An Improved Energy Efficient Duty Cycling Algorithm for IoT based Precision Agriculture

Dhall, Ruchi; Agrawal, Himanshu

doi:10.1016/j.procs.2018.10.159

Cited by 46 publications

(19 citation statements)

References 17 publications

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“…Reinforcement learning and meta-heuristic methods were utilized to find the optimal network configurations. The authors of [51] address the energy efficiency of data aggregations methods by proposing an improved duty cycling algorithm and comparing its performances to multiple existing approaches. The proposed approach utilizes two residual energy thresholds, one is used for the entire network and another one for the path of the Mobile Sink Node (MSN) which in this case is the base station.…”

Section: Related Workmentioning

confidence: 99%

Energy Efficient Distributed Processing for IoT

et al. 2020

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In the near future, the number of Internet connected objects is expected to be between 26-50 billion devices. This figure is expected to grow even further due to the production of miniaturized portable devices that are lightweight, energy, and cost-efficient. In this paper, the entire IoT-fog-cloud architecture is modeled, the service placement problem is formulated using Mixed Integer Linear Programming (MILP) and the total power consumption is jointly minimized for processing and networking. We evaluate the distributed processing paradigm for both the un-capacitated and capacitated design settings in order to provide solutions for the long-term and short-term basis, respectively. Furthermore, four aspects of the IoT processing placement problem are examined: 1) IoT services with non-splittable tasks, 2) IoT services with splittable tasks, 3) impact of processing overheads needed for inter-service communication and 4) deployment of special-purpose data centers (SP-DCs) as opposed to the conventional general-purpose data center (GP-DC) in the core network. The results showed that for the capacitated problem, task splitting introduces power savings of up to 86% compared to 46% with non-splittable tasks. Moreover, it is observed that the overheads due to inter-service communication greatly impacts the total number of splits. However much insignificant the overhead factor, the results showed that this is not a trivial matter and hence much attention needs to be paid to this area to make the best use of the resources that are available at the edge of the network.

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Section: Related Workmentioning

confidence: 99%

Energy Efficient Distributed Processing for IoT

et al. 2020

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“…This review identified several approaches for optimizing the power consumption in IoT solutions for smart farming. Among the identified solutions are the use of low energy protocols (e.g., BLE, ZigBee, Sigfox), reduction of data transmission in sensor nodes by an optimized duty cycle [ 177 , 178 , 192 ] and the use of message routing approaches that are more energy-efficient [ 72 , 193 ].…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, despite the fact that power-supply in IoT solutions for smart farming does not represent a specific layer of an IoT solution architecture [ 17 , 18 ], this topic has been covered in some of the reviewed papers. For example, [ 72 , 177 ] proposed improvements in algorithms for message routing and in duty cycles in sensor nodes. These approaches contribute to the reduction of power consumption by IoT devices.…”

Section: Considerationsmentioning

confidence: 99%

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A Systematic Review of IoT Solutions for Smart Farming

Navarro

Costa

Pereira

2020

Sensors

226

100

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The world population growth is increasing the demand for food production. Furthermore, the reduction of the workforce in rural areas and the increase in production costs are challenges for food production nowadays. Smart farming is a farm management concept that may use Internet of Things (IoT) to overcome the current challenges of food production. This work uses the preferred reporting items for systematic reviews (PRISMA) methodology to systematically review the existing literature on smart farming with IoT. The review aims to identify the main devices, platforms, network protocols, processing data technologies and the applicability of smart farming with IoT to agriculture. The review shows an evolution in the way data is processed in recent years. Traditional approaches mostly used data in a reactive manner. In more recent approaches, however, new technological developments allowed the use of data to prevent crop problems and to improve the accuracy of crop diagnosis.

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“…This paper also makes reference to the importance of duty cycling in the base stations but does not base this on the mobility of the MSN. This work is extended in [26] by additionally utilising two residual energy thresholds, one for the entire network and one for the path of the MSN. This is then used to modify the existing FarmBeats [25] algorithms in terms of duty cycling and path selection.…”

Section: Related Workmentioning

confidence: 99%

Mobility Aware Duty Cycling Algorithm (MADCAL) in Wireless Sensor Network with Mobile Sink Node

Thomson

Wadhaj

Tan

et al. 2019

2019 IEEE International Conference on Smart Internet of Things (SmartIoT)

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In Wireless Sensor Networks (WSNs) the use of Mobile Sink Nodes (MSNs) has been proposed in order to negate the "hotspot" issue. This where nodes closest to the sink node shall run out of energy fastest, affecting network lifetime. However, in using an MSN there remains the issue of nodes that sink passes closest to using most energy. In this study we propose a novel Mobility Aware Duty Cycling Algorithm (MADCAL), for use at the MAC layer. This algorithm provides a lightweight solution, utilising knowledge of the mobility pattern of the sink node in order to determine a dynamic communication threshold, within which static nodes shall awaken to communicate with the sink. This avoids the need for energy-heavy beacon messages. Using the Omnet++ simulation framework and tested on a lightweight implementation of a Carrier-sense multiple access (CSMA) based MAC protocol, we demonstrate a reduction in energy consumption up to 15% and up to 300% increase in frame delivery to the MSN, when compared to the existing duty cycling approach.

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An Improved Energy Efficient Duty Cycling Algorithm for IoT based Precision Agriculture

Cited by 46 publications

References 17 publications

Energy Efficient Distributed Processing for IoT

Energy Efficient Distributed Processing for IoT

A Systematic Review of IoT Solutions for Smart Farming

Mobility Aware Duty Cycling Algorithm (MADCAL) in Wireless Sensor Network with Mobile Sink Node

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