Transmission policy for wireless sensor networks

Wireless sensor networks (WSNs) are commonly recognized as the technological cornerstone in achieving ubiquitous computing, where future computing devices will be invisibly embedded in the world around us and accessed through simple and intelligent interfaces. From applications’ perspective, WSNs can be classified as data gathering and query-based networks, where in the former, sensors send their data proactively, either periodically or on detecting some events, and in the later, sensors only report in response to explicit user request. Irrespective of their data models, deployment of a WSN posses a number of technical challenges that stem primarily from i) the constraints imposed by simple sensor devices, e.g., limited power, transmission range, bandwidth, etc., ii) the node heterogeneity in terms of data rate, energy storage, deployment density, etc., and iii) the scalability since the network size and the number of nodes deployed can vary widely. Moreover, WSNs exhibit highly unbalanced traffic flow due to the many-to-one communication paradigm, where a large number of sensors communicate concomitantly with a central sink which reduces network lifetime drastically. This thesis aims to address these issues by developing transmission policy for transmitting sensor data which regulates the communication ranges and their associated duty cycles that nodes use over lifetime. The policy attains efficient and balanced energy usage among sensors despite the inherent unbalanced traffic flow of WSNs and thereby, significantly extends the network lifetime, a key deployment issue. We start by designing transmission policies for data gathering homogeneous sensor networks, where the transmission policies differ in terms of their degree of flexibility in using variable transmission ranges and associated duty cycles among nodes. Then, we extend our formulations for multi-tier heterogeneous sensor networks. We develop a traffic model considering collaboration among multiple services deploying multiple classes of heterogeneous sensors. Finally, we propose a distributed query optimization and processing framework for such collaborative heterogeneous sensor networks. Vigorous analytical and experimental analyses confirm the efficacy of the proposed new strategies over the notable works in the related area.