Energy Efficient Scheduling Of Sensing Activity In Wireless Sensor Networks Using Information Coverage
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Network lifetime is a key issue in wireless sensor networks where sensor nodes, distributed typically in remote/hostile sensing areas, are powered by ﬁnite energy batteries which are not easily replaced/recharged. Depletion of these ﬁnite energy batteries can result in a change in network topology or in the end of network life itself. Hence, prolonging the life of wireless sensor networks is important. Energy consumed in wireless sensor nodes can be for the purpose of i) sensing functions, ii) processing/computing functions, and ii) communication functions. For example, energy consumed by the transmit and receive electronics constitute the energy expended for communication functions. Our focus in this thesis is on the eﬃcient use of energy for sensing. In particular, we are concerned with energy eﬃcient algorithms for scheduling the sensing activity of sensor nodes. By scheduling the sensing activity we mean when to activate a sensor node for sensing (active mode) and when to keep it idle (sleep mode). The novel approach we adopt in this thesis to achieve eﬃcient scheduling of sensing activity is an information coverage approach, rather than the widely adopted physical coverage approach. In the physical coverage approach, a point is said to be covered by a sensor node if that point lies within the physical coverage range (or the sensing radius) of that sensor, which is the maximum distance between the sensor and the point up to which the sensor can sense with acceptable accuracy. Information coverage, on the other hand, exploits cooperation among multiple sensors to accurately sense a point even if that point falls outside the physical coverage range of all the sensors. In this thesis, we address the question of how to schedule the activity of various sensor nodes in the network to enhance network lifetime using information coverage. In the ﬁrst part of the thesis, we are concerned with scheduling of sensor nodes for sensing point targets using information coverage – example of a point-target being temperature or radiation level at a source or point that needs to be monitored. Deﬁning a set of sensor nodes which collectively can sense a point-target accurately as an information cover, we propose an algorithm to obtain Disjoint Set of Information Covers (DSIC) that can sense multiple point-targets in a given sensing area. Being disjoint, the resulting information covers in the proposed algorithm allow a simple round-robin schedule of sensor activation (i.e., activate the covers sequentially). We show that the covers obtained using the proposed DSIC algorithm achieve longer network life compared to the covers obtained using an Exhaustive-Greedy-Equalized Heuristic (EGEH) proposed recently in the literature. We also present a detailed complexity comparison between the DSIC and EGEH algorithms. In the second part of the thesis, we extend the point target sensing problem in the ﬁrst part to a full area sensing problem, where we are concerned with energy eﬃcient ‘area-monitoring’ using information coverage. We refer to any set of sensors that can collectively sense all points in the entire area-to-monitor as a full area information cover. We ﬁrst propose a low-complexity heuristic algorithm to obtain full area information covers. Using these covers, we then obtain the optimum schedule for activating the sensing activity of various sensors that maximizes the sensing lifetime. The optimum schedules obtained using the proposed algorithm is shown to achieve signiﬁcantly longer sensing lifetimes compared to those achieved using physical coverage. Relaxing the full area coverage requirement to a partial area coverage requirement (e.g., 95% of area coverage as adequate instead of 100% area coverage) further enhances the lifetime. The algorithms proposed for the point targets and full area sensing problems in the ﬁrst two parts are essentially centralized algorithms. Decentralized algorithms are preferred in large networks. Accordingly, in the last part of the thesis, we propose a low-complexity, distributed sensor scheduling algorithm for full area sensing using information coverage. This distributed algorithm is shown to result in signiﬁcantly longer sensing lifetimes compared to those achieved using physical coverage.
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