Federated learning is a collaborative training paradigm designed to protect private data and is widely used in the cooperative training of Internet of Things (IoT) devices. However, despite its focus on privacy protection, federated learning remains susceptible to poisoning attacks from malicious clients. These attacks can degrade system performance and potentially lead to data privacy breaches. Moreover, real-world IoT datasets are often heterogeneous, further increasing the difficulty of detecting malicious clients. Existing defense mechanisms often struggle to effectively identify malicious clients while maintaining high model performance. To address this issue, we propose a defense mechanism called Malicious client exclusion aggregation (MaEA). This method utilizes KL divergence to preliminarily filter out anomalous clients, aggregates the remaining (preliminarily filtered) clients to obtain a pre-center model, and then identifies and excludes malicious clients by measuring their deviations from this pre-center model. We executed a series of extensive experiments on the CIFAR-10 dataset to demonstrate the effectiveness of MaEA. The results demonstrate that our approach can efficiently detect and identify malicious clients while correcting model performance.

Smart homes depend on collaborative sequential prediction tasks to optimize energy consumption and appliance scheduling. Federated learning (FL) offers a promising approach by enabling decentralized model training to balance privacy and usability. Yet, standard FL techniques fail to effectively address data diversity and individual user preferences in smart home contexts. To address these issues, we propose P3FL: a Privacy-Preserving Personalized Federated Learning framework that integrates tailored model training and privacy enhancements for federated collaborative predictions and decision-making. Our framework introduces the Personalized Collaborative Decision-Making (PCDM) algorithm, which dynamically adapts to different household environments while ensuring privacy and personalization. P3FL combines a global model for knowledge aggregation with a personalized adaptation module to provide fine-tuned predictions based on user preferences, environmental factors, and device configurations. Theoretical convergence bounds analysis confirms the robustness and efficiency of PCDM under conditions of strong convexity, smoothness, and bounded variance. Extensive experiments on real-world smart home datasets demonstrate that P3FL outperforms state-of-the-art methods, with PCDM achieving a training accuracy of 92.14%. Our approach enhances operational efficiency and ensures personalized user satisfaction, privacy enhancement in smart homes.

Eigenvalue decomposition (EVD) is a fundamental yet time-consuming operation with extensive applications in Internet of Things (IoT). When the matrix dimension reaches millions, resource-limited IoT devices struggle to perform such computationally expensive operations. Edge computing, with its plentiful computing resources, offers an effective solution to this problem. However, privacy concerns arise because outsourced tasks may contain sensitive user data. In this article, we propose the first privacy-preserving, edge-assisted EVD outsourcing scheme that securely enables users to outsource EVD tasks to edge servers. We design a privacy-preserving matrix transformation method to encode the original data, ensuring that edge servers cannot access users’ private information. Additionally, we design a verification scheme that enables the user to verify the correctness of the results returned by the edge servers. Our protocol supports parallel computation by multiple edge servers, thus enhancing the efficiency of EVD. The feasibility of our proposed scheme is demonstrated through both theoretical and experimental perspectives.

The Internet of Vehicles (IoV), as one subset of the Internet of Things (IoT) in the smart transportation area, integrates vehicle networks with sensors and actuators. By connecting all sensors to the network, the IoV enables smart transportation (i.e., autonomous vehicles) and makes smart cities a reality. In smart transportation systems, roadside units (RSUs) capture all vehicle information and serve as gateways. However, smart transportation infrastructure has yet to mature in the current stage. RSUs are insufficient to support all vehicles. Meanwhile, the low computational capability of vehicles makes it challenging to recompute the driving route as the road environment changes. To address the problem of insufficient RSU coverage, one protocol called IEEE 802.11p enables vehicle-to-vehicle communication using relays. Nonetheless, data transfer among vehicles via relays is still time-consuming for a large-scale transportation network. To deal with the above issues, in this paper, we propose an IoV framework using digital twins (DTs) to digitize the IoV environment and assign nearby IoT gateways compatible with the RSU communication protocol. This framework lets DTs update the vehicle’s driving route based on real-time information. With a case study, we evaluate the efficacy of DT-assisted IoV based on communication latency and vehicle driving efficiency. Our evaluation results confirm that the proposed framework can efficiently enhance communication latency when the relay needs to pass through two or more vehicles and reduce travel time when vehicles receive updated route information at intersections.

The Internet of Vehicles (IoV) connects a massive amount of smart vehicles for inter/intra-vehicle information sharing. Data privacy issues, such as privacy leakage and privacy cost are the key challenges that hinder vehicle operators from sharing their data safely. Traditional privacy-preserving techniques, including Federated Learning (FL) and Differential Privacy (DP) techniques, can protect data privacy and security, but the high privacy cost severely limits learning performance. In addition, the IoV services place high demands on low communication latency, which can be obtained by reducing the communication bits, but it also limits the learning performance. Thus, how to solve the communication-privacy-accuracy tradeoffs to achieve low latency, high privacy preservation and model performance has been a complicated issue in IoV. In this paper, a privacy-enhancement differentially private federated learning framework (FedSDP) is proposed based on the shuffle model to ensure secure and efficient data sharing under the constraint of low latency in IoV. In our proposed framework, four privacy enhancement methods are proposed, including data subsampling, vehicle sampling, shuffle model and dummy points, to amplify the privacy and obtain higher learning performance. Then, a Top-K sparsification mechanism of the vehicle training process is proposed to reduce communication bits. Finally, the experimental results indicate that our approach can reduce the communication latency by 31.66%, enhance the privacy ϵc by 30.77% and improve the test accuracy by 48.56%, compared with the traditional SDP mechanism.

With the continued evolution of autonomous driving, the communication demand for in-vehicle networks (IVNs) has dramatically increased, and the traditional IVN solutions have become ineligible for the needs of new in-vehicle applications. Time-sensitive network (TSN) with the characteristics of determinism, low latency, high reliability, large bandwidth, and open industry standards, has been considered as the most promising technology for the next-generation IVNs. This article summarizes the current state and methodologies of in-vehicle TSN research through the following five aspects: 1) Quality of Service (QoS) strategy; 2) reliability; 3) clock synchronization; 4) network planning; and 5) network management. The corresponding research priorities and technical challenges are also addressed, respectively.

Green communications are playing critical roles in vehicular ad hoc networks (VANETs), while the deployment of a power efficient VANET is quite challenging in practice. To add more greens into such kind of complicated and time-varying mobile network, we specifically investigate the throughput and transmission delay performances for real-time and delay sensitive services through a repeated game theoretic solution. This paper has employed Nash Equilibrium in the noncooperative game model and analyzes its efficiency. Simulation results have shown an obvious improvement on power efficiency through such efforts.

Information security is key important when we are trying to interconnect the wireless body sensor network with the healthcare social network via mobile facilities. In this paper, we specially work on a secured electrocardiogram (ECG) signal transmission scheme to prevent further injuries for patients with heart diseases from human emotional stress. We proposed a dynamic encryption method via biometric information among frequency spectrums of ECG signals, which can guarantee both high classification rate (>90 %) and system energy efficiency. At the same time, cooperative relays are applied for an additional spatial diversity gains. Simulation results show that the improved transmission rate and signal power capacity can lower the probability of data intercept (LPI) and detection (LPD) by taking the advantages of both temporal and spatial diversities. The network security thereby can be further improved.

The proliferation of the mobile Internet and social networks reshapes the proportion of uploaded data in the entire Internet traffic. IFOM (IP Flow Mobility) technology, which offloads the cellular data to the WiFi or Femtocells or other complementary networks, plays a crucial role in improving the throughput of cellular systems. Although there have been many studies on the IFOM technology, most of them are done from a technical perspective, and the issues related the dissemination and utilization of the IFOM technology are largely overlooked. Unlike prior research works, this paper addresses issues involved with the IFOM technology from an economic perspective. Specifically, we model the competitions among multiple service providers supporting or not supporting the IFOM technology by leveraging the Game Theory, and then analyze the Nash Equilibrium for the ensuing game model. We also conduct extensive simulations to determine the factors that affect the market share and profit of the service providers. We believe that this research work will provide valuable guidance to service providers for the promotion and utilization of the IFOM technology.

In this work, we try to collect useful emotional stress information from electrocardiogram (ECG) signals via a real-time wearable Wireless Body Area Network (WBAN). Discrete Wavelet Transform (DWT) is applied on collected ECG signals for feature extraction, which carries important information for stress level identification. After the stress level is classified using K-Nearest Neighboring (KNN), adaptive convolutional coding is considered for ECG signal protection during transmission according to their various stress levels, which is able to provide an acceptable low Bit Error Rate (BER) and efficient energy consumption at the same time.

In multimedia communication, various data rates, security strategies, and data qualities are required for different content forms such as text, audio, images, video, and interactivity content forms. In this paper, we propose a secure and robust approach to achieve the multimedia multiple access (MA) communication using self-encoded spread spectrum (SESS). In this proposed system, SESS multiple access (SESS-MA) is a novel approach to multimedia system due to its unique secure and flexible spreading nature. Iterative detection is applied for an improved multimedia quality of service (QoS) at the receiver. The number of iterations needed is evaluated separately according to different multimedia contents. Simulation studies demonstrate that the proposed scheme ensures satisfactory data quality, security, and robustness.

In this paper, an Alamouti space time based cooperative wireless cellular network for multimedia transmission is proposed. According to various requirements of text, voice, image, video or medical signal transmissions, this multiplexing-diversity balanced structure is provided for such comprehensive multimedia transmission in wireless cellular networks. Simulation results of this Alamouti based cooperative network have been compared with Maximum Ratio Combining (MRC) method. Simulation results show the BER performance of proposed scheme outperforms several other spatial modulation methods.

In this paper, an Alamouti based cooperative wireless networks for multimedia transmission is proposed. According to various requirements of video, image, voice or medical signal transmissions, the multiplexing-diversity balanced structure is provided for such comprehensive multimedia network. Simulation results of this Alamouti based cooperative network have been compared with Maximum Ratio Combining (MRC) method. Simulation results show the BER performance of proposed schedule outperforms several other spatial modulation methods.

There is a strong need to explore green and harvestable energy in computer communications. However, adapting wireless network performance to harvested energy has largely been ignored in literature. In this paper, we propose a new resource allocation scheme to improve data delivery quality in energy harvesting enabled wireless networks. In the proposed approach, packet Automatic Repeat reQuest (ARQ) limit of each wireless node is adaptively adjusted according to harvested energy. To achieve such optimal retry adaptation, energy neutrality constraint is considered in the overall optimization process. Simulation results show that the proposed retry adaptation approach significantly improves packet delivery ratio by exploring the harvested energy.

In this paper, we present a cooperative transmission approach to reduce the end-to-end delay in the context of AODV based multi hop wireless networks. The underneath idea is to effectively increase the average reach of each hop so that data packets can arrive at the destination in less number of hops with lower end-to-end delay. The existing approaches of increasing transmitted power are not effective while they increase the network interferences. Unlike them, we exploit the concept of cooperative beamforming to reduce the end-to-end delay by increasing the effective communication distances and reducing the communication interferences in wireless ad-hoc networks.

Reducing transmission energy consumption is one of the most critical research issues in the area of Wireless Sensor Networks (WSNs). In this paper, we present our scheme for employing collaborative signal enhancement to achieve energy efficient image transmissions in WSNs. A collaborative signal enhancement approach is compelling due to its capability of saving individual energy consumption by spreading total transmission consumption over multiple sensors. Individual packets describing an embedded wavelet-encoded image exhibit a significantly unequal contribution towards image quality. By leveraging this fact we develop a strategy of appropriately selecting the number of collaborative sensors for each packet transmission in order to achieve the highest possible image quality given a restricted transmission energy consumption budget. Experimental results show that our proposed approach can provide about 2dB higher image quality under the same energy budget compared with the state-of-the-art collaborative transmission scheme in WSN literature.

In this paper, we propose an innovative low-cost biometric authentication scheme based on uniformed Gaussian mixture model (GMM) for secure communications within wireless body area sensor networks (WBASNs), to obviate the cost-prohibitive key exchange problem. In this approach, the sender Inter Pulse Interval (IPI) information is selected as the biometric key for authentication, and a statistical based signing scheme is developed using the uniformed GMM. Experimental analysis shows that the proposed approach effectively protects the communication channels in WBASN with a low computational complexity, significantly removing the need for secret key exchange and critical time synchronization overheads.