Ad Hoc Networks最新文献

Vehicular Ad Hoc Network (VANET) is a proven technology in Intelligent Transportation Systems (ITS) that provides various safety and non-safety applications. The Roadside Unit (RSU) is one of the essential components of the VANET, which allows vehicles to disseminate and exchange safety and infotainment information in a broader range. RSUs are also valuable for offloading the internet data traffic of vehicular users from cellular networks. However, due to high deployment and maintenance costs and security overhead, it is essential to optimally deploy these RSUs and place them in prominent places to increase the overall network coverage and efficiency. The existing deployment strategies either analyzed the road network on one or a few topological parameters or vehicular traffic parameters and considered equal or random weights for each parameter. None of them deal with the uncertainty of parameter weightage. Thus, this paper proposes a Fuzzy-Analytic Hierarchy Process-based optimal RSU deployment (Fuzzy-AHP-ORD) approach to finding the highly influential intersection nodes for RSU deployment. The Fuzzy-AHP-ORD considers different static and dynamic parameters concerning road topology and the vehicle’s traffic behavior. The weights of the parameter are calculated through Fuzzy-AHP, then the various intersection nodes of the road networks are ranked using the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) based MADM (Multiple Attribute Decision Making) method. The simulation results reveal the effectiveness of the proposed method in comparison to other benchmark methods in terms of coverage ratio, connection time, packet delivery ratio, and delay.

The Industrial Internet of Things (IIoT) plays a vital role in Industry 4.0, thanks to advanced wireless protocols like IEEE 802.15.4 Time-Slotted Channel Hopping (TSCH) and their incorporation in the IPv6 stack via 6TiSCH. These protocols rely on carefully constructed schedules generated by a Scheduling Function (SF). The Minimal Scheduling Function (MSF) serves as the standard SF established by the 6TiSCH WG, prescribing the management and adaptation of the communication schedule based on traffic patterns. However, MSF has a notable drawback in adapting to traffic variability and burstiness and fails to account for link quality when updating TSCH cells. To address these limitations, this paper introduces the Hybrid Proactive Scheduler with Adaptive Channel Selection (HPS). HPS calculates cell requirements by considering resource utilization, ETX (Expected Transmission Count), and buffer allocation. It incorporates a fast response mechanism for burst traffic, implemented through an autonomous scheduling mechanism. Additionally, HPS utilizes a whitelisting mechanism to select optimal communication channels. The proposed scheme is implemented in Contiki-ng and evaluated in both simulated environments and real-world testbed. Obtained results demonstrate the potential of HPS to significantly enhance reliability and reduce delays compared to state-of-the-art SFs. Specifically, HPS consistently demonstrates reliability levels surpassing 90% across diverse scenarios accompanied with a noticeable gain in latency and power consumption of approximately 70% and 25%, respectively. These findings highlight the effectiveness of HPS in improving network performance and addressing the limitations of existing SFs.

The widespread usage of Android-powered devices in the Internet of Things (IoT) makes them susceptible to evolving cybersecurity threats. Most healthcare devices in IoT networks, such as smart watches, smart thermometers, biosensors, and more, are powered by the Android operating system, where preserving the privacy of user-sensitive data is of utmost importance. Detecting Android malware is thus vital for protecting sensitive information and ensuring the reliability of IoT networks. This article focuses on AI-enabled Android malware detection for improving zero trust security in IoT networks, which requires Android applications to be verified and authenticated before providing access to network resources. The zero trust security model requires strict identity verification for every entity trying to access resources on a private network, regardless of whether they are inside or outside the network perimeter. Our proposed solution, DP-RFECV-FNN, an innovative approach to Android malware detection that employs Differential Privacy (DP) within a Feedforward Neural Network (FNN) designed for IoT networks under the zero trust model. By integrating DP, we ensure the confidentiality of data during the detection process, setting a new standard for privacy in cybersecurity solutions. By combining the strengths of DP and zero trust security with the powerful learning capacity of the FNN, DP-RFECV-FNN demonstrates the ability to identify both known and novel malware types and achieves higher accuracy while maintaining strict privacy controls compared with recent papers. DP-RFECV-FNN achieves an accuracy ranging from 97.78% to 99.21% while utilizing static features and 93.49% to 94.36% for dynamic features of Android applications to detect whether it is malware or benign. These results are achieved under varying privacy budgets, ranging from $ϵ=0.1$ to $ϵ=1.0$. Furthermore, our proposed feature selection pipeline enables us to outperform the state-of-the-art by significantly reducing the number of selected features and training time while improving accuracy. To the best of our knowledge, this is the first work to categorize Android malware based on both static and dynamic features through a privacy-preserving neural network model.

The limited energy source used in wireless sensor networks (WSNs) is a crucial aspect when designing a routing protocol. Developing a routing protocol that tries to maximize energy utilization can significantly improve the system's lifetime and balance energy consumption. A novel geometrical-based energy-efficient routing protocol called "EVRP" is presented in this paper. The Cluster generation process is based on the k-means algorithm for nodes grouping. To choose a cluster head (CH) for each cluster, a cost function is calculated for each node considering factors such as remaining energy level for the node, and the mean distance between the node and its neighbors. In the inter-cluster routing phase, a novel geometrical-based routing protocol is introduced considering factors such as the distance to a base station (BS), remaining energy level, and distance between CHs to find the optimal path between BS and CH. The cluster structure is integrated with direct communication in addition to a dynamic clustering mechanism based on the optimum number of CHs and a load balancing mechanism to reduce the load on CHs near BS. The simulation results demonstrate that in different scenarios, the proposed protocol can significantly increase the network lifetime, network throughput, and network stability by 52 %, 45 %, and 30 % respectively compared with the EECRAIFA protocol and by 100 %, 97.6 %, 64.3 % respectively compared with D2CRP protocol.

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.

The networking of industrial cyber–physical systems (CPS) introduces increased security vulnerabilities, necessitating advanced intrusion detection systems (IDS). Many current studies aiming to enhance IDS capabilities leverage Federated Learning (FL) technology for collaborative intrusion detection. However, devices deployed in an industrial setting in a distributed manner are vulnerable to cyber and poisoning attacks. Compromised clients can create malicious parameters to disrupt intrusion detection models, making them ineffective in identifying attacks. Nevertheless, existing FL-based intrusion detection methods exhibit suboptimal performance in detecting malicious clients and resisting poisoning attacks. To address these issues, we propose TICPS, a collaborative intrusion detection framework based on a trustworthy model update strategy to detect cyber threats from industrial CPS. The framework enables multiple industrial CPS to collaboratively construct an intrusion detection model and evaluate the security of each industrial CPS node using an update evaluation mechanism, ensuring effective intrusion detection even in the presence of poisoning. Extensive experiments on real-world industrial CPS datasets demonstrate that TICPS can effectively detect various types of cyber threats targeting industrial CPS. In particular, the framework achieves an intrusion detection accuracy of 94% even when the proportion of malicious agents reaches 80% under three typical poisoning attacks.

In this paper, we assume that a team of drones equipped with sensing and networking capabilities explore an unknown area via onboard sensors for surveillance, monitoring, target search or data collection purposes and deliver the sensed data to a ground control station (GCS) over multi-hop links. We propose a multi-drone path planner that jointly optimizes area coverage time and connectivity among the drones. We propose a novel connectivity metric that includes not only percentage connectivity of the drones to GCS, but also the maximum duration of consecutive time that the drones are disconnected from the GCS. To solve this optimization formulation, we propose a multi-objective evolutionary algorithm with novel operations. We use our solver to test single, two and many objective path planning problems and compare our Pareto-optimal solutions to benchmark weighted-sum based solutions. We show that as opposed to the single solution that weighted-sum methods provide based on prior information from the user, the proposed evolutionary multi-objective optimizers can provide a diverse set of solutions that cover a range of mission time and connectivity performance illustrating the trade-off between these conflicting objectives. The end-user can then choose the best path solution based on the mission priorities during operation.

Within the domain of the Internet of Things (IoT), wireless sensor networks (WSNs) play a pivotal role, facilitating advancements in sectors such as smart urban infrastructures, intelligent healthcare systems, and residential automation. Despite their versatility, WSNs grapple with challenges like limited energy reserves and constrained computational capacities, making energy conservation a paramount concern for IoT deployments underpinned by WSNs. This study introduces the Hybrid Routing Protocol for Efficient Energy (HRP-EE) designed to enhance the operational efficiency of WSNs. The HRP-EE protocol unfolds over three meticulous stages: the selection of cluster heads, the formation of clusters, and the establishment of routing pathways. Initially, the protocol evaluates nodes based on their residual energy and their Euclidean distance to the central sink (or gateway) device to designate apt cluster heads. Subsequently, these chosen cluster heads are integrated into the Voronoi diagram as central nodes to orchestrate cluster architectures. In the terminal stage, an innovative hybrid algorithm is instituted. This algorithm amalgamates the principles of the Minimum Spanning Tree for structuring intra-cluster communication trees and Dijkstra’s algorithm to ascertain the most efficient paths for inter-cluster data transmission from cluster heads to the sink device. The primary objective of this protocol is the judicious utilization of the sensor nodes’ energy, thereby optimizing the overall network longevity. To assess the proficiency of HRP-EE, we executed a series of simulations using NS2. Comparative analyses reveal that HRP-EE outperforms existing protocols such as LEACH-VA, PEGCP, and TBC, delivering superior energy efficiency, extended network lifespan, and enhanced throughput across both homogeneous and heterogeneous network architectures.

One of the most critical applications undertaken by Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) is reaching predefined targets by following the most time-efficient routes while avoiding collisions. Unfortunately, UAVs are hampered by limited battery life, and UGVs face challenges in reachability due to obstacles and elevation variations, which is why a coalition of UAVs and UGVs can be highly effective. Existing literature primarily focuses on one-to-one coalitions, which constrains the efficiency of reaching targets. In this work, we introduce a novel approach for a UAV-UGV coalition with a variable number of vehicles, employing a modified mean-shift clustering algorithm (MEANCRFT) to segment targets into multiple zones. This approach of assigning targets to various circular zones based on density and range significantly reduces the time required to reach these targets. Moreover, introducing variability in the number of UAVs and UGVs in a coalition enhances task efficiency by enabling simultaneous multi-target engagement. In our approach, each vehicle of the coalitions is trained using two advanced deep reinforcement learning algorithms in two separate experiments, namely Multi-agent Deep Deterministic Policy Gradient (MADDPG) and Multi-agent Proximal Policy Optimization (MAPPO). The results of our experimental evaluation demonstrate that the proposed MEANCRFT-MADDPG method substantially surpasses current state-of-the-art techniques, nearly doubling efficiency in terms of target navigation time and task completion rate.

As a data-centric network, the Mobile Crowd Sensing (MCS) collects and uploads sensing data through intelligent terminal devices carried by workers. However, due to resource limitations, the confidentiality, integrity and communication cost issues of sensing data have not been well coordinated and resolved in the actual MCS data collection process. In this regard, this paper proposes an edge computing-assisted MCS Chaotic Compressed Sensing Secure Data Collection scheme (CCS-SDC), which supports the secure collection of sensing data and saves communication cost. In CCS-SDC, workers first use the encryption algorithm based on chaos theory to encrypt the collected sensing data, and then adopt the hash location algorithm based on chaos theory to calculate the corresponding hash verification code of the sensing data. After receiving the encrypted sensing data transmitted by the worker, the edge server recomputes the hash verification code of the encrypted sensing data and verifies the integrity of the data, which can locate the changed sensing task data to a certain extent. Then the sensing data is compressed and sampled based on the generated chaos measurement matrix to reduce the amount of data transmission and further enhance the confidentiality of the sensing data. In addition, the same hash positioning algorithm is used between the edge server and the sensing platform to protect data integrity. For the changed data located by integrity verification, in addition to choosing to let workers re-sense and submit, the sensing platform can also choose to discard the changed sensing data under appropriate circumstances, and still reconstruct and decrypt the remaining data through the proposed algorithm to obtain effective original sensing data. The experimental evaluation results on real data sets show that CCS-SDC achieves the best effects, not only achieving lower sensing data communication cost than other related schemes, but also better protecting the confidentiality and integrity of sensing data, which is very useful for resource-constrained MCS data collection scenarios.