Peer-To-Peer Networking and Applications最新文献

The inherent challenges associated with the Internet of Things (IoT), such as vulnerability to cyber threats and privacy issues, need the development of novel solutions to ensure secure and efficient handling of data. Fog computing resolves these concerns by facilitating data processing in proximity to edge devices, minimising latency, and improving real-time decision-making. Blockchain boosts security in fog-based systems by providing a tamper-proof and transparent ledger. However, exclusively prioritising privacy in fog-based blockchains may impede the practical execution. This article presents the FogBlock Connect paradigm, which combines Fog computing and Blockchain through the implementation of a tailored Proxy Re-encryption (PRE) algorithm inspired by BBS98. This strategy guarantees enhanced data confidentiality while simultaneously upholding operational effectiveness in fog-based blockchains for Internet of Things applications. The efficiency and effectiveness of the suggested PRE algorithm over typical encryption methods are confirmed by comprehensive simulations utilising the Fobsim simulator. The FogBlock Connect paradigm entails the transmission of updates from nearby IoT devices to Fog servers for the purpose of creating and securely storing global updates, hence improving efficiency and performance. The paradigm ensures robust privacy measures, mitigates risks of single-point failures, and facilitates precise access control, establishing a basis for secure and resilient IoT applications. The CCA resistant formal security proof provides further validation for the strength and effectiveness of the suggested approach.

The Internet of Things (IoT) refers to a network where different smart devices are interconnected through the Internet. This network enables these devices to communicate, share data, and exert control over the surrounding physical environment to work as a data-driven mobile computing system. Nevertheless, due to wireless networks' openness, connectivity, resource constraints, and smart devices' resource limitations, the IoT is vulnerable to several different routing attacks. Addressing these security concerns becomes crucial if data exchanged over IoT networks is to remain precise and trustworthy. This study presents a trust management evaluation for IoT devices with routing using the cryptographic algorithms Rivest, Shamir, Adleman (RSA), Self-Adaptive Tasmanian Devil Optimization (SA_TDO) for optimal key generation, and Secure Hash Algorithm 3-512 (SHA3-512), as well as an Intrusion Detection System (IDS) for spotting threats in IoT routing. By verifying the validity and integrity of the data exchanged between nodes and identifying and thwarting network threats, the proposed approach seeks to enhance IoT network security. The stored data is encrypted using the RSA technique, keys are optimally generated using the Tasmanian Devil Optimization (TDO) process, and data integrity is guaranteed using the SHA3-512 algorithm. Deep Learning Intrusion detection is achieved with Convolutional Spiking neural network-optimized deep neural network. The Deep Neural Network (DNN) is optimized with the Archimedes Optimization Algorithm (AOA). The developed model is simulated in Python, and the results obtained are evaluated and compared with other existing models. The findings indicate that the design is efficient in providing secure and reliable routing in IoT-enabled, futuristic, smart vertical networks while identifying and blocking threats. The proposed technique also showcases shorter response times (209.397 s at 70% learn rate, 223.103 s at 80% learn rate) and shorter sharing record times (13.0873 s at 70% learn rate, 13.9439 s at 80% learn rate), which underlines its strength. The performance metrics for the proposed AOA-ODNN model were evaluated at learning rates of 70% and 80%. The highest metrics were achieved at an 80% learning rate, with an accuracy of 0.989434, precision of 0.988886, sensitivity of 0.988886, specificity of 0.998616, F-measure of 0.988886, Matthews Correlation Coefficient (MCC) of 0.895521, Negative predictive value (NPV) of 0.998616, False Positive Rate (FPR) of 0.034365, and False Negative Rate (FNR) of 0.103095.

Network function virtualization is a key enabling technology for the customization of network services in next-generation networks to support diverse applications. Most enterprise and network services contain specific network functions that are stitched together in a predefined sequence to form a service function chain. The deployment and scheduling of a service function chain onto the substrate network play a vital role in deciding the efficiency of resource utilization and the performance of network management. For a delay-sensitive network service request traversing a service function chain, the end-to-end packet delay is a crucial parameter that indicates the deployment performance. Transmission, propagation, processing, edge queueing, and virtualization delays all impact the order in which virtual network functions execute. Service level agreement violations and incorrect schedules are produced when the controller does not take edge queueing and virtualization delays into account. In this work, we propose a service function chain scheduling problem for the optimization of the end-to-end delay while considering transmission, propagation, queueing, virtualization, and processing delays. Then, we propose a scheduling approach based on the earliest finish times of the physical machines to minimize the end-to-end delay of the service function chain. The performance of the proposed service function chain scheduling approach using the earliest finish time is evaluated in terms of end-to-end delay, service level agreement violation ratio, resource utilization, and acceptance ratio. We compare our proposed algorithm with four existing approaches from the literature. Simulation results show that our proposed approach outperforms existing approaches in terms of end-to-end delay, service level agreement violation ratio, resource utilization, and acceptance ratio.

The inherent distributed and anonymity features of the blockchain system may cause illegal activities like improper content dissemination, illegal transactions, money laundering, etc., posing a severe threat to the blockchain. Due to the ultra-large scale of the public chain system, identifying key nodes in the transaction network is usually cost-intensive and time-consuming. In this paper, we propose a transaction graph-based scheme to identify key nodes in the public blockchain, where a multi-stage key node detection algorithm is proposed. Real Ethereum transaction data validates the performance of the proposed scheme. It is shown that with a data volume of millions of items, our multi-stage approach can effectively eliminate low-value information from the data and realize high-efficiency key node detection, with similar performance compared to traditional algorithms without filtering, and an extremely large improvement in algorithm execution time.

It is encouraging to see blockchain technology take off as a practical means of improving supply chain management. Blockchain can help to lower fraud, boost efficiency, and improve the trust of those involved in the supply chain by offering a secure, decentralized, and transparent platform for tracking and verifying transactions. Additionally, blockchain can help with the creation of smart contracts, which can automate the completion of some transactions and improve the supply chain’s overall efficiency. Despite ongoing challenges like scalability and interoperability, blockchain technology has the potential to transform supply chain management and build a more robust, sustainable, and reliable global economy. To increase transparency, accountability, and trust in the supply chain, this article suggests using a Proof of Reputation (PoR) consensus protocol in a blockchain-based supply chain management system. The protocol gives each participant a reputation score based on their previous actions and behavior, and uses this score to securely and decentralized validate transactions and add new blocks to the blockchain. The article offers a collection of Fair-Exchange Assessment Metrics for assessing node reputation as well as an assessment model for choosing the best consensus protocol based on particular needs and objectives. The proposed model, BCSC, outperforms the current model, BRBC, in terms of interference ratio, fair data exchange ratio, and process overhead, according to experimental results. The suggested method has the potential to boost the security, scalability, and effectiveness of supply chain blockchain systems.

The necessity for an advanced health monitoring system within healthcare systems has instigated the evolution of Wireless Body Area Networks (WBANs). It serves as a tool predominantly employed in diagnosing and addressing patient's health concerns through treatments. Securing highly confidential and sensitive patient data collected through sensors within WBANs is crucial, necessitating robust measures to prevent various forms of adversarial attacks and unauthorized access, mainly due to its critical role in healthcare applications. Hence, signcryption security is essential for ensuring the protection of medical information within WBANs. This research presents a novel perspective apart from existing investigations on signcryption, addressing a gap in the literature. The study thoroughly analyzes signcryption-based WBAN protocols to contribute valuable insights to the field. Recent signcryption literature has been assessedorganization, equipped with the master key to analyze WBAN architecture, security requirements, and critical challenges within WBANs to fulfill the outlined objectives. This review aims to perform a comparative analysis of existing signcryption security solutions and analyze the existing proposed security solutions for WBANs. The techniques were compared with the existing signcryption methods, aiming to comprehend the security issues and their underlying motives. Furthermore, it highlights the research challenges encountered in the security dimensions of signcryption in WBAN, establishing the foundation for future avenues of investigation in this rapidly developing sector of health monitoring technology. The survey aims to serve as a benchmark for researchers and application developers, offering reference points for further exploration in the field.

Federated learning is a distributed machine learning approach that enables participants to train models without sharing raw data, thereby protecting data privacy and facilitating collective information extraction. However, the risk of malicious attacks during client communication in federated learning remains a concern. Model poisoning attacks, where attackers hijack and modify uploaded models, can severely degrade the accuracy of the global model. To address this issue, we propose DWAMA, a federated learning-based method that incorporates outlier detection and a robust aggregation strategy. We use the robust Mahalanobis distance as a metric to measure abnormality, capturing complex correlations between data features. We also dynamically adjust the aggregation weights of malicious clients to ensure a more stable model updating process. Moreover, we adaptively adjust the malicious detection threshold to adapt to the Non-IID scenarios. Through a series of experiments and comparisons, we verify our method’s effectiveness and performance advantages, offering a more robust defense against model poisoning attacks in federated learning scenarios.

Traceability in the food industry has become essential to ensuring safety, quality, and regulatory compliance. Traditional traceability methods often lack transparency, efficiency, and security, leading to challenges in verifying product quality and adherence to health regulations. This paper addresses these challenges by presenting a unique blockchain-based framework/system to enhance the traceability of the food grain. Integrating sensors, Raspberry Pi units, IPFS, and Ethereum Blockchain creates a transparent and auditable supply chain, empowering every participant within the supply chain to verify quality and adherence to healthful regulations. The suggested framework combines machine learning (ML) with blockchain technology. ML is responsible for distinguishing between valid and invalid data within the agri-food supply chain in this setup. At the same time, blockchain ensures that only valid data is stored, maintaining its security and privacy. This is crucial for consumer trust and enabling regulatory bodies to conduct efficient online inspections and ensure adherence to best practices. Finally, the proposed system is evaluated using various performance metrics. In terms of scalability, as the volume of data transactions increases, the system’s scalability improves. The framework shows faster transaction commitments, reduced propagation delays, higher throughput, and lower latency with higher transaction volumes. Additionally, the security analysis confirms that the proposed system effectively addresses critical security and privacy concerns, including confidentiality, data integrity, availability, non-repudiation, and protection against cyber-attacks. The proposed blockchain-based traceability framework for food grains has shown substantial possibility in reducing fraud and improving transparency and consumer trust.

With the increasing popularity of the internet of things (IoT) and 5th generation mobile communication technology (5G), mobile edge computing (MEC) has emerged as an innovative approach to support smart devices (SDs) in performing computational tasks. Nevertheless, the process of offloading can be energy-intensive. Traditional battery-powered SDs often encounter the challenge of battery depletion when offloading tasks. However, with the advancements in wireless power transfer technology, SDs can now achieve a sustainable power supply by harvesting ambient radio frequency energy. This paper studies the computation offloading in wireless-powered MEC networks with device-to-device (D2D) assistance. The SDs are categorized into near and far SDs based on their proximity to the MEC server. With the support of near SDs, far SDs can reduce transmission energy consumption and overall latency. In this paper, we comprehensively consider the allocation of energy harvesting time, transmission power, computation resources, and offloading decisions for SDs, establishing a mathematical model aimed at minimizing long-term average delay under energy constraints. To address the time-varying stochastic nature resulting from dynamic task arrivals and varying battery levels, we transform the long-term problem into a deterministic one for each time slot by introducing a queue and leveraging Lyapunov optimization theory. We then solve the transformed problem using deep reinforcement learning. Simulation results demonstrate that the proposed algorithm performs effectively in reducing delay and enhancing task completion rates.

With the goal of enhancing traffic flow and decreasing road accidents, fifth-generation (5G)-assisted vehicular fog computing was developed through innovative studies in wireless network connection technologies. But, with such high speeds and open wireless networks built into the system, privacy and security are major issues. To ensure the safety of vehicular fog computing with 5G assistance, it is essential to verify vehicle-to-vehicle traffic communication. Numerous conditional privacy-preserving authentications (CPPA) solutions have been created to safeguard communications connected to traffic in systems. Nevertheless, utilising these CPPA approaches to validate signatures is computationally costly. Elliptic curve cryptography provides authentication and conditional privacy in this certificateless authentication method for 5G-assisted vehicular fog computing, which streamlines the process of verifying vehicle signatures. In contrast, the certificateless CPPA method rapidly authenticates a signature using blockchain technology, eliminating the need for any prior identification or validation of its legitimacy. According to our experiment carried out the AVISPA tool, there are no vulnerabilities in the system that could be exploited by a Doley-Yao threat. In comparison to older approaches, the proposed solution significantly reduces the computational, communication, and energy consumption expenses.