Peer-To-Peer Networking and Applications最新文献

Many drones work together in an ad hoc manner to form flying ad hoc networks. While these networks have opened up new possibilities for a wide range of applications like the commercial, and residential, they have also presented several problems, including high-speed nodes, limited density, and abrupt dynamic topology. Therefore, routing is a complex problem in such networks. The optimized link state routing protocol served as an inspiration for this plan. This article proposes a delay-conscious routing protocol for flying ad hoc networks and offers a new method for calculating the link lifespan among two unmanned aerial vehicles based on factors such as their distance apart, relative speed, and the direction in which they travel. An approach is presented in which the emperor penguin colony algorithm is used to select multi-point relay nodes. A node's ability to serve as a multi-point relay node is based on its remaining energy, link lifespan, neighboring degree, and eagerness. In sum up, the suggested approach generates paths between nodes taking energy and lifespan into account. The performance evaluation of the proposed routing is done against ML-OLSR and MP-OLSR. At a minimum, a 15% and 32% increase in latency and energy consumption were achieved by implementing the proposed technique.

Mobile Edge Computing (MEC) is a new computing paradigm that has shown great potential. How to extract the cooperative topological relationship between MEC servers to realize jointly computing is the key problem to solve the bottleneck of MEC computational capability. In previous studies, multi-MEC servers are regarded as unit computing nodes with the same cooperation relationship to jointly schedule offloading tasks, without considering the hierarchical and clustered topology of the server collaborative work. As a result, in the scenario of unbalanced distribution of computing resources, it is difficult to obtain the optimal joint scheduling strategy for offloading tasks according to the cooperation relationship and resource differences among MEC servers. Therefore, this paper considers introducing the topological relationship of group cooperation among multi-MEC servers to optimize the joint scheduling strategy, and proposes a Multi-Agent Hierarchical Graph Attention Soft Actor-Critic algorithm (MHSAC). Firstly, based on the differences in their own resources and the demands of the tasks they undertake, MEC servers are divided into series clusters. Then, a Hierarchical Graph Attention Network (HGAT) is used to model each agent to extract the physical communication topology information of the MEC server and the group topology information of multi-edge cooperation. The multi-agent soft Actor-Critic algorithm is used to obtain the offloading scheduling decision of multi-edge cooperation. Experiments show that the MHSAC algorithm that considering the topological relationship of multi-edge group cooperation can optimize load distribution under low latency and resource-limited requirements, achieving optimal load balancing values and task drop rates.

As blockchain technology advances, cross-chain asset transfer has become a critical issue in achieving interoperability between different blockchain networks. However, existing cross-chain solutions often require high trust requirements and complex communication protocols, which hinder usability and flexibility. To address these issues, this work introduces the temporary relay, a novel cross-chain asset transfer model without continuous blockchain network presence and frequent inter-chain communication. Technically, the temporary relay uses non-interactive zero-knowledge proofs to verify transactions and protect privacy while ensuring blockchain immutability and traceability after the temporary relay is shut down. We detail the construction of the temporary relay and analyze the security of the circuits constructed by the zero-knowledge proofs. Prototype implementation on the Substrate blockchain platform and experimental evaluation demonstrate the feasibility of the temporary relay. Furthermore, the verification time of zero-knowledge proofs in our model is short.

Homomorphic encryption (HE) is an innovative privacy protection technique supporting homomorphic addition and multiplication. It has been widely applied in the applications of peer-to-peer networks, such as secure data sharing and privacy-preserving search. Existing HE schemes can be roughly categorized into partially HE and fully HE (FHE). The former is computationally efficient but only supports either additive or multiplicative homomorphic operations. The latter can simultaneously support both additive and multiplicative operations, but the corresponding computational costs are intensive. Recently, some works leverage trusted execution environment (TEE) to optimize the efficiency of FHE. However, they suffer from the limitations of ciphertext expansion and the strong trust assumption for TEE. To address these limitations, we present a new fully homomorphic encryption scheme named Paillier FHE (PFHE) by employing TEE to extend the additive Paillier HE to support multiplicative operations and further optimizing the computational efficiency, where TEE is assumed to be semi-honest to avoid the strong trust assumption. Specifically, we first design a Paillier multiplication protocol (PMUL) to achieve the ciphertext multiplication without bootstrapping. Based on the protocol, we utilize the packing technique to design a Paillier inner product protocol (PVMUL) and a Paillier matrix multiplication protocol (PMMUL) to support the inner product and matrix multiplication operations efficiently. Moreover, we provide the detailed security analysis for our protocols. We compare our PFHE with typical fully homomorphic encryption libraries by experiments, and at the same security level, our scheme demonstrates significant advantages.

To enhance the security of medical data, the government and healthcare institutions must collect and analyze vast amounts of information, enabling the prompt detection of irregular patterns and timely issuance of accurate warnings. This is crucial for preventing and containing potential threats to medical data security. However, securely sharing and converting these data poses a significant challenge, particularly in open wireless access networks within healthcare settings. Proxy re-signature (PRS) offers not only signature conversion capabilities but also anonymity, safeguarding data reliability and authenticity. Nonetheless, current proxy re-signature techniques overlook the potential for algorithm substitution attacks (ASA). Therefore, we introduce a novel proxy re-signature scheme, leveraging cryptographic reverse firewall (CRF) technology, tailored specifically for the medical domain. Furthermore, we conducted rigorous security analysis and simulation experiments to validate the practical effectiveness of our scheme. This approach addresses the need for secure data sharing among various entities, including medical institutions, management centers, and research facilities, ensuring the integrity and confidentiality of critical medical information.

The surging interest in digitalization has revitalized research for digital business model. Mobile Internet of Things (IoT) terminals will reach billions and become an important infrastructure for a digital and intelligent society. In the digital business model of IoT terminals, the electronic seal of commercial contracts are especially investigated to provide the same level security as guaranteed by the traditional seal. However, the existing electronic seal is only applicable to the centralized network applications, which severely relies on the certificate authority. In this paper, we propose a blockchain-based multi-signature smart contract electronic seal scheme. The key of our design is to deploy a smart contract for business parties on Ethereum for secure electronic seal towards mobile IoT terminals. Unlike the traditional seal, this electronic seal is verified and managed collectively by all mining nodes. Combining it with optimizations in terms of a multi-signature algorithm encoded in the smart contract, our design achieves complete digital contract functionality for all business participants of terminals. As an example, we demonstrate an implementation on the Goerli testnet of the Ethereum network with the smart contract deployment. The security analysis and evaluation demonstrate that our design is secure in its multi-signature implementation and can be used in practice.

The demand-side management (DSM) research field has expanded due to rising energy consumption. In the traditional electrical grid, unknown energy usage results in high costs. This paper introduces a reinforcement learning-based self-adaptive learning-black widow optimization (RL-SAL-BWO) approach for dynamic load scheduling and power allocation, aimed at improving energy efficiency and reducing costs and energy consumption. The proposed strategy utilizes pricing signals and real-time load profiles to estimate the changing energy consumption within residential buildings. To optimize energy allocation across different appliances, this algorithm considers both energy efficiency and load characteristics. The RL agent, comprising action space, reward function, and Q-value function, is utilized for decision-making on power allocation and load scheduling. The SAL algorithm automatically adjusts the exploration rate and learning rate which leads to enhanced efficiency. By exploring the solution space, the BWO improves the learning process. Through the integration of RL, SAL, and BWO techniques, energy efficiency is increased, energy consumption is reduced, and electricity costs are lowered. The smart grid is utilized for estimating changes in energy consumption. The purpose of this is to estimate changes in energy consumption, aiding in informed decisions about energy management and infrastructure planning. The proposed approach is implemented using MATLAB R2021b software, followed by the evaluation and calculation of performance metrics. The findings demonstrate that the proposed strategy significantly enhances energy efficiency by 18.5%, reduces energy consumption by 31.91%, and decreases electricity costs by 40.66%. Furthermore, the computation time reduction of the proposed approach is 13.7 s.

Recent developments in low power sensors have prompted the creation of Visual Sensor Network (VSN). Coverage, availability, network life duration, and energy usage are important issues that arise in VSN. However several energy-efficient protocols have been developed, but those protocols have transmission collisions and energy loss due to increased data redundancy. In order to overcome these challenges, an Energy Efficient Routing based Sleep Scheduling Mechanism (EERSSM) is developed. Camera nodes are randomly deployed in the Visual Sensor Network, and the data is received from the network through a relay node. Energy, distance, and node stability are taken into account while identifying the relay node. The next step is to use a Similarity Graph guided Neural Network (SGGNN) to determine whether neighboring nodes detect similar data. If similar information is detected, a similarity measure is computed. When the value of the similarity measure exceeds the threshold, the node goes into the sleep stage while the other nodes are in the wakeup stage. A decision tree is used to calculate the sleep cycle depending on a few factors. The decision tree has a number of hyperparameters, and those parameters are tuned using Golden Eagle Optimization (GEO). When the update cycle is over, the node awakens and joins in the transmission procedure. This proposed energy efficient routing algorithm is tested with several metrics that attain better performance, like 14.42 J average residual energy, 93% packet delivery ratio, 9.3% throughput value, and 770 s network lifetime. Thus, the techniques used in the proposed approach are the better choice for solving the availability, energy consumption, and network lifetime issues in VSN.

With the continual evolution of network technologies, the Internet of Things (IoT) has permeated various sectors of society. However, over the past decade, the annual discovery of cyberattacks has shown an exponential surge, inflicting severe damage to economic development. Aiming at the high false alarm rate, poor classification performance and overfitting problems in current intrusion detection systems, this paper proposes an efficient hierarchical intrusion detection model named ET-DCANET. Initially, the extreme random tree algorithm is employed for feature selection to meticulously curate the optimal feature subset. Subsequently, the dilated convolution and dual attention mechanism (including channel attention and spatial attention) are introduced, and a strategy of gradual transition from coarse-grained learning to fine-grained learning is proposed by gradually narrowing the expansion rate of cavity convolution, and the DCNN and dual attention modules are progressively refined to effectively utilize the synergy of DCNN and Attention to extract spatial and temporal features. This gradual transition from coarse-grained learning to fine-grained learning helps to better balance global and local information when dealing with complex data, and improves the performance and generalization ability of the model. To confront the class imbalance issue within the dataset, a novel loss function, EQLv2, is introduced as a substitute for the conventional cross-entropy (CE) loss. This innovation directs the model's focus toward minority class samples, ultimately enhancing the overall performance of the model. The proposed model shows excellent intrusion detection on the NSL-KDD, UNSW-NB15, and X-IIoTID datasets with accuracy rates of 99.68%, 98.50%, and 99.85%, respectively.

Underwater Wireless Sensor Networks (UWSNs) must recognize sensor nodes and estimate their locations. A reliable underwater data forwarding relay is difficult to find in the acoustic communication environment. Underwater sensor node localization research has yet to accomplish accuracy, propagation error, restricted coverage, and QoS. A novel solution for static and dynamic UWSNs called the Reward-based distance vector Hop Localization (RDVHL) protocol is proposed. The reward for each underwater sensor node is computed periodically using the different reward measures in RDVHL. RDVHL first divides the deployed UWSNs dynamically into several clusters. Then Anchor Nodes (AN) are localized for each cluster using the reward measures periodically. After localizing the AN, they broadcast the localization information to the ordinary sensor nodes. The ordinary nodes are localized during the route formation for reliable data transmission using periodically computed reward scores. The correct localization of the sensor nodes enables the fast transmission of the underwater data from the source to the intended sink nodes. The proposed reward-based approach achieves accurate sensor node localization and reduces void communication and collisions in the acoustic communication channels. The RDVHL protocol outperforms the state-of-the-art in average throughput, average latency, average accuracy, and average energy consumption. RDVHL throughput increases by 12.54%. The average energy consumption drops 12.82%, localization inaccuracy drops 29.44%, and communication latency drops 3.72%.