Wireless sensor network deployment in mobile phones assisted environment

Abstract

Wireless sensor networks have been widely deployed to perform sensing constantly at specific locations, but their energy consumption and deployment cost are of great concern. With the popularity and advanced technologies of mobile phones, participatory urban sensing is a rising and promising field which utilizes mobile phones as mobile sensors to collect data, though it is hard to guarantee the sensing quality and availability under the dynamic behaviors and mobility of human beings. Based on the above observations, we suggest that wireless sensors and mobile phones can complement each other to perform collaborative sensing efficiently with satisfactory quality and availability.In this paper, a novel collaborative sensing paradigm which integrates and supports wireless sensors and mobile phones with different communication standards is designed. We propose a seamless integrated framework which minimizes the number of wireless sensors deployed, while providing high sensing quality and availability to satisfy the application requirements. The dynamic sensing behaviors and mobility of mobile phone participants make it extremely challenging to estimate their sensing quality and availability, so as to deploy the wireless sensors at the optimal locations to guarantee the sensing performance at a minimum cost. We introduce two mathematical models, a sensing quality evaluation model and a mobility prediction model, to predict the sensing quality and mobility of the mobile phone participants. We further propose a cost-effective sensor deployment algorithm to guarantee the required coverage probability and sensing quality for the system. Extensive simulations with real mobile traces demonstrate that the proposed paradigm can integrate wireless sensors and mobile phones seamlessly for satisfactory sensing quality and availability with minimized number of sensors.

Energy-Aware Participant Selection for Smartphone-Enabled Mobile Crowd Sensing

“…Gambs et al in [26] proposed a mobility model called mobility Markov chain (MMC), which incorporates the n previous visited locations. In [27] and [28] Zhang et al in [29] attempted to fill the gap by dividing the life cycle of the MCS process into four stages and using "4W1H" (i.e., what/when/where/who/how) to characterize the major research issues in each of the four stages of the MCS life cycle as well as across the whole MCS process. Zhang et al in [30] designed a novel community-centric framework for community activity prediction based on big data analysis and proposed an approach to extract community activity patterns by analyzing the big data collected from both the physical world and virtual social space.…”

An optimisation-based approach for wireless sensor deployment in mobile sensing environments

“…We then take the average of the coverage at each 30 second time slice over the entire duration of the experiment (see [11]). As a result, the output of the second phase is an N × N coverage matrix M , such that M [i, j] is the coverage (sensing quality), given as an integer percentage, provided by the mobile phones for cell (i, j) of the sensing field.…”

“…Chakrabarty et al [17] proposed a deployment scheme to reduce the deployment cost for heterogeneous kinds of sensors. Apart from that, wireless sensors have recently been suggested to improve the sensing quality in collaboration with the mobile phones [11]. Although a simple version of the deployment problem has been tackled by us in [11], it was only solved by a heuristic algorithm and only a small instance (size N 2 = 100) was experimented with.…”

“…Apart from that, wireless sensors have recently been suggested to improve the sensing quality in collaboration with the mobile phones [11]. Although a simple version of the deployment problem has been tackled by us in [11], it was only solved by a heuristic algorithm and only a small instance (size N 2 = 100) was experimented with. In this paper, we introduce an IP model to solve the deployment problem, which can lead to near optimal solutions for a much larger sensing field (size 100 ≤ N 2 ≤ 6400).…”

A new technique to enhance accuracy of WLAN fingerprinting based indoor positioning system

“…services [4]. The combination of Wi-Fi posItIOning system and embedded smartphones sensors can provide many precise location-based services especially for indoor applications, such as for navigating large and complex stores and shopping centers, tracking valuable goods which are being transported in metal containers, locating people such as children or the elderly who may be deep inside buildings, recovering stolen vehicles in the presence of easily-obtainable GPS j atmning devices, finding devices or equipments in hospitals.…”