ELECTROMAGNETISM METAHEURISTIC ALGORITHM FOR SOLVING THE STRONG MINIMUM ENERGY TOPOLOGY PROBLEM

Abstract: In this paper electromagnetism (EM) metaheuristic is used for solving the NPhard strong minimum energy topology problem (SMETP). Objective function is adapted to the problem so that it effectively prevents infeasible solutions. Proposed EM algorithm uses efficient local search to speed up overall running time. This approach is tested on two sets of randomly generated symmetric and asymmetric instances. EM reaches all known optimal solutions for these instances. The solutions are obtained in a reasonable running time even for the problem instances of higher dimensions

A branch-and-cut algorithm for the strong minimum energy topology in wireless sensor networks

This paper studies the strong minimum energy topology design problem in wireless sensor networks. The objective is to assign transmission power to each sensor node in a directed wireless sensor network such that the induced directed graph topology is strongly connected and the total energy consumption is minimized. A topology is defined to be strongly connected if there exists a communication path between each ordered pair of sensor nodes. This topology design problem with sensor nodes defined on a plane is an NP-Complete problem. We first establish a lower bound on the optimal power consumption. We then provide three formulations for a more general problem defined on a general directed graph. All these formulations involve an exponential number of constraints. Second formulation is stronger than the first one. Further, using the second formulation, we lift the connectivity constraints to generate stronger set of constraints that yield the third formulation. These lifted cuts turn out to be extremely helpful in developing an effective branch-and-cut algorithm. A series of experiments are carried out to investigate the performance of the proposed branch-and-cut algorithm. These computational results over 580 instances demonstrate the effectiveness of our approach.OR in energy Wireless sensor network Minimum energy topology Branch and bound Cutting

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