Energy dissipation model for wireless sensor networks: a survey

Roy, Nihar Ranjan; Chandra, P.

doi:10.1007/s41870-019-00374-y

Cited by 16 publications

(7 citation statements)

References 39 publications

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“…We should incorporate receiving and sending energy for IoT devices when calculating IoT device energy usage. Let ETrans(n, d) denote the cost of transmitting n bits of data over a distance of d meters and ERecv(n) denote the cost of receiving n bits of data over the same distance [59].…”

Section: Energy Consumption Modelmentioning

confidence: 99%

Cluster-Based Routing Protocol with Static Hub (CRPSH) for WSN-Assisted IoT Networks

et al. 2022

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The Internet of Things (IoT) is an evolving concept that has achieved prominence in the modern era. An autonomous sensor-equipped device is the major component of WSN-assisted IoT infrastructure. These devices intelligently sense the environment, automatically collect the data, and deliver the information to paired devices. However, in WSN-assisted IoT networks, energy depletion and hardware faults might result in device failures. Additionally, this might affect data transmission. A reliable route significantly reduces data retransmissions, which can help in congestion reduction and energy conservation. Generally, the sensor devices are typically deployed densely throughout the WSN-assisted IoT networks. A high number of sensor devices covering a monitoring area might result in duplicate data. The clustering method can be used to overcome this problem. The clustering technique reduces network traffic, whereas the multipath technique ensures path reliability. In CRPSH, we used the clustering technique to reduce the duplicate data. Moreover, the multipath approach can increase the reliability of the proposed protocol. CRPSH is intended to minimize the overhead associated with control packets and extend the network’s lifetime. The complete set of simulations is carried out using the Castalia simulator. The proposed protocol is found to reduce energy consumption and increase the lifetime of IoT infrastructure networks.

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Section: Energy Consumption Modelmentioning

confidence: 99%

Cluster-Based Routing Protocol with Static Hub (CRPSH) for WSN-Assisted IoT Networks

et al. 2022

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“…This is the energy related to data acquisition, energy due to processing, and the energy required for data transmission [51]. According to many previous studies [51][52][53][54], and some IWs commercial off-the-shelf components (Cf Table 1), the communication module is the biggest energy consumer in the IWS. For example, in [52], the amounts of energy for data sensing and for data processing of an accelerometer MMA7260Q are respectively estimated to be 0.000268 times and 0.044 times the energy dissipated for data communication.…”

Section: Iws Energy Consumption Modelmentioning

confidence: 99%

“…For example, in [52], the amounts of energy for data sensing and for data processing of an accelerometer MMA7260Q are respectively estimated to be 0.000268 times and 0.044 times the energy dissipated for data communication. In [54], the energy dissipated for communication is evaluated at 51% of the total energy cost of the IWS in industrial processes for which the sensor must be used for the activation of a pre-actuator (contactor or speed variator, for example). Given these observations, only the energy cost associated with data transmission will be considered in this work.…”

Section: Iws Energy Consumption Modelmentioning

confidence: 99%

Piezoelectric Energy Harvesting Prediction and Efficient Management for Industrial Wireless Sensor

2020

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The vibrations, due to their abundance in most industrial processes, constitute an attractive solution for the power supply of Industrial Wireless Sensor (IWS). However, the amount of energy that can be harvested presents numerous fluctuations due to the engines’ different operating modes (overload, full load, or even operation without charge). Most designs do not incorporate this fluctuation in the definition of the specifications of the autonomous IWS. This paper then presents a design method to ensure the node’s energy autonomy while maximizing its Quality of Service (QoS). To precisely define the specifications of the IWS, vibration measurements were carried out at its location for one month. The recorded data was used to propose a new Predictor of the Harvestable Energy from Vibrations (PHEV). A comparative evaluation of the proposed PHEV performances with a state-of-the-art predictor is carried out. The results obtained show that the PHEV makes it possible to minimize the Root Mean Square Error (RMSE) from 28.63 mW to 19.52 mW. A model of energy dissipation in IWS, considering the Internet of Things’ requirements, was established. The model is based on Long-Range (LoRa)/Long-Range Communication Wide Area Network (LoRaWan). The amount of data transmitted is then maximized according to the expected energy harvest rate by setting up a Maximization Data Size Protocol (MDSP). The proposed method makes it possible to ensure an acceptable QoS without resorting to reconfigurable circuits, which are sometimes bulky for miniature devices such as the IWS.

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“…Wireless sensor networks are of the growing technologies with various and broad applications in different fields such as industry, agriculture, military, etc [1]. A wireless sensor network is made of many sensor nodes distributed in the desired environment.…”

Section: -Introductionmentioning

confidence: 99%

Optimal Clustering-based Routing Protocol Using Self-Adaptive Multi-Objective TLBO For Wireless Sensor Network

Sedighimanesh¹,

Zandhessami²,

Alborzi³

et al. 2021

JIST

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Wireless sensor networks consist of many fixed or mobile, non-rechargeable, low-cost, and low-consumption nodes. Energy consumption is one of the most important challenges due to the non-rechargeability or high cost of sensor nodes. Hence, it is of great importance to apply some methods to reduce the energy consumption of sensors. The use of clusteringbased routing is a method that reduces the energy consumption of sensors. In the present article, the Self-Adaptive Multiobjective TLBO (SAMTLBO) algorithm is applied to select the optimal cluster headers. After this process, the sensors become the closest components to cluster headers and send the data to their cluster headers. Cluster headers receive, aggregate, and send data to the sink in multiple steps using the TLBO-TS hybrid algorithm that reduces the energy consumption of the cluster heads when sending data to the sink and, ultimately, an increase in the wireless sensor network's lifetime. The simulation results indicate that our proposed protocol (OCRP) show better performance by 35%, 17%, and 12% compared to ALSPR, CRPD, and COARP algorithms, respectively. Conclusion: Due to the limited energy of sensors, the use of meta-heuristic methods in clustering and routing improves network performance and increases the wireless sensor network's lifetime.

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Energy dissipation model for wireless sensor networks: a survey

Cited by 16 publications

References 39 publications

Cluster-Based Routing Protocol with Static Hub (CRPSH) for WSN-Assisted IoT Networks

Cluster-Based Routing Protocol with Static Hub (CRPSH) for WSN-Assisted IoT Networks

Piezoelectric Energy Harvesting Prediction and Efficient Management for Industrial Wireless Sensor

Optimal Clustering-based Routing Protocol Using Self-Adaptive Multi-Objective TLBO For Wireless Sensor Network

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