Within the rapidly expanding Internet of Vehicles (IoV) landscape, the demand for network services to accommodate data-intensive applications has become increasingly paramount. However, IoV faces significant challenges, including high latency, limited coverage in remote areas, network congestion, and privacy concerns in content popularity prediction. To address these challenges, we introduce the Scalable Optimisation for Networked Aerial-vehicles (SONA) scheme, which reduces latency through advanced caching techniques while enhancing coverage and ensuring required data rates using Unmanned Aerial Vehicles (UAVs). UAVs are deployed to augment coverage in areas lacking RoadSide Unit (RSU) support and to assist in scenarios where RSUs are overwhelmed, ensuring continuous data rate provision. Our scheme introduces a novel mathematical optimisation model and machine learning algorithms: Federated Learning (FL) to collaboratively predict content popularity without exposing user data, Reinforcement Learning (RL) to dynamically optimise UAV placement and energy-efficient deployment, and blockchain to enable secure, decentralized coordination between RSUs and UAVs for real-time decision-making. Simulation results demonstrate the effectiveness of our approach, achieving an average delay of 8 ms, an average cache hit rate of 88.43%, and satisfying desired data rate requirements in 84.85% of scenarios, significantly improving IoV performance.

Aiming at service-oriented design requirements for the Integrated Satellite Terrestrial network (ISTN), this paper proposes a cross-layer analysis framework and a distributed Cognitive Space Service Network architecture. These address challenges in traditional single-layer research, including un-quantified cross-layer deviations, incomplete link analysis, and a lack of multi-dimensional evaluation. A distributed on-orbit architecture for LEO satellites is constructed to enable cross-layer cooperation across physical-layer access, network-layer routing, and application-layer service matching. Beyond channel-fading-based analysis, a multi-link model incorporating node-induced interference is established. It derives uplink access success rate expressions, quantifies impacts of user density, link distance, and carrier bands on connectivity, and verifies interference-attenuation coupling via simulations. By integrating mutual information and entropy theory, a cross-layer deviation framework is built, using confluent hypergeometric distribution to model service matching uncertainty. This achieves quantitative modeling of “physical-network layer” cooperation gains and “network-application layer” adaptation deviations. The results provide theoretical tools for optimizing space-based intelligent networks. The architecture and methods directly support enhancing large-scale satellite network quality and constructing objective functions, providing a key technical path for service-oriented future space-ground integration systems.

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 advancement of low-Earth orbit satellite networks has increasingly drawn attention to the integration of smart grids with these satellites. This paper focuses on a lowEarth orbit satellite and drone-assisted smart grid network architecture. First, the communication relationship between multiple users and drone relays is analyzed. Then, the concept of ‘fault nodes,’ a category of users that necessitate the occupation of fixed resources for stable communication, is introduced. Moreover, this paper investigates the throughput optimization problem. We transform it into a resource-matching problem and propose a Q-learning approach based on improved reward function to efficiently solve it. Simulation results verify that our proposed scheme yields superior throughput when the number of faults is small, and can effectively avoid resources occupied by fault nodes when the number of faults is high.

Visible light communication (VLC) is an important research direction for 6th-generation mobile networks (6 G). When the image sensor realizes the multi-light source diffuse reflection VLC based on code division multiple access (CDMA), the spread spectrum sequence used cannot be too long due to the limitation of sensor hardware. The increase in the light sources will produce serious cross-correlation interference, which affects the communication performance of the system. This paper proposes a pilot-aided multi-light source VLC scheme. Firstly, at the optical transmitter, a light source's emission waveform combination is designed, and the orthogonal and non-orthogonal sequences are used as the spread spectrum sequences of the communication and the pilot light sources. The pilot light source provides the synchronous function for the communication light sources, effectively solving the problem of cross-correlation interference and signal synchronization between the communication light sources. Secondly, at the optical receiver, this paper proposes a demodulation algorithm for the diffuse reflection superimposed signal corresponding to the emission waveform combination, aimed at accurately recovering the information transmitted by the communication light sources. The simulation results show that the proposed scheme can achieve good lighting effect and meet the basic communication requirements.

The transportation system has seen rapid evolution with the coming of Industry 4.0. Self-driving cars and intelligent traffic control have replaced manually driven vehicles and the conventional traffic management system. To enhance their performance, these entities continuously need to exchange data amongst themselves regarding their position, speed, direction, obstacles on the road, traffic signal state and weather conditions. This data can be intercepted by any malicious eavesdropper and the entire smart transportation system can be compromised. To ensure secure and reliable communication among these autonomous vehicles and road infrastructure quantum communication is used. In the proposed Q-ShielD framework, two such quantum protocols are implemented, namely BB84 Quantum Key Distribution (QKD) and the Continuous Variable quantum protocol. The two protocols differ in the way they send the transmitted data. It was found that the Continuous Variable QKD performed better over BB84 in terms of Key Generation Rate, Quantum Bit Error Rate, and Distance covered in regular weather conditions.

Integrated satellite-terrestrial networks (ISTNs) are increasingly recognized for their global communication. However, the existing research mainly focuses on simplified ISTNs, where cooperative strategies between satellites and base stations (BSs) are not easily applicable to real-world scenarios. There is a pressing need to investigate more realistic and complex ISTNs to address this gap. To address this gap, we investigate a more realistic and complex ISTN configuration, characterized by a large number of BSs, each divided into interference and service areas. Based on two cooperative modes, i.e., overlay and underlay spectrum sharing, two multiple access schemes are proposed for complex ISTNs using promising rate-splitting technology. These schemes consider multiple constraints simultaneously, such as communication delay, information rate, and power limit. Furthermore, a delay-rate adaptive user grouping strategy is proposed according to communication delay and information rate. For these schemes, the corresponding weighted sum rate problems are formulated, and an improved alternating optimization (AO) method is designed to solve the nonconvex challenges in two spectrum sharing modes. Moreover, a satellite-terrestrial coordinated iteration strategy based on AO is proposed to reduce the computational complexity in underlay spectrum sharing. Simulation outcomes confirm the advantages of our proposed schemes compared to various standard schemes.

Due to their limited resources, Internet of Things (IoT) networks are vulnerable to attacks like aggressive denial-of-service (DoS) attacks aimed at draining device energy. IoT devices often have non-rechargeable or hard-to-recharge batteries, especially when deployed in hostile areas, making them prime targets for energy depletion attacks such as barrage attacks. To mitigate these threats, security systems must implement lightweight measures. This paper proposes a new game theory-based mechanism to defend IoT devices against energy depletion. Game theory effectively models the adversarial interactions between attackers and defenders. Our approach uses a dynamic game with incomplete information to derive optimal detection, defense, and attack strategies, establishing a Perfect Bayesian Nash Equilibrium (PBNE) to protect IoT device energy under constant attack. This dynamic game involves repeated interactions where at least one player lacks complete information about the other. The proposed model offers a high-performance solution for conserving IoT device energy. Simulation results demonstrate its effectiveness, showing that it can save, on average, 95.19% of the energy expended in receiving packets during an attack and can deter attackers. This approach ensures the sustainability of IoT networks against persistent energy depletion attacks.

Federated learning (FL) model marketplaces require qualified workers to collaboratively train customized models. However, recruiting optimal workers on a limited budget in non-independent and identically distributed (non-IID) data settings remains a fundamental issue. Moreover, inadequate quality verification exposes the marketplace to spoofing and poisoning attacks, while verifying data and model quality without accessing local storage remains a significant dilemma. To bridge the research gap, this paper proposes a knowledge-aware model customization scheme in FL model marketplaces, to facilitate zero-trust worker recruitment and verification while ensuring privacy preservation. Specifically, (i) we design a knowledge-aware quality evaluation mechanism by leveraging the knowledge of workers, i.e., soft-label predictions of their local models on a privacy-free reference dataset (provided by the customer), to assess their data quality in a privacy-preserving manner. (ii) We formulate the optimal worker recruitment problem under budget constraints as an NP-hard integer programming problem and design a dynamic programming-based optimal worker recruitment algorithm with budget feasibility and computational efficiency. (iii) We devise a two-stage zero-trust quality verification mechanism by utilizing zero-knowledge proof (ZKP) to exclude distrustful workers, thereby preventing spoofing and poisoning attacks. Extensive experimental results demonstrate that the proposed scheme enhances model customization performance by up to 34.3% on label-skewed non-IID data and 36.2% on feature-skewed non-IID data compared with existing representatives.

The integrated satellite-terrestrial network (ISTN) has attracted much interest due to its global information serviceability. Recently, rate-splitting multiple access (RSMA) has been widely investigated to achieve highly efficient access. Motivated by this, we design the joint RSMA scheme based on spectrum sharing for the downlink ISTN, where part data of terminals are shared by satellite and base station. Furthermore, the max-min rate (MMR) maximization problem is formulated, and an alternating optimization algorithm based on weighted minimum mean square error is introduced to solve the non-convex problem. Simulations show that the joint RSMA scheme has a higher MMR than baseline schemes.

This paper focuses on the RF index optimization of orthogonal time-frequency space (OTFS). Firstly, an optimization problem is designed to cover the three elements of EVM/DD domain filtering /PAPR. The analysis of this problem shows that an iterative optimization scheme can be designed to solve this problem. Like many traditional researches, the proposed problem can be solved by optimization methods like ADMM. However, traditional methods are opaque and difficult to control which leads to difficulties in deployment, this paper proposes a joint adjustment method of EVM/DD-domain-filtering/PAPR with adjustable joint parameters for practical use, which can adjust the indexes adaptively according to the demand. The simulation results show the system’s effectiveness using parameter adjustment to achieve index modification.

The multibeam satellite system is crucial for the next generation communication, providing seamless and various information services, such as broadcast and unicast messages. However, catering to the burgeoning number of users within limited spectrum resources presents formidable challenges. In response, rate-splitting multiple access (RSMA) has emerged, leveraging non-orthogonal transmission and precoding strategies concurrently. Therefore, we devise the non-orthogonal broadcast and unicast (NOBU) joint transmission framework using RSMA. Furthermore, amalgamating traditional precoding with frequency reuse techniques, we propose a novel centralized frequency reuse strategy, exhibiting commendable performance alongside reduced computational complexity. Furthermore, we maximize the weighted sum rate (WSR) and introduce an improved alternating optimization algorithm, adept at converting intricate non-convex problem into tractable convex counterpart. Simulation outcomes demonstrate that our proposed schemes have significant improvements in WSR performance and are promising for various practical applications.

Wireless Sensor Networks (WSNs) consist of devices communicating information wirelessly from a monitored field. Sensors are designed with limited energy resources pushing application designers to optimize energy consumption. It is common practice in WSNs to organize nodes in clusters with a device (designed as a cluster head) with superior energy resources. However, this clustered architecture exposes vulnerabilities, particularly to energy depletion attacks targeting the cluster head. Energy depletion attacks pose a significant threat to sensor node survival. To overcome such attacks, we propose a cyber deception defense mechanism based on game theory to model the actions between the attacker agent and the cluster head agent and hence, extract optimal strategies during conflicting interactions between the agents. In this paper, we propose using a game with incomplete information to find the Nash equilibrium point. Cyber deception, particularly the integration of a honeypot system, is employed to enhance the solution’s effectiveness in optimizing cluster head energy in the face of potential attacks. The proposed solution demonstrates its effectiveness in mitigating attacks of varying intensity against the cluster head.

Orthogonal time frequency space (OTFS) modulation can provide a stable signal in a highly dynamic environment with high speed. In this paper, the PAPR characteristics and peak-to-average ratio (PAPR) reduction methods of OTFS signal with superimposed pilot are studied, and a two-stage PAPR reduction scheme combining distributed superimposed pilot and precoding is proposed. Pilot dispersion is used in the first stage, and partial precoding is used in the second stage to optimize PAPR performance. Simulation results show that this method can reduce the PAPR of superimposed pilot OTFS signal. In addition, in order to make OTFS applicable to vehicle communication, the resolution of OTFS is also analyzed.

Orthogonal time-frequency space(OTFS) is an emerging waveform design, but it suffers from PAPR problem. This paper discusses the reasons for the PAPR increase of OTFS signal in practical applications, and designs a new OTFS frame structure that flexibly adjusts the resource domain size, and analyzes the applicability of this frame structure in IoT devices and miniature low-speed devices. Finally, the simulation results show the effectiveness of the proposed frame structure in reducing PAPR and increasing energy efficiency.

Group communications play a crucial role in enhancing the quality of service (QoS) of Internet of Things (IoT) networks, enabling efficient information dissemination while minimizing resource utilization. However, ensuring information security and privacy in IoT group communications necessitates the implementation of an efficient and lightweight key management scheme due to the limited capabilities of most IoT devices. This paper presents a novel key management protocol for group communications that employs distributed Blockchain technology in IoT networks. The proposed scheme considers nodes belonging to multiple groups. By utilizing an asymmetric key shared among group members, secure communication is established between outsiders and group members while preserving anonymity inside the group. A distinguishing feature of the protocol is its combination of group member anonymity and automatic key revocation facilitated by a Smart Contract. Furthermore, simulation results demonstrate the efficiency of the proposed scheme, consuming less than 300 mJ of energy and taking less than 7 seconds to establish a group key among 1000 nodes, outperforming several existing approaches in the literature in terms of computation and communication costs.

The proliferation of unmanned aerial vehicles (UAVs) opens up new opportunities for on-demand service provision anywhere and anytime, but also exposes UAVs to a variety of cyber threats. Low/medium interaction honeypots offer a promising lightweight defense for actively protecting mobile Internet of things, particularly UAV networks. While previous research has primarily focused on honeypot system design and attack pattern recognition, the incentive issue for motivating UAVs’ participation (e.g., sharing trapped attack data in honeypots) to collaboratively resist distributed and sophisticated attacks remains unexplored. This paper proposes a novel game-theoretical collaborative defense approach to address optimal, fair, and feasible incentive design, in the presence of network dynamics and UAVs’ multi-dimensional private information (e.g., valid defense data (VDD) volume, communication delay, and UAV cost). Specifically, we first develop a honeypot game between UAVs and the network operator under both partial and complete information asymmetry scenarios. The optimal VDD-reward contract design problem with partial information asymmetry is then solved using a contract-theoretic approach that ensures budget feasibility, truthfulness, fairness, and computational efficiency. In addition, under complete information asymmetry, we devise a distributed reinforcement learning algorithm to dynamically design optimal contracts for distinct types of UAVs in the time-varying UAV network. Extensive simulations demonstrate that the proposed scheme can motivate UAV’s cooperation in VDD sharing and improve defensive effectiveness, compared with conventional schemes.

Unmanned Aerial Vehicles (UAVs) have attracted extensive attention from both industry and academia owing to their high mobility, line-of-sight (LoS) characteristics of air-toground (A2G) channels, and low cost. However, the broadcast nature of wireless transmission and the LoS characteristics of A2G channels are vulnerable to eavesdropping attack, which leads to severe security issues. To enhance the security of UAV communication, we propose a framework that multiple UAVs cooperate to resist attacks (MURA). Specifically, we first propose an efficient incentive scheme based on the coalitional game to encourage UAVs to join the coalition. We prove that each UAV can maximize its utility by joining the coalition to form a grand coalition. Then, a secure UAV communication scheme is proposed to resist eavesdropping attack. Two types of scenarios are considered for UAV communication. In a completely rational scenario, in which participants make decisions aiming to maximize their utility, we utilize the Stackelberg game to model the interactions between UAVs and attacker. The existence and uniqueness of the equilibrium solution are proved, and the equilibrium solution is obtained. In an imperfectly rational scenario, the prospect theory (PT) is applied to capture the underlying rationality of the players. The PT valuations of the players, i.e., UAV and attacker, are deduced in detail. Meanwhile, the convergence of the PT valuations of UAV and attacker is proved. Finally, extensive simulation results show that the proposed scheme can effectively improve the utility of legal UAVs and ensure the security of the UAV networks compared with benchmarks.

In the last few years, there has been a massive increase in Internet of Things (IoT) devices and the data generated from these appliances. Devices involved in IoT networks can be challenging because of their resource-constrained nature, and security integration's on these devices are frequently disregarded. This results in attackers targeting more IoT devices. Thus, as the number of possible attacks on a network increases, it becomes more difficult for traditional intrusion detection systems (IDS) to deal with these attacks effectively. This paper presents a hybrid deep learning-based approach, a one- dimensional convolutional neural network, and long short-term memory (1D CNN-LSTM) algorithm, for anomaly detection that harnesses the power of the IoT, providing qualities to efficiently examine all traffic across the IoT. The comprehensive study was conducted utilizing the Bot-IoT dataset extracted from real network traffic, consisting of benign and malicious variants. Then, the anomaly detection including binary and multi-decision categories has been performed. The experimental results highlighted the superiority of the proposed model with an accuracy of 99.20% and lower false alarm with 0.80% compared to single CNN-based IDS.

This paper investigates the power efficiency of physical layer security (PLS) in intelligent reflecting surface (IRS)-assisted multi-user uplink channels. Existing research works usually focus on enhancing secrecy performance, and neglect measures to improve power efficiency. In this paper, the optimization problem is formulated to minimize the sum radio frequency (RF) power of multiple users in the uplink channel subject to secrecy outage probability constraint. This problem is solved by an alternating optimization (AO) algorithm that includes three optimization sub-problems, i.e., phase shift matrix, receiving matrix, and RF power optimization. Furthermore, to reduce the complexity of the proposed AO algorithm, a deep learning (DL)-based approach is proposed to optimize the sophisticated phase shift matrix optimization process. Simulation results demonstrate that the proposed scheme can significantly reduce the average RF power, and the DL-based scheme achieves similar performance as AO algorithm while reducing the time complexity significantly.

Nowadays, with the evolution of technology, sensor networks have experienced a real boom. Due to their constitutions, sensors suffer from low security and are therefore susceptible to different types of attacks. Wireless sensor networks (WSNs) deployed in hostile environments suffer particularly from energetic attacks, i.e. attacks aimed at shortening the life cycle of sensors. Sensors have limited energy resources; replacing or recharging nodes in hostile environments is difficult. Attacks that cause a drain on the energy level are the most common attacks in a hostile environment and can lead to the death of sensors such as sleep denial attacks. In this paper, we design a game model using a signaling game within clusters that enables both detection and defense against attackers. In this paper, we identify and impose penalties on nodes that practice sleep deprivation torture in WSNs. The simulations showed that the model is able to force the attacker to behave normally in a WSN.

To prevent implicit privacy disclosure in sharing gradients among data owners (DOs) under federated learning (FL), differential privacy (DP) and its variants have become a common practice to offer formal privacy guarantees with low overheads. However, individual DOs generally tend to inject larger DP noises for stronger privacy provisions (which entails severe degradation of model utility), while the curator (i.e., aggregation server) aims to minimize the overall effect of added random noises for satisfactory model performance. To address this conflicting goal, we propose a novel dynamic privacy pricing (DyPP) game which allows DOs to sell individual privacy (by lowering the scale of locally added DP noise) for differentiated economic compensations (offered by the curator), thereby enhancing FL model utility. Considering multi-dimensional information asymmetry among players (e.g., DO's data distribution and privacy preference, and curator's maximum affordable payment) as well as their varying private information in distinct FL tasks, it is hard to directly attain the Nash equilibrium of the mixed-strategy DyPP game. Alternatively, we devise a fast reinforcement learning algorithm with two layers to quickly learn the optimal mixed noise-saving strategy of DOs and the optimal mixed pricing strategy of the curator without prior knowledge of players' private information. Experiments on real datasets validate the feasibility and effectiveness of the proposed scheme in terms of faster convergence speed and enhanced FL model utility with lower payment costs.

Cellular Networks (CNs) are of paramount importance to enable Public Safety (PS) broadband services. During a disaster, due to damage to the infrastructures such as Base Stations (BSs), the access of PS Users (PSUs) to the core network can be disrupted even with the use of a relay station. To solve this problem and ensure the continuity of PS services under any circumstances, Proximity-based Services (ProSe) is a key enabling technology that allows Device-to-Device (D2D) communications to take place in licensed and unlicensed spectrum. In this paper, we propose a new architecture for PS Networks (PSNs), by using Non-Orthogonal Multiple Access (NOMA) technique and by placing the ProSe function and the ProSe application server in Multi-access Edge Computing (MEC). In this way, network availability for the maximum possible number of PSUs can be guaranteed. Moreover, they can access the required information with very low latency in the licensed spectrum. As well as they continue to operate in the unlicensed spectrum in a managed and secure manner. After the evaluation of the proposed architecture using the Simu5G network simulator, results reveal that by using NOMA, the spectral efficiency can be improved up to 28.8%. The benefits of using the MEC system in reducing latency and increasing reliability were clearly observed. In addition, we investigate the effect of increasing the number of requested applications and numerology on the task response time, which is a very important metric for PSUs.

Unmanned Aerial Vehicles (UAVs), also known as drones, have recently been proposed as flying base stations for providing reliable service to IoT devices. However, due to the lack of effective authentication schemes, UAVs are often hijacked by adversaries, which raises a high potential for sensitive information leakage. Therefore, designing a real-time authentication scheme is essential to enhance UAV safety. Up to the present, several works exist about pilot authentication by classifying radio-control signals. As propagating through the open environment, radio-control signals can be sniffed, analyzed, and simulated, posing significant threats to UAV security. For this reason, we propose a novel deep learning-based drone pilot authentication scheme, DP-Authentication, to protect UAVs from malicious radio-manipulated attacks. Specifically, we collect UAV flight data from the onboard PX4 flight stack and feed them into the authentication scheme to validate pilot legal status dynamically. As verified by comprehensive experiments, the proposed authentication scheme can authenticate pilots with an accuracy of 95.24% and detect malicious hijacking with an accuracy of 96.82%. Thanks to the low system overhead, it holds great promise for deployment on the UAV side to monitor pilot legal status in real-time.

The Industrial Internet of Things (IIoT) network is composed of devices that contain sensitive data, which makes them vulnerable to various security threats. Digital Certificates can be used to reinforce the security of the IIoT network, however, their management remains a major issue. Hence, in this article, we rely on trust management to deal with the whole certificate management process in IIoT networks, from revocation to verification. For this purpose, we organize the IIoT network into a clustering architecture where each cluster head (CH) hosts an agent, called CH-UR agent, that renews/revokes the certificates of its cluster member nodes. We apply signaling game theory to build a Certificate Revocation Game modeling the interactions between a member IIoT node and the CH-UR agent. Thus, upon the belief on the member node, updated by using the Bayesian rules, the best response strategy for the CH-UR agent can be obtained. Further, we propose a new efficient certificate verification scheme based on short-lived certificates (SLCs) and suitable for IIoT network requirements. The performance evaluation of our framework proves, first, the accuracy and convergence speed of our revocation mechanism to detect untrusted devices and on-off attacks. Second, the effectiveness of our clustering architecture to reduce the resource consumption resulting from the management of SLCs to 60% even with the increase of network density. Third, the effectiveness of the proposed certificate verification scheme to reduce the time needed to obtain the revocation information as well as the resulting storage and communication overhead to achieve this purpose.

The development of underwater wireless sensor networks (UWSNs) has attracted great interest from many researchers and scientists to detect and monitor unfamiliar underwater domains. To achieve this goal, collecting data with an underwater network of sensors is primordial. Moreover, real-time information transmission needs to be achieved through efficient and enabling technologies for node deployment and data collection in UWSN. The Internet of Things (IoT) helps in real-time data transmission, and it has great potential in UWSN, i.e., the Internet of Underwater Things (IoUT). The IoUT is a modern communication ecosystem for undersea things in marine and underwater environments. Intelligent boats and ships, automatic maritime transportation, location and navigation, undersea discovery, catastrophe forecasting, and avoidance, as well as intelligent monitoring and security are all intertwined with the IoUT technology. In this article, the enabling technologies of UWSN along with several fundamental key aspects are scrupulously explained. The study aims to inquire about node deployment and data collection strategies, and then encourages researchers to lay the groundwork for new node deployment and advanced data collection techniques that enable effective underwater communication techniques. Besides different types of communication media, applications of UWSNs are also part of this article. Various existing data collection protocols based on the deployment models are simulated using network simulator (NS 2.30) to analyze and compare the performance of state-of-the-art techniques.

The Internet of Drones (IoD) manages and coordinates communications between drones in Internet of Things (IoT) applications. Ensuring security and privacy in unmanned aerial vehicles (UAVs) networks, i.e., drones, is essential to protect data from cyber attacks. In this context, providing authentication is a major challenge due to the fact that drones are devices limited in power capabilities. The problem is aggravated by the dynamism of IoD networks due to the high mobility of drones, being sensitive to packet loss and handovers. Blockchain technology is attractive to address the problem of centralization of existing authentication protocols. In this article, we provide a decentralized, secure, and efficient authentication protocol, based on$\mu $Tesla, that relies on Blockchain to manage drone authentication. We analyze the security and performance of the proposed solution. Simulation results show that the proposed solution outperforms several approaches in the literature, achieving an authentication delay of less than 250 ms with a low information exchange of 1024 bits for 128-bit security level while maintaining low computational requirements.

To meet the future demands for ubiquitous communication coverage and temporary / unexpected computing resources, aerial computing networks have been envisioned as a new paradigm. Nevertheless, dynamic changes on the network make it particularly challenging to achieve global optimal resource allocation. As an emerging technology, digital twin (DT) can represent real objects in physical network by creating virtual models. With the help of DT, we can easily obtain comprehensive real-world high-fidelity state information for model training, so as to achieve intelligent efficient decision-making. Accordingly, DT-based aerial computing networks have emerged as a potential solution. Note that available researches mostly assumed simple ground user distribution like uniform distribution, and adopted binary / partial offloading in task processing, neglecting the task separability and data inter-dependency among subtasks. Toward this end, we introduce DT into aerial computing networks, and study the problem of intelligent UAV deployment and resource allocation. Specifically, we firstly propose a DT-assisted UAV deployment strategy and model the data inter-dependency among subtasks. After that, two DT-assisted hybrid (binary and partial) task offloading schemes are presented, i.e., heuristic greedy and DQN-based schemes. Extensive analysis and numerical results confirm the effectiveness of our proposed DT-assisted UAV deployment and hybrid task offloading strategies.

Technological interventions are becoming commonplace in everyday vehicles. But utilization of biosignals that can enhance the overall driving experience is still limited. Microsleep is one such issue that needs intervention, owing to the difficulty in its detection and social acceptance of using wearable BCI devices during transit. Microsleep is a short duration of sleep that lasts from few to several seconds. It could occur unconsciously without the person in context realizing it. This, therefore, happens before the deep sleep and could also occur when performing critical tasks such as driving on a highway. By using modern-day advancements in Internet of Things (IoT) and Machine Learning, we can provide efficient solutions to prevent accidents due to microsleep during vehicular transit. However, it is noteworthy that distinguishing microsleep using a single channel system is a challenge. We have explored this using datasets provided by International BCI Competition Committee. Given the fact that the participants’ values might not match the exact scenario, approaches for exploiting transitory phases using ANN/CNN have been developed and discussed in this paper. Transitory phases could include Wakefulness$\leftrightarrow $Non-Rapid Eye Movement-1 phase (NREM-1). Results show ≈95% increase in mean statistical agreements, which are represented by kappa values (CNN NREM$1~\rightarrow $CNN Transition) and ≈77% increase in mean kappa (ANN NREM$1~\rightarrow $ANN Transition). Hence, this work gives an initial indication whether classifiers trained on night sleep data can be used for microsleep detection in more real-world scenarios.

Owing to the exponential proliferation of internet services and the sophistication of intrusions, traditional intrusion detection algorithms are unable to handle complex invasions due to their limited representation capabilities and the unbalanced nature of Internet of Things (IoT)-related data in terms of both telemetry and network traffic. Drawing inspiration from deep learning achievements in feature extraction and representation learning, in this study, we propose an accurate hybrid ensemble deep learning framework (HEDLF) to protect against obfuscated cyber-attacks on IoT networks. To address complex features and alleviate the imbalance problem, the proposed HEDLF includes three key components: 1) a hierarchical feature representation technique based on deep learning, which aims to extract specific information by supervising the loss of gradient information; 2) a balanced rotated feature extractor that simultaneously encourages the individual accuracy and diversity of the ensemble classifier; and 3) a meta-classifier acting as an aggregation method, which leverages a semisparse group regularizer to analyze the base classifiers’ outputs. Additionally, these improvements take class imbalance into account. The experimental results show that when compared against state-of-the-art techniques in terms of accuracy, precision, recall, and F1-score, the proposed HEDLF can achieve promising results on both telemetry and network traffic data.

The proliferation of unmanned aerial vehicles (UAVs) opens up new opportunities for on-demand service provisioning anywhere and anytime, but it also exposes UAVs to various cyber threats. Low/medium-interaction honeypot is regarded as a promising lightweight defense to actively protect mobile Internet of things, especially UAV networks. Existing works primarily focused on honeypot design and attack pattern recognition, the incentive issue for motivating UAVs' participation (e.g., sharing trapped attack data in honeypots) to collaboratively resist distributed and sophisticated attacks is still under-explored. This paper proposes a novel game-based collaborative defense approach to address optimal, fair, and feasible incentive mechanism design, in the pres-ence of network dynamics and UAVs' multi-dimensional private information (e.g., valid defense data (VDD) volume, communication delay, and UAV cost). Specifically, we first develop a honeypot game between UAVs under both partial and complete information asymmetry scenarios. We then devise a contract-theoretic method to solve the optimal VDD-reward contract design problem with partial information asymmetry, while ensuring truthfulness, fair-ness, and computational efficiency. Furthermore, under complete information asymmetry, we devise a reinforcement learning based distributed method to dynamically design optimal contracts for distinct types of UAVs in the fast-changing network. Experimental simulations show that the proposed scheme can motivate UAV's collaboration in VDD sharing and enhance defensive effectiveness, compared with existing solutions.

During the last decade, the integration of artificial intelligence (AI) and the use of intrusion detection systems (IDSs) in the Internet of Things(IoT) networks have brought a new dimension to technological progress. Deep learning (DL) and machine learning (ML)-based IDS are vulnerable to adversarial perturbations. However, anomaly detection methods suffer from unbalanced and missing sample data, thus causing IDS training to be complicated. In this paper, we propose using conditional generative adversarial networks (cGANs) to enhance the training process by handling the unbalanced data and coping with the lack of specifics class samples, which may succeed in evading our Convolutional Neural Network-Long Short-Term Memory (CNNLSTM) based-IDS model. We evaluated our proposed IDS model before and after applying the adversarial training using the Bot-IoT dataset. Promising results showed that the accuracy of detecting Theft attacks could be increased by 40%. To the best of our knowledge, we are the first to suggest the combination of cGAN and CNNLSTM based-IDS system to enhance its performance.

Internet of Things (IoT) solutions have applications in various spheres of human life. There is a large number of IoT devices connected to the Internet and with a tendency to increase. Blockchain technology allows solving multiple security and centralization problems in these overcrowded networks. Fork occurrence affects the performance of many consensus mechanisms used in Blockchain, so it is important to analyze their performance considering different metrics and traffic patterns. This paper evaluates the performance of a blockchain-based IoT network in terms of the Age of Information (AoI) considering different traffic patterns and forking phenomena. Simulation results show that the AoI remains constant until the average time a batch stays in the queue is less than the average mining time and the decrease stops when the miners reach their maximum mining capacity. The simulation results allow to verify the agreement with the analytical model and to study the influence of the blockchain configuration parameters in the AoI, for different sensing mechanisms.

The security of distributed communications in UAV rescue networks is promising to be provisioned by blockchain technology. However, due to high mobility, distributed UAVs cannot timely connect to the backbone to synchronize blocks, which can result in severe security issues (such as Forged deposit address and Double spend attack). These issues has been neglected in literature. This paper proposes a UAVs assisted and incentive based blockchain offline transaction scheme to address the above issues when UAVs and ground users are offline. Particularly, we consider vehicle-to-vehicle (V2V) charging transactions in disaster areas. First, we built an offline channel between charging and discharging electric vehicles (EVs), and then, we design an accountable assertions based UAVs aided penalty algorithm to prevent various attacks. Then, considering selfishness of users, we formulate an incentive model based on Stackelberg game to encourage EVs to participate to the offline V2V charging transactions. Our simulation results demonstrate that our proposed scheme obtain the optimal utilities for EVs, which outperforms the conventional schemes.

As the increasing traffic patterns show asymmetric feature, directed pre-configured-cycle (p-cycle) has indicated the ability of better protection in elastic optical networks (EONs). In this paper, we investigate three different integer linear program (ILP) models of directed p-cycle without candidate cycle enumeration leveraging flow conservation. Three directed p-cycle designs are based on the same directed p-cycle strategy but differ from each other in how the flows can construct the directed p-cycles, namely individual link flow (ILF) directed p-cycle, aggregated link flows (ALF) directed p-cycle, and loop-eliminating flow (LEF) directed p-cycle, respectively. These ILPs aim to jointly minimize power consumption and spectrum usage of all directed p-cycles configured in EONs. The problem formulation involves directed p-cycle generation, modulation format (MF) selection, power consumption optimization, and spectrum allocation. Furthermore, the proposed directed p-cycle strategy is designed with a compact and novel MF adaptation relying on accurate protection path lengths. Simulations are conducted to compare the proposed ILPs with the conventional method which uses a rough upper bound on MF adaptation. Numerical results demonstrate that all of the three proposed ILPs have better performances on the joint objective, in which the improvement is up to 24.31%. Although the proposed ILPs are with the same performance on the objective due to the same directed p-cycle strategy, the LEF directed p-cycle shows the best efficiency.

Earthquake early-warning system (EEWS) is inevitable for saving human lives. The fast determination of the Earthquake’s (EQ’s) magnitude and its location is significant in disaster management and EQ risk mitigation. These parameters can be conveyed over the Internet-of-Things (IoT) network to alleviate an EQ disaster. In this article, a deep learning model based on integrating autoencoder (AE) and convolutional neural network (CNN) for a swift pinpointing of EQ magnitude and location after 3 s from the onset of the P-wave is proposed. Thus, we name it 3 s AE and CNN (3S-AE-CNN). The employed data set is observed by three stations from the Japanese Hi-net seismic network. We have trained our model on 12200 events (109.80 thousand 3-s-three-component seismic windows). The model facilitates the extraction of waveforms’ significant features leading to robust estimation of the EQ parameters. The proposed model predicts the magnitude and location of EQ with errors in magnitude, latitude, and longitude that reach 0.000028, 0.0000033, and 0.0001, respectively. The EQ’s parameters calculated by the proposed 3S-AE-CNN model are swiftly sent to a centralized IoT system that in turn directs the involved entity to take suitable action. The obtained results of the 3S-AE-CNN are compared to the conventional manual solution method, which represents the optimum solution mean. The 3S-AE-CNN shows an enhanced performance for the magnitude and location determination as compared with the benchmark method, which proves its effectiveness for EEWS.

Internet of Things (IoT) is a wireless network of various battery-powered sensing units. Due to the limited battery capacity, the nonaccessible/abandoned nodes demand more energy to be reached or reintegrated to keep the network connected. When a specific tree topology for routing, called destination-oriented directed acyclic graphs (DODAGs), is used, isolated nodes spend maximum energy on the assigned task during data transformation from the sensor field to the DODAG root. The nodes closer to the DODAG root need to rely on faraway nodes and resource-burden-constrained nodes to lead to the quick energy drain. It brings an idea of IoT network nodes in which an extra amount of energy provide to the longer time alive nodes. This article proposes an ARFOR-adaptive ranking fuzzy-based energy-efficient opportunistic routing protocol for sustainable IoT applications. The proposed protocol consists of a parent node (PN) that acts as a head node in a cluster to aggregate the packets to DODAG root; and a volunteer node (VN) acts as a forwarder to transfer the packets to PN with threshold energy limits to increase network lifetime during the transmission cycle. The proposed VN selection is based on fuzzy parameters, such as Canberra distance, residual energy, and threshold. The simulation outcomes depict that the ARFOR fairly justifies the network timeline requirement with maximum percentage area coverage. The percentage gain in terms of network lifetime is comparatively significant for a lower number of VN.

Adaptive multielement antennas can be leveraged to improve the reliability of wireless systems deployed in cluttered, depolarizing environments, such as those expected for Internet of Things (IoT) applications. While the performance improvement provided by such antennas has been well studied for individual links, multiple-hop networks have received little attention. In this work, we consider the problem of how devices in a multihop network should configure a three-element, tripolar antenna when deployed in a Rayleigh fading environment. We propose two switching-based antenna selection strategies: 1) Max-Sum and 2) Max–Min, each of which considers the channel conditions for communicating to nodes both higher and lower in the network hierarchy. We first derive the outage performance of the proposed schemes analytically and compare their performance with the well-known approaches of selection and switched diversity. Through simulations, which utilize empirical channel data from IoT devices equipped with a tripolar antenna, we show that the proposed Max-Sum and Max-Min schemes reduce antenna switching by over 80%, when compared to selection diversity. In addition, these two approaches lead to a median gain of 1.8 and 0.3 dB and a 1% diversity gain of 3.6 and 1.4 dB, respectively, relative to switched diversity.

While providing significant convenience for people, mobile applications (Apps) bring serious privacy leakage and invasion threats over certain platforms (e.g., Android) due to privacy violations. To protect users from these threats, a lot of works related to privacy violation detection have been proposed. However, few of them particularly check the violations, including lacking privacy policy, collecting privacy before statement, lacking account cancelation service, and stubborn permission request. Toward this end, we design an automatic detection tool namedPVDetectorto detect these violations in Android Apps. We extract and construct relevant threat forms by statically and dynamically analyzing Apps’ behaviors, and then fine tune these forms through threat-form-matching methods on problematic Apps. Finally, a comprehensive experiment is conducted to detect privacy violations on different Android application markets byPVDetector. Specifically, we detect 16 162 Android Apps (involving people’s various aspects of life) collected from six popular official application markets and three special categories. The experiment results indicate that the situation that Apps contain privacy violations is greatly serious in these markets and categories. We also randomly check the experiment results of 385 Apps. The check results illustrate that the detection accuracy ofPVDetectorcan reach 93%.

Edge computing (EC), is a technological game changer that has the ability to connect millions of sensors and provide services at the device end. The broad vision of EC integrates storage, processing, monitoring, and control of operations in the Edge of the network. Though EC provides end-to-end connectivity, speeds up operation, and reduces latency of data transfer, security is a major concern. The tremendous growth in the number of Edge Devices and the amount of sensitive information generated at the device and the cloud creates a broad surface of attack and therefore, the need to secure the static and mobile data is imperative. This article is a comprehensive survey that describes the security and privacy issues in various layers of the EC architecture that result from the networking of heterogeneous devices. Second, it discusses the wide range of machine learning and deep learning algorithms that are applied in EC use cases. Following this, this article broadly details the different types of attacks that the Edge network confronts, and the intrusion detection systems and the corresponding machine learning algorithms that overcome these security and privacy concerns. The details of machine learning and deep learning techniques for EC security are tabulated. Finally, the open issues in securing Edge networks and future research directions are provided.

The Internet of Things (IoT), while providing comprehensive interconnection and ubiquitous services, poses security issues by enabling resources sharing among various devices from different untrusted authorities. Blockchain, as a distributed ledger, provides a traceable and verifiable platform to ensure the secure access control in IoT. The existing works based on blockchain may bring up intolerable computing overhead and delay to the lightweight IoT devices. In this article, we propose a dynamic and lightweight attribute-based access control framework for blockchain-empowered IoT, to achieve secure and fine-grained authorization. The proposed scheme allows access to resources by evaluating attributes, operations, and the environment relevant to a request. The access policy is executed through smart contract in blockchain for security and flexibility. To further adapt to IoT device constraints, we design a access control framework based on decentralized application (DApp), which can maintain tamper proof in a timely manner and be adapt to the delay-intolerant application. When delay-intolerant access is required, access can be allowed according to local replica of the blockchain, without a consensus of blockchain network. Considering the time-varying attributes of IoT devices, a trust management scheme is proposed based on the Markov chain to resist the security fluctuation caused by the vulnerability of IoT devices. In the experiments, we deploy our system prototype on Ethereum to evaluate the feasibility and effectiveness of the scheme. The results show the proposed scheme can achieve secure, high throughput, and flexible access control in IoT.

Proof of work (PoW), as the representative consensus protocol for blockchain, consumes enormous amounts of computation and energy to determine bookkeeping rights among miners but does not achieve any practical purposes. To address the drawback of PoW, we propose a novel energy-recycling consensus mechanism named platform-free proof of federated learning (PF-PoFL), which leverages the computing power originally wasted in solving hard but meaningless PoW puzzles to conduct practical federated learning (FL) tasks. Nevertheless, potential security threats and efficiency concerns may occur due to the untrusted environment and miners’ self-interested features. In this paper, by devising a novel block structure, new transaction types, and credit-based incentives, PF-PoFL allows efficient artificial intelligence (AI) task outsourcing, federated mining, model evaluation, and reward distribution in a fully decentralized manner, while resisting spoofing and Sybil attacks. Besides, PF-PoFL equips with a user-level differential privacy mechanism for miners to prevent implicit privacy leakage in training FL models. Furthermore, by considering dynamic miner characteristics (e.g., training samples, non-IID degree, and network delay) under diverse FL tasks, a federation formation game-based mechanism is presented to distributively form the optimized disjoint miner partition structure with Nash-stable convergence. Extensive simulations validate the efficiency and effectiveness of PF-PoFL.

With continuous advances in novel ICT technologies (including Internet of Things (IoT), artificial intelligence (AI), cloud/fog/edge computing, mobile Internet, and smart terminals), smart cities have provided citizens a more convenient and smarter life with sustainable innovation, including client innovation, open innovation, public innovation, and cooperative innovation.A variety of value-added IoT applications for smart cities have been emerging, e.g., smart home, smart healthcare, smart transportation, smart energy management, smart retailing, smart grid, water system, smart building, smart factory, smart office, air pollution control, and so on.All the sensed information and facilities collaborate together to form economic and effective interaction, providing people with better working efficiency and life quality.Enhanced by ICT technologies, the public municipal services not only improve the government's performance, but also provide citizens with better life quality.Therefore, it has been an important indicator for every country in the world to improve city competitiveness through developing smart cities.The goal of this special issue is to publish both state-of-theart and predictive papers on recent advances in "Wireless Internet" selected from the 12th EAI International Wireless Internet Conference (WiCON 2019), which was held in Taichung (Taiwan) during November 26-27, 2019.After the event, an open call was published to encourage the contributions presented at WiCON 2019 to be extended and submitted to this special issue.After a rigorous review process, eleven high quality papers were selected for publication, which are briefly reviewed in the following.

International audience

Recently, the boosting growth of computation-heavy applications raises great challenges for the Fifth Generation (5G) and future wireless networks. As responding, the hybrid edge and cloud computing (ECC) system has been expected as a promising solution to handle the increasing computational applications with low-latency and on-demand services of computation offloading, which requires new computing resource sharing and access control technology paradigms. This work establishes a software-defined networking (SDN) based architecture for edge/cloud computing services in 5G heterogeneous networks (HetNets), which can support efficient and on-demand computing resource management to optimize resource utilization and satisfy the time-varying computational tasks uploaded by user devices. In addition, resulting from the information incompleteness, we design an evolutionary game based service selection for users, which can model the replicator dynamics of service subscription. Based on this dynamic access model, a Stackelberg differential game based cloud computing resource sharing mechanism is proposed to facilitate the resource trading between the cloud computing service provider (CCP) and different edge computing service providers (ECPs). Then we derive the optimal pricing and allocation strategies of cloud computing resource based on the replicator dynamics of users’ service selection. These strategies can promise the maximum integral utilities to all computing service providers (CPs), meanwhile the user distribution can reach the evolutionary stable state at this Stackelberg equilibrium. Furthermore, simulation results validate the performance of the designed resource sharing mechanism, and reveal the convergence and equilibrium states of user selection, and computing resource pricing and allocation.

With considerable demand for higher throughput, greater capacity, and lower latency for consumers, the Internet of Things (IoT) network is anticipated to meet the desired security and privacy requirements. This study provides high transaction throughput on critical IoT applications, particularly Bitcoin security against double-spending attacks. To this end, we investigated the signaling game approach to model the interaction between two miners while considering players behavior (malicious or honest miners) and the incoming transaction throughput. To the best of our knowledge, this is the first work that exploits the signaling game to cover the incoming transactions randomness waiting for validation, which influences the honest miners behavior. With extensive simulations, we show that our proposed signaling game reduces the impact of double-spending attacks on IoT transactions. The results also illustrate the benefit of using the signaling game to model the interaction between two miners while handling the incomplete information of the incoming transactions and the type of miners.

Currently, the Internet of Things (IoT) is widely used by the emerging of internet-integrated wireless devices in daily life. In order to communicate and exchange data with each other, IoT devices must pass by a gateway that belongs to the same network. But, considering the mobility of such devices and their constraints, it becomes difficult to trust and to connect from a gateway to another. Therefore, this paper introduces a blockchain-based secure handover protocol. It ensures an anonymous mutual authentication solution between a mobile IoT device and a visited gateway by using Zero-Knowledge Proof (ZKP) protocol. To do, simulations have been performed thanks to a discrete events simulator. In addition to a good security level, compared with the most used protocol (DTLS), our simulation results lead to better performance in terms of temporal complexity, energy consumption, and communication cost.

Reports of possible third-party interference in the electoral process, unauthorized voting, disenfranchisement, and technical failure raise questions about the transparency of elections around the world. At the same time, e- voting has partially replaced the traditional paper ballot-based system, with the advantages of high human labor efficiency, low cost, and low probability of error in the process. However, their use has not been extended due to the security issues of the developed solutions. Blockchain technology has been proposed to support e-voting processes to ensure the integrity, resilience and verifiability of votes, but blockchain alone is not able to guarantee the anonymity and privacy demanded by voters. In this paper, we propose a voting mechanism that uses Blockchain and a cryptographic Blind Signature protocol to guarantee anonymity and privacy to voters. The performance in terms of computational complexity and scalability is evaluated via simulation. The analysis of simulation results shows that the blockchain-based voting mechanism proposed in this paper achieves good performance in terms of computational cost and scalability.

Vehicular ad hoc Networks have emerged as a viable alternative for enabling user applications on moving vehicles. However, maintaining acceptable levels of Quality of Service and message latency still remains a challenging task. Several solutions have been proposed for improving performance of these networks. Clustering has been considered as one of the important mechanism that structures vehicles into organize groups. However, high deployment overheads and lack of security are the major bottlenecks hindering its deployment. Software defined networking has been emerged as a promising solution on account of its characterstics such as dynamic access control and scalabilty. In view of this, TruClu: a trust based clustering mechanism that creates vehicular clusters for a Software Defined Vehicular Network is proposed. Cluster formation and cluster head selection in TruClu is based on vehicular mobility and trust value that alleviates the drawbacks of traditional clustering and also enabling trust based communication in the network. The performance of TruClu is evaluated through extensive simulations and obtained results indicate the comparable performance of the proposed scheme in terms of standard performance parameters.

Ensuring communication for Public Safety Users (PSUs) in all events, and specifically in disaster situations, is one of the main challenges of Cellular Networks (CNs). Various types of communications, such as in-band and outband communications, can be used to integrate the Public Safety Network (PSN) into the CN. In this paper, we focus on in-band overlay Device-to-Device (D2D) communication, which effectively reduces the interference caused by Cellular Users (CUs), and ensures the availability of Resource Blocks (RBs) for PSUs at any time and for different events. Furthermore, by using a Non-Orthogonal Multiple Access (NOMA) based system and the Particle Swarm Optimization (PSO) algorithm, the user throughput and the resource wastage problem are improved. Our goal is to provide the necessary resources to PSUs and, at the same time, to maximize the use of these resources. Compared to the traditional Orthogonal Frequency-Division Multiple Access (OFDMA) system, the simulation results show the efficiency of our proposed PSO-based NOMA system in terms of average user throughput and user sum-throughput, and also show that the relation between throughput and fairness among users is a requirement-dependent tradeoff, where we can achieve optimal fairness by decreasing the total throughput.

The autonomous underwater vehicle (AUV) has gradually become an important platform for performing various underwater tasks. Due to the shortcomings resulting from a single AUV's poor detection, information processing and moving capabilities, more and more tasks are completed in a cooperative manner by multiple AUVs. However, most of the existing works do not consider security factors in the process of multi-AUV cooperation. In this paper, we propose a novel cooperative tracking scheme towards an underwater moving target, performed by an intelligent AUV swarm. In this scheme, a cooperative multi-agent reinforcement learning (MARL) based tracking algorithm is proposed following a centralized training with distributed execution (CT-DE) manner. After centralized training in the designed secure private network, no information sharing is required during the mission execution. This feature ensures the security of the whole system, especially in a complex confrontation scenario. In addition, we build models of the AUV underwater dynamics and the target sonar detection, which make the algorithm applicable to real target tracking enabled AUV swarms. Then, based on the multi-agent deep deterministic policy gradient (MADDPG) algorithm, we design an end-to-end AUV control algorithm. Simulation results validate that the proposed algorithm can achieve competitive performance in tracking success rate and tracking stability against baselines, while ensuring the security of the entire system.

In Internet of Things (IoT) system architectures where static and mobile IoT devices connected to gateway devices for forwarding and processing their collected data, authentication and secure communication between IoT device and gateways are crucial requirements. This paper proposes a mobility-enabled DTLS-based authentication scheme. It maintains mutual secure authentication between an IoT device and a gateway while transferring the connection between the visited gateways. We propose a scheme that implements a session resumption procedure to allow the authentication between a moving IoT device and a visited gateway. The performance of the proposed scheme is evaluated in terms of handshake time, processing time, energy consumption, and memory overhead. The results demonstrate its feasibility for limited resource devices.

Unmanned aerial vehicles (UAVs) have attracted extensive attention from both industry and academia owing to their high mobility, and characteristics of line of sight (LoS) propagation. However, wireless communication is vulnerable to eavesdropping attacks because of the broadcast characteristics. To enhance secure UAV communications with the ground nodes, we propose a novel framework that multiple UAVs cooperate to resist attack (MURA). First, we propose an incentive mechanism based on coalitional game to encourage legal UAVs to join the coalition. We prove that each legal UAV can only maximize its profits by joining the coalition to form a major coalition. Then, a secure UAV communication scheme is proposed to resist the eavesdropping attacks. Two types of scenarios are considered for the UAV communication: in a completely rational scenario, we utilize the Stackelberg game to model the interactions between the legal UAVs and attacker. In an imperfectly rational scenario, the cumulative prospect theory (PT) is applied to the game to capture the underlying rationality of the players. Finally, simulation results show that the proposed scheme can significantly improve the security of the UAV network compared with traditional schemes.

Since early 2020, the COVID-19 global pandemic has significantly impacted almost every aspect of the human society throughout the world. Until now, middle of 2021, although with all the efforts on pandemic intervention and vaccination, COVID-19 is still hovering around the world, resulting in more than 177 million confirmed cases and 3.8 million deaths.

With the evolution of the Internet of Vehicles (IoV), there has been an overwhelming increase in the number of connected vehicles in recent times. Due to this reason, massive amounts of data generated by connected vehicles makes traditional host-centric approach inevitable in IoV ecosystem. Moreover, the existing TCP/IP based congestion control mechanisms cannot be directly applied in IoV environment as there is a requirement of content sharing among vehicles with reduced delay and high throughput. So, in this article,11.This article is an extended version of paper entitled “Deep Learning-based Content Centric Data Dissemination Scheme for Internet of Vehicles“ published in IEEE ICC, 20-24 May 2018, Kansas City, USADiLSe: A Lattice-based Secure and Dependable Data Dissemination Scheme for Social Internet of Vehicles is designed, which works in three modules. The first module, i.e., deep learning based content centric data dissemination scheme, works in three phases. 1) In the first phase, the connection probability of vehicles is computed to identify stable and reliable connections using Weiner process model. 2) In the second phase, a convolutional neural network based scheme is presented for estimating the social relationship score among vehicle-to-vehicle pair. 3) In the third phase, a content centric data dissemination scheme is presented. However, the mobility of vehicles in IoV ecosystem gives them the liberty to move in/out of the network without IP assignment. This makes it necessary to replicate the content at each node for providing fault tolerance. So, in the second module, a data replication scheme for fault tolerance in IoV network is designed, which is followed by an access control mechanism for read/right access for network content in third module. Finally, in the last module, a crucial lattice-based exchange and authentication scheme using blockchain is also designed for handling secure communication in IoV ecosystem. The proposed scheme is evaluated on a highway topology using extensive simulations. The results obtained prove the efficacy of the proposed scheme concerning various performance metrics.

To accommodate the surge of data traffic in unmanned aerial vehicle (UAV) applications, software defined air-ground integrated networks (SD-AGNs) hold great potentials for efficient resource allocation and intelligent security countermeasures for UAVs. In SD-AGNs, virtualized bandwidth, computing and security resources owned by terrestrial mobile edge computing (MEC) nodes can be dynamically allocated to satisfy UAVs' diverse demands in data transmission and security protection. However, with complicated cooperative interactions among MEC nodes and competition among UAVs, it is of great challenge to allocate both the security and wireless resource in SD-AGNs. In this paper, we propose a security-aware resource sharing scheme for UAVs to jointly allocate bandwidth and security resource in SD-AGNs, using a game-theoretic approach. Specifically, we first investigate a software-defined collaborative mechanism to promote resource utilization for MEC nodes through coalition formation and resource sharing within each coalition. Then, a coalitional game model is presented to construct the Nash-stable coalition structure for MEC nodes. Furthermore, by modeling the interactions among UAVs as a non-cooperative game, their optimal demands of wireless and security resource, as well as the Nash equilibrium, are analyzed in the competitive environment. Simulation results show that the proposed scheme can effectively improve resource efficiency and reduce average delay.

Industrial IoT network (IIoT) is the result of integrating Internet of Things into the industrial processes to make manufacturers more efficient and reactive. This integration brings with it new security and privacy challenges because of the sensitive information exchanged between devices and the infrastructure. In order to prevent malicious nodes from abusing the IIoT system, this paper investigates a new distributed certificate revocation protocol. Hence, we propose cluster-based certificate revocation mechanism for IIoT network by using game theory. Indeed, our proposed signaling game allows to make a decision on a targeted node after evaluating its behaving. In this approach, we assume an IIoT network organized as a set of clusters called communities. Each community consists of a set of member nodes and a community leader who uses a multi-stage game to renew the certificate of well behaving member nodes and revoke the certificate of malicious member nodes. Based on the Perfect Bayesian Equilibrium, we describe a reactive algorithm implementing the certificates update mechanism. By simulations, we evaluate the convergence speed, the dynamic and the accuracy of the leader's posterior belief on nodes with different behaviors.

This paper considers value based information collection for underwater acoustic sensor networks (UWASNs). In the considered system, the sensor nodes collect, store and update monitoring information with an initial value related to associated events. The value of information (VoI), however, decays with time. An autonomous underwater vehicle (AUV) is dispatched to retrieve data from the sensor nodes through acoustic communication. Our objective is to find the optimal traversal path for the AUV to maximize the VoI of the whole network. To achieve this goal, we first establish a realistic model for characterizing the behaviors of AUV and sensor nodes as well as the challenging environment, based on which the expression of the total VoI is derived. Then, we formulate the problem as a combinatorial optimization problem. We provide an optimal solution for this problem based on the branch and bound (BB) method, in which the lower bound (LB) and upper bound (UB) calculation strategies are specifically designed. A near-optimal heuristic algorithm based on the ant colony method is also adopted for further reducing computation complexity. Finally, simulations validate the effectiveness of the proposed algorithms.

WiFi fingerprint-based positioning system emerges to offer fundamental location information for indoor mobile users. It facilitates the check-in to point of interest (POI) through submitting received signal strength (RSS) fingerprints in order to evaluate the crowd traffic. However, the crowd traffic evaluation with RSS fingerprints is vulnerable to the malicious check-in attacks. Attackers who are not at the target POI may still submit the self-modified RSS fingerprints located at the target POI in order to illegally increase its crowd traffic and eventually profit from this fake information. In this paper, we propose a defense scheme against malicious check-in based on access point (AP) selection to significantly reduce the success rate of fingerprint modification from attackers. Specifically, we first exploit fingerprint distance between POIs for AP selection. Then, we explore the mutual information between different POI classes to select APs with high robustness. In addition, the level set method (LSM) is developed to search the optimal modified fingerprint to assess attacker's costs. The extensive simulation results show that the proposed scheme can effectively resist attackers with high accuracy and facilitate crowd traffic evaluation of target POI according to the submitted RSS fingerprints.

Wireless sensor networks (WSNs) provide a significant contribution to the advancement of the Internet of Things (IoT) as it can transfer massive volume of data to the Internet with the help of low-power and low-cost self-operating sensing devices. This article deals with the congestion problem which is one of the critical issues in IPV6 over low-power and lossy wireless personal area network (6LoWPAN)-based network as it causes significant packet loss that leads to degraded throughput and extra energy consumption. We propose a congestion control mechanism for 6LoWPAN by using the noncooperative game theory. The proposed method named noncooperative gaming for energy-efficient congestion control (NGECC) determines the optimal data transfer rate of all the source nodes (leaf nodes) to avoid congestion at the intermediate nodes. NGECC considers both the channel occupation and the buffer overflow as the main factors of the packet loss in the network. For the simulation of the NGECC method, we use Contiki OS and Cooja simulator, which operates based on the IEEE 802.15.4 standard and implements the 6LoWPAN protocol stack. The performance of NGECC has been evaluated by comparing it with the analytical results, the default routing protocol for low-power and lossy network functioning, and two other existing methods. The simulation results show that the NGECC outperforms the existing methods in terms of sending rate of data packets, packet delivery ratio, throughput, weighted fairness index, energy consumption, and delay.

Multielement antenna systems have been shown to provide improved performance over single-element antennas in harsh, depolarizing propagation environments. However, how best to leverage these systems is still a challenge for low-cost implementations, e.g., for the Internet of Things (IoT) devices. In this article, we present a three-branch switched diversity scheme that models the transition between the elements of a tripolar antenna by means of a hidden Markov model (HMM), parameterized from the measurement data. The proposed technique determines antenna polarization before each transmission using the received signal strength indication (RSSI) values, their probabilistic relationship with antenna elements, and transition probabilities among the diversity branches. The simulation and experimental results show that in high multipath environments, similar to those expected for many IoT networks, selection of the most likely antenna element before every transmission using the HMM approach leads to a median gain of 0.4 and -0.9 dB and a 1% diversity gain of 2.4 and -6.8 dB over conventional switched diversity and selection diversity, respectively, with no additional hardware costs. In addition, a hardware implementation demonstrates that the proposed method can reduce power drain from the battery by at least 15% as compared with the selection and switched diversity techniques, with no degradation in packet delivery rates.

Unmanned aerial vehicles (UAVs) have been widely used to provide enhanced information coverage as well as relay services for ground Internet-of-Things (IoT) networks. Considering the substantially limited processing capability, the IoT devices may not be able to tackle with heavy computing tasks. In this article, a multi-UAV-aided mobile-edge computing (MEC) system is constructed, where multiple UAVs act as MEC nodes in order to provide computing offloading services for ground IoT nodes which have limited local computing capabilities. For the sake of balancing the load for UAVs, the differential evolution (DE)-based multi-UAV deployment mechanism is proposed, where we model the access problem as a generalized assignment problem (GAP), which is then solved by a near-optimal solution algorithm. Based on this, we are capable of achieving the load balance of these drones while guaranteeing the coverage constraint and satisfying the quality of service (QoS) of IoT nodes. Furthermore, a deep reinforcement learning (DRL) algorithm is conceived for the task scheduling in a certain UAV, which improves the efficiency of the task execution in each UAV. Finally, sufficient simulation results show the feasibility and superiority of our proposed load-balance-oriented UAV deployment scheme as well as the task scheduling algorithm.

Automobile manufacturers around the world are increasingly deploying Industrial Internet of Things (IIoT) devices in their factories to accompany the Industrial Revolution 4.0. Security and privacy are the main limitations to the integration of Internet of Things (IoT) into industrial processes. Therefore, it is necessary to protect industrial data contained in IIoT devices and keep them confidential. As a step towards this direction, in this paper, we propose a dynamic trust management model suitable for industrial environments. We propose also to change the traditional centralized architecture of IIoT networks in automotive plants into a hybrid architecture based on a set of new industrial relationship rules. The performance evaluation in this work is done in two parts. In the first part, we compare our proposed architecture with the traditional architecture of the plant's IIoT network. The results of this comparison show that our architecture is more suitable to simplify trust management of IIoT devices. In the second part, we demonstrated the ability, the adaptiveness and the resiliency of our proposed trust model against behavioral changes of IIoT nodes in malicious environments.

Due to the vast, increasing number of devices connected to the internet, their high connectivity, diversity, heterogeneity, and the fact that these devices are incapable of carrying a strong security protocols, a number of security and privacy challenges arise. Thus, the IoT- BlockChain brings a lot of benefits, yet still several challenging issues of security, storage and privacy are being faced. Therefore, this present research work proposes a new architecture with an objective to provide robust security with unlimited storage capability that can be adapted to the IoT resources constraints by introducing the new component Security and Storing Manager (SSM) added to the BlockChain. The concept behind the SSM mechanism is based on both encrypting the main data which will be stored in the locked blocks scattered on the internet and storing only the hash of these data in the BlockChain. Through this process, the SSM achieves a role like burial of the data in an unknown place with an unknown name. Also, the access to the BlockChain is restricted by using the smart contract in the new proposed architecture which allows the BlockChain to manage the data access policy. We evaluated the performance of the proposed architecture using various performance measures. The results of our evaluation show that performance is improved by reducing the size of the BlockChain and achieving the security goals. Consequently, SSM paves the way to make our BlockChain smart.

One of the factors limiting the performance of Internet of Things (IoT) devices is the multipath fading caused by reflectors and attenuators present in the environment where these devices are deployed. While existing diversity techniques mitigate the fading and multipath effects, their usage and suitability in compact IoT devices has not yet been explored. This work proposes a Markov model approach for managing the configuration of multi-polarized antennas in a slowly varying, high multipath environments similar to the those expected for industrial Internet of Things (IoT) networks. By leveraging space-variant channel measurement data, collected at 2.4 GHz frequency band inside of a reverberation chamber, a state transition matrix is formed by examining the received power at different polarizations of a tripolar antenna. Next, we construct a Markov chain which estimates suitable antenna polarization when the signal strength of the currently active branch goes below the threshold. Simulation results show that our proposed data driven approach for managing polarization diversity performs satisfactorily with approximately 95% successful packet delivery rate compared to 98%, 93% and 88% achieved by ideal tripolar selection diversity, tripolar switched diversity and a single linear polarized antenna systems, respectively.

The tremendous increase of computation-heavy applications has posed great challenges in terms of enhanced service coverage and high-speed data processing in the Fifth Generation (5G) networks. As responding, the integrated fog and cloud computing (FCC) system has been expected as an efficient approach to support low-latency and on-demand computing services. This work considers the computing resource market in an FCC system operated by one cloud computing service provider (CCP) and multiple fog computing service providers (FCPs), in which the CCP shares its cloud computing resource among FCPs and itself to serve users with computational tasks. To facilitate the resource trading between the CCP and FCPs, a Stackelberg differential game based resource sharing mechanism is proposed. In this mechanism, performance discrepancy is introduced as a penalty factor to denote the mismatch between the resource supply and demand, which will encourage all computing providers (CPs) to make their trading decisions that can truthfully reflect their resource capacity and requirements. In addition, an evolutionary game based replicator dynamics is established to analyze the users' service selection among CPs. Based on the established hierarchical game framework, interactions between user selection and computing resource sharing are investigated. The performance of the designed resource sharing mechanism is validated in the simulations, which also reveal the convergence and equilibrium states of user selection, resource pricing and resource allocation.

Cloud-Fog computing architectures are new paradigms designed to add advantages to the existing architectures for Internet of Things (IoT). This paper proposes an inter-operable cloud-fog based IoT architecture for healthcare. It describes its architecture, environmental context, and user context. The proposed architecture supports the mobility of the patients as well as the diversity of the medical cases. The features of the individual modules are discussed and the interconnection between the different underlying modules and tiers is explained. Task scheduling and allocation approach is proposed to effectively balance healthcare tasks distribution. The performance evaluation of the proposed approach is presented with different number of tasks and cloud nodes. The simulation results show acceptable results in terms of miss ratio, cost, and latency.

Exploiting Content-Centric Networking (CCN) caching capabilities, in which contents are cached on intermediate nodes can be beneficial in IoT, as it can decrease the latency, reduce required transmission hops, limit traffic load on content producer and improves availability. This article presents a new scheme for caching the contents in IoT environments. In the proposed method, devices are grouped into clusters in which the cluster heads act as the cache controller. In addition, we consider a global SDN/cache controller which is responsible for orchestrating cache decisions in the whole IoT network. This central management of the caching system improves the efficiency of resource usage in the IoT network. In the proposed scheme caching, decision is made in two steps: 1) determining the candidate contents and 2) determining the candidate nodes. In each step, several metrics are taken into account, and the Analytical Hierarchy Process (AHP) is used as a Multi-Criteria Decision Making (MCDM) approach. Simulation results demonstrate that our proposed caching method can achieve up to 40% more cache hit and at the same time reduce the content retrieval time.

In this paper, we propose a distributed detection system for hierarchical information acquisition based on autonomous underwater vehicle (AUV) and underwater fixed sensor networks. Different from the previous information collection systems, where the AUV traverses each node to obtain information, we propose a layered network architecture in this work, which is composed of an underwater fixed sensor networks layer and an AUV information acquisition layer. Such information acquisition system does not need to modify the original underlying fixed sensor networks, resulting from its flexible deployability. Additionally, because of the power sensitivity of sensor nodes in underwater fixed sensor networks, an improved algorithm based on classical low energy adaptive clustering hierarchy (Leach) algorithm is proposed in this work. Simulation results validate that the proposed algorithm can effectively improve the life cycle of sensor networks. At the same time, for the AUV information acquisition layer, we propose an angle optimization path planning algorithm based on the ant colony algorithm, which effectively takes the angle and path length as joint optimization objects. Experiments show that introducing the angle optimization jointly not only helps to optimize the AUV rotation angle, but also contributes to improving the convergence of the algorithm.

In interweave Cognitive Radio Networks (CRNs), monitoring the spectrum to detect unused portions (holes) is done by the spectrum sensing function however, it consumes both time and energy. So, some protocols use prediction to estimate the channel availability. One of these protocols use Hidden Markov Model (HMM) but in a very simple way. So, it does not perform well in several cases. In this paper, we propose two new protocols for cognitive radio channel availability prediction. Both protocols use HMM but in a more advanced way. They divide the data into two sets, thus create two HMM models. The first protocol uses Bayes theorem together with these two models, while the second one uses Support Vector Machine (SVM) with the two models HMM parameters. Evaluation of the two protocols proves that both protocols perform better than the old one that uses HMM in a classical way. It also proves that using SVM with HMM parameters is better than using HMM only. This is because dividing the data into two sets for training the protocols with, gives more flexibility to both protocols.

Physical (PHY)-layer security has received considerable interest as a way to safeguard data confidentiality and achieve security and privacy in wireless networks. Authentication between two devices is a challenging problem. In this paper, a machine learning algorithm is proposed to detect and identify rogue transmitters relying on a low-dimensional channel feature vector that is obtained by the Karhunen-Loeve transform (KLT). Specifically, a Linde-Buzo-Gray algorithm is designed for improving the reliability and robustness of the proposed scheme, where a Gaussian Mixture Model (GMM) is employed to learn and track the changes of physical layer properties. Simulation results demonstrate that the proposed authentication scheme achieves a higher spoofing detection rate compared to other existing methods.

The Internet of Vehicles (IoV) has emerged as a new spin-off research theme from traditional vehicular ad hoc networks. It employs vehicular nodes connected to other smart objects equipped with a powerful multisensor platform, communication technologies, and IP-based connectivity to the Internet, thereby creating a possible social network called Social IoV (SIoV). Ensuring the required trustiness among communicating entities is an important task in such heterogeneous networks, especially for safety-related applications. Thus, in addition to securing intervehicle communication, the driver/passengers honesty factor must also be considered, since they could tamper the system in order to provoke unwanted situations. To bridge the gaps between these two paradigms, we envision to connect SIoV and online social networks (OSNs) for the purpose of estimating the drivers and passengers honesty based on their OSN profiles. Furthermore, we compare the current location of the vehicles with their estimated path based on their historical mobility profile. We combine SIoV, path-based and OSN-based trusts to compute the overall trust for different vehicles and their current users. As a result, we propose a trust-aware communication architecture for social IoV (TACASHI). TACASHI offers a trust-aware social in-vehicle and intervehicle communication architecture for SIoV considering also the drivers honesty factor based on OSN. Extensive simulation results evidence the efficiency of our proposal, ensuring high detection ratios >87% and high accuracy with reduced error ratios, clearly outperforming previous proposals, known as RTM and AD-IoV.

Online social networks have now become the most popular platforms for people to share information with others. Along with this, there is a serious threat to individuals' privacy. One privacy risk comes from the sharing of co-owned data, i.e., when a user shares a data item that involves multiple users, some users' privacy may be compromised, since different users generally have different opinions on who can access the data. How to design a collaborative management mechanism to deal with such a privacy issue has recently attracted much attention. In this paper, we propose a trust-based mechanism to realize collaborative privacy management. Basically, a user decides whether or not to post a data item based on the aggregated opinion of all involved users. The trust values between users are used to weight users' opinions, and the values are updated according to users' privacy loss. Moreover, the user can make a tradeoff between data sharing and privacy preserving by tuning the parameter of the proposed mechanism. We formulate the selecting of the parameter as a multi-armed bandit problem and apply the upper confidence bound policy to solve the problem. Simulation results demonstrate that the trust-based mechanism can encourage the user to be considerate of others' privacy, and the proposed bandit approach can bring the user a high payoff.

202001 bcma

In a Wireless Sensor Network (WSN), issues like redundant nodes and duplicated data resulting from deploying nodes within each other's sensing area are still a challenge for random deployment strategy. This paper investigates the issue of identifying and relocating redundant nodes in a WSN where sensors are tracking events. First, a directed bipartite network, which consists of two types of vertices - nodes and detected events, and edges joining nodes with events, is constructed from raw sensory data to identify redundant nodes. Next, these redundant nodes are either relocated or put into sleep mode using circle packing technique to enhance coverage while minimizing energy usage during relocation. Simulation results show that the proposed algorithm can identify approximately 10% redundant nodes while detecting 98% events accurately. Our proposed redundant node identification algorithm has runtime complexity of O(U) + O(E), where U is the number of sensors and E is the total number of edges, the algorithm is also able to increase efficiency in back-end data processing by reducing approximately 20% duplicate data. Results also showed that, our redundant node relocation strategy improves coverage and energy efficiency of the network.

The recent proliferation of IoT based networks in diverse applications has seen various embedded devices deployed in harsh propagation environment. These conditions often induce polarization mismatch between transmitter and receiver antennas and degrade overall network performance. Our work proposes two techniques to prevent power loss due to polarization mismatch for IoT based sensor networks. We first propose the Neighbor Matching (NM) technique to find optimal polarization pattern by solving a Binary Integer Linear Programming optimization problem for routers in a network with fixed routes for data packets. Using empirical data and simulation results, we show that the NM technique has a successful data delivery ratio of approximately 88% compared to 71% for networks which do not consider polarization mismatch. Next, we propose Opportunistic Polarization Matching (OPM) technique for networks with flexible data packet forwarding routes to find polarizations for nodes and routers. Combined with load balancing approach, the proposed OPM technique improves polarization matching by 13% than simple diversity technique. We are also able to show that along with high link reliability both NM and OPM reduce power loss and extend lifetime of nodes.

Large scale network systems such as Internet, smart grids and social networks become an indispensable part of our daily life. However, due to their inherent vulnerability as well as the limited management and operational capability, these network systems are constantly under the threat of malicious attackers. In such attack-defense scenarios, it is particularly significant to make the best use of defenders' limited resources and capability. In this paper, we propose a networked Colonel Blotto game, where the attackers and defenders allocate the limited resources on network nodes, and their utility depends on certain network performance metrics, which are defined for evaluating the performance of the whole network system. Furthermore, considering the complexity of the equilibrium analysis in large scale network systems, a co-evolution based algorithm is proposed for obtaining the practical action sets as well as achieving the mixed-strategy Nash equilibrium. Finally, relying on three real- world network systems, i.e., computer networks, Internet of vehicles and online social networks, simulation results show the effectiveness and feasibility of our proposed model, which is conducive to the design, management and maintenance of real-world network systems.

Successive location recommendation has recently emerged as an important service in Location-Based Social Networks (LBSNs). It aims at recommending the next location(s) to visit to a user given its current and previous locations. Although several recommenders have been proposed, only few works have considered the sequential correlations among locations in addition to other influential factors in recommendation. In this paper, a novel sequential rule mining-based approach called Location Recommender (LocRec) is proposed. Our proposal is designed to perform successive location recommendation for users of LBSN by considering sequential, social and temporal influence factors. The proposed approach first extracts the set of location sequences from mobility data and then generates recommendation rules from it. Based on the concept of recommendation influence factor, two rule-based recommenders are designed, namely temporal-based and social-based recommenders. An experimental evaluation was conducted on a real large-scale LBSN dataset to compare the performance of the proposed recommenders. Obtained results show that the tolerance to order-variations and the use of a window constraint enhance the performance of LocRec. They also depict that LocRec outperforms classical sequential-based models for successive location recommendation.

In order to face the security risks and challenges imposed by the openness nature of the Internet of Things (IoT), this paper proposes a distributed advanced analytical trust model based on a Markov chain. The main objective of this paper is to study, improve and adapt a distributed trust model already proposed for Vehicular Ad hoc Networks (VANETs) to the IoT context. This adaptation uses specific parameters to the IoT networks and considers all limitations of IoT devices as well as the less mobile nature of these devices than vehicles. In this model, a set of neighbor nodes organize themselves into groups to evaluate and monitor another IoT node called monitored node depending on its behavior in the network. Our model uses an estimation algorithm to remove spams provided by malicious neighbors during the process of updating the monitored node trust. The numerical results illustrate the adaptability, the robustness and the strong resistance of our adapted trust model against the dynamic behaviors of the IoT devices and against the various popular attacks related to trust.

Recent breakthrough in the wireless energy transfer have attracted attention of the researchers due to its potential to prolong the lifetime of Wireless Sensor Networks (WSNs) and to eliminate energy bottlenecks. Through wireless charging vehicles sensor node's energy can be replenished periodically by using Wireless Charging Vehicles (WCVs). However, for a large scale WSN, the capacity of WCVs, positioning and coordination between them, and limited resources of WCVs have to be considered to implement a scalable charging system. Additionally, creating an efficient charging route for the WCVs considering the dynamic energy consumption of sensor nodes is also crucial. Since, WCVs have limited capacity and low system resources, the classic TSP-based Optimization algorithms, which requires high-performance computing, are not suitable for them. In this paper, we proposed GHSC (Guided Hilbert for Specified Cluster), which is based on the Hilbert space filling curve. We consider a clustered WSN with multiple WCVs, where each cluster is assigned to a single WCV. GHSC creates a primary charging route by considering a cluster as an unit square and dividing the square into small sub-squares. As the Hilbert curve goes through each sub-squares, the algorithm assigns a rank for each of them based on their position along the curve. The algorithm then map the nodes into vertices of the sub-squares and rank the nodes as well. Once ranking is done, the algorithm creates a charging route based on the node's ranking. This heuristic approach of the GHSC algorithm can find effective charging tours for the WCVs with a time complexity of (O(n log n) + Ω(nk)). Compared to other Global Optimization algorithms, GHSC was able to find tours, which was at most a logarithmic factor longer than the shortest tour.

Ensuring the required trustiness among communicating peers is an important task in Vehicular Adhoc Networks (VANETs), especially for safety-related applications where the margin of error is extremely undesired. Most the safety applications are a kind of decision aided system, and final decision is always taken by humans. Thus, in addition to securing inter-vehicle communication, the human factor must be also considered. With the appearance of 5G technology it became possible to connect VANET to any other network including Online Social Networks (OSNs). In this paper, we took advantage of this possibility to connect VANET and OSN, for the purpose of estimating the drivers honesty based on their OSN profiles. Afterward, we combined both inter-vehicle and OSN-based trust to compute the overall trust about the different vehicles and their drivers. Conducted simulation show that our proposal offers more than 5% detection ratio than the classical inter-vehicle solution. Furthermore, it also reduced the detection error ratio by about 3% with a reduced standard deviation for both detection and error ratios.

Network Function Virtualization (NFV) is emerging as an efficient mean to deploy and manage network and telecommunication services. With wireless access networks, NFV has to take into account the radio resources at wireless nodes in order to provide an end-to-end optimal virtual network function (VNF) allocation. This topic has been well-studied in existing literature, however, the effects of variations of networks over time have not been addressed yet. In this paper, we provide a model of the adaptive and dynamic VNF allocation problem considering VNF migration. Moreover, we also consider service function chains (SFCs) with QoS constraints. Then we formulate the optimisation problem as an Integer Linear Programming (ILP) and provide a heuristic algorithm for allocating multiple SFCs. The proposed approach allows SFCs to be reallocated so as to obtain the optimal solution over time. The results confirm that the proposed algorithm is able to optimize the network utilization while limiting the reallocation of VNFs which could interrupt services.

Vehicular Named Data Network (VNDN) uses NDN as an underlying communication paradigm to realize intelligent transportation system applications. Content communication is the essence of NDN, which is primarily carried out through content naming, forwarding, intrinsic content security, and most importantly the in-network caching. In vehicular networks, vehicles on the road communicate with other vehicles and/or infrastructure network elements to provide passengers a reliable, efficient, and infotainment-rich commute experience. Recently, different aspects of NDN have been investigated in vehicular networks and in vehicular social networks (VSN); however, in this paper, we investigate the in-network caching, realized in NDN through the content store (CS) data structure. As the stale contents in CS do not just occupy cache space, but also decrease the overall performance of NDN-driven VANET and VSN applications, therefore the size of CS and the content lifetime in CS are primary issues in VNDN communications. To solve these issues, we propose a simple yet efficient multi-metric CS management mechanism through cache replacement (M2CRP). We consider the content popularity, relevance, freshness, and distance of a node to devise a set of algorithms for selection of the content to be replaced in CS in the case of replacement requirement. Simulation results show that our multi-metric strategy outperforms the existing cache replacement mechanisms in terms of Hit Ratio.

In remote mobile social networks, caching is a very promising technique to alleviate the burden of space cloudlet (e.g., cache-enabled satellite user terminal) and to improve subscribers' user experience in terms of content retrieval latency. In this paper, we proposed a social relationship aware caching placement approach for remote mobile social networks. Social relationships between users are used to designate a set of helpers with caching capability, which can cache popular files proactively when the cloudlet is idle. Furthermore, the caching placement problem is formulated as an optimization problem to minimize the average content retrieval latency. Then, we reformulate the problem into a monotone submodular optimization problem with a partition matroid constraint; moreover, an efficient greedy algorithm with 1-[1/e] approximation ratio is proposed to solve it. Simulation results show that the proposed social aware greedy caching placement approach significantly outperforms the traditional approaches in terms of content retrieval latency and hit ratio.

In a multi-cloud environment, consumers can access multiple cloud services using a single heterogeneous computing architecture. In such an environment, multiple instances of the same cloud service and its component may be geographically dispersed. So, cloud service broker (CSB) exploits the heterogeneity of multi-cloud environment to provide high performance at a low price to its consumers. The consumer tasks are allocated to the geo-dispersed cloud service components for execution of various services. For this purpose, an optimal service components identification and task allocation are major concerns keeping in view of the heterogeneity in multi-cloud environment. For this purpose, a scheduling algorithm, which takes care of location, price, and performance is required. Therefore, in this paper, SLOPE: A Self Learning Optimization and Prediction Ensembler for Task Scheduling in Multi-cloud Environment is proposed. SLOPE works in two phases, 1) In first phase, Bayes theorem is used to design a self-learning algorithm, to compute the conditional probability (strength) of each service component in order to select the probable rule string, and 2) a roulette wheel method is used to select an optimal scheduling policy for a given service request. SLOPE helps to identify the best possible service component from the pool of resources on the basis of dynamic factors and then schedule a service request to the selected component. Unlike most of the other existing approaches, SLOPE builds an efficient schedule for service selection. Experimental results demonstrate that SLOPE performs better in comparison to other competing schemes of its category.

Nowadays, information security is extremely critical issues for every organization to protect information from the useless data on the manipulation of network traffic or intrusion. Intrusion detection system has one of the important roles to prevent data or information from malicious behaviors because its capable of detecting attacks in several available environments. Thereafter, many researches concentrate on developing new algorithms to treat the Dataset by different way. In this work, we suggest a new proposed PCA-fuzzy Clustering-KNN method that means ensemble of Analysis of Principal Component and Fuzzy Clustering with K-Nearest Neighbor feature selection technics. However, we perform two main class classifications to construct our suggested model. Then, to check the robustness of model we used as well-known Dataset NSL-KDD used for analysis of anomaly. This Dataset is based on benchmark data used for intrusion detection, KDDCup 1999. Therefore, we analyse NSL-KDD Dataset using PCA-fuzzy Clustering-KNN analytic and try to define the performance of incident using machine learning algorithms, the algorithm learns what type of attacks are found in which classes in order to improve the classification accuracy and reduce high false alarm rate and detects the maximum of detection rate from Dataset as shown by the numerical results.

This paper introduces the concept of a new service for social Internet of Vehicles (IoV)-based clouds called incentives-based vehicle witnesses as a service (IVWaaS), which employs vehicles moving on the road as the witnesses to designated events. Specifically, we focus on two key enablers, a new secure and privacy preserving service framework as well as a new incentive mechanism to promote the wide adoption of the aforementioned social service. In IVWaaS, when confronted any events, the vehicles in the vicinity with mounted cameras collaborate with other roadside cameras to take pictures of the site of interest around them, and send the pictures to the cloud infrastructure anonymously so that the privacy of the vehicles can be preserved. To stimulate active participation from the users, we also introduce a new privacy-aware incentives mechanism called privacy-aware proportionate receipt collection, in which the contributors are credited according to their contribution to the service and can claim their incentives in a privacy-aware fashion. Service providers can also use the stored pictures as “on-demand picture service.” Other law enforcement agencies can obtain the stored pictorial information and use it as forensics in the investigations.

To ensure robustness in wireless networks, monitoring the network state, performance and functioning of the nodes and links is crucial, especially for critical applications. This paper targets Internet of Things (IoT) networks. In the IoT, devices (things) are vulnerable due to security risks from the Internet. Moreover, they are resource-constrained and connected via lossy links. This paper addresses the optimized scheduling of the monitoring role between the embedded devices in IoT networks. The objective is to minimize energy consumption and communication overhead of monitoring, for each node. Several subsets of the potential monitoring nodes are generated by solving a minimal vertex cover (VC) problem with constraint generation. Assuming periodical functioning, VCs are optimally assigned to time periods in order to distribute the monitoring role throughout the entire network. The assignment of VCs to periods is modeled as a multiobjective generalized assignment problem. To further optimize the energy consumption of the monitors, they are sequenced across time periods to minimize the state transitions of nodes. This part of the problem is modeled as a traveling salesman path problem. The proposed model is tested on randomly generated instances and the experimental results illustrate its effectiveness to optimize the scheduled monitoring for fault tolerance in IoT networks.

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.

A fundamental problem in designing sensors network is locating their position. The data collected from the sensors can be used to detect, track and organize objects of interest. In this paper, we present and evaluate an improvement of the famous DV-HOP algorithm in order to increase the localization accuracy and reduce energy consumption. The benefits of the suggested algorithm are twofold. First, it uses a new technique for solving an N-equation system and a weighted least squares method (WLS) to minimize the error of the expected distance between anchor and unknown nodes. Second, this method uses the hop-size average of the anchor node, which is computed by unknown nodes, to reduce the overall communication cost between nodes. This yields a significant reduction in both energy consumption and execution time. The performance of our proposed approach was evaluated and compared to other classical algorithms. Results show that significant enhancement is achieved within the proposed algorithm when measuring different metrics such as energy, execution time and localization error while varying simulation parameters such as the total number of nodes, percentage of anchor node and communication range.

Interference, which can be caused by Secondary Users (SUs) to other Primary Users (PUs) or SUs, is of major concern for Cognitive Radio Networks (CRNs). We study a spectrum allocation problem, where our primary focus is on maximizing the throughput of the CRN while protecting the PUs from interference and controlling the co-channel interference among the SUs. We build a CRN, where initially, all SUs explore prospective links for communicating between them and send the sensed data to a centralized Base Station (SU BS). The SU BS aggregates the received information and discovers conflicting links among the SUs by using an interference graph. Conflict among links affects the SINR, which in essence hampers the efficiency of the CRN. We form an optimization problem, which is a binary integer linear programming problem, with an objective to determine the optimal set of conflict-free links that satisfies the interference constraints. We propose the CFLA (Conflict-free Link Assignment) algorithm, which uses a neural network for capacity-aware link distribution. Simulation results show that the algorithm has less time complexity than the evolutionary algorithms such as genetic algorithm in particular and perform steadily for large scale CRNs as well.

Distributing scarce spectral resources among the unlicensed users has been an attractive research area for Cognitive Radio Network's (CRN) research community. A resource distribution technique, which emphasizes fairness, ensures allocation of resources for all Secondary Users (SUs) irrespective of their data rates and may cause efficiency loss for the CRN. On the other hand, a throughput based resource allocation approach considers SUs with high data rates only and consequently SUs with low data rates face negative experiences as they starve from resources. Our work aims to balance between the fairness and the efficiency of a CRN. We formulate an objective function, which is a nonlinear convex function, to achieve maximum weighted sum rate for the SUs while ensuring fairness to all. We use Primal Dual Interior Point Method to solve the optimization problem and define a weight factor to obtain balance between the fairness and the throughput of the CRN. Finally we present an online iterative algorithm which maximizes the weighted sum rate of the SUs while guaranteeing QoS to the Primary Users (PUs). Numerical results exhibit that our method achieves higher throughput while ensuring adequate fairness to the SUs, compared to other traditional fairness schemes.

In recent years, there have been significant efforts to standardize a routing protocol for Low-power and Lossy Networks (LLNs). This effort has culminated in standard IPv6 routing protocol for LLNs (RPL). The main interest of RPL is to improve routing in an LLN minimizing the usage of network resources. For this, RPL builds acyclic graphs and applies an Objective Function (OF) which is responsible of choosing the preferred parent and the best links during the construction of the Destination Oriented Directed Acyclic Graph (DODAG). This paper introduces a new OF, based on a Non-Linear Length (NL-OF) which takes into account any number of metrics and constraints for QoS routing. NL-OF ensures that each path in the DODAG respects the input constraints. The NL-OF can be used to meet the requirements of sensible applications, such as real-time applications. A significant part of this work aims at studying the theoretical aspect of the NL-OF. Finally, using Cooja simulator, we evaluate the performance of NL-OF. We show that our new Objective Function gives a good result and outperforms the three existing OFs when considering three QoS parameters which are end-to-end Delay, Packet Loss and Jitter.

Internet of Things is one of the most promising domain for developing new and smart applications. For some applications, Quality of Service requirement may be imposed. However, the things connected in this kind of networks are often vulnerable and dotted by limited energy (Low-power and Lossy Networks, LLNs). These networks need special solutions for routing and RPL is a standard IPv6 routing protocol to respond to needs. RPL build Destination Oriented Directed Acyclic Graphs (DODAGs) for routing. Recently, some propositions have been formulated to satisfy QoS requirements in LLNs using RPL even if there are several QoS constraints. A greedy DODAG construction has been proposed based on a Non-Linear Length which takes into account any number of constraints for QoS routing. However, it is known that the multi-constrained QoS routing is an NP-hard problem and its solution is not always a simple acyclic graph (not a tree). In this paper we present the computation of the exact solution in LLNs between a Border Router and the nodes respecting a set of QoS constraints. We propose the modification of RPL for this. Our analysis also covers a set of parameterized polynomial algorithms. We evaluate the performance of the proposed algorithms in term of execution time and scalability, the number of missing nodes during the construction of the DODAG and the quality of the paths from the Border Router to each node in the constructed DODAG.

In this paper we address the impact of the security cost in terms of energy consumption, processing time, and traffic load on quality of services (QoS) in Wireless Sensor Networks (WSNs). Offering security services (authentication, confidentiality, and integrity) and QoS (throughput, delay, and reliability) guarantee in WSNs is still challenging issue. The security and QoS are opposite parameters, and then security services must be dynamically and optimally adapted to QoS and network constraints (e.g. energy efficiency). Therefore, designing such solution that optimizes multiple conflicting objectives is computationally intractable. We propose a new solution based on multi-objective optimization using genetic algorithm (NSGA-II) for security, QoS, and energy efficiency in WSNs. Resource constraints as well as QoS requirements are respected through use of optimal security level based on evolutionary strategy. The obtained simulation results illustrate that the energy efficiency and the security level optimization is reached with different set of optimal security settings adapted to the QoS and the energy requirements.

Human action recognition is a challenging field that have been addressed with many different classification techniques such as SVM or Random Decision Forests and by considering many different kinds of information joints, key poses, joints rotation matrix, angles for example. This paper presents our approach for action recognition that considers only information given by the 3D joints from the skeleton and trains a two stage random forest to classify them. We extract skeletal features by computing all angles between any triplet of joints and all distances between any pair of joints then organizing them into a feature vector for each static pose. Complex dynamic actions are then described by sequences of such feature vectors. We evaluate our approach on the most recent and the largest benchmark, MSRC-12 Kinect Gesture Dataset, and compare our results with the state-of-the-art methods on this dataset.

The introduction of wireless charging is a major breakthrough for Wireless Sensor Networks (WSNs) as it can solve the bottleneck of a limited network lifetime. Wireless charging is done via mobile chargers (MCs) that go to the nodes and recharge them using magnetic resonant coupling. Coordination among the MCs and adapting the routing with the heterogeneous charge depletion rate of the nodes is crucial for a realistic and scalable charging scheme. We adopt a cluster-first route-second approach to design an efficient charging policy for WSNs while considering the capacity constraint of the MCs and dynamic charging requests from the sensor nodes. In particular, we proposed KMEC (Extended K-means for Equal Clustering), which extends the K-means algorithm to create equal-sized clusters of sensor nodes and then assign the clusters to the MCs to ensure that each charger gets equal loads. For routing, we proposed the Dynamic Active Zone strategy, which adapts with the real time charging demands from the nodes. Simulation results demonstrate that our proposed algorithms perform better than the existing schemes and can be used competently for large scale WSNs.

Abstract This paper introduces a new vehicle localization approach for global positioning system‐challenged road segments (e.g., tunnels), which takes advantage of roadside units (RSUs) and in‐vehicle inertial navigation system (INS). In the proposed approach, namely RSU/INS‐aided localization system (RIALS), vehicles only need one RSU in their transmission range for an accurate positioning. The beacons received from the RSU along with the information provided by the INS system are used for establishing and maintaining particular locus circles. After linearization, the system of locus circle equations is solved using the linear least square estimation technique, and the estimated vehicle's position is obtained. In the presence of speed variations and existing ranging and INS estimation errors, the proposed RIALS adaptively sets the sufficient number of required locus circles, aiming at keeping the localization error below a given threshold. We study the effects of ranging and INS estimation errors on localization accuracy of RIALS from analytical and simulation perspectives. Results of extensive simulations show that the localization error is more sensitive to the ranging error than the INS error. Moreover, the network traffic overhead of the proposed method is considerably lower than other competitive localization approaches. Copyright © 2015 John Wiley & Sons, Ltd.

In this paper, we study the problem of efficient safety message dissemination in Vehicular Ad-Hoc Networks (VANETs). The objective is to reduce vehicular communication interferences for safety message delivery while guaranteeing the timeliness and the reliability of messages to avoid accidents. Therefore, we propose a heuristic algorithm called Time-Limited Reliable Broadcast Incremental Power (TRBIP) to construct a safety message delivery tree. Extensive simulation results show that the proposed algorithm outperforms its counterparts in terms of message timeliness, reliability and interference reduction.

Vehicular networks have attracted attention for recent years due to their various and emerging applications supporting secure and convenient driving. Regarding specific features of vehicular networks, we propose a new Social-aware Vehicular DTN protocol (SocVe) respectively for a type of safety applications such as emergency support services. We evaluate our protocol in short contact and intermittent connection scenarios extracting from mobility data set in Hanoi city. We conduct comparative performance evaluation of SocVe in multiple scenarios with different destination centralities against a geographical protocol.

Multichannel operation has been suggested for vehicular networks in order to support simultaneously safety and non-safety applications. Without awareness of lower layers, safety messages may be generated out of the control channel interval that leads to synchronous contention at the beginning of control channel interval (CCHI). The jamming attacks at this period are difficult to detect as they can be confused with collisions in contention. In this work, we proposed a detection method that can distinguish between packet lost due to normal collisions and due to jamming attacks. An analytical model is also introduced and validated in order to study the efficiency and the precision of the proposed detection method. Obtained numerical results from analytical model and network simulation show that our detection method has a high detection probability under reactive jamming attacks.

Current anonymity techniques for mobile opportunistic networks typically use obfuscation algorithms to hide node's identity behind other nodes. These algorithms are not well suited to sparse and disconnection prone networks with large number of malicious nodes and new opportunistic, adaptive. So, new, opportunistic, adaptive fully localized mechanisms are needed for improving user anonymity. This paper proposes reputation aware localized adaptive obfuscation for mobile opportunistic networks that comprises of two complementary techniques: opportunistic collaborative testing of nodes' obfuscation behaviour (OCOT) and multidimensional adaptive anonymisation (AA). OCOT-AA is driven by both explicit and implicit reputation building, complex graph connectivity analytics and obfuscation history analyses. We show that OCOT-AA is very efficient in terms of achieving high levels of node identity obfuscation and managing low delays for answering queries between sources and destinations while enabling fast detection and avoidance of malicious nodes typically within the fraction of time within the experiment duration. We perform extensive experiments to compare OCOT-AA with several other competitive and benchmark protocols and show that it outperforms them across a range of metrics over a one month real-life GPS trace. To demonstrate our proposal more clearly, we propose new metrics that include best effort biggest length and diversity of the obfuscation paths, the actual percentage of truly anonymised sources' IDs at the destinations and communication quality of service between source and destination.

Broadcasting plays vital role in vehicle-to-vehicle communication. However, it must face to challenges such as lack of acknowledgment, no retransmission, hidden terminals and low link quality that leads to unreliable communication because of message loss caused by collisions and poor link conditions. It is especially critical for safety messages in vehicular networks. To make broadcast more reliable, some rebroadcast methods have been proposed but they have to deal with problems of broadcast redundancy, delay and requirement of cross-layer information. In this work, we propose a new rebroadcast method, at MAC layer level, that uses a polling scheme where messages are retransmitted only when requested. To evaluate our scheme, we propose an analytical model specified for IEEE 802.11p with multichannel operations. We compute packet delivery rate in two cases: the case of one access category as in DCF and the case of prioritized broadcasting with several access categories based on EDCA. Our polling scheme shows significant improvement in both cases compared to standard even in limited operating time due to the switching channel.

Many applications of Wireless Sensor Networks (WSN) require to achieve the positions of the sensor nodes within a given time bound. In this paper we study the relative and physical localizability of WSN in a given time bound. We propose a new distributed and time bounded localization algorithm based on Multidimensional Scaling (MDS) method in WSN called D-MDS localization time algorithm. We compare the proposed algorithm to the existing algorithm based on the well-known Trilateration method. The simulation results show that the proposed algorithm outperforms the existing approach based on Trilateration method in terms of the number of localized nodes in the network and the number of anchors required to physically localize the sensors. The D-MDS localization time algorithm localizes a large number of nodes for a low node degree in a time bound. Moreover it is able to physically localize the network with a low number of anchors compared with the algorithm based on Trilateration method.

ABSTRACT In this paper, we propose a distributed and dynamic public key infrastructure for vehicular ad hoc networks. We aim to achieve the fundamental security requirements, particularly the authentication, the confidentiality, and a reliable vehicle‐to‐vehicle data exchange. To make the certification authority (CA) reachable by all vehicles, we distribute its role among a set of dynamically elected vehicles. The election of dynamic CAs is based on a clustering algorithm where the cluster heads will be CAs in their clusters. The cluster heads are elected following two criteria: security and mobility. Due to the important role of the CA in each cluster and to protect it from DOS attacks, we introduce a VANETs dynamic demilitarized zone for vehicular ad hoc networks. Its role is to handle the certification requests sent to the CA from unknown vehicles, and hence, it avoids compromising it. Additionally, we detail the certificates management in the proposed public key infrastructure, and we propose a mechanism to provide anonymous vehicle‐to‐vehicle communications using pseudonyms. To study the feasibility of our distributed architecture and particularly the clustering algorithm, we propose a probabilistic model considering the speed of vehicles and taking into account the safety distance between vehicles. We carried out a set of simulations to evaluate the performance of the proposed clustering algorithm in both urban and highway environments. Hence, we study the effects of the transmission range, the speed of vehicles, and the number of trusted vehicles in the network on the stability and the efficiency of the overall proposed architecture. We also study some delays characterizing the certificates management. Our simulation results show that the security of the proposed architecture closely depends on the number of trusted vehicles in the network, and the stability depends on the mobility of vehicles on the road and on the total number of trusted vehicles. Copyright © 2013 John Wiley & Sons, Ltd.

ABSTRACT This paper addresses the problem of localization in sensor networks where, initially, a certain number of sensors are aware of their positions (either by using GPS or by being hand‐placed) and are referred to as anchors. Our goal is to localize all sensors with high accuracy, while using a limited number of anchors. Sensors can be equipped with different technologies for signal and angle measurements. These measures can be altered by some errors because of the network environment that induces position inaccuracies. In this paper, we propose a family (AT‐Family) of three new distributed localization techniques in wireless sensor networks: free‐measurement (AT‐Free) where sensors have no capability of measure, signal‐measurement (AT‐Dist) where sensors can calculate distances, and angle‐measurement (AT‐Angle) where sensors can calculate angles. These methods determine the position of each sensor while indicating the accuracy of its position. They have two important properties: first, a sensor node can deduce if its estimated position is close to its real position and contribute to the positioning of others nodes; second, a sensor can eliminate wrong information received about its position. This last property allows to manage measure errors that are the main drawback of measure‐based methods such as AT‐Dist and AT‐Angle techniques. By varying the density and the error rate, simulations show that the three proposed techniques achieve good performances in term of high accuracy of localized nodes and less energy consuming while assuming presence of measure errors and considering low number of anchors. Copyright © 2012 John Wiley & Sons, Ltd.

Road safety can be improved a lot with the help of Cooperative Collision Warning System, in which vehicles cooperate with each other for relaying safety messages to avoid accidents. Once a vehicle is in an abnormal status, which may lead to potential accidents, an emergency safety message will be generated and sent out to warn surrounding vehicles. In this paper, we address the routing problem of reliable dissemination of emergency safety messages in Vehicle Ad-Hoc Networks (VANETs). We aim to minimize the total transmission energy and reduce the transmission interference for delivering emergency safety messages to endangered vehicles while guaranteeing timeliness of safety messages and transmission reliability of inter-vehicle communications. The emergency safety message dissemination problem is formulated as a spanning tree optimization problem with both hop-count constraint and reliability-height constraint. A mixed integer programming model (MILP) is proposed for solving the studied problem optimally, which can be used as a benchmark for evaluating future fast heuristic algorithms. Simulations are conducted to validate our proposed MILP model.

Social routing protocols are typically used to transfer messages among users and services in mobile opportunistic networks. Adaptive mechanisms are needed for achieving user anonymization and providing sufficient level of user anonymity due to the constant changes in underlying topology, mobility patterns and density of users and their queries. This paper describes a novel flexible and adaptive approach, AdaptAnon that is suitable for dynamic and heterogeneous mobile opportunistic networks. Our approach is multidimensional and combines multiple heuristics based on user profiles, analysis of user connectivity and history of anonymization in order to predict and decide on the best set of nodes that anonymize the sending node. Our results of extensive experiments show that AdaptAnon achieves higher quality of anonymization in terms of both the number of nodes and the diversity of nodes in the anonymization layer for varying query intensity and over different sender and destination degrees of connectivity while neither decreasing success ratios nor increasing latency. We show that AdaptAnon outperforms state of the art single dimensional anonymization approaches when run over three different real-life traces.

Location-tracking is a major privacy problem, especially with high penetration of smart-phones and GPS-enabled devices around the world. In this paper, we propose a Hybrid and Social-aware Location-Privacy in Opportunistic mobile social networks (HSLPO), a collaborative and distributed obfuscation protocol that offers location-privacy k-anonymity. HSLPO discovers the users' own social network and use it to obfuscate requests and hide the original sender's location from the Location based service (LBS) while minimizing the overhead needed to perform obfuscation. We performed extensive simulations over a map-based pseudo realistic environment to compare HSLPO to exiting protocols. Results show that HSLPO can reach higher location-privacy levels and quality of service, in term of success ratio, than other protocols while maintaining low overheads.

In this paper we propose a trust model based on a Markov chain in order to formalize the trust metric variation and its stability in the context of Vehicular Ad hoc Networks (VANETs). The proposed model takes into account not only the dynamic trust metric variation according to the vehicles behaviors, but also the constraints related to the monitoring process. In our model each vehicle can act as monitor and update the trust metric of its neighbors according to their behavior in the network. In addition, our model can be customized through different parameters like the trust interval and the number of transitions needed to reach the highest trust level. This flexibility enables to adapt the model according to the application context. The performance evaluation of the proposed model is presented with different parameters and two types of disruptive vehicles are taken into account: malicious and selfish. The obtained results show the resistance, the robustness and the incentive of the proposed model against the fluctuations of the vehicles behaviors.

In the last few years, the localization issue in wireless sensor networks has gained a lot of popularity for providing flexible and novel location-aware applications. However, throughout the variety of research works in this topic, most interests focused purely on the localization scheme itself rather than the security issue of the localization. In this paper, we propose a new and original secure version of AT-Dist, a distance-based multihop localization algorithm previously explored but under a non-adversarial environment. This version, called SecAT-Dist, proposes a lightweight security scheme that combines RC4-based hash function and symmetric encryption system to enable sensor nodes to authenticate received beacon location information and protects hop-count information from being arbitrary or selfishly changed. Furthermore, we introduce a trust model based on a set of confidence index taking benefits from specific characteristics of distance-based multihop localization algorithms. Analytically, we show that the number of trust anchors, among the total number including malicious anchors, is reduced. Simulation results show that our security scheme is more resistant to external attacks and allows great number of nodes to be located with accuracy.

IEEE 802.11 wireless transmissions suffer from a big security flaw as they are vulnerable to Denial of Service Attacks (DoS) which can degrade enormously their performance, especially their achieved throughput. One of the most harmful forms of these attacks is the jamming. In this paper, we provide an analytical model to study the effect of jamming on WLANs. Hence, we implement a physical layer jamming to show the effect at the MAC layer level (i.e. cross-layer jamming). Different jamming scenarios, with different modulation techniques such as DSSS and OFDM, were distinguished in order to evaluate the most accurately possible the effect of jamming on WLANs performance. Extensive simulations, under ns-3 tool, are conducted to validate the correctness of the theoretical analysis. We show that low rates (e.g. 1Mbps) are more resistant to jamming than high rates (e.g. 27Mbps). Also, we show that energy consumption when jamming depends only on communication time. Valuable observations are provided to derive solutions for jamming.

We propose an efficient dynamic architecture of the Public Key infrastructure (PKI) for vehicular ad hoc networks (VANETs) based on a trust model and a novel distributed clustering algorithm. Furthermore, we introduce the notion of a VANET Dynamic Demilitarized Zone (VDDZ), composed of the set of confident vehicles located within the immediate neighborhood of each clusterhead (CH) to assume the Registration Authorities (RA). The VDDZ aims to secure the elected CAs by shielding them from unknown or malicious vehicles. A host of simulations using urban and highway scenarios are conducted to investigate the effects of the transmission range, the speed of vehicles and the number of confident vehicles in the network on the security level, the stability and the efficiency of our proposed architecture. In particular, the results show a strong dependency of the security and the stability on the assumed percentage of trust nodes and the mobility models.

Coupling the high data rates of IEEE 802.11p-based VANETs and the wide coverage area of 3GPP networks (e.g., UMTS), this paper envisions a VANET-UMTS integrated network architecture. In this architecture, vehicles are dynamically clustered according to different related metrics. From these clusters, a minimum number of vehicles, equipped with IEEE 802.11p and UTRAN interfaces, are selected as vehicular gateways to link VANET to UMTS. Issues pertaining to gateway selection, gateway advertisement and discovery, service migration between gateways (i.e., when serving gateways lose their optimality) are all addressed and an adaptive mobile gateway management mechanism is proposed. Simulations are carried out using NS2 to evaluate the performance of the envisioned architecture incorporating the proposed mechanisms. Encouraging results are obtained in terms of high data packet delivery ratios and throughput, reduced control packet overhead, and minimized delay and packet drop rates.

In this paper, we propose a dynamic PKI for vehicular ad hoc networks aiming to distribute the role of the central certification authority (CA) among a set of dynamic chosen CAs. The election of dynamic CAs is based on a clustering algorithm where the cluster heads (CH) perform the role of CAs. The clustering process is based on both security and mobility. Furthermore, we define a new approach called the dynamic demilitarized zone (DDMZ) formed by confident nodes located at 1-hop from the CH of the same cluster and intended to perform the registration authorities (RA). Extensive simulations are conducted to evaluate the performance of our clustering algorithm and to investigate the impact of transmission range, speed of vehicles and inter vehicles distances on the stability of the security infrastructure.

Along with recent Internet security threats, different security measures have emerged. Whilst these security schemes ensure a level of protection against such threats, they sometimes have significant impact on perceived Quality of Service (QoS). There is thus need to retrieve ways for an efficient integration of security requirements with their QoS counterparts. In this paper, we devise a Quality of Protection framework that tunes between security requirements and QoS using a multi-attribute decision making model. The performance of the proposed approach is evaluated and verified via a use case study using computer simulations.

Vehicle Ad Hoc Networks (VANETs), based on IEEE 802.11p, and 3G & beyond networks are characterized by their high date transmission rates and wide range communication, respectively. This paper presents an architecture that integrates between the two, making advantage of the features of each. Design guidelines pertaining to vehicle clustering and gateway management are defined. The former aims for enhancing the link stability within the VANET, whereas the latter sustains inter-connectivity of the VANET with the backhaul 3G & beyond network. Simulations are carried out using NS2 to evaluate the performance of the integrated network architecture and encouraging results are obtained in terms of high data packet delivery ratio, reduced control packet overhead, and reduced packet drop rate.

On behalf of the Organizing Committee, we would like welcome you to the Sixth Annual IEEE International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob). The tremendous advances in wireless communications and mobile computing, combined with the rapid evolution in smart appliances and devices have generated new challenges and problems requiring solutions that rely on interactions between different network layers and applications in order to offer advanced mobile services. WiMob'2010 addresses three main areas: Wireless Communications, Mobile Networking, Ubiquitous Computing and Applications. IEEE WiMob has grown to become one of the major events in these areas, and it aims to stimulate interactions among participants and enable them to exchange new ideas and practical experiences in their respective areas.

Security has become a primary concern in order to provide protected communication in mobile networks. Unlike the wired networks, the unique characteristics of mobile networks pose a number of nontrivial challenges to security design, such as open peer-to-peer network architecture, shared wireless medium, stringent resource constraints, highly dynamic network topology and absence of a trusted infrastructure. Ubiquitous roaming impacts on a radio access system by requiring that it supports handover between neighbouring cells and different networks. Also, mobile networks are more exposed to interferences than wired networks. There are several components that contribute to this: adjacent channels, co channels, Doppler shifts, multipath and fading. This SI aims to identify and explore the different issues and challenges related to security aspects in mobile networks. It is dedicated to present the state-of-the-art research results and findings on Security in Mobile and Wireless networks. We accepted 12 papers out of a total of 31 submissions from all over the world. Each paper was reviewed by at least three reviewers and has undergone through two cycles of revisions. The first paper introduces the notion of malnets: networks of adversary-controlled wireless routers targeted to a physical geography. Similar to Internet worms such as Slammer and Code-Red, malnets are created by the recursive compromise of targeted devices. However, unlike their traditionally wired counterparts, malnet worms exploit only other routers that are within their transmission range. The malnet creates a parallel wireless infrastructure that is (a) completely under control of the adversary, and (b) spans a targeted physical area, creating a valuable infrastructure for a variety of virtual and physical attacks. A study of the propagation characteristics of commercial routers and model inter-router connectivity using publicly available war-driving data is given. The resulting characterization is applied to well-known epidemiological models to explore the success rates and speeds of malnet creation across cities such as New York, Atlanta and Los Angles. Finally, a sampling of available exploits is used to demonstrate the construction of multi-vector, multi-platform worms capable of targeting wireless routers. Wireless vehicular communications pose significant challenges for the deployment of next generation roadside services. Some important issues that must be tackled are security, billing and reliability while guarantying a scalable service delivery. The second paper addresses the assignation of secure service session parameters upon the reception of on-demand service requests by an incumbent services district domain, and studies and analyses the performance of the underlying mechanisms. Three types of service request protocols are introduced: single-hop, extended connectivity and multi-hop service requests. A detailed analytical model and cost study for the access protocols are presented. The analysis study covers the estimation of total cost in terms of latency for each access protocol with different mobility characteristics and vehicle densities within the service coverage area and across different serving district domains. The third paper noted that providing secure routing in mobile ad hoc networks (MANETs) is far more difficult than establishing secure routing in wired networks or static wireless networks, because node mobility and the relative scarcity of bandwidth render prior solutions ineffective. Further solutions based on securing link or path information do not work well in MANETs because the dynamic nature of links requires extensive use of flooding to establish effective countermeasures. The authors argue in their paper that secure routing in MANETs must be based on the end-to-end verification of physical-path characteristics aided by the exploitation of path diversity to increase the probability of finding secure paths. They hence applied this approach to the design of the Secure Routing through Diversity and Verification (SRDV) protocol and proved that the countermeasures used in SRDV can defend against a variety of known attacks to routing protocols, including attacks involving collusion, and the fabrication and modification of routing packets. The fourth paper proposes a new architecture based on an efficient trust model and Secure Distributed Clustering Algorithm (SDCA) in order to distribute a certification authority (CA) for ensuring the distribution of certificates in each cluster. They use a combination of a fully self-organized security for trust models like PGP adapted to adhoc technology and the clustering algorithm with respect to trust and mobility metrics. Furthermore, they present a new approach: the Dynamic Demilitarized Zone (DDMZ) to protect the CA in each cluster. The principal idea of DDMZ consists in selecting the dispensable nodes, also called registration authorities (RA); these nodes must be confident and located at one-hope from the CA. Their roles are to receive, filter and treat the requests from any unknown node to the CA. This approach allows to avoid the single point of failure in each cluster. Moreover, they propose a probabilistic model to define the direct connectivity between confident nodes in order to study the resistance degree of the DDMZ against different attacks. The performance evaluation of the proposed SDCA shows the robustness and the availability of DDMZ. The effects of direct connectivity and transmission range on the stability and security of the network are analysed. In the fifth paper, authors observe that, in MANETs, there is an increasing interest in providing anonymity for witnesses, i.e. those users who share their knowledge in detecting either malicious or selfish users. As well, it is a challenging problem to prevent the misuse of anonymous sources. They proposed the PlainClothesMan (PCM) protocol to provide anonymity for the witness who helps identify malicious or selfish users. Once there are more than a certain number of claims from distinct users against the same user, she is identified as a malicious or selfish user. Moreover, in PCM, the misuse of the witness anonymity is prevented in such a way that malicious users who broadcast the same invalid claims repeatedly can be identified. Two exemplary scenarios are designed and simulated to model the necessities of witness anonymity in MANETs. Simulation results show that, witness anonymity is very important for ensuring proper and efficient executions of fundamental functionalities of MANETs, e.g. certificate revocation and fairness, and PCM is both effective and efficient in providing such a type of anonymity The sixth paper provides an overview of Internet security to meet the IP Multimedia Subsystem (IMS). The IMS, a platform designed for providing various multimedia services, requires high availability and trustability. However, the IMS specifications do not explicitly introduce the protection mechanisms. By focusing on the security issues of IMS, the seventh paper discusses and surveys the potential security threats on IMS. Those security threats include: (a) routing attacks and DNS attacks that result from vulnerabilities of IP technology, and (b) Denial of Service (DoS) attacks and the privacy control threats that are IMS-specific. (c) Spam threats such as Spam over Internet Telephony (SPIT), Spam over Instant Messaging (SPIM) and presence spam. On the other hand, this paper also reviews the existing work, available countermeasures and tools for preventing these threats from harm. The seventh article addresses schemes to provide key recovery capability for mobile communications. In recent year, wireless communication network has been the most convenient and powerful way for all computer users to access the resources from the Internet. However, the lack of suitable key recovery mechanism that meets the special security and performance requirements for wireless communication networks is still an open issue. By addressing this issue, the eighth paper proposes a novel security mechanism with key recovery capability satisfying the requirement of privacy protection and monitoring while maintaining low computational complexity. The scheme provides a ‘backdoor’ for an authorized agency to monitor suspected communications while protecting legal users from unauthorized disclosure of their data privacy. With the security and performance analysis of the proposed protocol, the author shows that the scheme is suitable for deploying in wireless communication network. The eighth paper introduces the analysis of performance overheads for securing the signalling plane in beyond 3G networks. Although the countermeasures are urgent for mobile network such as IMS, the cost should also be considered. For signalling plane, since no built-in protection mechanism was defined for SIP, 3GPP technical specifications introduce IPSec to protect SIP messages. However, the additional protection can lead to significant computational and spatial overheads both in the core network and in the terminals, which might affect their performance. By focusing on the possible performance overhead from protecting SIP by additional IPSec methods, the ninth paper presents and validates the analysis of potential performance overheads brought by security mechanisms developed to protect signalling messages in beyond-3G core networks and mobile terminals. According to the analysis result, the overheads for both cases can significantly impact the performance of the entire architecture. The ninth article addresses the impact of key assignment on VANET privacy. The privacy issue is also an important topic for VANET. To know the best way of key assignment for maintaining user privacy from (1) the CA or (2) non-CA entities, the tenth paper gives the analysis of the privacy provided by three key assignment approaches for VANETs: (1) all vehicles share (a) a copy of the same key and (b) a copy of multiple same keys, (2) each unique key is held by only one vehicle and (3) each key is held by several vehicles. According to the mathematical and logical analysis result, the authors found that ‘keys should be unique to vehicles and vehicles should be given multiple keys.’ Besides, the author also verified that ‘it is impossible to provide good privacy and fast revocation when keys are shared among vehicles.’ The tenth paper proposes a Dirichlet reputation system in reliable routing of wireless ad hoc network. In peer-based network such as wireless ad hoc networks, the reliable routing is most important for maintaining the normal operation and good throughput of the whole network. However, the misbehaviour of some nodes will impact the routing reliability. To predict and select a reliable path for each nodes of wireless ad hoc network, the 11th paper proposes Dirichlet reputation model based on Bayesian inference theory that evaluates reliability of each node in terms of packet delivery. To adjust the responsiveness of the reputation system, the moving window mechanism is applied to change the speed of updating nodes' reputation: the larger size implies the fast misbehaviour detection and the smaller size slows the speed of redeeming bad reputation. According to the simulation result, the negative impacts of misbehaving nodes can be reduced and the throughput can be improved by integrating the proposed reputation system into the routing protocol of wireless ad hoc network. The 11th paper suggests an efficient reputation based hybrid key management architecture for ad hoc networks. In fact, it is difficult to deploy the common key management approaches into the wireless ad hoc network. To address this issue, the twelfth paper proposes adaptable hybrid public key management architecture for wireless ad hoc networks based on a clustering algorithm—RECA. The reputation system is applied to compute the trust levels information. All trustworthy cluster-heads that managed clusters are elected based on the trust levels. In addition, each cluster-head is a CA for the members of the cluster and the key management among all the cluster-heads is done by applying totally distributed CA. According to the simulation result, the enhancement of scalability and certification services availability can be achieved. The last article offers a new cluster based algorithm for Black Hole Intrusion Detection System (BHIDS). This paper focused on intrusion detection on wireless ad hoc network. Only applying the traditional firewall or cryptography is not enough to protect the wireless ad hoc network from active attack. This paper addresses this issue and proposes a 2-layered cluster-based BHIDS for mobile, ad hoc networks. By splitting the nodes in the network into 2-layered clusters, the processing and communication overhead between the cluster heads at layer 1 and 2 are reduced the cluster heads of each layer can cooperate to detect the potential attacks. The features of monitoring include: mobility of nodes, variation in number of attacking nodes, packet delivery rate and density of the network. The simulation result shows marked improvement on packet dropping compared with the other work. Furthermore, the authors found that the stability of routes in sparse network is more than dense network. We would like to thank the contributors as well the reviewers towards the success of this special issue. We specially thank Professor Hsiao-Hwa Chen, Editor-in-Chief, Wiley's Security and Communication Networks Journal, for his advice and help during the process of putting together this Special Issue. We hope that you will enjoy reading this Special Issue.

Abstract In this paper, we propose a new architecture based on an efficient trust model and secure distributed clustering algorithm (SDCA) in order to distribute a certification authority (CA) for ensuring the distribution of certificates in each cluster. We use the combination of a fully self‐organized security for trust models like pretty good privacy (PGP) adapted toad hoctechnology and the clustering algorithm which is based on the use of trust and mobility metrics, in order to select the clusterhead and to establish a public key infrastructure (PKI) in each cluster for authentication and exchange of data. Furthermore, we present a new approach: the dynamic demilitarized zone (DDMZ) to protect the CA in each cluster. The principal idea ofDDMZconsists in selecting the dispensable nodes, also called registration authorities (RAs); these nodes must be confident and located at one‐hope from theCA. Their roles are to receive, filter and treat the requests from any unknown node to theCA. With this approach, we can avoid the single point of failure in each cluster. Moreover, we propose a probabilistic model to define the direct connectivity between confident nodes in order to study the resistance degree of theDDMZagainst different attacks. In addition, we evaluate the performance of the proposedSDCAand we estimate the robustness and the availability ofDDMZthrough the simulations. The effects of direct connectivity and transmission range on the stability and security of the network are analyzed. The simulation's results confirm that the proposed architecture is scalable, secure, and more resistant against attacks. Copyright © 2009 John Wiley & Sons, Ltd.

In this paper, we propose a cognitive radio based Medium Access Control (MAC) protocol for packet scheduling in wireless networks. Cognitive MAC protocols allow a class of users, called secondary users, to identify the unused frequency spectrum and to communicate without interfering with the primary users. In our proposed MAC protocol, each secondary user is equipped with two transceivers. One of the transceivers is used for control messages while the other periodically senses and dynamically utilizes the unused data channel. The secondary users report the status of channels on control channel and negotiate on the selected data channel itself for onward data transmission. Each channel is used by different set of secondary users. Contrary to existing protocols, data transmission takes place in the time slot in which spectrum opportunity is found. We develop a new analytical model, while taking into account the backoff mechanism. Our simulation results show that throughput increases with the increase in the number of channels.

A number of key issues arise in the implementation of scalable multicast protocols for wireless mobile ad hoc networks (MANETs), namely energy consumption and data delivery over unstable/mobile nodes. To improve scalability of these protocols, clustering has been proposed. Clustering allows reducing the number of mobile nodes participating in multicast routing algorithms, which in turn significantly reduces the routing-related control overhead. In this paper, we propose a clustering algorithm, called RSIDS (restful stability based insomnious distributed sensors), which considers both stability and residual energy of neighboring nodes when selecting critical nodes (i.e. cluster heads and gateways). RSIDS uses passive clustering (in opposition to active clustering) to form the clustering structure. The critical nodes selection enables the selection of most stable nodes with high residual energy as critical nodes; the goal is to minimize re-clustering (and thus re-branching for multicast protocols) that may generate considerable overhead and packet losses and increase the lifespan of the network. We show, via simulations, that RSIDS outperforms existing clustering schemes, in terms of packet delivery ratio and network lifetime, when used with the MAODV (multicast ad hoc on demand distance vector) routing protocol.

Inter-Vehicle Communications have recently attracted research from both academia and industry. In such networks, vehicles should be able to communicate among each other (V2V) as well as with roadside Infrastructure units (V2I). These units provide some services such as driver information systems and Internet access. Because of the high speed and high mobility of vehicles, establishing and maintaining a connection to these units is very challenging. We introduce a new protocol which uses the characteristics of vehicle movements to predict the vehicle behavior and select a route with the longest life time to connect to the wired network. It aims at spreading the advertisement messages through multi-hops without flooding the network, do seamless hand-overs and select the most stable routes to these units. We performed some simulations and compared the performance of our work with some well-known protocols.

Abstract The intrusion detection system (IDS) for mobilead hocnetworks (MANET) consists in monitoring the nodes' behavior, in order to detect the malicious activity of nodes. Many existing solutions deal with the problem at each layer separately. But new kinds of misbehavior attacks are cross‐layer attacks. And such smart misbehaviors cannot be detected at the level of one layer. In this paper, we propose a new cross‐layer approach based on physical, MAC, and routing layers for a monitoring mechanism. A new analytical model is proposed to illustrate the parameters' effect on these different layers. The impact of the signal to noise ratio (SNR) and the distance between monitor and monitored nodes are clearly introduced. Moreover, the difference between the carrier sense, the interference range, and the transmission range is taken into account in our model. The proposed model improves the evaluation of the nodes' cooperation and reduces the risk of having any false positive rate. The analytical study and simulation results illustrate our purpose. In addition, with the simulations' results, we illustrate the impact of the distance between monitor and monitored nodes on the monitoring mechanism. Finally, we show that our cross‐layer mechanism has a lower false positive rate than the classical Watchdog mechanism in different network's parameters such as the nodes' density, the speed mobility, and the different traffic loads. Copyright © 2008 John Wiley & Sons, Ltd.

Abstract In this paper, we focus on the medium access control (MAC), particularly the IEEE 802.11 and we deal with some hidden vulnerabilities based on the control packets CTS (clear to send) and ACK (acknowledgment). Through these vulnerabilities, we show two new smart attacks which were not dealt by the solutions proposed recently like the attack based on the RTS (request to send) packet vulnerability. The malicious node can exploit these vulnerabilities on the MAC protocol, in order to corrupt the monitoring and routing processes. Furthermore, we demonstrate the attacks through algorithms and we show how vulnerabilities can be exploited and how these attacks can be implemented by the attacker. The impact of these attacks is presented through simulation and implementation. Simulation and exprimental results show the impact of the attacks on the network. In addition, the experimental results demonstrate the feasibility of these real attacks and their exploitation. These experimentations allow us to confirm the simulation's results. Furthermore, in order to prevent these attacks, the solutions based on control packet authentication are presented. We propose two kinds of the solution one is cryptography independent and the other one is cryptography dependent. The evaluation and analysis of these solutions are investigated by analytic and simulations analysis. The simulations' results of the proposed solution show that the attacks are prevented and the negative impacts are significantly reduced. In addition, the security cost of the proposed solutions are investigated. Hence, the security costs are insignificant in comparison with the negative impact of these attacks. Copyright © 2008 John Wiley & Sons, Ltd.

In this paper, we propose an anonymous protocol to secure nodes which have important roles in the network. We focus in the clustering approach to secure the mobile ad hoc networks (MANETs). In each cluster, a confident node is selected to ensure the certification authority (CA) roles; however, the cluster security depends in the security of the CA node. Therefore, we present an anonymous dynamic demilitarized zone (ADDMZ) to protect the CA node identity and to avoid the single point of failure in the cluster. ADDMZ is formed by a set of confident nodes which have a high trust level between them and their goal is to filter the communication between the cluster member node and the CA node. Moreover, we draw one's inspiration from military defence mechanisms such as: camouflage and identity change mechanisms. We present protocol to realize these mechanisms by using the identity based cryptographic from bilinear maps. The security analysis is proposed to discuss the proposed protocols.

Several routing protocols have been proposed for mobile wireless sensor networks. Some are based on variants of flooding algorithms leading to redundant copies of message unnecessarily. Despite various optimizations, such routing methods still remain inefficient. This paper deals with region-based routing which has been introduced to reduce the number of messages in the network. We propose an energy efficient routing algorithm, called Ellipse-routing, which is based on region-based routing. A virtual ellipse is built thanks to source and destination positions. So, only nodes within this region forward a message. For a given energy consumption model, we select a suitable ellipse factor and a transmission range, leading to a delivery rate close to 100% while minimizing energy consumption. Then, we extend the proposed scheme to take into account position errors. Performances of proposed algorithms are shown thanks to simulations.

Although IEEE 802.11 provides several transmission rates, a suitable rate adaptation taking into account the relative fairness among all competitive stations, according to the underlying channel quality remains a challenge in Mobile Ad hoc Networks (MANETs). The absence of any fixed infrastructure and any centralized control makes the existing solutions for WLANs like CARA (collision-aware rate adaptation) [4] not appropriate for MANETs. In this paper, we propose a new analytical model with a suitable approach to ensure a relative fairness among all competitive nodes of a particular channel. Our model deals with the channel quality while respecting the nodes, based on transmission successes and failures in a mobility context. Finally, each node calculates its own probability to access the channel in a distributed manner. We evaluate the performance of our scheme with others in the context of MANET via extensive and detailed simulations. The performance differentials are analysed using varying network load and transmission range. The simulation results illustrate that our proposed approach ensures a better tradeoff between fairness and throughput.

Determining where a given sensor is physically located is a challenging issue. In this paper, we address the localization problem where, initially, a certain number of sensors called anchors are aware of their positions. Our goal is to localize all sensors with high accuracy, while using a limited number of anchors. So, we focus on localization techniques based on angle of arrival information between neighbor nodes. This paper proposes an original angle-based localization technique, called AT-Angle, which allows to verify two important properties: first, a sensor node can eliminate wrong received information about its position; second, it deduces if its estimated position is closed to its real position. In this last case, the sensor node becomes an estimated anchor and contributes to the positioning of others nodes. Simulations show that AT-Angle achieves good precision for located nodes despite the introduction of position errors and the small number of anchors.

Presents the introductory welcome message from the conference proceedings.

To avoid the single point of failure for the certificate authority (CA) in MANET, a decentralized solution is proposed where nodes are grouped into different clusters. Each cluster should contain at least two confident nodes. One is known as CA and the another as register authority RA. The Dynamic Demilitarized Zone (DDMZ) is proposed as a solution for protecting the CA node against potential attacks. It is formed from one or more RA node. The problems of such a model are: (1) Clusters with one confident node, CA, cannot be created and thus clusters' sizes are increased which negatively affect clusters' services and stability. (2) Clusters with high density of RA can cause channel collision at the CA. (3) Clusters' lifetime are reduced since RA monitors are always launched (i.e., resource consumption). In this paper, we propose a model based on mechanism design that will allow clusters with single trusted node (CA) to be created. Our mechanism will motivate nodes that does not belong to the confident community to participate by giving them incentives in the form of trust, which can be used for cluster's services. To achieve this goal, a RA selection algorithm is proposed that selects nodes based on a predefined selection criteria function. Finally, empirical results are provided to support our solutions.

In this paper, we propose a new explicit multicast protocol for MANET, called EM2NET. The objective of EM2NET is to efficiently support large number of small multicast groups in MANETs. EM2NET strikes a nice balance between the characteristics of explicit multicast protocols (e.g., Xcast and E2M) and these of tree/mesh-based multicast protocols (e.g., ODMRP and MAODV). Indeed, it combines the encoding of destinations addresses in the packet header and the maintenance of multicast state information in carefully selected nodes. Simulations show that EM2NET outperforms (a) explicit multicast protocols and (b) tree/mesh based protocols in the case of small size multicast groups.

The monitoring process consists in evaluating the behaviour of nodes in networks in order to detect if the monitored nodes well-behave or misbehave. Many existing solutions deal the problem at each layer separately. Actually new kinds of misbehaviour attacks are cross-layer. So, such smart misbehaviours cannot be detected at the level of one layer. In this paper, we propose a new cross-layer approach based on physical, MAC and routing layers for a monitoring mechanism. An analytical model is proposed to illustrate the parameters' effect on these different layers. The impact of the signal to noise rate (SNR), the distance between monitor and monitored nodes are clearly introduced. Moreover, the difference between the carrier sense, the interference and the transmission ranges is taken into account in our model. The simulations' results show the effectiveness of the proposed analytical model, we reach until 90% of observation's correction in some cases.

Thanks to recent technological progress, autonomous wireless sensor networks have experienced considerable development. Currently, they are used in the areas of health care, environment, military etc. For a number of sensor-based applications, the knowledge of the positions of sensors is required or, at least, preferable. In this paper, we propose a new method to locate a large number of nodes in wireless sensor networks where only a subset of them are landmarks (i.e., know their positions). Our method is AOA-based (angle of arrival) and it is called A2L (angle to landmark). Compared, via simulations, to previous methods such as APS and AHLoS, A2L considerably increases the number of located nodes with accurate precision while using a smaller node degree.

Providing a security solution for mobile ad-hoc networks (MANETs) is not an easy task. This is due to the unique characteristics of MANETs, such as the lack of a pre- existent infrastructure, the dynamic topology of the network, the non-existence of a control authority and the constraints of device resources. In this paper, we introduce the monitoring and cluster manager modules to improve our distributed hierarchical architecture. Moreover, we study the concept of dynamic demilitarized zone (DDMZ) defined in our hierarchical architecture to avoid a single point of failure in MANETs. The DDMZ is formed by the dispensable nodes which belong to the confident community. The confident community is formed by sets of confident nodes which have high trust levels and collaborate with each other to ensure secure services. We propose a probabilistic model to define the direct connectivity between confident nodes in order to study the resistance degree of DDMZ against different attacks. Furthermore, we estimate the robustness and the availability of DDMZ and we also analyze the effects of direct connectivity and transmission range on the stability and security of the network.

In sensor networks, several applications such as habitat monitoring and moving objects tracking, require the knowledge of nodes positions. Position estimation most often includes errors due to the measurements of distance and incoming angles between neighbors. Erroneous positions are propagated from a node to other nodes exacerbating the degree of errors in the estimation of the positions of these nodes. In this paper, we propose a new localization method, called HA-A2L. Compared, via simulations, to previous methods, such as APS and A2L, HA-A2L considerably increases the number of located nodes with far better accuracy.

Mobile wireless sensors need to know their localizations in many control and monitoring applications. Among all sensors, some know their exact position (i.e., they are equipped with GPS or they are positioned by human intervention). These sensors are called anchors. Some sensors can have different capabilities allowing them to calculate either distances or angles when they receive messages from others nodes. So, they only use anchor positions to obtain an estimated position. However, when sensors are mobile they cannot continuously calculate their position because of the energy constraints. This paper concerns the localization problem in the case where all nodes in the network (anchors and others sensors) are mobile. The authors propose three techniques following the capabilities of nodes. Thus, each node obtains either an exact position or an approximate position with the knowledge of the maximal error born. Also, the authors adapt the periods where nodes invoke their localization. Simulation results show the performances of our methods in term of accuracy and determinate the technique the more adapted related to the network configurations.

Explicit Multicast protocols are newly proposed multicast schemes that support very large numbers of small groups using unicast services. In this paper, we propose a new scalable multicast protocol for wireless ad hoc networks, called Explicit Multicast for MANET (EM2NET). It is based on Xcast scheme. The basic idea behind EM2NET is forwarding data only between Intercepting Nodes (INs) that are determined dynamically; an IN can be a receiver belonging to a multicast group or a branching node. EM2NET is scalable and supports mobility better than existing protocols. It supports load balancing, between INs on the multicast tree, and better energy conservation.

We present Predictable Random Backoff (PRB) algorithm that is capable of mitigating the impacts of selfish hosts on well-behaved hosts in wireless local area networks (WLAN) and mobile ad hoc networks (MANETs). Here, hosts fail to follow the operation of PRB are easily detected. We present an accurate analytical model to compute the system throughput using a three-dimensional Markov chain and evaluate the performance of PRB under both normal case and selfish case. PRB and BEB (Binary Exponential Backoff) perform similarly in the former case. However, PRB can effectively mitigate the impacts of MAC selfish misbehavior and guarantee a fair share of the wireless channel for well behaved hosts.

Wireless sensor nodes need to know their localizations in many control and monitoring applications such as routing, target tracking, etc... To determine node localization, many techniques have been proposed in the literature. This paper proposes a rule-based method, called MuR (method using rules), that allows to locating nodes with high accuracy. The rules are based on information of located nodes (called anchors). They resolve ambiguity when a node can be located at more than one position. MuR is compatible with existing localization techniques; it can be used as a preliminary step before the execution of one of these techniques. Simulation results show the effectiveness of MuR locating a maximum number of nodes

IVG (inter vehicle geocast) is a GPS-based inter-vehicle communication mechanism used for alarm message dissemination among vehicles in a highway in risk situations. In this paper, we propose an improvement to IVG towards supporting its interoperability in environments where "GPS-U" GPS-unequipped vehicles are present. They are vehicles that are not equipped with GPS devices or they have some problems to obtain their positions, via GPS, due to some obstacles. The proposed solution allows GPS-U vehicle to compute its position with the help of its neighbors that are equipped with GPS devices, called GPS-E neighbors. Analyses show that the optimal performances of IVG can be reached even when the rate of GPS-U vehicle is 40%.

This paper presents a synchronization protocol for real-time multimedia application in wireless networks. A multimedia stream is multicast from one to some mobile terminals (MTs). Source and receivers can both be mobile. The protocol allows MTs to receive a multicast stream without breaks in playback as MTs move from cell to cell. The scheme is general because we make no assumption about clocks synchronization and messages are not time-stamped. However, it is used in a network having bounded delays. Verifications show that synchronization properties are respected. Moreover, simulations show that the strategy of known locations reduces the handoff duration compared to the one of unknown locations and then preserves the synchronization.

This paper proposes a dynamic hierarchical architecture of k-local groups, where k represents the number of local groups composing the group communication system S. Each local group is defined as a finite set of processes. The proposed architecture ensures real-time causal ordering between alive local group members. This structure allows us to compensate for the local clock drift by synchronizing the local clocks, and ensures real-time causal delivering. We validate this model by using CPN tools (coloured Petri nets) to obtain a state space graph. The objective is to study the behaviour of this model, by verifying different properties such as boundedness and liveness. Simulation results show that the hierarchical architecture respects all studied properties. Therefore the real-time causal ordering is guaranteed.

With the evolution of network technologies, integrated services networks make possible real-time multimedia applications. However, the delay jitter and the absence of a physical global clock may disrupt the temporal relationships among media units composing theses multimedia applications. The /spl Delta/-causal ordering is designed to ensure real-time delivery of messages by respecting the causal order. This /spl Delta/ value represents the limited lifetime during which a message m can be used by a destination process. In this paper, we develop a new real-time causal ordering concept, in the context of multicast communication, where all participants can start to playback the same message. We validate this model by using CPN (coloured Petri nets) tools to obtain a state space graph. The objective is to study the behavior of this model, by verifying different properties such as, boundedness, and liveness. The simulation result shows that this model respects the real-time causal ordering, in the context of multicast communications.

With the increase use of the progress of distributed multimedia applications, the ability of networks to handle temporal relationships among media units is becoming more and more important. Yet delay jitter, and the absence of global clock may disrupt these temporal relationships. This paper proposes a hierarchical architecture of k-local groups, where k represents the number of local groups composing the distributed system G. Each local group is defined as a finite set of processes. The proposed architecture allows us to solve the local clocks drift by synchronizing the local clocks. The clocks synchronization is resolved by according to a time reference noted VMT (virtual master time). This hierarchical topology ensures real-time causal ordering, and guarantees a dynamic reconfiguration, in a failure detection, by insuring at least a partial real-time communication between alive local groups.

The evolution of communication technology makes possible distributed real-time multimedia applications. Unlike traditional data traffic, real-time multimedia traffic requires that temporal relationships among media units must be maintained, and needs that all sites impose a consistent causal order to receive the same media at a given time. Yet delay jitter, the absence of a global clock, and a crash failure may disrupt these temporal relationships. This paper proposes new hierarchical architecture of k-local groups, where k represents the number of local groups composing the group communication system. This architecture ensures real-time causal ordering and guarantees an automatic reconfiguration, in presence of a crash failure, by ensuring at least a partial real-time communication between active local groups, while keeping the amount of control information within a reasonable size.

Consider a system of two communicating finite state machines that exchange messages over two unbounded, one-directional, FIFO channels. In our main result, we show that the boundedness problem is decidable for the class of such systems, where the two CFSMs are identical and constituted of only initial elementary circuits, and where the two CFSMs are linear. To analyse such systems, we introduce a reduction relation on the words. We then give examples to illustrate our purpose.>