Peer-To-Peer Networking and Applications最新文献

Objective: To evaluate neck pain, disability, and deep neck flexor (DNF) performance of individuals with temporomandibular disorders (TMD).

Methods: Eighty individuals were divided into the following: arthrogenic TMD (n = 40), myogenic TMD (n = 12), and mixed TMD (n = 28). Neck pain intensity, neck disability, and DNF performance were evaluated.

Results: Individuals with arthrogenic TMD reported lower intensity of neck pain when compared to mixed TMD (p = 0.01). Individuals with arthrogenic TMD had less neck disability than individuals with myogenic TMD (p = 0.037) and mixed TMD (p < 0.001). A moderate positive correlation was found between neck pain and neck disability (p < 0.001). No differences were found for DNF performance.

Conclusion: Neck pain and disability differs according to subtype of TMD, but performance of the deep neck flexors does not. Neck pain intensity and neck disability were correlated in patients with TMD.

This Research work addresses the pressing need within cloud computing for a trust-based service mechanism that effectively manages the burgeoning volume and variety of data while mitigating privacy concerns. The primary aim is to address pressing security challenges within cloud networks through a novel approach tailored to enhance privacy preservation mechanisms. Experiments were done on a variety of datasets using a hybrid privacy-preserving strategy to assess the efficacy of the suggested solution. The datasets were divided into both testing and training sets for the experimental design, using a 70% validation ratio for training. The method's performance was compared with that of existing strategies, including caching and spatial K-anonymity (CSKA) and privacy-preserving incentive and rewarding (PPIR), using precision, recall, and F-measure analysis. The findings show that the suggested strategy performs better than the baseline approaches in a number of assessment measures, indicating its greater capacity to protect privacy in cloud environments. Specifically, the approach achieved an average precision of 0.85, significantly surpassing the precision values of existing techniques by 8-10%. Moreover, the method exhibited an average recall of 0.84, indicating its robustness in recalling values across all test samples. Across various experiments, our method consistently achieved impressive F1 scores ranging from 0.80 to 0.85, underscoring its robustness in maintaining a balance between precision and recall. Furthermore, with an accuracy hovering around 0.85, our approach demonstrated remarkable proficiency in accurately classifying instances while preserving privacy in cloud environments. These promising results underscore the efficacy of the proposed approach in enhancing privacy preservation mechanisms within cloud networks, paving the way for more secure and reliable cloud computing infrastructures. By leveraging a hybrid privacy-preserving method, the paper offers a holistic approach to address the complex problems faced by cloud networks in safeguarding sensitive information. The experimental evaluation demonstrates the efficacy of the proposed approach, highlighting its superior performance compared to existing techniques.

Internet of Medical Things (IoMT) has great potential in delivering medical services. In IoMT, data users (e.g., doctors) may want to process data collected by sensors attached to data owners’ body (e.g., patients). As sensors lack computing resources, confidential outsourcing the data to a server becomes necessary due to its sensitivity. Using homomorphic encryption raises limitations in secure processing. First, as decrypting the processed result requires the data owners’ secret key, they must be online or share it with data users. Second, when processing is performed on the data of multiple data owners, the interaction becomes harder. Finally, if the processed result is sensitive, it lacks confidentiality as data owners may access it. In this paper, we propose a non-interactive homomorphic multi-party computation (HMPC) protocol, addressing the limitations efficiently. In HMPC, data owners encrypt their data with their own key and store it in a cloud server. Then, data users select the required data from the cloud server and outsource their own encrypted data to the server for processing. Afterwards, they decrypt the result regardless of the circuit computed and without interaction with the data owners. Our security and performance analyses demonstrate that HMPC is provably secure and applicable.

Multicast has emerged as a primary communication pattern in datacenter networks due to the increasing demand for distributed data-parallel applications. To accelerate multicast traffic, the emerging reconfigurable circuit technology, which can establish circuit connections among switches, has been proposed as a promising paradigm for datacenter networks. This paper investigates how to accelerate the non-preemptive multicast flows in a demand-aware manner in reconfigurable datacenter networks. Firstly, the problem of scheduling circuit switches to minimize the average completion time is formulated and proved to be NP-hard. To address the conflicts between different multicast flows under the bandwidth constraint, a connection based hypergraph is constructed and then a two round matching algorithm is proposed under the bandwidth constraint. Additionally, to further reduce the average completion time, we introduce a method to utilize the remaining capacity of the ToR switches by splitting the unscheduled flows. The proposed algorithm is proved to have an approximation ratio of (2sqrt{2n}), where n represents the number of Top-of-Rack (ToR) switches. Finally, the extensive simulations demonstrate the effectiveness of the proposed algorithm in reducing the average completion time of flows compared to state-of-the-art algorithms.

With the rapid development of medical information technology, the widespread adoption and application of electronic medical data have prompted more and more healthcare institutions to choose to store medical data in cloud servers to facilitate easier sharing. Attribute-based encryption is utilized for sharing electronic medical data to achieve fine-grained access control. However, storing access policies in plaintext can easily expose user privacy. Additionally, during the data sharing process, placing data retrieval in the cloud prevents secure and reliable searches. To address these issues, this paper proposes a blockchain-based privacy preserving and keyword-searchable scheme for medical data sharing(BPPKS). Access policies are transformed into vector-matrix form, concealing attributes within access policies to prevent the leakage of authorized user privacy information. Leveraging blockchain’s transparency, tamper-resistance, and integrity verification features, smart contracts are used for retrieval and verification, enabling secure and reliable data searches while ensuring the integrity of medical data. Simultaneously, some complex decryption operations are delegated to the cloud servers, reducing the decryption load for users to a constant level. Finally, security analysis demonstrates that this scheme can withstand adaptive chosen keyword attacks (IND-CKA), and performance evaluations show higher efficiency in computation and storage aspects.

Heterogeneous wireless sensor networks (HWSNs) are energy-constrained networks. Data aggregation can conserve the energy of HWSN. Clustering protocols and data processing can be used at individual nodes to reduce the amount of transfers and extend the network's lifespan. Considering these advantages, the proposed research introduces an efficient energy supply and data aggregation using effective techniques. Initially, cluster head (CH) election and data transmission are done using an information entropy based-clustering algorithm (IECA). After successful data transmission, an efficient energy supply scheme is enabled between cluster members (CMs) and sink nodes. Then, data aggregation is performed in CH using Planar Flow-Based Variational Auto-Encoder-based data aggregation (PF-VAE-DA). Before performing data aggregation, the useless and redundant data is compressed using a Long-short-term-memory-based auto-encoder (LSTM-based auto-encoder). The compressed data is aggregated in CHs. Before transferring the aggregated data to the sink, efficient data stream collection is performed to equalize the data size utilizing self-adaptive adjustment of sliding window size (SASWS). Finally, the optimal path is selected to transmit the aggregated data from CH to the sink. The performance of the proposed method is evaluated for various performance metrics. The aim of the proposed study is to enhance the accuracy of sensing data by introducing a novel deep learning-based data aggregation approach. This will extract significant features from vast amounts of data and carry out data aggregation. In addition, to improve the dependability of aggregated data transfer, an effective Energy Supply Policy based on data transmission patterns is implemented. The results show that the proposed method outperforms other methods in terms of network energy consumption, packet delivery ratio (PDR), packet dropping ratio, data aggregation rate, transmission delay, and network lifetime. The proposed approach uses 50% less energy than the other methods. The model's transmission delay ranges from 0.1 to 0.4 s as the number of nodes increases. The proposed network contains 282 active nodes at the 400th round, which is much more than the existing networks.

Vehicular Ad-Hoc Network (VANET), provides considerable real-time traffic information services that enhance safety and traffic effectiveness. However, as most of the VANET systems are centralized in nature prone to single-point failure, and vulnerable to attacks there will be reasonable delay in communication. In this paper, a lightweight privacy-preserving authentication scheme for peer-to-peer communication using blockchain (DLPA) while considering the resource-constrained nature of VANET is proposed. We have designed and deployed smart contracts using public blockchain to resist the vehicle impersonation attack, to identify illegal vehicle’s identity and thereby non-repudiation will be achieved. Vehicle-to-Vehicle (V2V) authentication and peer-to-peer communication are attained without the involvement of a Trusted Authority (TA) and to eliminate the trusted third party who is responsible for generating the key. Furthermore, DLPA has achieved handover authentication of vehicles so that vehicles need not be re-authenticated when they enter into a new Road Side Unit (RSU) limit. The proposed scheme is implemented in different Ethereum-powered test networks using Remix IDE to demonstrate the feasibility and to analyze the performance of the smart contract in terms of transaction cost and execution cost. In addition to that, security proof and analysis are performed to unveil that our proposed scheme preserves the privacy of the communicating parties, semantic security of the session key, and resistance against various known threats and attacks. Finally, the performance analysis of the scheme is done by calculating the communication and computation costs. The computation cost for our DLPA scheme for V2V authentication is 0.27461 ms and for V2RSU authentication is 0.15622 ms. The communication cost for both V2V and V2RSU authentication is 640 bits. While analyzing the result, the proposed protocol has a minimal cost when compared with other blockchain-based authentication schemes in VANET.

In the expanding field of the Internet of Vehicles (IoV) where network communication meets technology there is a pressing need, for robust data management and security. This study introduces IoVBlockSecure a protocol based on technology that aims to ensure collection and validation of data within the dynamic and decentralized realm of vehicle networks. The primary objective of IoVBlockSecure is to enhance data security, integrity, efficiency, and scalability in IoV. It achieves this through the utilization of smart contracts advanced blockchain technology and consensus protocol. The framework tackles scalability challenges by integrating both off chain and on chain data storage solutions thereby boosting the security and reliability of data from roadside units (RSUs) and vehicles. Additionally IoVBlockSecure incorporates techniques, a unique consensus mechanism, and a sequential numbering system for data points to optimize data processing and validation. Furthermore, the framework demonstrates its adaptability and operational efficiency by implementing Layer 2 solutions for off chain activities. Comprehensive performance evaluations were conducted to assess aspects such as fault tolerance, data integrity, security measures effectiveness, transaction latency, system throughput, consensus efficacy, and block processing time, across node counts and operational loads. The evaluations conducted confirm that the model is robust and effective demonstrating capabilities, in processing blocks and achieving consensus when transaction latencies increase and system throughputs vary. The framework shows resilience and reliability achieving levels of data integrity, security, and fault tolerance. While these findings validate the potential of IoVBlockSecure in meeting the demands of IoV networks they also highlight areas for improvement in optimizing throughput and latency for optimal performance.

To enhance the scalability of blockchain ledger structure, researchers have abandoned the traditional single-chain structure and used the structure of Directed Acyclic Graph (DAG) to achieve high-concurrency processing of transactions. The DAG-based ledger requires a specific topological structure and block sorting rules to ensure the temporality and security of the ledger structure. This paper proposes a hybrid protocol called Mainstay with a double-layer structure, including the DAG-Ledger layer and the NC-Chain layer. The DAG-Ledger layer uses PHANTOM structure to confirm the transaction block order; the NC-Chain uses dynamic committee elections, voting, and fast verification to form a blockchain similar to the Nakamoto style, and each block of the NC-Chain requires committee members to calculate the Verifiable Delay Function (VDF) to generate a quorum certificate(QC), which can ensure the stability and security of the DAG-Ledger layer ledger sequence. By experimental comparison, we conclude that the Mainstay protocol can effectively reduce the probability of malicious attackers successfully attacking the ledger structure.

Security and energy efficiency are regarded as the significant problems in the pervasive wireless networks. Since widespread wireless networks rely on battery-operated nodes, it is necessary to progress an energy-efficient scheme to lower energy consumption and increase the networks lifespan. The existing approaches fail to accomplish both objectives at the same time. Therefore, this paper proposes a Blockchain abetted Energy Efficient Archerfish Hunting and Namib Beetle Optimization Algorithm espoused Clustering Protocol for Wireless Sensor Network (BC-EEAHNBOA-CP-WSN). The proposed method operates in two phases: (i) determining the optimal cluster heads, (ii) determining the optimal trust path. The Archerfish Hunting Optimizer and Namib Beetle Optimization Algorithm are used to precisely select the cluster heads, while the optimized trust paths are secured using blockchain technology. This paper combines the development of Energy Efficient Archerfish Hunting and Namib Beetle Optimization Algorithm (EEAHNBOA) with blockchain-enabled secure data transmission, introduces a clustering method based on AFHO-NBOA for efficient cluster head selection using a fitness function that incorporates energy, node density, neighboring nodes' distance, and sink distance, and ensures safe data transfer between cluster members and cluster heads using blockchain. The proposed BC-EEAHNBOA-CP-WSN approach is executed in MATLAB 2018a, its effectiveness is examined using metrics. The results demonstrate that the proposed method achieves a 23.31%, 45.16%, and 18.72% higher packet delivery ratio, and a 15.56%, 47.31%, and 19.96% longer network lifetime compared to existing methods. By fusing blockchain technology with sophisticated optimization algorithms, this research advances the state of the art by improving WSN security and energy efficiency. The implications of this work suggest significant improvements in the lifespan and reliability of wireless sensor networks, which are crucial for a wide range of applications.