This paper addresses the joint optimization of per-user equipment (UE) bandwidth allocation and UE-access point (AP) association to maximize weighted sum-rate while satisfying heterogeneous quality-of-service (QoS) requirements across enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC) slices in the uplink of a network slicing-enabled user-centric cell-free (CF) massive multiple-input multiple-output (mMIMO) system. The formulated problem is NP-hard, rendering global optimality computationally intractable. To address this challenge, it is decomposed into two sub-problems, each solved by a computationally efficient heuristic scheme, and jointly optimized through an alternating optimization framework. We then propose (i) a bandwidth allocation scheme that balances UE priority, spectral efficiency, and minimum bandwidth demand under limited resources to ensure fair QoS distribution, and (ii) a priority-based UE-AP association assignment approach that balances UE service quality with system capacity constraints. Together, these approaches provide a practical and computationally efficient solution for resource-constrained network slicing scenarios, where QoS feasibility is often violated under dense deployments and limited bandwidth, necessitating graceful degradation and fair QoS preservation rather than solely maximizing the aggregate sum-rate. Simulation results demonstrate that the proposed scheme achieves up to 52% higher weighted sum-rate, 140% and 58% higher QoS success rates for eMBB and URLLC slices, respectively, while reducing runtime by up to 97% compared to the considered benchmarks.

This paper addresses joint admission control and per-user equipment (UE) bandwidth allocation to maximize weighted sum-rate in network slicing-enabled user-centric cell-free (CF) massive multiple-input multiple-output (mMIMO) systems when aggregate quality-of-service (QoS) demand may exceed available bandwidth. Specifically, we optimize bandwidth allocation while satisfying heterogeneous QoS requirements across enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC) slices in the uplink. The formulated problem is NP-hard, rendering global optimality computationally intractable. We decompose it into two sub-problems and solve them via computationally efficient heuristics within a sequential framework. We propose (i) a hierarchical admission control scheme that selectively admits UEs under bandwidth scarcity, prioritizing URLLC to ensure latency-sensitive QoS compliance, and (ii) an iterative gradient-based bandwidth allocation scheme that transfers bandwidth across slices guided by marginal utility and reallocates resources within slices. Simulation results demonstrate that the proposed scheme achieves near-optimal performance, deviating from an interior point solver-based benchmark by at most 2.2% in weighted sum-rate while reducing runtime by 99.7%, thereby enabling practical real-time deployment. Compared to a baseline round-robin scheme without admission control, the proposed approach achieves up to 1085% and 7% higher success rates for eMBB and URLLC slices, respectively, by intentionally sacrificing sum-rate to guarantee QoS. Sensitivity analysis further reveals that the proposed solution adapts effectively to diverse eMBB/URLLC traffic compositions, maintaining 47-51% eMBB and 93-94% URLLC success rates across varying load scenarios, confirming its robustness for resource-constrained large-scale deployments.

Sixth-generation (6G) networks must deliver ultra-low latency and near-100 percent reliability to support massive-scale Internet of Things (IoT) deployments and Hyper-Reliable Low-Latency Communications (HRLLC). Grant-free access protocols permit devices to transmit without prior scheduling; nevertheless, this uncoordinated transmission introduces uncertainty at the receiver, necessitating Active User Detection (AUD) to ascertain which devices are active. Quantum search methods—most notably Grover’s algorithm—can accelerate AUD, yet they require knowing the optimal number of iterations, which depends on the (typically unknown and time-varying) number of valid solutions induced by the current activity pattern and channel/noise conditions. To overcome this, we formulate an optimization problem that optimizes the number of Grover iterations to maximize detection accuracy and minimize computational cost without any prior activity information. We then apply a Deep Q-Network (DQN) to learn, via deep reinforcement learning, an adaptive policy for selecting the iteration count. Simulation results verify that the DQN converges to an optimal strategy and outperforms two baseline schemes under varying fading conditions and active-user transmit powers.

Quantum computing may offer new approaches for advancing machine learning, including in complex tasks such as anomaly detection in network traffic. In this paper, we introduce a quantum generative adversarial network (QGAN) architecture for multivariate time-series anomaly detection that leverages variational quantum circuits (VQCs) in combination with a time-window shifting technique, data re-uploading, and successive data injection (SuDaI). The method encodes multivariate time series data as rotation angles. By integrating both data re-uploading and SuDaI, the approach maps classical data into quantum states efficiently, helping to address hardware limitations such as the restricted number of available qubits. In addition, the approach employs an anomaly scoring technique that utilizes both the generator and the discriminator output to enhance the accuracy of anomaly detection. The QGAN was trained using the parameter shift rule and benchmarked against a classical GAN. Experimental results indicate that the quantum model achieves a accuracy high along with high recall and F1-scores in anomaly detection, and attains a lower MSE compared to the classical model. Notably, the QGAN accomplishes this performance with only 80 parameters, demonstrating competitive results with a compact architecture. Tests using a noisy simulator suggest that the approach remains effective under realistic noise-prone conditions.

The evolution towards 6G networks promises a massive increase in connected devices and demanding use cases, intensifying the challenge of managing limited spectrum resources efficiently. This paper addresses this challenge in an underlay cognitive radio network framework where secondary users (SUs) employ the code domain non-orthogonal multiple access (NOMA) mechanism for communication while incorporate energy harvesting (EH) to enhance their operational longevity and support green communication principles. Specifically, we assume SUs utilize EH via a wireless powered communication network (WPCN) process. A difficulty within this combined cognitive radio and WPCN scenario is the precise and efficient identification of active SUs for effective resource allocation and interference management. While traditional active user identification methods exist, they can face challenges, including computational complexity and experiencing limitations in accuracy under certain conditions. To address this issues we proposes the application of Grover’s quantum search technique. Furthermore, we investigate the impact of the number of users on the detection success probability and the trade-off between this probability and energy efficiency in this scenario. A comparison between the proposed approach and a non-quantum search technique is also provided.

In this work, we explore the use of the computation reuse concept at the edge server. We design a network-based computation reuse architecture for IoT applications. The architecture caches previously executed results and utilizes them to address newly arrived similar tasks without performing computation from scratch. By doing so, we eliminate redundant computations, enhance resource utilization, and reduce task completion time. We deployed this architecture and assessed its performance at both the networking and application levels. From the networking perspective, we achieved an up to 80% reduction in task completion time and up to 60% reduction in resource utilization, alongside a 63% decrease in energy consumption. From the application perspective, we achieved up to 90% in computation correctness and accuracy.

In this paper, we present matching-based services offloading schemes for compute-less IoT networks. We adopt the matching theory to match service offloading to the appropriate edge server(s). Specifically, we design, Whistle, a vertical many-to-many offloading scheme that aims to offload the most invoked and highly reusable services to the appropriate edge servers. We further extend Whistle to provide horizontal one-to-many computation reuse sharing among edge servers which leads to bouncing less computation back to the cloud. We evaluate the efficiency and effectiveness of Whistle with a real-world dataset. The obtained findings show that Whistle is able to accelerate the task completion time by 20%, reduce the computation up to 77%, and decrease the communication up to 71%. Theoretical analyses also prove the stability of the designed schemes.

Open Radio Access Networks (Open RAN) have gained significant traction in modern telecommunications due to their cost-efficiency and scalability. However, their open architecture introduces critical security challenges. To fortify Open RAN security, this paper proposes a novel approach that combines the principles of Zero Trust Architecture (ZTA) with the use of transformer-based models. ZTA operates on the premise that all network entities are untrusted until authenticated, providing a robust security foundation. By integrating transformers, renowned for their efficiency in processing unstructured data, we enable real-time analysis of time series data within specified windows, enhancing anomaly detection. We leverage a dataset constructed from a functional 5G test network to evaluate the effectiveness of our transformer-based approach. Our findings demonstrate the superior performance of our approach compared to other benchmark methods.

This paper explores the application of a federated learning-based multi-agent reinforcement learning (MARL) strategy to enhance physical-layer security (PLS) in a multi-cellular network within the context of beyond 5G networks. At each cell, a base station (BS) operates as a deep reinforcement learning (DRL) agent that interacts with the surrounding environment to maximize the secrecy rate of legitimate users in the presence of an eavesdropper. This eavesdropper attempts to intercept the confidential information shared between the BS and its authorized users. The DRL agents are deemed to be federated since they only share their network parameters with a central server and not the private data of their legitimate users. Two DRL approaches, deep Q-network (DQN) and Reinforce deep policy gradient (RDPG), are explored and compared. The results demonstrate that RDPG converges more rapidly than DQN. In addition, we demonstrate that the proposed method outperforms the distributed DRL approach. Furthermore, the outcomes illustrate the trade-off between security and complexity.

Anticipated advancements in 5G wireless networks and beyond would necessitate an increased emphasis on security measures to accommodate the projected rise in demand for connections and services. Therefore, this paper aims to investigate the physical layer security (PLS) to evaluate the privacy of authorized users in multi-cellular networks, which represent a fundamental architecture in next-generation networks. Each cell is assumed to include a base station (BS) that serves multiple users. This scenario also takes into account the presence of several eavesdroppers. Every BS functions as a reinforcement learning (RL) agent that must undergo training in order to optimize security. To enhance the safety and speed of training, a federated learning (FL) technique is utilized. In this approach, a central unit regularly receives the neural network (NN) weights from the agents, updates them, and then transfers the result back to the agents to update their model. We examine and compare two deep RL methodologies, specifically deep Q-network, and Reinforce deep policy gradient. The findings of our research demonstrate the influence of the number of eavesdroppers on security, as well as the impact of the number of cells and the aggregation frequency of neural network parameters.

As 5G networks continue to be deployed and 6G networks begin to be envisioned, mobile network operators (MNOs) are embarking on a revolutionary transformation of the way they manage their networks. Various technology bricks are currently considered paramount in this transformation, including radio access network (RAN) slicing. The concept of an open radio access network (Open RAN) promises to provide more flexibility to support RAN slicing. However, RAN slicing in an O-RAN architecture raises a major challenge in achieving efficient resource sharing among slices, due to the diverse and permanent changes in RAN slices’ QoS requirements. To overcome this challenge in a RAN environment involving an MNO and multiple mobile virtual network operators (MVNOs), we propose a two-level RAN slicing mechanism. The first level is executed on a long time-scale to allocate radio resources from the MNO to MVNOs while the second level is executed on a shorter time-scale to allocate MVNO resources to users. This mechanism improves the performance of the RAN slicing operation by enabling users to obtain the required resources as quickly as possible and with a high level of granularity. We formulate the two-level problem as two mathematical optimization problems and we study their NP hardness. To efficiently solve the two-level problem, we first propose a game-theoretic solution to solve the first-level resource allocation problem using a matching algorithm. Next, we propose a deep reinforcement learning (DRL) algorithm that uses the double deep$Q$-network procedure to solve the second-level resource allocation problem. The two proposed algorithms are coupled such that the DRL algorithm uses the solution obtained using the game-theoretic matching algorithm. We show through extensive simulations that the proposed two-level solution outperforms the current state-of-the-art solutions and achieves efficient performance.

Cloud Service Providers (CSPs) allow data owners to migrate their data to resource-rich and powerful cloud servers and provide access to this data by individual users. Some of this data may be highly sensitive and important and CSPs cannot always be trusted to provide secure access. It is also important for end users to protect their identities against malicious authorities and providers, when they access services and data. Attribute-Based Encryption (ABE) is an end-to-end public key encryption mechanism, which provides secure and reliable fine-grained access control over encrypted data using defined policies and constraints. Since, in ABE, users are identified by their attributes and not by their identities, collecting and analyzing attributes may reveal their identities and violate their anonymity. Towards this end, we define a new anonymity model in the context of ABE. We analyze several existing anonymous ABE schemes and identify their vulnerabilities in user authorization and user anonymity protection. Subsequently, we propose a Privacy-Preserving Access Control Scheme (PACS), which supports multi-authority, anonymizes user identity, and is immune against users collusion attacks, authorities collusion attacks and chosen plaintext attacks. We also propose an extension of PACS, called Statistical Privacy-Preserving Access Control Scheme (SPACS), which supports statistical anonymity even if malicious authorities and providers statistically analyze the attributes. Lastly, we show that the efficiency of our scheme is comparable to other existing schemes. Our analysis show that SPACS can successfully protect against Collision Attacks and Chosen Plaintext Attacks.

The Internet of Things (IoT) is witnessing rapid adoption across various fields due to the advances in wire-less communication and low-power devices. However, achieving energy sustainability in IoT applications is a non-trivial task. Energy Neutral Operation (ENO) has emerged as a promising approach to address this issue. To this end, duty cycle scheduling is a prominent power management approach to attain ENO. The dynamic nature of the IoT environment poses a great challenge to determine individual nodes' duty cycle due to intermittent energy supply and variation in QoS requirements and traffic intensity. Artificial Intelligence (AI) and Machine Learning (ML) techniques can enhance QoS performance when integrated with ENO solutions. Therefore, our work considers employing Reinforcement Learning (RL) to compute the duty cycle of individual nodes based on the network's energy and traffic conditions. This work evaluates the performance of the RL solution in the context of multi-hop communication. The results are compared to a modified version of a regression-based duty-cycling solution from the literature.

As 5G networks continue to evolve, they are becoming increasingly intricate and diverse, accommodating a vast array of devices. This complexity poses significant challenges when it comes to safeguarding these networks against cyber-attacks. While the core infrastructure of 5G is shifting towards virtualization and is being deployed by multiple vendors, Radio Access Networks (RANs) have traditionally been delivered as tightly integrated solutions, often lacking interoperability. Open RAN (O- RAN) emerges as a flexible and cost-effective approach to designing and deploying mobile networks. It allows for the integration of mobile radio access networks from various vendors through the use of disaggregated and O- RAN technologies. Nevertheless, the introduction of components from multiple vendors into the supply chain increases complexity, making it difficult to ensure the security of each individual component. Additionally, O- RAN's attack surface expands due to seamless access for numerous devices. In this dynamic landscape, adopting a zero-trust architecture (ZTA) presents an attractive framework for bolstering security in open networks. We introduce an intelligent architectural concept design that leverages key zero-trust principles to enhance information security within the inherently untrusted O-RAN environment. Moreover, we propose a solution that combines deep reinforcement learning techniques with traditional machine learning methods to fortify security in Open RAN. Finally, we evaluate the performance of our proposed solution using the UNSW network dataset and demonstrate its superior performance across selected metrics.

Open Radio Access Networks (O-RAN) are gaining momentum because of their ability to provide greater vendor flexibility, cost-effectiveness, and scalability, making it an attractive choice for network operators. However, securing O-RAN has become an essential concern due to the inherent vulnerabilities and risks associated with their open nature. Zero trust architecture (ZTA) can help address the security issues associated with O-RAN. ZTA is a security model that assumes that all devices, users, and applications are potentially hostile and cannot be trusted until verified. In addition to ZTA, the integration of deep learning techniques can allow for the detection and prevention of sophisticated cyber threats in real-time. In this work, we propose a novel approach to securing O-RAN using ZTA and Deep Sarsa reinforcement learning algorithms. First, we developed a ZTA model using an open-source approach that can be used as the base architecture for our study. Then, we present our proposed approach, which uses Deep Sarsa to learn the optimal policy for enforcing access control rules on the network resources based on user authentication data from ZTA. Finally, we evaluate our model using real data sets and show that it performs better than other approaches in terms of accuracy, F1 score, and precision. Our results demonstrate that combining ZTA with reinforcement learning is a promising way to help secure O-RAN while still providing flexible access control policies for operators.

Immersive systems and$360^{\circ}$video have grown in popularity in recent years. Nevertheless, due to the high bandwidth needs and massive size of these videos, streaming them presents an important issue. A possible solution to this problem is to employ edge caching, which keeps a portion of the video closer to the viewer to reduce latency and assure a higher Quality of Experience (QoE). In this paper, we present a caching policy approach for$360^{\circ}$video streaming that employs a tile-based graph representation of videos. Our method focuses on finding the most relevant tiles for caching. The suggested solution is intended to work efficiently for many videos competing for a single cache, ensuring that the most relevant tiles are cached to maximize performance. It exhibits robustness even in scenarios with a limited number of users, while still being able to effectively handle a vast amount of videos. Our technique can adapt to different video content and user needs by using the graph structure of the videos, making it a flexible and scalable solution for cache management in video streaming. Performance evaluation demonstrate the effectiveness of our method that outperforms existing caching strategies in terms of Cache Hit Ratio (CHR).

Data dissemination is critical for 6G autonomous driving (AD) service because of the extensive demand for real-time traffic information. However, the heavier data transmission burden and more stringent requirements of AD service bring challenges for current data dissemination methods. In this paper, we first propose a novel digital twin (DT)-based semantic dissemination architecture to better support 6G AD service. Under this architecture, an energy-efficient semantic communication mechanism is developed to reduce the data dissemination burden while keeping low semantic model update costs. Meanwhile, the DT network is leveraged to disseminate semantic data in parallel with the physical vehicular networks, which alleviates the physical transmission contention and improves the dissemination efficiency. Second, we design a deep-reinforcement-learning (DRL)-driven semantic data dissemination scheme for the proposed architecture, named Proximal-policy-optimization for Digital-twin-aided Data Dissemination (PD3), which seeks the optimal DT transfer and semantic transmission scheduling strategy. Finally, experimental results show that our approach surpasses the state-of-the-art methods by 18.36% lower dissemination delay and 4.51% higher dissemination ratio on average.

The development of the Metaverse is completely changing how business is done in the physical world. The Metaverse considerably improves intelligent manufacturing by mapping out operations and spreading them into virtual space. The Metaverse can access data from numerous production and operation lines thanks to the Internet of Things (IoT), enabling efficient data analysis and decision-making. However, the problem of sharing sensitive and private data remains a challenge when integrating the Metaverse with IoT. Federated learning (FL) has emerged as a distributed machine learning (ML) setting that can overcome the security problems related to data sharding With FL, several devices can work together to create an ML model under the direction of a central server while maintaining the privacy and security of their local training data. FL in the Metaverse continues to face significant challenges due to a lack of transparency, learning forgetting caused by streaming industrial data, and problems with non-independent and identically dispersed (non-iid) data. In this paper, we develop a FL framework for transparent and secure model learning in the Metaverse using blockchain technology. The blockchain ledger stores and verifies the model updates which ensures that all updates are tamper-proof and transparent to all parties involved. Furthermore, we propose a scheduling approach to distribute the bandwidth between reliable devices, hence minimizing communication across FL devices and giving devices with reliable behavior priority. The numerical result demonstrates that our framework performed better on the chosen indicators.

In this paper, a reinforcement learning technique is employed to maximize the performance of a cognitive radio network (CRN). In the presence of primary users (PUs), it is presumed that two secondary users (SUs) access the licensed band within underlay mode. In addition, the SU transmitter is assumed to be an energy-constrained device that requires harvesting energy in order to transmit signals to their intended destination. Therefore, we propose that there are two main sources of energy; the interference of PUs’ transmissions and ambient radio frequency (RF) sources. The SU will select whether to gather energy from PUs or only from ambient sources based on a predetermined threshold. The process of energy harvesting from the PUs’ messages is accomplished via the time switching approach. In addition, based on a deep Q-network (DQN) approach, the SU transmitter determines whether to collect energy or transmit messages during each time slot as well as selects the suitable transmission power in order to maximize its average data rate. Our approach outperforms a baseline strategy and converges, as shown by our findings.

Future 6G networks are expected to surpass the advances made in 5G, by providing the faster speeds, lower latency and extended coverage needed for emerging transformative applications, while at the same time achieving greater energy and spectral efficiency, as well as enhanced security and reliability. While 6G is currently in its initial phases of development and standardization, it is already foreseen to incorporate not just incremental technical enhacements but also pioneering innovations compared to its forerunner, 5G. Indeed, quantum technologies are expected to play an important role in 6G. This goes beyond mechanisms such as quantum key distribution for secure communications; it includes the integration of quantum computing for advanced data processing within 6G networks. As 6G networks are set to integrate artificial intelligence and machine learning even more intrinsically into their operation, the concept of quantum machine learning (QML) emerges as a promising opportunity to enable swift data processing, network optimization, and increased security and privacy. In this presentation, we will look at the fundamentals of quantum computing and quantum machine learning, explore the possibilities they offer for future 6G networks, and the potential for revolutionary advances they offer, while presenting some the important challenges associated with their integration into 6G networks.

Digital twins (DTs), defined as the virtual representation of a real-world entity or system, act as a mirror to provide a way to simulate, predict physical behaviors, and possibly control the real-world entity where applicable. Originating in the industry, advances in computing capacity and recent progress in artificial intelligence (AI)-based analytics make DTs attractive to a broader set of use cases including mobile networks.

6G communication networks are expected to become an integral part of the infrastructure needed for developing a smart society in the future. Addressing the challenges on the road towards realizing 6G network requirements in terms of quality of service, user experience, and security, is therefore of utmost importance. The digital twin (DT) technology can potentially improve the efficiency, reliability, and security of 6G networks. Digital twins for mobile networks (DTMNs) are seen as a key factor in harnessing the full benefits of 6G. Using digital twins can help address several problems, including network optimization, fault diagnosis, and fault management. Furthermore, DTMNs can characterize the physical entities in a 6G network and their relationships to each other, build their virtual models, and use simulation, learning, and reasoning capabilities to make predictions and support informed decision-making,

Recent advances in the Internet of Things (IoT) offer a plethora of new opportunities for several intelligent services and applications. As the IoT connects a massive number of devices, inevitable security threats must be addressed. On the one hand, machine learning (ML), especially federated learning (FL), is proposed as a promising distributed ML paradigm to improve attack detection performance in the IoT network due to its privacy-preserving and lower latency advantages. On the other hand, blockchain is proposed as a decentralized technology to establish a secure and decentralized environment for IoT devices. However,$F$L and blockchain solutions are not well suited for the IoT context that suffers from resource limitations, such as limited communication bandwidth and scarce computing resources of IoT devices. In addition, traditional FL and blockchain solutions are unable to guarantee the reliability of data. In this paper, we present a decentralized FL framework powered by blockchain for security attack protection in IoT systems. In addition, we propose an oracle blockchain network that protects privacy and guarantees data reliability. The oracle blockchain acts as a trusted third party to verify the reliability of data and pattern formation at the network edge. Finally, we will formulate a resource allocation problem to allocate the necessary bandwidth to selected devices meticulously. The goal is to minimize communication between devices in the framework and prioritize devices with reliable behavior.

In this paper, we investigate the collaborative computation offloading method in satellite edge computing by allowing computation tasks to be executed by multiple satellites with computing capacity. The main purpose is to optimize the resource allocation to minimize the energy consumption of the network, which is formulated as a non-convex optimization problem. To solve it efficiently, we first provide the optimal task allocation scheme and then divide the original optimization problem into two subproblems based on an alternative optimization method. Although two subproblems are still non-convex, we can apply successive convex approximation method to deal with them and design an iterative algorithm to solve them. Finally, simulation results demonstrate the superiority and effectiveness of our proposed algorithm.

The number of road accidents has remained stable in recent years. By using the latest technologies such as vehicle-to-vehicle communications, it is possible to improve road safety and reduce the number of road fatalities, especially for vulnerable road users (VRUs). There are two existing radio access technologies (RAT) for vehicle-to-everything (V2X) communications, i.e., Wi-Fi -based by IEEE (802.11p and its next-generation standard 802.11bd), and cellular-based by 3GPP (LTE-V2X and 5G NR-V2X). Although many works have evaluated and compared the performance of V2V RAT communications, very little work has been done to compare the performance of these technologies in the context of vehicle-VRU communications. In this paper, we present, to the best of our knowledge, the first work that evaluates the performance of each RAT in the context of vehicle-to-pedestrian (V2P) and vehicle-to-cyclist (V2C) communications. Using four performance metrics, namely packet error rate (PER), packet reception rate (PRR), throughput, and latency, we examined whether each RAT can meet the requirements of safety applications intended for implementation in urban areas. The answer to this question is yes. However, each RAT has its own performance profile. In terms of PER and PRR, 802.11bd has an advantage, while in terms of throughput and latency, 5G NR-V2X performs better.

IoT connects a large number of physical objects with the Internet that capture and exchange real-time information for service provisioning. Traditional network management schemes face challenges to manage vast amounts of network traffic generated by IoT services. Software-defined networking (SDN) and information-centric networking (ICN) are two complementary technologies that could be integrated to solve the challenges of different aspects of IoT service provisioning. ICN offers a clean-slate design to accommodate continuously increasing network traffic by considering content as a network primitive. It provides a novel solution for information propagation and delivery for large-scale IoT services. On the other hand, SDN allocates overall network management responsibilities to a central controller, where network elements act merely as traffic forwarding components. An SDN-enabled network supports ICN without deploying ICN-capable hardware. Therefore, the integration of SDN and ICN provides benefits for large-scale IoT services. This article provides a comprehensive survey on software-defined information-centric Internet of Things (SDIC-IoT) for IoT service provisioning. We present critical enabling technologies of SDIC-IoT, discuss its architecture, and describe its benefits for IoT service provisioning. We elaborate on key IoT service provisioning requirements and discuss how SDIC-IoT supports different aspects of IoT services. We define different taxonomies of SDIC-IoT literature based on various performance parameters. Furthermore, we extensively discuss different use cases, synergies, and advances to realize the SDIC-IoT concept. Finally, we present current challenges and future research directions of IoT service provisioning using SDIC-IoT.

This paper studies the problem of minimizing the age of information (AoI) in cellular vehicle-to-everything communications. To provide minimal AoI and high reliability for vehicles’ safety information, non-orthogonal multiple access is exploited. We reformulate a resource allocation problem that involves half-duplex transceiver selection, broadcast coverage optimization, power allocation, and resource block (RB) scheduling. First, to obtain the optimal solution, we formulate the problem as a mixed-integer nonlinear programming problem and then study its NP-hardness. The negative result of NP-hardness motivates us to design efficient sub-optimal solutions. Consequently, we model the problem as a single-agent Markov decision process (MDP). The MDP model helps in solving the problem efficiently using fingerprint deep reinforcement learning (DRL) techniques such as deep-Q-network (DQN) methods. Nevertheless, applying DQN is not straightforward due to the curse of dimensionality implied by the large and mixed action space that contains discrete RB scheduling decisions and continuous power and coverage optimization decisions. Therefore, to solve this mixed discrete/continuous problem efficiently simply and elegantly, we propose a decomposition technique that consists of first solving the discrete subproblem using a matching algorithm based on state-of-the-art stable roommate matching and then solving the continuous subproblem using DRL algorithm that is based on deep deterministic policy gradient (DDPG). We validate our proposed method through Monte Carlo simulations where we show that the decomposed matching and DRL algorithm successfully minimizes the AoI and achieves almost 66% performance gain compared to the best benchmarks for various vehicles’ speeds, transmission power, or packet sizes. Further, we prove the existence of an optimal value of broadcast coverage at which the learning algorithm provides the optimal AoI.

Federated Learning (FL) is expected to play a prominent role for privacy-preserving machine learning (ML) in autonomous vehicles. FL involves the collaborative training of a single ML model among edge devices on their distributed datasets while keeping data locally. While FL requires less communication compared to classical distributed learning, it remains hard to scale for large models. In vehicular networks, FL must be adapted to the limited communication resources, the mobility of the edge nodes, and the statistical heterogeneity of data distributions. Indeed, a judicious utilization of the communication resources alongside new perceptive learning-oriented methods are vital. To this end, we propose a new architecture for vehicular FL and corresponding learning and scheduling processes. The architecture utilizes vehicular-to-vehicular(V2V) resources to bypass the communication bottleneck where clusters of vehicles train models simultaneously and only the aggregate of each cluster is sent to the multi-access edge (MEC) server. The cluster formation is adapted for single and multi-task learning, and takes into account both communication and learning aspects. We show through simulations that the proposed process is capable of improving the learning accuracy in several non-independent and-identically-distributed (non-i.i.d) and unbalanced datasets distributions, under mobility constraints, in comparison to standard FL.

In this paper, we address the resource provisioning problem for service function chaining (SFC) in terms of the placement and chaining of virtual network functions (VNFs) within a multi-access edge computing (MEC) infrastructure to reduce service delay. We consider the VNFs as the main entities of the system and propose a mean-field game (MFG) framework to model their behavior for their placement and chaining. Then, to achieve the optimal resource provisioning policy without considering the system control parameters, we reduce the proposed MFG to a Markov decision process (MDP). In this way, we leverage reinforcement learning with an actor-critic approach for MEC nodes to learn complex placement and chaining policies. Simulation results show that our proposed approach outperforms benchmark state-of-the-art approaches.

Federated learning (FL) is a distributed machine learning (ML) technique that enables collaborative training in which devices perform learning using a local dataset while preserving their privacy. This technique ensures privacy, communication efficiency, and resource conservation. Despite these advantages, FL still suffers from several challenges related to reliability (i.e., unreliable participating devices in training), tractability (i.e., a large number of trained models), and anonymity. To address these issues, we propose a secure and trustworthy blockchain framework (SRB-FL) tailored to FL, which uses blockchain features to enable collaborative model training in a fully distributed and trustworthy manner. In particular, we design a secure FL based on the blockchain sharding that ensures data reliability, scalability, and trustworthiness. In addition, we introduce an incentive mechanism to improve the reliability of FL devices using subjective multi-weight logic. The results show that our proposed SRB- FL framework is efficient and scalable, making it a promising and suitable solution for federated learning.

This paper studies the problem of massive Internet of things (IoT) access in beyond fifth generation (B5G) networks using non-orthogonal multiple access (NOMA) technique. The problem involves massive IoT devices grouping and power allocation in order to respect the low latency as well as the limited operating energy of the IoT devices. The considered objective function, maximizing the number of successfully received IoT packets, is different from the classical sum-rate-related objective functions. The problem is first divided into multiple NOMA grouping subproblems. Then, using competitive analysis, an efficient online competitive algorithm (CA) is proposed to solve each subproblem. Next, to solve the power allocation problem, we propose a new reinforcement learning (RL) framework in which a RL agent learns to use the CA as a black box and combines the obtained solutions to each subproblem to determine the power allocation for each NOMA group. Our simulations results reveal that the proposed innovative RL framework outperforms deep-Q-learning methods and is close-to-optimal.

Recently, blockchain has gained momentum as a novel technology that gives rise to a plethora of new decentralized applications (e.g., Internet of Things (IoT)). However, its integration with the IoT is still facing several problems (e.g., scalability, flexibility). Provisioning resources to enable a large number of connected IoT devices implies having a scalable and flexible blockchain. To address these issues, we propose a scalable and trustworthy blockchain (STB) architecture that is suitable for the IoT; which uses blockchain sharding and oracles to establish trust among unreliable IoT devices in a fully distributed and trustworthy manner. In particular, we design a Peer-To-Peer oracle network that ensures data reliability, scalability, flexibility, and trustworthiness. Furthermore, we introduce a new lightweight consensus algorithm that scales the blockchain dramatically while ensuring the interoperability among participants of the blockchain. The results show that our proposed STB architecture achieves flexibility, efficiency, and scalability making it a promising solution that is suitable for the IoT context.

Multi-access edge computing (MEC) is a key enabler to reduce the latency of vehicular network. Due to the vehicles mobility, their requested services (e.g., infotainment services) should frequently be migrated across different MEC servers to guarantee their stringent quality of service requirements. In this paper, we study the problem of service migration in a MEC-enabled vehicular network in order to minimize the total service latency and migration cost. This problem is formulated as a nonlinear integer program and is linearized to help obtaining the optimal solution using off-the-shelf solvers. Then, to obtain an efficient solution, it is modeled as a multi-agent Markov decision process and solved by leveraging deep Q learning (DQL) algorithm. The proposed DQL scheme performs a proactive services migration while ensuring their continuity under high mobility constraints. Finally, simulations results show that the proposed DQL scheme achieves close-to-optimal performance.

FEderated Edge Learning (FEEL) has emerged as a leading technique for privacy-preserving distributed training in wireless edge networks, where edge devices collaboratively train machine learning (ML) models with the orchestration of a server. However, due to frequent communication, FEEL needs to be adapted to the limited communication bandwidth. Furthermore, the statistical heterogeneity of local datasets’ distributions, and the uncertainty about the data quality pose important challenges to the training’s convergence. Therefore, a meticulous selection of the participating devices and an analogous bandwidth allocation are necessary. In this paper, we propose a data-quality based scheduling (DQS) algorithm for FEEL. DQS prioritizes reliable devices with rich and diverse datasets. In this paper, we define the different components of the learning algorithm and the data-quality evaluation. Then, we formulate the device selection and the bandwidth allocation problem. Finally, we present our DQS algorithm for FEEL, and we evaluate it in different data poisoning scenarios.

Low-latency IoT applications, such as autonomous vehicles, augmented/virtual reality devices, and security applications, require high computation resources to make decisions on the fly. However, these kinds of applications cannot tolerate offloading their tasks to be processed on a cloud infrastructure due to the experienced latency. Therefore, edge computing (EC) is introduced to enable low latency by moving the tasks processing closer to the users at the edge of the network. The edge of the network is characterized by the heterogeneity of edge devices (EDs) forming it; thus, it is crucial to devise novel solutions that take into account the different physical resources of each ED. In this article, we propose a resource representation scheme, allowing each ED to expose its resource information to the supervisor of the edge node through the mobile EC application programming interfaces proposed by the European Telecommunications Standards Institute. The information about the ED resource is exposed to the supervisor of the edge node each time a resource allocation is required. To this end, we leverage a Lyapunov optimization framework to dynamically allocate resources at the EDs. To test our proposed model, we performed intensive theoretical and experimental simulations on a testbed to validate the proposed scheme and its impact on different system's parameters. The simulations have shown that our proposed approach outperforms other benchmark approaches and provides low latency and optimal resource consumption.

This article studies the problem of online user grouping, scheduling, and power allocation for massive Internet of Things (IoT) access in beyond 5G networks using nonorthogonal multiple access (NOMA). NOMA has been identified as a promising technology to accommodate a large number of devices using a limited number of radio resources. In this work, the objective is to maximize the number of served devices while allocating their transmission powers such that their real-time requirements as well as their limited operating energy are respected. First, we formulate the problem as a mixed-integer nonlinear program (MINLP) that can be transformed to MILP for some special cases. Second, we study its NP-hardness in different cases. Then, by dividing the problem into multiple NOMA grouping and scheduling subproblems, an efficient online competitive algorithm is proposed to solve each subproblem. Next, we show how to use the proposed online algorithm as a black box and how to combine the obtained solutions to each subproblem in a reinforcement learning setting to obtain the power allocation for each NOMA group. Our analyses are supplemented by simulation results to illustrate the performance of the proposed algorithms in comparison to optimal and state-of-the-art methods.

Nanomachines promise to enable new medical applications, including drug delivery and real time chemical reactions' detection inside the human body. Such complex tasks need cooperation between nanomachines using a communication network. Wireless Ad hoc networks, using molecular or electromagnetic-based communication have been proposed in the literature to create flexible nanonetworks between nanomachines. In this paper, we propose a Wired Ad hoc NanoNETwork (WANNET) model design using actin-based nano-communication. In the proposed model, actin filaments self-assembly and disassembly is used to create flexible nanowires between nanomachines, and electrons are used as carriers of information. We give a general overview of the application layer, Medium Access Control (MAC) layer and a physical layer of the model. We also detail the analytical model of the physical layer using actin nanowire equivalent circuits, and we present an estimation of the circuit component's values. Numerical results of the derived model are provided in terms of attenuation, phase and delay as a function of the frequency and distances between nanomachines. The maximum throughput of the actin-based nanowire is also provided, and a comparison between the maximum throughput of the proposed WANNET, vs other proposed approaches is presented. The obtained results prove that the proposed wired ad hoc nanonetwork can give a very high achievable throughput with a smaller delay compared to other proposed wireless molecular communication networks.

In the smart grid, huge amounts of consumption data are used to train deep learning models for applications such as load monitoring and demand response. However, these applications raise concerns regarding security and have high accuracy requirements. In one hand, the data used is privacy-sensitive. For instance, the fine-grained data collected by a smart meter at a consumer's home may reveal information on the appliances and thus the consumer's behaviour at home. On the other hand, the deep learning models require big data volumes with enough variety and to be trained adequately. In this paper, we evaluate the use of Edge computing and federated learning, a decentralized machine learning scheme that allows to increase the volume and diversity of data used to train the deep learning models without compromising privacy. This paper reports, to the best of our knowledge, the first use of federated learning for household load forecasting and achieves promising results. The simulations were done using Tensorflow Federated on the data from 200 houses from Texas, USA.

The rapid growth of communication networking, ubiquitous sensing, and signal processing has spurred the emergence of the Internet of Things (IoT) era. As a novel cutting-edge technology, the IoT enables a plethora of smart-devices equipped with diverse computing, sensing, and actuation capabilities to be connected to the Internet. Thus, it promises to provide a revolutionary and fully connected “smart” world while greatly developing economies and enhancing the quality of life. IoT is indeed an emergent global phenomenon, where real-time remote access to data and applications opens new unprecedented opportunities for ubiquitous monitoring and managing. In such dynamic, interconnected, and heterogeneous environment where the context conditions (location, time, situation sensitivity, etc.) are continuously and frequently changing, context-aware and adaptive solutions for data access are required to respond to the applications' needs. Nevertheless, until now, no schemes provide concrete context-aware access control mechanisms in IoT. In this article, we design a novel context-aware attribute-based access control (CAABAC) that considers the dynamic context changes. The proposed approach incorporates the contextual information with the ciphertext-policy attribute-based encryption (CP-ABE) to guarantee adaptive contextual access to data. The extensive analysis and simulations prove both the effectiveness and efficiency of the proposed scheme. Specifically, context-aware and adaptive remote access is enabled while outperforming other benchmarked schemes in terms of storage, communication, and computational cost.

Fog computing is emerging as a new paradigm to deal with latency-sensitive applications, by making data processing and analysis close to their source. Due to the heterogeneity of devices in the fog, it is important to devise novel solutions which take into account the diverse physical resources available in each device to efficiently and dynamically distribute the processing. In this paper, we propose a resource representation scheme which allows exposing the resources of each device through Mobile Edge Computing Application Programming Interfaces (MEC APIs) in order to optimize resource allocation by the supervising entity in the fog. Then, we formulate the resource allocation problem as a Lyapunov optimization and we discuss the impact of our proposed approach on latency. Simulation results show that our proposed approach can minimize latency and improve the performance of the system.

The rapid technological advancements in wireless communications, ubiquitous sensing and mobile networking have paved the way for the emergence of the Internet of Things (IoT) era, where “anything” can be connected “anywhere” at “anytime”. However, the flourish of IoT still faces various security and privacy preserving challenges that need to be addressed. In such pervasive and heterogeneous environment where the context conditions dynamically and frequently change, efficient and context-aware mechanisms are required to meet the users' changing needs. Therefore, it seems crucial to design an adaptive access control scheme in order to remotely control smart things while considering the dynamic context changes. In this paper, we propose a Context-Aware Attribute-Based Access Control (CAABAC) approach that incorporates the contextual information with the Ciphertext-Policy Attribute-based Encryption (CP-ABE) to ensure data security and provide an adaptive contextual privacy. From a security perspective, the proposed scheme satisfies the security requirements such as confidentiality, context-aware privacy, and resilience against key escrow problem. Performance analysis proves the efficiency and the effectiveness of the proposed scheme compared to benchmark schemes in terms of storage, communication and computational cost.

Distributed architectures of the SDN control plane require a careful design for balancing the load among controllers. Solutions proposed for SDN load balancing usually use switch migration operations. However, an efficient switch migration means triggering the operation at the right moment, and judiciously choosing the migrated switch and the destination controller. Here, we propose a switch migration scheduling algorithm to improve the migration efficiency, and ensure load balancing between controllers. Our algorithm uses a multi-step ARIMA forecasting model to predict the long-term controllers load. When an overload is predicted, a switch migration operation is scheduled in advance. After validating the accuracy of the ARIMA forecasting model, we evaluated the performance of the algorithm by analyzing the response time of controllers. Numerical results confirm the performance of the proposed algorithm.

The tremendous progress in nanotechnology over the last century, makes it possible to engineer tiny nanodevices, which they need a nano-communication network to interact. Two solutions are proposed in literature to create a nano-communication system, either by using the classical electromagnetic paradigm with Terahertz band, or using the bio-inspired molecular communication. However, Terahertz is suffering from molecular absorption and scattering losses at nano level, and the achievable throughput of molecular communication is very low. In this paper, we propose a new solution to establish a wired nano-communication. Self-assembled actin-based is a new method that takes advantage of actin filaments self-assembly to create a nano wire between a transmitter and a receiver, and use electrons as information carriers. VPython framework is used in this paper to perform stochastic simulations of the nano wire formation. The algorithms used for the simulations are presented. The stability of the constructed nano wire is analyzed, and the error probability is calculated. Self-assembled actin-based method promises a fast and stable nano-communication system with a very high achievable throughput.

The blockchain is a distributed technology which allows establishing trust among unreliable users who interact and perform transactions with each other. While blockchain technology has been mainly used for crypto-currency, it has emerged as an enabling technology for establishing trust in the realm of the Internet of Things (IoT). Nevertheless, a naive usage of the blockchain for IoT leads to high delays and extensive computational power. In this paper, we propose a blockchain architecture dedicated to being used in a supply chain which comprises different distributed IoT entities. We propose a lightweight consensus for this architecture, called LC4IoT. The consensus is evaluated through extensive simulations. The results show that the proposed consensus uses low computational power, storage capability and latency.

Path loss is a main challenge in Molecular Communications. When molecules carry information based only on a natural diffusion, the number of molecules that can be received is inversely proportional to the square distance between the transmitter and the receiver, thus hugely impacting the received signal strength. The use of a Multi-Input Multi-Output (MIMO) technique can improve the performance of molecular communications by increasing the data rate. In this paper, we studied the receiver used in molecular MIMO communications. We focused on three important parameters for the receiver design, which are the channel distance, the distance between the detectors constructing the receiver and the detectors diameter. To optimize the design of a 3×3 MIMO receiver, we used AcCoRD simulator to obtain 3D stochastic simulations for each scenario. We evaluated the simulation results by studying the error probability and the number of molecules representing the signal strength. We then proposed two optimization problems that aim at optimizing the receiver parameters choice, and two algorithms to solve the problems. The study shows that a judicious choice of the three parameters combination can optimize MIMOs receiver design, which can decrease the error probability and improve the performance of Molecular Communication.

Fog computing has emerged as a new paradigm in mobile network communications, aiming to equip the edge of the network with the computing and storing capabilities to deal with the huge amount of data and processing needs generated by the users devices and sensors. Optimizing the assignment of users to fogs is, however, still an open issue. In this paper, we formulated the problem of users-fogs association, as a matching game with minimum and maximum quota constraints, and proposed a Multi-Stage Differed Acceptance (MSDA) in order to balance the use of fogs resources and offer a better response time for users. Simulations results show that the performance of the proposed model compared to a baseline matching of users, achieves lowers delays for users.

SDN technology has arrived to solve several limitations of standard networks such as flexibility, scalability and programmability. In a data center environment, adopting the SDN approach where switches are statically linked to controllers creates load balancing problems. This issue is due to the traffic variation between controllers and switches, which influences the response time of the controllers. In this paper, we propose a dynamic assignment of switches to controllers by formulating the problem as a one-to-many matching game with a minimum quota that each controller has to achieve. This quota represents the utilization of the processing capacity of the controller. In addition, an efficient algorithm is defined to ensure a stable matching between switches and controllers in order to maintain load balancing and reduce the latency of controllers. Numerical results confirm the performance of our proposed model compared to a static assignment of switches in terms of load balancing and minimization of the response time especially, when the network becomes too loaded.

Diffusion-based Molecular Communication is a bioinspired system, which uses random walk diffusive molecules as carriers of the information between the transmitter and the receiver. One of the main challenges of that system is the Inter-Symbol-Interference (ISI), caused by the channel memory and represented by a heavy tail in the impulse response. While most prior work has proposed the use of enzymes to catalyze the degradation of the remaining molecules, which mitigates ISI and increases the data rate, the enzymes will decrease the signal strength by degrading also the molecules carrying the information. In this paper, we propose the use of non-enzymatic reactions to degrade only the received molecules, which increases the amplitude of the received signal and at the same time mitigates ISI, enhancing by that the signal strength and the achievable throughput. In this study, we focused on photolysis reactions, which use light to instantly degrade the molecules. We studied the optimal time of light emission with 3D stochastic simulations, using AcCoRD simulator. Simulation results show an improvement of the received signal when using non-enzymatic reactions, compared to enzymatic systems. The performance of the proposed method was evaluated using interference-to-total-received molecules (ITR).

The increasing number of intelligent electric vehicles (EVs) has stimulated challengeable problems (i.e., confidentiality, privacy) for the smart grid in the last few years. These challenges impact the exchange of sensitive information between EVs and smart grid which offer different sort of services (i.e., planning itineraries, booking charging stations (CSs)). In this paper, we propose a new architecture to secure communication exchange between EVs and the smart grid. The proposed architecture ensures both confidentiality of communications and privacy of EVs, and includes authentication and authorization in order to secure service access for EVs. Simulations were performed under different scenarios to show the performance of our proposed scheme. The results have shown the proposed architecture ensures good response time when implementing the security modules.

In recent years, the number of electric vehicles (EVs) on the road has been steadily increasing. At the same time, availability of the charging infrastructure on the road is still limited. In this paper, we propose a new scheme to guide EVs toward charging stations so as to minimize their waiting time and power energy consumption to get a charging service. The scheme uses wireless communication between EVs and the smart aggregator. It takes into account the state-of- charge (SoC) of EVs, their position, and available charging stations on the road. It also considers traffic, and occupancy of charging stations. Simulations were performed to assess the performance of our proposed scheme. Results show that the scheme effectively minimizes energy consumption and waiting times for EVs.

M2M communications have recently been introduced in smart grid and vehicular networking environments. Its principles can improve electrical vehicular networking while offering two-way communication between Electric Vehicles (EVs) and Electric Vehicle Supply Equipment (EVSEs). In this paper, we first study the impact of a very large number of connected EVs when attempting to use the random access in LTE- Advanced to communicate with the grid. Second, we propose an effective solution for avoiding congestion on the random access channel of LTE- Advanced for massive EV-2-EVSE communications. In this solution, we differentiate between two classes of service of EVs communications, giving priority to charging demands over other types of messages lower priority messages (promotions, subscriptions, mechanical checks, etc.). Finally, we propose an efficient admission control mechanism to manage EVs M2M traffic on LTE- Advanced and to provide QoS to charging demand messages in terms of strict delay to avoid both a long latency of EV users and a network overload in high offered load conditions.

We present MAP, a contention-free MAC protocol for fast and reliable multi-hop data dissemination in vehicular ad hoc networks which takes advantage of two-packet collisions to improve network capacity. MAP addresses both the problem of unwanted collisions due to hidden nodes in CSMA/CA-based networks, and the problem of excessive control messages in dense TDMA-based vehicular networks. MAP operation introduces: (i) a dynamic subdivision of the space into multiple sub-spaces using one relay node each, and (ii) a deterministic medium access that is free of control messages, while being favorable to physical-layer networking coding transmissions. MAP implements judicious broadcast phases to guarantee fast and reliable transmissions, while nodes can join or leave the dissemination process anytime. Numerical results show that with MAP, we can achieve fast and reliable all-to-all dissemination in large multi-hop VANETS compared to conventional techniques.

The objectives set for smart grids are as diverse as they are exciting and ambitious. Instead of overloads, bottlenecks and blackouts, smart grids will ensure the reliability, sustainability and efficiency of the Electric Vehicle Supply Equipment (EVSEs). Therefore, EVSEs and Electric Vehicles (EVs) must integrate wireless communication between them to discover the availability and make pre-reservations of charging time slots. In this paper, we propose a novel multi-objective EV Charging Slots Assignment (CSA) optimization model that, besides balancing energy usage between EVSEs, minimizes the latency time of EVs. Moreover, we integrate our optimization model to a communication protocol between EVs and EVSEs that allows a reliable reservation process, called Reliable Broadcast for EV Charging Assignment (REBECA) [1]. Then, we propose a centralized heuristic to efficiently solve our model as an offline CSA process.

In this work, we focus our study to improving the energy efficiency of mobile cellular users in ultra-dense LTE HetNets. The hyper-dense co-channel deployment of indoor LTE small cell networks (SCNs) within LTE macro cell networks (MCNs) will aggravate the effect of cross-tier interferences caused by the uplink transmissions of macro-indoor users located inside the overlapping zones of small base station (SBS) coverage areas. Hence, degrading the uplink performance at the level of SBSs adopting closed access policy. In order to eliminate the severe cross-tier interferences, each SBS attempts to open the access for macro-indoor users that accept only the SBS with Max-SINR offer. This will lead to network congestion problems in several SCNs. Wherefore, we formulate our problem as a many-to-many matching game. Then, we introduce an algorithm that computes the optimal many-to-many stable matching which consist of assigning each macro-indoor user with multi-homing capabilities to the most suitable set of SBSs and vice versa based on their preference profiles. With regard to the conventional Max-SINR association scheme, our solution can effectively improve the energy efficiency of cellular users. Moreover, it can ensure load balancing in ultra-dense LTE HetNets.

Recently, Jung et al. [1] proposed a data access privilege scheme and claimed that their scheme addresses data and identity privacy as well as multi-authority, and provides data access privilege for attribute-based encryption. In this paper, we show that this scheme, and also its former and latest versions (i.e. [2] and [3] respectively) suffer from a number of weaknesses in terms of finegrained access control, users and authorities collusion attack, user authorization, and user anonymity protection. We then propose our new scheme that overcomes these shortcomings. We also prove the security of our scheme against user collusion attacks, authority collusion attacks and chosen plaintext attacks. Lastly, we show that the efficiency of our scheme is comparable with existing related schemes.

Although the number of electric vehicles (EVs) on the road has been steadily increasing in the last few years, the problems of autonomy and limited driving range of EVs still represent a big challenge for automotive industry. In this paper, we first propose a secure architecture where EVs and the smart grid exchange information for itinerary planning and charging time-slots' reservations at charging stations. The architecture ensures privacy, and includes authentication and authorization in order to secure EVs sensitive information. Second, we introduce a new scheme for EV itinerary planning, which takes into account the state-of-charge of the EV, its destination, and available charging stations on the road. The scheme minimizes the waiting time of the EV and its overall energy consumption to attain destination. MATLAB and CPLEX simulations were performed to show the performance of our proposed scheme. Simulation proved that our model is able to optimize paths in terms of energy consumption and waiting time.

Vehicular sensing has gained prominence in recent years with its use in entities, including traffic management centers, forensic authorities, and air pollution control units. It also provides end users with real-time street images, parking summaries, and road congestion status. To reduce bandwidth usage and improve the content value, the sensed data must be aggregated. Data aggregation is said to be efficient when the destination (i.e., a node that serves as a data collection point in the network) is capable of receiving sensed data from a significant proportion of vehicles. However, when a large number of vehicles attempt to send sensed data, the network becomes congested eventually causing packet losses and collisions. Thus, if aggregation is performed without considering key factors, such as number of vehicles and network dynamics, it is difficult to ensure the efficient collection of sensed data at the destination. In this paper, we propose a dynamic hierarchical aggregation scheme in which sensed data is aggregated using a hierarchy. Moreover, the hierarchy is dynamically updated based on theoretically estimated delivery efficiency. In particular, we perform partition and merge operations within the hierarchy to achieve an improved value of delivery efficiency. The simulation results show that the proposed scheme ensures efficient data collection even with stringent delay requirements and achieves scalability with respect to a number of vehicles in the network.

Mobile cloud computing is a revolutionary computing paradigm for mobile application which enables storage and computation migration from mobile users to resources rich and powerful cloud servers, but emerges various privacy concerns. Attribute based encryption is a public key encryption that ensures the security of stored data in the cloud and provides fine grained access control using defined policies and constraints. Location of a device is one of the contextual policies which is used to improve data security, authenticate users and provide access to services and useful information for mobile users. However, unlike other policies and attributes used in attribute based encryption, location of mobile users are dynamic. In this paper, we investigate providing Location Based Services (LBS) for attribute based access control in mobile cloud. More specifically, we propose a multi-authority attribute based access control scheme and protect users privacy against malicious authorities. The proposed scheme uses dynamic location of a mobile user as contextual information about that user, employs coarse location as an attribute in attribute based encryption to achieve K-anonymity, and filters the returned results for more accuracy. The attribute based encryption is integrated with proxy re-encryption to outsource the computation to a cloud server with “unlimited” computational power. The proposed scheme achieves efficiency by reducing computational cost on resource-constrained mobile users.

With the increasing deployment of smart devices for the Internet of Things (IoT) using 802.15.4 in spaces where 802.11 devices are already deployed, wireless channels are getting densely populated in the 2.4 GHz ISM band, especially when nodes operate in saturation conditions. In addition to the fact that 802.15.4 and 802.11 use different CSMA/CA protocols with considerable different backoff durations, 802.15.4 is designed for ultra-low power, low rate WPAN while 802.11 is designed for higher power and high data rate WLAN. In this paper, we aim to tackle the resulting issues due to this asymmetric coexistence. Especially we propose a simple but efficient backoff mechanism for 802.15.4 in which the backoff duration is adaptively chosen, when WiFi transmissions are detected during the clear channel assessment. Through extensive simulations, we demonstrate the efficiency of the proposed adaptive backoff mechanism and the considerable improvements of the 802.15.4 performance even in case of erroneous decision regarding the type of packet detected during the clear channel assessment.

In wireless networks, spectral efficiency is a vital issue in the wake of ever increasing bandwidth usage. To improve throughput, in wired and wireless networks, network coding is one of the promising solutions which has been used for a quite some time. On getting coding opportunity in a hop, coding gain is around 100% if physical layer networking, unlike linear network coding which offers maximum coding gain up-to 33%. However, PNC has symbol-level synchronization, carrier-frequency synchronization, and carrier-phase synchronization. The throughput elevate with number of nodes involved in physical layer network coding. In the literature, there is no proposal which increases the involvement of number nodes to elevate overall throughput of the network. In this paper, we are forcing multiple opposite direction routes to share common road segments without compromising delay and reliability requirements. We designed a new particle swarm optimization (PSO) mechanism which offers maximum throughput gain for the entire network. Through network coding such voluntary congestion is solved. Thus, higher packet delivery ratio can be achieved while keeping lower end to end delay which is verified by graphs.

In this paper, we propose methods to enable efficient data delivery in vehicular ad-hoc networks (VANETs). We target scenarios where data are collected by vehicles and need to be transmitted. Dynamic clusters are formed while vehicles move in order to make data transmissions more robust and scalable. We use mathematical optimization solutions to optimize transmission scheduling so as to maximize throughput and minimize delay in delivering data. This paper defines an optimization model which addresses the max-min flow allocation problem by decomposing it into a master problem and a subproblem. Our proposed scheme implements a contention free based medium access control where physical conditions of channel have been fully analyzed. Extensive simulations were performed for different scenarios to show the performance of the proposed Distributed Cluster Based (DCB) transmission scheduling scheme.

Vehicular communication technology leverages communication equipment and infrastructure to improve road safety and provide useful services for road user. In order to operate properly, many of these services need continuous data gathering to assess road situations accurately. In this paper, we analyse the impact of communication technology penetration rate and the proportion of application-equipped vehicles on the efficiency of a Lane Changing Advisory Application in improving travel delay and traffic fluidity. We model the system analytically and perform extensive simulations at different penetration rates both with a microscopic traffic simulator and a network simulator. Vehicle-to-vehicle communication outcomes influence application efficiency and therefore driver behaviour. The mobility pattern is fed back into the traffic simulator in a closed loop in order to assess traffic fluidity. The results of the study present the impact of DSRC penetration ratio on the application performance and by consequence on road traffic fluidity. They show that even at low penetration rates of 10% and 25%, total travel time and traffic fluidity are enhanced.

This paper presents a distributed clustering algorithm, called DCEV, which constructs multi-hop clusters. DCEV places vehicles into non-overlapping clusters which have adaptive size based on their relative mobility. The cluster formation is based on a D-hop clustering scheme where each node selects its cluster head in at most D-hop distance. To create clusters, DCEV uses a new metric to let vehicles choose the most stable route to their desired cluster head within their D-hop neighbourhood. For this purpose, each node calculates the mean relative mobility value of each discovered route (end-to-end relative mobility). DCEV considers the route which has the least end-to-end relative mobility as the most stable route. Extensive simulations were conducted for different scenarios to validate the performance of DCEV clustering algorithm. Results show that DCEV efficiently manages to build stable clusters.

In this work, we propose an approach for optimizing Mobile Cloud Computing (MCC) resources allocation using stochastic networks optimization based on Lyapunov optimization. The approach targets reducing users' requests experienced delay while enhancing resource availability. We use Lyapunov drift plus penalty optimization to ensure design stability and to minimise the delay cost function. We perform extensive simulations under different charge conditions in order to prove the effectiveness of the approach in handling user requests with lower delays. Simulation results show that the approach enhances the experienced delay while ensuring a good utilization of resource pools.

In this paper, we propose parallel interference cancellation (PIC) for link activation in VANETs. Link activation (LA) stands for activating a set of communication links which can transmit simultaneously without transmission collisions. We consider multi-hop VANET scenarios where vehicles are clustered using d-hop clustering algorithms such as proposed in [1]. We model the interference cancellation as a mixed integer programming (MIP) optimization problem where wireless link conditions are analyzed. The proposed parallel interference cancellation method can be used for scheduling of transmissions and resource sharing inside the constructed clusters. Simulations were performed for different scenarios to show the performance of the improved LA.

Mobile Cloud Computing (MCC) leverages mobile devices and infrastructure equipment to increase services accessibility. It uses increased devices computing capability to enhance services usability and ensure high availability. This growth in performances results in an increased interest for platforms use to accommodate a multitude of applications. To support such an increase in demand, new designs for resource management have to be implemented in order to reach usage optimality. In this work, we propose to design new algorithms to optimize MCC resources management techniques based on stochastic networks optimization. Our approach is focused on energy consumption optimization on the cloud data center side while ensuring resources elasticity to adapt to users' demands and insure a highly available platform. We elected an overclocking technique to enhance servers' capabilities and Lyapunov improvisation to ensure design stability and to minimize the energy cost. We perform extensive simulations under different charge conditions in order to prove the design effectiveness in ensuring the service with lower power consumption. Simulations results confirm the effectiveness of the proposed resources management design.

A major challenge in vehicular ad-hoc networks (VANETs) is the ability to account for resource sharing and location management so that multicasting/routing functions and bandwidth reservations can be organized efficiently. By creating clusters of vehicles we are able to control resource sharing and management functions in VANETs that are highly dynamic. In this paper, a D-hop clustering algorithm, called DHCV is presented which organizes vehicles into non overlapping clusters which have adaptive sizes according to their respective mobility. The D-hop clustering algorithm creates clusters in such a way that each vehicle is at most D hops away from a cluster head. To construct multi-hop clusters, each vehicle chooses its cluster head based on relative mobility calculations within its D-hop neighbours. The algorithm can run at regular intervals or whenever the network formation changes. One of the features of this algorithm is tendency to re-elect the surviving cluster heads whenever the network structure changes. Extensive simulation results have been done under different scenarios to show the performance of our clustering algorithm.

VANETs) that provides a vehicle with data about 1-hop neighboring vehicles. Data provided by this service can be used to support safety applications, such as the efficient selection of forwarders for safety messages and dissemination of early warnings to drivers about potential dangers of the road. This paper discusses the limitations of the beaconing service in providing vehicles with safety-related information. It also proposes a mechanism to let each vehicle have additional information about its surroundings in order to get an extended perception of its environment. This can help in considerably reducing accidents on the roads. Through simulations, we show that the additional overhead caused by the exchange of additional data can be kept low enough to prevent significant impact on overall network performance.

Abstract In wireless network‐based node localization, the received signals are hampered by complex phenomena, such as shadowing, noise, and multi‐path fading. In this work, the localization is stated as an ill‐posed problem that can be solved by compressed sensing (CS) technique. A three dimensional (3D)‐CS approach using the ratio of received signal strength (R2S2) and the time difference of arrival metrics was proposed to improve the localization accuracy of multiple target nodes in 3D wireless networks, and to reduce deployment complexity and processing time. Simulation and experimental tests were conducted in a large multi‐floors building using the strength of the received signals and the radio map of the localization area. The results indicated that the 3D‐CS approach is reliable for identifying the floor number and estimating the horizontal position. The localization precision is less affected by the propagation medium variation than the conventional 2D‐CS method. The localization mean error is lower when the number of access points increases, and the radio map spacing decreases. In addition, the accuracy of the 3D‐CS approach was assured as well as the building material characteristics, position of access points, and wireless‐terminal real transmission power are unknown. Copyright © 2015 John Wiley & Sons, Ltd.

This paper deals with channel assignment for cloud network users based on the information from application layer. We assume a multiple user network where the services requested by the users are categorized into real-time (RT) and delay- tolerant (DT) services. We assign each wireless channel to a user who needs RT service. Inspired by the cognitive networks, we aim to assign the same channel to a second user who needs DT service, while minimizing the interference. Our objective is to simultaneously perform these assignments such that the SNR value of the worst user is maximized. This assignment significantly outperforms the separate assignment of the channels to RT users and then, to DT users. The resulting assignment problem turns out to be NP- hard. We propose a very simple, yet effective algorithm to solve this problem. In the second part of the paper, we will prove that the results of our suggested solution stay in a specific neighborhood of the optimal answer. In order to prove this fact, we statistically analyze the optimal solution, where we derive a general framework to express the statistical behavior of the optimal solution. Then we prove that both optimal solution and our solution achieve the same physical channel diversity.

High Electric Vehicle (EV) penetration increases smart grid solicitation especially with various EV charging demands at peak load times. The EV charging process at public supply station (EVPSS) has to be managed in the way to promote the EV satisfaction levels while preserving smart grid stability. The waiting time for the EV charging service is an important factor in assessing the effectiveness of any interaction system between EVs and smart grid. In this paper, we present a system-guidance model to minimize the waiting time for an EV to be plugged-in for the charging service at public supply stations. We propose an algorithm for directing vehicles to charging stations in a way to minimize their searching time to join a supply station. The simulations conducted to evaluate its performance while satisfying the defined constraints proved the effectiveness of the proposed approach.

Vehicular sensing has gained significant attention in recent years, thanks to its enormous benefits to many entities including traffic management centers, forensic authorities and air pollution control units. To reduce redundancy and improve the content, the collected data must be aggregated. Delay-sensitive sensing applications require the aggregated data be collected with a certain delay. In this paper, we propose a hierarchical aggregation scheme for image sensing in vehicular networks. The hierarchy is dynamically updated based on observed network conditions. In particular, partition and merge operations are performed on the hierarchy to satisfy the delay requirement. The simulation results show that the proposed scheme outperforms existing scheme in terms of efficient data collection and redundancy elimination.

In wireless networks, spectral efficiency is an important issue due to limited available bandwidth. Traditionally, when a receiver collects simultaneous transmissions, packets collide and the much meaningful information cannot be retrieved. Thus, through suitable scheduling, simultaneous transmissions to the receiver are avoided. However, in physical layer network coding, simultaneous transmissions are allowed and the combined signal is equivalent of XOR operation on air. A path from a source to a destination in unicast routing in VANET is called a flow; multiple flows may have common road segments. There will be heavy contention in such shared road segments resulting in lower throughput, lower packet delivery ratio and higher end to end delay. In this paper, we propose a multihop physical layer network coding structure to be used by multiple flows of unicast routing paths. We analyze throughput, end-to-end delay and coding gain with respect to number of routing flows and number of forwarders involved in network coding.

Vehicle density information is crucial for efficient functioning of many vehicular applications, including emergency notification, driver assistance, and infotainment applications. This information is used for evacuation planning in accident scenarios, finding alternate routes in the case of road congestion, and providing stable routing paths for uninterrupted internet connections. In a city scenario, it is a tedious task to continuously collect and share large volumes of data containing density information. In this paper, we propose a mechanism to create a density map for city environments. A hierarchy (i.e., tree) is established, where a node represents a road segment and the density is used to determine the height of the node; the root node represents the road segment having highest density. The purpose of this tree is to collect and aggregate density information starting from the leaves until the root node is reached. Then, the aggregated density information (i.e., density map) is forwarded down the hierarchy. For efficient aggregation of density information, we adopt an effective curve-fitting method where data are represented in an equation. Simulation results show that the proposed mechanism allows highly accurate computing of density map while generating low network overhead.

Abstract As electric vehicles (EVs) become more popular, public charging stations for such vehicles will become common. Because the load introduced by such stations on the grid is high, the smart grid will need to balance the load among charging stations in an area while minimizing the charging waiting time. To achieve this goal, we propose two models where vehicles communicate beforehand with the grid to convey information about their charging need and location. In the first model, we develop a mathematical formalism for handling requests for charging vehicles at public charging station based on queuing theory. The second model extends the first one by considering priority queues with two EV classes, high and low, and a cut‐off service discipline. Both models are evaluated while considering mobility of vehicles in an urban scenario and time‐of‐use pricing. Finally, we propose two algorithms for directing vehicles to charging stations in a way to minimize either their waiting time to plug‐in or their waiting time to charge completion. Simulation results show the effectiveness of the proposed approaches when considering both real EV and charging station characteristics and constraints. Copyright © 2014 John Wiley & Sons, Ltd.

An urban scenario is the center stage for vehicles to roam around the concrete jungle. Any sorts of wireless communication would be affected by hidden terminal problems, fading and interferences. Unintended nodes are unnecessarily bothered by such huge volume of microwave communications. The most common forms of communication are beaconing messages, which let the vehicles know about its neighboring vehicles and possibly choose an appropriate forwarder for safety and non-safety messages. However, such influx of broadcast messages may lead to beacon overhead and congestion resulting in low message reception as well as excessive delay. Interferences due to inter-street beacon messages may affect emergency messages, channel arbitration messages and other control messages which share a common channel as specified by DRSC/WAVE. This paper proposes a scheme to cancel interferences due to inter-street beacon communications by adaptive transmission control, while maintaining application layer transmission range, through multi-hop beacon forwarding and network coding. The simulations show that our scheme has higher packet delivery ratio and higher successful channel utilization compared to CSMA/CA protocols.

In the next few years, as the number of EVs will become important, the smart grid will be solicited to satisfy high power demands. To deal with this problem, efficient power charge scheduling techniquesare required. In this paper, a scheduling protocol with a load management technique is introduced. The scheduling protocol is aimed first at minimizing peak loads due to multiple EVs charging at home while using pricing policies. Second, it aims at coordinating charging and discharging processes to achieve cost optimization and improve grid stability. An analytical formulation is given for the scheduling problem with a load management strategy. The simulation results showed the effectiveness of the proposed approach in minimizing peak loads, optimization cost and improving grid stability while satisfying the defined constraints.

Vehicular ad hoc networks (VANETs) leverage communication equipment and infrastructure to improve road safety. These networks, by the rapid change of their topology and their broadcasting dissemination nature, can experience mainly two major problems; (1) the broadcasting storm and (2) network disconnection. In this paper, we focus on safety-related messages and propose a new approach to avoid the broadcasting storm and under various network load. Our scheme, called RMDS, is based on two main concepts; the critical distance and the distance of relevance. It combines an asymmetric power-range adjustment and message frequency tuning aiming to reduce network load. It integrates a new approach to prioritize locally generated messages over relayed ones according to the distance from the event originator. Simulation results confirm the effectiveness of the proposed scheme and its network performance under various traffic constraints.

Urban Sensing has become an increasingly popular service in vehicular networks. It allows consumers to access a repository of data collected by sensors embedded in vehicles. Aggregation is a viable approach to convert the sensed data into a usable form. An efficient way to perform aggregation is to divide the network into a number of geographical regions, called aggregation regions. In this paper, we propose a novel mechanism to construct aggregation regions based on the location of RSUs (road-side units). Besides, we propose an optimization strategy to determine the optimal location of aggregation that minimizes the delay of vehicular communications.

Beacon messages are periodic 1-hop broadcast messages which are sent by each vehicle to let neighboring vehicles/infrastructures be aware of its position, speed, and change of direction. Beacon information plays a crucial role for many applications including active safety applications through which vehicles can predict the position of neighboring vehicles and be able to take instant decisions to avoid any emergency situation. However, such influx of broadcast messages may lead to beacon overhead and congestion. These result in low message reception as well as excessive delay. Beacon overhead and congestion may affect emergency messages, channel arbitration messages and other control messages which share a common channel as specified by DRSC/WAVE. This paper proposes a mechanism for controlling beacon overhead by adopting packet level network coding. The simulations prove that our scheme has higher packet delivery ratio and higher successful channel utilization compared to CSMA/CA protocol.

Smart cities leverage technology to analyze data to make decisions, anticipate problems and coordinate resources to operate efficiently. Data produced by sensors embedded in vehicles moving on streets enable sensing applications for smart cities that were infeasible in the past due to high deployment costs. In this paper, we propose a novel framework for collection, aggregation and retrieval of data. The framework considers vehicles and road-side units as the main entities. To collect data, the city road network is divided into a number of sensing regions. We discuss the aggregation operations for each type of event. A retrieval mechanism is also proposed to deliver content in real-time. The simulations results demonstrate that the proposed framework outperforms existing vehicular sensing approaches in terms of delay and accuracy.

In this work, we introduce a priority-aware deterministic access protocol called Vehicular Deterministic Access VDA. VDA is based on 802.11p/DSRC and allows vehicles to access the shared medium in collision-free periods. Particularly, VDA supports two types of safety services emergency and routine safety messages with different priorities and strict requirements on delay. To avoid long delays and high packet collisions, VDA allows vehicles to access the wireless medium at selected times with a lower contention than would otherwise be possible within a two-hop neighborhood by the classical 802.11p Enhanced Distributed Channel Access or Distributed Coordination Function schemes. A non-VDA-enabled vehicle, that is, a vehicle not configured with the optional VDA capability over 802.11p, may start transmitting on the shared channel just before or during the VDA opportunities reserved for vehicles with VDA capabilities. To avoid the aforementioned issues and prevent interfering transmissions from VDA-enabled vehicles and non-VDA-enabled vehicles, we also proposed a novel scheme called extended VDA. We analyzed the impact of several design tradeoffs between the contention free period/contention period dwell time ratios on the performance of safety applications with different priorities for VDA and extended VDA. Simulations show that the proposed schemes clearly outperform the backoff-based schemes currently used by 802.11p in high communication density conditions while bounding the transmission delay of safety messages and increasing the packet reception rate. Copyright © 2012 John Wiley & Sons, Ltd.

Main applications in vehicular networks are primarily looking for road safety improvement. To achieve this goal, positiondata information of all interfering road users must be conveyed effectively. This proposal examines the performance made possible using WAVE/DSRC standard when including pedestrians/two-wheels. First, we propose a specific message format for encapsulating pedestrian and two-wheel position-data information. Second, we propose a complete system for collision avoidance at intersection level. Subsequently, we perform tests to analyze the system performance in terms of packets reception rate and transmission delay using WAVE/DSRC standard in two modes of operation. Namely, we perform a comparison between using only the control channel to announce and forward messages as a safety service, and using one of the dedicated channels 172 or 184 after announcing the service on the control channel. Taking into account different levels of network densities, we investigate the modes of operation that provide lower congestion and channel saturation. The findings in this work are relevant to the design of data transmission patterns for pedestrian collision avoidance systems at intersections when using VANETs.

In this paper, a scheduling protocol for electric vehicle (EV) home charging with time of use pricing is introduced. This work addresses the problem of EVs charging at home by adopting an appropriate charging process protocol over Power Line Communications (PLC). The scheduling protocol is aimed at minimizing peak loads on distribution feeders due to multiple EVs charging while using a time-of-use pricing policy. Energy efficiency and performance are both taken into account. An appropriate analytical formulation of the scheduling problem is given together with the proposed scheduling protocol. Simulations demonstrate the effectiveness of the proposed approach in minimizing peak loads while satisfying the defined constraints.

As electric vehicles become more popular, public charging stations for such vehicles will become common. Since the load introduced by such stations on the grid is high, the smart grid will need to balance the load among charging stations in an area while minimizing the waiting time for users to have their vehicles charged. In this paper, we present an approach for balancing the load among charging stations in an area while minimizing the charging time of electric vehicles. We propose a model where vehicles communicate beforehand with the grid to convey information about their charging status, and develop a mathematical model of handling requests for charging vehicles at public charging station based on queuing theory. Finally, we propose an algorithm for directing vehicles to charging stations in a way to minimize their waiting time to charge completion. The simulation results show the effectiveness of the proposed approach when considering both real electric vehicle and charging station characteristics and constraints.

In this study, we propose a four‐dimensional modulation, referred to as modulation network coding (MNC), to address the problem of decoding a signal from multiple source node transmissions in mobile fast fading channels. The MNC scheme judiciously mixes a two‐dimensional (2D) pilot symbol with a 2D information symbol, and makes use of a π /4 rotated M‐pulse‐amplitude modulation (PAM) constellation to guaranty an effective decoding of all the interfering symbols even in the case of a smaller channel Doppler spread compared with the period of the MNC symbol. In addition, because the MNC scheme makes use of an additional dimension introduced through orthogonal pulse waveform, the use of the pilot symbols does not reduce the effective information rate. The analytical and simulation results show that it is possible to achieve a low symbol error probability with a good signal‐to‐noise ratio when controlling a few parameters impacting the performance of the system such as the synchronisation in time and in frequency of the source nodes.

Vehicular ad hoc networks (Vanets) play a critical role in enabling essential emergency safety applications (e.g., Forward Collision Warning) and subsequently help the driver deal with emergency situations, These emergency applications have strict different QoS requirements mainly on latency time [1]. In this work, we propose a Power Control Scheme, called PCS that adjusts the transmission power level to guarantee short delays for safety messages over 802.11p/DSRC, especially for occasional emergency messages. PCS aims to minimize contention within two-hop neighborhood of a vehicle under high-offered load conditions. Numerical results show the effectiveness of PCS scheme when coupled with either the traditional random access mechanism [2] or the priority-aware deterministic access protocol based on 802.11p/DSRC [3].

Compressive sensing (CS) intends to recover signals at a sampling rate significantly (much) lower than that classically used according to the Nyquist theorem. This allows avoiding unnecessary sampling and complexity. In this paper, a Three-Dimensional Compressive Sensing (3D-CS) approach is proposed for nodes localization in wireless networks. In 3D-CS-R2S2 approach, which is based on the ratio of received signal strength (RSS), a 3D sparsity basis and a 3D measurement matrix are used as radio map and noisy measurements respectively in order to recover the target position. A specific multi-linear algebra procedure was developed using N-way array products, together with an adequate decomposition. Both allow formulating the localization problem in a way that is solvable by an ℓ1-minimization algorithm based on CS theory. 3D-CS-R2S2 improves localization accuracy even if propagation conditions change significantly and/or the effective isotropic radiated power (EIRP) is unknown. Additionally, it enables practical Real Time Localization Systems (RTLS) development since 3D-CS-R2S2 can be functional with a reduced number of base stations without compromising position recovery accuracy. The simulation results show the efficiency of the method that not only succeeds to recover a target position but also improves localization accuracy in presence of noise.

In this paper we investigate the improvements in the capability of neighborhood prediction when the Physical-Layer Network Coding (PLNC) is used for relaying messages in Vehiculars Ad Hoc Networks (VANETs). We compute the probability that a link between two nodes is available at a given time in a three-node cooperative network, and we demonstrate that the use of PLNC, compared to the use of Network Coding (NC)-based or traditional routing (TR)-based relaying techniques, leads to a better accuracy of the neighborhood prediction. We also demonstrate that the accuracy of the prediction is tightly related to the mobility model used and that this tight relationship can be relaxed by using PLNC-based relaying neighborhood prediction (PRNP). The results demonstrate that PRNP can improve the accuracy of neighborhood prediction due to the high network capacity of PLNC-based networks.

In this paper, we propose a new method for assessing the sociability scalar of a mobile peer by the network, most importantly with no involvement of the mobile peer. The sociability metric can help in the Application Layer Traffic Optimization (ALTO) guidance in Mobile Peer-to-Mobile Peer (MP2MP) scenario to scale up the database search of an ALTO server. The proposed method models encounters of mobile peers with predetermined areas, such as cells, tracking areas, gateway service areas, etc, depending on the targeted granularity. The obtained metrics, pertaining to inter-mobile peer relationship (i.e., sociability) and mobile peers mobility, are adopted to ALTO in a MP2MP scenario. In addition, metrics reflecting the energy budget of a mobile peer, the type of a mobile terminal, history of a mobile terminal in sharing contents with other mobile peers, etc, can be also taken into account by ALTO in the peer recommendation.

Vehicular ad hoc networks (VANETs) leverage communication equipment and infrastructures to improve road safety. These networks, by the rapid change of their topology, can experience mainly two major problems; (1) the broadcasting storm and (2) the network disconnection due respectively to high vehicles density and their velocity. In this paper, we propose a new unicast data dissemination scheme based on distances estimation using Received Signal Strength (RSS) measurements and congestion detection by mean of a newly designed metric; called Multi-metric Unicast Data Dissemination Scheme (MUDDS). MUDDS adapts the transmission range so that congestion can be avoided. It performs the best available link choice to guarantee both reliable transmission and minimum delivery delay. MUDDS focuses on the broadcasting storm and the network disconnection problems simultaneously. Simulation results confirm the effectiveness of the proposed on-demand adaptation and relaying scheme and its impact on network performance under various traffic constraints.

The smart grid is a new concept of electricity supply operation and management that will enable consumers and utilities to better control the electricity usage. This is possible because of the two way electricity and information communication between all nodes in the grid. For Electric Vehicles (EVs) travelling on the road, and because of the necessary battery charging times, there is a need for wireless communication between the EVs and the Electric Vehicle Supply Equipment (EVSEs) (charging stations) to discover the availability and make pre-reservations of charging time slots. In this paper, we introduce a new communication protocol between EVs and EVSEs that allows a reliable reservation process. The scheme, called Reliable Broadcast for EV Charging Assignment (REBECA) processes information about electricity usage in EVSEs and allows to reserve charging time slots for vehicles. REBECA also takes into account balancing energy usage between EVSEs while minimizing the latency time of EVs. Simulations results show the effectiveness of REBECA scheme.

In Physical-Layer Network Coding (PNC), the symbol detected at the relay node in two-way relay channels (TWRC) is the superposition of the symbols transmitted by the sinks. We refer to these symbols as pnc-symbols. In traditional modulation constellations such as Quadrature Amplitude Modulation (QAM) and Phase Shift Keying (PSK), the demodulator may fail to optimally identify pnc- symbols formed from pairs of central symmetrical symbols (CSS) because their superposition may yield to the same output point (i.e., to the same decision region) due to the presence of a central symmetry point. In order to avoid such ambiguity, we propose a non central symmetry constellation (CSC), called 4-TRAQAM, which is used by the sinks such that the decision regions of the output points are pair-wise disjoint.We show that the sinks in the PNC-based TWRC can be considered as a single transmitter using the same modulation as the real sinks with a higher order.We further derive the average energy of pnc- symbols and the error probability of the derived PNC-based TWRC modulation, and we show that the 4- TRAQAM provides the best trade-off between the average energy and the disjointness of the decision regions, compared to other used QAM.

In recent works, a priority-aware deterministic access protocol that is based on 802.11p/DSRC was introduced to allow vehicles to access the shared medium in collision-free periods. The VANET Deterministic Access (VDA) protocol as introduced in [8] has no mechanism that prevents a non VDA-enabled vehicle from accessing the channel in a scheduled VDA opportunity (VDAOP). A non VDA-enabled vehicle, i.e. a vehicle not configured with the optional VDA capability over 802.11p, may start transmitting on the shared channel just before or during the VDAOPs reserved for vehicles with VDA capabilities. Also, non VDA-enabled vehicles may be prevented from accessing the shared channel due to the transmission of VDA-enabled vehicles during their respective VDAOPs with a higher priority (shorter AIFS). In this work, we propose a new enhanced VDA scheme, called EVDA that avoids the above issues and prevents interfering transmissions from VDA-enabled vehicles and non VDA-enabled vehicles. We also analyzed the impact of several design tradeoffs between the Contention Free Period (CFP)/Contention Period (CP) Dwell-time ratios on the performance of safety applications with different priorities with EVDA. Simulations show that the proposed scheme clearly outperforms the VDA scheme in high communications density conditions while bounding the transmission delay of safety messages and increasing the packet reception rate.

Recent studies have shown that network coding (NC) directly applied at the physical layer -physical layer network coding (PLNC)- is a promising technique for two-way relay channels (TWRC). Unfortunately, to date, most existing PLNC works have mainly addressed cases of time-invariant channels rather than more realistic multipath time-varying wireless channels. To make further progress in understanding how to optimally implement and design PLNC techniques, we present in this paper, a simple but efficient equivalent interference channel (EIC) model for time-varying TWRC in the flat fading condition. The model captures important parameters such as the phase difference due to the asymmetry of the TWRC in real environment. Based on the EIC model, we derive and analyze some important analytical expressions for the statistical properties of the processes that describe the fading at the different stages of the PLNC in TWRC.

The lifespan of wireless sensors installed on the road for vehicular applications is a critical issue since costly road work and maintenance operations are necessary to replace sensors that cannot be powered. For many of these sensors, the energy of communication represents the largest proportion of the total energy consumed. In this work, we show how we use compressive sensing (CS) to significantly reduce the amount of communications necessary to transmit information about traffic measured by wireless magnetic sensors installed on the road. CS is a new concept in signal acquisition where one seeks to minimize the number of measurements to be taken from signals while still retaining the information necessary to approximate them well. Through measurements of signals carried on wireless sensor nodes, and also with simulations, we show that CS can significantly expand the lifetime of the sensors used and caters for new applications of wireless vehicular sensing that would otherwise be too costly to maintain.

In this work, we present the design of an efficient Deterministic medium Access (DA) for Dedicated Short-Range Communication (DSRC) vehicular safety communication over IEEE 802.11p, called Vehicular DA (VDA). VDA supports two types of safety services (emergency and routine safety messages) with different priorities and strict requirements on delay, especially for emergency safety messages. VDA processes both types of safety messages to maintain a balance between two conflicting requirements: reducing chances of packets collisions and lowering the transmission delay. To avoid long delays and high packets collisions, VDA allows vehicles to access the wireless medium at selected times with a lower contention than would otherwise be possible within two-hop neighborhood by the classical 802.11p EDCA or DCF schemes. Particularly, our scheme provides an efficient adaptive adjustment of the Contention Free Period (CFP) duration to establish a priority between emergency and routine messages. Simulations show that the proposed scheme clearly outperforms the classical DCF scheme used by 802.11p in high-offered load conditions while bounding the transmission delay of safety messages.

Location estimation is a fundamental middleware for enabling location based services. Different Radio propagation models, customarily used in wireless networks planning, can be useful for localizing mobile nodes by estimating the transmitter receiver distance from the Received Signal Strength (RSS). However, most of these localization methods need a prior knowledge of the Effective Isotropic Radiated Power (EIRP) to determine a target location and may suffer from imprecisions that can undermine the purpose of localization. In this paper, we propose a new technique called TR2S2 (Trilateration based on Ratio of Received Signal Strength). Though also based on RSS, the method improves location estimation accuracy compared to classical trilateration algorithms and does not need a knowledge of the EIRP. TR2S2 was applied using different deterministic and statistical radio propagation models in different settings. The results show that location estimation is every time more accurate than other compared methods.

The reliability of most safety applications that are based on vehicular communications, depends in turn on the reliability of data received by each vehicle from its neighbors. Routine messages exchanged in Vehicular Ad hoc Networks (VANETs) include crucial information for safety applications such as direction, position, etc. A vehicle failure and/or a malicious vehicle transmitting false information may affect the data collection scheme and cause a disturbance for safety applications. In such a scenario, (1) the faulty/malicious vehicle should be detected rapidly and (2) routine messages exchange should be updated in consequence. To be able to detect the faulty/malicious vehicle, we developed a mechanism that collects, at a single vehicle, data regarding each neighbour transmission, and extracts the temporal correlation rules between vehicles implicated in transmissions in the neighbourhood. With the mechanism, called VANETs Association Rules Mining (VARM), a mining process will take place during a-priori constant historical period. The associations rules formulated during the mining process will be used to detect a faulty or malicious vehicle, i.e., a vehicle which is not correlated with vehicles in the neighbourhood following these rules. To react after this kind of anomaly detection, an 1:N technique is used as a protection for reestablishing the accuracy of the data collection process between vehicles communicating in the neighbourhood. Simulation results demonstrate the efficiency of the VARM scheme.

Mobility management is a critical issue in vehicular networks. In this paper, we consider the case of highly mobile users in heterogeneous wireless environments. We propose a mobility management, based on a recently proposed mobile IP-based mobility management architecture, optimizing the calculation of its dynamic registration message frequency. The new calculation takes into account both the size of the radio access networks and the velocity of the mobile users. Simulation results show that this approach yields an effective control of the policy function and alleviates the high signaling cost introduced by high registration message frequencies. The derived mobility management thus allows an efficient control of the use of registration messages at congested access networks and guarantees appropriate handoff decisions.

Effective engineering-change management (ECM) is a real challenge in mechanical engineering industry and manufacturing companies. Computer-aided design systems are usually connected to other systems such as ERP or product data management, but currently this integration does not provide effective means to manage engineering change (EC). While communication between multidisciplinary teams working on a project is known to have a significantly positive impact on the ECM, the communication between disciplines is generally performed solely through message exchange. Experts could feel the need to meet to agree on the requested changes, which in turn translates into longer design and manufacturing processes. There is a need for a system that assists human experts in making decisions about ECs. Such a system will considerably reduce the processing time following a change-request procedure. This paper proposes a collaborative tool named EchoMag, which assists designers and experts during the change-management process. The proposed system ensures the coherence of data between the various disciplines involved in the change process. EchoMag also assists experts in making decisions by proposing alternative solutions when change requests are not agreed upon. Software agents were used to implement EchoMag for which a prototype was developed. Results of the implementation are discussed.

We propose a new algorithm for estimating the location of an outdoor RFID node, based on the received signal strength (RSS) of a transmitted message by a set of readers with known locations. The algorithm supposes omni-directional RFID readers that are prevalent for outdoor long range RFID systems. The method does not require knowledge of the Effective Isotropic Power (EIPR) of the transmitting tag, and bounds the position of the node within a small delimited candidate area. Our simulation results demonstrate that the Received Signal Compensation Based Localization (CBL) method outperforms other compared RSS-based methods in improving the precision of the bounded area.

Abstract 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. This 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 in our work defined as single‐hop (SHI‐RQ), extended connectivity (EC‐RQ), and multi‐hop (MHI‐RQ) service requests. A detailed analytical model and cost study for the access protocols are presented. Our 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 analytical results are consistent with the experimental one and show that the access protocols cost in terms latency remains acceptable for a realistic number of serviced vehicles even at high speeds. Copyright © 2009 John Wiley & Sons, Ltd.

Computer-aided design organizations worry much about the management of the communication between the various multidisciplinary teams working on the same project. The good management of this communication is very important for the success of the engineering change management process. Each discipline must approve or reject a change request according to its constraints and inform the other disciplines involved. Currently, the communication between the various disciplines is performed using messages. The approval or the rejection of a change request can be made only if human experts representing these disciplines meet and negotiate, which in turn can be very time consuming. Organizations thus, have a great need for a system which could automate the communication between experts and assist them in making sound engineering change decisions. Such a system could also help by reducing the time needed to verify an engineering change request therefore accelerating the production start-up and the time to market. This paper proposes an agent-based system that can manage the engineering change requests efficiently. Actually, the proposed system checks if the engineering change request does not create any inconsistency with the constraints stemming from the various disciplines. It also assists experts in making decisions by proposing alternative solutions. Each discipline is represented by an expert agent. When an inconsistency is discovered, a negotiation process among expert agents is launched. The system proposed could be easily connected to various CAD tools.

Wireless vehicular communications possess significant challenges for their massive deployment in next generation vehicular networks. Some important issues that must be tackled deal with security, billing and scalability in order to provide reliable services in the case of non-safety applications. In this paper we study a service provisioning protocol intended for Vehicular-to-Infrastructure (V2I) environments where service access is granted by administrative areas in the form of service district domains. To provide scalability of the service, it is necessary to share the current user service parameters between active district domains. Our analysis covers the average response time for requesting on-demand services as well as for sharing service parameters between district domains.

The self-organizing and decentralized features of wireless mobile ad hoc networks make them suitable for a wide variety of applications. In this paper, we explore their use in rescue and relief applications in emergency situations. We propose to study the efficiency of some routing and MAC protocols under the client-server architecture. The presence of dynamic and adaptive routing and MAC protocols will enable ad hoc networks to be formed quickly, and ensure communications during the rescue operations. Extensive simulations were performed to show the impact of both the routing and MAC layer choice over multiple QoS parameters (delay, throughput, energy, etc.) in small and large scales rescue areas. We conclude the paper by some remarks that may be very useful for the relevant agencies to enhance the efficiency of rescue and relief operations.

With the recent developments in wireless networks, different radio access technologies are used in different places depending on capacity in terms of throughput, cell size, scalability etc. In this context, mobile users, and in particular highly mobile users and vehicular networks, will see an increasing number and variety of wireless access points enabling Internet connectivity. Such a heterogeneous networking environment needs, however, an efficient mobility management scheme offering the best connection continuously. In this paper, a mobility management architecture focusing on efficient network selection and timely handling of vertical and horizontal hand-overs is proposed. The solution is based on Mobile IP where hand-over decisions are taken based upon calculations of a metric combining delay and delay jitter. For efficiency reasons, the frequency of binding updates is dynamically controlled, depending on speed and variations in the metric. The dynamic frequency of binding updates helps the timely discovery of congested access points and cell edges so as to allow efficient hand-overs that minimize packet drops and hand-over delays. Results show that the overall signaling cost is decreased and changes in networking conditions are detected earlier compared to standard Mobile IP.

In an effort to develop a simulation framework for evaluating the performance of wireless access technologies for vehicular networks, the authors have studied the internal structure of the simulator, NS-2, as well as the draft wireless technology IEEE 802.11p. This intended framework aims at replicating, as far as possible, the access technology characteristics in the simulator and with the help of realistic scenarios intends to give an objective view of the performance of applications destined for intelligent transport systems. The framework is first designed to meet the requirements set for vehicle to infrastructure communications, with a view of later extending it to vehicle to vehicle communications for vehicular ad hoc networks.

This paper investigates the key elements for a scalable solution of authentication, authorization, and accounting for service delivery in vehicular networks. Different approaches were studied to identify their advantages and disadvantages according to main evaluation parameters, two of which are scalability and latency. Upcoming research efforts need to address these elements to provide an appropriate service provisioning framework for the future deployment of vehicular networks.

Achieving real-time voice communication over a mobile wireless ad hoc network poses many challenges both in speech coding and network protocols. In this paper, we investigate the real-time voice transmission capacity of ad hoc networks using a multiple description coding (MDC) scheme along with a new multi-path routing protocol called MSBR. The MDC generates two or more complementary bitstreams from the bitstream of the speech encoder, each sent along different routes. This allows the receiver to maintain acceptable speech quality even with missing packets. MSBR algorithm attempts to find multiple reliable stable routes to the destination node. Simulations using the GloMoSim network simulator and speech audio evaluation setups were used to study the performances of the approach in different network scenarios. Results show that the MDC scheme, together with MSBR, makes an effective use of the network and provides good performances for real time voice transmission

This paper presents a multi-agent architecture and a supporting data structure suited for the verification of constraints in a mechanical engineering design context. The data structure is composed of three levels, namely an environment level, providing a link to existing mechanical engineering software, an expert level, capturing product requirements and trade knowledge through constraints formulation and finally a product level, dealing with specific products under development. Constraints and models are assigned distinctive attributes which are then exploited by the agents in their verification strategies which are rightly termed as constraints or model driven. Multi-agent task planning provides an additional level of optimization for the operation of agents in their mission to check the product against the various constraints throughout the design process.