Participant Selection Strategies Based on Crowd Sensing for River Environmental Monitoring

Abstract

The surrounding environment of rivers in city is often damaged and polluted.How to effectively monitor rivers has gradually attracted the attention of public,government and academia.At present,traditional monitoring methods are facing with high cost,insufficient coverage and other defects.With the increasing popularity of intelligent mobile devices,a new idea of using crowd sensing to efficiently monitor the river environment is proposed in this paper.The problem can be described as the assumption that each river reach contains c monitoring points,and then r users are selected according to the movement tracks of a large number of users to jointly complete the monitoring of all river reaches in s periods.It is stipulated that the smaller the number of users r,the less the monitoring cost.The stepwise-greedy strategy,the global-greedy strategy and the integer-programming stra-tegy are designed to solve this problem,that is,to select the least number of participants to achieve the monitoring goal of “s durations-c ranges-r users”.In this paper,the above strategies are applied to environmental monitoring of some rivers in Taijiang,Fuzhou.Experimental results show that the above strategies can obtain better solutions than the random strategy,and the integer-programming strategy has the best performance.However,with the increase of the scale of the problem,the implicit enumeration algorithm used to solve the small-scale integer programming will be unable to solve the situation.Motivated by this,this paper designs a discrete particle swarm optimization algorithm based on greedy initialization(GI-DPSO).Although this algorithm can solve large-scale integer programming,it is time-consuming.Considering the monitoring cost and computational cost comprehensively,it is suggested that the integer-programming strategy can be adopted for small-scale problems and the global-greedy strategy can be adopted for large-scale problems.

Keywords

• river environmental monitoring|crowd sensing|greedy strategy|integer programming|discrete particle swarm optimization algorithm