Harvested Energy Prediction Technique for Solar-Powered Wireless Sensor Networks

Sah, Dipak Kumar; Hazra, Abhishek; Kumar, Ramesh; Amgoth, Tarachand

doi:10.1109/jsen.2022.3208730

Cited by 16 publications

(14 citation statements)

References 24 publications

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“…3. The proposed routing method (EHEER) is evaluated against the recently proposed routing methods (GABEEC, 18 PSOBS, 19 GADA-LEACH, 20 GAPSO-H, 21 ROTEE 22 and ERAS 23 ) on the benchmark for various performance evaluating metrics, namely, stability duration, network operation period, throughput and network's remaining energy.…”

Section: Major Contributionsmentioning

confidence: 99%

“…As can be seen in reference Figure 4, the suggested work makes use of EH-enabled nodes, which bear the additional energy expense, so that the network's total energy consumption is kept to a minimum. Although they all begin with the same amount of energy, the protocols GABEEC, 18 PSOBS, 19 GADA-LEACH, 20 GAPSO-H, 21 ROTEE 22 and ERAS 23 cover a smaller number of rounds as compared to the EHEER protocol. 3.…”

Section: Metrics To Examine Performancementioning

confidence: 99%

“…(486,712), GADA-LEACH 20 (506,203), GAPSO-H 21 (792,941), ROTEE 22 (1,869,578) and ERAS 23 (1,789,781). The improved performance of this statistic is mostly attributable to a longer average time between node deaths.…”

Section: Metrics To Examine Performancementioning

confidence: 99%

“…As it could be seen from Figure 6, the proposed protocol outperforms the other protocols, namely, the protocols GABEEC, 18 PSOBS, 19 GADA-LEACH, 20 GAPSO-H, 21 ROTEE 22 and ERAS. 23 This is because of the energy-optimized method of selecting the CH and the optimized data forwarding from the CH, which eventually reduces the burden on CH nodes. The EH nodes help with data forwarding, eventually reducing the burden on the CH nodes.…”

Section: Metrics To Examine Performancementioning

confidence: 99%

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Energy harvesting‐enabled energy‐efficient routing using spotted hyena optimization in wireless sensor network

Gupta,

Kumar Rout

2024

Int J Communication

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SummaryWith the continuous proliferation of sensing technology, it has become possible to utilize energy harvesting (EH)‐enabled sensor nodes for a variety of applications. However, conventional wireless sensor networks (WSNs), that is, those without EH‐enabled nodes, still have limited applicability due to their limited battery resources. Further, the utilization of EH‐enabled nodes in the network not only imposes a financial burden on the user but also limits its performance due to its dependence on environmental conditions. To address this concern, in this paper, we propose the EH‐enabled energy‐efficient routing (EHEER) technique for green communication in WSN. The predominant concern being addressed in this paper is the selection of cluster head (CH), which helps in gathering, aggregating and forwarding the data from the cluster‐based routing paradigm. We use the spotted hyena optimizer (SHO) algorithm for optimizing the fitness parameters for CH selection, namely, energy ratio, distance considerations, node density, load balancing and the network's average energy. We use EH‐enabled nodes in the network strategically so as to keep control over the costs incurred in the network. The simulation outcomes empirically prove the efficacy of the proposed work, as it effectively increases the network stability and operational period by a huge margin as compared to the existing techniques.

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Section: Major Contributionsmentioning

confidence: 99%

Section: Metrics To Examine Performancementioning

confidence: 99%

Section: Metrics To Examine Performancementioning

confidence: 99%

Section: Metrics To Examine Performancementioning

confidence: 99%

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Energy harvesting‐enabled energy‐efficient routing using spotted hyena optimization in wireless sensor network

Gupta,

Kumar Rout

2024

Int J Communication

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“…The prediction accuracy is difficult to meet the requirements. Many scholars have studied the prediction technology of renewable energy (Sah et al, 2022). However, when forecasting techniques are limited, the deterministic optimization model shown in Formula ( 23) often seems too "risky" and needs to consider the influence of uncertainty in the model.…”

Section: Two Stage Robust Planning Model and Solution Strategy 251 De...mentioning

confidence: 99%

Two stage robust planning of park integrated energy system considering low carbon

Chen

Su²,

Wu³

2023

Front. Ecol. Evol.

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The development of integrated energy systems is a significant way of improving energy efficiency and encouraging renewable energy absorption. However, the uncertainty of renewable energy and loads raise severe challenges to integrated energy system planning. Besides, under the low carbon background, the relationship between reducing carbon emissions and increasing the absorption of renewable energy is not investigated clearly in the planning of the park integrated energy system. Thus, this paper proposes a two-stage robust planning method considering low carbon and uncertainty. Firstly, the input-output model of energy conversion equipment in the park integrated energy system is described. Secondly, the objective function and constraints and are set up. Specifically, the carbon emission limits and renewable energy penetration target are introduced in the constraints. On this basis, the two-stage robust planning model with min-max-min structure is established, after transformation, column-and-constraint generation is used for the solution. Case study shows the ability and cost of the park integrated energy system to promote low-carbon emission reduction and renewable energy consumption. Moreover, it proves that although there is some synergy between the low carbon target and the renewable energy consumption target, maximum renewable energy consumption does not mean minimum carbon emissions, and vice versa, and in some cases increasing renewable energy penetration does not necessarily reduce carbon emissions.

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KEFSAR: A Solar-Aware Routing Strategy For Rechargeable IoT Based On High-Accuracy Prediction

Wang

Huang

et al. 2023

The Computer Journal

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The high energy density of solar energy gives wireless sensor networks advantages in outdoor monitoring applications. However, long-term stable monitoring is challenging due to frequent weather changes, shading by buildings and trees, etc. The existing research usually uses two technologies to solve the above problems: (1) the energy prediction algorithm, and (2) the energy-aware routing strategy. However, in an actual deployment, frequent weather changes can significantly reduce the accuracy of the existing prediction algorithms. When using the algorithms as the support for energy-aware routing, the network lifetime is less than ideal. The existing routing strategies are in need of further improvement. Because of its lack of environmental adaptability, nodes consume energy quickly and have a high mortality rate. Therefore, aiming at the long-term stability of solar wireless sensor networks, this paper proposes a prediction algorithm based on classification and recurrent neural networks, and integrates the shadow judgement method from our previous research to correct the predicted values. Furthermore, we propose a routing optimization model that can flexibly adjust the target according to the solar intensity. The experimental results show that the prediction and routing scheduling algorithm can significantly improve the energy prediction accuracy (30–50%) and prolong the network lifetime (10–42%) in outdoor small sensor scenarios.

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Harvested Energy Prediction Technique for Solar-Powered Wireless Sensor Networks

Cited by 16 publications

References 24 publications

Energy harvesting‐enabled energy‐efficient routing using spotted hyena optimization in wireless sensor network

Energy harvesting‐enabled energy‐efficient routing using spotted hyena optimization in wireless sensor network

Two stage robust planning of park integrated energy system considering low carbon

KEFSAR: A Solar-Aware Routing Strategy For Rechargeable IoT Based On High-Accuracy Prediction

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