A4 Vertaisarvioitu artikkeli konferenssijulkaisussa
Hierarchal Placement of Smart Mobile Access Points in Wireless Sensor Networks using Fog Computing
Tekijät: Majd A, Sahebi G, Daneshtalab M, Plosila J, Tenhunen H
Toimittaja: Kotenko I, Cotronis Y, Daneshtalab M
Konferenssin vakiintunut nimi: Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP)
Julkaisuvuosi: 2017
Journal: Proceedings: Euromicro Workshop on Parallel and Distributed Processing
Kokoomateoksen nimi: Proceedings of the 25th Euromicro International Conference on Parallel, Distributed and Network-Based Processing PDP 2017
Tietokannassa oleva lehden nimi: 2017 25TH EUROMICRO INTERNATIONAL CONFERENCE ON PARALLEL, DISTRIBUTED AND NETWORK-BASED PROCESSING (PDP 2017)
Lehden akronyymi: EUROMICRO WORKSHOP P
Sarjan nimi: Euromicro Conference on Parallel Distributed and Network-Based Processing
Aloitussivu: 176
Lopetussivu: 180
Sivujen määrä: 5
ISSN: 1066-6192
DOI: https://doi.org/10.1109/PDP.2017.27
Recent advances in computing and sensor technologies have facilitated the emergence of increasingly sophisticated and complex cyber-physical systems and wireless sensor networks. Moreover, integration of cyber-physical systems and wireless sensor networks with other contemporary technologies, such as unmanned aerial vehicles (i.e. drones) and fog computing, enables the creation of completely new smart solutions. By building upon the concept of a Smart Mobile Access Point (SMAP), which is a key element for a smart network, we propose a novel hierarchical placement strategy for SMAPs to improve scalability of SMAP based monitoring systems. SMAPs predict communication behavior based on information collected from the network, and select the best approach to support the network at any given time. In order to improve the network performance, they can autonomously change their positions. Therefore, placement of SMAPs has an important role in such systems. Initial placement of SMAPs is an NP problem. We solve it using a parallel implementation of the genetic algorithm with an efficient evaluation phase. The adopted hierarchical placement approach is scalable; it enables construction of arbitrarily large SMAP based systems.
