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With the development of the IoT, Wireless Rechargeable Sensor Networks (WRSNs) derive more and more application scenarios with diverse performance requirements. In scenarios where the energy consumption rate of sensor nodes changes dynamically, most existing charging scheduling methods are not applicable. The incorrect estimation of node energy requirement may lead to the death of critical nodes, resulting in missing events. To address this issue, we consider both the spatial imbalance and temporal dynamics of the energy consumption of the nodes, and minimize the Event Missing Rate (EMR) as the goal. Firstly, an Energy Consumption Balanced Tree (ECBT) construction method is proposed to prolong the lifetime of each node. Then, we transform the goal into Maximizing the value of the Evaluation function of each node’s Energy Consumption Rate prediction (MEECR). Afterwards, the setting of the evaluation function is explored and the MEECR is further transformed into a variant of the knapsack problem, namely “the alternating backpack problem”, and solved by dynamic programming. After predicting the energy consumption rate of the nodes, a charging scheduling scheme that meets the Dual Constraints of Nodes’ energy requirements and MC’s capability (DCNM) is developed. Simulations demonstrate the advantages of the proposed method. Compared to the baselines, the EMR was reduced by an average of 35.2% and 26.9%.

Wireless Power Transfer (WPT) has become a key technology to extend network lifetime in Wireless Rechargeable Sensor Networks (WRSNs). The traditional omnidirectional recharging method has a wider range of energy radiation, but it inevitably results in more energy waste. By contrast, the directional recharging mode enables most of the energy to be focused in a predetermined direction that achieves higher recharging efficiency. However, the MC (Mobile Charger) in this mode can only supply energy to a few nodes in each direction. Thus, how to set the location of staying points of the MC, its service sequence and its charging orientation are all important issues related to the benefit of energy replenishment. To address these problems, we propose a Fuzzy Logic-based Directional Charging (FLDC) scheme for Wireless Rechargeable Sensor Networks. Firstly, the network is divided into adjacent regular hexagonal grids which are exactly the charging regions for the MC. Then, with the help of a double-layer fuzzy logic system, a priority of nodes and grids is obtained that dynamically determines the trajectory of the MC during each round of service, i.e., the charging sequence. Next, the location of the MC’s staying points is optimized to minimize the sum of charging distances between MC and nodes in the same grid. Finally, the discretized charging directions of the MC at each staying point are adjusted to further improve the charging efficiency. Simulation results show that FLDC performs well in both the charging benefit of nodes and the energy efficiency of the MC.

Abstract The secrecy outage performance for a cooperative cognitive radio energy‐harvesting network is analyzed. The cognitive network is composed of an energy‐constrained cognitive source (CS), multiple energy‐constrained cognitive relays (CRs) and a cognitive destination (CD) as well as an eavesdropper (E) coexists with a primary network consisting of a primary transmitter (PT) and a primary receiver (PR). The CS and CRs are equipped with energy harvesters for collecting energy from the radio frequency signal from PT and their transmit powers are limited by the interference threshold at PR. To prevent confidential information leaking to E, an optimal relay selection (ORS) scheme and a suboptimal relay selection (SRS) scheme are proposed. In ORS scheme, the whole channels state information (CSI) of wireless links is available to CRs while SRS only needs to know the CSI of main channels from CRs to CD. Moreover, the closed‐form expressions of secrecy outage probabilities for both ORS and SRS schemes are derived. For the purpose of comparison, the classical round‐robin relay selection (RRRS) is also analyzed in terms of secrecy outage probability. Furthermore, the numerical results show that ORS achieves the best performance and RRRS performs the worst in terms of secrecy outage probability.

Aiming at the security problem of edge server, a trust evaluation algorithm was proposed, which integrated server cooperation reputation and device user feedback to improve the security of edge computing context.In the process of interaction, a fusion algorithm based on objective information entropy theory was used to aggregate the cooperation reputation between servers, and a partial homomorphic encryption algorithm was used to prevent the leakage of user data in the process of interaction.After the interaction, the highly trusted device node was selected to calculate the feedback trust, which overcame the malicious feedback of the traditional mechanism.When calculating the global trust, the expected score of the server and the adaptive weight calculation method were considered, which overcame the limitations of the traditional trust scheme.The experimental results show that the proposed trust calculation scheme has low time complexity and spatial complexity, and can effectively resist the attack of malicious nodes.

Abstract In this paper, the secrecy performance in a MIMO cognitive radio (CR) relay network with energy harvesting (EH) and transmit antenna selection/maximal ratio combining (TAS/MRC) is invstigated, where the DF relaying protocol and multiple colluding passive eavesdroppers are considered. To improve the security of wireless transmission, two antenna selection schemes are proposed, namely, the optimal transmit antenna selection (OTAS) scheme and suboptimal transmit antenna selection (STAS) scheme. For the purpose of comparison, the space‐time transmission (STT) scheme is introduced as a baseline. The exact and asymptotic closed‐form secrecy outage probability (SOP) expressions for OTAS, STAS and STT schemes are derived over Rayleigh fading channels. An extension of the TAS framework to an artificial noise (AN) aided MIMO network is further presented and an AN aided transmit antenna selection (AN‐TAS) scheme is proposed, in which the unselected antennas at R are used to emit AN for interfering with Es. Numerical results show that the OTAS and STAS schemes perform better than STT scheme in terms of SOP. Meanwhile, the SOP of AN‐TAS scheme is much smaller than that of OTAS, STAS and STT schemes in the high SNR region, indicating the benefit of applying AN in MIMO network.

Small target detection has been widely used in applications that are relevant to everyday life and have many real-time requirements, such as road patrols and security surveillance. Although object detection methods based on deep learning have achieved great success in recent years, they are not effective in small target detection. In order to solve the problem of low recognition rate caused by factors such as low resolution of UAV viewpoint images and little valid information, this paper proposes an improved algorithm based on the YOLOv5s model, called YOLOv5s-pp. First, to better suppress interference from complex backgrounds and negative samples in images, we add a CA attention module, which can better focus on task-specific important channels while weakening the influence of irrelevant channels. Secondly, we improve the forward propagation and generalisation of the network using the Meta-ACON activation function, which adaptively learns to adjust the degree of linearity or nonlinearity of the activation function based on the input data. Again, the SPD Conv module is incorporated into the network model to address the problems of reduced learning efficiency and loss of fine-grained information due to cross-layer convolution in the model. Finally, the detection head is improved by using smaller, smaller-target detection heads to reduce missed detections. We evaluated the algorithm on the VisDrone2019-DET and UAVDT datasets and compared it with other state-of-the-art algorithms. Compared to YOLOv5s, mAP@.5 improved by 7.4% and 6.5% on the VisDrone2019-DET and UAVDT datasets, respectively, and compared to YOLOv8s, mAP@.5 improved by 0.8% and 2.1%, respectively. For improving the performance of the UAV-side small target detection algorithm, it will help to enhance the reliability and safety of UAVs in critical missions such as military reconnaissance, road patrol and security surveillance.