Multi-Objective Optimization for Location Prediction of Mobile Devices in Sensor-Based Applications | Zendy

Qinglan Peng | Zendy; Mengchu Zhou | Zendy; Qiang He | Zendy; Yunni Xia | Zendy; Chunrong Wu | Zendy; Shuiguang Deng | Zendy

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Multi-Objective Optimization for Location Prediction of Mobile Devices in Sensor-Based Applications

Author(s) -

Qinglan Peng,

Mengchu Zhou,

Qiang He,

Yunni Xia,

Chunrong Wu,

Shuiguang Deng

Publication year - 2018

Publication title -

ieee access

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.587

H-Index - 127

ISSN - 2169-3536

DOI - 10.1109/access.2018.2869897

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

A mobile ad hoc network (MANET) can be constructed when a group of mobile users need to communicate temporarily in an ad hoc manner. It allows mobile services to be shared through device-to-device links and composed by combining a set of services together to create a complex, value-added, and cross-organizational business application. Nevertheless, various challenges, especially the reliability and quality-of-service of such a MANET-based mobile service composition, are yet to be properly tackled. Most studies and related composition strategies assume that mobile users are fully stable and constantly available. However, this is not realistic in most real-world scenarios where mobile users are mobile. The mobility of mobile users impact the reliability of corresponding mobile services and consequently impact the success rate of mobile service compositions. In this paper, we propose a reliability-aware mobile service composition approach based on prediction of mobile users' positions. We model the composition problem as a multi-objective optimization problem and develop an evolutionary multi-objective optimization-based algorithm to solve it. Extensive case studies are performed based on a real-world mobile users' trajectory data set and show that our proposed approach significantly outperforms traditional ones in terms of composition success rate.

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