Towards Improved Lifespan for Wireless Sensor Networks: A Review of Energy Harvesting Technologies and Strategies

Ehlali, Souad; Sayah, Awatef

doi:10.24018/ejece.2022.6.1.396

Cited by 8 publications

(4 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As WSNs are manufactured using resource-limited components, the significance of lifetime and energy factors is inevitable. In order to improve the lifetime, energy optimization, and transmission coverage quality, Ehlali et al [25] and Pal et al [26] developed different types of coverage analysis models and lifespan improvement strategies for WSNs. However, most of the existing systems were not taking WUGSN characteristics and channel uncertainties under research constraints seriously; this is needed and this problem is estimated to be resolved.…”

Section: Related Workmentioning

confidence: 99%

Multi-Channel Assessment Policies for Energy-Efficient Data Transmission in Wireless Underground Sensor Networks

Rajasoundaran

Stanislaus

Ramasamy³

et al. 2023

Energies

View full text Add to dashboard Cite

Wireless Underground Sensor Networks (WUGSNs) transmit data collected from underground objects such as water substances, oil substances, soil contents, and others. In addition, the underground sensor nodes transmit the data to the surface nodes regarding underground irregularities, earthquake, landslides, military border surveillance, and other issues. The channel difficulties of WUGSNs create uncertain communication barriers. Recent research works have proposed different types of channel assessment techniques and security approaches. Moreover, the existing techniques are inadequate to learn the real-time channel attributes in order to build reactive data transmission models. The proposed system implements Deep Learning-based Multi-Channel Learning and Protection Model (DMCAP) using the optimal set of channel attribute classification techniques. The proposed model uses Multi-Channel Ensemble Model, Ensemble Multi-Layer Perceptron (EMLP) Classifiers, Nonlinear Channel Regression models and Nonlinear Entropy Analysis Model, and Ensemble Nonlinear Support Vector Machine (ENLSVM) for evaluating the channel conditions. Additionally, Variable Generative Adversarial Network (VGAN) engine makes the intrusion detection routines under distributed environment. According to the proposed principles, WUGSN channels are classified based on the characteristics such as underground acoustic channels, underground to surface channels and surface to ground station channels. On the classified channel behaviors, EMLP and ENLSVM are operated to extract the Signal to Noise Interference Ratio (SNIR) and channel entropy distortions of multiple channels. Furthermore, the nonlinear regression model was trained for understanding and predicting the link (channel behaviors). The proposed DMCAP has extreme difficulty finding the differences of impacts due to channel issues and malicious attacks. In this regard, the VGAN-Intrusion Detection System (VGAN-IDS) model was configured in the sensor nodes to monitor the channel instabilities against malicious nodes. Thus, the proposed system deeply analyzes multi-channel attribute qualities to improve throughput in uncertain WUGSN. The testbed was created for classified channel parameters (acoustic and air) with uncertain network parameters; the uncertainties of testbed are considered as link failures, noise distortions, interference, node failures, and number of retransmissions. Consequently, the experimental results show that DMCAP attains 10% to 15% of better performance than existing systems through better throughput, minimum retransmission rate, minimum delay, and minimum energy consumption rate. The existing techniques such as Support Vector Machine (SVM) and Random Forest (RF)-based Classification (SMC), Optimal Energy-Efficient Transmission (OETN), and channel-aware multi-path routing principles using Reinforcement Learning model (CRLR) are identified as suitable for the proposed experiments.

show abstract

Section: Related Workmentioning

confidence: 99%

Multi-Channel Assessment Policies for Energy-Efficient Data Transmission in Wireless Underground Sensor Networks

Rajasoundaran

Stanislaus

Ramasamy³

et al. 2023

Energies

View full text Add to dashboard Cite

show abstract

“…The energy source is an essential part of any energy harvesting architecture because it determines the total amount of energy used and the rate at which it can be used. Each potential energy source possesses unique characteristics, the magnitude of which can be predicted and controlled by us [39,40]. It is optional to make an accurate prediction of the amount of energy available before harvesting when using a controllable energy source to supply harvestable energy at any time required.…”

Section: Energy-harvesting Sourcesmentioning

confidence: 99%

Energy harvesting for devices in wireless sensor networks: A Review

Nguyen¹,

Nguyen²,

Tran³

et al. 2023

EAI Endorsed Trans IoT

View full text Add to dashboard Cite

Recent years have witnessed several technological breakthroughs in wireless sensor networks (WSN), yet energy continues to be an indispensable resource despite these advancements. The amount of energy that is available in a WSN has a direct bearing on how well it functions, how well it performs, and how long it will continue to operate. Because of the limitations imposed on them by cost and size, sensor nodes almost always come outfitted with a constrained amount of energy. As a direct consequence of this, their batteries will need to be replaced at regular intervals. However, the replacement is only sometimes a viable alternative; in fact, there are some situations in which it is unlikely to be achievable and entirely improbable. Because of this, there is an urgent need for more feasible solutions, which include energy harvesting or wireless energy transfer, as well as the creation of power at the sensor nodes themselves or their delivery of power to them. These options are among the options that are now available. This study intends to accomplish the following three primary goals: In the first step of this process, we will investigate prospective renewable energy resources and information on their qualities and uses in wireless sensor networks (WSN). Second, this study examines various methods for charging batteries and the various ways each of these methods might be applied to wireless sensor networks (WSN).

show abstract

“…This can be achieved by choosing very low power components to realize the electronic board and adopting a smart acquisition logic to maintain the sensor in a low-power condition (usually called "sleep" mode) when data gathering is not required. On the other hand, an inflow of energy must be guaranteed to recharge the sensor battery using an energy harvester [26]. Different types of harvesting sources are described in the literature, such as piezoelectric harvesters [27], micro wind turbines [28] and thermo-electric generators (TEG) [29].…”

Section: Introductionmentioning

confidence: 99%

Development and Field Validation of Wireless Sensors for Railway Bridge Modal Identification

et al. 2023

View full text Add to dashboard Cite

Bridges are strategic infrastructures which are subject to degradation during their lifetime. Therefore, structural health monitoring is becoming an essential tool in this field to drive maintenance activities. Conventional vibration monitoring systems relying on wired sensors present several limitations for continuous monitoring projects on a huge number of structures. In this work, a smart wireless monitoring system is developed for bridge modal identification with the aim of providing an alternative tool to wired sensors in this field. The main peculiarities of the designed wireless accelerometers are the low cost, the ease of installation on the structure, and the long-term autonomy granted by the use of energy harvesting techniques. To assess their measurement performance, some prototypes were installed for a field test on a railway bridge and significant data were acquired. Through the processing of the collected data, bridge main natural frequencies were estimated, and their values were in good agreement with the reference ones obtained with a conventional system. The assessment of the developed solution paves the way to the instrumentation of many bridges with the aim of performing continuous monitoring activities using simple diagnostic indicators, such as the variation of frequencies in time.

show abstract

Towards Improved Lifespan for Wireless Sensor Networks: A Review of Energy Harvesting Technologies and Strategies

Cited by 8 publications

References 22 publications

Multi-Channel Assessment Policies for Energy-Efficient Data Transmission in Wireless Underground Sensor Networks

Multi-Channel Assessment Policies for Energy-Efficient Data Transmission in Wireless Underground Sensor Networks

Energy harvesting for devices in wireless sensor networks: A Review

Development and Field Validation of Wireless Sensors for Railway Bridge Modal Identification

Contact Info

Product

Resources

About