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Journal articles on the topic 'Energy Harvesting Sensor Network'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Lee, Chao Yang, and Chu Sing Yang. "Perpetual Topology Control in Energy Harvesting Sensor Network." Applied Mechanics and Materials 556-562 (May 2014): 2487–91. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.2487.

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Harvesting ambient energy to power Wireless Sensor Networks (WSNs) is a promising approach. However, due to low recharging rates and the dynamics of renewable energy, energy harvesting sensors are unable to provide sufficient energy for sustained operation. This work designs a novel perpetual topology control that can enhance the energy efficiency and prolong network lifetime in energy harvesting sensor network. The proposed perpetual topology control (PTC) algorithm aims to ensure WSN sustainability and make the harvesting ambient energy usefully. Experimental results demonstrate the superior

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Liu, Fen, Wendong Xiao, Shuai Chen, and Chengpeng Jiang. "Adaptive Dynamic Programming-Based Multi-Sensor Scheduling for Collaborative Target Tracking in Energy Harvesting Wireless Sensor Networks." Sensors 18, no. 12 (2018): 4090. http://dx.doi.org/10.3390/s18124090.

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Collaborative target tracking is one of the most important applications of wireless sensor networks (WSNs), in which the network must rely on sensor scheduling to balance the tracking accuracy and energy consumption, due to the limited network resources for sensing, communication, and computation. With the recent development of energy acquisition technologies, the building of WSNs based on energy harvesting has become possible to overcome the limitation of battery energy in WSNs, where theoretically the lifetime of the network could be extended to infinite. However, energy-harvesting WSNs pose

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Sutapa, Sarkar Bhavani.V I.Hameem Shanavas V.Nallusamy. "ENERGY HARVESTING METHOD IN WIRELESS SENSOR NETWORK." International Journal of Education (IJE), Vol. 1, No. 1, December 2013 1, no. 1 (2019): 01–08. https://doi.org/10.5281/zenodo.3257076.

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With the advent of modern micro mechanical system technology and wireless communication wireless sensor networks are finding a lot of application in modern day life. The design of the sensor network depends on the specific application. This paper gives a description of the components of the wireless sensor nodes used. It also describes how the lifetime of a wireless sensor network can be increased by the use of energy harvesting sensor nodes.

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Getahun, Masresha, M. Azath, Durga Prasad Sharma, Amin Tuni, and Abel Adane. "Efficient Energy Utilization Algorithm through Energy Harvesting for Heterogeneous Clustered Wireless Sensor Network." Wireless Communications and Mobile Computing 2022 (April 4, 2022): 1–17. http://dx.doi.org/10.1155/2022/4154742.

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The usefulness of wireless sensor networks has fascinated the world’s attention. Usage of low-power microcontrollers and wireless sensors to handle real-world problems such as environmental, medicinal, and structural monitoring has exploded. Wireless sensor nodes are extremely tiny and are designed for low-duty applications such as recording physical characteristics. Wireless sensor network operations such as sensing, calculations, and communication take extensively more energy than these low-powered sensor nodes. They are used both in attainable and inaccessible areas and are usually powered

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Thabit, Ahmed A., Mahmoud Shuker Mahmoud, Ahmed Alkhayyat, and Qammer H. Abbasi. "Energy harvesting Internet of Things health-based paradigm: Towards outage probability reduction through inter–wireless body area network cooperation." International Journal of Distributed Sensor Networks 15, no. 10 (2019): 155014771987987. http://dx.doi.org/10.1177/1550147719879870.

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In today’s healthcare environment, the Internet of Things technology provides suitability among physicians and patients, as it is valuable in numerous medicinal fields. Wireless body sensor network technologies are essential technologies in the growth of Internet of Things healthcare paradigm, where every patient is monitored utilising small-powered and lightweight sensor nodes. A dual-hop, inter–wireless body sensor network cooperation and an incremental inter–wireless body sensor network cooperation with energy harvesting in the Internet of Things health-based paradigm have been investigated

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Yakine, Fadoua, and Adil Kenzi. "Energy Harvesting in wireless communication: A survey." E3S Web of Conferences 336 (2022): 00074. http://dx.doi.org/10.1051/e3sconf/202233600074.

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Wireless Sensor Network is an emerging technology that has the potential to be used in futuristic applications. Sensor nodes are energy-constrained. They rely on batteries with limited capacity which impact their lifetime or mobility. To address this problem, energy harvesting technology is a solution that aims to avoid the premature energy depletion of nodes. It recharges their batteries using an energy harvesting system from the environment. In this review work, we present the concept of energy harvesting technology (EH) and Energy-Harvesting for Wireless Sensor Network (EH-WSN). We then dis

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Fan, Zuzhi, and Xiaoli Liu. "Energy Synchronized Transmission Control for Energy-harvesting Sensor Networks." International Journal of Computers Communications & Control 11, no. 2 (2016): 194. http://dx.doi.org/10.15837/ijccc.2016.2.2049.

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Energy harvesting and recharging techniques have been regarded as a promising solution to ensure sustained operations of wireless sensor networks for longterm applications. To deal with the diversity of energy harvesting and constrained energy storage capability, sensor nodes in such applications usually work in a duty-cycled mode. Consequently, the sleep latency brought by duty-cycled operation is becoming the main challenge. In this work, we study the energy synchronization control problem for such sustainable sensor networks. Intuitively, energy-rich nodes can increase their transmission po

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Kaur, Pardeep, Preeti Singh, and Balwinder S. Sohi. "Traffic Models for Energy Harvesting Based Wireless Sensor Networks." Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) 13, no. 2 (2020): 219–26. http://dx.doi.org/10.2174/1872212113666190306145721.

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Background: Energy consumption is an important parameter in wireless sensor networks since it affects the lifetime of sensor nodes. Methods: Battery powered wireless sensor networks cannot sustain for long hence impractical for real-time applications. With energy harvesting and relevant protocols, this issue of extending the lifetime of nodes has been solved largely. The performance can be enhanced further if proper traffic analysis and modeling are done as a proactive approach. Results: A proper understanding of the traffic dynamics provides a base for further network optimization and detecti

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9

Zareei, Mahdi, Cesar Vargas-Rosales, Mohammad Hossein Anisi, et al. "Enhancing the Performance of Energy Harvesting Sensor Networks for Environmental Monitoring Applications." Energies 12, no. 14 (2019): 2794. http://dx.doi.org/10.3390/en12142794.

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Fast development in hardware miniaturization and massive production of sensors make them cost efficient and vastly available to be used in various applications in our daily life more specially in environment monitoring applications. However, energy consumption is still one of the barriers slowing down the development of several applications. Slow development in battery technology, makes energy harvesting (EH) as a prime candidate to eliminate the sensor’s energy barrier. EH sensors can be the solution to enabling future applications that would be extremely costly using conventional battery-pow

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Ijemaru, Gerald K., Kenneth Li-Minn Ang, and Jasmine KP Seng. "Wireless power transfer and energy harvesting in distributed sensor networks: Survey, opportunities, and challenges." International Journal of Distributed Sensor Networks 18, no. 3 (2022): 155014772110677. http://dx.doi.org/10.1177/15501477211067740.

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Distributed sensor networks have emerged as part of the advancements in sensing and wireless technologies and currently support several applications, including continuous environmental monitoring, surveillance, tracking, and so on which are running in wireless sensor network environments, and large-scale wireless sensor network multimedia applications that require large amounts of data transmission to an access point. However, these applications are often hampered because sensor nodes are energy-constrained, low-powered, with limited operational lifetime and low processing and limited power-st

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11

Gaudence, Tesha, and Eliphas Frank Tongora. "ENERGY HARVESTING AND RECHARGING FOR WIRELESS SENSOR NETWORKS." POLISH JOURNAL OF SCIENCE, no. 55 (October 19, 2022): 29–33. https://doi.org/10.5281/zenodo.7223717.

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Minimizing energy consumption for wireless sensor networks lifetime is one of the main problems in wireless sensor networks. The problem is long term autonomous operation of wireless sensor networks. This implies harvesting and recharging the sensor nodes during it is operation by employing the energy harvesting method using the sunlight available. The optimized solar energy harvesting and recharging wireless sensor networks operate for an infinite wireless sensor networks lifetime. The simulation results indicate the highest stability for energy harvesting and recharging the wireless sensor n

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Antonova, Hanna. "The Main Aspects of Wireless Sensor Nodes for Digital Agriculture." Cybernetics and Computer Technologies, no. 2 (June 9, 2024): 74–86. http://dx.doi.org/10.34229/2707-451x.24.2.8.

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Introduction. Wireless sensor networks are a part of information and communication technologies and the basis of the Internet of Things technologies. Data are collected, transmitted and processed in real time with the wireless sensor networks. The typical WSN consists of the large number wireless sensor nodes and the coordinator. The wireless network is based on wireless communication standards. Today, WSNs are used in the variety of industries such as medicine, military and digital agriculture. The purpose is to provide a comprehensive analysis of a wireless sensor node for use in digital agr

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Bassil, Chirine, Hussein EL GHOR, Jawad Khalife, and Nizar Hamadeh. "A New Clustering Routing Protocol for Homogeneous Wireless Sensor Networks Powered by Renewable Energy Sources." Scalable Computing: Practice and Experience 21, no. 4 (2020): 637–48. http://dx.doi.org/10.12694/scpe.v21i4.1787.

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The technology of wireless sensor networks (WSNs) is in constant development and it made great progress in many applications. One of the most popular problems in WSNs is the limited energy storage power at every sensor node. This paper aims to propose and develop a new distributed clustering algorithm for energy harvesting wireless sensor networks denoted by DEH-WSN (Energy Harvesting for Distributed Clustering Wireless Sensor Networks Protocol) that relies on matching between clustering and energy harvesting in a distributed topology. DEH-WSN uses initial and residual energy capacity of the n

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Kim, Sunyong, Chiwoo Cho, Kyung-Joon Park, and Hyuk Lim. "Increasing network lifetime using data compression in wireless sensor networks with energy harvesting." International Journal of Distributed Sensor Networks 13, no. 1 (2017): 155014771668968. http://dx.doi.org/10.1177/1550147716689682.

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In wireless sensor networks powered by battery-limited energy harvesting, sensor nodes that have relatively more energy can help other sensor nodes reduce their energy consumption by compressing the sensing data packets in order to consequently extend the network lifetime. In this article, we consider a data compression technique that can shorten the data packet itself to reduce the energies consumed for packet transmission and reception and to eventually increase the entire network lifetime. First, we present an energy consumption model, in which the energy consumption at each sensor node is

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da Rocha, Helbert, Paolo Caruso, João Pereira, Pedro Serra, and Antonio Espirito Santo. "Discussion on Secure Standard Network of Sensors Powered by Microbial Fuel Cells." Sensors 23, no. 19 (2023): 8227. http://dx.doi.org/10.3390/s23198227.

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Everyday tasks use sensors to monitor and provide information about processes in different scenarios, such as monitoring devices in manufacturing or homes. Sensors need to communicate, with or without wires, while providing secure information. Power can be derived from various energy sources, such as batteries, electrical power grids, and energy harvesting. Energy harvesting is a promising way to provide a sustainable and renewable source to power sensors by scavenging and converting energy from ambient energy sources. However, low energy is harvested through these methods. Therefore, it is be

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Yi, Jun Min, Min Jae Kang, and Dong Kun Noh. "Solar Energy Harvesting Wireless Sensor Network Simulator." Journal of the Korea Institute of Information and Communication Engineering 19, no. 2 (2015): 477–85. http://dx.doi.org/10.6109/jkiice.2015.19.2.477.

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Saleh, Mohammed Mehdi, Ruslan Saad Abdulrahman, and Aymen Jaber Salman. "Energy‑harvesting and energy aware routing algorithm for heterogeneous energy WSNs." Indonesian Journal of Electrical Engineering and Computer Science 24, no. 2 (2021): 910. http://dx.doi.org/10.11591/ijeecs.v24.i2.pp910-920.

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Wireless sensor networks are regarded as the most essential components of contemporary technologies since they are in charge of sensing and monitoring processes, which are the primary functions of these technologies. Because these nodes rely on an unchangeable battery and are randomly deployed in the environment, node energy management is the most essential issue to consider when designing algorithms to enhance the network's life. Clustering is a wireless sensor network (WSN) routing technique that has been implemented in order to extend network lifetime. Also, it is trendy to increase the ene

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18

Mohd Nabil Iqbal Ahmad, Aznida Abu Bakar Sajak, and Hassan Dao. "Green IoT Based on Tropical Weather: The Impact of Energy Harvesting in Wireless Sensor Network." Journal of Advanced Research in Applied Sciences and Engineering Technology 40, no. 1 (2024): 35–44. http://dx.doi.org/10.37934/araset.40.1.3544.

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Wireless Sensor Networks (WSNs) are a key component of Green IoT, as they play a critical role in many applications. However, a major challenge faced by WSNs is their limited energy capacity, which can impede their effectiveness. To address this issue, energy harvesting techniques are used to harness ambient energy and power the nodes, eliminating the need for frequent battery replacements or recharging. This study proposes a solar energy harvesting technique to prolong the lifespan of each wireless sensor node in a network. The aim of the research is to assess the impact of energy harvesting

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Saleh, Mohammed Mehdi, Ruslan Saad Abdulrahman, and Aymen Jaber Salman. "Energy‑harvesting and energy aware routing algorithm for heterogeneous energy WSNs." Indonesian Journal of Electrical Engineering and Computer Science 24, no. 2 (2021): 910–20. https://doi.org/10.11591/ijeecs.v24.i2.pp910-920.

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Wireless sensor networks are regarded as the most essential components of contemporary technologies since they are in charge of sensing and monitoring processes, which are the primary functions of these technologies. Because these nodes rely on an unchangeable battery and are randomly deployed in the environment, node energy management is the most essential issue to consider when designing algorithms to enhance the network's life. Clustering is a wireless sensor network (WSN) routing technique that has been implemented in order to extend network lifetime. Also, it is trendy to increase the

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20

Qu, Ming Zhe. "Research on the Applications and Characteristics of the Wireless Sensor Network." Applied Mechanics and Materials 538 (April 2014): 498–501. http://dx.doi.org/10.4028/www.scientific.net/amm.538.498.

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A wireless sensor network (WSN) consists of spatially distributed autonomous sensors to monitor physical or environmental conditions, such as temperature, sound, pressure, etc. and to cooperatively pass their data through the network to a main location. The more modern networks are bi-directional, also enabling control of sensor activity. The development of wireless sensor networks was motivated by military applications such as battlefield surveillance; today such networks are used in many industrial and consumer applications, such as industrial process monitoring and control, machine health m

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21

Rathore, Rajkumar Singh, Suman Sangwan, Kabita Adhikari, and Rupak Kharel. "Modified Echo State Network Enabled Dynamic Duty Cycle for Optimal Opportunistic Routing in EH-WSNs." Electronics 9, no. 1 (2020): 98. http://dx.doi.org/10.3390/electronics9010098.

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Minimizing energy consumption is one of the major challenges in wireless sensor networks (WSNs) due to the limited size of batteries and the resource constrained tiny sensor nodes. Energy harvesting in wireless sensor networks (EH-WSNs) is one of the promising solutions to minimize the energy consumption in wireless sensor networks for prolonging the overall network lifetime. However, static energy harvesting in individual sensor nodes is normally limited and unbalanced among the network nodes. In this context, this paper proposes a modified echo state network (MESN) based dynamic duty cycle w

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Han, Yu, Jian Su, Guangjun Wen, Yiran He, and Jian Li. "CPEH: A Clustering Protocol for the Energy Harvesting Wireless Sensor Networks." Wireless Communications and Mobile Computing 2021 (April 11, 2021): 1–14. http://dx.doi.org/10.1155/2021/5533374.

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In the last decade, energy harvesting wireless sensor network (EHWSN) has been well developed. By harvesting energy from the surrounding environment, sensors in EHWSN remove the energy constraint and have an unlimited lifetime in theory. The long-lasting character makes EHWSN suitable for Industry 4.0 applications that usually need sensors to monitor the machine state and detect errors continuously. Most wireless sensor network protocols have become inefficient in EHWSN due to neglecting the energy harvesting property. In this paper, we propose CPEH, which is a clustering protocol specially de

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Lei, Kuncheng, and Zhenrong Zhang. "System Performance Analysis of Sensor Networks for RF Energy Harvesting and Information Transmission." Future Internet 15, no. 5 (2023): 172. http://dx.doi.org/10.3390/fi15050172.

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This paper investigates the problem of RF energy harvesting in wireless sensor networks, with the aim of finding a suitable communication protocol by comparing the performance of the system under different protocols. The network is made up of two parts: first, at the beginning of each timeslot, the sensor nodes harvest energy from the base station (BS) and then send packets to the BS using the harvested energy. For the energy-harvesting part of the wireless sensor network, we consider two methods: point-to-point and multi-point-to-point energy harvesting. For each method, we use two independen

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Hussain, Md Naeem, Md Abdul Halim, Md Yakub Ali Khan, Salah Ibrahim, and Abrarul Haque. "A Comprehensive Review on Techniques and Challenges of Energy Harvesting from Distributed Renewable Energy Sources for Wireless Sensor Networks." Control Systems and Optimization Letters 2, no. 1 (2024): 15–22. http://dx.doi.org/10.59247/csol.v2i1.60.

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Wireless Sensor Networks (WSNs) have drawn a lot of interest from a variety of industries, such as industrial automation, healthcare, and environmental monitoring. Typically, these networks are made up of sensor nodes that run on batteries and depend on energy-efficient operation to extend their lifetime. Renewable and sustainable energies are suitable for wireless sensor networks. Energy harvesting from dispersed renewable sources, such as solar, wind, biomass, and vibration, has emerged as a possible approach to alleviate the limits associated with limited battery life. The state-of-the-art

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S., V. Saravanan. "Efficient and Energy Scheme for Wireless Rechargeable Sensor Network." Indonesian Journal of Electrical Engineering and Computer Science 9, no. 2 (2018): 265–66. https://doi.org/10.11591/ijeecs.v9.i2.pp265-266.

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The wireless rechargeable sensor network is attractive crucial and important in recent years for the advancement of wireless energy communication skill. The previous explore shown that not all of sensors can be recharged due to the limitation of power capacity to mobile chargers can carry. If a sensor playing a critical role in a sensing task cannot function as usual due to the exhausted energy, then the sensing task will be interrupted. Therefore, this paper proposes a novel recharging mechanism taking the priorities of sensors into consideration such that mobile chargers can recharge the sen

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Kim, Teasung, Joohan Park, Jeehyeong Kim, Jaewon Noh, and Sunghyun Cho. "REACH: An Efficient MAC Protocol for RF Energy Harvesting in Wireless Sensor Network." Wireless Communications and Mobile Computing 2017 (2017): 1–8. http://dx.doi.org/10.1155/2017/6438726.

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This paper proposes a MAC protocol for Radio Frequency (RF) energy harvesting in Wireless Sensor Networks (WSN). In the conventional RF energy harvesting methods, an Energy Transmitter (ET) operates in a passive manner. An ET transmits RF energy signals only when a sensor with depleted energy sends a Request-for-Energy (RFE) message. Unlike the conventional methods, an ET in the proposed scheme can actively send RF energy signals without RFE messages. An ET determines the active energy signal transmission according to the consequence of the passive energy harvesting procedures. To transmit RF

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Dvir, Efi, Mark Shifrin, and Omer Gurewitz. "Cooperative Multi-Agent Reinforcement Learning for Data Gathering in Energy-Harvesting Wireless Sensor Networks." Mathematics 12, no. 13 (2024): 2102. http://dx.doi.org/10.3390/math12132102.

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This study introduces a novel approach to data gathering in energy-harvesting wireless sensor networks (EH-WSNs) utilizing cooperative multi-agent reinforcement learning (MARL). In addressing the challenges of efficient data collection in resource-constrained WSNs, we propose and examine a decentralized, autonomous communication framework where sensors function as individual agents. These agents employ an extended version of the Q-learning algorithm, tailored for a multi-agent setting, enabling independent learning and adaptation of their data transmission strategies. We introduce therein a sp

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Grossi, Marco. "Energy Harvesting Strategies for Wireless Sensor Networks and Mobile Devices: A Review." Electronics 10, no. 6 (2021): 661. http://dx.doi.org/10.3390/electronics10060661.

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Wireless sensor network nodes and mobile devices are normally powered by batteries that, when depleted, must be recharged or replaced. This poses important problems, in particular for sensor nodes that are placed in inaccessible areas or biomedical sensors implanted in the human body where the battery replacement is very impractical. Moreover, the depleted battery must be properly disposed of in accordance with national and international regulations to prevent environmental pollution. A very interesting alternative to power mobile devices is energy harvesting where energy sources naturally pre

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Kumar V., Shiva, Rajashree V. Biradar, and V. C. Patil. "Design and Performance Analysis of Hybrid Energy Harvesting and WSN Application for More Life Time and High Throughput." International Journal of Circuits, Systems and Signal Processing 16 (January 17, 2022): 686–98. http://dx.doi.org/10.46300/9106.2022.16.85.

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the technology of wireless sensor-actuator networks (WSANs) is widely employed in the applications of IoT due to its wireless nature and it does not involve any wired structure. The wireless systems that are battery-driven can easily reconfigure the existing devices and sensors efficiently in the manufacturing units without employing any cable for power operation as well as for communication. The wireless sensor-actuator networks that are based on IEEE 802.15.4 consumes significantly less power. These networks are designed and built cost-effectively by considering the capacity of battery and e

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M. T. Javaid, S. Y. Siddiqui, S. M. Hassan, et al. "MODEL FOR SOLAR ENERGY HARVESTING AND OPTIMIZATION IN WIRELESS SENSOR NETWORKS." Pakistan Journal of Science 76, no. 03 (2024): 509–20. https://doi.org/10.57041/vol76iss03pp509-520.

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Presented in this thesis is the energy harvesting and management model concerning wireless sensor network. Wireless sensor network, bears resemblance as routers. However, wireless sensor network cannot afford large batteries because of their small size. Continues operation of these devices result in rapid consumption of available energy. It is difficult to replace and recharge the battery due to limited accessibility. Sometime these devices are installed in remote areas or areas with uneven terrain such as hilly areas. One approach to counter this problem is to harness the ambient energy which

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Kaur, Jaspreet, and Amit Kumar Bindal. "Resource Aware Hybrid Energy Harvesting for Wireless Sensor Networks." Journal of Computational and Theoretical Nanoscience 16, no. 10 (2019): 4117–24. http://dx.doi.org/10.1166/jctn.2019.8490.

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Sensors consume the resources to perform different operations, and energy of the nodes may be depleted due to excessive computational load; thus, may reduce the overall network lifespan as well as coverage area. Traditional energy harvesting schemes provides energy to the nodes in linear way but these schemes depend over a single source as well as these do not interact with the routing protocol. In this paper, a Hybrid Energy Harvester scheme for wireless sensor network is introduced which can utilize multiple energy sources for harvesting and also interact with the routing protocols to fulfil

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Yi, Jun Min, Min Jae Kang, and Dong Kun Noh. "SolarCastalia: Solar Energy Harvesting Wireless Sensor Network Simulator." International Journal of Distributed Sensor Networks 11, no. 6 (2015): 415174. http://dx.doi.org/10.1155/2015/415174.

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Shwe, Yee Win, and Yung C. Liang. "Smart dust sensor network with piezoelectric energy harvesting." International Journal of Intelligent Systems Technologies and Applications 9, no. 3/4 (2010): 253. http://dx.doi.org/10.1504/ijista.2010.036580.

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Bhat, Akshatha Hari, and Balachandra Achar H V. "E2BNAR: Energy Efficient Backup Node Assisted Routing for Wireless Sensor Networks." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 3s (2023): 193–204. http://dx.doi.org/10.17762/ijritcc.v11i3s.6181.

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In Wireless Sensor Networks (WSNs), each sensor node can only use so much power before recharging. If energy is depleted too quickly, nodes will fail one by one, bringing down the network as a whole. To this end, a design is needed to reduce the burden on the sensor nodes' power supplies while extending the network's useful life. This paper proposes a new approach, called Energy Efficient Backup Node Assisted Routing, to accomplish this (E2BNAR). Each primary node in the network has a group of backup nodes to ensure the network continues functioning. Assuming that the sensor nodes are capable

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Doublali, Asmaa, Abdlilah Jilbab, Chakib Bojji, and Rachida Idchabani. "Smart wall by wireless sensor network toward building energy optimization." E3S Web of Conferences 336 (2022): 00032. http://dx.doi.org/10.1051/e3sconf/202233600032.

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Buildings design and operation are responsible for occupant comfort. Buildings facades and walls can be engineered to control solar energy for photovoltaic electricity generation, daylighting, heating, ventilation, thermal insulation, and energy storage. Adaptive facades and intelligent walls integrate real-time control technologies to adapt to the occupant’s requirements and preferences. Data transmission and information control in the modern building are gaining increasing importance. Implementing wireless network systems based on wireless communication technologies and protocols with low en

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Wu, Hao, and Yong Chen. "Optimal Time Assignment Policy for Maximizing Throughput in Cognitive Sensor Network with Energy Harvesting." Sensors 18, no. 8 (2018): 2540. http://dx.doi.org/10.3390/s18082540.

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A cognitive sensor network with energy harvesting (EH-CSN) is a promising paradigm to address the issues both in spectrum efficiency and in energy efficiency. The cognitive sensors (CSs) equipped with energy harvesting devices are assumed to operate in a harvesting-sensing-transmission mode and permitted to access the idle licensed frequency bands without causing any harmful jamming to the primary user. By identifying the time fractions of harvesting, sensing, and transmission, we can discuss some design considerations for the EH-CSN. In the meantime, considering the possibility that the prima

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Haq, Inam Ul, Qaisar Javaid, Zahid Ullah, et al. "E2-MACH: Energy efficient multi-attribute based clustering scheme for energy harvesting wireless sensor networks." International Journal of Distributed Sensor Networks 16, no. 10 (2020): 155014772096804. http://dx.doi.org/10.1177/1550147720968047.

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Internet of things have emerged enough due to its applications in a wide range of fields such as governance, industry, healthcare, and smart environments (home, smart, cities, and so on). Internet of things–based networks connect smart devices ubiquitously. In such scenario, the role of wireless sensor networks becomes vital in order to enhance the ubiquity of the Internet of things devices with lower cost and easy deployment. The sensor nodes are limited in terms of energy storage, processing, and data storage capabilities, while their radio frequencies are very sensitive to noise and interfe

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Liu, Jian Hua, and Wei Qin Tong. "Rechargeable Sensor Energy Maintenance Scheme in the Internet of Things." Applied Mechanics and Materials 197 (September 2012): 649–55. http://dx.doi.org/10.4028/www.scientific.net/amm.197.649.

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Internet of things applications using RFID sensors are a challenging task due to the limited capacity of batteries. Thus, energy efficient updating and maintenance have become more critical design with RFID sensor network. This paper is dedicated to combine energy harvesting and maintenance, sensor network, and resource discovery to develop a rechargeable sensor energy maintenance scheme. To deal with key sensor nodes and low energy path maintenance, the proposed approach consists of the following:(1)key point energy maintenance for RFID sensor through multi-path similarity analysis;(2)path en

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Vignesh, S. R., and Rajeev Sukumaran. "Underwater energy harvesting model for agricultural applications using stochastic network calculus." International Journal of Electrical and Computer Engineering (IJECE) 15, no. 2 (2025): 2031. https://doi.org/10.11591/ijece.v15i2.pp2031-2041.

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Underwater wireless sensor network (UWSN) is a specialized type of wireless sensor network (WSN) designed for underwater communication among sensor nodes deployed in oceans for monitoring purposes such as observing marine life, detecting pollutants, and keeping track of oceanographic conditions. Managing limited energy in harsh underwater environments presents unique challenges compared to terrestrial networks. This research addresses this challenge by developing a reliable energy harvesting model. It analyzes the effects of delay and energy storage constraints on the energy harvesting rate (E

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Vignesh, S. R., and Rajeev Sukumaran. "Underwater energy harvesting model for agricultural applications using stochastic network calculus." International Journal of Electrical and Computer Engineering (IJECE) 15, no. 2 (2025): 2031–41. https://doi.org/10.11591/ijece.v15i2.pp2031-2041.

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Underwater wireless sensor network (UWSN) is a specialized type of wireless sensor network (WSN) designed for underwater communication among sensor nodes deployed in oceans for monitoring purposes such as observing marine life, detecting pollutants, and keeping track of oceanographic conditions. Managing limited energy in harsh underwater environments presents unique challenges compared to terrestrial networks. This research addresses this challenge by developing a reliable energy harvesting model. It analyzes the effects of delay and energy storage constraints on the energy harvesting rate (E

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Zhou, Pengzhan, Cong Wang, and Yuanyuan Yang. "Design of Self-sustainable Wireless Sensor Networks with Energy Harvesting and Wireless Charging." ACM Transactions on Sensor Networks 17, no. 4 (2021): 1–38. http://dx.doi.org/10.1145/3459081.

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Energy provisioning plays a key role in the sustainable operations of Wireless Sensor Networks (WSNs). Recent efforts deploy multi-source energy harvesting sensors to utilize ambient energy. Meanwhile, wireless charging is a reliable energy source not affected by spatial-temporal ambient dynamics. This article integrates multiple energy provisioning strategies and adaptive adjustment to accomplish self-sustainability under complex weather conditions. We design and optimize a three-tier framework with the first two tiers focusing on the planning problems of sensors with various types and distri

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You, Lei, Xin Su, and Yu Tong Han. "Dynamic Resource Allocation for Compressive-Sensing-Based Wireless Visual Sensor Networks with Energy Harvesting." Applied Mechanics and Materials 303-306 (February 2013): 187–90. http://dx.doi.org/10.4028/www.scientific.net/amm.303-306.187.

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Wireless visual sensor network (WVSN) is emerging with many potential applications. The lifetime of a WVSN is seriously dependent on the energy shored in the battery of its sensor nodes as well as the adopted compression and resource allocation scheme. In this paper, we use the energy harvesting to provide almost perpetual operation of the networks and compressed-sensing-based encoding to decrease the power consumption of acquiring visual information at the front-end sensors. We propose a dynamic algorithm to jointly allocate power for both compressive-sensing-based visual information acquisit

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Jiao, Dongbin, Liangjun Ke, Shengbo Liu, and Felix Chan. "Optimal Energy-Delay in Energy Harvesting Wireless Sensor Networks with Interference Channels." Sensors 19, no. 4 (2019): 785. http://dx.doi.org/10.3390/s19040785.

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In this work, we investigate the capacity allocation problem in the energy harvesting wireless sensor networks (WSNs) with interference channels. For the fixed topologies of data and energy, we formulate the optimization problem when the data flow remains constant on all data links and each sensor node harvests energy only once in a time slot. We focus on the optimal data rates, power allocations and energy transfers between sensor nodes in a time slot. Our goal is to minimize the total delay in the network under two scenarios, i.e., no energy transfer and energy transfer. Furthermore, since t

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Manikandan, A. "Deep Learning Based Energy Efficiency in Wireless Sensor Network." Journal of Artificial Intelligence, Machine Learning and Neural Network, no. 11 (September 30, 2021): 50–57. http://dx.doi.org/10.55529/jaimlnn.11.50.57.

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Wireless Sensor Network (WSN) comprise of huge amount of sensor nodes. These nodes sense the data from their surroundings and pass this information to the sink node using cluster head. Due to the emergence of new technology, it is widely used in distinct applications such as habitat monitoring, health science, border surveillance etc. There are several issues in WSN such as Quality of Service (QoS), localization, routing and data aggregation. Sensor nodes have limited energy, so there is a need to enhance the energy efficiency across the network. This paper focuses on two mechanisms of energy

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Falah Abbood, Mohammed, Mohammed Falih Kadhim, and Ahmed Raheem Kadhim. "Improving multimedia data transmission quality in wireless multimedia sensor networks though priority-based data collection." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 4 (2022): 3595. http://dx.doi.org/10.11591/ijece.v12i4.pp3595-3606.

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<span>Wireless multimedia sensor networks (WMSNs) are special kinds of wireless sensor networks (WSN) that can send multimedia data such as audio and video streams. Sensors used in WMSNs are smart, tiny, and resource constraint sensor nodes (SNs) distributed in a large area. Typically, multimedia data are large in comparison to other data types. As a result, WMSNs have to deal with high volumes of packet transmission, leading to a high rate of packet loss and network congestion. Network congestion can significantly affect the quality of service and usually lead to high energy consumption

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Mohammed, Falah Abbood, Falih Kadhim Mohammed, and Raheem Kadhim Ahmed. "Improving multimedia data transmission quality in wireless multimedia sensor networks though priority-based data collection." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 4 (2022): 3595–606. https://doi.org/10.11591/ijece.v12i4.pp3595-3606.

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Wireless multimedia sensor networks (WMSNs) are special kinds of wireless sensor networks (WSN) that can send multimedia data such as audio and video streams. Sensors used in WMSNs are smart, tiny, and resource constraint sensor nodes (SNs) distributed in a large area. Typically, multimedia data are large in comparison to other data types. As a result, WMSNs have to deal with high volumes of packet transmission, leading to a high rate of packet loss and network congestion. Network congestion can significantly affect the quality of service and usually lead to high energy consumption. Thus, to i

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Xu, Xiang Nan, Ming Bo Xiao, and Wei Yan. "Clustering Routing Algorithm for Heterogeneous WSN with Energy Harvesting." Applied Mechanics and Materials 733 (February 2015): 734–39. http://dx.doi.org/10.4028/www.scientific.net/amm.733.734.

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Focus on the character of energy harvesting sensor network in heterogeneous sensor network and some shortage in SEP algorithm, an improved algorithm for EH-SEP is been proposed. EH-SEP considers both residual energy and energy support of nodes in cluster-head election process .Improved algorithm achieves higher probability that the advanced nodes with high residual energy to be cluster-head, and lower probability that the traditional nodes with low residual energy to be cluster-head. During the state of data sensing, this paper adopted multiple hop data transmission to avoid long distance comm

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Isarakorn, Don, Thapanun Sudhawiyangkul, and Songmoung Nundrakwang. "Energy Analysis in Zigbee Based Wireless Sensor Node Powered by Piezoelectric Energy Harvester." Advanced Materials Research 931-932 (May 2014): 1328–32. http://dx.doi.org/10.4028/www.scientific.net/amr.931-932.1328.

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Recently, an integration of wireless sensor network with energy harvesters is getting more interested because it can extend the life time of battery in a sensor node, which is very important in many applications. This paper presents the concept of energy analysis in Zigbee based wireless sensor network, which is powered by a piezoelectric energy harvester to optimize the algorithms used in wireless sensor network. In this work, difference aspects related to piezoelectric energy harvester, characteristic of power consumption in Zigbee based wireless sensor node and concept of optimizing algorit

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Al Kabi, Amin. "Enhancement of Energy Harvesting Efficiency in Mobile Wireless Sensor Networks." WSEAS TRANSACTIONS ON COMMUNICATIONS 21 (May 19, 2022): 129–34. http://dx.doi.org/10.37394/23204.2022.21.18.

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Mobile wireless sensor networks suffer from the restricted availability of energy supplies. In this research work, a proposed method for extending the lifetime of energy-constrained mobile wireless sensor networks (MWSNs) is presented. This method is based on the fact that RF signal carries both information and energy at the same time. Hence, by increasing the efficiency of energy harvesting from radio frequency (RF) signals, the lifetime of the wireless network can be significantly extended. The Simultaneous Wireless Information and Power Transfer (SWIPT) technique enables harvesting of energ

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Hammad, Ahmed, M. A. Mohamed, and Heba M. Abdel-Atty. "Enhancement of the performance of wireless sensor networks using the multihop multiantenna power beacon path selection method in intelligent structures." PLOS ONE 17, no. 11 (2022): e0276940. http://dx.doi.org/10.1371/journal.pone.0276940.

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Sensor nodes are the building blocks of wireless sensor networks (WSNs), which may gather, analyze, and transmit various types of information to a certain destination. Data collection and transmission to the destination are the main responsibilities of sensor nodes at specified time intervals. However, one of the biggest issues with WSNs is the creation of energy-efficient wireless network algorithms. In this paper, a multi-hop multi-antenna power beacon path selection (MMPS) protocol is proposed. The proposed approach consists of a source, a destination, relays, power beacons generating radio

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