Owing to the easy deployment and mobile flexibility, Unmanned Aerial Vehicle (UAV) assisted Mobile Edge Computing (MEC) has been deemed as one potential technology for handling the computation-intensive tasks at terminal devices (TDs). In this work, a MEC architecture assisted by UAVs is designed which achieves efficient offloading, computing, and downloading for tasks from multiple TDs via aerial to aerial collaboration of two UAVs. In this architecture, the task offloading process contains two parts, i.e., the offloading from TDs to a mobile UAV which flies around TDs, and the offloading from the mobile UAV to a hovering UAV which hovers in the air. The computing tasks from TDs will be divided into three parts allocated to the TDs themselves, and both two UAVs. Upon completion of computation, the computation results are downloaded to the TDs. The optimization objective is to seek for an optimal task division strategy to attain the weighted total energy consumption minimization for all devices. Since the formulated optimization problem is not convex, we develop a two-step iteration algorithm which jointly optimizes computing frequency, task allocation volume, as well as UAV's trajectory based on the method of block coordinate descent. Simulation results confirm the effectiveness and performance advantages of the designed algorithm.

Ensuring safe and reliable data sharing is crucial for the development of Internet of Vehicles (IoV) technology. To provide a trusted data environment for IoV and enable traditional consensus algorithms to meet the high dynamic requirements of the IoV. In this article, we propose a blockchain-based data sharing scheme for IoV (BLDTS) to achieve secure and trusted sharing. First, we design a false information identification strategy that utilizes a bayesian inference model to determine the authenticity of shared data with the assistance of reputation value. Second, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is used to construct a novel lightweight consensus mechanism based on vehicle reputation values and traffic environment factors, and the nodes with high scores were selected as participants in the consensus, which can reduce the computational overhead. Finally, experimental results show that our scheme has advantages in improving the accuracy of false message identification and consensus efficiency.

Wearable activity recognition is crucial for ubiquitous computing, enhancing human-machine interaction, medical monitoring, and personalized services. As wearable devices collect user activity data that often contain personal privacy information, federated learning (FL) is increasingly applied to protect user data privacy. However, in real-world scenarios, users' data are commonly exhibit heterogeneity, manifesting as non-independent and identically distributed (non-IID) characteristics, which presents challenges for FL methods. Traditional FL client selection approaches with heterogeneous data can cause global model drift, reducing the accuracy of activity recognition models. In this paper, we propose Data Contribution-Based Federated Learning (DCBFL) method, an adaptive FL training approach by selecting clients to counter the problem caused by heterogeneous data. Specifically, we first utilize a conditional generator on the server to construct an auxiliary dataset, which is used to train an auxiliary model as a benchmark to measure the degree of heterogeneity in each client's data. Furthermore, we reasonably differentiate the data contributions of clients based on the degree of data heterogeneity and select suitable clients for FL training, effectively utilizing heterogeneous data information, mitigating global model drift. The comprehensive experiments are conducted on five public activity recognition datasets under non-IID conditions in this work. The experimental results show that DCBFL outperforms existing baseline methods, showcasing superior performance.

Throughput-optimal dynamic broadcasting is an essential cornerstone for the efficient operation of Multi-hop Wireless Networks (MWNs). Most existing algorithms for this problem were developed assuming static interference environments and network connectivity. However, wireless interference environments and network connectivity are inherently time-varying in real-world scenarios, primarily due to uncontrollable interference sources and unreliable links. Such time-varying characteristics make these existing algorithms less robust. In this paper, we study the robust throughput-optimal dynamic broadcasting for MWNs with multi-dimensional time-varying characteristics in terms of interference environments, network connectivity, and data arrival. We model the time-varying link existence states using a random process and characterize the time-varying interference environments through a dynamic variant of the classical Signal-to-Interference-plus-Noise-Ratio (SINR) model. In this variant, the SINR model parameters are dynamically adjusted over time by an adversary. Based on this, we first design a Robust Throughput-optimal Dynamic Broadcast (RTDB) algorithm which makes efficient slot-based max-weight link scheduling, power allocation, and data forwarding decisions in each time slot. We then prove its throughput-optimality in time-varying acyclic directed MWNs under the dynamic SINR model. The effectiveness of RTDB is validated via numerous simulations.

With the rapid advancement and application of the Internet of Medical Things (IoMT), personal health records (PHRs) are now increasingly comprised of data collected by Internet of Things (IoT) devices and medical records documented by healthcare professionals. Personal health record (PHR) sharing demonstrates great potential in improving the accuracy of disease diagnosis. However, PHR sharing also brings risks such as illegal access and personal information leakage. Some works explored using blockchain or attribute-based encryption (ABE) to solve these privacy leakage problems, but those solutions did not pay attention to the user’s attribute privacy. In this work, we combine a linear secret sharing scheme (LSSS) and zero-knowledge succinct non-interactive argument of knowledge (zkSNARK) scheme to design an efficient zero-knowledge proof protocol called zk-AHSNARK. It can verify the user’s attribute permissions while also hiding attribute information. Based on zk-AHSNARK, we propose a novel PHR sharing scheme that protects attribute privacy. Data security is ensured by storing encrypted data in the interplanetary file system (IPFS). In addition, we introduce keyword ciphertext search to achieve fast data retrieval, and we implement the search and verification algorithms via a smart contract, ensuring the trustworthiness and integrity of the execution. Finally, through a large number of simulations, we demonstrated the suggested scheme’s viability and security.

Disseminating continuous packet flows injected at multiple location-random source nodes to all network nodes, known as the multi-source dynamic global broadcast problem, is a fundamental building block for wireless multi-hop networks to run smoothly and efficiently. Previous studies on dynamic global broadcast all assume reliable communications. However, in realistic wireless networks, there exist unpredictable transmission failures caused by the randomized signal interference from uncorrelated wireless networks sharing the same spectrum or even malicious attackers. In this paper, by integrating the Signal-to-Interference-plus-Noise-Ratio (SINR) model, multi-channel communication mode, and randomized malicious channel jamming controlled by an adaptive adversary, we present an SINR-based adversarial channel jamming model to capture the unpredictable transmission failures in a wireless multi-hop network. We first propose a distributed Jamming-resilient Multi-source Static Broadcast (JMSB) algorithm based on random channel selection and message transmissions for multi-hop wireless networks under the above SINR-based adversarial channel jamming model. We then propose a distributed stable Jamming-resilient Multi-source Dynamic Broadcast (JMDB) algorithm which iterates JMSB repeatedly and efficiently in a two-stage manner. We derive the maximum supportable broadcast throughput of JMDB under the stability guarantee, i.e., the expected boundedness on the queue length of each network node and expected broadcast latency for each injected packet. Simulation results shows the stability and throughput efficiency of our proposed JMDB algorithm.

Multi-source global broadcast is a fundamental problem in multi-hop wireless networks. The Static Multi-source Global Broadcast problem (SMGB), which considers static packet injection at all source nodes, has been extensively studied in recent years. However, packets are more likely to be continuously injected over time in realistic multi-hop wireless networks. In this paper, we focus on studying the Dynamic Multi-source Global Broadcast problem (DMGB), in which packets are continuously injected to$k$($k\geq 2$) source nodes in the network according to a widely-used dynamic packet injection model and the objective is to disseminate each injected packet across the whole network quickly. We solve this DMGB problem under the Signal-to-Interference-plus-Noise-Ratio (SINR) interference model. Specifically, we first present a distributed randomized algorithm for solving the SMGB problem. We then iterate this SMGB algorithm repeatedly to construct a distributed DMGB algorithm. We prove the proposed DMGB algorithm is stable, i.e., the expected number of packets in each node’s message queue is bounded at any time and further the expected global broadcast latency for each injected packet is bounded. Simulation results validate the effectiveness of the proposed DMGB algorithm.

Global broadcasting is a fundamental problem in wireless multi-hop networks. In this paper, we propose two distributed deterministic algorithms for global broadcasting based on the Signal-to-Interference-plus-Noise-Ratio (SINR) model. In both algorithms, an arbitrary node can become the source node, and the rest of the nodes are divided into different layers according to their distance to the source node. A broadcast message is propagated from the source node to all the other nodes in a layer by layer fashion. Our first proposed algorithm (named TEGB) selects a Maximal Independent Set (MIS) for each layer. Subsequently, multiple subsets of the MIS are carefully selected so as to allow the most concurrent transmissions. Our theoretical analysis shows that TEGB has the time complexity of O(D log n), where n is the total number of nodes in the network and D is the diameter of the network. Compared with the popular algorithm DetGenBroadcast proposed in the work of Jurdzinski et al.(2013), TEGB has a logarithmic improvement in running time. Furthermore, we develop the second algorithm (named TBGB) to reduce the number of duplicated broadcast messages at each layer. To be specific, TBGB attempts to form a unidirectional spanning tree of the network. On the spanning tree, only the non-leaf nodes transmit the broadcast message. Therefore, the redundant broadcasts in the same layer are eliminated. Our theoretical analysis shows that TBGB has the time complexity of O(DΔ log n), where Δ is the maximum node degree.