PERFORMANCE ANALYSIS AND OPTIMIZATION OF QUERY-BASED WIRELESS SENSOR NETWORKS

This dissertation is concerned with the modeling, analysis, and optimization of large-scale, query-based wireless sensor networks (WSNs). It addresses issues related to the time sensitivity of information retrieval and dissemination, network lifetime maximization, and optimal clustering of sensor nodes in mobile WSNs. First, a queueing-theoretic framework is proposed to evaluate the performance of such networks whose nodes detect and advertise significant events that are useful for only a limited time; queries generated by sensor nodes are also time-limited. The main performance parameter is the steady state proportion of generated queries that fail to be answered on time. A scalable approximation for this parameter is first derived assuming the transmission range of sensors is unlimited. Subsequently, the proportion of failed queries is approximated using a finite transmission range. The latter approximation is remarkably accurate, even when key model assumptions related to event and query lifetime distributions and network topology are violated. Second, optimization models are proposed to maximize the lifetime of a query-based WSN by selecting the transmission range for all of the sensor nodes, the resource replication level (or time-to-live counter) and the active/sleep schedule of nodes, subject to connectivity and quality-of-service constraints. An improved lower bound is provided for the minimum transmission range needed to ensure no network nodes are isolated with high probability. The optimization models select the optimal operating parameters in each period of a finite planning horizon, and computational results indicate that the maximum lifetime can be significantly extended by adjusting the key operating parameters as sensors fail over time due to energy depletion. Finally, optimization models are proposed to maximize the demand coverage and minimize the costs of locating, and relocating, cluster heads in mobile WSNs. In these models, the locations of mobile sensor nodes evolve randomly so that each sensor must be optimally assigned to a cluster head during each period of a finite planning horizon. Additionally, these models prescribe the optimal times at which to update the sensor locations to improve coverage. Computational experiments illustrate the usefulness of dynamically updating cluster head locations and sensor location information over time

Modeling search costs in wireless sensor networks

Abstract — We develop approximate closed-form expressions of expected minimum search energy costs for data-centric wireless sensor networks showing the search performance with respect to the network size N and the number of randomly placed copies of the target event r. We consider both unstructured sensor networks, which use blind sequential search for querying, and structured sensor networks, which use efficient hash-based querying. We also consider two kinds of deployments: a fixed transmit power (FTP) model and the geometric random graph (GRG) model. We find that the search cost of unstructured networks under the FTP deployment is proportional to N and inversely proportional to (r + 1) regardless of the spatial dimension d in which nodes are deployed, while that of the GRG is proportional to N(log N) η d r+1 where η is the path-loss exponent. The search cost of structured networks under the FTP deployment is found to be proportional to d √ N / d √ r, while that of the GRG deployment is proportional to d √ N(log N) η−1 d √. In all r cases, we also provide bounds on the coefficient of proportionality. I