As the scale of wireless sensor networks (WSNs) continues to expand, challenges such as excessive network energy consumption and load imbalance have become increasingly severe. Existing non-uniform clustering protocols rely on fixed parameters and local information, lack the ability to dynamically perceive global energy differences, and are thus difficult to adapt to the dynamic changes of large-scale networks for balancing energy consumption and load. To address this issue, this paper proposes an adaptive cluster radius multi-hop routing protocol (ACRM) suitable for large-scale WSNs. The protocol provides a decision-making basis for the adjustment of nodes’ personalized competition radii and auction-based cluster head selection through an energy disparity factor quantified based on the Gini coefficient. On this basis, cluster head selection is modeled as a static game with incomplete information. Through an auction mechanism, cluster head seats are allocated according to the principle of maximizing bid prices, and a price decay strategy is introduced to prevent overloading of low-energy nodes. In the routing phase, intra-cluster routing employs hierarchical decision-making to select a subset of nodes for multi-hop communication to reduce energy consumption; while inter-cluster routing establishes multi-hop paths based on a direction-aware scoring mechanism that integrates node direction and energy, effectively avoiding path detours and reverse transmissions. Additionally, unlike existing non-uniform clustering protocols, ACRM effectively addresses the issues of cluster head overload near the base station and excessive energy consumption from frequent clustering through directly connected node offloading and adaptive periodic reconfiguration. Simulation results show that in a 500m×500m network scenario, the network stability period of ACRM reaches 636 rounds, which is over 100% higher than that of protocols such as LEACH, LEACH-OR, EEUC, and DEBUC, approximately 61.8% higher than PUAG, and 18.4% higher than UCRTD; significant improvements are also observed in the overall network lifetime and the number of data packets received by the base station.

The Internet of Things (IoT) environment contains many different types of devices, each with different functionalities, communication protocols, and security capabilities, which makes the IoT a complex challenge for security protection. Therefore, network intrusion detection (NID) is needed to detect intrusions in the network to secure the IoT. In recent years, deep learning (DL)-based intrusion detection systems have achieved excellent results, but they tend to require high-computational resources and storage space, which is not feasible for most IoT devices. In this article, we propose a lightweight intrusion detection model based on self-knowledge distillation (SKD), namely, tied block convolution lightweight deep neural network (TBCLNN), which improves the detection accuracy while also reducing the number of model parameters and computational cost. Specifically, we use the binary Harris Hawk optimization algorithm (bHHO) for dimensionality reduction of traffic features. We use lightweight convolution, such as tied block convolution (TBC), to design lightweight neural network (LNN) models with residual and inverse residual structures. Moreover, we propose an improved SKD loss function to solve the sample imbalance problem and compensate for the performance degradation caused by lightweight neural networks. The multiclassification accuracy of our proposed method exceeds 99% on all three publicly available IoT datasets. The experimental results show that our method has a small model size and requires only low-computational resources, making it suitable for resource-constrained IoT intrusion detection.

Cluster head (CH) nodes near the base station (BS) die prematurely due to the need to perform more communication tasks, which can lead to disruption of network connectivity and makes it difficult to achieve the goal of load balancing in Wireless Sensor Networks (WSNs), this problem is known as hot spot problem. To solve this problem, non-uniform clustering strategies have been proposed. However, all the current related non-uniform clustering protocols have some drawbacks, such as the lack of a theoretical basis for the value of the multi-hop threshold distance between clusters, the limited attention to the data transmission process, and the insufficient load balancing of the protocols in the face of complex and variable networks. Based on the above problems, we propose an unequally clustered routing protocol based on multi-hop threshold distance (UCRTD) for WSNs. First, this paper analyzes the energy-saving threshold distance for multi-hop communication in conjunction with the energy consumption model of WSNs, and based on the multi-hop energy-saving threshold distance, a strategy for selecting the best energy-saving relay node is proposed. In intra-cluster communication, considering that medium-sized networks form larger clusters, cluster members (CMs) within the cluster that are farther away from the CH take multi-hop communication. For inter-cluster communication, to maximize the network lifetime, the most energy-efficient CH node with the highest residual energy is selected in the routing phase for alternate multi-hop transmission, and this strategy effectively prolongs the network lifetime and also ensures the load balance of the network. Simulation results show that the proposed UCRTD effectively prolongs the network lifetime and maintains good load balancing under multiple network environments when compared with four existing EEUC, EBUC, EADUC, and EAUCA unequal clustering protocols as well as LEACH protocol.

In the field of intrusion detection, existing deep learning algorithms have limited capability to effectively represent network data features, making it challenging to model the complex mapping relationship between network data and attack behavior. This limitation, in turn, impacts the detection accuracy of intrusion detection systems. To address this issue and further enhance detection accuracy, this paper proposes an algorithm called the Fourier Neural Network (FNN). The core of FNN consists of a Deep Fourier Neural Network Block (DFNNB), which is composed of a Hadamard Neural Network (HNN) and a Fourier Neural Network Layer (FNNL). In a DFNNB, the HNN is responsible for sampling the network intrusion data samples in different time domain spaces. The FNNL, on the other hand, performs a Fourier transform on the samples outputted by the HNN and maps them to the frequency domain space, followed by a filtering process. Finally, the data processed by filtering are transformed back to the time domain space for subsequent feature extraction work by the DFNNB. Additionally, to enhance the algorithm’s detection accuracy and filter out noise signals, this paper also introduces a High‐energy Filtering Process (HFP), which eliminates noise signals from the data signal and reduces interference on the final detection result. Due to the ability of FNN to process network data in both the time domain space and the frequency domain space, it possesses a stronger capability in expressing data features. Finally, this paper conducts performance evaluations on the KDD Cup99, NSL‐KDD, UNSW‐NB15, and CICIDS2017 datasets. The results demonstrate that the proposed FNN‐based IDS model achieves higher detection rates, lower false alarm rates, and better detection performance than classical deep learning and machine learning methods.