Initial Deployment Of Sensors Research Articles

Coverage area enhancement in wireless sensor network

In the wireless sensor network, coverage area may be enhanced after an initial deployment of sensors. Though, some research works propose how to decrease the coverage hole by increasing sensing range or movement assisted sensor deployment, these are not suitable for energy constraint wireless sensor network, as longer mobility distance or higher power level consume more energy. In this paper, we address the increasing coverage area through smaller mobility of nodes. We find out the coverage hole in the monitoring region, which is not covering by any sensing disk of sensor. Then, we address the new position of mobility nodes to increase the coverage area. The simulation result shows the mobile nodes can recover the coverage hole perfectly. The coverage holes is recovered by mobility on the existing recovery area, which cannot be lost. Moreover, hole detection time in our proposed protocol is better than existing algorithm.

A Two-Phase Coverage-Enhancing Algorithm for Hybrid Wireless Sensor Networks.

Providing field coverage is a key task in many sensor network applications. In certain scenarios, the sensor field may have coverage holes due to random initial deployment of sensors; thus, the desired level of coverage cannot be achieved. A hybrid wireless sensor network is a cost-effective solution to this problem, which is achieved by repositioning a portion of the mobile sensors in the network to meet the network coverage requirement. This paper investigates how to redeploy mobile sensor nodes to improve network coverage in hybrid wireless sensor networks. We propose a two-phase coverage-enhancing algorithm for hybrid wireless sensor networks. In phase one, we use a differential evolution algorithm to compute the candidate’s target positions in the mobile sensor nodes that could potentially improve coverage. In the second phase, we use an optimization scheme on the candidate’s target positions calculated from phase one to reduce the accumulated potential moving distance of mobile sensors, such that the exact mobile sensor nodes that need to be moved as well as their final target positions can be determined. Experimental results show that the proposed algorithm provided significant improvement in terms of area coverage rate, average moving distance, area coverage–distance rate and the number of moved mobile sensors, when compare with other approaches.

Optimal Sensor Deployment for Manufacturing Process Monitoring Based on Quantitative Cause-Effect Graph

This paper proposes a new sensor deployment strategy based on quantitative cause-effect graph (QCEG) to handle the heterogeneity among the properties of sensors and faults. A QCEG is developed to model the cause-effect relationship between the system faults and sensor readings. A multi-objective optimization is performed to facilitate the monitoring of single-station multistep manufacturing process (SMMP). A stream of fault information model is built to describe the propagation of fault state in the SMMP. By means of state-space transformation, a detection factor is used to provide the initial sensor deployment. The optimal sensor deployment in an SMMP is achieved by an improved shuffled frog leaping algorithm (ISFLA), which minimizes the fault unobservability, maximizes the system stability, and minimizes the cost for the whole system, under the constraints on detectability, stationarity, and limited resources. Two experimental investigations on an assembly unit and a manufacturing unit are conducted to verify the methodology. Comparative studies demonstrate that the proposed QCEG is able to overcome the shortcomings of directed graph (DG) in handling sensor heterogeneity and multiple objectives. As a goal-oriented swarm-intelligence search strategy, the ISFLA performs better than the popular integer programming in dealing with the multi-objective optimization problem.

Optimal reduction from an initial sensor deployment along the deck of a cable-stayed bridge

The ambient vibration measurement is an output-data-only dynamic testing where natural excitations are represented, for instance, by winds and typhoons. The modal identification involving output-only measurements requires the use of specific modal identification techniques. This paper presents the application of a reliable method (the Stochastic Subspace Identification - SSI) implemented in a general purpose software. As a criterion toward the robustness of identified modes, a bio-inspired optimization algorithm, with a highly nonlinear objective function, is introduced in order to find the optimal deployment of a reduced number of sensors across a large civil engineering structure for the validation of its modal identification. The Ting Kau Bridge (TKB), one of the longest cable-stayed bridges situated in Hong Kong, is chosen as a case study. The results show that the proposed method catches eigenvalues and eigenvectors even for a reduced number of sensors, without any significant loss of accuracy.