Wireless Sensor Networks (WSNs) are being applied widely for data collection, especially to carry out the mission critical tasks. Therefore, one of the most challenging tasks of Mission Critical Sensors and Sensor Networks (MC-SSN) is the development of energy efficient (EE) routing protocols. Comparing with flat routing protocols, more EE can be achieved in hierarchical routing protocols. In this paper, we propose an enhanced balanced energy efficient network-integrated super-heterogeneous (E-BEENISH) routing protocol, by analyzing communication energy consumption of the clusters and large range of energy levels in heterogeneous WSNs. E-BEENISH is based on weighted election probabilities of each node to become a cluster head (CH) according to the remaining energy and the distance from the sink to node. Moreover, we also study the impact of heterogeneity of nodes in terms of energy. Studying the sensitivity of our stable election protocol, we conclude heterogeneity parameters capturing energy imbalance in the network, and find that E-BEENISH yields the longer stability region for suitable weight of energy and distance. Our results show by simulation that E-BEENISH can improve system lifetime by an order of magnitude compared to obtained using current clustering protocols, which is crucial for many applications.

In Internet of Things (IoT) applications with multihop networking, not only traditional energy efficiency but also many distinct features should be considered when designing routing protocols, including different security requirements, heterogeneity, and scalability. In this article, an energy-efficient multilevel secure routing (EEMSR) protocol in IoT networks is proposed. Considering that clustering is a reasonable solution of conserving energy, a cluster-based multihop routing protocol is utilized to reduce the high communication overhead due to the scalability of IoT networks. In particular, more reasonable analytic hierarchy process and genetic algorithms are adopted to assign accurate weight and optimize intercluster routing in which heterogeneous IoT networks are considered to support large amount of heterogeneous IoT entities and services. Moreover, multiple trust levels are adopted to defend the different attacks by calculating the trust factor on the clustering and routing, including data perception trust, data fusion trust, and communication trust. It is shown that the proposed algorithm outperforms the existing algorithms in terms of network lifetime, throughput, packet delivery ratio, network energy balance, and adaptability.

The maritime joint sensing and communication system (MSCS) has recently emerged as a promising solution to address the maritime spectrum scarcity issue for high-efficiency communication and environmental sensing. To mitigate the significant path loss experienced over the complex sea surface, the intelligent reflecting surface (IRS) is integrated into the MSCS to enhance the signal quality by dynamically adjusting the phases of its reflecting elements. Building upon this foundation, we aim to improve the sensing performance by maximizing the sensing signal-to-noise ratio while maintaining normal maritime communication, which involves optimizing active and passive beamforming vectors. Under the unpredictable environmental information and high-complexity and high-overhead channel information estimation in MSCS, we propose a heuristic algorithm based on genetic evolution to tackle this problem. To ensure the algorithm convergence and the feasibility of available solutions, we introduce an individual processing approach and an elitist reservation strategy in each genetic generation. Numerical results and simulations validate the convergence and efficacy of the proposed algorithm. Additionally, we analyze the effects of critical parameters and IRS structure on the algorithm performance.

In this paper, we propose a novel precoding algorithm based on an unsupervised learning graph attention network (ULGAT) to improve the performance of the sum rate in reconfigurable intelligent surface (RIS) assisted communication systems. Specifically, an unsupervised learning scheme is used to optimise the precoding of RIS-assisted multi-user downlink systems, which significantly reduces the difficulty of obtaining samples. Then, a graph attention network (GAT) based on the multi-head attention is employed to refine the performance of the precoding, where the underlying topology formed is fully utilized by the channel matrix and phase shift matrix. The proposed ULGAT algorithm can obtain better performance by exploiting the learning capability of the GAT and making full use of the network topology with unsupervised learning to produce datasets at a low cost. The results of the simulation demonstrate that the proposed ULGAT algorithm significantly outperforms the existing algorithms in terms of the sum rate and generalization capability.

Industrial Internet of Things (IIoT) networks, as the application of IoT networks to modern industry, are growing rapidly with the digital transformation accelerates. Mobile sensors are widely adopted in monitoring key information of massive IIoT networks in an ad hoc fashion or retrofitting on to an existing infrastructure. Hence, owing to the characteristics of scalability, unstable locations and highly unstructured, achieving perfect prediction for mobile multi-sensor is a challenging problem. In this paper, a dynamic space-time prediction algorithm combining temporal convolutional network and graph convolutional network (TCN-GCN) is proposed to provide reliable multiple nodes prediction for detection and maintenance in IIoT networks. In particularly, an adaptive learning graph process is exclusively designed according to the dynamic characteristics of mobile sensors. Moreover, the improved dilated time convolution network and dynamic graph convolution network are combined to effectively capture the time dependence and topology information. Furthermore, an advanced loss function is applied to avoid overfitting. Numerical simulations show that the proposed TCN-GCN prediction algorithm exhibits higher effectiveness in accuracy and complexity than convolutional long short-term memory (ConvLSTM), temporal graph convolutional network (T-GCN) and residual graph convolutional long short-term memory (Graph-ResLSTM).

Hybrid precoding has been recognized as promising and effective for practical 5G communication. It is generally challenging to obtain the sample for deep learning-based hybrid precoding due to its need of massive precoding vector and channel matrix. To effectively solve this issue, a novel coordinated hybrid precoding algorithm based on unsupervised learning graph attention networks (CHP-ULGAT) is first developed by making full use of the underlying topology formed by the channel matrix. Subsequently, a more realistic situation of existing an ultra-low execution time is considered. A sub-optimal coordinated hybrid precoding based on unsupervised learning convolutional neural networks (CHP-ULCNN) is proposed to further reduce complexity. Moreover, we present effective ways to design the multi-matrix operation and the loss function to address the practicability of the algorithm. Extensive simulation results show that the proposed hybrid-precoding algorithms have obvious advantages in spectral efficiency (SE) and energy efficiency (EE) improvement with ultra-low computational complexity, considering the different number of RF chains and deployment scenarios.