As one of the physical pest control technologies, solar insecticidal lamps (SILs) align with the development trend of modern agriculture, which emphasizes green and environmentally friendly practices. The integration of SILs with wireless sensor networks (WSNs) has led to the development of a new type of agricultural Internet of Things (IoT), known as SIL-IoT, which aids in better agricultural decision-making. In this paper, the problem of SIL deployment in SIL-IoT is investigated. The existing deployment methods still face challenges such as unrestricted deployment locations and a single evaluation metric. Therefore, to address these shortcomings, we propose a Greedy-based Layered Deployment (GBLD) optimization method. The proposed method consists of two layers: boundary coverage layer and the infill coverage layer. Firstly, in the boundary coverage layer, SILs are deployed layer by layer from the boundary to the center, aiming to cover the boundary points as much as possible. Secondly, based on the first layer, the remaining large area is infill covered to achieve the minimum deployment cost and total weight, satisfying both coverage and connectivity requirements. Furthermore, To meet the application requirements of SIL-IoT in various scenarios, a demand coverage algorithm is proposed that satisfies the specified coverage requirements while minimizing deployment cost and weight. The simulation results demonstrate that the proposed GBLD method can reduce the deployment cost by 3%-5% and can effectively meet the deployment requirements of SIL-IoT application scenarios with different coverage requirements.

Coverage maximization under full connection constraints involves many factors and poses huge challenge in mobile wireless sensor networks. Most of research works on this issue are based on the disk model, which have high complexity and long iterations, and therefore cannot be applied to dynamic complex networks. In this paper, the problem of confident information coverage maximization under full connectivity constraints (CIC-CC) is defined based on the confident information coverage model (CIC). To address this problem, a 3-stage connectivity constrained coverage maximization algorithm (3-CCC) is proposed with the time complexity of O(T*n2). 3-CCC contains three stages: maximizing coverage (MC), full connectivity (FC), and maximizing connectivity constrained coverage (MCCC). These three stages can be used in whole or in part to achieve coverage maximization with connectivity constraints depending on the network state. The stable matching mechanism, greedy algorithm, and Q-learning are utilized to improve the algorithm’s efficiency. Experiments show that the proposed algorithm has good performance in terms of iteration number, running time, and coverage rate.

In recent years, Synthetic Aperture Radar (SAR) ship detection has seen significant improvements due to the rapid development of deep learning. However, when ship targets are densely arranged or exhibit multi-scale variations, there are still issues such as significant differences in aspect ratios, resulting in false alarms, missed detections, and low detection accuracy. To overcome these challenges, this paper introduces a novel detection model, PEGNet, based on Faster R-CNN. Firstly, to identify ship targets at different scales, the Path Aggregation Feature Pyramid Network (PAFPN) was integrated into the feature fusion structure, which enhances the network’s feature representation and robustness. Secondly, the Efficient Multi-Scale Attention (EMA) was employed to strengthen detection accuracy by reducing noise interference and enhancing feature stability. Thirdly, the Guided Anchoring Region Proposal Network (GA-RPN) was introduced to produce anchors that more accurately reflect the actual positions and scales of targets, which improves localization precision and lowers the missed detection rate. The performance of PEGNet was test on the SSDD and HRSID datasets, achievingmAPscores of 71.1% and 67.9%, respectively. Compared to the baseline network, this represents improvements of 2.5% and 7.6%. This result highlighting the method’s superior performance compared to other approaches.

The large-scale applications of wireless sensor networks (WSNs) place higher demands on their reliability. WSNs deployed in the intelligent tunnel are often affected by environmental interference or malicious intrusions, which lead to untrusted paths and affect the reliability of transmission. A trusted path ensures that the collected information can be successfully and reliably transmitted to the sink node. To solve the transmission reliability problem of wireless sensor networks, a trusted multi-source shortest path-based transmission reliability (TMSPR) algorithm is proposed in this paper. A lightweight trust management model with a node relation matrix (RM) is applied to identify and exclude untrusted nodes, thereby establishing secure transmission links. Meanwhile, the shortest transmission path is selected based on the minimum path to save the energy of the nodes. The information transmitted through trusted multi-source shortest paths (TMSPs) can successfully reach the sink node, which significantly improves the transmission reliability of the network. Furthermore, a transmission reliability indexTRelis defined as a probabilistic measure to assess reliability. Simulation results demonstrate that the proposed algorithm exponentially reduces both computation time and memory usage, while enhancing transmission reliability by approximately 5%.

The Internet of Things (IoT) has recently experienced a significant increase in the frequency of cyberattacks, leading to an urgent need for high security and reliability in intelligent IoT applications. Ensuring that interconnected devices within the system operate as expected and provide accurate data has become an essential concern. Reliable coverage can provide a trusted data source for the system. Comprehensively considering various factors such as node multi-state, potential intrusions, and interferences, a trust-based intrusion-tolerant coverage reliability evaluation algorithm (T-ITCR) is proposed to evaluate the coverage reliability based on the trust-based reliable confident information coverage model (T-RCIC). In T-ITCR, trust management is deeply integrated throughout the evaluation process, facilitating dynamic adjustments in node states, network connectivity, and node coverage weights. Malicious nodes are identified and excluded to guarantee the security of data sensing and transmission. Furthermore, to predict node states more accurately, a precise energy assessment mechanism is conducted based on node interaction processes. A significant number of experiments have demonstrated the performance of the proposed algorithm. Consequently, the T-ITCR algorithm demonstrates its ability to efficiently detect malicious intrusions and adjust network states, which significantly strengthens the security and reliability of the networks.

The widespread applications of the Hybrid Internet of Things (HIoT) have put forward higher requirements for network reliability. Coverage reliability is one of the important metrics of reliability, and reliable coverage ensures network data perception and transmission to improve the Quality of Service (QoS). In this article, we define Confident Information Coverage Reliability (CICR) based on the Confident Information Coverage Model (CIC), which comprehensively considers sensor multistate, sensor energy, coverage rate, and connectivity robustness to evaluate coverage reliability. Furthermore, a Tensor-based Confident Information Coverage Reliability Algorithm (T-CICR) is proposed based on tensor modeling to evaluateCICR. The algorithm uses a tensor-based Markov model to predict sensor multistate. Three tensors of coverage rate, sensor multistate, and sensor energy are constructed to provide unified representations. Simulation results show that our proposed algorithm can significantly improve coverage reliability in terms of duty cycle, coverage rate requirement, sensing range, Root Mean Square Error (RMSE) threshold, connectivity robustness requirement, and link reliability.

The emergence of new applications of Internet of Things (IoT) makes its security and reliability become one of the most concerning issues and requires more breakthroughs. To ensure reliable operation of IoT, network reliability measures are essential for quantifying the performance of such networks. In this paper, we focus on the problem of coverage reliability of IoT intrusion detection systems based on Attack-Defense Game Design. A comprehensive coverage reliability algorithm is proposed based on Monte Carlo simulations. The algorithm employs Byzantine attack and defense ideas to determine network node attributes and uses confident information model to calculate the network coverage area. Furthermore, we propose a system reliability metric based on the analytic hierarchy process method, which takes advantage of node attributes, network coverage and connectivity. The metric is used to compare algorithms in simulated experiments, and a series of simulation comparisons illustrate the superiority and usability of the proposed approach.

The development of 5G has brought new opportunities for the application of Internet of Things (IoT). The integration of 5G and IoT technologies promote high availability, resilience, and reliability of the network infrastructures. IoT deployment optimization is the core issue of 5G IoT. Traditionally, IoT node deployment methods mostly used disk coverage model or probabilistic detection coverage model, which only utilizes the sensing capability of a single IoT node, which results in higher deployment costs. In this article, we study the network resilience of coverage estimation error and solve the coverage problem of resilient deployment of smart nodes in 5G IoT. The coverage formulation in the deployment optimization method is defined based on the confident information coverage (CIC). In order to obtain the optimal deployment with a given coverage quality and with a given budget, the mixed-integer linear programming models (CICILP-COST) and (CICILP-ERROR) are proposed based on the CIC model. After analyzing the model complexity, the proposed models are solved by the variable relaxation algorithm (CICVR-COST) and dichotomous search algorithm (CICDS-ERROR), respectively. Simulations on air pollution datasets in Lyon, France, show that the proposed model yields a lower cost optimal deployment than existing peer schemes.