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International Journal of Sensors, Wireless Communications and Control

Editor-in-Chief

ISSN (Print): 2210-3279
ISSN (Online): 2210-3287

Research Article

An Efficient Power Management Strategy of a Solar Powered Smart Camera-road Side Unit Integrated Platform

Author(s): Qutaiba Ibrahim*

Volume 12, Issue 7, 2022

Published on: 10 November, 2022

Page: [521 - 534] Pages: 14

DOI: 10.2174/2210327913666221024160809

Price: $65

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Abstract

Background: This paper proposes efficient employment of a self-powered VANET infrastructure. Miscellaneous techniques and algorithms are suggested to help the realization of such a framework.

Objective: The current work attempts to enhance the network architecture of the Green VANET by adopting the self-powered fog computing concept for better networking, computing, and storage performance.

Methods: The green fog layer consists of three components: a self-powered edge server, Wireless Solar Routers (WSRs), and a new device resulting from the integration between a solar-powered Smart Camera (SC) and a solar-powered Road Side Unit (RSU) in order to create a better sensing mechanism of the road traffic.

Results: A proper power management strategy is suggested and installed locally in the self-powered devices to decrease their power utilization by 80% and lengthen their batteries' lifetime from 17 to 64 hours.

Conclusion: The different methods and algorithms suggested in this paper are realized and tested using an experimental framework based on a mix of evaluation kits. It is noticed that the suggested power management algorithm can adjust the duty cycling according to the accessible energy levels, and thus, the SC-RSU nodes and the WSRs keep on working in a pre-managed and arranged manner.

Graphical Abstract

[1]
Vouk MA. Cloud computing issues, research and implementations. CIT J Comput Inf Technol 2008; 16(4): 235-46.
[http://dx.doi.org/10.2498/cit.1001391]
[2]
Rountree D, Castrillo I. The Basics of Cloud Computing: Understanding the Fundamentals of Cloud Computing in Theory and Practice. (1st ed.), Syngress; USA 2013.
[3]
Chiang M, Zhang T. Fog and IoT: an overview of research opportunities. IEEE Internet Things J 2016; 3(6): 854-64.
[http://dx.doi.org/10.1109/JIOT.2016.2584538]
[4]
Dastjerdi AV, Buyya R. Fog computing: Helping the internet of things realize its potential. Computer 2016; 49(8): 112-6.
[http://dx.doi.org/10.1109/MC.2016.245]
[5]
Feng J, Liu Z, Wu C, Ji Y. AVE: Autonomous vehicular edge computing framework with ACO-based scheduling. IEEE Trans Vehicular Technol 2017; 66(12): 10660-75.
[http://dx.doi.org/10.1109/TVT.2017.2714704]
[6]
Talia D. Clouds for scalable big data analytics. Computer 2013; 46(5): 98-101.
[http://dx.doi.org/10.1109/MC.2013.162]
[7]
Ali QI. GVANET project: an efficient deployment of a self‐powered, reliable and secured VANET infrastructure. IET Wirel Sens Syst 2018; 8(6): 313-22.
[http://dx.doi.org/10.1049/iet-wss.2018.5112]
[8]
Ahmed U, Lin JC-W, Srivastava G, Yun U, Singh AK. Deep active learning intrusion detection and load balancing in software-defined vehicular networks. In: IEEE Transactions on Intelligent Transportation Systems. 2022; pp. 1-9.
[http://dx.doi.org/10.1109/TITS.2022.3166864]
[9]
Khan AA, Abolhasan M, Ni W. 5G next generation VANETs using SDN and fog computing framework. In. 2018 15th IEEE Annual Consumer Communications & Networking Conference (CCNC). 12-15 January 2018; Las Vegas, NV, USA;. IEEE 2018.
[http://dx.doi.org/10.1109/CCNC.2018.8319192]
[10]
Bibi R, Saeed Y, Zeb A, et al. Edge AI-based automated detection and classification of road anomalies in VANET using deep learning. Comput Intell Neurosci 2021; 2021: 1-16.
[http://dx.doi.org/10.1155/2021/6262194] [PMID: 34630550]
[11]
Kumar M, Raw RS. A Novel Routing Protocol for Hierarchical Software Defined Vehicular Adhoc Network. In. 9th International Conference on Computing for Sustainable Global Development (INDIACom); 23-25 March 2022. New Delhi, India. IEEE 2022; pp. 771-5.
[http://dx.doi.org/10.23919/INDIACom54597.2022.9763267]
[12]
Zhang K, Mao Y, Leng S, He Y, Zhang Y. Predictive offloading in cloud-driven vehicles: using mobile-edge computing for a promising network paradigm. IEEE Veh Technol Mag 2017; 12(2): 36-44.
[http://dx.doi.org/10.1109/MVT.2017.2668838]
[13]
Basudan S, Lin X, Sankaranarayanan K. A privacy-preserving vehicular crowdsensing-based road surface condition monitoring system using fog computing. IEEE Inter Things J 2017; 4(3): 772-82.
[http://dx.doi.org/10.1109/JIOT.2017.2666783]
[14]
Shrivastava AL, Dwivedi RK. A secure design of the smart vehicular iot system using blockchain technology. In. 9th International Conference on Computing for Sustainable Global Development (INDIACom). 23-25 March 2022; New Delhi, India: IEEE 2022; pp. 23-25 March 2022; New Delhi, India: IEEE 2022; pp. 616-20.
[http://dx.doi.org/10.23919/INDIACom54597.2022.9763216]
[15]
Mohammed SJ, Hasson ST. Modeling and simulation of data dissemination in vanet based on a clustering approach. In. International Conference on Computer Science and Software Engineering (CSASE). 15-17 March 2022; Duhok, Iraq:. IEEE 2022; pp. 54-9.
[http://dx.doi.org/10.1109/CSASE51777.2022.9759671]
[16]
Truong NB, Lee GM, Ghamri-Doudane Y. Software defined networking-based vehicular adhoc network with fog computing. In. IFIP/IEEE International Symposium on Integrated Network Management (IM). 11-15 May 2015; Ottawa, ON, Canada. IEEE 2018; pp. 1202-7.
[http://dx.doi.org/10.1109/INM.2015.7140467]
[17]
Liu Z, Xiu C, Ye C. Improving urban resilience through green infrastructure: an integrated approach for connectivity conservation in the central city of shenyang, china complexity. Complex 2020. Available from: https://www.semanticscholar.org/paper/Improving-Urban-Resilience-through-Green-An-for-in-Liu-Xiu/6c3c78ce028bdcad4dbce52158dfbdc8f671f7f8
[18]
Peter N. Fog computing and its real time applications. Int J Emerg Technol Adv Eng 2015; 5(6): 266-9.
[19]
Ali Q. Design, implementation & optimization of an energy harvesting system for VANETS’ road side units (RSU). IET Intell Transp Syst 2014; 8(3): 298-307.
[http://dx.doi.org/10.1049/iet-its.2012.0206]
[20]
Ibrahim Q. Enhanced power management scheme for embedded road side units. IET Comput Digit Tech 2016; 10(4): 174-85.
[http://dx.doi.org/10.1049/iet-cdt.2015.0135]
[21]
Ali QI. Event driven duty cycling: an efficient power management scheme for a solar‐energy harvested road side unit. IET Electr Syst Transp 2016; 6(3): 222-35.
[http://dx.doi.org/10.1049/iet-est.2015.0036]
[22]
Ali Q. Green communication infrastructure for vehicular ad hoc network (VANET). J Electr Eng 2016; 16(2): 10.
[23]
Ali Q. Security issues of solar energy harvesting road side unit (RSU). Iraqi J Electric Electr Eng 2015; 11(1): 18-31.
[http://dx.doi.org/10.37917/ijeee.11.1.3]
[24]
Ali QI. Securing solar energy‐harvesting road‐side unit using an embedded cooperative‐hybrid intrusion detection system. IET Inf Secur 2016; 10(6): 386-402.
[http://dx.doi.org/10.1049/iet-ifs.2014.0456]
[25]
Mavrinac A, Chen X. Modeling coverage in camera networks: A survey. Int J Comput Vis 2013; 101(1): 205-26.
[http://dx.doi.org/10.1007/s11263-012-0587-7]
[26]
Devarajan D, Radke RJ, Chung H. Distributed metric calibration of ad hoc camera networks. ACM Trans Sens Netw 2006; 2(3): 380-403.
[http://dx.doi.org/10.1145/1167935.1167939]
[27]
Taj M, Cavallaro A. Distributed and decentralized multi camera tracking. IEEE Signal Process Mag 2011; 28(3): 46-58.
[http://dx.doi.org/10.1109/MSP.2011.940281]
[28]
Lei W. Camera sensor activation scheme for target tracking in wireless visual sensor networks. Inter J Distrib Sensor Netw 2013; 9(4)
[http://dx.doi.org/10.1155/2013/397537]

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