Node density optimisation using composite probabilistic sensing model in wireless sensor networks

2019 ◽  
Vol 9 (4) ◽  
pp. 181-190 ◽  
Author(s):  
Nitika Rai ◽  
Rohin Daruwala
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Wireless Sensor ◽  
Node Density ◽  
Sensing Model ◽  
Probabilistic Sensing Model

scholarly journals An Effective Sensor Deployment Scheme that Ensures Multilevel Coverage of Wireless Sensor Networks with Uncertain Properties

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1831 ◽  
Author(s):  
Yu-Ning Chen ◽  
Wu-Hsiung Lin ◽  
Chiuyuan Chen
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Detection Probability ◽  
Sensor Node ◽  
Fundamental Problem ◽  
Wireless Sensor ◽  
Sensor Deployment ◽  
Sensing Model ◽  
Definition Of ◽  
Probabilistic Sensing Model

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.

Energy-Efficient Coverage Based on Probabilistic Sensing Model in Wireless Sensor Networks

2010 ◽  
Vol 14 (9) ◽  
pp. 833-835 ◽  
Author(s):  
Jiming Chen ◽  
Junkun Li ◽  
Shibo He ◽  
Youxian Sun ◽  
Hsiao-Hwa Chen
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Energy Efficient ◽  
Wireless Sensor ◽  
Sensing Model ◽  
Probabilistic Sensing Model

scholarly journals A Novel Location Source Optimization Algorithm for Low Anchor Node Density Wireless Sensor Networks

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1890
Author(s):  
Zhongliang Deng ◽  
Shihao Tang ◽  
Xiwen Deng ◽  
Lu Yin ◽  
Jingrong Liu
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Optimization Algorithm ◽  
Fuzzy Comprehensive Evaluation ◽  
Wireless Sensor ◽  
Cooperative Localization ◽  
Fuzzy Evaluation ◽  
Node Density ◽  
Positioning Accuracy ◽  
Anchor Node

Location information is one of the basic elements of the Internet of Things (IoT), which is also an important research direction in the application of wireless sensor networks (WSNs). Aiming at addressing the TOA positioning problem in the low anchor node density deployment environment, the traditional cooperative localization method will reduce the positioning accuracy due to excessive redundant information. In this regard, this paper proposes a location source optimization algorithm based on fuzzy comprehensive evaluation. First, each node calculates its own time-position distribute conditional posterior Cramer-Rao lower bound (DCPCRLB) and transfers it to neighbor nodes. Then collect the DCPCRLB, distance measurement, azimuth angle and other information from neighboring nodes to form a fuzzy evaluation factor set and determine the final preferred location source after fuzzy change. The simulation results show that the method proposed in this paper has better positioning accuracy about 33.9% with the compared method in low anchor node density scenarios when the computational complexity is comparable.

scholarly journals Energy-Balanced Density Control to Avoid Energy Hole for Wireless Sensor Networks

2012 ◽  
Vol 8 (1) ◽  
pp. 812013 ◽  
Author(s):  
Jie Jia ◽  
Jian Chen ◽  
Xingwei Wang ◽  
Linliang Zhao
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Transmission Mechanism ◽  
Wireless Sensor ◽  
Node Density ◽  
Energy Hole ◽  
Density Control ◽  
Network Load ◽  
Great Relevance ◽  
Sensing Radius

Density control is of great relevance for wireless sensor networks monitoring hazardous applications where sensors are deployed with high density. Due to the multihop relay communication and many-to-one traffic characters in wireless sensor networks, the nodes closer to the sink tend to die faster, causing a bottleneck for improving the network lifetime. In this paper, the theoretical aspects of the network load and the node density are investigated systematically. And then, the accessibility condition to satisfy that all the working sensors exhaust their energy with the same ratio is proved. By introducing the concept of the equivalent sensing radius, a novel algorithm for density control to achieve balanced energy consumption per node is thus proposed. Different from other methods in the literature, a new pixel-based transmission mechanism is adopted, to reduce the duplication of the same messages. Combined with the accessibility condition, nodes on different energy layers are activated with a nonuniform distribution, so as to balance the energy depletion and enhance the survival of the network effectively. Extensive simulation results are presented to demonstrate the effectiveness of our algorithm.

scholarly journals NDSL: Node Density-Based Subregional Localization in Large Scale Anisotropy Wireless Sensor Networks

2015 ◽  
Vol 11 (11) ◽  
pp. 821352
Author(s):  
Zhanyong Tang ◽  
Jie Zhang ◽  
Liang Wang ◽  
Jinzhi Han ◽  
Dingyi Fang ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Large Scale ◽  
Wireless Sensor ◽  
Node Density
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