Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

ACO-GCN: A FAULT DETECTION FUSION ALGORITHM FOR WIRELESS SENSOR NETWORK NODES

Wireless Sensor Network (WSN) has become a solution for real-time monitoring environments and is widely used in various fields. A substantial number of sensors in WSNs are prone to succumb to failures due to faulty attributes, complex working environments, and their hardware, resulting in transmission error data. To resolve the existing problem of fault detection in WSN, this paper presents a WSN node fault detection method based on ant colony optimization-graph convolutional network (ACO-GCN) models, which consists of an input layer, a space-time processing layer, and an output layer. First, the users apply the random search algorithm and the search strategy of the ant colony algorithm (ACO) to find the optimal path and locate the WSN node failures to grasp the overall situation. Then, the WSN fault node information obtained by the GCN model is learned. During the data training process, where the WSN fault node is used for error prediction, the weights and thresholds of the network are further adjusted to increase the accuracy of fault diagnosis. To evaluate the performance of the ACO-GCN model, the results show that the ACO-GCN model significantly improves the fault detection rate and reduces the false alarm rate compared with the benchmark algorithms. Moreover, the proposed ACO-GCN fusion algorithm can identify fault sensors more effectively, improve the service quality of WSN and enhance the stability of the system

A comprehensive survey on cultural algorithms

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