Book chapter
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, pp.1-19
Advances in Computer and Electrical Engineering (ACEE) , IGI Global
2021
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
Details
- Title
- An Energy-Efficient Model for Opportunistic Data Collection in IoV-Enabled SC Waste Management
- Authors
- Gerald Ijemaru (Author) - University of the Sunshine Coast, Queensland, School of Science, Technology and EngineeringEricmoore T Ngharamike (Author) - Federal University Oye EkitiEmmanuel U Oleka (Author) - North Carolina Agricultural and Technical State UniversityAugustine O Nwajana (Author) - University of Greenwich
- Contributors
- Augustine O Nwajana (Editor) - University of GreenwichIsibor Ihianle (Editor) - Nottingham Trent University
- Publication details
- Handbook of Research on 5G Networks and Advancements in Computing, Electronics, and Electrical Engineering, pp.1-19
- Series
- Advances in Computer and Electrical Engineering (ACEE)
- Publisher
- IGI Global
- Date published
- 2021
- DOI
- 10.4018/978-1-7998-6992-4.ch001; 10.4018/978-1-7998-6992-4
- ISSN
- 2327-0403; 2327-039X
- ISBN
- 9781799869948
- Organisation Unit
- University of the Sunshine Coast, Queensland; School of Science, Technology and Engineering
- Language
- English
- Record Identifier
- 99534606502621
- Output Type
- Book chapter
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