Abstract
Sensor networks have been used in a rapidly increasing number of applications in many fields. This work generalizes a sensor deployment problem to place a minimum set of wireless sensors at candidate locations in constrained 3D space to k-cover a given set of target objects. By exhausting the combinations of discreteness/continuousness constraints on either sensor locations or target objects, we formulate four classes of sensor deployment problems in 3D space: deploy sensors at Discrete/Continuous Locations (D/CL) to cover Discrete/Continuous Targets (D/CT). We begin with the design of an approximate algorithm for DLDT and then reduce DLCT, CLDT, and CLCT to DLDT by discretizing continuous sensor locations or target objects into a set of divisions without sacrificing sensing precision. Furthermore, we consider a connected version of each problem where the deployed sensors must form a connected network, and design an approximation algorithm to minimize the number of deployed sensors with connectivity guarantee. For performance comparison, we design and implement an optimal solution and a genetic algorithm (GA)-based approach. Extensive simulation results show that the proposed deployment algorithms consistently outperform the GA-based heuristic and achieve a close-to-optimal performance in small-scale problem instances and a significantly superior overall performance than the theoretical upper bound.
Highlights
Sensor networks have been widely used in many agricultural, military, and industrial applications.The technological advances in both sensing and communication have significantly improved the quality of sensors at a reduced cost, making it possible to deploy more sensors than before to achieve quality through quantity
We provide a rigorous proof that the number of divisions intersected by m circles in a 2D plane is tightly upper bounded by (m2 − m + 1), and prove that the algorithm for Discrete L with Continuous T (DLCT) has an approximation ratio of (ln(k · n · m) + 1), where m denotes the number of candidate locations for possible sensor deployment
The problems we study in this paper differ from the aforementioned research efforts in several aspects: (i) We consider a set of constrained sensor deployment problems in 3D space; (ii) We exhaust the combinations of various deployment constraints posed on candidate sensor locations and target objects, which can be either discrete points or continuous areas; (iii) We require the deployed sensors to k-cover a given set of target objects and maintain communication connectivity
Summary
Sensor networks have been widely used in many agricultural, military, and industrial applications. For CLCT, we take a hexagon-based discretization approach to discretize the continuous candidate sensor locations or target objects into a number of hexagons, and covert this problem to either DLCT or CLDT. For each of these four problems, we further consider a connected version where all sensors in the deployment region must be connected for communication, referred to as C-DLDT, C-DLCT, C-CLDT, and C-CLCT. Extensive simulations show that these algorithms consistently outperform the GA-based heuristic and achieve a close-to-optimal performance in small-scale problem instances and a significantly superior overall performance than the theoretical upper bound These results shed light on the efficiency of the proposed deployment schemes and their great potential for practical sensor network applications.
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