Peer-To-Peer Networking and Applications最新文献

The sensors available in the smartphones are useful to explore a diverse range of city dynamics (e.g. noise pollution, road condition, traffic condition, etc.). The potential of the smartphone sensors coupled with their widespread availability help to emerge a new paradigm of sensing known as participatory sensing. It uses the power of smartphone equipped sensors to collect, store, and analyze data with high spatiotemporal granularity. In a participatory sensing based system, a task provider (also known as a crowdsourcer) may have a set of sensing tasks regarding different dynamics of a city. Here, adequate users’ participation is necessary to acquire a sufficient amount of data which is a key factor for the participatory sensing based systems to provide good service quality. The task providers appoint a set of task executors (smartphone users i.e. participants of crowdsensing tasks) to execute those sensing tasks. But, existing works on sensing task allocation suffer from lack of good incentive mechanisms that are attractive for the task executors. In order to address this issue, in this paper, a double auction based incentive mechanism called TATA (Truthful Double Auction for Task Allocation) is proposed for participatory sensing. TATA performs fair allocation of tasks which is leading to efficient incentive mechanism. In the case of TATA, the fair allocation of sensing tasks of the task providers to the task executers indicates that the proposed double auction mechanism is able to satisfy the truthfulness property in order to resist market manipulation (i.e., untruthful bidding and asking). Specifically, TATA achieves all the desirable properties like individual rationality, truthfulness (i.e. incentive compatibility), budget balance, etc. TATA is also computationally efficient and yields high system efficiency. Additionally, the performance of the proposed incentive mechanism is evaluated and compared with the existing mechanisms through extensive simulations based on the real-world data from Amazon Mechanical Turk. TATA yields high utility and satisfaction for the task providers and executors as compared to the existing mechanisms.

The power system stands as a crucial infrastructure pivotal to the country’s modern economic, security and social development. This paper addresses challenges in insulator fault detection on power transmission towers, leveraging the advancements in unmanned aerial vehicles equipped with target detection methods. We propose a novel method for insulator defect detection based on YOLOv5 (You Only Look Once), aiming to mitigate the issues associated with high missed detection rates. Small insulator faults and the limitation of unmanned aerial vehicle on-board capacity make it difficult to detect comprehensively. Firstly, the cluster analysis was carried out on the training data to obtain 9 kinds of better preset anchors for insulator detection, which improved the accuracy of the model to identify the location of targets. Secondly, the base-model is used to detect the insulator region, and the detection results are input into the sub-model to detect the location of faults, so as to form a cascade model, and make full use of the advantages of the two models to solve the problem of high missed detection rate. Finally, a lightweight attention module combining channel attention module and spatial attention module is added in YOLOv5 to improve the base-model’s attention to insulator region and suppress complex background features. Experimental results show that compared with the original model, the average precision of the proposed method for insulator detection is increased by 6.9%, and the missed detection rate of the fault location is 30% lower. Significant improvements in insulator detection performance have been achieved using the method proposed in this paper. It can not only effectively improve the detection accuracy, but also make the missed detection rate lower to meet the requirements of insulator defect detection and fault warning applications in complex environments, which proves that it has a wide range of application prospects in practice, especially in the field of power industry.

With the advent of the Fourth Industrial Revolution, the use of the smart grid is becoming more and more widespread. However, a large number of distributed smart grid devices currently have insecure authentication and low information sharing. As the use of blockchain technology can provide a secure and trustworthy interaction environment for smart grids, this paper proposes a blockchain-based smart grid security architecture, which resolves the conflict between distributed smart grid devices and centralized management is resolved. In the proposed architecture, we have newly designed blocks and gateway nodes, which improve the security and credibility of smart grid device identity authentication. Then we design a Computing Balance based Exchange (CBE) algorithm to improve the interaction efficiency between smart grid devices. In addition, the multi-layer smart contract based on smart grid is proposed to solve the problems of lack of mutual trust and information sharing between smart grid devices. Finally, the blockchain-based smart grid security architecture and multi-layer smart contract can achieve secure interaction and efficient identity authentication among smart grid devices.

Effective communication between smart transportation and smart vehicles is carried out using Vehicular Ad-Hoc Networks (VANETs). Here, the VANET systems that exist nowadays have issues regarding user privacy and authentication. In internal vehicles, the fake message broadcasting should be stopped to protect the vulnerability of these vehicles from privacy issues. Additionally, the traditional manner of storing transmitted data lacks a decentralized and distributed security system, making it easily vulnerable for third parties to provoke malicious activities within the VANET system. VANET is an autonomous and open-access network, so, privacy and security are the main issues. Hence, it is essential to rectify the complications that are present in the traditional security and privacy preservation models in the VANET system. Thus, an innovative privacy preservation and security scheme with fog enabled VANET system is implemented by considering the complications in the existing models. The major that take place in the recommended framework are (a) Node Authentication, (b) Privacy Preservation, and (c) Message Verification. Initially, the node authentication is performed in the recommended framework using an Adaptive Deep Bayesian network (ADBN) in order to ensure an enhanced permissibility rate in the vehicular node. Then, messages are authenticated to protect the virtue of the messages. The parameters in the ADBN are tuned using the aid of Integrated Fire Hawk with Tunicate Swarm Algorithm (IFHTSA). Next, the privacy preservation procedure in the VANET model is carried out using Hybrid Attribute-Based Advanced Encryption Standard (HABAES) encryption techniques. The keys obtained on the encryption of the messages are signed digitally. Moreover, the suggested model utilized the fog node for the analysis instead of Road-Side Units (RSUs), because of its effectiveness in minimizing the latency rate with an increased throughput rate. In the message verification node, once the Fog Edge Node (FEN) receives the signed message from the vehicles, then it checks the validity of the vehicle node by comparing it with the signed messages. Finally, the experimentation is done based on various standard performance metrics. However, the developed model achieves 94% and 93% in terms of accuracy and precision. Hence, the suggested technique offers minimal computation and communication overhead in different experimental observations over the classical technique.

Vehicular Ad-hoc Networks (VANETs) have received extensive consideration from the industry and the research community because of their expanding emphasis on constructing Intelligent Transportation Systems (ITS) to enhance road safety. ITS is a collection of technologies and applications that aim to improve transportation safety and mobility while lowering the number of accidents. In VANET, routing protocols play a significant role in enhancing communication safety for the transportation system. The high mobility of nodes in VANET and inconsistent network coverage in different areas make routing a challenging task. As a result, ensuring that the VANET routing protocol has the maximum packet delivery ratio (PDR) and low latency is of utmost necessity. Due to the high dynamicity of the VANET environment, position-based routing protocols are paramount for VANET communication. VANET is subjected to frequent network disconnection due to the varied speeds of moving vehicles. Managing and controlling network connections among V2V and V2I is the most critical issue in VANET communication. Therefore, reliable routing protocols that can adapt to frequent network failures and select alternative paths are still an area to be explored further. Majorly, VANET routing protocols follow the greedy approach; once the local maximum is reached, the packets start dropping, resulting in a lower packet delivery ratio. Therefore, lower PDR is still an issue to be resolved in VANET's routing protocols. This paper investigates recent position-based routing protocols proposed for VANET communication in urban and highway scenarios. It also elaborates on topology-based routing, which was initially used in VANET, and its research gaps, which are the major reason for the advent of position-based routing techniques proposed for VANET communication by various researchers. It provides an in-depth comparison of different routing protocols based on their performance metrics and communication strategies. The paper highlights various application areas of the VANET, research challenges encountered, and possible solutions. Further, a summary and discussion on topology-based and position-based routing protocols mark the strengths, limitations, application areas, and future enhancements in this domain.

The authenticated key agreement (AKA) method used in the Internet of Things (IoT) provides identity authentication and agreed symmetric keys to encrypt large amounts of communication messages for devices and servers. With the rapid development of quantum computers and quantum algorithms, classical cryptographic algorithms become vulnerable to attacks by adversaries, leading to significant risks in IoT communication systems. Numerous lattice-based authentication key agreement (AKA) schemes have emerged to fortify communication systems against quantum attacks. However, due to the large size of the lattice cryptography public key, an excessive number of communication rounds can cause significant time delays. Meanwhile, many current lattice-based AKA schemes rely on weak security models like BR, CK, and ROR. These models can only capture partial adversary attacks. To this end, we propose a lower communication rounds lattice-based anonymous authenticated key agreement (LA-AKA) protocol under the seCK model. This protocol aims to achieve lower communication rounds under the robust security model, ensuring heightened security and efficiency within IoT communication systems.

The rapid expansion of the mobile Internet has led to online social networks becoming an increasingly integral part of our daily lives, this offers a new perspective in the study of human behavior. Existing methods can not effectively monitor the real-time evolution of user interests based on the previous diffusion behavior of influence disseminators and to anticipate future diffusion behavior of users. In order to address these challenges, this study proposes a knowledge-infused deep learning-based event tracking model named DIEET (Diffusion and Interest Evolution behavior modeling for Event Tracking). This model accurately predicts the propagation and interest evolution behavior in event tracking by considering both propagation and interest evolution behavior. Specifically, the DIEET model incorporates the interval time, the number of times, the sequence interval time, and finally user preference for the event of interest, greatly improving the accuracy and efficiency of event evolution prediction. The experiments conducted on real Twitter datasets detail the proposed DIEET models’ ability to greatly improve the tracking of the state of user interest alongside the popularity of event propagation, and DIEET also has superior prediction performance compared to state-of-the-art models in terms of identifying user dynamic interest. Therefore, the aforementioned model offers promising potential in the ability for predicting and tracking the evolution of user interest and event propagation behavior on online social networks.

Intelligent technology and devices have affected nearly every aspect of human lives. The type of connection and means of communication among such a huge number of devices has led to development of Internet of Things (IoT), broad sector that has considerably increased awareness of the problem of energy management and lengthened the lifespan of networks. The design of networks has been made more difficult by complex communications. In this paper, a Hybrid Multipath Routing Cluster head prediction based on software defined networking enabled IoT and Heterogeneous context-aware graph convolution network Using Eagle Swarm Optimization (SDN-IOT-HCAGCN) is proposed. In this method, cluster is formed by coati optimization and cluster head selection by Heterogeneous context-aware graph convolution network, after that Eagle Swarm Optimization is used for multipath routing. The simulations are executed in MATLAB. The proposed technique is evaluated through different metrics, likes delay, number of dead nodes, packet delivery ratio, number of alive node, energy consumption and network life time. The proposed SDN-IOT-HCAGCN model provides 34.49%, 26.44% and 20.51% highest number of alive sensor nodes, 39.45%, 22.44% and 30.24% lowest number of delay when analyzed with the existing models, like Intelligent Energy-Aware Routing Protocol in Mobile IoT depending upon SDN (SDN IEAR-IOT-SDN), Edge-based Optimal Routing in SDN-enabled Industrial Internet of Things (EOR-SDN-IOT), Cooperative Self-scheduling routing protocol basis IoT communication for increasing lifetime duty cycled energy efficient protocol in SDN controlled embedded network (CSR-IOT-SDN) respectively.

With the rapid advancement of Internet of vehicles and autonomous driving technology, there is a growing need for increased computing power in vehicle operations. However, the strict latency requirements of vehicle tasks may pose challenges to communication and computing resources within the vehicle edge computing network. This paper introduces a two-stage joint optimization to address challenges, focusing on minimizing vehicle task latency and optimizing resource allocation. In addition, the task completion rate is considered an important indicator to ensure safety and reliability in practical application scenarios. Next, we propose a global adaptive offloading and resource allocation optimization model named GOAL. The GOAL model dynamically adjusts the weight coefficients of the reward function to optimize the model, integrating the actor-critic algorithm to effectively adapt to uncertain environments. Through experimental comparisons of various weight coefficients for task arrival rates and reward functions, we were able to determine the optimal hyperparameters for our proposed model. The simulation results show that the GOAL model outperforms the benchmark methods by over 30% in reward value. It also performs better in terms of task delay and energy consumption. Additionally, the GOAL model has a higher task completion rate compared to the benchmark methods, and it exhibits strong search capabilities and faster convergence speed.

The agriculture supply chain is an integral part of the whole agriculture sector. A top-notch cost efficient supply chain management system that can protect itself from outside security attacks and also from malicious activities by supply chain participants without third party intervention is highly demanded in the agriculture field. In this paper, a smart contract based forward and reverse agriculture supply chain management system is proposed that takes into account both internal and external security attacks. Here, peer-to-peer Certificate Authorities provide the rate of the crops and the farmer puts this in the forward supply chain with its price as well as communicates directly with distributors for making transactions with customers through the smart contracts. After receiving the requested crops from the farmer through the distributor, the customer initiates payment contract for the transaction. On the other hand, if a customer is not satisfied, he or she initiates a reverse supply chain. In order to maintain its level of security, this model applies blockchain technology in conjunction with a session key technique. The system computes cost analysis to ensure the immutability of costs and predict future expenses as well as compared with benchmark data. The Real or Random (RoR) based formal as well as informal security analysis is presented here to demonstrate the system’s robustness against a wide range of potential security threats and the malicious behavior of different entities. Our results analysis demonstrates the system evaluation in terms of throughput, latency, transmitted message size among different entities and gas consumption. It is observed that the proposed model outperforms than other existing approaches.