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

By Mohammad Abu Alsheikh, Shaowei Lin, Dusist Niyato, Hwee Pink Tan. 2014

889 citations recorded by the source corpus.

Wireless sensor networks (WSNs) 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 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.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering, Environmental Engineering

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Citations in the corpus

Deep Learning for IoT Big Data and Streaming Analytics: A Survey (2017, methodology): “Moreover, the focus of [13] was on traditional machine learning methods, whereas this article focuses on advanced and DL”
Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges (2017, neutral): “[11] IEEE COMST 2013 177 ML for Cognitive Radios ≈ × × × [12] IEEE COMST 2014 152 ML for WSNs ≈ × × √”
A Survey of Machine Learning Techniques Applied to Self-Organizing Cellular Networks (2017, background): “However, differently from [22] and [23], which surveyed”
Machine Learning in IoT Security: Current Solutions and Future Challenges (2019, methodology): “a number of ML-based techniques have been used to detect anomalies and intrusions in the IoT networks and their different breeds [155], [156].”
Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues (2018, background): “While in [20], the applications of machine learning in wireless sensor networks (WSNs) are discussed, and the advantages”
A comprehensive survey on machine learning for networking: evolution, applications and research opportunities (2018, background): “[18], though comprehensive in their coverage ofML techniques in networking, are specialized to specific network technology i.”
Threat analysis of IoT networks using artificial neural network intrusion detection system (2016, background): “The gradient of the cost – function can thus be calculated [17]–[19].”
IoT Security Techniques Based on Machine Learning: How Do IoT Devices Use AI to Enhance Security? (2018, methodology): “■ Computation and communication overhead: Many existing ML-based security schemes have intensive computation and communication costs and require a large number of training data and a complicated feature-extraction process [9].”
Deep Reinforcement Learning: An Overview (2017, background): “, 2014) and wireless sensor networks (Alsheikh et al., 2014) play an important role in Industry 4.”
Machine Learning Based Solutions for Security of Internet of Things (IoT): A Survey (2020, background): “In the past few years, ML techniques have been advanced remarkably for IoT security purposes [85], [86].”
State-of-the-Art Deep Learning: Evolving Machine Intelligence Toward Tomorrow’s Intelligent Network Traffic Control Systems (2017, methodology): “Furthermore, Alsheikh et al. [118] recommended deep learning methods [21] and the non-negative matrix factorization algorithm [162] for more efﬁcient unsupervised learning methods for the WSNs.”
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks (2019, background): “Both WSNs and M2M communications can be viewed as a part of the IoT.”
Toward Massive Machine Type Communications in Ultra-Dense Cellular IoT Networks: Current Issues and Machine Learning-Assisted Solutions (2018, methodology): “The above-mentioned types of ML techniques have been applied mainly in the centralized framework [148].”
A Very Brief Introduction to Machine Learning With Applications to Communication Systems (2018, background): “Unlike other review papers such as [9]–[11], the presentation aims at highlighting conditions under which the use of machine learning is justified in”
Spatial anomaly detection in sensor networks using neighborhood information (2017, methodology): “Depending on the detection method used, these updates may be intrinsic and lightweight, or may require the reprocessing of all the acquired data [28] .”