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

Deep Predictive Coding Neural Network for RF Anomaly Detection in Wireless Networks

Intrusion detection has become one of the most critical tasks in a wireless network to prevent service outages that can take long to fix. The sheer variety of anomalous events necessitates adopting cognitive anomaly detection methods instead of the traditional signature-based detection techniques. This paper proposes an anomaly detection methodology for wireless systems that is based on monitoring and analyzing radio frequency (RF) spectrum activities. Our detection technique leverages an existing solution for the video prediction problem, and uses it on image sequences generated from monitoring the wireless spectrum. The deep predictive coding network is trained with images corresponding to the normal behavior of the system, and whenever there is an anomaly, its detection is triggered by the deviation between the actual and predicted behavior. For our analysis, we use the images generated from the time-frequency spectrograms and spectral correlation functions of the received RF signal. We test our technique on a dataset which contains anomalies such as jamming, chirping of transmitters, spectrum hijacking, and node failure, and evaluate its performance using standard classifier metrics: detection ratio, and false alarm rate. Simulation results demonstrate that the proposed methodology effectively detects many unforeseen anomalous events in real time. We discuss the applications, which encompass industrial IoT, autonomous vehicle control and mission-critical communications services.Comment: 7 pages, 7 figures, Communications Workshop ICC'1

Discrete R-Contiguous bit Matching mechanism appropriateness for anomaly detection in Wireless Sensor Networks

Resource exhaustion is one of the main challenges for the security of Wireless Sensor Networks (WSNs). The challenge can be addressed by using algorithms that are light weighted. In this paper use of light-weighted R-Contiguous Bit matching for attack detection in WSNs has been evaluated. Use of R-Contiguous bit matching in Negative Selection Algorithm (NSA) has improved the performance of anomaly detection resulting in low false positive, false negative and high detection rates. The proposed model has been tested against some of the attacks. The high detection rate has proved the appropriateness of R-Contiguous bit matching mechanism for anomaly detection in WSNs

Wireless home automation networks for indoor surveillance: technologies and experiments

The use of wireless technologies for critical surveillance and home automation introduces a number of opportunities as well as technological challenges. New emerging technologies give the opportunity to exploit the full potential of the internet of things paradigm by augmenting existing wired installations with smart wireless architectures. This work gives an overview of requirements, characteristics, and challenges of wireless home automation networks with special focus on intrusion detection systems. The proposed wireless network is based on several sensors that are deployed over a monitored area for detecting possible risky situations and triggering appropriate actions in response. The network needs to support critical traffic patterns with different characteristics and quality constraints. Namely, it should provide a periodic low-power monitoring service and, in case of intrusion detection, a real-time alarm propagation mechanism over inherently unreliable wireless links subject to fluctuations of the signal power. Following the guidelines introduced by recent standardization, this paper proposes the design of a wireless network prototype at 868 MHz which is able to satisfy the specifications of typical intrusion detection applications. A proprietary medium access control is developed based on the low-power SimpliciTI radio stack (Texas Instruments Incorporated, San Diego, CA, USA). Network performance is assessed by experimental measurements using a test-bed in an indoor office environment with severe multipath and nonline-of-sight propagation conditions. The measurement campaigns highlight the potential of the sub-GHz technology for cable replacing