Energy Efficient Scheduling for Networked IoT Device Software Update

Bui, Ngoc Hai; Pham, Chuan; Nguyen, Kim Khoa; Cheriet, Mohamed

doi:10.23919/cnsm46954.2019.9012742

Cited by 8 publications

(5 citation statements)

References 13 publications

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“…Simulation results show that this algorithm can achieve near-optimal values. This is a difference of only 3.2% from the minimum in the best case [20].…”

Section: Energy Efficient Scheduling For Networked Iot Device Softwar...mentioning

confidence: 81%

High Performance Priority Packets Scheduling Mechanism for Big Data in Smart Cities

Alassery¹

2022

Computers, Materials &Amp; Continua

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Today, Internet of Things (IoT) is a technology paradigm which convinces many researchers for the purpose of achieving high performance of packets delivery in IoT applications such as smart cities. Interconnecting various physical devices such as sensors or actuators with the Internet may causes different constraints on the network resources such as packets delivery ratio, energy efficiency, end-to-end delays etc. However, traditional scheduling methodologies in large-scale environments such as big data smart cities cannot meet the requirements for high performance network metrics. In big data smart cities applications which need fast packets transmission ratio such as sending priority packets to hospitals for an emergency case, an efficient scheduling mechanism is mandatory which is the main concern of this paper. In this paper, we overcome the shortcoming issues of the traditional scheduling algorithms that are utilized in big data smart cities emergency applications. Transmission information about the priority packets between the source nodes (i.e., people with emergency cases) and the destination nodes (i.e., hospitals) is performed before sending the packets in order to reserve transmission channels and prepare the sequence of transmission of theses priority packets between the two parties. In our proposed mechanism, Software Defined Networking (SDN) with centralized communication controller will be responsible for determining the scheduling and processing sequences for priority packets in big data smart cities environments. In this paper, we compare between our proposed Priority Packets Deadline First scheduling scheme (PPDF) with existing and traditional scheduling algorithms that can be used in urgent smart cities applications in order to illustrate the outstanding network performance parameters of our scheme such as the average waiting time, packets loss rates, priority packets end-to-end delay, and efficient energy consumption.

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“…Simulation results show that this algorithm can achieve near-optimal values. This is a difference of only 3.2% from the minimum in the best case [20].…”

Section: Energy Efficient Scheduling For Networked Iot Device Softwar...mentioning

confidence: 81%

High Performance Priority Packets Scheduling Mechanism for Big Data in Smart Cities

Alassery¹

2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

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“…The algorithm must choose among "n" possible options with three variables each and nine coefficients to the cost equation. Thus, for step 1, the complexity is defined in Equation (24).…”

Section: Algorithm Complexity Evaluationmentioning

confidence: 99%

“…The proposals [ 11 , 24 , 25 ] use CC as an option for task execution. Other approaches use fog computing to allow local processing in IoT devices such as the works [ 14 , 26 , 27 ] or without applying the CC [ 28 ].…”

Section: Related Workmentioning

confidence: 99%

An Algorithm to Minimize Energy Consumption and Elapsed Time for IoT Workloads in a Hybrid Architecture

Anjos

Gross

Matteussi

et al. 2021

Sensors

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Advances in communication technologies have made the interaction of small devices, such as smartphones, wearables, and sensors, scattered on the Internet, bringing a whole new set of complex applications with ever greater task processing needs. These Internet of things (IoT) devices run on batteries with strict energy restrictions. They tend to offload task processing to remote servers, usually to cloud computing (CC) in datacenters geographically located away from the IoT device. In such a context, this work proposes a dynamic cost model to minimize energy consumption and task processing time for IoT scenarios in mobile edge computing environments. Our approach allows for a detailed cost model, with an algorithm called TEMS that considers energy, time consumed during processing, the cost of data transmission, and energy in idle devices. The task scheduling chooses among cloud or mobile edge computing (MEC) server or local IoT devices to achieve better execution time with lower cost. The simulated environment evaluation saved up to 51.6% energy consumption and improved task completion time up to 86.6%.

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“…Compared to its competitors, such as Long Range Wide Area Network (LoRaWAN) and Sigfox, C-IoT protocols can offer better performance in many areas, including energy consumption of UEs [1]. This can be done primarily in three ways: i) improving the scheduling and routing of information through the network [2] [3], ii) processing data using more energy efficient methods (e.g. cloud computing) [4] [5], and iii) introducing sleep modes for nodes in the network [6] [7].…”

Section: Introductionmentioning

confidence: 99%