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

Intrusion detection in IPv6-enabled sensor networks.

In this research, we study efficient and lightweight Intrusion Detection Systems (IDS) for ad-hoc networks through the lens of IPv6-enabled Wireless Sensor Actuator Networks. These networks consist of highly constrained devices able to communicate wirelessly in an ad-hoc fashion, thus following the architecture of ad-hoc networks. Current state of the art IDS in IoT and WSNs have been developed considering the architecture of conventional computer networks, and as such they do not efficiently address the paradigm of ad-hoc networks, which is highly relevant in emerging network paradigms, such as the Internet of Things (IoT). In this context, the network properties of resilience and redundancy have not been extensively studied. In this thesis, we first identify a trade-off between the communication and energy overheads of an IDS (as captured by the number of active IDS agents in the network) and the performance of the system in terms of successfully identifying attacks. In order to fine-tune this trade-off, we model networks as Random Geometric Graphs; these are a rigorous approach that allows us to capture underlying structural properties of the network. We then introduce a novel IDS architectural approach that consists of a central IDS agent and set of distributed IDS agents deployed uniformly at random over the network area. These nodes are able to efficiently detect attacks at the networking layer in a collaborative manner by monitoring locally available network information provided by IoT routing protocols, such as RPL. The detailed experimental evaluation conducted in this research demonstrates significant performance gains in terms of communication overhead and energy dissipation while maintaining high detection rates. We also show that the performance of our IDS in ad-hoc networks does not rely on the size of the network but on fundamental underling network properties, such as the network topology and the average degree of the nodes. The experiments show that our proposed IDS architecture is resilient against frequent topology changes due to node failures

Time constrained fault tolerance and management framework for k-connected distributed wireless sensor networks based on composite event detection

Wireless sensor nodes themselves are exceptionally complex systems where a variety of components interact in a complex way. In enterprise scenarios it becomes highly important to hide the details of the underlying sensor networks from the applications and to guarantee a minimum level of reliability of the system. One of the challenges faced to achieve this level of reliability is to overcome the failures frequently faced by sensor networks due to their tight integration with the environment. Failures can generate false information, which may trigger incorrect business processes, resulting in additional costs. Sensor networks are inherently fault prone due to the shared wireless communication medium. Thus, sensor nodes can lose synchrony and their programs can reach arbitrary states. Since on-site maintenance is not feasible, sensor network applications should be local and communication-efficient self-healing. Also, as per my knowledge, no such general framework exist that addresses all the fault issues one may encounter in a WSN, based on the extensive, exhaustive and comprehensive literature survey in the related areas of research. As one of the main goals of enterprise applications is to reduce the costs of business processes, a complete and more general Fault Tolerance and management framework for a general WSN, irrespective of the node types and deployment conditions is proposed which would help to mitigate the propagation of failures in a business environment, reduce the installation and maintenance costs and to gain deployment flexibility to allow for unobtrusive installation