Wireless Sensor Network (WSN) is an essential technology for the Internet-of-Things (IoT) and intelligence-based applications. In the case of Intelligent Transportation Systems (ITS), the WSNs play an important role in safety and efficient traffic management. Therefore, there is enormous demand for energy efficient WSNs for dynamic resource allocation in vehicles and infrastructures. This work presents a Multi-Input Multi-Output (MIMO) technique model in WSNs, which addresses the Cluster Head (CH) recognition issue for MIMO sensor networks by using Back Propagation Neural Network (BPNN). The conventional CH identification suffers from a lack of location identification due to the dynamic and real-time environment. Thus, to obtain more precise positioning accuracy, the proposed work uses BPNN combined with a distributed gradient drop technique to calculate the position of the unknown CH. This reduces the distance estimation error, and the particle swarm optimization technique is further used to obtain the optimal weight and threshold of the network. The work is validated by using mathematical analysis, simulations, and comparison with existing techniques. The proposed model shows a better performance in terms of energy consumption, error rate, and computation time.

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The birth of beyond 5G (B5G) and emerge of 6G has made personal and industrial operations more reliable, efficient, and profitable, accelerating the development of the next-generation Internet of Things (IoT). The Industrial equipment in 6G contains a large number of wireless sensors, which collect a large amount of data by sensing the surrounding environment, but the data is not always useful. The emergence of data mining has undoubtedly found a breakthrough point for extracting effective information from massive data. In the pursuit of lower latency, edge computing has also begun to develop. Eventually, 6G can make intelligent decisions in real-time and realize automated equipment operations. However, with the application of various technologies, the energy consumption of the system has increased, but the energy carried by the sensor is still limited. This paper addresses the energy consumption problem with a system model of industrial wireless sensor networks based on a multi-agent system (MAS) for industrial 6G applications. The method uses distributed artificial intelligence (DAI) to cluster the sensor nodes in the system to find the main node and predict its location. The work initially uses the backpropagation neural network (BPNN) and convolutional neural network (CNN), which are respectively introduced for optimization. Furthermore, we analyze the correlation of mutual clusters to allocate resources to individual nodes in each cluster efficiently. The simulation results show that the proposed method reduces the waste of resources caused by redundant data, improves the energy efficiency of the whole network, along with information preservation.

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Resource allocation in the Internet of Things (IoT) applications for Wireless Sensor Networks (WSNs) is a challenging problem that requires tasks processing from the appropriate sensor nodes without compromising the Quality-of-Service (QoS). Due to heterogeneity in sensors, the inter-cluster and intra-cluster cooperative communication between sensor nodes hinders the overall resource allocation of the network in terms of energy consumption and response time. Therefore, this paper establishes a multi-agent clustering WSN model, i.e., Adaptive Distributed Artificial Intelligence (ADAI) technique with a hierarchical resource allocation strategy to address the issue of resource allocation in these types of network. For the inter-cluster power allocation, we are considering QoS and energy consumption factors with DAI. Moreover, for intra-cluster resource allocation, this paper introduces Adaptive Particle Swarm Optimization (APSO), which uses its objective functions as the node distance and respective energy loads. The mathematical analysis and simulation results validate the propose method in terms of energy consumption and response time of the network.