrics for vehicular networks using network simulator OMNeT++ with Veins framework and SUMO vehicular mobility datasets of Ireland urban and highway scenarios for rush and non-rush hours. We have made our code available online at GitHub that will be time-saving for researchers who start developing protocols for vehicular networks. We believe such analysis results can help in designing communication or dissemination protocols in vehicular networks and understanding vehicular traffic for further applications.

The proliferation of airborne drones, while instrumental to a broad range of applications, has led to an increased number of regulatory non-compliance incidents. The ubiquitous unmanned aerial vehicles (UAVs) are posing security risks, since they have started to be used for cybercrimes. Effective detection of illicit drones in restricted areas is paramount. Evolved drones are more and more sophisticated, and sometimes they do not emit RF-based signals, making inapplicable RF-based detection solutions. Different from existing work, this paper introduces a neural sensor fusion framework for drone detection based on both audio and video data to accurately identify drones and differentiate them from similar objects at long distances. Our design adopts a late fusion approach using the Weighted Average and Random Forest algorithm on the visual and auditory classification pipeline. Specifically, we process infrared data using a You Only Look Once (YOLO) v5 model due to its balance between inference time and accuracy. For the audio stream, we evaluate Long Short-Term Memory (LSTM) and Convolutional Recurrent Neural Network (CRNN) models and demonstrate the superiority of the CRNN model through Mel-Frequency Cepstral Coefficients (MFCC) features. To demonstrate the robustness of our audio-visual fusion approach, we validate it in extensive scenarios, with impaired audio/video data. Our results demonstrate that multimodal fusion significantly improves drone detection, outperforming traditional single-modality systems in complex environments. Additionally, our system provides predictions rapidly, in just 0.382 seconds, making it well-suited for real-time applications.

Visible Light Communication (VLC) is expected to enable a wide range of applications in the next generation wireless networks. These applications are recognized as sensitive and prone to dangerous threats. So far, VLC research activities have been more focused on developing high data rate solutions and more robust systems for both indoor and outdoor applications, with a reduced focus on the security aspects. This is mostly due to the fact that VLC systems are based on short range and occurs in line of sight and it is then considered inherently secure by design. The specific characteristics of VLC systems make the traditional countermeasures adopted in radio-frequency-based systems not applicable, with a concrete need to conceive ad hoc solutions. Basing on these premises, in this work we consider a physical layer perspective by analyzing the intrinsic properties of visible light signals so as to develop a secure by design VLC system to be used in downlink. By exploiting the light bias, we guarantee an improved security level in respect of eavesdropper attack by granting also a good illumination level. A key aspect of this work is that the proposed solution does not rely on external devices or extra hardware.

The simultaneous task of data transmission and illumination achievable with Visible Light Communication is continuing to raise the interest of research and industry community. The joint use of the same infrastructure for illumination and communication may enable new location-based services, by implementing sensing functionalities. In this paper, we propose a new multi-dimensional modulation scheme combining different techniques, in order to increase the spectral and energy efficiency, while meeting the optical power emission constraints. Specifically, Pulse Position Modulation Pulse Amplitude Modulation and Frequency Shift Keying are effectively combined in a joint fashion, in order to exploit time, amplitude and frequency information. The innovation of using a smart combination of those modulation formats is based on the exploitation of specific features of the different approaches. In particular, we combine time, amplitude and frequency features to enhance the robustness of the system, without sacrificing the data rate performance as for coded systems. The major implication of such approach is twofold. In this way, we grant a constant illumination level per symbol and, moreover, we outperform in terms of reliability coded modulations for the same level of spectral efficiency. Theoretical results, validated through experimental evaluations, demonstrate that the combined approach achieves very good performance.

Channel hopping is a well-known methodology to dynamically allocate frequency resources to nodes in wireless communication systems. The aim of channel hopping is to mitigate possible interference issues caused by both legitimate and malicious users. The state-of-the-art channel hopping-based jamming mitigation techniques are becoming increasingly smart by using game theory or machine learning. However, we show in this paper that, although the victim may employ smart channel hopping strategies, these are not always effective in mitigating attacks. In this paper, we implement an effective jamming attack strategy to impair the effectiveness of channel hopping mitigation strategies. We test our attack on a testbed network composed by real devices, and test its effectiveness against three mitigation strategies: i) incremental channel hopping, ii) random channel hopping, and iii) smart channel hopping. We show that, by implementing our attack, an attacker can reduce the number of successfully transmitted packets by at least 28.5%, even against the smart channel hopping strategy,

The use of Optical Wireless Communications is largely adopted in both indoor and outdoor applications. Recently, it is also extending to underwater scenarios, as an alternative technology to the acoustic one, which is traditionally adopted. Knowing more precisely the position of artifacts, technical/scientific instruments and operators in the marine environment increases the operational potential and consequently, the interest in the progress of knowledge is guaranteed. Furthermore, the possibility of operating in both shallow and deep waters and the opportunity to be a new underwater navigation system increases the possibility of success of underwater activities, in the context of the Internet of Underwater Things (IoUT). This paper introduces an underwater all-optical IoT network architecture for both communication and positioning tasks. We initially discuss the main issues for data transmission via underwater visible light communications, and present an IoUT schematic based on Light Emitting Diode (LED) devices accordingly deployed on the seabed and illuminating a given space of interest. Then, a simple but accurate fingerprinting-based localization approach is presented, able to estimate the position of an object moving within the space of interest (e.g., a diver or an Autonomous Underwater Vehicle). The positioning error is auto-adjusted in case of variable channel coefficients, mainly due to changes in water turbidity space distribution. The achieved results have been assessed in case of two different paths, where variable distances from the positioning/communication LEDs are assumed.

Dynamic allocation of frequency resources to nodes in a wireless communication network is a well-known method adopted to mitigate potential interference, both unintentional and malicious. Various selection approaches have been adopted in literature, to limit the impact of interference and keep a high quality of wireless links. In this paper, we propose a different channel selection method, based on trust policies. The trust management approach proposed in this work relies on the node’s own experience and trust recommendations provided by its neighbourhood. By means of simulation results in Network Simulator NS-3, we demonstrate the effectiveness of the proposed trust method, while the system is under jamming attacks, in respect of a baseline approach. We also consider and evaluate the resilience of our approach in respect of malicious nodes, providing false information regarding the quality of the channel, to induct bad channel selection of the node. Results show how the system is resilient in respect of malicious nodes, keeping around 10% of throughput more than an approach only based on the own proper experience, considering the presence of 40% of malicious nodes, both single and collusive attacks.

Channel hopping provides a defense mechanism against jamming attacks in large scale Internet of Things (IoT) networks. However, a sufficiently powerful attacker may be able to learn the channel hopping pattern and efficiently predict the channel to jam.

A wireless system in outdoor environment including a reconfigurable intelligent metasurface designed to perform beam-steering is presented. The metasurface unit-cell has been designed to work in the range 37–40 GHz and the designed meta-atom is insensitive to the angle of incidence until 30°. We model and evaluate the effect of interferers on the tracking performances. In particular, we model the channel including interferers and we consider the effect of interferers for different signal powers and different directions on the radiation pattern generated by the metasurface. In general, we notice that the sidelobe level increases when the number of scatterers and their power increase and also when the angle of incidence of the signals scattered from the cluster is increased. Interestingly, for certain configurations, e.g. at 225°, the radiation pattern at 50° is better than at 30°, showing that the behavior is not linear.

The inherent openness of wireless communication techniques has made them vulnerable to jamming attacks. However, the effectiveness of a jamming attack depends on numerous parameters and varies according to the state of the environment. At the same time, attacks are becoming increasingly sophisticated and attempt to evade basic detection methods. Consequently, there is a real need to evaluate this new type of attack to improve the robustness of the detection and mitigation methods. A simulating tool to assess the impact of jamming attacks on wireless networks has become essential to gain effectiveness against attackers. This paper proposes a module of jamming attack for the discrete network simulator 3 (ns-3). This module, adaptable to any type of jamming attack strategy, provides a set of essential metrics allowing their evaluation. We evaluate the module by comparing the impacts of different types of jamming attacks already carried out in a real environment.

Saturation of optical receivers, caused by sunlight, represents a major issue in outdoor VLC system design for vehicular applications. This effect reduces maximum communication range, causing performance degradation when transmitter and receiver are in strict proximity. In addition, when saturation phenomena occur, only a fraction of total transmitted power is really exploited by the receiver, resulting in a significant waste of energy. The aim of this work, is to provide a novel analytical perspective, properly validated by experimental tests, in which the effects of saturation are related to the performance of outdoor VLC systems, in terms of signal to noise ratio (SNR). This analysis is further exploited for proposing an adaptive energy saving approach, which decreases the amplitude of transmitting power when saturation phenomena are detected by the receiver. A use case evaluation shows a reduction of 30% on average energy per bit transmitted, without any significant degradation of signal to noise ratio. Conversely, the adaptive control significantly improves SNR when transmitter and receiver are in close proximity and the system is fully approaching saturation.

Wide use of light emitting diode (LED) in cars and road sides unites is encouraging the exploitation of Visible Light Communication (VLC) paradigm in vehicular applications. However, real scenarios are characterized by poor Signal to Noise Ratio (SNR) conditions and, heavy interference among devices in the same area, that make effective handover operations critical for properly supporting mobility. A novel approach to perform handover in Vehicular Visible Light Networks (V-VLN), based on the evaluation of Interference to Noise Ratio (INR) and Interferer to Interference (IIR) Ratio, is proposed in this work, in place of the conventional approaches based on Signal to Noise Ratio (SNR) evaluation. Our approach has been numerically validated, taking into account Adaptive Modulation Scheme (AMS) and a VLC device moving at different speeds. Furthermore, a comparison with SNR-based approach has been provided. Simulation results show how INR-based handover mechanism outperforms SNR-based handover in terms of handover rate, the average delivered data per handover, and handover delay ratio.

With the increase of Internet of Things (IoT) applications, securing their communications is an important task. In multi-hop wireless networks, nodes must unconditionally trust their neighbours when performing routing activities. However, this is often their downfall as malicious nodes can infiltrate the network and cause disruptions during routing. We grant nodes the ability to evaluate the behaviour of their neighbours and, through consensus inspired from blockchain's miners, agree on the credibility of each node. The resulting metric is expressed as a node's reputation allowing, in the case of a malicious node, to isolate it from network operations. By illustrating this in an AODV-like multi-hop routing protocol, we can influence route selection no longer based solely upon the shortest number of hops, but also the highest overall reputation. Simulation results revealed that our approach can decrease packet drop rates by ≈ 48% in a static context when subjected to multiple black hole attacks compared to the original routing protocol.

The Internet of Things (IoT) represents an eclectic paradigm that is still growing in popularity. However, security aspects are a major concern for IoT devices due to their applications and the amount of sensitive data they provide. Simultaneously, the energy constraint in IoT networks remains a significant issue due to their limited resources. To reduce their energy consumption, several IoT protocols have integrated the energy-saving mode which offers four operating modes. On the basis of these four states, we derive an analysis framework, named HARPAGON, allowing an attacker to maximize his attack efficiency and minimize his impact in terms of energy consumption. Indeed, the effectiveness of many attacks depends principally on the state of the attacker and the victim at the same time. HARPAGON with the help of Markov chain theory allows modeling the interaction between the attacker and its victims. In this article, we demonstrate the effectiveness of the framework coupled with a jamming attack by comparing it to other types of jamming attacks. Experimental results reveal HARPAGON combined with a jamming attack drastically reduces the performance of the network, with an impact on the packet error rate (PER), which is around 13% higher than the reactive attack and with a reduced energy budget in respect of the other two well-known “green” jamming attacks.

The control logic of a reconfigurable meta-surface in mmWave is investigated in order to perform real-time beam steering in mobility contexts. When it is necessary to track an object or a person in movement, it is required to change the direction of the signal transmitted/reflected by the meta-surface. To do so, the meta-surface has to be reconfigured to modify the generated radiation pattern. Here a specific meta-surface, working around 78.5 GHz, is adopted, which consists of many unit-cells containing a diode to make its electromagnetic behaviour reconfigurable. In order to enable the system to adaptively compute the new coding schemes, namely to change the states of the meta-surface unit-cells, a machine-learning algorithm is adopted. In particular, we propose a Genetic Algorithm, integrate it in the architecture of our meta-surface and evaluate the performance. Results show that a good accuracy and a good convergence time are achieved for real-time beam steering functionality and the capacity of the system equipped with RIM (reconfigurable intelligent metasurface) is increased.

Reconfigurable Intelligent Meta-surfaces (RIMs) are particular devices able to control and manipulate radio frequency wireless signals. This promising technology allows to improve the reliability of wireless networks, thanks to the capacity of reflecting the desired signals through appropriate phase shifts. The joint use of RIMs and Computer Vision (CV) technology is the main objective of this paper. This synergistic approach is used to correctly identify the specific configuration of a radiation pattern, to be used as input for computing optimal coding sequences of the RIM. Indeed, by the means of a CV algorithm it is possible to infer a connectivity graph related to a real scenario, where people is moving. The information about network nodes such as their distance, the relative position, etc. is used for feeding an intelligent logic, able to compute the optimal configuration for re-directing the signals towards a given receiver target node.Numerical results show the huge potentiality of this combined approach in terms of interference reduction. It has been observed that for high traffic load, it is possible to reduce the average interference in the network of 40%. Furthermore, an analysis including the positioning estimation error of the CV algorithm has been addressed, in order to consider how it affects the interference reduction. Results show that, even though there is an increasing effect of interference, when the error is accounted, the interference reduction impact is still important.

In vehicular networks, vehicles carry various types of data that need to be offloaded to the RoadSide Units (RSUs) through Vehicle-to-Infrastructure (V2I) communications when vehicles come into their coverage. Since, RSUs are not widely deployed, vehicles have intermittent connectivity with RSUs. The data that vehicles carry to offload could be urgent data (such as accident data of nearby incident or emergency health data) that needs to be offloaded to the RSUs as soon as possible. Therefore, the consideration of Quality of Service (QoS) provisioning is imperative for data offloading in vehicular networks. In this paper, we propose V2I-Q, a V2I data offloading scheme with QoS provisioning by using three QoS functions: traffic classification, overload control and admission control. Traffic classification organizes the data into three priorities: high, medium and low. Overload control avoids overloading the RSUs to enable it to receive high priority data as soon as possible. Admission control allows RSU s to stop servicing existing vehicles offloading low priority data in order to receive high priority data from other vehicles. The performance evaluation shows that V2I-Q is able to offload more high priority data by incurring lower maximum offloading delay as compared to the traditional V2I data offloading schemes.

In vehicular networks, RoadSide Units (RSUs) are not available everywhere, therefore, it is not possible for vehicles to stay connected with RSUs all the time and to send their data directly to RSUs anytime using Vehicle-to-Infrastructure (V2I) data offloading. Hence, in these cases, Vehicle-to-Vehicle (V2V) communications are used to offload data to RSUs through other vehicles. Data to offload could be urgent (e.g., accident data), therefore, it is important to consider the Quality of Service (QoS) provisioning. In this paper, we propose a QoS-aware data offloading scheme for vehicular networks with QoS provisioning (DOVEQ) that considers both V2I and V2V data offloading. DOVEQ models the connectivity of vehicles with RSUs and vehicles, offloading capacity and estimation of reaching RSUs. It provides QoS using traffic classification, overload control and admission control. The performance evaluation of DOVEQ shows that DOVEQ outperforms other schemes by offloading more amount of important data with lesser offloading delay and running time.

Wireless networks are prone to jamming-type attacks due to their shared medium. An attacker node can send a radio frequency signal and if this signal interferes with the “normal” signals of two communicating nodes, the communication can be severely impacted. In this paper, we examine radio interference attacks from the jamming node perspective. In particular, we assume a “greedy” jamming node, whose main twofold objectives are to attack and interfere the communication of a transmitter and a receiver node, by minimizing its energy consumption and maximizing the detection time. The two communication nodes are static during the attack window time, while the attacker node can adapt its distance from the transmitter in order to select the most suitable range for a successful interference. In order to take into account the distance factor for the effectiveness of the attack, we derive an optimization model for representing the attack and we will study the key factors that allow effective and efficient implementation of a jamming attack, namely a) the energy b) the detection time and c) the impact on the transmission in terms of lowering the PDR. Three different types of attacks will be analyzed, 1) Constant Jamming, 2) Random Jamming and 3) Reactive Jamming. Simulation results show that the effectiveness of a jamming attack in respect to the others not only depends on the position of the jamming node but also on the distance between the transmitter and receiver nodes.

Visible Light Communication (VLC) relies on the dual use of existing light infrastructure for wirelessly send data. VLC is regarded as a “green”, cost-effective and secure communication technology. Moreover, VLC has been always considered immune to Radio Frequency (RF) waves. The only type of interference that has always been included in the channel model is the optical interference. Nevertheless, all the interference components potentially impacting on the performance of the system should be integrated in the transmitting model in order to improve the performance of the communication system. In this paper, we investigate on the interference and immunity of the VLC systems in respect of frequencies spanning the spectrum up to 1 GHz. We provide a detailed evaluation of the immunity of a VLC system by the means of experiments realized in “controlled” environment, i.e. an anechoic chamber, by considering interference at different power levels. Results are very interesting, since at specific frequency values, the VL transmitted signal results too “interfered” and noisy to be correctly recovered. In order to evaluate the impact of this interference, we have estimated the Bit Error Rate (BER) for different index modulations of Pulse-Position Modulation (mPPM).

A reconfigurable metasurface unit-cell is investigated in order to obtain a 0°/180° phase shift of the incident millimeter electromagnetic wave and its reflection for automotive applications. The meta-atom is realized starting from a ground and a dielectric layer, over which a metallic pattern is deposited. A diode is added to the pattern in order to make the structure reconfigurable in terms of phase. By tuning the substrate parameters (dielectric thickness h and dielectric constant εr) and with the shape and dimensions of the pattern on the top of it, the unit-cell that satisfies the requirements is found. The S11of the structure is shown both in modulus and phase and compared for the two configurations of the diode. Moreover, a vehicular scenario where a whole reconfigurable intelligent metastructure based on this unit cell is described. The ideal instantaneous rate is also derived.

Vehicular Social Networks (VSNs) consist of groups of individuals (i.e., people) who may share common interests, preferences and needs in the context of temporal spatial proximity on roads. In this environment, the impact of human social factors, such as mobility, willingness to cooperate and personal preferences, on vehicular connectivity is taken under consideration, thus extending the concept of Vehicular Ad-hoc Networks. In VSNs, vehicles are classified based on their social degree, a vehicle considered to be a “social” one if it accesses the vehicular social network and posts messages with a frequency higher than a given threshold. Therefore, to speed up the data dissemination process within a vehicular social network, a packet should be forwarded to those vehicles showing high social activity. In a previous paper, we introduced a new probabilistic-based broadcasting scheme called SCARF (SoCial-Aware Reliable Forwarding Technique for Vehicular Communications), and we analytically demonstrated its effectiveness in packet transmission reduction while guaranteeing network dissemination. In this paper, we assess SCARF in more realistic scenarios with real traffic traces, and we compare it with other similar techniques. We show that SCARF outperforms other approaches in terms of delivery ratio, while guaranteeing acceptable time delay values and average number of forwardings.

In this work, we demonstrate that Vehicular Social Networks (VSNs) require specific data dissemination techniques. To this aim, we consider the Barabási model and we study the impact of its use for data dissemination in VSNs. Moreover, the use of a probabilistic data dissemination protocol based on social features [1] proves that the time-evolving dynamics of the vehicular social network graph presents a small-world structure, where nodes tend to connect through clusters whose average distance is low on average. This result highlights how VSNs are a specific class of vehicular ad hoc networks with peculiar features that distinguish them from classical online social networks.

Numerous medium access control (MAC) have been proposed for Low-power Lossy Networks (LLNs) over the recent years. They aim at ensuring both energy efficiency and robustness of the communication transmissions. Nowadays, we observe deployments of LLNs for potentially critical application scenarios (e.g., plant monitoring, building automation), which require both determinism and security guarantees. They involve battery-powered devices which communicate over lossy wireless links. Radio interfaces are turned off by a node as soon as no traffic is to be sent or relayed. Denial-of-sleep attacks consist in exhausting the devices by forcing them to keep their radio on. We here focus on jamming attacks whose impact can be mitigated by approaches such as time-division and channel hopping techniques. We use the IEEE 802.15.4e standard to show that such approaches manage to be resistant to basic jamming but yet remain vulnerable to selective jamming. We discuss the potential impacts of such onslaughts, depending on the knowledge gained by the attacker, and to what extent envisioned protections may allow jamming attacks to be handled at upper layers.

Visible Light Communication (VLC) exploits optical frequencies, diffused by usual LED lamps, for adding data communication features to illuminating systems. This paradigm has attracted a growing interest in both scientific and industrial community in the latter decade. Nevertheless, classical wireless communication mechanisms for physical and Medium Access Control (MAC) layers are hardly available for VLC, due to the massive external interference caused by sunlight. A correct signal carrier recover in high noise conditions represent a significant challenge. In this work, it is shown that the synchronization frame length affects the performance of the system in terms of Bit Error Ratio (BER). Since different external conditions require different minimum preamble lengths, we considered an Artificial Intelligence (AI) approach, based on multi-arm bandit formulation, for obtaining a low impact in both BER and goodput of the communication. A low-cost hardware VLC system, implementing a learning algorithm on a Frequency Shift Keying Modulation (FSK), has been designed and tested in different environmental conditions. Experimental results show that a proper choice of preamble length overcomes, in terms of BER and goodput, the classical approach based on fixed preambles.

Localization systems based on Visible Light Communication (VLC) are considered as good candidates for indoor environments, due to their high accuracy, low costs and the possibility of reusing existing infrastructures for both lighting and positioning. However, high level of environmental noises, mainly due to sunlight, significantly affect the performance of VLC positioning systems. A novel approach, for easily measuring environmental noises and compensating their effects, has been proposed in this work. Frequency Division Multiplexing (FDM) is adopted to divide the total bandwidth into a series of non-overlapping frequency sub-bands corresponding to each signal, while an estimation of Signal to Noise Ratio, obtained through real time Power Spectral Density measure, is exploited to compensate error positioning due to sunlight and other wide-band external optical nice sources. Proposed approach has been validated through experimental tests, carried out using a simple deployment of low power lamps, extremely low cost hardware and a Software Defined approach. In the region under test, receiver positions have been experimentally detected according to an improved accuracy in comparison with classical FDM approach, confirming the correctness of proposed technique, according to low Signal to Noise Ratio levels.

Intelligent inter-vehicle communication is a key research field in the context of vehicular networks that applies in real-life applications (e.g., management of accidents, intelligent fuel consumption, smart traffic jams, etc.). Considering different roles of nodes based on their “social aptitude” to relay information could provide a social component in the vehicular structure that can be useful in getting a clear prediction of the topological evolution in time and space proving to be very effective in managing intelligent data forwarding. In this work we characterize a vehicular network as a graph using the link layer connectivity level and we classify nodes on the basis of specific attributes characterizing their “social aptitude” to forward data. Two forwarding approaches are presented, based on different socialites that allow to (i) select the most social node (i.e., a social hub) or (ii) choose among various social nodes.

Recently, Internet-of-Things (IoT) applications have been moving toward a network of intelligent objects with social capabilities, defined as the Social IoT (SIoT). Different types of relationships exist among things, thus forming social connections such as parental-object relationship (POR), co-work-object relationship (CWOR), co-location-object relationship, ownership-object relationship, and so on.

Recent progress in the field of nanotechnology is enabling communication between nanodevices to be considered also for in-vivo applications. In particular, nanodevices enabling light-controlled process have started to attract a lot of interest in the research community, due to potential applications like healthcare wearable devices, intra-body applications, and optogenetics, whose main objective is to stimulate neurons by the means of the light signals. Leveraging on these premises, we consider upconversion phenomenon, namely the ability of specific nanoparticles to convert low energy radiations (e.g., near-infrared radiations) into higher energy radiation (e.g., visible light) via a non-linear process. Energy transfer as viable communication mean has been already considered based on the Förster-Resonance-Energy Transfer. In this paper, we focus on phonon-assisted energy transfer, and we analyze the requirements and conditions for generating optical signals from a genuine signal processing point of view. More specifically, based on information theory approach, we derive the conditions to minimize the error probability. We show that the dopant concentration, (i.e., the concentration of elements drugging a pure material in order to change its features) mostly influences the correct generation of the light signal. The performed analysis is based on experimental results.

Modern vehicles are equipped with several interconnected sensors on board for monitoring and diagnosis purposes; their availability is a main driver for the development of novel applications in the smart vehicle domain. In this paper, we propose a Docker container-based platform as solution for implementing customized smart car applications. Through a proof-of-concept prototype-developed on a Raspberry Pi3 board-we show that a container-based virtualization approach is not only viable but also effective and flexible in the management of several parallel processes running on On Board Unit. More specifically, the platform can take priority-based decisions by handling multiple inputs, e.g., data from the CANbus based on the OBD II codes, video from the on-board webcam, and so on. Results are promising for the development of future in-vehicle virtualized platforms.

To meet the food demand of the future, farmers are turning to the Internet of Things (IoT) for advanced analytics. In this case, data generated by sensor nodes and collected by farmers on the field provide a wealth of information about soil, seeds, crops, plant diseases, etc. Therefore, the use of high tech farming techniques and IoT technology offer insights on how to optimize and increase yield. However, one major challenge that should be addressed is the huge amount of data generated by the sensing devices, which make the control of sending useless data very important. To face this challenge, we present a Bayesian Inference Approach (BIA), which allows avoiding the transmission of high spatio-temporal correlated data. In this paper, BIA is based on the PEACH project, which aims to predict frost events in peach orchards by means of dense monitoring using low-power wireless mesh networking technology. Belief Propagation algorithm has been chosen for performing an approximate inference on our model in order to reconstruct the missing sensing data. According to different scenarios, BIA is evaluated based on the data collected from real sensors deployed on the peach orchard. The results show that our proposed approach reduces drastically the number of transmitted data and the energy consumption, while maintaining an acceptable level of data prediction accuracy.

The potentialities of the Terahertz (THz) frequency band in the context of nano-scale communications are increasing, thanks to specific features that allow issues related to the spectrum scarcity and capacity limitation to be overcome. Apart from high molecular absorption and very high reflection loss that represent the main phenomena in THz band, in this paper, we investigate the chirality effects that affect the propagation medium, in the frequency range (4 - 10) THz. It is observed that in this interval, the chiral parameter shows resonance peaks in specific frequencies. In this paper, we investigate the channel capacity in a special medium affected by chirality effects, such as biomolecules, DNA chains, and so on. Specifically, we analyze the signal propagation in a chiral medium where a giant optical activity (GOA) is present. This effect is typical of the so-called chiral-metamaterials. Through simulation results we distinguish the behavior of a chirality-affected channel with GOA in line-of-sight and non-line-of-sight propagations, assuming different power allocation techniques and also comparing the performance to the case of No GOA.

Cognitive radio technology is a promising solution to the exponential growth in bandwidth demand sustained by increasing number of ubiquitous connected devices. The allocated spectrum is opened to the secondary users conditioned on limited interference on the primary owner of the band. A major bottleneck in cognitive radio systems is to find the best available channel quickly from a large accessible set of channels. This work formulates the channel exploration-exploitation dilemma as a multi-arm bandit problem. Existing theoretical solutions to a multi-arm bandit are adapted for cognitive radio and evaluated in an experimental test-bed. It is shown that a Thompson sampling based algorithm efficiently converges to the best channel faster than the existing algorithms and achieves higher asymptotic average throughput. We then propose a multihop extension together with an experimental proof of concept.

Among all the Internet of Things (IoT) applications, the Smart City concept has received significant attention in the last few years. The main motivation behind this interest is attributable to population growth and urbanization trend. Cities need, indeed, to be ready to face new challenges - e.g., traffic congestion, wast management, etc. - caused by this new amount of citizens. To address those issues, many IoT solutions have already been proposed and many others are still under investigation; anyway, those initiatives are all based on different standards and protocols, while the Smart City concept requires integration among all the stakeholders. In this paper, we present the VITAL architecture, which aims to integrate Internet- Connected Objects (ICOs) among multiple IoT platforms and ecosystems. In particular, we introduce the "ICOs and Services Discovery" module, which makes completely transparent, for users, the exploration of data-sources that are appropriate for his/her business context. This mechanism is at the basis of the Cloud of Things paradigm and a key feature as the platform agnostic property is an essential goal for VITAL.

This paper focuses on the coverage of specific Zones of Interest that can change dynamically over time by using a swarm of flying robots. The mobility of the flying devices is achieved by the design of two distributed and local algorithms. The first algorithm is based on Particle Swarm Optimization (PSO) and Virtual Forces Algorithm (VFA). We modify the classical PSO approach to propose a totally distributed algorithm, which only requires the flying robots to receive local information from the neighbors to update their velocity and trajectory (PSO-S). This new distributed version of the PSO is combined with a distributed version of the Virtual Forces Algorithm. The second algorithm is a distributed implementation of the VFA (VFA-D). To the best of our knowledge, these two approaches are novel in their distributed character, scalability and implementability on resource-constrained devices. We show that the proposed algorithms are reactive, i.e. able to capture in an effective fashion the events happening within the field even if the position of the events changes over time. To show the effectiveness of the proposed techniques, we perform extensive simulations to compare both the PSO-S and the VFA-D schemes with a centralized version of the VFA. Simulations show the good performance in terms of coverage and traveled distance as well as the high reactivity of both PSO-S and VFA-D when the ZoI changes.

In this paper we focus on the problem of dynamic event coverage. We assume that no knowledge about either event position or duration is given a priori. Nonetheless, the events need to be monitored and covered thanks to mobile wireless sensors. Thus, mobile sensors have to discover the events and move towards a new Zone of Interest (ZoI) when the previous monitored event is over. An efficient, distributed and localized solution of this problem would be immediately exploitable by several applications domains, such as environmental, civil, etc. We propose two novel approaches to deal with dynamic event coverage. The first one is a modified version of the PSO, where particles (mobile sensors, nodes or devices in the following) update their velocity by using only local information coming from their neighbors. In practice, the velocity update is performed by considering neighbors' sensed events. Our distributed version of PSO is integrated with a distributed version of the Virtual Force Algorithm (VFA). Virtual Force approach has the ability to “position” nodes with no overlap, by using attractive and repulsive forces based on the distance between nodes. The other proposed algorithm is a distributed implementation of the VFA by itself. Both techniques are able to reach high levels of coverage and show a satisfying reactivity when the ZoI changes. This output parameter is measured as the capability for the sensors to “follow” a sequence of events happening in different ZoIs. The effectiveness of our techniques is shown through a series of simulations and comparisons with the classical centralized VFA.

In this paper, we address the problem of re-establishing the network connectivity in post-disaster scenarios, where the original wireless infrastructure has been partitioned into multiple network fragments (called islands), operating on different frequencies. To this purpose, we propose the utilization of swarms of dedicated repairing units, called Stem-Nodes (SNs). SNs are provided with Cognitive Radio (CR) and self-positioning capabilities, in order to offer maximum reconfigurability in terms of mobility and wireless technologies supported. Moreover, swarms of SNs can self-organize into STEM-Mesh structure, that works as a dynamic backbone to connect heterogeneous islands using different technologies (e.g. Wi-Fi, Wi-MAX, etc). In this paper, we present three contributions pertaining to STEM-Mesh: (i) we describe a distributed motion control scheme (based on virtual springs approach) that enables SNs to self-organize into dynamic STEM-Mesh structures, (ii) we introduce a discovery scheme, through which SNs can explore the scenario in both spatial and frequency domains, and possibly connect the islands to the STEM-Mesh backbone and (iii) we validate the correctness of the proposed scheme, by verifying the optimal placements of the SNs composing the STEM-Mesh on a simplified scenario (e.g. chain topology). Finally, we evaluate through Omnet++ simulations the ability of STEM-Mesh to maximally re-establish connectivity on partitioned network scenarios.

In this paper, we introduce two families of distributed algorithms to control the movement of groups of flying robots that are monitoring an event by moving over the field where the event takes place, while optimizing some specific objective. In order to show the effectiveness of our algorithms, we formulate the Sport Event Filming (SEF) problem. The objective of the problem is to maximize the satisfaction of event viewers while minimizing the distance traveled by the camera-drones. We propose two families of solutions to solve the dynamic version of the problem, where the flying robots do not have any knowledge of the input sequence and move in reaction to the movements of the protagonists of the event. The first family (Nearest Neighbor)is based on a technique used in robotic systems, whereas the second family (Ball Movement Interception) is designed based on specific characteristics of the SEF problem. We present extensive simulation results for both families in terms of average viewer satisfaction and travelled distance for the flying robots, when several parameters vary.

We are currently witnessing the increasing diffusion of new location-aware smartphones applications that make intensive use of positioning information, however, the widespread and pervasive GPS technology is gradually giving way to new or revised less power-hungry communication technologies able to guarantee almost the same accuracy. Within this general context, mainly motivated by the current green communication paradigm, this paper proposes an energy-efficient and cost-effective localization architecture based on the improvement of classical cell-tower schemes coupled with a dynamic fingerprinting update phase in order to face the natural changes in the radio environments. The proposed system architecture has been implemented and tested in a real scenario to measure the performances in terms of accuracy and energy saving that will make it preferable to the traditional GPS-based systems in the next future. The obtained results show the effectiveness of the considered approach that makes possible to estimate the current position of a mobile user with a very small error (≈ 20m).

This paper proposes a distributed Neural/Genetic algorithm able to compute both the more suitable positioning and transmission modulation schemes for fixed/mobile wireless nodes equipped with software defined radio abilities. Devices considered in this work are able to move towards new positions by applying the concept of controlled mobility. The selection of the more suitable modulation scheme is realized through the SDR (Software Defined Radio) paradigm. The synergistic combination of controlled mobility and SDR in a totally distributed way, allows to obtain a high degree of self-configurability; moreover, the extreme adaptability to the network conditions and application level constraints in terms of coverage and guaranteed connectivity, make the proposed approach well suited for quite different communication scenarios such as classical monitoring or disaster recovery. The obtained results, validated throughout an intensive simulation campaign, show how the controlled mobility paradigm applied to the wireless devices and the intrinsic re-configuring SDR capabilities, increase the performance of the network both in terms of coverage and connectivity respect to other algorithms.

The paper proposes the integration of new cognitive capabilities within the well known OpenBTS architecture in order to make the system able to react in a smart way to the changes of the radio channel. In particular, the proposed spectrum sensing strategy allows the OpenBTS system to be aware of other active transmissions by forcing to choose a new radio channel, within the GSM frequency band, when a licensed primary user has to transmit on a busy channel. The implemented scheme, representing a solid step forward in the cognitive direction, has been validated throughout a detailed testbed pointing out strengths and limitations in realistic communication environments.

Power consumption is an essential issue in wireless multimedia sensor networks (WMSNs) due to the elevated processing capabilities requested by the video acquisition hardware installed on the generic sensor node. Hence, node placement scheme in WMSNs greatly impacts the overall network lifetime. In this context, the paper first proposes a suitable hardware architecture to implement a feasible WMS node based on off-the-shelf technology, then it shows how the energy spaced placement of the wireless nodes reduces the energy consumption in comparison with the classical evenly spaced placement, without affecting the video quality of multimedia traffic.

In this paper, controlled mobility is used in a novel fashion in order to achieve the best placement for prolonging the lifetime and for maximizing the energy efficiency of the wireless sensor network. Basically, a new source of energy consumption is introduced when the mobility of nodes is exploited. We design a new mechanism for the nodes, which, by virtualizing the movement phase, makes nodes reach the best placement and, at the same time, it counterbalances the effects of energy wasting caused by the movement. We validated the results by extensive simulation campaigns where different network parameters vary. In all the cases, the performance of the network, in terms of lifetime and energy efficiency, has been found fairly superior to the schemes existing in literature.

The IEEE 802.16 standard is currently one of the most interesting broadband wireless access. One key issue of mesh networks is the MAC (medium access control) layer. In this paper we analyze the coordinated distributed scheduling scheme (CDS) of the standard IEEE 802.16. Since some fundamental parameters in this scheme has been left un-standardized, we focus on the dynamic setting of these parameters in order better performance in terms of throughput and average end-to-end data packets delay to be obtained. The standard describes a data scheduler in a general way, based on a three-way-handshake mechanism, but details about data scheduler scheme have been left un- standardized. In order to obtain a better evaluation in terms of performance of the control schedule, we implemented a simplified data scheduler and we show how this simplified data schedule allows smaller data delay transmissions to be realized.

The IEEE 802.16 standard is currently one of the most interesting broadband wireless access. One key issue of mesh networks is the MAC (Medium Access Control) layer. The scheduling and assignment of transmission opportunities can be realized in centralized or distributed manner. In this paper we analyze the IEEE 802.16 election based mechanism for the Coordinated Distributed Scheduling (CDS). In order to analyze performance of this scheduling mechanism we implemented a new MAC module for the IEEE 802.16 mesh mode in a well-known simulation tool, ns2. Moreover, we developed a queue size based approach to dynamically set a typical parameter of the CDS, called XmtHoldoffExponent (XHE). This dynamic adaptation mechanism is able to enhance the performance of the network in terms of throughput and average end-to-end data packet delay despite of the increased average setup latency, that is the average interval time of acquiring a new transmission opportunity..

Exactly determining the geographical position of a telecommunication device is an open research challenge for the personal and mobile communications community. In this paper we explore the terminal localization problem in a GSM system, and we develop a set of carrier independent solutions. Specifically, we introduce four different techniques, all based on the 6-strongest cells traditionally used in the GSM standard. Through extensive simulations we show that our technique can achieve remarkable results without requiring any additional equipment in the core network or the mobile devices.

The design of an efficient medium access control (MAC) for wireless ad hoc networks or wireless sensor networks is challenging. These kinds of network can benefit from communication protocols that avoid frame collision: time division multiple access (TDMA). However, ad hoc networking and sensor networks enable novel communication paradigms which require the definition of new quality-of-service (QoS) parameters and frameworks for QoS support. The relevant QoS requirements are frequently antagonist and, consequently, an appropriate tradeoff has to be determined to fit all of them. In the distributed TDMA paradigm the speed of schedule exchange is important to have updated information about collision-free slots when the topology network changes. The high speed of the schedule update is paid in terms of power consumption. In this work, an analytical framework allowing an evaluation of the tradeoff between energy efficiency and responsiveness in the update process in a generic, distributed TDMA system is derived. The main objective of this work is to maximize the probability that the schedule updating process is realized in an interval time [0, t], given a constraint in terms of power consumption.

In this paper, we study the problem of joint routing and link scheduling to support high data rates for broadband wireless multi-hop networks. We first address multi-path routing in a static wireless mesh network. We review the premise that a routing protocol should prefer disjoint path construction and argue that using disjoint path limits route reliability in wireless mesh networks (WMNs) compared to using multiple loop-free paths that need not be disjoint. The new multi-path parallel routing protocol (MPRP) developed runs over a TDMA MAC protocol based on the coordinated distributed scheduling scheme (CDS) of the Std. IEEE 802.16. Moreover, we propose a variant of the coordinated distributed scheduling scheme called distributed scheduling scheme (DSS). Through extensive simulations, we show that multi-path routing protocol outperforms in terms of throughput and average end-to-end delay a well-known routing protocol ad-hoc on demand distance vector routing. Both protocols, MPRP and AODV run over the MAC protocol with the CDS or the DSS schemes. The synergic effect of multiple paths and conflict-free schedules generated at MAC layer alleviate the bottleneck created in a scenario in which only a node, the Internet Gateway, is assumed as destination and the others nodes are either source nodes or relay nodes. Compared to CDS we show that our DSS is more robust in different wireless network topologies.

In this paper we propose a new totally distributed scheduling scheme, the randomized-MAC (R-MAC), for wireless mesh networks. This mechanism is based on a function that randomly selects an available control slot. In each frame a generic node will try to reserve a new control slot. This control slot is a kind of permission to reserve new information slots and for exchanging its scheduling information with its neighbors. The scheduling process is executed across the entire network at the same time. Nodes do not wait in some particular order to schedule their transmission. This makes sure that the mechanism is fair. We implemented this new scheme in a well-known simulator, ns-2. Moreover, we implemented the coordinated distributed scheduling (CDS) scheme of the Std. IEEE 802.16 and we compared R-MAC and the standard distributed scheme. In practice, through extensive simulations we have been able to sketch some inherent characteristics of the standard scheme and we tried to develop a mechanism that permits to overcome some limits of the CDS. We will show how our mechanism outperforms CDS mechanism in terms of throughput and data packet end-to-end delay.

This paper presents an analytical framework for evaluating a tradeoff between the schedule information freshness and the energy consumption in a TDMA environment. In practice, we investigate the importance of the speed of schedules updating in a totally distributed TDMA MAC protocol. In order to do that, we developed an analytical framework based on Markov chain that permits to measure a tradeoff between the cost associated with the update process and the freshness of information. The power consumption associated with the schedule updating process and the speed of the schedule updating process are considered as two antagonis QoS parameters. The main objective of this paper is to maximize the probability that the Schedule Update Process is realized in an interval time [0, t], given a constraint in terms of power consumption 0 ≤ cost ≤ c. We will show an application of this analytical framework to a totally distributed TDMA protocol, the E-TDMA.

In this paper we propose a comparison between different MAC protocols for wireless ad-hoc networks. Specifically we consider a well-known protocol adopted at the MAC layer, that is the Std IEEE 802.11 and a totally distributed Mac protocol based on a contention phase and a TDMA phase, the Evolutionary-TDMA. Moreover, we propose a new totally distributed TDMA Mac protocol, that based on the information of 1 and 2 hop neighbors permits schedules to be built. This protocol permits conflict-free schedules to be built in the first phase called Control Phase, but there is a Re-distribution phase based on a random function that do not guarantees that schedules are totally conflict-free. The MAC protocol proposed in this work is called Randomized-MAC, because it is based on a random function in the Re-distribution phase. Randomized-MAC is implemented in a well-known simulation tool, NS2. In NS2 we conducted extensively simulation campaigns that permit to conclude that even if schedules built with our schemes are not necessarily conflict-free, Randomized-MAC permits better performance in terms of throughput and delay to be obtained.

In this paper we propose and develop a new simple routing protocol for wireless mesh network. This protocol, called multipath parallel routing protocol MPRP, is a reactive, distance vector routing protocol. It uses a simple mechanism to build multiple paths for a couple of nodes source and destination. These multiple paths are used in a parallel fashion. Multiple paths are not necessarily disjoint but they are loop-free. Moreover, we implemented the coordinated distributed scheme (CDS) of the Std. IEEE 802.16. We used a well-known simulation tool, NS2 in order to compare performance of the multipath protocol and the ad hoc on demand distance vector routing protocol (AODV), both running over the MAC layer in which is incorporated the CDS scheme. Simulation results show as multiple paths used in a parallel fashion permit better results in terms of throughput and delay to be obtained.

This article presents a role dynamic management algorithm for sensor devices. The network scenario considers a wireless sensor network in which each node can assume different roles. Specifically, attention is focused on two different roles: the active and the passive role. When a sensor device assumes an active role it uses a greater amount of energy greater than the passive role. The scope of this work is to introduce a dynamic management strategy to better distribute the energy in the network varying dynamically roles of the nodes under different mobility conditions. This dynamic algorithm is applied to an energy-efficient protocol based on a cross-layered approach (ESR and EMAC) and which ensures a good node lifetime. Two different algorithms have been developed to ensure a dynamic management of the node roles with the ESR and EMAC approach. Both these approaches have been evaluated through a simulation tool OMNeT++ and their performance have been compared with the original ESR and EMAC.

Mobile ad hoc networks are characterized by multi-hop wireless links, the absence of any cellular infrastructure, and frequent host mobility. Due to the dynamic nature of the network topology and the resource constraints, routing in MANETs is a very challenging task. The advantage of multi-path routing in MANETs is not obvious because the traffic along the multiple paths will interfere with each other. The effect of the route coupling in this environment can severely limit the gain offered by multipath routing strategies. Route coupling refers to the interference between two or more multiple paths located physically close enough to interfere with each other during data communication. In this paper, a new multipath distance-vector routing protocol, the geographic multipath protocol (GMP) is proposed. This reactive algorithm computes alternate routes loop-free and zone-disjoint paths. In this way the paths are more independent of each other and the correlation-interference is very low. This permits a higher load balancing in the network and a major effectiveness of the multiple paths as far as the performance measures considered are concerned. Simulation experiments have shown that GMP outperforms unipath distance vector routing (AODV) and multipath distance vector routing