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Multi-unarmed aerial vehicle (UAV) collaborative mission planning is a key technology for the intelligent development of UAV clusters at this stage, where mission assignment under multiple constraints is a core part of UAV mission planning technology. The poor planning ability of an UAV swarm often leads to resource waste and revenue reduction during mission execution, while the adoption of task allocation in the multi-target tracking of UAV clusters guarantees the highest profit. Algorithms of traditional pigeon-inspired optimization(PIO) and contract network protocol reflect defects of low timeliness and high time costs, respectively. Compared with these methods, the multilevel PIO algorithm maximizes the efficiency and profitability of the overall tracking task. A staged UAV cluster task allocation architecture was constructed to complete the global optimal initialization before tracking, and then a parallel auction contract network was employed to further perform the local optimal redistribution of each UAV. The simulation results suggest that this scheme not only guides cluster allocation in stages but also decreases the negotiatory time of a UAV cluster to in-crease the integral profitability under multiple constraints, such as prohibited areas.

With the rapid development of large‐scale satellite constellations and the intelligent progression of satellites, distributed task planning algorithms have garnered extensive research. However, traditional distributed algorithms face drawbacks such as increased communication burdens and slower response to emergency tasks when dealing with task planning for large satellite clusters. This paper introduces improvements to the traditional contract network protocol to address these shortcomings. Firstly, a method for satellites to determine their own dominant tasks is proposed with the aim of alleviating the superfluous negotiation communication among satellites. Secondly, in the context of batch task upload scenarios, a multitask single‐bid methodology is designed. Furthermore, a provisional task group was constituted to conduct small‐scale intersatellite negotiations for exceptional and urgent tasks for the purpose of minimizing the response time of the system. The simulation results indicate that the algorithm marginally enhances the computing memory of satellites but decreases the intersatellite communication traffic.

This paper addresses the challenge of conducting cover searches for unmanned surface vessels operating in unknown waters. To tackle this problem, we propose a cover algorithm that combines job partitioning with a joint network protocol. The algorithm starts by dividing the map area based on an exploration-based approach, followed by task area calculation and assignment using the Boustrophedon technique. Subsequently, a distributed joint network protocol is utilized to dynamically allocate search tasks among the members of the USV (unmanned surface vessel) group, maximizing the overall search efficiency. Three basic strategies are designed for collaboration between USVs (namely, obstacle recognition, distributed communication, and regional transfer), facilitating the real-time allocation of water coverage tasks among unmanned vessels until the entire body of water is completely covered. Simulation experiments demonstrate the effectiveness of the proposed algorithm. Compared to several non-cooperative area coverage algorithms, our algorithm reduces calculation task usage time and total travel distance for the cluster. Furthermore, the proposed algorithm performs well in dynamic environments, efficiently handling coverage search tasks. Notably, the B-CNP (Boustrophedon-contract network protocol) algorithm proposed in this paper achieves an approximate 3.22% reduction in path length compared to the BA* (Boustrophedon-A*) algorithm.

This paper focuses on cooperative multi-task assignment and re-assignment problems when multiple unmanned aerial vehicles (UAVs) attack multiple known targets. A unified multi-objective optimization framework for UAV cooperative task assignment and re-assignment is studied in this paper. In order to simultaneously optimize the losses and benefits of the UAVs, we establish a multi-objective optimization model. The amount of tasks that each UAV can perform and the number of attacks on each target are limited according to the ammunition capacity of each UAV and the value of each target. To solve this multi-objective optimization problem, a multi-objective genetic algorithm suitable for UAV cooperative task assignment is constructed based on the NSGA-II algorithm. At the same time, a selection strategy is used to assist decision-makers in choosing one or more solutions from the Pareto-optimal front. Moreover, to deal with emergencies such as UAV damage and to detect of new targets, a task re-assignment algorithm based on the contract network protocol (CNP) is developed. It can be implemented in real-time while only slightly sacrificing the ability to seek the optimal solution. Simulation results demonstrate that the methods developed in this paper are effective.

Vehicular Ad Hoc Networks (VANETs) are essential for the success of Intelligent Transportation Systems (ITS), providing real-time communication between vehicles and infrastructure. However, the highly dynamic and decentralized nature of VANETs introduces significant challenges in ensuring trust and security across the network, including security threats, communication overhead, and energy inefficiencies. This paper presents a novel blockchain-based trust management framework that addresses these issues by incorporating lightweight consensus mechanisms, optimized data propagation strategies, and energy-aware protocols. Our approach reduces communication overhead by selectively propagating trust updates, leading to a 35% decrease in overall network traffic compared to traditional broadcast-based systems. In terms of trust accuracy, our model achieves over 95% accuracy in detecting malicious nodes, significantly outperforming existing solutions. The proposed system demonstrates the identification and penalization of malicious behaviors such as Sybil attacks and false reporting with a 25% improvement in detection rate, while maintaining low latency (an average reduction of 30% compared to PoW-based systems) and efficient energy consumption, reducing energy use by up to 40%. The proposed model also incorporates a hybrid Proof of Stake (PoS) and Practical Byzantine Fault Tolerance (PBFT) consensus mechanism, which further enhances its scalability and fault tolerance. Simulation results show that our framework converges to accurate trust values faster than traditional methods, ensuring that reliable trust evaluations are made in real-time, even under high mobility conditions. The combination of these optimizations ensures that our framework is not only secure but also highly efficient, capable of supporting scalable and resilient VANET deployments. Furthermore, our decentralized approach ensures that trust decisions are made in real-time without the need for a centralized authority, making the system more adaptable to the high-mobility conditions of VANETs. This research offers a comprehensive solution for VANETs trust management, significantly improving communication efficiency, trust accuracy, and energy consumption while maintaining robust security and scalability. Our proposed blockchain-based trust management system provides a secure, energy-efficient, and scalable solution for VANETs, setting the stage for future developments in secure vehicular communication networks.

The emergence of biomedical sensor devices, wireless communication, and innovation in other technologies for healthcare applications result in the evolution of a new area of research that is termed as Wireless Body Area Networks (WBANs). WBAN originates from Wireless Sensor Networks (WSNs), which are used for implementing many healthcare systems integrated with networks and wireless devices to ensure remote healthcare monitoring. WBAN is a network of wearable devices implanted in or on the human body. The main aim of WBAN is to collect the human vital signs/physiological data (like ECG, body temperature, EMG, glucose level, etc.) round-the-clock from patients that demand secure, optimal and efficient routing techniques. The efficient, secure, and reliable designing of routing protocol is a difficult task in WBAN due to its diverse characteristic and restraints, such as energy consumption and temperature-rise of implanted sensors. The two significant constraints, overheating of nodes and energy efficiency must be taken into account while designing a reliable blockchain-enabled WBAN routing protocol. The purpose of this study is to achieve stability and efficiency in the routing of WBAN through managing temperature and energy limitations. Moreover, the blockchain provides security, transparency, and lightweight solution for the interoperability of physiological data with other medical personnel in the healthcare ecosystem. In this research work, the blockchain-based Adaptive Thermal-/Energy-Aware Routing (ATEAR) protocol for WBAN is proposed. Temperature rise, energy consumption, and throughput are the evaluation metrics considered to analyze the performance of ATEAR for data transmission. In contrast, transaction throughput, latency, and resource utilization are used to investigate the outcome of the blockchain system. Hyperledger Caliper, a benchmarking tool, is used to evaluate the performance of the blockchain system in terms of CPU utilization, memory, and memory utilization. The results show that by preserving residual energy and avoiding overheated nodes as forwarders, high throughput is achieved with the ultimate increase of the network lifetime. Castalia, a simulation tool, is used to evaluate the performance of the proposed protocol, and its comparison is made with Multipath Ring Routing Protocol (MRRP), thermal-aware routing algorithm (TARA), and Shortest-Hop (SHR). Evaluation results illustrate that the proposed protocol performs significantly better in balancing of temperature (to avoid damaging heat effect on the body tissues) and energy consumption (to prevent the replacement of battery and to increase the embedded sensor node life) with efficient data transmission achieving a high throughput value.

With the increasing growth rate of smart home devices and their interconnectivity via the Internet of Things (IoT), security threats to the communication network have become a concern. This paper proposes a learning engine for a smart home communication network that utilizes blockchain-based secure communication and a cloud-based data evaluation layer to segregate and rank data on the basis of three broad categories of Transactions (T), namely Smart T, Mod T, and Avoid T. The learning engine utilizes a neural network for the training and classification of the categories that helps the blockchain layer with improvisation in the decision-making process. The contributions of this paper include the application of a secure blockchain layer for user authentication and the generation of a ledger for the communication network; the utilization of the cloud-based data evaluation layer; the enhancement of an SI-based algorithm for training; and the utilization of a neural engine for the precise training and classification of categories. The proposed algorithm outperformed the Fused Real-Time Sequential Deep Extreme Learning Machine (RTS-DELM) system, the data fusion technique, and artificial intelligence Internet of Things technology in providing electronic information engineering and analyzing optimization schemes in terms of the computation complexity, false authentication rate, and qualitative parameters with a lower average computation complexity; in addition, it ensures a secure, efficient smart home communication network to enhance the lifestyle of human beings.

In this paper, we propose a novel blockchain-based contractual routing (BCR) protocol for a network of untrusted IoT devices. In contrast to conventional secure routing protocols in which a central authority (CA) is required to facilitate the identification and authentication of each device, the BCR protocol operates in a distributed manner with no CA. The BCR protocol utilizes smart contracts to discover a route to a destination or data gateway within heterogeneous IoT networks. Any intermediary device can guarantee a route from a source IoT device to a destination device or gateway. We compare the performance of BCR with that of the Ad-hoc On-Demand Distance Vector (AODV) routing protocol in a network of 14 devices. The results show that the routing overhead of the BCR protocol is 5 times lower compared to AODV at the cost of a slightly lower packet delivery ratio. BCR is fairly resistant to both Blackhole and Greyhole attacks. The results show that the BCR protocol enables distributed routing in heterogeneous IoT networks.

Transactive energy systems (TES) are modern mechanisms in electric power systems that allow disparate control agents to utilise distributed generation units to engage in energy transactions and provide ancillary services to the grid. Although voltage regulation is a crucial ancillary grid service within active distribution networks (ADNs), previous work has not adequately explored how this service can be offered in terms of its incentivisation, contract auditability, and enforcement. Blockchain technology shows promise in being a key enabler of TES, allowing agents to engage in trustless, persistent transactions that are both enforceable and auditable. To that end, this study proposes a blockchain based TES that enables agents to receive incentives for providing voltage regulation services by (i) maintaining an auditable reputation rating for each agent that is increased proportionately with each mitigation of a voltage violation, (ii) utilising smart contracts to enforce the validity of each transaction and penalise reputation ratings in case of a mitigation failure, and (iii) automating the negotiation and bidding of agent services by implementing the contract net protocol as a smart contract. Experimental results on both simulated and real-world ADNs are executed to demonstrate the efficacy of the proposed system.

Ad hoc network is a special network with centerless and dynamic topology. Due to the free mobility of the nodes, routing security has been a bottleneck problem that plagues its development. Therefore, a multi-path QoS (quality of service) routing security algorithm based on blockchain by improving the traditional AODV (ad hoc on-demand distance vector) protocol (AODV-MQS) is proposed. Firstly, a chain of nodes is established in the network and the states of all nodes by making the intermediate nodes on the chain are saved. Secondly, the smart contract in the blockchain is set to filter out the nodes that meet the QoS constraints. Finally, two largest unrelated communication paths are found in the blockchain network through smart contract, one of which is the main path and the other is the standby path. Simulation experiments show that the performance of the proposed algorithm is better than other algorithms, especially in an unsafe environment.