Peer-To-Peer Networking and Applications最新文献

In the context of Internet of Things (IoT)-based Wireless Sensor Networks (WSNs) for smart agriculture, ensuring efficient resource utilization, prolonged network lifespan and robust security mechanisms is paramount. This paper addresses these challenges by introducing an optimized secure cluster-based routing protocol with blockchain. The algorithm initiates with node ID assignment, followed by the use of Distributed Fuzzy Cognitive Maps (DFCM) to select Cluster Heads (CHs) based on energy, proximity to the Base Station (BS) and neighbor count. DFCM aims for balanced CH distribution to optimize energy usage. The secure routing protocol, employing Earthworm-based Deer Hunting Optimization Algorithm (EW-DHOA) and blockchain, ensures reliable data transmission. Through extensive comparative analyses with existing techniques, including GA-PSO, CI-ROA, ACI-GSO and P-WWO, our approach consistently outperforms in critical parameters. At varying node densities, the proposed method demonstrates a substantial improvement in network lifetime, achieving a 60% increase over GA-PSO and maintaining a superior average of 3200 rounds. Energy consumption is notably reduced, with a 33.3% improvement compared to GA-PSO at a density of 100 nodes. The packet delivery ratio reaches 98%, showcasing a 4% enhancement over the best-performing existing technique P-WWO. Throughput at a density of 500 nodes achieves an impressive 33.3% increase, reaching 0.8 Mbps. Notably, our methodology excels in preserving active nodes, sustaining a network lifetime of 66.7% more than competing techniques at the 3500th round. The proposed approach demonstrates a higher detection rate, ranging from 75% to 90% and exhibits a significantly higher convergence rate. Therefore, our Optimized Secure Cluster-Based Routing Protocol with Blockchain-Based Integrity Checking presents a comprehensive and superior solution for enhancing the efficiency, resilience and security of WSNs in smart agriculture.

Low Earth orbit (LEO) communication satellite constellation provides network service to remote areas without terrestrialnetwork coverage. In sparsely populated areas, the deployment of ground stations is also scarce, due to geographic constraints and the lack of terrestrial backhaul. Due to the constant orbiting of the satellite constellation, location management in LEO satellite-assisted vehicular networks faces the challenges of the dual mobility of access points and users. In this paper, we propose a privacy-preserving location management scheme in an LEO satellite network with sparsely deployed ground stations. Specifically, the proposed scheme exploits the RSA-based accumulator and the Non-Interactive Proof-of-Knowledge of Exponent ((textsf{NI})-(textsf{PoKE})) protocol to verify the linkages between satellites and vehicles. Meanwhile, our scheme combines the homomorphic Symmetric Homomorphic Encryption ((textsf{SHE})) cryptosystem, the Secure Less than ((textsf{SLESS})) protocol, and the KdTree structure, to identify the potential set of accessing satellites. Security analysis shows that the proposed scheme achieves privacy preservation and authentication. Performance evaluations show that ours achieve high computation and communication efficiency.

In the rapidly advancing field of medical image analysis, accurate and efficient segmentation of retinal vessels is paramount for diagnosing ocular diseases, especially diabetic retinopathy. With the increasing emphasis on environmental sustainability, this paper presents BranchFusionNet, a novel lightweight neural network architecture tailored for retinal vessel segmentation. Embodying the principles of energy conservation, BranchFusionNet integrates multi-branch and lightweight dual-branch modules to optimize computational demands without sacrificing segmentation precision. This study not only contributes to the domain of retinal vessel segmentation but also showcases the potential of crafting energy-conscious deep learning methodologies in medical imaging applications.

With the development of big data and communication technologies, the Internet of Things (IoT) has permeated all aspects of smart cities. IoT smart city application scenarios are distributed with a large number of edge servers to accomplish large-scale data collection, transmission, analysis, and decision-making. However, in many emergency services, network communication faces data congestion and insufficient computational resources for nodes. To alleviate the situation that some nodes operate efficiently with insufficient cache and resource shortages, edge servers need to collaborate to handle tasks together and form an edge service community to realize fast message reception, response, and processing. Based on this, this study proposes an optimized edge server group collaboration architecture strategy in IoT smart cities application (ESGCA). It is based on the coalition to accomplish the optimal edge service community generation to collaborate on the messaging task. We design a multivariate discrete particle swarm optimization algorithm based on the discrete nearest past position update policy to improve the search utility. The algorithm can effectively solve the problem that current algorithms are prone to falling into local optimal solutions, long running times, and instability in the case of too many transmission tasks and edge nodes. Experimental results show that in the environment of insufficient node cache space and urgent transmission tasks, our ESGCA method can equalize the energy consumption of nodes, conserve computational resources, reduce the message transmission delay and the data loss rate.

With development of e-commerce and intelligent devices, large amounts of asset transaction data and traders’ identity information have been generated. It poses huge challenges to the data security and privacy when data are stored in a centralized trading system. Here, considering the need of data sharing and data privacy in asset trading, we proposed a blockchain-based privacy protecting framework to accommodate both aspects of these requirements. The proposed framework combines a multi-channel access control model and smart contracts to enhance the security and transparency of asset trading within permissioned channels while maintaining privacy for transactions occurring outside of these channels. Furthermore, regarding market efficiency and transaction fairness, we design trading algorithm of double auction to make transaction more automated and intelligent. Finally, we implement asset trading prototype based on Hyperledger Fabric and evaluate the performance of model. The experimental results show that the proposed multi-channel blockchain-based framework realized intra-channel date sharing and extra-channel privacy protection. Our framework achieves enhanced security, high efficiency and strong traceability for assert trading.

The integration of IoT (Internet of Things) in the energy sector has the potential to transform the way it generates, distributes, and consumes energy. IoT can enable real-time monitoring, control, and optimization of energy systems, leading to improved efficiency, reliability, and sustainability. This work is an attempt to provide an in-depth analysis of the integration of the IoT in the energy sector, examining the characteristics of IoT, its components, and protocols. It also explores the architecture of IoT, the latest advancements and challenges in the field of IoT, including the IoT communications model, IoT sensor boards, and the current challenges facing the industry and related security threats, and also provides suggestions for solutions to address IoT vulnerabilities. The work further delves into IoT in the energy sector aspect and explores the latest advancements and challenges in the field of IoT, including IoT in energy generation, smart cities, smart grids, smart buildings, and intelligent transportation. Additionally, the work explores the challenges of applying IoT in the energy sector discusses future trends in IoT in the energy sector, and aims to provide a detailed understanding of the latest developments and challenges of IoT in the energy sector, as well as its potential impact on the future of the industry. The work critically analyzes securing IoT devices and offers practical solutions to mitigate risks associated with IoT vulnerabilities. This work serves as a valuable resource for researchers, policymakers, and practitioners interested in understanding the impact of IoT on energy security.

Graphical Abstract

Taxonomy of the study.

Cloud storage has attracted widespread attention due to its advantages such as convenience, low cost, and easy scalability. With the explosion of cloud data volumes, security and performance issues are becoming major concerns. Countermeasures like data deduplication and integrity auditing techniques have been extensively studied. However, traditional auditable deduplication protocols face significant challenges related to confidentiality, reliability, and efficiency. Aiming to address these challenges, a blockchain-based auditable deduplication scheme for multi-cloud storage is proposed in this paper. Specifically, this scheme leverages blockchain technology and bilinear pairing cryptosystem to achieve an auditable deduplication mechanism that not only saves storage space but also checks data integrity. Furthermore, convergent encryption and threshold secret sharing algorithms are utilized to enhance storage confidentiality and system reliability. Batch auditing and anti-repeated auditing techniques are also adopted to improve auditing efficiency. Finally, security analysis and performance evaluation reveal that the proposed scheme achieves the expected security and performance requirements.

Diverse routing and security protocols are implemented to enhance the efficacy when performing the packet transmission, however, finding the optimal path is highly challenging since it reduces the transmission consistency over the sensor network. Here, the security is enhanced in Wireless Sensor Network (WSN) routing where a new meta-heuristic algorithm and deep learning framework are suggested. The designed WSN model consists of various models like trust model, routing model, clustering model, and energy efficient model. Moreover, in the trust model, malicious node or attack detection is done by “Bidirectional Long-Short Term Memory (Bi-LSTM) with Recurrent Neural Network (RNN)”. By selecting the optimal path, an energy-efficient routing is implemented for secure data transmission. Here, the secure routing is implemented through the hybrid optimization algorithm named Exploration-based Pelican Black Hole Optimization (E-PBHO). The overall performance is enhanced by evaluating the standard performance measures like alive and dead nodes, network lifetime, throughput, and energy consumption. Here, the developed model provides 92% and 93% in terms of accuracy and sensitivity. Thus, the empirical outcome of the suggested model offers superior performance over than the existing approaches.

MANET is widely utilized for interactions among devices without relying on a central governing body. The frequent changes in network topologies expose it to various security vulnerabilities. Enhancing security within MANET is of paramount concern and demands resolution. Addressing these security-related issues stands as the sole approach to ensuring proper data sharing in MANET. Numerous security-oriented techniques have been introduced, but they have encountered several challenges, leading to increased costs and time complexity. These difficulties have necessitated additional resources to be allocated towards mitigating issues and grappling with the growing complexity, thereby introducing a layer of complexity that can impede seamless implementation and efficacy. In this context, a Dynamic Algorithm Switching (DAS) algorithm is proposed to strengthen security in MANET. The process begins with Node clustering, followed by cluster formation through the implementation of the Modified Density-Based Spatial Clustering of Applications (DBSCAN). The proposed method incorporates Cluster Head selection (CHS), which in turn enhances energy efficiency within MANET. The effectiveness of the DAS method is evaluated through experimentation, measuring key aspects such as Packet Delivery Ratio (PDR), end-to-end delay, throughput97.9, network lifetime, and energy consumption with the attained values 98.7%, 15.81mas, 97.9% 952 s as well as 11 J/s respectively. However the existing approaches such as ANFIS-EESC, QoSDSBS, EBR and IBADConBE achieved rate of PDR as 98.3%, 97.6%, 97.1% and 96.9% that diminished the performance. Comparative analysis demonstrates that the proposed method outperforms other techniques across all metrics, establishing its superior efficiency.

In recent years, Indoor Positioning Systems (IPS) have emerged as a critical technology to enable a diverse range of Location-based Services (LBS) across different sectors, such as retail, healthcare, and transportation. Despite their strong demand and importance, existing implementations of IPS face significant challenges concerning accuracy and privacy. The accuracy issue is mainly rooted in the inherent characteristics of Received Signal Strength (RSS), which is widely integrated into current IPS as it only requires readily available WiFi infrastructure. Several studies have demonstrated that RSS suffers from instability and inaccuracy in the presence of environmental changes, making it an inadequate choice for precise IPS. Furthermore, most state-of-the-art IPS encounter privacy and data security issues as they often require users to share their privacy-sensitive location data with a centralized server. Unfortunately, centralized data collection and processing potentially expose users to privacy breaches. To tackle these shortcomings, we advocate for a comprehensive, accurate, and multifaceted solution that enables users to harness the benefits of IPS without provoking privacy concerns. First, we address the positional inaccuracy problem by combining the strengths and synergies between RSS and Channel State Information (CSI). Fusing these complementary metrics delivers increased stability against environmental fluctuations. Thereby, it provides a robust foundation for reliable and accurate positioning outcomes. Second, to address the privacy challenge, we integrate Federated Learning (FL) into the proposed solution to enable the collaborative development of machine learning-based IPS models while ensuring that user data remains decentralized. We conducted a comprehensive assessment to evaluate the performance of the proposed IPS and the corresponding overheads compared to established baseline techniques that utilize either RSS or CSI independently. The results indicate significant enhancements, highlighting our solution’s ability to effectively address accuracy and privacy challenges.