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Journal articles on the topic 'Energy network'

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Author: Grafiati

Published: 14 March 2023

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1

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 (November 1, 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 energy levels of the node battery by utilizing various energy harvesting techniques in order to extend the network lifetime. In this paper, a new energy-aware clustering algorithm (EHEARA) has been proposed. The proposed algorithm is based on a dynamic clustering function and adopts a solar energy harvesting scheme in order to improve network lifetime. Furthermore, the active-sleep mechanism was used to distribute node activity and balance communication among nodes within clusters and cluster heads with the base station. The proposed algorithm is simulated using matrix laboratory (MATLAB), and the results show that it outperforms the low energy adaptive clustering hierarchy (LEACH), distributed energy efficient clustering (DEEC), and stable election protocol (SEP) algorithms in terms of network lifetime, energy consumption, and network throughput.

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2

Chib, Manvi, and Tamanna Pathania. "The Review of Energy Efficient Network." International Journal of Trend in Scientific Research and Development Volume-3, Issue-4 (June 30, 2019): 1006–8. http://dx.doi.org/10.31142/ijtsrd24036.

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Abasaheb, Jundale Poonam, and Patil Yogita Dattatraya. "Energy Efficient Compression for Wireless Sensor Network." International Journal of Trend in Scientific Research and Development Volume-2, Issue-5 (August 31, 2018): 1820–24. http://dx.doi.org/10.31142/ijtsrd18171.

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4

Teh, Jiashen, Ching-Ming Lai, and Yu-Huei Cheng. "Composite reliability evaluation for transmission network planning." AIMS Energy 6, no. 1 (2018): 170–86. http://dx.doi.org/10.3934/energy.2018.1.170.

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Conti, Stefania, Santi A. Rizzo, Nunzio Salerno, and Giuseppe M. Tina. "Distribution network topology identification based on synchrophasor." AIMS Energy 6, no. 2 (2018): 245–60. http://dx.doi.org/10.3934/energy.2018.2.245.

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Vasudev, Arpitha, A. M. Sowmya, and G. Manjula. "Applying Intermittent Energy Distribution for Evading Energy Holes in Wireless Sensor Network." Bonfring International Journal of Software Engineering and Soft Computing 6, Special Issue (October 31, 2016): 217–19. http://dx.doi.org/10.9756/bijsesc.8281.

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7

Ma, De Xin, Jian Ma, Peng Min Xu, Cai Xia Song, and Ying Pang. "Solar-Powered Wireless Sensor Network’s Energy Gathering Technology." Applied Mechanics and Materials 477-478 (December 2013): 396–99. http://dx.doi.org/10.4028/www.scientific.net/amm.477-478.396.

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We analyze the solar-powered wireless sensor network's energy gathering techniques, aiming to prolong the lifetime of wireless sensor network. We summarize wireless sensor network node's energy autonomy system, its characteristics in detail and new technology adopts, provides some suggestions and new ideas in the design and research of solar-powered wireless sensor networks.

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Lam, Albert Y. S., Ka-Cheong Leung, and Victor O. K. Li. "Vehicular Energy Network." IEEE Transactions on Transportation Electrification 3, no. 2 (June 2017): 392–404. http://dx.doi.org/10.1109/tte.2017.2649887.

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Gelenbe, Erol, and Omer H. Abdelrahman. "An Energy Packet Network model for mobile networks with energy harvesting." Nonlinear Theory and Its Applications, IEICE 9, no. 3 (2018): 322–36. http://dx.doi.org/10.1587/nolta.9.322.

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Elgibaly, Ahmed A., Mohamed Ghareeb, Said Kamel, and Mohamed El-Sayed El-Bassiouny. "Prediction of gas-lift performance using neural network analysis." AIMS Energy 9, no. 2 (2021): 355–78. http://dx.doi.org/10.3934/energy.2021019.

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11

Nonde, Leonard, Taisir E. H. El-Gorashi, and Jaafar M. H. Elmirghani. "Energy Efficient Virtual Network Embedding for Cloud Networks." Journal of Lightwave Technology 33, no. 9 (May 1, 2015): 1828–49. http://dx.doi.org/10.1109/jlt.2014.2380777.

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12

Liu, Xu, Zhongbao Zhang, Junning Li, and Sen Su. "Clustering-based energy-aware virtual network embedding." International Journal of Distributed Sensor Networks 13, no. 8 (August 2017): 155014771772671. http://dx.doi.org/10.1177/1550147717726714.

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Virtual network embedding has received a lot of attention from researchers. In this problem, it needs to map a sequence of virtual networks onto the physical network. Generally, the virtual networks have topology, node, and link constraints. Prior studies mainly focus on designing a solution to maximize the revenue by accepting more virtual networks while ignoring the energy cost for the physical network. In this article, to bridge this gap, we design a heuristic energy-aware virtual network embedding algorithm called EA-VNE-C, to coordinate the dynamic electricity price and energy consumption to further optimize the energy cost. Extensive simulations demonstrate that this algorithm significantly reduces the energy cost by up to 14% over the state-of-the-art algorithm while maintaining similar revenue.

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13

XUE, Yusheng. "Energy internet or comprehensive energy network?" Journal of Modern Power Systems and Clean Energy 3, no. 3 (March 24, 2015): 297–301. http://dx.doi.org/10.1007/s40565-015-0111-5.

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14

Ray, Shweta. "Intelligent-Driven Green Resource Allocation for Industrial Internet of Things In 5g Heterogeneous Network." International Journal for Research in Applied Science and Engineering Technology 10, no. 7 (July 31, 2022): 55–67. http://dx.doi.org/10.22214/ijraset.2022.45161.

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Abstract: Mobile networks' energy consumption is rising in tandem with the volume of traffic and the number of people utilising mobile technology. To maintain the long-term survival of the next generation of mobile networks, there must be an emphasis on energy efficiency. By minimising the network's power consumption and proposing an energy-efficient network architecture, this thesis addresses the issue of increasing 5G and beyond network efficiency. The first component of this thesis focuses on base stations (BSs), the most energy-intensive part of mobile networks. Mobile network providers offer us with a data set that contains information on the amount of traffic on their system. The poor temporal granularity of mobile network traffic data makes it difficult to train ML systems for sleep mode management choices. Bursty arrivals are taken into consideration while generating mobile network traffic statistics.

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Nesterenko, Sergiy, and Julia Nesterenko. "Costs evaluation methodic of energy efficient computer network reengineering." Odes’kyi Politechnichnyi Universytet. Pratsi, no. 2 (August 20, 2016): 41–45. http://dx.doi.org/10.15276/opu.2.49.2016.10.a.

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Rizvi, S. S. H., M. Z. Ahmad, J. Ahmed, and A. Karim. "Active Node Based Sensor Wireless Network for Energy Management." International Journal of Engineering and Technology 4, no. 5 (2012): 590–93. http://dx.doi.org/10.7763/ijet.2012.v4.439.

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17

Xiao, Wen Hong, and Xiang Dong Cai. "A Novel Wireless Sensor Network Model Based on Complex Network Theory." Advanced Materials Research 546-547 (July 2012): 1276–82. http://dx.doi.org/10.4028/www.scientific.net/amr.546-547.1276.

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The key issue of wireless sensor networks is to balance the energy costs of the entire network, to enhance the robustness of the entire sensor network. Sensor networks as a special kind of complex network, in particular, environmental constraints, and more from the traditional complex networks, such as Internet networks, ecological networks, social networks, is to introduce a way of wireless sensor networks way of complex networks theory and analytical method, the key lies in, which is a successful model of complex network theory and analysis methods, more suitable for the application of wireless sensor networks, in order to achieve certain characteristics of some wireless sensor networks to optimize the network. Considering multi-hop transmission of sensor network, this paper has proposed a maximum restriction on the communication radius of each sensor node; in order to improve the efficiency of energy consumption and maintain the sparsely of the entire network, this paper has also added a minimum restriction on the communication radius of each sensor node to the improved model; to balance the energy consumption of the entire network, The simulation results show that proposed improvements to the entire network more robust to random failure and energy costs are more balanced and reasonable. This is more applicable to wireless sensor networks.

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Manasa, P., K. Shaila, and K. R. Venugopal. "TCHCN: Trust Computation for Hierarchical Clustering Network to Improve Network Lifetime in Wireless Sensor Network." Journal of Computational and Theoretical Nanoscience 17, no. 12 (December 1, 2020): 5509–15. http://dx.doi.org/10.1166/jctn.2020.9446.

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Network Lifetime of sensor node plays a fundamental role in Wireless Sensor Networks, since they drain out energy during the communication. The hierarchical networks assist in hindering energy loss by exchanging the information at various levels. The Hierarchical levels formed avoid overburdening of single level cluster zones and helps the network to retain energy of cluster level heads. Malicious attack disturbs the regular functionality of a routing algorithm and deviates QoS performance in the network. The existing LEACH-Mobile protocol adopts low energy clustering techniques but, falls short in providing security of the network when there is malicious attack. The proposed Trust Computation for Hierarchical Clustering Network protocol proves itself to be trustworthy by having various trust measuring metrics. It enhances the lifetime by adopting low energy hierarchical protocol and shows improvement of 12% in terms of energy consumption.

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Poullikkas, Andreas, Savvas Papadouris, George Kourtis, and Ioannis Hadjipaschalis. "Storage Solutions for Power Quality Problems in Cyprus Electricity Distribution Network." AIMS Energy 2, no. 1 (2014): 1–17. http://dx.doi.org/10.3934/energy.2014.1.1.

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20

Fan, Zuzhi, and Xiaoli Liu. "Energy Synchronized Transmission Control for Energy-harvesting Sensor Networks." International Journal of Computers Communications & Control 11, no. 2 (January 26, 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 power in order to improve network performance, while energy-poor nodes can lower transmission power to conserve its precious energy resource. In particular, we propose an energy synchronized transmission control scheme (ESTC) by which each node adaptively selects suitable power levels and data forwarders according to its available energy and traffic load. Based on the large-scale simulations, we validate that our design can improve system performance under different network settings comparing with common uniform transmission power control strategy. Specially, ESTC can enable the perpetual operations of nodes without sacrificing the network lifetime.

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21

Muhammad Ridzuan, Mohd, and Sasa Djokic. "Energy Regulator Supply Restoration Time." Energies 12, no. 6 (March 19, 2019): 1051. http://dx.doi.org/10.3390/en12061051.

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In conventional reliability analysis, the duration of interruptions relied on the input parameter of mean time to repair (MTTR) values in the network components. For certain criteria without network automation, reconfiguration functionalities and/or energy regulator requirements to protect customers from long excessive duration of interruptions, the use of MTTR input seems reasonable. Since modern distribution networks are shifting towards smart grid, some factors must be considered in the reliability assessment process. For networks that apply reconfiguration functionalities and/or network automation, the duration of interruptions experienced by a customer due to faulty network components should be addressed with an automation switch or manual action time that does not exceed the regulator supply restoration time. Hence, this paper introduces a comprehensive methodology of substituting MTTR with maximum action time required to replace/repair a network component and to restore customer duration of interruption with maximum network reconfiguration time based on energy regulator supply requirements. The Monte Carlo simulation (MCS) technique was applied to medium voltage (MV) suburban networks to estimate system-related reliability indices. In this analysis, the purposed method substitutes all MTTR values with time to supply (TTS), which correspond with the UK Guaranteed Standard of Performance (GSP-UK), by the condition of the MTTR value being higher than TTS value. It is nearly impossible for all components to have a quick repairing time, only components on the main feeder were selected for time substitution. Various scenarios were analysed, and the outcomes reflected the applicability of reconfiguration and the replace/repair time of network component. Theoretically, the network reconfiguration (option 1) and component replacement (option 2) with the same amount of repair time should produce exactly the same outputs. However, in simulation, these two options yield different outputs in terms of number and duration of interruptions. Each scenario has its advantages and disadvantages, in which the distribution network operators (DNOs) were selected based on their operating conditions and requirements. The regulator reliability-based network operation is more applicable than power loss-based network operation in counties that employed energy regulator requirements (e.g., GSP-UK) or areas with many factories that required a reliable continuous supply.

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22

Ahmed, Aseel K., and Abbas Akram Khorsheed. "Open network structure and smart network to sharing cybersecurity within the 5G network." Indonesian Journal of Electrical Engineering and Computer Science 27, no. 1 (July 1, 2022): 573. http://dx.doi.org/10.11591/ijeecs.v27.i1.pp573-582.

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The <span>next-generation communication system incorporates information technology (IT) and operations technology (OT) for generating, delivering, and collecting, and obtaining communication power. We plan to include a brief outline of internet of thing (IoT) communication and its context, along with security concerns that arise for IoT data on the network and some methods for detecting and avoiding cyber security threats. With the rise of the 5G networks, we introduce the smart network's emergent technology and its opportunities and more cybersecurity issues. Whereas, finding or responding to a power outage is an essential part of system security That is why we will discuss the innumerable advantages of 5G networks and we must also cover the inevitable problems that we will encounter in power delivery. The use of smart IoT communication technologies is becoming more common in the energy sector, particularly with the network (5G. The smart network and energy flow integration Real-time data on generation, electricity distribution, and energy consumption is measured using computers and cutting-edge technologies. This information aids utility companies in managing electricity supply and demand, as well as price. While enhanced communication and information technologies are unquestionably crucial to </span>the smart network.

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23

Hwang, Kwang-Il. "Survey on Network Protocols for Energy Network Infrastructure based on Smart Utility Networks." KIPS Transactions on Computer and Communication Systems 1, no. 3 (December 31, 2012): 119–24. http://dx.doi.org/10.3745/ktccs.2012.1.3.119.

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van der Linden, Ruud, Ryvo Octaviano, Huib Blokland, and Tom Busking. "Security of Supply in Gas and Hybrid Energy Networks." Energies 14, no. 4 (February 3, 2021): 792. http://dx.doi.org/10.3390/en14040792.

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Reliable energy supply becomes increasingly complex in hybrid energy networks, due to increasing amounts of renewable electricity and more dynamic demand. Accurate modeling of integrated electricity and gas distribution networks is required to quantify operational bottlenecks in these networks and to increase security of supply. In this paper, we propose a hybrid network solver to model integrated electricity and gas distribution networks. A stochastic method is proposed to calculate the security of supply throughout the networks, taking into account the likelihood of events, operational constraints and dynamic supply and demand. The stochastic method is evaluated on a real gas network case study. The calculated security of supply parameters provide insight into the most critical parts of the network and can be used for future network planning. The capabilities of the coupled hybrid energy network simulation are demonstrated on the real gas network coupled to a simplified electricity network. Results demonstrate how combined simulation of electricity and gas networks facilitate the control design and performance evaluation of regional hybrid energy networks.

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25

Shih, Mau-Hsiang, and Feng-Sheng Tsai. "Neural Network Dynamics without Minimizing Energy." Abstract and Applied Analysis 2013 (2013): 1–4. http://dx.doi.org/10.1155/2013/496217.

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Content-addressable memory (CAM) has been described by collective dynamics of neural networks and computing with attractors (equilibrium states). Studies of such neural network systems are typically based on the aspect of energy minimization. However, when the complexity and the dimension of neural network systems go up, the use of energy functions might have its own limitations to study CAM. Recently, we have proposed the decirculation process in neural network dynamics, suggesting a step toward the reshaping of network structure and the control of neural dynamics without minimizing energy. Armed with the decirculation process, a sort of decirculating maps and its structural properties are built here, dedicated to showing that circulation breaking taking place in the connections among many assemblies of neurons can collaborate harmoniously toward the completion of network structure that generates CAM.

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Avinash Jethi, Preeti Sharma and Rakesh Kuma. "Analysis of Energy Efficient Techniques of WSN." International Journal for Modern Trends in Science and Technology 7, no. 07 (February 20, 2022): 58–62. http://dx.doi.org/10.46501/ijmtst050239.

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The wireless sensor networks is the decentralized type of network in which sensor devices sense information and pass it to base station. Due to dynamic topology of the network security, data aggregation and energy consumption are the major issue which affects network performance. In the past years many techniques are designed to improve lifetime of lifetime of sensor networks. In this review paper, energy efficient techniques of wireless sensor networks are reviewed in terms of certain parameters

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27

Abad, Mohammad Seydali Seyf, Jennifer A. Hayward, Saad Sayeef, Peter Osman, and Jin Ma. "Tidal Energy Hosting Capacity in Australia’s Future Energy Mix." Energies 14, no. 5 (March 8, 2021): 1479. http://dx.doi.org/10.3390/en14051479.

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This paper outlines a methodology to determine the amount of renewable energy that can be accommodated in a power system before adverse impacts such as over-voltage, over-loading and system instability occur. This value is commonly known as hosting capacity. This paper identifies when the transmission network local hosting capacity might be limited because of static and dynamic network limits. Thus, the proposed methodology can effectively be used in assessing new interconnection requests and provides an estimation of how much and where the new renewable generation can be located such that network upgrades are minimized. The proposed approach was developed as one of the components of the AUSTEn project, which was a three-year project to map Australia’s tidal energy resource in detail and to assess its economic feasibility and ability to contribute to the country’s energy needs. In order to demonstrate the effectiveness of the proposed approach, two wide area networks were developed in DIgSILENT PowerFactory based on actual Australian network data near two promising tidal resource sites. Then, the proposed approach was used to assess the local tidal hosting capacity. In addition, a complementary local hosting capacity analysis is provided to show the importance of future network upgrades on the locational hosting capaity.

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Hoglund, Anette, Johan Pettersson, Helene Hallberg, Lars Humla, and Erik Sanders. "Energy-optimized network modernization." Ericsson Technology Review 2022, no. 6 (June 2022): 2–11. http://dx.doi.org/10.23919/etr.2022.9904677.

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29

Yonghua, Luo, Wu Jiawei, and Qiu Shangming. "Energy Aware Wireless Sensor Network Low Energy Consumption Full Network Coverage Algorithm." International Journal of Grid and Distributed Computing 9, no. 7 (July 31, 2016): 73–82. http://dx.doi.org/10.14257/ijgdc.2016.9.7.08.

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30

Chen, Tao, Yang Yang, Honggang Zhang, Haesik Kim, and Kari Horneman. "Network energy saving technologies for green wireless access networks." IEEE Wireless Communications 18, no. 5 (October 2011): 30–38. http://dx.doi.org/10.1109/mwc.2011.6056690.

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31

Lin, Rongping, Shan Luo, Haoran Wang, and Sheng Wang. "Energy-aware virtual network embedding in flexi-grid networks." Optics Express 25, no. 24 (November 14, 2017): 29699. http://dx.doi.org/10.1364/oe.25.029699.

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32

Song, Yujae, Wooyeol Choi, and Seungjae Baek. "Network switching strategy for energy conservation in heterogeneous networks." PLOS ONE 12, no. 2 (February 27, 2017): e0172318. http://dx.doi.org/10.1371/journal.pone.0172318.

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33

Agarwal, Madan Mohan, Mahesh Chandra Govil, Madhavi Sinha, and Saurabh Gupta. "Fuzzy based Data Fusion for Energy Efficient Internet of Things." International Journal of Grid and High Performance Computing 11, no. 3 (July 2019): 46–58. http://dx.doi.org/10.4018/ijghpc.2019070103.

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Internet of Things will serve communities across the different domains of life. The resource of embedded devices and objects working under IoT implementation are constrained in wireless networks. Thus, building a scheme to make full use of energy is key issue for such networks. To achieve energy efficiency, an effective Fuzzy-based network data Fusion Light Weight Protocol (FLWP) is proposed in this article. The innovations of FLWP are as follows: 1) the simulated network's data fusion through fuzzy controller and optimize the energy efficiency of smart tech layer of internet of things (Energy IoT); 2) The optimized reactive route is dynamically adjusted based on fuzzy based prediction accurately from the number of routes provided by base protocol. If the selection accuracy is high, the performance enhances the network quality; 3) FLWP takes full advantage of energy to further enhance target tracking performance by properly selecting reactive routes in the network. Authors evaluated the efficiency of FLWP with simulation-based experiments. FLWP scheme improves the energy efficiency.

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34

Liu, Wen Lai. "Design of Energy-Saving Control Software Algorithms in Large Industrial Network." Applied Mechanics and Materials 644-650 (September 2014): 828–31. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.828.

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In the operation process of large industrial control network, with conventional fuzzy PID control algorithm for industrial control networks energy-saving control, excessive industrial networks will aggravate machine wear of the single network, thereby reduce the effect of energy-saving for industrial network. This paper presents an approach for industrial network energy-saving control based on non-uniform data production rate. According to the relationship between the network load and loss, the loss model of industrial control network can be established. Adaptive linear genetic method is utilized to control industrial control network energy-saving load, so as to achieve energy-saving control of industrial control network. Experimental results show that the algorithm can effectively improve the energy-saving efficiency of industrial control network, and achieve satisfactory results.

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Dou, Zhenlan, Chunyan Zhang, Renjie Dai, Siming Wei, Jihang Zhang, Lingling Wang, and Chuanwen Jiang. "Resilience enhancement strategy of multi-energy coupling distribution network considering movable energy storage equipment." Journal of Physics: Conference Series 2418, no. 1 (February 1, 2023): 012070. http://dx.doi.org/10.1088/1742-6596/2418/1/012070.

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Abstract With the deepening of energy transformation, multi-energy coupling has become an effective method to alleviate the energy gap and improve the energy utilization rate. As a bridge connecting users and the power system, the distribution network is becoming multi-energy coupling as well. Therefore, to ensure a stable supply of power, heat, and other energy sources, it is of great significance to study the resilience improvement of multi-energy coupling distribution networks. This paper proposes a strategy for improving the resilience of multi-energy coupling distribution networks considering movable energy storage equipment (MESS) configuration. Firstly, the multi-energy coupling distribution network model is established, and the mathematical model of each piece of equipment is established considering the coupling of the power grid and heat grid. Secondly, based on the loss of load probability (LOLP), considering the importance of the load, it is expanded to the loss of important load probability as an index to evaluate the resilience of distribution networks. Thirdly, a distribution network resilience improvement model based on the configuration and operation of the MESS is established, and the optimal configuration capacity and operation strategy of the MESS is solved by taking the minimum operation cost as the objective function, i.e., the configuration cost of the MESS, the load shedding cost and the cost of wind and PV abandonment. Finally, based on the IEEE-33 node distribution network, a multi-energy coupling distribution network model is built, which verifies that the method proposed in this paper can effectively reduce the total cost and improve the resilience of the multi-energy coupling distribution network.

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36

Gabbar, Hossam A., and Ahmed S. Eldessouky. "Energy Semantic Network for Building Energy Management." Intelligent Industrial Systems 1, no. 3 (September 2, 2015): 213–31. http://dx.doi.org/10.1007/s40903-015-0023-8.

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37

Zhao, Zhidong, Kaida Xu, Guohua Hui, and Liqin Hu. "An Energy-Efficient Clustering Routing Protocol for Wireless Sensor Networks Based on AGNES with Balanced Energy Consumption Optimization." Sensors 18, no. 11 (November 14, 2018): 3938. http://dx.doi.org/10.3390/s18113938.

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To further prolong the lifetime of wireless sensor network (WSN), researchers from various countries have proposed many clustering routing protocols. However, the total network energy consumption of most protocols is not well minimized and balanced. To alleviate this problem, this paper proposes an energy-efficient clustering routing protocol in WSNs. To begin with, this paper introduces a new network structure model and combines the original energy consumption model to construct a new method to determine the optimal number of clusters for the total energy consumption minimization. Based on the balanced energy consumption, then we optimize the AGglomerative NESting (AGNES) algorithm, including: (1) introduction of distance variance, (2) the dual-cluster heads (D-CHs) division of the energy balance strategy, and (3) the node dormancy mechanism. In addition, the CHs priority function is constructed based on the residual energy and position of the node. Finally, we simulated this protocol in homogeneous networks (the initial energy = 0.4 J, 0.6 J and 0.8 J) and heterogeneous networks (the initial energy = 0.4–0.8 J). Simulation results show that our proposed protocol can reduce the network energy consumption decay rate, prolong the network lifetime, and improve the network throughput in the above two networks.

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Sadia Batool and Mohtishim Siddique. "Energy Efficient Schemes for Wireless Sensor Network (WSN)." Lahore Garrison University Research Journal of Computer Science and Information Technology 1, no. 1 (March 31, 2017): 54–61. http://dx.doi.org/10.54692/lgurjcsit.2017.01016.

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Conservation of energy is the main design issue in wirelesssensor network (WSN) which is usually available at each node. Althoughdifferent solutions have been introduced for typical wireless networks,cellular networks, MANET, and other short-range wireless local areanetworks, yet they are not often much feasible for a large scale WSN.For this purpose, multiple mobile sink nodes can be deployed to increasethe life of sensor network. The purpose can be achieved by splitting thelife time into equal time interval known as rounds. Similarly, byemploying multiple sink nodes can also make the sensor network moreenergy efficient. Another way to make the sensor network energyefficient is to logically divide the deployment area into static clusters. Byadopting the strategy of static cluster, energy consumption can beminimized. The two major wireless standards used by WSN are 802.15.4and Zigbee [1],[2] .They are low-power protocols. Maximum distance isaround 100m (at 2.4 GHz). However, performance is an issue. In orderto assure the Wireless sensor network (WSN)s survivability and increasethe lifetime of network in such environments, various energy efficiencyschemes have been proposed in the literature. Energy is a valuablecommodity in wireless networks due to the limited battery of the handydevices. The energy problem becomes stiffer in ad-hoc WSN).

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Lai, Baixi, Ping Yi, Yu Sui, and Qingquan Zhang. "Energy distribution in EV energy network under energy shortage." Neurocomputing 444 (July 2021): 179–88. http://dx.doi.org/10.1016/j.neucom.2020.08.090.

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40

Kumar, Dr D. R. V. A. Sharath, and P. A. Lovina. "Information Encryption Methods to Reduce Network-on-Chip Energy Efficiency." International Journal of Psychosocial Rehabilitation 23, no. 1 (February 20, 2019): 455–65. http://dx.doi.org/10.37200/ijpr/v23i1/pr190258.

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Belapurkar, Nainesh, and Dr Manmohan Singh. "Energy Emphasis Fault Tolerance Routing Scheme for Wireless Sensor Network." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (April 30, 2018): 616–22. http://dx.doi.org/10.31142/ijtsrd11017.

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B, Venkateswarulu Naik, and Dr S. Rama Krishna. "An Energy-aware Distributed Clustering Protocol in Wireless Sensor Network." International Journal of Trend in Scientific Research and Development Volume-1, Issue-6 (October 31, 2017): 1130–38. http://dx.doi.org/10.31142/ijtsrd5798.

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Prof.MS.Vaishali R, Prof MS Vaishali R., Prof D. K. shende Prof.D.K.shende, and Prof MS Shubhangi Prof. MS. Shubhangi. "Energy Optimization And Power Scheduling In Low Power Sensor Network." International Journal of Scientific Research 1, no. 3 (June 1, 2012): 40–42. http://dx.doi.org/10.15373/22778179/aug2012/15.

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Virmani, Dr Deepali. "Maximizing Network Lifetime Operator for Wireless Sensor Networks." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 4, no. 2 (March 18, 2013): 267–72. http://dx.doi.org/10.24297/ijct.v4i2a2.3172.

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Optimizing and enhancing network lifetime with minimum energy consumption is the major challenge in field of wireless sensor networks. Existing techniques for optimizing network lifetime are based on exploiting node redundancy, adaptive radio transmission power and topology control. Topology control protocols have a significant impact on network lifetime, available energy and connectivity. In this paper we categorize sensor nodes as strong and weak nodes based on their residual energy as well as operational lifetime and propose a Maximizing Network lifetime Operator (MLTO) that defines cluster based topology control mechanism to enhance network lifetime while guarantying the minimum energy consumption and minimum delay. Extensive simulations in Java-Simulator (J-Sim) show that our proposed operator outperforms the existing protocols in terms of various performance metrics life network lifetime, average delay and minimizes energy utilization.

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Karimi, Ahmad. "Multicast Energy Aware Routing â€Žin Wireless Networks." Computer Engineering and Applications Journal 5, no. 3 (October 29, 2016): 127–33. http://dx.doi.org/10.18495/comengapp.v5i3.184.

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Multicasting is a service for disseminating data to a group of hostsand it is of paramount importance in applications with a close collabo-ration of network hosts. Due to limited energy available in the wirelessdevices, energy management is one of the most important problems inwireless networks. Energy aware routing strategies help us to mini-mize the energy costs for communication as much as possible and toincrease the network lifetime. In this paper, we address the problemof energy efficient routing to increase the lifetime of the network. Wepresent three new strategies for online multicast energy aware routingin wireless networks to increase the network lifetime.

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Jeyasekar, A., Siddharth Nanda, and Annie Uthra. "Green SDN: Trends of Energy Conservation in Software Defined Network." International Journal of Engineering & Technology 7, no. 3.12 (July 20, 2018): 9. http://dx.doi.org/10.14419/ijet.v7i3.12.15853.

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Because of the exponential growth of the data in the Internet, storing and computing the data become a challenging issue. Therefore the data center networks are used to provide the infrastructure for storing and computing the data. Most of the network devices present in the data center network is comparatively idle for most of the time and resulting in a waste of energy. To overcome it, the Software Defined Network is proposed which allows the administrators to manage the network devices using a centralized controller and enables programmable network devices. It optimizes the utilization of network resources efficiently and results in significant amount of energy saving. There are several approaches proposed for optimizing the energy in the Software Defined Network which takes a network towards green energy and lower carbon print. This paper presents a survey on energy conservation techniques used in the software defined networks which makes the networks more capable and productive. We also provide a brief comparison of possible energy conservation techniques and guidelines for future research.

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Shutters, Shade T., Keith Waters, and Rachata Muneepeerakul. "Triad Analysis of Global Energy Trade Networks and Implications for Energy Trade Stability." Energies 15, no. 10 (May 17, 2022): 3673. http://dx.doi.org/10.3390/en15103673.

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An international push to decarbonize economies has initiated a major transition in the global energy system and has begun to disrupt the intricate network of energy trade. As trade patterns begin to reconfigure, it is important that policy makers understand how vulnerabilities of the existing network may present obstacles to a smooth energy transition. We analyze the topology of the global energy trade network in aggregate, for various energy commodities, and for individual countries. Using the network science technique of triad analysis, which examines the prevalence of 3-node subnetworks in a target network, we calculate triad significance profiles for each network. We then analyze whether various triads are under- or over-represented in our networks and find that triads associated with stability appear more frequently than expected, whereas triads associated with conflict appear less frequently than expected. We further find that the global energy trade network is quite robust against disruptions, maintaining its topological characteristics even after random removal of 80% of the network’s nodes. However, when analyzing individual countries, we find that some exhibit a high prevalence of unstable triads or a low prevalence of stabilizing triads, suggesting that vulnerabilities in global energy trade are more pronounced in some countries than others.

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Husni, Emir, and Aska Narendra. "Energy efficient network methods on local area network." International Journal of Internet Protocol Technology 7, no. 4 (2013): 203. http://dx.doi.org/10.1504/ijipt.2013.058647.

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Wan, Zhiping, Zhiming Xu, Jiajun Zou, Shaojiang Liu, Weichuan Ni, and Shitong Ye. "An IPv6 Passive-Aware Network Routing Algorithm Based on Utility Value Combined with Deep Neural Network." Journal of Sensors 2021 (November 28, 2021): 1–9. http://dx.doi.org/10.1155/2021/7193788.

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Passive sensing networks can maintain the operation of the network by capturing energy from the environment, thereby solving the energy limitation problem of network nodes. Therefore, passive sensing networks are widely used in data collection in complex environments. However, the complexity of the network deployment environment makes passive sensing nodes unable to obtain stable energy from the surroundings. Therefore, better routing strategies are needed to save network energy consumption. In response to this problem, this paper proposes an IPv6 passive-aware network routing algorithm for the Internet of Things. This method is based on the characteristics of passive sensing networks. By analyzing the successful transmission rate of the network node transmission link, transmission energy consumption, end-to-end transmission delay, and waiting delay of IPv6 packets, the utility evaluation function of the route is obtained. After the utility evaluation function is obtained, the network routing is selected through the utility evaluation function. Then, the utility value and the deep neural network method are combined to train the classification model. The classification model assigns the best routing strategy according to the characteristics of the current network, thereby improving the energy consumption and delay performance of the network.

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Jia, Jie, Jian Chen, Xingwei Wang, and Linliang Zhao. "Energy-Balanced Density Control to Avoid Energy Hole for Wireless Sensor Networks." International Journal of Distributed Sensor Networks 8, no. 1 (January 1, 2012): 812013. http://dx.doi.org/10.1155/2012/812013.

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Density control is of great relevance for wireless sensor networks monitoring hazardous applications where sensors are deployed with high density. Due to the multihop relay communication and many-to-one traffic characters in wireless sensor networks, the nodes closer to the sink tend to die faster, causing a bottleneck for improving the network lifetime. In this paper, the theoretical aspects of the network load and the node density are investigated systematically. And then, the accessibility condition to satisfy that all the working sensors exhaust their energy with the same ratio is proved. By introducing the concept of the equivalent sensing radius, a novel algorithm for density control to achieve balanced energy consumption per node is thus proposed. Different from other methods in the literature, a new pixel-based transmission mechanism is adopted, to reduce the duplication of the same messages. Combined with the accessibility condition, nodes on different energy layers are activated with a nonuniform distribution, so as to balance the energy depletion and enhance the survival of the network effectively. Extensive simulation results are presented to demonstrate the effectiveness of our algorithm.

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