Vehicular ad-hoc networks (VANETs), considered a pivotal component of intelligent transportation systems (ITS), are susceptible to both established and emerging security vulnerabilities. However, existing authenticated key management schemes fail to provide effective conditional anonymity during decentralized authentication process. Meanwhile, scalable and reliable vehicular pseudonym management is absent, resulting in potential privacy leakage. Furthermore, conventional group key agreement schemes inherently fail to properly accommodate the highly dynamic topological characteristics of vehicular environments, which significantly limits their practical applicability. To address these challenges, the blockchain-assisted anonymous authentication and tree-based group key agreement design is proposed in this paper. Firstly, the pairing-free decentralized authentication mechanism is designed to enable mutual authentication between vehicles and roadside units (RSUs). Secondly, the threshold-varying pseudonym management system is designed, leveraging secret sharing and smart contracts to ensure conditional privacy preservation. This mechanism utilizes the multi-RSU consensus to recover the user's real identity, enabling traceability of malicious entities. Thirdly, the self-balancing tree-based group key agreement mechanism is proposed, optimizing key generation efficiency in dynamic vehicular environments. Crucial security requirements can be satisfied via the security analysis, whereas the performance evaluation substantiates the superiority of the proposed scheme over existing approaches.

Deep learning based side-channel analysis (DL-SCA) has achieved remarkable success in recovering cryptographic keys from embedded devices by exploiting physical leakages such as power consumption and electromagnetic emissions, posing a serious threat to the security of cryptographic implementations. However, a major challenge arises in cross-device attacks, where a model trained on profiling devices cannot be directly applied to attack a different device. This is because domain discrepancies emerge from variations in chip architecture, manufacturing, operational conditions, and data acquisition methods between these two devices. Many existing DL-SCA schemes have not adequately addressed the challenges posed by the differences. Therefore, we propose a novel cross-device SCA framework based on an autoencoder, which leverages the encoder—decoder architecture to align feature distributions across devices in the latent space while simultaneously preserving discriminative leakage features through the reconstruction process. To achieve this, Maximum Mean Discrepancy (MMD) is integrated into the loss function and applied to the latent representations, effectively narrowing the distribution gap between profiling and attack devices. Operating in the latent space allows our approach to avoid the training instability of adversarial methods and provides an efficient end-to-end solution for domain alignment. Building on this framework, we further introduce three multi-domain adaptation methods. Experimental results, evaluated in terms of Partial Guessing Entropy (PGE), demonstrate that cross-device attacks can be effectively executed even with device discrepancies, with most keys being successfully recovered within 1,000 traces. Moreover, the proposed adaptation techniques significantly reduce the number of traces required for successful key recovery.

In recent years, applications are becoming increasingly computation-intensive and delay-sensitive owing to the rapid growth of Internet of Things (IoT) devices among ground users (GUs). Mobile edge computing (MEC) presents crucial computational support, but conventional MEC services often fail in remote areas and in disaster scenarios. This study presents a hierarchical aerial computing platform leveraging uncrewed aerial vehicles (UAVs) and high-altitude platform (HAP) to meet the computation demands and latency requirements of various IoT applications for GUs. We propose a joint offloading decision, user association, and resource allocation (JOUR) scheme, utilizing binary offloading from GUs to UAVs and partial offloading from UAVs to HAP. The proposed scheme minimizes the energy consumption and latency while maximizing the load balancing. A matching game-based algorithm addresses the GUs offloading decision and GUs-UAVs association, followed by an enhanced soft actor-critic (ESAC) algorithm for UAV partial offloading decision, UAV computation resource allocation, and HAP computation resource allocation. Our simulation results demonstrate the effectiveness of the JOUR scheme in reducing the energy consumption and latency, while improving the load balancing and task completion rates. This demonstrates its potential for optimizing the hierarchical aerial computing platforms in dynamic IoT environments.

In an air–ground integrated network (AGIN), low-altitude unmanned aerial vehicles (UAVs) and a high-altitude platform (HAP) operate synergistically to support computationally expensive and delay-critical applications of mobile ground devices (GDs). UAVs obtain tasks from GDs, execute the tasks, and offload some of the tasks to the HAP. In AGINs, the trajectory control of a UAV swarm should provide optimal coverage to randomly distributed mobile GDs. The limited resources of UAVs, such as energy, computation, caching, and bandwidth, result in further challenges. Therefore, a joint optimization problem is formulated in this study to minimize the task execution delay and energy consumption of UAVs by optimizing the UAVs trajectory, GD association, task-offloading ratio, and resource allocation. The limited resources, maximum task execution delay, task queue size, and mobility of UAVs are regarded as key constraints. Solving the problem is intricate owing to the complex mixed-integer nonlinear constraints coupled with a large continuous and discrete decision space. To track the dynamics in AGINs and efficiently solve the problem above, we utilize a swarming behavior-integrated multi-agent gated recurrent unit-based actor and multi-head attention-based critic network (SMA-GAC) framework. Results of simulative evaluation show that the proposed SMA-GAC outperforms baseline methods.

Owing to high flexibility and rapid deployment, unmanned aerial vehicles (UAVs) can offer network coverage for Internet of things (IoT) devices in post-disaster scenarios. UAVaided mobile edge computing (MEC) provides computational support and facilitates optimal decision-making processes for ground-based IoT devices. However, existing literature has separately examined both data aggregation and computational offloading. In this paper, we introduce a Joint Data Aggregation and Computational Offloading (JDACO) scheme for UAVenabled IoT systems in post-disaster scenarios. JDACO’s primary objective is to minimize the overall energy consumption and latency in the aggregation and computation processes. It achieves this by employing UAVs as MEC servers and deploying multiple UAVs. We initially design an objective function to assess the costs associated with the aggregation and offloading processes. Subsequently, we frame the optimization problem as a Markov model and employ a multi-agent deep reinforcement learning algorithm. This approach utilizes value decomposition with the double deep Q-Network algorithm to optimize data aggregation and enable a cost-effective offloading process through cooperative learning. Our experimental results demonstrate that our proposed JDACO scheme surpasses existing methods in terms of training time reduction, processed data volume, energy efficiency, and mission duration by 20%, 11.4%, 5.6%, and 11.2%, respectively, compared to the conventional schemes while serving up to 98% of IoT devices.

Collaborative unmanned aerial vehicle (UAV) swarm networks can effectively execute various emerging missions such as surveillance and communication coverage. However, due to high mobility and constrained transmission range, packet routing encounters mutual interferences, link breakages, and unexpected delays. In such networks, routing performance is coupled with trajectory control, frequency allocation, and relay selection. In this study, we propose a joint trajectory control, frequency allocation, and packet routing (JTFR) algorithm, in which link utility is maximized by considering the link stability, signal-to-interference-plus-noise ratio, queuing delay, and residual energy of UAVs. The proposed JTFR employs adaptive distributed multi-agent deep deterministic policy gradient coupled with the swarming behavior to obtain the optimal solution. For each UAV, an actor network is established by utilizing a long short-term memory-based state representation layer containing two-hop neighbor information to adopt the dynamic time-varying topology. Subsequently, a scalable multi-head attentional critic network is set up to adaptively adjust the actor network policy of each UAV by collaborating with neighbors. The extensive simulation results show that JTFR outperforms existing routing protocols by 30-60% less end-to-end delay, 15-32% better packet delivery ratio, and 20-46% less energy consumption.

Flyingad hocnetworks (FANETs) have emanated over the last few years for numerous civil and military applications. Owing to underlying attributes, such as a dynamic topology, node mobility in 3-D space, and the limited energy of unmanned aerial vehicles (UAVs), a routing protocol for FANETs is challenging to design. Exiting topology-based routing is unsuitable for highly dynamic FANETs. Location-based routing protocols can be preferred for FANETs owing to their scalability, but are based on one-hop neighbor information and do not contemplate the reachability of further appropriate nodes for forwarding. Owing to the rapid mobility of UAVs, the topology frequently changes; thus, some route entries in the routing table can become invalid and the next-hop nodes may be unavailable before a timeout. That is, the routing decision based on one-hop neighbors cannot assure a successful delivery. In this study, we propose a novel$Q$-learning-based topology-aware routing (QTAR) protocol for FANETs to provide reliable combinations between the source and destination. The proposed QTAR improves the routing decision by considering two-hop neighbor nodes, extending the local view of the network topology. With the${Q}$-learning technique, QTAR adaptively adjusts the routing decision according to the network condition. Our simulation results reveal that QTAR outstrips the existing routing protocols in respect of various performance metrics under distinct scenarios.

Unmanned aerial vehicles (UAVs), commonly known as drones, are currently being used to combat the COVID-19 pandemic through applications, including the delivery of medical supplies, aerial spraying, and public space monitoring. In a pandemic, drone-based delivery is a promising and highly efficient method to reduce transportation time, cost, and exposure to infection. However, owing to both the limited battery lifetime and the limited functions of UAV in-flight missions, it is difficult to implement multiple deliveries over long distances in a single transportation mission. In this article, we study how to extend the drone flight time with charging stations and ensure multiple deliveries in a single mission. For multiple long-distance deliveries, optimization methods are required to design the delivery area networks of customers, charging stations, and delivery routes. We propose a joint routing and charging strategy (JRCS) comprising three phases to perform multiple deliveries in a single mission. We first split the customers of a delivery area using a clustering algorithm according to their distance from the nearest charging station and the maximum flight range. The second phase provides flight segmentation and intersegment routes between the depot, customer locations, and charging stations based on the maximum flight range and safe flight distance. The joint consideration of drone routes with charging stations minimizes the number of charging stations and ensures safe delivery. Finally, we formulate mixed-integer linear programming to solve the drone delivery route problem. According to simulation results, the proposed JRCS outperforms existing delivery approaches in terms of various performance metrics.

Aerial surveillance using unmanned aerial vehicles (UAVs) provides an on-demand and cost-effective solution to smart-city monitoring needs, owing to their three-dimensional positioning adjustment and autonomy. The optimal deployment of a UAV swarm, also known as a flying ad hoc network (FANET), to achieve on-demand coverage of mobile ground targets (MGTs) is challenging in terms of controlling UAV mobility to maximize coverage while maintaining quality of service. Data routing from UAVs to a base station (BS) without awareness of the updated topology causes link breakages, excessive retransmissions, high congestion, and energy holes. Therefore, we propose a joint topology control and routing (JTCR) protocol comprising three modules to perform crowd surveillance. The first JTCR module provides virtual force-based mobility control (VFMC), which controls the mobility of UAVs to track MGTs, ensuring stable bi-connectivity. The second module provides energy-efficient mobility-aware fuzzy clustering that clusters the FANET to aggregate the sensed data to each cluster head (CH) by utilizing the UAV mobility provided by the VFMC. The third module provides topology-aware Q-routing, which routes the aggregated data from CH UAVs to the BS by selecting an optimal path in terms of delay, path stability, and energy consumption. According to our performance study, the proposed JTCR outperforms existing routing protocols in terms of tracking-coverage rate, connectivity rate, the number of retransmissions, packet delivery ratio, end-to-end delay, and energy consumption. This is mainly enabled by the realistic mobility control of the UAV swarm at the reasonable cost of control overhead.

With the increasing growth of internet-of-things (IoT) devices, effective computation performance has become a critical issue. Many services provided by IoT devices (e.g., augmented reality, location-tracking, traffic systems, and autonomous driving) require intensive real-time data processing, which demands powerful computational resources. Mobile edge computing (MEC) has been introduced to effectively handle this problem reliably over the internet. The inclusion of a MEC server allows computationally intensive tasks to be offloaded from IoT devices. However, communication overhead and delays are major drawbacks. With the advantages of high mobility and low cost, unmanned aerial vehicles (UAVs) can mitigate this issue by acting as MEC servers. The offloading decisions for such scenarios involve service latency, energy/power consumption, and execution delays. For this reason, this study reviews UAV-enabled MEC solutions in which offloading was the focus of research. We compare the algorithms qualitatively to assess features and performance. Finally, we discuss open issues and research challenges in terms of design and implementation.

With the development of wireless communication and the pervasive Internet of Things, vehicular ad hoc networks (VANETs) have attracted much attention in recent years. As a platform for vehicular communication and management, VANETs require the guarantee of security and efficiency for the resources constrained and unreliable networks. In this article, a data-sharing scheme is proposed for VANETs where both efficient key updating and dynamic property of VANETs are supported. In particular, the symmetric balanced incomplete block design (SBIBD) in combinatorics is introduced to ensure secure and efficient VANETs while the concept of indistinguishability obfuscation is employed to support efficient key updating. In order to simultaneously satisfy the resources constrained and real-time requirements of VANETs, both serial and parallel construction of the SBIBD is implemented by the shift register. Taking advantage of this efficient sequential logical circuit, we eliminate the complicated construction of the SBIBD, thereby, making it effective for the resource-constrained environment. Theoretical and experimental analyses indicate that the proposed scheme is practical for VANETs with high security and efficiency.

In recent years, unmanned aerial vehicle (UAV) networks have been a focus area of the academic and industrial research community. They have been used in many military and civilian applications. Emergency communication is one of the essential requirements for first responders and victims in the aftermath of natural disasters. In such scenarios, UAVs may configure ad hoc wireless networks to cover a large area. In UAV networks, however, localization and routing are challenging tasks owing to the high mobility, unstable links, dynamic topology, and limited energy of UAVs. Here, we propose swarm-intelligence-based localization (SIL) and clustering schemes in UAV networks for emergency communications. First, we propose a new 3-D SIL algorithm based on particle swarm optimization (PSO) that exploits the particle search space in a limited boundary by using the bounding box method. In the 3-D search space, anchor UAV nodes are randomly distributed and the SIL algorithm measures the distance to existing anchor nodes for estimating the location of the target UAV nodes. Convergence time and localization accuracy are improved with lower computational cost. Second, we propose an energy-efficient swarm-intelligence-based clustering (SIC) algorithm based on PSO, in which the particle fitness function is exploited for intercluster distance, intracluster distance, residual energy, and geographic location. For energy-efficient clustering, cluster heads are selected based on improved particle optimization. The proposed SIC outperforms five typical routing protocols regarding packet delivery ratio, average end-to-end delay, and routing overhead. Moreover, SIC consumes less energy and prolongs network lifetime.

In this paper, the performance of homogeneous cellular networks (HMCNs) and heterogeneous cellular networks (HTCNs) is evaluated and compared. The HTCN discussed in this paper consists of three kinds of cells: macrocells, microcells and femtocells. The macrocells are evenly deployed. The microcells are densely deployed in the offices and public areas and sparsely deployed in the universities areas. The femtocells are deployed in the residential areas. And, a user mobility pattern is defined to model real communication environment. Our simulation results show that the HTCN requires less power (in Watt/km 2 /Mbps) and achieves higher throughput compared to the HMCN.

Wireless sensor networks (WSNs) usually consist of small cheap sensors and they are extensively used for many applications including environment monitoring. In this paper, we focus on the problem that node density in a randomly deployed WSN differs region by region. In other words, sensor nodes are deployed non-uniformly over the network. Such a non-uniform topology makes clustering protocols less efficient. In this paper, we propose a new clustering algorithm for non-uniformly deployed sensor networks, which is called BCA (Balanced Clustering Algorithm). In BCA, each node determines the probability that the node itself becomes the cluster head (CH) by taking the node density, which is defined as the number of nodes within the node's sensing range in this paper, into account. As a result, the coverage area of each cluster is almost equally distributed and unused redundant nodes are turned into sleep mode. Therefore, a large deviation of coverage areas of clusters in a network is remarkably decreased and the unnecessary duplication of sensing and transmission is also minimized. Because of the sleeping nodes not sending duplicated information over entire network, inefficient energy consumption is significantly reduced and thus the network lifetime is prolonged. According to the results of performance evaluation, the proposed BCA reduces energy consumption and increases the network lifetime as well as distributes the detection area of each cluster more uniformly than the existing protocols.

In interference-rich and noisy environment, wireless communication is often hampered by unreliable communication links. Recently, there has been active research on cooperative communication that improves the communication reliability by having a collection of radio terminals transmit signals in a cooperative way. This paper proposes a medium access control (MAC) algorithm, called Cooperative Diversity MAC (CD-MAC), which exploits the cooperative communication capability of the physical (PHY) layer to improve robustness in wireless ad hoc networks. In CD-MAC, each terminal proactively selects a partner for cooperation and lets it transmit simultaneously so that this mitigates interference from nearby terminals, and thus, improves the network performance. For practicability, CD-MAC is designed based on the widely adopted IEEE 802.11 MAC. For accurate evaluation, this study presents and uses a realistic reception model by taking bit error rate (BER), derived from Intersil HFA3861B radio hardware, and the corresponding frame error rate (FER) into consideration. System-level simulation study shows that CD-MAC significantly outperforms the original IEEE 802.11 MAC in terms of packet delivery ratio and end-to-end delay.

Clustering protocol that is used in wireless sensor network is an efficient method that extends the lifetime of the entire network. However, when this method is applied to an environment in which collected data of the sensor node easily overlap, sensor nodes unnecessarily consumes so much energy. In the case of clustering technique that use a threshold, the lifetime of the network is extended but the degree of accuracy of collected data is low. Therefore it is hard to trust the data and improvement is needed. In addition, it is hard for the clustering protocol that uses multi-hop transmission to normally collect data because the selection of a cluster head node occurs at random and therefore the link of nodes is often disconnected. Considering the whole networks, nodes nearer the sink have to take heavier traffic load. Therefore, nodes around the sink would deplete their energy faster, leading to what is called an energy hole around the sink. Accordingly this paper suggested a cluster-formation algorithm that reduces unnecessary energy consumption and that works with an alleviated link disconnection. According to the result of performance analysis that used environment data, the suggested method lets the nodes consume less energy than the existing clustering method and the transmission efficiency is increased and the entire lifetime is prolonged by about 30%.

Recently, vehicular sensor networks (VSNs) have emerged as a new wireless sensor network paradigm that is envisioned to revolutionize driving experiences and traffic control systems. Many existing routing protocols for VSNs have good performance on data routing in a city environment. In this paper, we propose a traffic control aware routing for VSNs, which is called PUT. It considers two modules of (i) the traffic control aware selection of vertices through which a packet is passed toward its destination and (ii) the greedy forwarding strategy by which a packet is forwarded between two adjacent vertices. The simulation results show that the proposed PUT outperforms conventional protocols in terms of packet delivery ratio, end-to-end delay and routing overhead.

This paper proposes a link quality aware routing protocol for MANETs resulting in robust delivery and high performance by finding out a reliable path with strong links. During route discovery, the strong links are effectively exploited by forwarding the route request (RREQ) packet with the highest link quality or signal to interference plus noise ratio (SINR) among the multiple RREQ packets received. Compared to the conventional protocols such as AODV, in which only the first-arrived RREQ is forwarded and the others are ignored, the proposed scheme may not have the minimum hop-count route but the one with more number of hops. However, the discovered route is a reliable path with high data rate because it consists of strong links, resulting in high performance as well as robust routing. According to our performance study, it is shown that packet delivery ratio is improved by up to 70% and per-route goodput is dramatically increased by a factor of up to 12. It is also shown that the acceptable value of the RREQ waiting time (Tw) is 1 msec in the simulated environment, which is enough to achieve fairly good performance.

This paper proposes a new process mapping scheme called adjacency-based mapping (AM) for irregular cluster systems assuming that the two-dimensional mesh process topology is specified as an interprocess communication pattern. The proposed AM tries to map neighboring processes in virtual process topology to adjacent processors in physical processor topology. Simulation study shows that the proposed AM results in better mapping quality and shorter interprocess latency compared to the conventional approaches.

In sensor networks, clustering protocol such as LEACH is an efficient method to increase whole networks lifetime. However, this protocol result in high energy consumption at the cluster head node. Hence, this protocol must changes the cluster formation and cluster head node in each round to prolong the network lifetime. But this method also causes a high amount of energy consumption during the set-up process of cluster formation. In order to improve energy efficiency, in this paper, we propose a new cluster formation algorithm named EEVAR (an Energy Efficient Variable Area Routing protocol). This scheme decreases unnecessary repetitious set-up process and prolongs steady-state process. As a result of analysis and comparison, our scheme reduces energy consumption of nodes, and improve the efficiency of communications in sensor networks compared with current clustering methods.

This paper proposes a medium access control (MAC) algorithm, called Cooperative Diversity MAC (CD-MAC), which exploits the cooperative communication capability to improve robustness inwirelessadhocnetworks. In CD-MAC, each terminal proactively selects a relay for cooperation and lets it transmit simultaneously when it is beneficial in mitigating interference from nearby terminals and thus improving the network performance. For practicability, CD-MAC is designed based on the widely adopted IEEE 802.11 MAC. System-level simulation study shows that CD-MAC significantly outperforms the original IEEE 802.11 MAC in terms of packet delivery ratio.

This paper presents Linux/SimOS, a Linux operating system port to SimOS, which is a complete machine simulator from Stanford. The motivation for Linux/SimOS is to alleviate the limitations of SimOS, which only supports proprietary operating systems. The contributions made in this paper are two-fold: First, the major modifications that were necessary to run Linux on SimOS are described. Second, a detailed analysis of the UDP/IP protocol and M-VIA is performed to demonstrate the capabilities of Linux/SimOS. The simulation study shows that Linux/SimOS is capable of capturing all aspects of communication performance, including the effects of the kernel, device drivers, and network interface.

This paper reevaluates the multicast protocols for MANETs in terms of energy efficiency and proposes a new robust multicast protocol, called two-tree multicast (TTM). Multicast protocols can be broadly categorized into two types, tree-based multicast and mesh-based multicast, based on the network structure along which multicast packets are delivered to multiple receivers. Mesh-based protocols are more robust to mobility and result in high packet delivery ratio. On the other hand, multicast trees are more energy efficient than multicast meshes. This is because mesh-based protocols depend on broadcast flooding within the mesh and therefore, mobile nodes in the mesh must receive all multicast packets during the multicast communication. The proposed TTM uses two trees, a primary and an alternative backup tree, to improve energy efficiency compared to the mesh-based protocols and to offer a better energy balance and packet delivery ratio than the free-based protocols. Performance evaluation study shows that the proposed TTM saves energy consumption by a factor of 1.9/spl sim/4.0 compared to the mesh-based multicast. In terms of combined performance metric, energy per delivered packet, TTM shows up to 80% and 40% improved performance than the mesh-based multicast and the conventional shared tree multicast, respectively.

Mapping virtual process topology to physical processor topology is one of the most important issues in parallel computing. The mapping problem for switch-based cluster systems of irregular topology is very complicated due to the connection irregularity and routing complexity. This paper proposes two mapping schemes for irregular cluster systems, which try to map the nearest neighbors in the process topology to physically adjacent processors. In addition, an application-oriented performance metric, weighted cardinality, is introduced to represent the quality of mapping. A simulation study shows that, for a virtual topology of a 16/spl times/16 mesh, the proposed mapping schemes result in better mapping quality and about 15/spl sim/20% shorter communication latency compared to random mapping. The proposed algorithms should also be beneficial when they are applied to metacomputing and cluster of cluster systems, where the communication costs are an order of magnitude different depending on the relative position of the processor nodes.

This paper proposes a Barrier Tree for Meshes (BTM) to minimize the barrier synchronization latency for two-dimensional (2D) meshes. The proposed BTM scheme has two distinguishing features. First, the synchronization tree is 4-ary. The synchronization latency of the BTM scheme is asymptotically /spl theta/(log/sub 4/ n), while that of the fastest scheme reported in the literature is bounded between /spl Omega/(log/sub 3/ n) and /spl theta/(n/sup 1/2/), where n is the number of member nodes. Second, nonmember nodes are neither involved in the construction of a BTM nor actively participate in the synchronization operations, which avoids interference among different process groups during synchronization. This not only results in low setup overhead, but also reduces the synchronization latency. The low setup overhead is particularly effective for the dynamic process model provided in MPI-2. Extensive simulation study shows that, for up to 64/spl times/64 meshes, the BTM scheme results in about 40/spl sim/70 percent shorter synchronization latency and is more scalable than conventional schemes.