Wireless Sensor Networks (WSNs) have promising features that presents unique opportunities to observe both urban and rural environments. Unlike traditional monitoring systems, WSNs components are small, inexpensive, and easy to deploy. These characteristics of WSNs offer not only capability of monitoring wider and broader areas but also prolonging the observation time without any intervention. However, implementation of WSNs brings about its own theoretical and practical challenges particularly in regards to energy efficiency, reliability, fault tolerance, and scalability.
We employ distributed algorithms as our basic tool to overcome these challenges. Distributed algorithms research has an extensive repertoire of reliable and scalable methods. Starting with these methods, we design WSN specific solutions where energy efficiency, resource limitations and unreliable communication are key concerns.
We specifically target reliability and energy efficiency issues. While reliability is studied within distributed algorithms research, common models and assumptions are not compatible with WSNs. The issue of energy efficiency is not studied at all, instead constraints such as memory limitations and execution time take precedence.
We utilize “programming abstractions” and “mobility” approaches to provide solutions for reliability and energy efficiency problems. Through programming abstractions approach, we aspire to generate more expressive primitives simplifying the implementations of distributed algorithms. For constructing these primitives in reliable and energy efficient manners, we make use of some of the characteristics of the low power radios frequently used in WSN nodes.
Mobility, more specifically mobile basestations approach, addresses the spatio-temporal features of WSNs. We propose that relocating basestation in cooperation with WSN eliminates many challenges emerging due to the dynamic nature of data generation in applications such as tracking and detection.
We present analytical discussions, discrete event simulations, as well as experimental results from WSN deployments. Our initial results indicate that our approaches have the potential of improving reliability and energy efficiency in WSN deployments.