Computer Communications最新文献

Federated Learning (FL) has emerged as a powerful model for training collaborative machine learning (ML) models while maintaining the privacy of participants’ data. However, traditional FL methods can exhibit limitations such as increased communication overhead, vulnerability to poisoning attacks, and reliance on a central server, which can impede their practicality in certain IoT applications. In such cases, the necessity of a central server to oversee the learning process may be infeasible, particularly in situations with limited connectivity and energy management. To address these challenges, peer-to-peer FL (P2PFL) offers an alternative approach, providing greater adaptability by enabling participants to collaboratively train their own models alongside their peers. This paper introduces an original framework that combines P2PFL and Homomorphic Encryption (HE), enabling secure computations on encrypted data. Furthermore, a defense approach against poisoning attacks based on cosine similarity is introduced These techniques enable users to collectively learn while preserving data privacy and accounting for ideal energy optimization. The proposed approach has demonstrated superior performance metrics in terms of accuracy, F-scores, and loss when compared to other similar approaches. Furthermore, it offers robust privacy and security measures, leading to an enhanced security level, with improvements ranging from 5.5% to 14.6%. Moreover, we demonstrate that the proposed approach effectively reduces the communication overhead. The proposed approach resulted in impressive reductions in communication overhead ranging from 63.8% to 79.6%. The implementation of these security models was cumbersome, but we have made the code publicly available for your reference ¹.

Unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) networks have recently been considered to be a support for ground MEC networks to enhance their computation capability. However, the line-of-sight (LOS) channels between the UAV and Internet of Things (IoT) devices can be interfered by various obstacles, such as trees and buildings, resulting in a considerable reduction in the capacity of MEC networks. To solve this issue, a system that combines multiple reconfigurable intelligence surfaces (RISs) with a UAV-enabled MEC network is proposed in this study. A UAV equipped with edge servers is treated as an aerial computing platform for IoT devices, and multi-RISs are utilized to simultaneously improve the communication quality between enhanced UAV and IoT devices. To maximize the sum computation bits of the system, a problem that jointly optimizes the time slot partition, local computation frequency, transmit power of the devices, UAV receive beamforming vectors, phase shifts of the RISs, and the trajectory of the UAV is formulated. The problem is a typical nonconvex optimization problem; therefore, we propose a recursive algorithm based on the successive convex approximation (SCA) and block coordinate descent (BCD) technology to find an approximate optimal solution. Simulation results demonstrate the effectiveness of the proposed algorithm compared with various benchmark schemes.

The exponential growth of Internet of Things (IoT) devices has triggered a substantial increase in cyber-attacks targeting these systems. Recent statistics show a surge of over 100 percent in such attacks, underscoring the urgent need for robust cybersecurity measures. When a cyber-attack breaches an IoT network, it initiates the dissemination of malware across the network. However, to counteract this threat, an immediate installation of a new patch becomes imperative. The time frame for developing and deploying the patch can vary significantly, contingent upon the specifics of the cyber-attack. This paper aims to address the challenge of pre-emptively mitigating cyber-attacks prior to the installation of a new patch. The main novelties of our work include: (1) A well-designed node-level model known as Susceptible, Infected High, Infected Low, Recover First, and Recover Complete $\left({\mathrm{SI}}_H{I}_L{R}_F{R}_C\right)$. It categorizes the infected node states into infected high and infected low, according to the categorization of infection states for IoT devices, to accelerate containment strategies for malware propagation and improve mitigation of cyber-attacks targeting IoT networks by incorporating immediate response within a restricted environment. (2) Development of an optimal immediate response strategy (IRS) by modeling and analyzing the associated optimal control problem. This model aims to enhance the containment of malware propagation across IoT networks by swiftly responding to cyber threats. Finally, several numerical analyses were performed to fully illustrate the main findings. In addition, a dataset has been constructed for experimental purposes to simulate real-world scenarios within IoT networks, particularly in smart home environments.

An extended connected dominating set (ECDS) in a wireless network with cooperative communication (CC) is a subset of nodes such that its induced subgraph is connected and each node outside the ECDS is covered by either one neighbor or several quasineighbors in the ECDS. Traditionality, the size of virtual backbone (VB) is the only factor considered in the problem of CDS construction. However, diameter is also an important factor to evaluate VB. In this paper we consider the problem of constructing quality ECDSs in unit disk graphs under CC with both of these two factors. We propose a two-phase centralized algorithm BD-ECDS to construct an ECDS for a given UDG with CC, which has a constant performance ratio (PR) and diameter. To obtain the PR of this two-phase centralized algorithm, we first give an upper bound of the EDS and use this upper bound to prove that the size of the ECDS under CC generated by the centralized algorithm is no greater than $120\left|ECD{S}_{opt}\right|-2$, where $ECD{S}_{opt}$ is the size of the minimum ECDS. Furthermore, our theoretical analysis and simulation results show that our algorithm BD-ECDS is superior to previous approaches.