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In the realm of network intrusion detection using machine learning, feature selection aims for computational efficiency, enhanced performance, and model interpretability, preventing overfitting and optimizing data visualization. This paper proposes a filtering method for feature selection, which optimizes information quantity and linear correlation between resultant features. The method identifies Pareto dominant pairs of informative and correlated features, constructs a graph, and selects key features based on betweenness centrality in its connected components. The proposal yields a more concise and informative dataset representation. Experimental results, using three diverse datasets, demonstrate that the proposal achieves more than 95% accuracy in classifying network attacks with just 14% of the total number features in original datasets. [ABSTRACT FROM AUTHOR]

Considering the ubiquity of WiFi enabled devices and user's mobility, location privacy within WiFi networks is one of the focus points of many researchers. Preferred Network Lists (PNL), within which devices store a list of names of previously used hotspots - Service Set Identifiers (SSIDs), is transmitted in clear by a portion of devices as a part of WiFi connection protocol. PNL has proven to be one exceptionally interesting source of private data on user's previous whereabouts. However, since the PNL datasets are anonymized, most of the available work focuses on groups of users as opposed to one particular user. In this paper we work towards finding the name of the person behind the device's PNL. We introduce a novel SSID - location tag matching function, followed by an algorithm used for intersecting large PNL datasets with localization tags on Instagram social network. The algorithm enables us to match the user's MAC address and PNL with his full name, photos and activities. We find that deanonymization of a MAC address provides serious implications for potential long term tracking. We tested our work in real life conditions on a large scale music festival. To approach the ground truth we conducted hand check tests performed by 10 testers who concluded that more than 50% of the proposed matches were correct. [ABSTRACT FROM AUTHOR]

Strip-shaped Wireless Sensor Networks (WSNs) with branches are commonly used in various long and narrow applications, such as mines, factories, subways, and pipelines, and they face serious energy hole problems caused by multi-hop communication. The Mobile Data Collector (MDC) can alleviate the energy hole problem. Current solutions have two limitations: one is the balance between energy consumption and delay, and the other is the overly ideal network model, e.g., the square region or circular area. This paper focuses on strip-shaped networks and proposes a novel mobile data collection method to find a trade-off between energy preservation and data delivery delay. Firstly, the MDC path is planned by solving the diameter of the tree in the network, resulting in reduced delay. Secondly, the network energy consumption is further reduced by clustering and optimal transmission distance adjustment. Then, a network lifetime balancing mechanism is designed to balance network energy between backbone and branches. Finally, the performance of the algorithm proposed in this paper has been studied in four types of strip-shaped WSNs and compared with four existing MDC methods with evaluation metrics of maximum node energy consumption, network delay and weighted sum of both. The simulation results demonstrate that the proposed algorithm is applicable to different types of strip-shaped WSNs with branches and achieves excellent network performance, which can effectively balance network energy consumption and data collection delay. [ABSTRACT FROM AUTHOR]

The phenomenon of urbanization, characterized by the migration of people from rural to urban areas, has led to an expansion of existing urban challenges while introducing new ones. Among these, mobility is a primary concern due to its far-reaching impacts on personal health, safety, social, economic, and environmental aspects. Information and communication technologies (ICT) have been identified as effective solutions to address these issues, leveraging the Internet of Things (IoT) and smart city infrastructure. However, the mainstream approach in smart cities is characterized by a vertical-siloed pattern, addressing individual problems (mobility, pollution, energy management, healthcare, safety, and security) in isolation, without actively engaging citizens, people, and communities as stakeholders. This paper proposes a paradigm shift towards a holistic, multilateral approach to address mobility, incorporating diverse perspectives, stakeholder needs, and problem-solving strategies. By integrating smart city infrastructure, smart vehicles, and personal devices, an all-encompassing solution is implemented through direct interaction and cooperation between these entities. The resulting City-Vehicle Participatory-Opportunistic Cooperative Route Navigation system (CV POp-CoRN) enables the enforcement of mobility policy trade-offs, reconciling city, vehicle, and people requirements across various domains, including safety, emergency response, traffic management, travel time optimization, vehicle maintenance, pollution mitigation, and special event management. The paper presents the CV POp-CoRN framework, comprising route navigation policies, a heuristics for trading them off, the system design and architecture, and a model for assessing and demonstrating the effectiveness of the proposed approach and the feasibility of the solution. • Taxonomy definition of multi-objective/perspective urban route navigation policies. • Heuristic to identify RN policy trade-offs combining city and citizen-vehicle needs. • A Cloud-Fog-Edge framework to enforce cooperative policies from city to vehicle devices. • Presentation of a parametric model of the overall system. • Modelization of Smart City infrastructure enabling the CV POp-CoRN System. [ABSTRACT FROM AUTHOR]

This paper introduces a new priority-aware routing protocol for mobile Ad-hoc networks to be utilized in emergencies, which is based on AODV. Mobile Ad-hoc networks find extensive use in various domains including military operations, environmental monitoring, healthcare, disaster response, smart transportation systems, unmanned aerial vehicles, and smart homes. During emergencies, communication can be severely restricted or even impossible due to the congestion of physical communication channels and unexpected technical failures in the infrastructure. Mobile Ad-hoc networks offer a solution to maintain continuous and reliable communication under such challenging conditions. In emergency scenarios, it is crucial for any node in the network to promptly deliver urgent messages to the intended destination, especially when certain nodes require ongoing active communication. The proposed routing protocol effectively addresses this requirement through its priority-aware mechanisms. The protocol ensures that nodes not involved in emergency tasks select the least congested route to prevent any delays or disruptions in the transmission of critical emergency data. This approach guarantees seamless communication for emergency nodes while allowing non-emergency nodes to communicate with each other as well. The study proposed in this paper introduces a new priority-aware routing protocol based on AODV for mobile Ad-hoc networks in emergencies. The packet transmission ratio of emergency nodes within the network is improved while maintaining the overall network performance unaffected. The adoption of proposed mechanisms to enhance performance does not necessitate an expansion in the size of data and control packets. These mechanisms do not inflict any supplementary latency or incur packet loss expenses on the network. The proposed protocol has been implemented and evaluated using ns-3 simulation software across various emergency scenarios. The results show that emergency nodes using the proposed protocol, achieve better packet delivery ratios compared to the original AODV, DSR, P-AODV, and AOMDV protocols, with improvements of 10.8%, 15.9%, 6.2%, and 5.9% respectively. This improvement in the packet delivery ratio for emergency data traffic is achieved without causing any disruptions in the overall network communication flow. [ABSTRACT FROM AUTHOR]

Opportunistic Mobile Ad Hoc Networks (MANETs) offer versatile solutions in contexts where the Internet is unavailable. These networks facilitate the transmission between endpoints using a store-carry-forward strategy, thereby allowing information to be stored during periods of disconnection. Consequently, selecting the next hop in the routing process becomes a significant challenge for nodes, particularly because of its impact on Quality of Service (QoS). Therefore, routing strategies are crucial in opportunistic MANETs; however, their deployment and evaluation in real scenarios can be challenging. In response to this context, this paper introduces a monitoring software-driven tool designed to evaluate the QoS of routing algorithms in physical opportunistic MANETs. The implementation and its components are detailed, along with a case study and the outcomes provided by an implementation of the proposed solution. The results demonstrate the effectiveness of the implementation in enabling the analysis of routing protocols in real scenarios, highlighting significant differences with simulation results: mobility patterns in simulations tend to be inaccurate and overly optimistic, leading to a higher delivery probability and lower latency than what is observed in the real testbed. [Display omitted] • Deploying routing protocols in real scenarios is costly, but simulations often give optimistic results. • This paper introduces a solution to expedite QoS study in MANETs, managing communications and network events. • The approach deploys routing protocols on mobile devices, aiding in QoS evaluation and AI training. • The proposal is implemented in a proof-of-concept smartphone app, offering detailed performance outcomes. • After comparison with the real testbed, simulations highlight optimistic movement models and ignore interferences. [ABSTRACT FROM AUTHOR]

In conventional studies on vehicular edge computing, researchers frequently overlook the high-speed mobility of vehicles and the dynamic nature of the vehicular edge environment. Moreover, when employing deep reinforcement learning to address vehicular edge challenges, insufficient attention is given to the potential issue of the algorithm converging to a local optimal solution. This paper presents a content caching solution tailored for vehicular edge cloud computing, integrating content prediction and deep reinforcement learning techniques. Given the swift mobility of vehicles and the ever-changing nature of the vehicular edge environment, the study proposes a content prediction model based on Informer. Leveraging the Informer prediction model, the system anticipates the vehicular edge environment dynamics, thereby informing the caching of vehicle task content. Acknowledging the diverse time scales involved in policy decisions such as content updating, vehicle scheduling, and bandwidth allocation, the paper advocates a dual time-scale Markov modeling approach. Moreover, to address the local optimality issue inherent in the A3C algorithm, an enhanced A3C algorithm is introduced, incorporating an ɛ -greedy strategy to promote exploration. Recognizing the potential limitations posed by a fixed exploration rate ɛ , a dynamic baseline mechanism is proposed for updating ɛ dynamically. Experimental findings demonstrate that compared to alternative content caching approaches, the proposed vehicle edge computing content caching solution substantially mitigates content access costs. To support research in this area, we have publicly released the source code and pre-trained models at https://github.com/JYAyyyyyy/Informer.git. [ABSTRACT FROM AUTHOR]

With the steep growth of Electric Vehicles (EV's), the consequent demand of energy for charging puts significant load to powergrids. Renewable Energy Sources enabled microgrids can alleviate the problem of energy demand and trade the energy locally in Peer-to-Peer (P2P) manner, where seller (microgrid) and buyer (EV's) "meet" to trade electricity directly on agreed term without any intermediary. However, a foolproof system required for audit and verification of transaction record between seller and buyer to address privacy and security in untrusted and opaque local energy trading market (LETM). Centralized public blockchain enabled system (for audit the transaction records and storage) based on conventional learning models faces mainly two issues in the LETM. (a) if, centralize system runs out of energy and tear down then whole energy trading plunges treated as single point of failure (b) Conventional learning models fail to converge optimal point in case of large state and action space (large number of EV's and their energy demand). The primary objective of this paper to provide secure system for LETM, 1) Distributed nature of Consortium Blockchain used that solve the problem of single point of failure to audit and storage of transaction and profile info of microgrids and EV's. 2) Quantum based Reinforcement Learning (QRL) easily handles the large number of EV's energy supply and demand for smoothly run LETM. In this context, this paper presents Blockchain and Quantum Machine Learning driven energy trading model for EVs (B-MET). A utility maximization problem formulated as Markov Decision Process (MDP) and their solution provided by using QRL focusing on join optimization of selling price, loan amount and quantity of shared energy. MDP is a mathematical framework used to model decision-making in situations where outcomes are partly random and partly under the control of a decision-maker, i.e., the future state depends only on the current state and action, not on the sequence of events that preceded it. QRL method combines quantum theory with traditional RL. It is inspire by the principles of state superposition and quantum parallelism. Convergence analysis and performance results attest that B-MET convergences faster, maximizes the utility with lower confirmation delay in P2P energy trading as compare to state of the art techniques. [ABSTRACT FROM AUTHOR]

The data security of CAN bus system is receiving increasing attention with the rapid development of Internet of Vehicles (IoV). However, traditional ciphers are not the best choice due to the limitations of computation, real-time, and resources of Electronic Control Units in vehicles. Thus, this paper proposes a lightweight block cipher IoVCipher to protect the security of IoV. It is designed focus on the latency and area in round-based architectures (both encryption and decryption) to meet this resource-constrained environments. For this purpose, two S-boxes with low latency and tiny area are constructed in this paper, one involution and one non-involution. Considering the decryption latency, a low latency subkey generation method is designed. In addition, this paper proposes a new extended MISTY structure that makes the encryption and decryption of hardware implementations similar. In comparison to other low-latency lightweight block ciphers such as PRINCE, QARMA, MANTIS and LLLWBC, IoVCipher achieves an effective balance between latency and area in the round-based architecture, and IoVCipher has low latency, low area, and low energy in the fully unrolled architecture. Finally, IoVCipher is implemented on a real-time speed acquisition and encryption testbed to simulate encrypted transmission of real-time speed in a CAN bus environment. [ABSTRACT FROM AUTHOR]

With the widespread popularity of smart phones, watches and other devices, mobile crowdsensing (MCS) has gradually entered the public's field of vision. However, the widespread use of MCS technology is accompanied by an increasing risk of workers' personal privacy being violated. Typically, workers are required to submit location information in order to participate in task assignments, and the privacy of a worker's location information is a key factor in determining worker participation. Therefore, in order to solve the problem of workers location privacy leakage in the process of task allocation, this paper proposes a location privacy protection method based on local differential privacy (VLDPP). VLDPP constructs a task map based on Voronoi diagram according to the task location, and each task location is mapped into a task area to hide the task location. A local coordinate system is constructed based on the task area, and all workers in the area have their relative location coordinates recalculated and encoded, and then the encoding is perturbed by local differential privacy, thus ensuring workers location privacy. The worker uploads the perturbed location information to the server, which determines the workers in the task by calculating the worker's acceptance rate and completes the task allocation. In addition, this paper improves the availability of perturbed worker location information by dividing the task area at a secondary level. This paper uses the internationally recognized World Check-In Gowalla dataset for experimental evaluation, which shows that the proposed method has good performance in terms of data availability and efficiency, providing adequate privacy guarantees. • This paper presents a hierarchical task map based on Voronoi diagram, which enables workers to determine the nearest task point, thus reducing the travel distance. • This paper proposes a location protection method based on local differential privacy to ensure user privacy while encouraging users to participate in tasks. • The grouping coding obfuscation method proposed in this paper can save privacy budget and is verified by real data sets. The method in this paper is superior to other methods in terms of time, error and travel distance. [ABSTRACT FROM AUTHOR]

With the rapid development of mobile communications, the dense deployment of small base stations (SBSs) in heterogeneous wireless networks (HW-Nets) has met the explosive growth in demand from terminals. However, due to the irregular deployment of SBSs, users frequently handover base stations in pursuit of high-quality services, which inevitably leads to load imbalance among SBSs. To alleviate load imbalance in HW-Nets and improve the efficiency of seamless handover, this paper proposes a distributed auction algorithm scheme based on dual active protocol stack (DAPS), which transforms the wireless access selection of terminals into the terminal-base station association process during movement to globally optimize network resource utilization. At the same time, this paper constructs a terminal-base station association graph and uses auction algorithms to solve the maximum weighted matching of the graph. The DAPS-based scheme can effectively avoid the problem of excessively long outage time caused by handovering outages. Simulation results show that compared with general distributed algorithms, the distributed auction algorithm based on DAPS can effectively balance the load of base stations and reduce the mobile outage latency by 59%. [ABSTRACT FROM AUTHOR]

The rapid proliferation of consumer UAVs, or drones, is reshaping the wireless communication landscape. These agile, autonomous devices find new life as UE in cellular networks. This paper explores their integration, emphasizing the myriad applications, standardization efforts, challenges, and research community solutions. Key areas of investigation include the complexities of 3D deployment, channel modelling, and energy efficiency. Moreover, we highlight the open questions and research opportunities these flying UEs present. The evolving landscape of UAV integration into cellular networks promises transformative enhancements for next-generation communications, addressing challenges while fostering innovation across industries. The paper encapsulates the essential aspects of UAV integration within the cellular ecosystem, offering a concise yet comprehensive overview of this dynamic field, where UAVs as UEs redefine wireless communication with promise and complexity. [ABSTRACT FROM AUTHOR]

Cyber–physical systems (CPSs) integrate network and physical components. The relevant network security issues have been receiving consistent attention. This paper considers cyber–physical systems whose control resilience is characterized by uncertainty, mechanical motion constraints, and exposure to cyber-attacks. To address these problems, this paper proposes a game-theoretic method based on constraint following theory where the moving trajectory requirements are considered as constraints. There can be adversary network environment which may launch cyberattacks and even deny providing service. With a creative control design, it is validated that the controlled system is resilient to cyberattack disturbance and other spoofing or mixed threat attacks. Three game rules, one non-cooperative game and two Stackelberg competitions, are adopted in this study to obtain the optimal control design parameter choice. It is proven that the optimal choice is the same from the three rules, thus making this optimal choice generic (i.e., nonspecific) and important. The superiority of the optimal control design parameter is demonstrated in simulations. [ABSTRACT FROM AUTHOR]

Recently, there has been a substantial interest in on-device Machine Learning (ML) models to provide intelligence for the Internet of Things (IoT) applications such as image classification, human activity recognition, and anomaly detection. Traditionally, ML models are deployed in the cloud or centralized servers to take advantage of their abundant computational resources. However, sharing data with the cloud and third parties degrades privacy and may cause propagation delay in the network due to a large amount of transmitted data impacting the performance of real-time applications. To this end, deploying ML models on-device (i.e., on IoT devices), in which data does not need to be transmitted, becomes imperative. However, deploying and running ML models on already resource-constrained IoT devices is challenging and requires intense energy consumption. Numerous works have been proposed in the literature to address this issue. Although there are considerable works that discuss energy-aware ML approaches for on-device implementation, there remains a gap in the literature on a comprehensive review of this subject. In this paper, we provide a review of existing studies focusing on-device ML models for IoT applications in terms of energy consumption. One of the key contributions of this study is to introduce a taxonomy to define approaches for employing energy-aware on-device ML models on IoT devices in the literature. Based on our review in this paper, our key findings are provided and the open issues that can be investigated further by other researchers are discussed. We believe that this study will be a reference for practitioners and researchers who want to employ energy-aware on-device ML models for IoT applications. [ABSTRACT FROM AUTHOR]

The reconfigurable intelligent surface (RIS) assisted crowd spectrum detection (CSD) has emerged as a promising approach for achieving higher enhancements in both spectrum efficiency (SE) and energy efficiency (EE). Nevertheless, due to the double fading effect in reflecting links, the detection performance gains obtained through current passive RIS are negligible, especially in typical communication scenarios. As a result, the performance enhancement in passive RIS-assisted CSD is ineffective. To address the issue, this paper incorporates the advanced active RIS into CSD to assist the spectrum requestor (RU) in achieving notable and effective improvement in detection performance gains. Through amplifying the amplitude of reflected signals, active RIS can further enhance the received signal power at the RU while consuming more energy than the passive RIS. Therefore, for enabling both effective and efficient CSD with the assistance of active RIS, this paper investigates the detection efficiency (DE) maximization problem by jointly optimizing the RU's sampling number and reward budget that is paid to compensate active RIS controllers for amplifying reflected signals. In a word, the key point of this paper is to evaluate whether the active RIS-assisted CSD can outperform the passive RIS-assisted CSD in both effective and efficient detection performance gains by joint parameter optimization. Quadratic transform (QT) and particle swarm optimization (PSO) methods are leveraged to solve the DE maximization problem. Finally, extensive simulation results demonstrate that, when taking the no-RIS mechanism as the benchmark, the proposed active RIS-assisted CSD can outperform the conventional passive-RIS mechanism in terms of significantly improved detection performance gains and a higher DE in most simulation scenarios. [ABSTRACT FROM AUTHOR]

This paper deals with the physical layer security for multi-unmanned aerial vehicle (UAV) assisted transmission of integrated sensing and communication (ISAC) signals from a dual-function base station (BS) to the ground user. Here, an active eavesdropper (Eve) on the ground eavesdrops on the information transmitted by the UAV network to the user. We propose a Stackelberg game model for the game problem of information security transmission between UAV and Eve, with perfect or imperfect channel state information (CSI) and cost information. To deal with the Stackelberg game problem, the dual optimization theory and KKT conditions are used to solve it, then we prove the existence and uniqueness of Stackelberg equilibrium (SE). At the same time, considering that the influence of ISAC signal's sensing of Eve's location on the distance between Eve and the user, and thus on the utility function of both sides of the game, this paper uses the Cramer–Rao bound (CRB) to represent the accurate performance of location sensing. Then the relaxation method is used for the non-convex optimization problem, and convex optimization tools can be used subsequently. Finally we notice that both operations have their own optimal resolution strategy, it is difficult to reach agreement, therefore an overall algorithm is proposed to solve the whole problem iteratively. In the simulation results, the effect of power on game and location sensing, and the effect of the distance between Eve and the user on the utility function of both sides of the game are given. In addition, we give the relationship between the CRB threshold and the transmit power, and verify the convergence of the game and overall algorithm. [ABSTRACT FROM AUTHOR]

This paper proposes an Unmanned aerial vehicle (UAV) aided content management system in communication-challenged disaster scenarios. Without cellular infrastructure in such scenarios, communities of stranded users can be provided access to situation-critical content using a hybrid network of static and traveling UAVs. A set of relatively static anchor UAVs with vertical as well as lateral links can provide content access to its local users. A set of ferrying UAVs with only lateral links, but with wider mobility, can provision content to users while visiting their communities. The objective is to design a content dissemination system that learns caching policies on-the-fly for maximizing content availability to the users. This paper proposes a distributed Federated Multi-Armed Bandit (MAB) Learning technique for UAV-caching decisions in the presence of geo-temporal differences in content popularity and heterogeneity in content demands. The proposed mechanism is able to combine the expected reward maximization attribute of Multi-Armed Bandit, and distributed intelligence sharing nature of Federated Learning for caching decision at the UAVs. It is demonstrated that Federated aggregation of individual MAB models can improve system performance while making the learning fast and adaptive. This analysis is done for different user-specified tolerable access delay, heterogeneous popularity distributions, and inter-community geographical characteristics. The paper does functional verification and performance evaluation of the proposed caching framework under a wide range of network size, UAV distribution, content popularity, and ferrying UAV trajectories. [ABSTRACT FROM AUTHOR]

This paper constructs a NOMA-based UAV-assisted Cellular Offloading (UACO) framework and designs a UAV path selection and resource offloading algorithm (UPRA) based on deep reinforcement learning. This paper focuses on the coupling relationship between path selection and resource offloading during the movement of UAVs. A joint optimization problem between UAV three-dimensional path design and resource offloading is proposed, considering the UAV's autonomous obstacle avoidance in complex environments and the influence of obstacles in 3D space on the channel model. In particular, a constrained clustering-assignment algorithm is designed by improving the K-means algorithm and combining it with the assignment algorithm in order to achieve periodic clustering of random motion users and UAV task assignment. In addition, a semi-fixed hierarchical power allocation algorithm is embedded in the designed DQN algorithm to improve the convergence performance of the learning algorithm in this paper. Simulation results show that: the NOMA-based design is able to improve the spectrum utilization efficiency and communication throughput of the UAV network system. Compared with the artificial potential field, the proposed algorithm is able to solve the problem of falling into suboptimal solutions in path selection and improve the communication throughput. In addition, this paper explores the impact of the reward function on the training convergence and the results in deep reinforcement learning. The excellent adaptability of the designed algorithm in dynamic networks as well as in complex environments is demonstrated by random deployment of users and varying the maximum user movement speed. [ABSTRACT FROM AUTHOR]

Recently, the use of unmanned aerial vehicles (UAV)s for accomplishing various tasks has gained a significant interest from both civilian and military organizations due to their adaptive, autonomous, and flexibility nature in different environments. The characteristics of UAV systems introduce new threats from which cyber attacks may benefit. Adaptive security solutions for UAVs are required to counter the growing threat surface. The security of UAV systems has therefore become one of the fastest growing research topics. Machine learning based security mechanisms have a potential to provide effective countermeasures that complement traditional security mechanisms. The main motivation of this survey is to the lack of a comprehensive literature review about reinforcement learning based security solutions for UAV systems. In this paper, we present a comprehensive review on the security of UAV systems focusing on deep-reinforcement learning-based security solutions. We present a general architecture of an UAV system that includes communication systems to show potential sources of vulnerabilities. Then, the threat surface of UAV systems is explored. We explain attacks on UAV systems according to the threats in a systematic way. In addition, we present countermeasures in the literature for each attack on UAVs. Furthermore, traditional defense mechanisms are explained to highlight requirements for reinforcement based security solutions on UAVs. Next, we present the main reinforcement algorithms. We examine security solutions with reinforcement learning algorithms and their limitations in a holistic approach. We also identify research challenges about reinforcement based security solutions on UAVs. Briefly, this survey provides key guidelines on UAV systems, threats, attacks, reinforcement learning algorithms, the security of UAV systems, and research challenges. [ABSTRACT FROM AUTHOR]

This paper investigates a Reconfigurable Intelligent Surface (RIS)-assisted multi-UAV data collection system, in which unmanned aerial vehicles (UAVs) collect data from Internet of Things (IoT) devices. The RIS, mounted on building surfaces, plays a vital role in preventing obstruction and improving the communication quality of the IoT-UAV transmission link. Our aim is to minimize the energy consumption of this system, including the transmission energy consumption of IoT devices and the hovering energy consumption of UAVs, by optimizing the deployment of UAVs and the phase shifts of RIS. To achieve this goal, a multi-UAV deployment and phase shift of RIS optimization algorithm (MUDPRA) is proposed that consists of two phases. In the first phase, a joint differential evolution (DE) algorithm with a two-layer structure featuring a variable population size, namely DEC-ADDE, is proposed to optimize the UAV deployment. Specifically, each UAV's location is encoded as an individual, with the whole UAV deployment is considered as the population in DEC-ADDE. Thus, a differential evolution clustering (DEC) algorithm is employed initially to initialize the population, which allows for obtaining better initial UAV deployment without the need for a predefined number of UAVs. Subsequently, an adaptive and dynamic DE algorithm (ADDE) is employed to produce offspring population to further optimize UAV deployment. Finally, an adaptive updating strategy is adopted to adjust the population size to optimize the number of UAVs. In the second phase, a low-complexity method is proposed to optimize the phase shift of RIS with the aim of enhancing the IoT-UAV data transmission rate. Experimental results conducted on eight instances involving IoT devices ranging from 60 to 200 demonstrate the effectiveness of MUDPRA in minimizing energy consumption of this system compared to six alternative algorithms and three benchmark systems. [ABSTRACT FROM AUTHOR]

Vehicle edge computing (VEC) enhances the distributed task processing capability within intelligent vehicle-infrastructure cooperative systems (i-VICS) by deploying servers at the network edge. However, the proliferation of onboard sensors and the continual emergence of new applications have exacerbated the inadequacy of wireless spectrum resources and edge server resources, while the high mobility of vehicles reduces reliability in task processing, resulting in increased communication and task processing delays. To address these challenges, we propose a mobile-aware Many-to-Many Parallel (MTMP) offloading scheme that integrates: a) millimeter-wave (mmWave) and cellular vehicle-to-everything (C-V2X) to mitigate excessive communication delays; and b) leveraging the underutilized resources of surrounding vehicles and parallel offloading to mitigate excessive task processing delays. To minimize the average completion delay of all tasks, this paper formulates the objective as a min-max optimization problem and solves it using the maximum entropy method (MEM), the Lagrange multiplier method, and an iterative algorithm. Extensive experimental results demonstrate the superior performance of the proposed scheme in comparison with other baseline algorithms. Specifically, our proposal achieves a 47 % reduction in task completion delay under optimal conditions, a 31.3 % increase in task completion rate, and a 30 % decrease in program runtime compared to the worst-performing algorithm. [ABSTRACT FROM AUTHOR]

Due to the impact of turbulence, atmospheric optical wireless channel exhibits characteristics such as time-varying, space-varying and natural randomness, which can be used as a common natural random source for shared secret key extraction. Wireless laser communication technology boasts advantages like high bandwidth and fast transmission, which is conducive to improving key generation rate. Additionally, the strong anti-interference of laser signal helps to reduce key disagreement rate. Moreover, the laser beam's good directionality effectively decreases the risk of eavesdropping on key information. Given its advantages and a scarcity of research in this regard, this paper proposes a scheme of shared secret key extraction from atmospheric optical wireless channels with multi-scale information reconciliation. In the scheme, to increase the cross-correlation coefficient of signal samples at the two legitimate parties, a preprocessing algorithm is designed based on a denoising algorithm and a threshold-based outliers elimination algorithm, and the denoising algorithm is inspired by the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDan); moreover, a multi-level quantization algorithm based on Equilibrium-Optimizer(EO) is developed to balance and optimize distribution of sample points in the sample space; furthermore, to simplify the process of and decrease the computational complexity of information reconciliation, a concept of a multi-scale information reconciliation is formed, on the basis of which three algorithms, B-MSIR, I-MSIR and C-MSIR, are formulated. Finally, its performance is verified by numerical simulations and experiments, and the results show it has better performance in terms of the key disagreement rate, the key generation rate and the key randomness compared with several state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

This paper delves into optimizing network lifetime (NL) subject to connected-coverage requirement, a pivotal issue for realistic wireless sensor network (WSN) design. A key challenge in designing WSNs consisting of energy-limited sensors is maximizing NL, the time a network remains functional by providing the desired service quality. To this end, we introduce a novel NL metric addressing target-specific coverage requirements that remedies the shortcomings imposed by conventional definitions like first node die (FND) and last node die (LND). In this context, while we want targets to be sensed by multiple sensors for a portion of the network lifetime, we let the periods, during which cells are monitored by at least one sensor, vary. We also allow the ratios of multiple and single tracking times to differ depending on the target and incorporate target-based prioritization in coverage. Moreover, we address role assignment to sensors and propose a selective target-sensor assignment strategy. As such, we aim to reduce redundant data transmissions and hence overall energy consumption in WSNs. We first propose a unique 0-1 mixed integer programming (MIP) model, to analyze the impact of our proposal on optimal WSN performance, precisely. Next, we present comprehensive comparative studies of WSN performance for alternative NL metrics regarding different coverage requirements and priorities across a wide range of parameters. Our test results reveal that by utilizing our novel NL metric total coverage time can be improved significantly, while facilitating more reliable sensing of the target region. [ABSTRACT FROM AUTHOR]

Intelligent Transportation Systems (ITS) faces significant challenges in achieving its goal of sustainable and efficient transportation. These challenges include real-time data processing bottlenecks caused by high communication latency and security vulnerabilities related to centralized data storage. We propose a novel architecture that leverages Edge Computing and Distributed Ledger Technology (DLT) to address these concerns. Edge computing pushes cloud services, such as vehicles and roadside units, closer to the data source. This strategy reduces latency and network congestion. DLT provides a secure, decentralized platform for storing and sharing ITS data. Its tamper-proof nature ensures data integrity and prevents unauthorized access. Our architecture utilizes these technologies to create a decentralized platform for ITS data management. This platform facilitates secure processing, storage, and data exchange from various sources in the transportation network. This paper delves deeper into the architecture, explaining its essential components and functionalities. Additionally, we explore its potential applications and benefits for ITS. We describe a case study focusing on a data marketplace system for connected vehicles to assess the architecture's effectiveness. The simulation results show an average latency reduction of 83.35% for publishing and 87.57% for purchasing datasets compared to the cloud architecture. Additionally, transaction processing speed improved by 18.73% and network usage decreased by 96.67%. The proposed architecture also achieves up to 99.61% reduction in mining centralization. • An architecture based on Edge Computing and DLT for secure and efficient ITS. • Edge Computing offers low-latency communication and efficient bandwidth utilization. • DLT creates a tamper-proof and decentralized ledger to store ITS data securely. [ABSTRACT FROM AUTHOR]

The deployment of Roadside Units (RSUs) in the Cellular-Vehicle to Everything enabled Internet of Vehicles is crucial for the transition from individual intelligence of vehicles to collective intelligence of vehicle-road collaboration. In this paper, we focus on improving the adaptability of RSU deployment to real scenarios, and optimizing deployment costs and vehicle-oriented service performance. The RSU deployment problem is modeled as a Multi-objective Optimization Problem (MOP), with a cost model integrating the purchase and installation costs, and a service-oriented Quality of Service (QoS) model adopting the total time the RSUs cover the vehicles as the evaluation metric. Specifically, we propose an RSU reliable coverage analysis method based on Packet Delivery Ratio model to estimate the coverage distances in different scenarios, which will be used in QoS calculation. Then, an evolutionary RSU deployment algorithm is designed to solve the MOP. The performance of the proposed method is simulated and discussed in real road network and dynamic scenarios. The results prove that our method outperforms the baseline method in terms of significant cost reduction and total coverage time improvement. [ABSTRACT FROM AUTHOR]

In the context of rapidly urbanizing smart cities reliant on IoT networks, efficient load management is critical for sustainable energy use. This paper proposes an AI-enhanced Multi-Stage Learning-to-Learning (MSLL) approach tailored for secure load management in IoT networks. The proposed approach leverages MMStransformer, a transformer-based model designed to handle multivariate, correlated data, and to capture long-range dependencies inherent in load forecasting. MMStransformer employs a multi-mask learning-to-learning strategy, optimizing computational efficiency without compromising prediction accuracy. The study addresses the dynamic and complex nature of smart city data by integrating diverse environmental and operational variables. Security and privacy concerns inherent in IoT networks are also addressed, ensuring secure data handling and communication. Experimental results demonstrate the efficacy of the proposed approach, achieving competitive performance compared to traditional methods and baseline models. The findings highlight the potential of AI-driven solutions in enhancing load forecasting accuracy while ensuring robust security measures in smart city infrastructures. This research contributes to advancing the state-of-the-art in AI applications for sustainable urban development and energy management. [ABSTRACT FROM AUTHOR]

Urban transportation networks are vital for the economic and environmental well-being of cities and they are faced with the integration of Human-Driven Vehicles (HVs) and Connected and Autonomous Vehicles (CAVs) challenge. Most of the traditional traffic management systems fail to effectively manage the dynamic and complex flows of mixed traffic, mainly because of large computational requirements and the restrictions that control models of traffic lights directly based on extensive and continuous training data. Most of the times, the operational flexibility of CAVs is severely compromised for the safety of HVs, or CAVs are given high priority without taking into account the efficiency of HVs leading to lower performance, especially at low CAV penetration rates. On the other hand, the existing adaptive traffic light approaches were usually partial and could not adapt to the real-time behaviors of the traffic system. Some systems operate with inflexible temporal control plans that cannot react to variations in traffic flow or use adaptive control strategies that are based on a limited set of static traffic conditions. This paper presents a novel traffic light control approach utilizing the BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies) clustering algorithm combined with digital twins for a more adaptive and efficient system. The BIRCH is effective in processing large datasets because it clusters data points incrementally and dynamically into a small set of representatives. The suggested method does not only enable better simulation and prediction of traffic patterns but also makes possible the real-time adaptive control of traffic signals at signalized intersections. It also improves traffic flow, reduces congestion, and minimizes vehicle idling time by adjusting the green and red light durations dynamically based on both real-time and historical traffic data. This approach is assessed under different traffic intensities, which include low, moderate, and high, while efficiency, fuel consumption, and the number of stops are being compared with the traditional and the existing adaptive traffic management systems. • Studying traditional and adaptive traffic control systems comprehensively. • Improving data processing for real-time and historical traffic light control and prediction. • Evaluating the suggested method across high, moderate, and low traffic conditions. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) have opened new communication possibilities by being able to access remote areas. Their ability to serve a large number of users based on demand and adaptability is a key strength. In Sixth Generation (6G) networks, UAVs are highly valued for their cost-efficiency and versatile deployment. However, the mobility of UAVs introduces different types of interference issues, resulting in a decrease in network performance and quality of service (QoS) for edge users. To address these challenges, this paper introduces a clustering-based solution involving three main steps. Firstly, UAVs are deployed in three-dimension (3D) space based on user requests using mini-batch K-mean clustering Subsequently, re-clustering is explored to tackle load balancing within clusters. Finally, outliers and boundary users are classified to enhance QoS for edge users. This model effectively reduces interference and boosts UAV reliability in terrestrial networks. Also, a case study is presented to show how UAVs can mitigate interference in maritime communication within terrestrial networks. Numerical results demonstrated that the proposed scheme increases throughput by 33.06% and reduces energy consumption and time delay by 73.15% and 9.15%, respectively, as compared to the existing baseline schemes. [ABSTRACT FROM AUTHOR]

Internet applications such as video gaming virtual/ augmented reality necessitate efficient fifth-generation (5G) millimeter-wave (mmWave) cellular networks. The Transmission Control Protocol (TCP) is an essential protocol for network connectivity. However, TCP faces challenges in efficiently utilizing the available bandwidth of 5G mmWave cellular networks while maintaining low latency, mainly due to constraints like Non-Line of Sight (NLoS) conditions. This paper introduces Round-Trip-Time Variations-TCP (RTTV-TCP), enhancing TCP performance in 5G mmWave cellular networks. Simulation scenarios for a 5G mmWave cellular network have been conducted to evaluate RTTV-TCP's performance, comparing it to legacy TCP variants such as NewReno, HighSpeed, CUBIC, Bottleneck Bandwidth and Round-trip propagation time (BBR), FB-TCP (Fuzzy Based-TCP). The results demonstrate that RTTV-TCP achieves higher average throughput than these TCP variants while maintaining the same level of delay in 5G mmWave cellular networks. RTTV-TCP outperforms NewReno and CUBIC by a very significant margin, demonstrating a 208% improvement compared to HighSpeed and a 6% increase compared to BBR protocol in the worst Packet Error Rate (PER) scenario and when the buffer size matches the Bandwidth Delay Product (BDP). [ABSTRACT FROM AUTHOR]

The introduction of fifth-generation (5G) technology marks a significant milestone in next-generation networks, offering higher data rates and new services. Achieving optimal performance in 5G and beyond 5G (B5G) systems requires addressing key requirements like increased capacity, high efficiency, improved performance, low latency, support for many connections, and quality of service. It is well-known that suboptimal network configuration, hardware impairments, or malfunctioning components can degrade system performance. The physical layer of the radio access network, particularly channel estimation and synchronization, plays a crucial role. Hence, this paper offers an in-depth evaluation of the 5G Physical Downlink Shared Channel (PDSCH), along with its related channel models such as the Clustered Delay Line (CDL) and the Tapped Delay Line (TDL). This work assesses 5G network performance through practical and IA-based channel estimation and synchronization techniques, and anticipates numerologies for B5G networks. Extensive simulations leveraging the Matlab 5G New Radio (NR) toolbox assess standardized channel scenarios in both macro-urban and indoor environments, following configurations set by the 3rd Generation Partnership Project (3GPP). The numerical results offer valuable insights into achieving the maximum achievable throughput across various channel environments, including both line-of-sight (LoS) and non-line-of-sight (NLoS) conditions. The throughput comparisons are performed under assumptions of ideal, realistic, and convolutional neural networks (CNN)-based channel estimation with both perfect and realistic synchronization conditions. Importantly, the study pinpoints certain physical layer elements that have a pronounced impact on system performance, providing essential insights for devising effective strategies or refining CNN-based methods for forthcoming mobile B5G networks. [ABSTRACT FROM AUTHOR]

Wireless power transfer (WPT) provides a promising technology for energy replenishment of wireless rechargeable sensor networks (WRSNs), where wireless chargers can be deployed at fixed locations for charging nodes simultaneously within their effective charging range. Optimal charger placement (OCP) for sustainable operations of WRSN with cheaper charging cost is a challenging and difficult problem due to its NP-completeness in nature. This paper proposes a novel reinforcement learning (RL) based approach for OCP, where the problem is firstly formulated as a charging cluster determination problem with a fixed clustering radius and then tackled by the reinforcement learning-based charging cluster determination (RL-CCD) algorithm. Specifically, nodes are coarsely clustered by the K-Means++ algorithm, with chargers placed at the cluster center. Meanwhile, RL is applied to explore the potential locations of the cluster centers to adjust the center locations and reduce the number of clusters, using the number of nodes in the cluster and the summation of distances between the cluster center and nodes as the reward. Moreover, an experience-strengthening mechanism is introduced to learn the current optimal charging experience. Extensive simulations show that RL-CCD significantly outperforms existing algorithms. [ABSTRACT FROM AUTHOR]

In emergency rescue, target search and other mission scenarios with Unmanned Aerial Vehicles (UAVs), the Relay UAVs (RUs) and Mission UAVs (MUs) can collaborate to accomplish tasks in unknown environments. In this paper, we investigate the problem of trajectory planning and power control for the MU and RU collaboration. Firstly, considering the characteristics of multi-hop data transmission between the MU and Ground Control Station, a multi-UAV collaborative coverage model is designed. Meanwhile, a UAV control algorithm named MUTTO is proposed based on multi-agent reinforcement learning. In order to solve the problem of the unknown information about the number and locations of targets, the geographic coverage rate is used to replace the target coverage rate for decision making. Then, the reward functions of two types of UAVs are designed separately for the purpose of better cooperation. By simultaneously planning the trajectory and transmission power of the RU and MU, the mission target coverage rate and network transmission rate are maximized while the energy consumption of the UAV is minimized. Finally, numerical simulations results show that MUTTO can solve the UAV network control problem in an efficient way and has better performance than the benchmark method. [ABSTRACT FROM AUTHOR]

The advent of wireless technologies has led to the development of novel services for end-users, with stringent needs and requirements. High availability, very high throughput, low latency, and reliability are all of them crucial performance parameters. To address these demands, emerging technologies, such as non-terrestrial networks or millimeter wave (mmWave), are being included in 5G and Beyond 5G (B5G) specifications. mmWave enables massive data transmissions, at the expense of a more hostile propagation, typical for high frequency bands. Consequently, the inherent instability of the physical channel significantly affects the upper layers of the protocol stack, resulting in congestion and data losses, which might strongly hinder the overall communication performance. These challenges can be addressed not only at the link layer, but at any affected layer. QUIC is a new transport protocol designed to reduce communications latency in many ways. Among other features, it enables the use of multiple streams to effectively manage data flows sent through its underlying UDP socket. This paper introduces an implementation of priority-based stream schedulers along with the design of a flexible interface. Exploiting the proposed approach, applications are able to set the required scheduling scheme, as well as the stream priorities. The feasibility of the proposed approach is validated through an extensive experiment campaign, which combines Docker containers, the ns-3 simulator and the Mahimahi framework, which is exploited to introduce realistic mmWave channel traces. The results evince that an appropriate stream scheduler can indeed yield lower delays for time-sensitive applications by up to 36% under unreliable conditions. [Display omitted] • Adaptable multi-stream scheduling in QUIC. • Performance evaluation of QUIC over mmWave. • Emulation of real QUIC implementation over ns-3 and Mahimahi. [ABSTRACT FROM AUTHOR]

With the rapid development of Internet of Things (IoT) technology in industrial, agricultural, medical and other fields, IoT terminal devices face security and privacy challenges when sharing data. Among them, ensuring data confidentiality, achieving dual-side privacy protection, and performing reliable data integrity verification are basic requirements. Especially in resource-constrained environments, limitations in the storage, computing, and communication capabilities of devices increase the difficulty of implementing these security safeguards. To address this problem, this paper proposes a resource-constrained anonymous data-sharing scheme (ADS-RC) for the IoT. In ADS-RC, we use elliptic curve operations to replace computation-intensive bilinear pairing operations, thereby reducing the computational and communication burden on end devices. We combine an anonymous verifiable algorithm and an attribute encryption algorithm to ensure double anonymity and data confidentiality during the data-sharing process. To deal with potential dishonest behavior, this solution supports the revocation of malicious user permissions. In addition, we designed a batch data integrity verification algorithm and stored verification evidence on the blockchain to ensure the security and traceability of data during transmission and storage. Through experimental verification, the ADS-RC scheme achieves reasonable efficiency in correctness, security and efficiency, providing a new solution for data sharing in resource-constrained IoT environments. [ABSTRACT FROM AUTHOR]

The new radio unlicensed (NR-U) technology is proposed by 3GPP to extend NR to the unlicensed spectrum because of the shortage of the licensed spectrum. Different from the ground and fixed communication equipment-based unlicensed spectrum access scheme, the unmanned aerial vehicle (UAV) mobile platform-based unlicensed spectrum access scheme is not only related to incumbent users but also its trajectory and resource allocation. Therefore, this paper proposes a hybrid unlicensed spectrum access scheme for the UAV-assisted unlicensed mobile edge computing (MEC) communication (UAUM) system, where each flight time slot of the UAV is divided into two parts: power free (PF) and power controlled (PC) stages. In the PF stage, the transmit power is only restrained by the unlicensed spectrum regulations, and thus the UAV can provide high-rate services for real-time downlink users (RDUs) and uplink computing users (UCUs). In the PC stage, the transmit power of the UAV is mainly restrained by the interference to WiFi devices, and thus UAV can be allowed to provide low-rate services for non-realtime downlink users (NDUs) without affecting WiFi users. Based on the proposed scheme, a multi-variable optimization problem regarding trajectory, bandwidth, transmit power, and duty cycle is formulated to maximize the total offloaded computing bits on the premise of ensuring the quality of experience of RDUs, NDUs, and WiFi users under the maximum energy budget. To solve this problem efficiently, we propose an iterative algorithm based on the block coordinate descent method and successive convex approximation technique to decompose the original problem into four optimization subproblems of trajectory, bandwidth, transmit power and duty cycle, which are then solved alternatively in an iterative manner. A large number of simulation results demonstrate that in terms of spectrum efficiency and total offloaded computing bits, the proposed algorithm outperforms other unlicensed spectrum access schemes and optimization algorithms. The other performances of the proposed algorithm are deeply evaluated to prove its effectiveness and feasibility. [ABSTRACT FROM AUTHOR]

Ensuring secure authentication between participating entities in VANETs has emerged as a critical challenge. Most of existing schemes mainly consider authentication issue in single administrative domain and suffer from various limitations that include privacy-preserving and malicious entity tracking. This paper proposes a double-layer blockchain-assisted conditional privacy-preserving cross-domain authentication scheme (DBCPCA) that leverages blockchain technology and certificate-less signatures to address these challenges. In DBCPCA, the upper-layer blockchain is used in cross-domain authentication by sharing inter-domain information among multiple different administrative domains. The lower-layer blockchain is employed in intra-domain authentication. In DBCPCA, we also introduce an anonymity mechanism to protect the real identity of a vehicle while enabling the system to trace a malicious vehicle, thereby addressing conditional privacy-preserving concerns. In addition, a security analysis of the proposed scheme demonstrates that it can meet our specified security objectives. Finally, we make a detailed experimental comparison with the most relative solutions such as BCPPA and BCGS. The results show that the DBCPCA scheme reduces the time cost by at least 66.68 % compared to the BCPPA scheme during the signature generation phase. During the signature verification phase, the DBCPCA scheme reduces the time cost by at least 62.39 % compared to the BCGS scheme. [ABSTRACT FROM AUTHOR]