Double exponential smoothing: an alternative to Kalman filter-based predictive tracking
EGVE '03 Proceedings of the workshop on Virtual environments 2003
Sink-to-sensors reliability in sensor networks
ACM SIGMOBILE Mobile Computing and Communications Review
Mobility improves coverage of sensor networks
Proceedings of the 6th ACM international symposium on Mobile ad hoc networking and computing
The role of Wireless Sensor Networks in the area of Critical Information Infrastructure Protection
Information Security Tech. Report
Optimal and approximate approaches for deployment of heterogeneous sensing devices
EURASIP Journal on Wireless Communications and Networking
Grid-Based Access Scheduling for Mobile Data Intensive Sensor Networks
MDM '08 Proceedings of the The Ninth International Conference on Mobile Data Management
Assessing sensor reliability for multisensor data fusion within the transferable belief model
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Optimal deployment of large wireless sensor networks
IEEE Transactions on Information Theory
| Hi-index | 0.00 |
In this paper we investigate the problem of heterogeneous sensor deployment in preferential areas. The problem considers many of the sensors characteristics such as mobility, state-switching, reliability, and mobility cost; in addition, the problem takes into consideration that the monitored field areas may differ in their monitoring requirements from time to time. Different prediction methods namely Markov, double exponential smoothing, triple exponential smoothing, simple average, and weighted average are used to predicate the monitoring field preferential areas. Our approach in solving such problem starts by formulating the problem mathematically to show its complexity and to solve small-scale problems optimally. Then, we propose three different algorithms for large scale problems. The first algorithm deals with structured deployment where the monitored field is assumed accessible. The second and third algorithms deal with real time deployment where the sensed data is used for future planning and sensor relocation. The second algorithm is a centralized algorithm while the third algorithm is a distributed algorithm. An extensive set of experiments are conducted to show the performance of the proposed methods and algorithms.