The radio frequency identification (RFID) technology is frequently used for large-scale RFID network planning, which brings thought-provoking RFID network planning (RNP) problems such as the uncertain number of readers, high interference rate of readers, poor economic benefits, and weak robustness. In this paper, we propose a mayfly sparrow search hybrid algorithm with Circle chaotic map and Lévy flight, which is named MASSA. MASSA maps out the position of the readers by adding the idea of the mayfly algorithm (MA) into the optimization process of the sparrow search algorithm (SSA). Circle chaos mapping and Lévy flight are added in the SSA optimization process and the nonlinear inertia coefficients to balance the relationship between global search and local search. The MASSA is compared with SSA, Improved sparrow search algorithm (ISSA), MA, particle swarm optimization algorithm (PSO), and gray wolf algorithm (GWO). The first part of the experiment uses the CEC2017 test suite to evaluate algorithm performance and analysis strategy effectiveness. The results encourage the application of MASSA to solve RNP problems. In the second part, experiments were conducted with four objective functions of coverage, interference, power, and load balance. Two different experiments were carried out with fixed number of readers and flexible number of readers under random label conditions. Experimental results show that MASSA has good planning performance, better economy, and robustness.

With the continuous expansion of the Internet of Things (IoT), and despite reductions in communication latency enabled by edge computing, the decentralized nature of node structures poses significant security challenges. Blockchain is considered a critical technology for enhancing IoT security. However, traditional consensus exhibits limitations in efficiency and security when facing the exponential growth of IoT nodes necessitating further optimization and innovation. This paper proposes the threshold signature and reputation-enhanced Byzantine fault tolerance (TRBFT) algorithm. First, we integrate IoT with edge computing architectures, conduct a comprehensive evaluation of node reputation based on behavioral quality and contribution to consensus, and periodically update and categorize nodes into different sets with varying permissions. Secondly, the algorithm ensures that the selection of primary nodes strikes a balance between reliability and randomness, while employing a segmented screening scheme that progressively restricts the participating nodes in consensus, aiming to enhance accuracy. Subsequently, the communication structure is improved by incorporating threshold signature with reputation weight, which reduces communication complexity. Ultimately, simulation results demonstrate that TRBFT reduces the average consensus delay by 71.1% and increases the average throughput by 3.95 times compared to the advanced consensus HotStuff. In scenarios with faulty nodes, TRBFT increases the chain growth rate by 65.12% and decreases the block interval by 54.26% relative to HotStuff. The findings show the superior scalability and fault tolerance of TRBFT, making it better suited for large-scale IoT scenarios integrated with edge computing.

In this paper, a credit identity ring optimization Blockchain algorithm (CRBFT) based on Practical Byzantine Fault Tolerance (PBFT) is proposed to address the security problem for the network of Internet of things (IoT). The contributions of this work are as following: firstly, combined with the Edge Computing architecture, CRBFT introduces the improved credit grading protocol and improved ring signature scheme into the Blockchain network, rewards or punishes the nodes through their behavior, changes the way of selecting the primary node and ensures that the identity of the primary node will not be disclosed, so as to prevents the primary node from adaptively attack by Byzantine nodes, improves the reliability of the nodes participating in the consensus and enhances the security of the system; secondly, in order to reduce the communication traffic among nodes, CRBFT optimize the consensus protocol, not only reduces the time complexity of the traffic in the consensus stage from O (n ² ) to O (n), but also improves the consensus efficiency of the system while ensuring the system security; finally, aims to measure the performance of CRBFT, this paper is simulated and tested in three aspects: throughput (transactions per second), consistency delay, and communication overhead. Performance evaluation shows that CRBFT achieves lower communication overhead than PBFT. When the number of nodes is the same, CRBFT can obtain higher TPS and lower consistency delay. The results show that compared with PBFT, CRBFT can provide very competitive results.

This paper proposes utilizing federated learning (FL), a distributed learning paradigm, to process large, decentralized, and heterogeneous edge data in the context of Internet of Things (IoT) devices. However, heterogeneity and high communication costs are two primary challenges that hinder the efficacy of federated learning. To overcome these challenges, we have designed an algorithm, FedACADMM, which applies the adaptive consensus alternating direction method of multipliers (ACADMM) to federated learning clients (i.e., the edge mobile devices) to tackle the heterogeneity problem in federated networks. Importantly, the cost per round of communication for FedACADMM remains consistent with FedAvg and FedProx without adding any extra workload. Furthermore, our experimental results demonstrate that FedACADMM outperforms baseline methods with a realistic set of federated datasets, displaying enhanced convergence robustness. Notably, in highly heterogeneous scenarios, FedACADMM exhibits significant stability and rapid convergence, requiring at least 75% fewer communication rounds to achieve the target accuracy.