scholarly journals Mobile Subscriber Profiling and Personal Service Generation using Location Awareness

2018 ◽  
Vol 18 (3) ◽  
pp. 105-112
Author(s):  
K. OZTOPRAK
Keyword(s):  
Location Awareness ◽  
Personal Service ◽  
Mobile Subscriber

Coaching and professional/personal service: performance improvement and scientific method

2018 ◽  
Vol 59 (4) ◽  
Author(s):  
Gianfranco Tomei ◽  
Francesco Tomei ◽  
Carmina Sacco ◽  
Pasquale Ricci ◽  
Franco Pagliara ◽  
...  
Keyword(s):  
Performance Improvement ◽  
Scientific Method ◽  
Service Performance ◽  
Personal Service

Research on Smart Space Oriented Location Awareness Method

2009 ◽  
Vol 20 (3) ◽  
pp. 671-681
Author(s):  
Liang MING ◽  
Gang ZHAO ◽  
Gui-Hai XIE ◽  
Chun-Lei WANG
Keyword(s):  
Location Awareness ◽  
Smart Space

A Comparative Study of Computational Intelligence Algorithms for Sensor Localization

2019 ◽  
Vol 9 (2) ◽  
pp. 224-236
Author(s):  
Vaishali R. Kulkarni ◽  
Veena Desai ◽  
Raghavendra Kulkarni
Keyword(s):  
Firefly Algorithm ◽  
Heuristic Algorithms ◽  
Computing Time ◽  
Sensor Nodes ◽  
Convergence Time ◽  
Localization Error ◽  
Location Awareness ◽  
Cultural Algorithm ◽  
Localization Accuracy ◽  
Multi Stage

Background & Objective: Location of sensors is an important information in wireless sensor networks for monitoring, tracking and surveillance applications. The accurate and quick estimation of the location of sensor nodes plays an important role. Localization refers to creating location awareness for as many sensor nodes as possible. Multi-stage localization of sensor nodes using bio-inspired, heuristic algorithms is the central theme of this paper. Methodology: Biologically inspired heuristic algorithms offer the advantages of simplicity, resourceefficiency and speed. Four such algorithms have been evaluated in this paper for distributed localization of sensor nodes. Two evolutionary computation-based algorithms, namely cultural algorithm and the genetic algorithm, have been presented to optimize the localization process for minimizing the localization error. The results of these algorithms have been compared with those of swarm intelligence- based optimization algorithms, namely the firefly algorithm and the bee algorithm. Simulation results and analysis of stage-wise localization in terms of number of localized nodes, computing time and accuracy have been presented. The tradeoff between localization accuracy and speed has been investigated. Results: The comparative analysis shows that the firefly algorithm performs the localization in the most accurate manner but takes longest convergence time. Conclusion: Further, the cultural algorithm performs the localization in a very quick time; but, results in high localization error.

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