On Efficient Deployment of Wireless Sensors for Coverage and Connectivity in Constrained 3D Space
- New Jersey Institute of Technology, Newark, NJ (United States)
- Xi'an International Univ., Shaanxi (China)
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.
- Research Organization:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
- Sponsoring Organization:
- USDOE; National Science Foundation (NSF)
- Grant/Contract Number:
- AC05-00OR22725; CNS-1560698
- OSTI ID:
- 1628549
- Journal Information:
- Sensors, Vol. 17, Issue 10; ISSN 1424-8220
- Publisher:
- MDPI AGCopyright Statement
- Country of Publication:
- United States
- Language:
- English
Web of Science
Improved Many-Objective Optimization Algorithms for the 3D Indoor Deployment Problem
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journal | January 2019 |
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