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An energy-efficient model for opportunistic data collection in IoV-enabled SC waste management

An energy-efficient model for opportunistic data collection in IoV-enabled SC waste management

Ijemaru, Gerald K., Ngharamike, Ericmoore T., Oleka, Emmanuel U. and Nwajana, Augustine O. ORCID: 0000-0001-6591-5269 (2021) An energy-efficient model for opportunistic data collection in IoV-enabled SC waste management. Handbook of Research on 5G Networks and Advancements in Computing, Electronics, and Electrical Engineering. IGI Global. ISBN 978-1799869924 (doi:https://doi.org/10.4018/978-1-7998-6992-4.ch001)

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Abstract

Recent advancements in technological research have seen the use of mobile data collectors (MDCs) or data MULEs for wireless sensor network (WSN) applications. In the context of smart city (SC) waste management scenarios, vehicular networks or the internet of Vehicles (IoV) can be exploited as MDCs or data MULEs for data collection and transmission purposes from the sparsely distributed smart sensors that are attached to the smart bins to an access point or sink node and further deployed for waste management operations. A major challenge with the traditional methods of data collection using static sink nodes is the high energy consumption of the sensor-nodes. The use of MDCs has been well studied and shown to be energy efficient. To the best of the authors' knowledge, this scheme has not been exploited for waste management operations in a SC. Compared to the centralized schemes, the data MULE scheme presents several advantages for data collection in WSN applications. This chapter proposes an energy-efficient model for opportunistic data collection in IoV-enabled SC waste management operations.

Item Type: Book Section
Uncontrolled Keywords: waste management, smart cities (SCs), internet of things (IoT), internet of vehicles (IoV), big data, data mules, mobile data collectors (MDCs)
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Faculty / Department / Research Group: Faculty of Engineering & Science
Faculty of Engineering & Science > School of Engineering (ENN)
Last Modified: 02 Jun 2021 12:59
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
Selected for REF2021: None
URI: http://gala.gre.ac.uk/id/eprint/32870

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