Energy-Efficient Collaborative Outdoor Localization for Participatory Sensing

Wang, Wendong; Xi, Teng; Ngai, Edith C.-H.; Song, Zheng

doi:10.3390/s16060762

Cited by 8 publications

(5 citation statements)

References 23 publications

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“…In such LBSs, smartphones commonly obtain location information mostly via GPS receiver, as it offers the highest accuracy among the location providers available in modern smartphones [16] (e.g., Wi-Fi Positioning System and cellular cell tower location providers). Nevertheless, if fine spatial granularity is required by a given LBS, the GPS receiver must be queried continuously with a high sampling rate, which produces a high energy consumption because of the inherent characteristics of its infrastructure (synchronization and communication with GPS satellites [17]), as well as in an increased use of computational resources [18].…”

Section: Introductionmentioning

confidence: 99%

Full On-Device Stay Points Detection in Smartphones for Location-Based Mobile Applications

Pérez-Torres¹,

Torres-Huitzil²,

Galeana-Zapién³

2016

Sensors

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The tracking of frequently visited places, also known as stay points, is a critical feature in location-aware mobile applications as a way to adapt the information and services provided to smartphones users according to their moving patterns. Location based applications usually employ the GPS receiver along with Wi-Fi hot-spots and cellular cell tower mechanisms for estimating user location. Typically, fine-grained GPS location data are collected by the smartphone and transferred to dedicated servers for trajectory analysis and stay points detection. Such Mobile Cloud Computing approach has been successfully employed for extending smartphone’s battery lifetime by exchanging computation costs, assuming that on-device stay points detection is prohibitive. In this article, we propose and validate the feasibility of having an alternative event-driven mechanism for stay points detection that is executed fully on-device, and that provides higher energy savings by avoiding communication costs. Our solution is encapsulated in a sensing middleware for Android smartphones, where a stream of GPS location updates is collected in the background, supporting duty cycling schemes, and incrementally analyzed following an event-driven paradigm for stay points detection. To evaluate the performance of the proposed middleware, real world experiments were conducted under different stress levels, validating its power efficiency when compared against a Mobile Cloud Computing oriented solution.

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Section: Introductionmentioning

confidence: 99%

Full On-Device Stay Points Detection in Smartphones for Location-Based Mobile Applications

Pérez-Torres¹,

Torres-Huitzil²,

Galeana-Zapién³

2016

Sensors

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“…30 Besides, CS also inspired a new research trend in crowdsensing. Wang et al 31,32 proposed a framework called compressive crowdsensing task allocation (CCS-TA) to dynamically select a minimum number of sub-areas for sensing task allocation in each sensing cycle, while deducing the missing data of unallocated sub-areas under a probabilistic data accuracy guarantee. Cheng et al 33 designed a framework called DECO to detect false values for crowdsensing in the presence of missing data.…”

Section: Csmentioning

confidence: 99%

Sparse sensing data–based participant selection for people finding

Tian

Tang

2019

International Journal of Distributed Sensor Networks

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With the emerging of participatory sensing, crowdsensing-based lost people finding is arising. As a special locationcentric task, participant selection is a key factor to determine success or failure of lost people finding result. Besides traditional influence, like Quality of Information contribution, candidate's spatial proximity to the lost people is crucial in participant selection procedure. In order to evaluate how possible a candidate can approach lost people, the probability distribution of their tracing points should be predicted. However, the sparse sensing data problem has been a bottleneck of estimating people's probable position. To overcome these issues, we study the problem of selecting optimal participants according to each candidate's spatial proximity and Quality of Information contribution. In the first procedure, we proposed a Received Signal Strength Indicator Positioning-Determined Compressive Sensing algorithm to interpolate missing Received Signal Strength Indicator data. Then, a location-important marking method is put forward to select a set of high-quality data for estimating missing people's location. In the third procedure, a double-condition greedy participant selection approach, which guarantees candidates' spatial proximity and Quality of Information contribution, is executed to select optimal participants. Simulation results demonstrate that the proposed mechanism outperforms the other algorithms both in accuracy of positioning and quality of uploaded sensing data.

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“…The next problem is how to define the distance between two quasi identifier attributes form different tuples [ 37 ]. In this article, quasi identifier attributes are divided into two main categories, the continuous attributes and the discrete attributes.…”

Section: Clustering Model Based On Micro Aggregationmentioning

confidence: 99%

Data Privacy Protection Based on Micro Aggregation with Dynamic Sensitive Attribute Updating

Shi

Zhang

Chao

et al. 2018

Sensors

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With the rapid development of information technology, large-scale personal data, including those collected by sensors or IoT devices, is stored in the cloud or data centers. In some cases, the owners of the cloud or data centers need to publish the data. Therefore, how to make the best use of the data in the risk of personal information leakage has become a popular research topic. The most common method of data privacy protection is the data anonymization, which has two main problems: (1) The availability of information after clustering will be reduced, and it cannot be flexibly adjusted. (2) Most methods are static. When the data is released multiple times, it will cause personal privacy leakage. To solve the problems, this article has two contributions. The first one is to propose a new method based on micro-aggregation to complete the process of clustering. In this way, the data availability and the privacy protection can be adjusted flexibly by considering the concepts of distance and information entropy. The second contribution of this article is to propose a dynamic update mechanism that guarantees that the individual privacy is not compromised after the data has been subjected to multiple releases, and minimizes the loss of information. At the end of the article, the algorithm is simulated with real data sets. The availability and advantages of the method are demonstrated by calculating the time, the average information loss and the number of forged data.

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Energy-Efficient Collaborative Outdoor Localization for Participatory Sensing

Cited by 8 publications

References 23 publications

Full On-Device Stay Points Detection in Smartphones for Location-Based Mobile Applications

Full On-Device Stay Points Detection in Smartphones for Location-Based Mobile Applications

Sparse sensing data–based participant selection for people finding

Data Privacy Protection Based on Micro Aggregation with Dynamic Sensitive Attribute Updating

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