Peer-To-Peer Networking and Applications最新文献

Wireless Sensor Networks (WSN) are composed of small sensor nodes that either transmit their sensed data to the sink node directly or transmit it to its respective cluster head, which then transmits it to the sink node. However, this consumes a lot of network bandwidth and energy from the constrained sensor nodes. To address these constraints, Mobile Agents (MA) paradigm can be used in WSNs, which may lead to better energy and bandwidth conservation. When a single mobile agent is insufficient to complete a task, multiple mobile agents can be deployed to perform in parallel and reduce network latency. The set of sensor nodes and their sequence that MAs must migrate to complete a task is called an itinerary. The planning of the itinerary is the most prominent and significant issue related to the MA-based system, including the determination of an appropriate number of MAs to be dispatched, determining the set of sensor nodes and their sequence to be visited by MAs. This paper proposes a fuzzy-based algorithm to partition Wireless Sensor Networks into a set of sensor nodes, called domains, for enhancing the efficiency of the WSN in terms of its prolonged operation. Experimental evaluations are conducted to compare the proposed algorithm with benchmarked algorithms. The paper suggests that the proposed algorithm's integration with MA-based systems can enhance their performance and prolong the WSN's lifetime.

The rise of online social networks has fundamentally transformed the traditional way of social interaction and information dissemination, leading to a growing interest in precise community detection and in-depth network structure analysis. However, the complexity of network structures and potential issues like singularity and subjectivity in information extraction affect the accuracy of community detection. To overcome these challenges, we propose a new community detection algorithm, known as the Hierarchical Louvain (H-Louvain) algorithm. It enhances the performance of community detection through a multi-level processing and information fusion strategy. Specifically, the algorithm integrates graph compression techniques with the Hyperlink-Induced Topic Search (HITS) algorithm for initial network hierarchical partitioning, simultaneously filtering out low-quality posts and users while retaining critical information. Furthermore, the proposed method enhances semantic representation by automatically determining an appropriate number of attribute vector dimensions and obtaining attribute weight information through the calculation of self-authority values and the "minimum distance" attribute of posts. Lastly, the method creates an initial user training set through network re-partitioning in hierarchical layers and improves the Louvain algorithm for community partitioning by estimating the comprehensive influence of nodes. Extensive experimentation has demonstrated that the H-Louvain algorithm outperforms state-of-the-art comparative algorithms in terms of accuracy and stability in community detection based on real-world Twitter datasets.

Software defined networking (SDN) is a novel networking paradigm that aims to achieve global management for the underlying forwarding plane based on its centralization concept. Unfortunately, the newly designed paradigm does not consider security issues related to unauthenticated and unauthorized activities across various SDN layers. Recently, blockchain (BC) technology has proven successful in providing a decentralized, immutable, and fault tolerant ledger. In this study, we take advantage of blockchain characteristics to provide mutual host-controller, PacketIn/PacketOut and host-host authentication methods. We also provide secure Address Resolution Protocol (ARP) and Identity Resolution Protocol (IRP) to protect layer 3 and layer 2 of the SDN network. In addition, both SDN hosts and controllers utilize lattice-based signatures based on Dilithium scheme and Key Encapsulation Methods (KEMs) based on Kyber scheme to provide protection against quantum adversaries. We also compare our work with AuthFlow (Mattos and Duarte in Ann Telecommun 71:607–615, 2016). The results show that HostSec is more secure than AuthFlow due to its ability to detect both host-based and switch-based PacketIn attacks and also reduces the load on the SDN controller. Overall, the experimental results suggest a trade-off between improved security and lower latency.

Efficient Energy Consumption and Network Lifetime are significant concerns in wireless sensor networks and allied disciplines. Clustering is one of the available solutions, but optimized cluster head selection is a prime issue nowadays. Many solutions have been given for solving this issue considering some attributes, but with time, meta-heuristics algorithms have become widely used for real-world applications. The nature-inspired algorithm is seeking researchers' wide attention as it gives the capability to self-learn and perform better. In this paper, we have proposed the Dragon Fly algorithm with multi-attribute decision-making inspired by the dynamic and static behavior of the dragonfly. The Proposed IDA (Innovative Dragonfly Algorithm) is a hybrid approach in which dragonfly and multi-attributes are combined for optimal cluster head selection. The proposed method is a way to compute multi-attributes of sensor nodes for ranking them and selecting optimized cluster heads. The energy consumption of IDA is 0.4896, NBA (Novel Bio-Inspired Algorithm) is 0.4321, FLPSOC (Fuzzy Logic and PSO-based energy efficient clustering) is 0.4421, and ESO-LEACH (PSO-based energy efficiency) is 0.4678 at which means proposed IDA is better than other compared algorithms in energy consumption. The throughput of IDA is 64.99, which is better than existing different compared algorithms. The number of alive nodes in the proposed method is, and that of compared algorithms is; thus, IDA has enhanced network lifetime compared to others. The IDA algorithm is compared with NBA, FLPSOC, and ESO-LEACH, validating that the proposed algorithm performs better than the classical and compared algorithm.

Nowadays, smart healthcare and Electronic Health Record (EHR) play a significant role in simplifying the experience of medical treatment and cuts down the trivial work of paramedics. However, this poses significant challenges for organizations, including the management, transfer and protection of EHR which is vital for correct detection. To overcome these challenges, cloud computing presented which offers various attractive features for EHR management such as data availability, elasticity, and ubiquitous computation. However, the use of cloud computing introduces confidentiality and privacy concerns which can lead to human health threat. A trivial solution for this problem is encryption which makes searching a desired document infeasible, more specifically in big data scenarios. Although searchable encryption is a candidate solution, it suffers from keyword guessing attacks, malicious service providers, lack of trust, data integrity, and fairness. In this paper, we propose BACASE-SH framework that leverages blockchain technology to EHR integrity and availability insurance, fair payment, and establish trust among the involved parties. Moreover, we present a certificate-less authenticated asymmetric searchable encryption scheme to address challenges such as data owner (patient) and data user (physician) identity and keyword guessing attacks. The scheme is implemented using a smart contract, and the evaluation results show its efficiency.

Internet of Things (IoT) security refers to different aspects of security, including methods, tactics, and technologies used to protect these devices from unauthorized access. However, it is connected with multiple physical devices to perform huge tasks simultaneously and secure the data transmitted through the IoT network. Furthermore, the IoT is used to transmit sensitive data and validate security performance. Mostly Machine Learning (ML) algorithms are widely utilized for the process of anomaly detection. However the application of the ML model fails to detect attacks in IoT, to overcome this, a novel Stacked Long Short Term Memory based Willow Catkin Optimization (SLSTM-WCO) algorithm is proposed to detect intrusion anomalies in IoT networks. The complex patterns and abnormalities are predicted by determining the regularization method. Also, the deep learning (DL) model such as stacked LSTM detects the anomaly accurately and improves the effectiveness. The detection performance is validated by using benchmark datasets such as BoT-IoT, IoT network Intrusion, IoT-23, MQTT, and MQTTset which enhanced the efficiency. The outcome of the SLSTM-WCO method improved accuracy by 99.49% and improved anomaly detection in IoT networks compared to existing methods.

The requirement and the purpose of the IoT approach have developed substantially over the most recent few years. Here, IoT collects information from actual things, stores it, and then moves it to various organizations. Here, we use a Mobile Ad-Hoc Network (MANET) using IoT. MANET is highly delicate to malware which includes passive and active for the organization. Additionally, this paper shows the security angle-based IoT model utilizing AI. The black hole attack is one among these attacks which drops the entire information traffic and corrupts the organization's execution. In this way, it requires the designing of the novel Adaptive Defense Reinforcement Mountaineering Team Search (ADR-MTS) algorithm that distinguishes and safeguards the organization from the blackhole attack node. The role of ADR-MTS calculation recognizes the source directing and the sum of nodes that are been accomplished by the routing mechanism. This routing method assists with upgrading the course between the both objective node and the source node. The simulation analysis that performs MATLAB shows the improvement with regards to Packet Delivery Ratio (PDR). To improve the system efficiency a similar examination is performed against the current methodologies and from the study, the ADR-MTS calculation gives gainful outcomes concerning the location of black holes in the MANET-based IoT organizations The ADR-MTS method achieved a PDR of 98.7% and scalability of 98.5% and these results demonstrate the efficiency of the ADR-MTS method in comparison to existing methods.

The state-of-the-art framework for VANETs, Vehicles in Network Simulation (VEINS), is primarily sparse and fragmented. The combination of VANETs and VEINS can improve road safety, efficiency, and user experience for connected and autonomous vehicles. This research examined existing trends and knowledge gaps to provide actionable insights for technical contexts and researchers. Therefore, this systematic literature evaluation was conducted to create a full classification of the article ecosystem. The literature applies the VEINS framework to simulate and evaluate in-vehicle personalized entertainment recommendations based on real-time traffic data and user preferences. We examine service metrics for VANET-integrated vehicle content exchange. Three databases were consulted throughout this study: Scopus, ScienceDirect, and IEEE Xplore. The databases had extensive VANET-related research built on the VEINS framework. Then, screening was completed based on the services considerations. The topic is thoroughly covered in this categorization. Taxonomy proposes categories and subcategories. The initial group includes papers discussing different aspects of VANET-based VEINS framework applications (35/9878 total). The second group consists of pieces that focus on the answer (15/98 total). Network-related articles (48/98 total) make up the final section. This work concludes with a discussion of the VEINS framework’s design and bidirectional connectivity. This study could be helpful for researchers working on VANETs and the VEINS framework by highlighting areas where further development is necessary.

Green edge-assisted unmanned aerial vehicle (eUAV) network is an emerging network architecture enhanced with the energy efficiency technology that provides an ubiquitous communication for air and ground. It is considered a promising technology that can maintain a balance between a clean environment and a rich human life to build a sustainable world. However, the security issues exposed by UAVs have raised concerns about the trust and security for adopting eUAV network for communication. To this end, a trust assessment as a service (TaaS) scheme for secure communication of green eUAV network is proposed. The TaaS can effectively collect the valid data of UAVs about diverse and dynamic quality of service (QoS) attributes related to energy efficiency. To predict the actual data of UAVs about QoS attributes in the real green eUAV environment, an service level object calibration method is presented in TaaS. In addition, to accurately obtain the trust level of UAVs in the eUAV network, TaaS presents a trust level assessment method integrated the interval multi-attribute decision-making method and the objective weight assignment method based on deviation maximization. A case study with open source dataset and a performance analysis experiment are conducted to show that the proposed TaaS scheme can accurately and effectively assess the trust level of UAVs while outperforming other traditional trust assessment methods.

Certificateless Proxy Signature (CLPS) offers a comprehensive authentication mechanism to ensure the optimal solutions from modern forgery attacks in Industrial Internet of Things (IIoTs) environment. CLPS is part of certificateless public key cryptography (CL-PKC) and has the benefits of eradicating many issues like key distribution problem. Although costly computational processing capabilities are consumed by smart devices during authentication process. This article proposed an authentication scheme for CLPS by using the mathematical cryptographic curve concepts of Hyper Elliptic Curve (HEC) to provide a secure and optimized communication approach in IIoTs environment. The arises key distribution problem in CLPS is also fixed. The scrutinized security analysis of proxy signature and delegation signature were performed to tackle different attacks like Machine-in-the-middle Attack (MiTM), Replay Attack, Key Replacement Attack, Impersonation Attack, and Chosen Message Attack. Due to HEC-DLP the forgery attack cannot succeeded. Finally, the proposed work is formally verified through the AVISPA tool. The comprehensive performance evaluation shows that the total computational cost is reduced to 49.48% and the communication overhead size is reduced to 49.57% with a comparative analysis of existing schemes. Thus, the proposed approach eradicates key distribution problems with an equal security level of RSA and ECC.