Browsing by Author "Chen, Yu-Ning"

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An Effective Sensor Deployment Scheme that Ensures Multilevel Coverage of Wireless Sensor Networks with Uncertain Properties
(2020-04-01)
Chen, Yu-Ning
;
Lin, Wu-Hsiung
;
Chen, Chiuyuan
;
應用數學系
;
Department of Applied Mathematics
The coverage problem is a fundamental problem for almost all applications in wireless sensor networks (WSNs). Many applications even impose the requirement of multilevel (k) coverage of the region of interest (ROI). In this paper, we consider WSNs with uncertain properties. More precisely, we consider WSNs under the probabilistic sensing model, in which the detection probability of a sensor node decays as the distance between the target and the sensor node increases. The difficulty we encountered is that there is no unified definition of k-coverage under the probabilistic sensing model. We overcome this difficulty by proposing a "reasonable" definition of k-coverage under such a model. We propose a sensor deployment scheme that uses less number of deployed sensor nodes while ensuring good coverage qualities so that (i) the resultant WSN is connected and (ii) the detection probability satisfies a predefined threshold p(th), where 0 < p(th) < 1. Our scheme uses a novel "zone 1 and zone 1-2" strategy, where zone 1 and zone 2 are a sensor node's sensing regions that have the highest and the second highest detection probability, respectively, and zone 1-2 is the union of zones 1 and 2. The experimental results demonstrate the effectiveness of our scheme.
Sensor Deployment under Probabilistic Sensing Model
(2018-01-01)
Chen, Yu-Ning
;
Chen, Chiuyuan
;
應用數學系
;
Department of Applied Mathematics
In recent years, with the rapid development of wireless sensor networks (WSNs), sensors are widely used to monitor a region of interests (ROI). Therefore sensor deployment becomes one of the important issues that need to be solved because it determines the cost of constructing the WSN and affects how well the ROI is monitored. Sensors can be deployed in a pre-planned manner or in an ad-hoc manner. Moreover, the sensing model of a WSN can be binary disk model or probabilistic sensing model. Most of previous researches focus on binary disk model, which assumes that sensors can accurately detect targets within their sensing ranges. Recently, probabilistic sensing model has been proposed; in this model, the probability for a sensor to detect a target decays with the distance between the sensor and the target. Probabilistic sensing model is therefore more realistic than the binary disk model. In [2], the sensor deployment problem in a pre-planned manner under probabilistic sensing model is solved by using a transformation from probabilistic sensing model to binary disk model. We find that such a transformation wastes too many sensors, and it can be avoided. In this paper, we solve the same problem (i.e., pre-planned deployment under probabilistic sensing model) by using a "direct" method. We show that even under a simplified probabilistic sensing model, there are examples such that more than 86% of sensors used in [2] can be saved.