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Artículos de revistas sobre el tema "Energy-Efficient Localization"

Autor: Grafiati
Publicado: 16 de noviembre de 2024
Última modificación: 31 de julio de 2025

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1

Jurdak, Raja, Peter Corke, Alban Cotillon, Dhinesh Dharman, Chris Crossman, and Guillaume Salagnac. "Energy-efficient localization." ACM Transactions on Sensor Networks 9, no. 2 (2013): 1–33. http://dx.doi.org/10.1145/2422966.2422980.

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2

Liu, Haifeng, Feng Xia, Zhuo Yang, and Yang Cao. "An energy-efficient localization strategy for smartphones." Computer Science and Information Systems 8, no. 4 (2011): 1117–28. http://dx.doi.org/10.2298/csis110430065l.

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In recent years, smartphones have become prevalent. Much attention is being paid to developing and making use of mobile applications that require position information. The Global Positioning System (GPS) is a very popular localization technique used by these applications because of its high accuracy. However, GPS incurs an unacceptable energy consumption, which severely limits the use of smartphones and reduces the battery lifetime. Then an urgent requirement for these applications is a localization strategy that not only provides enough accurate position information to meet users' need but al
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3

Choi, Taehwa, Yohan Chon, and Hojung Cha. "Energy-efficient WiFi scanning for localization." Pervasive and Mobile Computing 37 (June 2017): 124–38. http://dx.doi.org/10.1016/j.pmcj.2016.07.005.

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4

Abdellatif, Mohamed. "GreenLoc: Energy Efficient Wifi-Based Indoor Localization." Qatar Foundation Annual Research Forum Proceedings, no. 2011 (November 2011): CSP20. http://dx.doi.org/10.5339/qfarf.2011.csp20.

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5

Abu-Mahfouz, Adnan M., and Gerhard P. Hancke. "ALWadHA Localization Algorithm: Yet More Energy Efficient." IEEE Access 5 (2017): 6661–67. http://dx.doi.org/10.1109/access.2017.2687619.

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6

Taheri, Mostafa, and Seyed Ahmad Motamedi. "Energy-efficient cooperative localization in mobile WSN." IEEJ Transactions on Electrical and Electronic Engineering 12, no. 1 (2016): 71–79. http://dx.doi.org/10.1002/tee.22346.

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7

Taheri, Mostafa, and Seyed Ahmad Motamedi. "Transceiver Optimization for ToA-Based Localization of Mobile WSN." Journal of Circuits, Systems and Computers 25, no. 09 (2016): 1650100. http://dx.doi.org/10.1142/s0218126616501000.

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One of the main parameters in wireless sensor networks (WSNs) is the design of energy-efficient protocols. And accuracy is another central goal of localization. Since sensor nodes run on battery power, any WSN application and accurate localization needs to be energy-efficient. In this paper, the accuracy of localization is increased by accurate measurement of the distance between the mobile sensors. Limit error in multiple-input multiple-output (MIMO) has been calculated by CRB method. Virtual MIMO (VMIMO) technique can obtain better localization precision and the localization is energy-effici
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8

Wang, Wendong, Teng Xi, Edith Ngai, and Zheng Song. "Energy-Efficient Collaborative Outdoor Localization for Participatory Sensing." Sensors 16, no. 6 (2016): 762. http://dx.doi.org/10.3390/s16060762.

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9

Bui, ThiOanh, Pingping Xu, Wenxiang Zhu, Guilu Wu, and Nanlan Jiang. "Energy-Efficient Localization Game for Wireless Sensor Networks." IEEE Communications Letters 21, no. 11 (2017): 2468–71. http://dx.doi.org/10.1109/lcomm.2017.2731966.

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10

Aly, Heba, Anas Basalamah, and Moustafa Youssef. "Accurate and Energy-Efficient GPS-Less Outdoor Localization." ACM Transactions on Spatial Algorithms and Systems 3, no. 2 (2017): 1–31. http://dx.doi.org/10.1145/3085575.

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11

Panda, Tanuja. "Energy Efficient Anchor-Based Localization Algorithm for WSN." IOSR Journal of Computer Engineering 1, no. 3 (2012): 13–20. http://dx.doi.org/10.9790/0661-0131320.

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12

Guo, Peng, Tao Jiang, and Kui Zhang. "Novel Energy-Efficient Miner Monitoring System with Duty-Cycled Wireless Sensor Networks." International Journal of Distributed Sensor Networks 8, no. 1 (2012): 975082. http://dx.doi.org/10.1155/2012/975082.

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Target monitoring is an important application of wireless sensor networks. In this paper, we develop an energy-efficient miner monitoring system with sensor nodes. To keep monitoring miners' activities in tunnels, periodical localization and timely data transmission are both required. Since the localization and data transmission much depend on the media access control (MAC) scheme, codesign of localization and MAC scheme is actually needed for the resource-constrained system, which is seldom discussed in existing related works. Moreover, as sensor nodes form an ultra-sparse network with linear
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13

Xia, Feng, Xue Yang, Haifeng Liu, Zhang Da, and Wenhong Zhao. "Energy-efficient opportunistic localization with indoor wireless sensor networks." Computer Science and Information Systems 8, no. 4 (2011): 973–90. http://dx.doi.org/10.2298/csis110406063x.

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Localization challenges researchers for its contradictive goals, i.e., how to tackle the problem of minimizing energy consumption as well as maintaining localization precision, which are two essential trade-offs in wireless sensor network systems. In this paper, we propose an Energy-Efficient Opportunistic Localization (EEOL) scheme to satisfy the requirement of positional accuracy and power consumption. We explore the idea of opportunistic wakeup probability to wake up an appropriate numbers of sensor nodes, while ensuring the high positional accuracy. Sensor nodes can be triggered by the opp
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14

Yaghoubi, Forough, Ali-Azam Abbasfar, and Behrouz Maham. "Energy-Efficient RSSI-Based Localization for Wireless Sensor Networks." IEEE Communications Letters 18, no. 6 (2014): 973–76. http://dx.doi.org/10.1109/lcomm.2014.2320939.

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15

Tsung-Han Lin, P. Huang, Hao-Hua Chu, and Chuang-Wen You. "Energy-Efficient Boundary Detection for RF-Based Localization Systems." IEEE Transactions on Mobile Computing 8, no. 1 (2009): 29–40. http://dx.doi.org/10.1109/tmc.2008.84.

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16

Uddin, Nizam, and Ibrahim Elshafiey. "Efficient energy localization for hybrid wideband hyperthermia treatment system." International Journal of RF and Microwave Computer-Aided Engineering 28, no. 3 (2018): e21238. http://dx.doi.org/10.1002/mmce.21238.

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17

T P, Mohankumar, and D. Ramesh. "New Strategies for Boosting Localization Accuracy in Wireless Sensor Nodes." International Journal of Innovative Research in Computer Science and Technology 12, no. 6 (2024): 1–6. http://dx.doi.org/10.55524/ijircst.2024.12.6.1.

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Wireless Sensor Networks (WSNs), accurate and energy-efficient localization of sensor nodes remains a challenging task despite significant advancements. Current geolocation algorithms often struggle with scalability, adaptability, and energy efficiency, particularly in large-scale, dynamic environments where node failures or random shifts occur. This paper proposes a novel Secure Node Localization (SABWP-NL) approach, combining Self-Adaptive Binary Waterwheel Plant Optimization (SABWP) and Bayesian optimization to enhance localization accuracy, scalability, energy efficiency, and robustness. T
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18

Dev, Jayashree, and Jibitesh Mishra. "Energy-Efficient Object Detection and Tracking Framework for Wireless Sensor Network." Sensors 23, no. 2 (2023): 746. http://dx.doi.org/10.3390/s23020746.

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Object detection and tracking is one of the key applications of wireless sensor networks (WSNs). The key issues associated with this application include network lifetime, object detection and localization accuracy. To ensure the high quality of the service, there should be a trade-off between energy efficiency and detection accuracy, which is challenging in a resource-constrained WSN. Most researchers have enhanced the application lifetime while achieving target detection accuracy at the cost of high node density. They neither considered the system cost nor the object localization accuracy. So
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19

Goyat, Rekha, Mritunjay Kumar Rai, Gulshan Kumar, Rahul Saha, and Tai-Hoon Kim. "Energy Efficient Range-Free Localization Algorithm for Wireless Sensor Networks." Sensors 19, no. 16 (2019): 3603. http://dx.doi.org/10.3390/s19163603.

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In this paper, an energy-efficient localization algorithm is proposed for precise localization in wireless sensor networks (WSNs) and the process is accomplished in three steps. Firstly, the beacon nodes discover their one-hop neighbor nodes with additional tone requests and reply packets over the media access control (MAC) layer to avoid collision of packets. Secondly, the discovered one-hop unknown nodes are divided into two sets, i.e. unknown nodes with direct communication, and with indirect communication for energy efficiency. In direct communication, source beacon nodes forward the infor
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20

Molla, Jahir Pasha, Dharmesh Dhabliya, Satish R. Jondhale, et al. "Energy Efficient Received Signal Strength-Based Target Localization and Tracking Using Support Vector Regression." Energies 16, no. 1 (2023): 555. http://dx.doi.org/10.3390/en16010555.

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The unpredictable noise in received signal strength indicator (RSSI) measurements in indoor environments practically causes very high estimation errors in target localization. Dealing with high noise in RSSI measurements and ensuring high target-localization accuracy with RSSI-based localization systems is a very popular research trend nowadays. This paper proposed two range-free target-localization schemes in wireless sensor networks (WSN) for an indoor setup: first with a plain support vector regression (SVR)-based model and second with the fusion of SVR and kalman filter (KF). The fusion-ba
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21

Yuan, Yali, Chencheng Liang, Xu Chen, Thar Baker, and Xiaoming Fu. "Adaptive Fuzzy Game-Based Energy-Efficient Localization in 3D Underwater Sensor Networks." ACM Transactions on Internet Technology 22, no. 2 (2022): 1–20. http://dx.doi.org/10.1145/3406533.

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Numerous applications in 3D underwater sensor networks (UWSNs), such as pollution detection, disaster prevention, animal monitoring, navigation assistance, and submarines tracking, heavily rely on accurate localization techniques. However, due to the limited batteries of sensor nodes and the difficulty for energy harvesting in UWSNs, it is challenging to localize sensor nodes successfully within a short sensor node lifetime in an unspecified underwater environment. Therefore, we propose the Adaptive Energy-Efficient Localization Algorithm (Adaptive EELA) to enable energy-efficient node localiz
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22

Shams, Rehan, Pablo Otero, Muhammad Aamir, and Fozia Hanif. "E2JSL: Energy Efficient Joint Time Synchronization and Localization Algorithm Using Ray Tracing Model." Sensors 20, no. 24 (2020): 7222. http://dx.doi.org/10.3390/s20247222.

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In underwater wireless sensor networks (UWSNs), localization and time synchronization are vital services that have been tackled independently. By combining localization and time synchronization, could save nodes energy and improve accuracy jointly. Therefore, it is of great significance to study joint synchronization and localization of underwater sensors with low energy consumption. In this paper, we propose the energy-efficient joint framework of localization and time synchronization, in which the stratification effect is considered by using a ray-tracing approach. Based on Snell’s law, ray
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23

Chen, Junfeng, Samson Hansen Sackey, Joseph Henry Anajemba, Xuewu Zhang, and Yurun He. "Energy-Efficient Clustering and Localization Technique Using Genetic Algorithm in Wireless Sensor Networks." Complexity 2021 (August 2, 2021): 1–12. http://dx.doi.org/10.1155/2021/5541449.

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Localization is recognized among the topmost vital features in numerous wireless sensor network (WSN) applications. This paper puts forward energy-efficient clustering and localization centered on genetic algorithm (ECGAL), in which the residual energy, distance estimation, and coverage connection are developed to form the fitness function. This function is certainly fast to run. The proposed ECGAL exhausts a lesser amount of energy and extends wireless network existence. Finally, the simulations are carried out to assess the performance of the proposed algorithm. Experimental results show tha
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24

Rout, Saroj Kumar, Amiya Kumar Rath, and Chidananda Bhagabati. "Energy Efficient Dynamic Node Localization Technique in Wireless Sensor Networks." Indian Journal of Science and Technology 10, no. 15 (2017): 1–8. http://dx.doi.org/10.17485/ijst/2017/v10i15/93919.

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25

Elavarasan, R., and K. Chitra. "Efficient Localization Based Optimal Energy Routing for Wireless Sensor Networks." Journal of Computational and Theoretical Nanoscience 14, no. 6 (2017): 2968–75. http://dx.doi.org/10.1166/jctn.2017.6369.

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26

Salazar Gonzalez, Jose L., Luis Miguel Soria Morillo, Juan A. Alvarez-Garcia, Fernando Enriquez De Salamanca Ros, and Antonio R. Jimenez Ruiz. "Energy-Efficient Indoor Localization WiFi-Fingerprint System: An Experimental Study." IEEE Access 7 (2019): 162664–82. http://dx.doi.org/10.1109/access.2019.2952221.

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27

Wu, Pan, Xiaobing Wu, Guihai Chen, Mengfan Shan, and Xiaojun Zhu. "A few bits are enough: Energy efficient device-free localization." Computer Communications 83 (June 2016): 72–80. http://dx.doi.org/10.1016/j.comcom.2016.01.010.

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28

Liu, Xi, Yiju Zhan, and Jian Cen. "An Energy-Efficient Crowd-Sourcing-Based Indoor Automatic Localization System." IEEE Sensors Journal 18, no. 14 (2018): 6009–22. http://dx.doi.org/10.1109/jsen.2018.2842239.

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29

Peng, B., and A. H. Kemp. "Energy-efficient geographic routing in the presence of localization errors." Computer Networks 55, no. 3 (2011): 856–72. http://dx.doi.org/10.1016/j.comnet.2010.10.020.

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30

Alhmiedat, Tareq. "Fingerprint-Based Localization Approach for WSN Using Machine Learning Models." Applied Sciences 13, no. 5 (2023): 3037. http://dx.doi.org/10.3390/app13053037.

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The area of localization in wireless sensor networks (WSNs) has received considerable attention recently, driven by the need to develop an accurate localization system with the minimum cost and energy consumption possible. On the other hand, machine learning (ML) algorithms have been employed widely in several WSN-based applications (data gathering, clustering, energy-harvesting, and node localization) and showed an enhancement in the obtained results. In this paper, an efficient WSN-based fingerprinting localization system for indoor environments based on a low-cost sensor architecture, throu
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31

Aouali, Kaouthar, Najib Kacem, Noureddine Bouhaddi, Elyes Mrabet, and Mohamed Haddar. "Efficient broadband vibration energy harvesting based on tuned non-linearity and energy localization." Smart Materials and Structures 29, no. 10 (2020): 10LT01. http://dx.doi.org/10.1088/1361-665x/abaa95.

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32

Algobail, Afnan, Adel Soudani, and Saad Alahmadi. "Energy-efficient scheme for target recognition and localization in wireless acoustic sensor networks." International Journal of Distributed Sensor Networks 15, no. 11 (2019): 155014771989140. http://dx.doi.org/10.1177/1550147719891406.

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The development of wireless acoustic sensor networks has driven the use of acoustic signals for target monitoring. Most monitoring applications require continuous network connectivity and data transfers, which can rapidly exhaust nodes’ energy. Consequently, sensors must collaborate in an adequate architecture to perform target recognition and localization tasks and then to send the results to a remote server with a reduced data volume. The design of an energy-efficient scheme that achieves acoustic target recognition and localization remains an open research problem. Accordingly, this article
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33

Yang, Yankan, Baoqi Huang, Zhendong Xu, and Runze Yang. "A Fuzzy Logic-Based Energy-Adaptive Localization Scheme by Fusing WiFi and PDR." Wireless Communications and Mobile Computing 2023 (January 7, 2023): 1–17. http://dx.doi.org/10.1155/2023/9052477.

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Fusing WiFi fingerprint localization and pedestrian dead reckoning (PDR) on smartphones is attractive because of their obvious complementarity in localization accuracy and energy consumption. Although fusion localization algorithms tend to improve localization accuracy, extra hardware and software involved will result in extra computations, such that energy consumption is inevitably increased. Thus, in this study, we propose a novel fusion localization scheme based on fuzzy logic, which aims to achieve ideal localization accuracy by consuming as little energy as possible. Specifically, energy-
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34

Suresh Limkar, Et al. "Energy-Efficient Localization Techniques for Wireless Sensor Networks in Indoor IoT Environments." Journal of Electrical Systems 19, no. 2 (2024): 47–57. http://dx.doi.org/10.52783/jes.690.

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For Wireless Sensor Networks (WSNs) to operate as efficiently as possible in Indoor Internet of Things (IoT) environments, energy-efficient localization approaches are essential. We investigate several localization approaches, such as trilateration based on Received Signal Strength Indicator (RSSI), Proximity Based Technique, Inertial Navigation, Ultrasound-based, and Magnetic Field-based approaches, in the context of energy efficiency. RSSI-based trilateration, which provides good accuracy with little energy consumption, uses measurements of signal intensity to infer device positions. In case
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35

Mukhtiar, Ahmed, Salleh Mazleena, Ibrahim Channa M., and Foad Rohani Mohd. "Energy Efficient Routing Protocols for UWSN: A Review." TELKOMNIKA Telecommunication, Computing, Electronics and Control 15, no. 1 (2017): 212–19. https://doi.org/10.12928/TELKOMNIKA.v15i1.4706.

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The Underwater Sensor Network (UWSN) is main interesting area due to its most valuable applications like: disaster preventions, distributed tactical surveillance, undersea exploration, seismic monitoring, environmental monitoring and many more. The design of energy efficient routing protocol however is a challenging issue because in underwater environment the batteries of the sensor nodes cannot be recharged easily. Majority of the researchers have adapted the terrestrial WSN methodologies to overcome this problem but in underwater environment the terrestrial WSN approach is not feasible due t
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36

Frattini, Flavio, Christian Esposito, and Stefano Russo. "ROCRSSI++." International Journal of Adaptive, Resilient and Autonomic Systems 2, no. 2 (2011): 51–70. http://dx.doi.org/10.4018/jaras.2011040104.

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Localization within a Wireless Sensor Network consists of defining the position of a given set of sensors by satisfying some non-functional requirements such as (1) efficient energy consumption, (2) low communication or computation overhead, (3) no, or limited, use of particular hardware components, (4) fast localization, (5) robustness, and (6) low localization error. Although there are several algorithms and techniques available in literature, localization is viewed as an open issue because none of the current solutions are able to jointly satisfy all the previous requirements. An algorithm
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37

Mohammad, Hafiz Mohamed, Abu Bakar Kamalrulnizam, Fauzi Isnin Ismail, Zulkifli M. Zaki M., Herman, and Tul Zuhra Fatima. "Dynamic mobile anchor path planning for underwater wireless sensor networks." TELKOMNIKA (Telecommunication, Computing, Electronics and Control) 19, no. 4 (2021): 1126–36. https://doi.org/10.12928/telkomnika.v19i4.19016.

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In an underwater wireless sensor network (UWSN), the location of the sensor nodes plays a significant role in the localization process. The location information is obtained by using the known positions of anchor nodes. For underwater environments, instead of using various static anchor nodes, mobile anchor nodes are more efficient and cost-effective to cover the monitoring area. Nevertheless, the utilization of these mobile anchors requires adequate path planning strategy. Mzost of the path planning algorithms do not consider irregular deployment, caused by the effects of water currents. Conse
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38

Ahmed, Mukhtiar, Mazleena Salleh, M. Ibrahim Channa, and Mohd Foad Rohani. "RMEER: Reliable Multi-path Energy Efficient Routing Protocol for Underwater Wireless Sensor Network." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 6 (2020): 4366–73. https://doi.org/10.11591/ijece.v8i6.pp4366-4373.

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Underwater Wireless Sensor Networks (UWSNs) is interesting area for researchers. To extract the information from seabed to water surface the the majority numbers of routing protocols has been introduced. The design of routing protocols faces many challenges like deployment of sensor nodes, controlling of node mobility, development of efficient route for data forwarding, prolong the battery power of the sensor nodes, and removal of void nodes from active data forwarding paths. This research article focuses the design of the Reliable Multipath Energy Efficient Routing (RMEER) which develops the
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39

Chen, Hua-yan, Mei-qin Liu, and Sen-lin Zhang. "Energy-efficient localization and target tracking via underwater mobile sensor networks." Frontiers of Information Technology & Electronic Engineering 19, no. 8 (2018): 999–1012. http://dx.doi.org/10.1631/fitee.1700598.

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40

Sureshkumar, V., T. Sandeep Reddy, Aishwarya Malepati, and N. Radhika. "Energy Efficient Mobility Prediction based Localization Algorithm for Mobile Sensor Networks." Research Journal of Applied Sciences, Engineering and Technology 8, no. 4 (2014): 571–77. http://dx.doi.org/10.19026/rjaset.8.1007.

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41

Yuan, Yazhou, Cailian Chen, Xinping Guan, and Qiuling Yang. "An Energy-Efficient Underground Localization System Based on Heterogeneous Wireless Networks." Sensors 15, no. 6 (2015): 12358–76. http://dx.doi.org/10.3390/s150612358.

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42

Wei, Chun-Yi, and Hsuan-Yi Pan. "Adaptive Zone–Assisted Iterative Localization in Energy-Efficient Wireless Sensor Networks." IEEE Sensors Journal 21, no. 23 (2021): 27186–94. http://dx.doi.org/10.1109/jsen.2021.3120883.

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43

Saeed, Nasir, and Haewoon Nam. "Energy Efficient Localization Algorithm With Improved Accuracy in Cognitive Radio Networks." IEEE Communications Letters 21, no. 9 (2017): 2017–20. http://dx.doi.org/10.1109/lcomm.2017.2712802.

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44

Gu, Yu, and Fuji Ren. "Energy-Efficient Indoor Localization of Smart Hand-Held Devices Using Bluetooth." IEEE Access 3 (2015): 1450–61. http://dx.doi.org/10.1109/access.2015.2441694.

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45

Arya, Azin, Philippe Godlewski, Marine Campedel, and Ghislain du Chene. "Radio Database Compression for Accurate Energy-Efficient Localization in Fingerprinting Systems." IEEE Transactions on Knowledge and Data Engineering 25, no. 6 (2013): 1368–79. http://dx.doi.org/10.1109/tkde.2011.241.

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46

Shigeng Zhang, Jiannong Cao, Chen Li-Jun, and Daoxu Chen. "Accurate and Energy-Efficient Range-Free Localization for Mobile Sensor Networks." IEEE Transactions on Mobile Computing 9, no. 6 (2010): 897–910. http://dx.doi.org/10.1109/tmc.2010.39.

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47

Akter, Mahmuda, Md Obaidur Rahman, Md Nazrul Islam, Mohammad Mehedi Hassan, Ahmed Alsanad, and Arun Kumar Sangaiah. "Energy-Efficient Tracking and Localization of Objects in Wireless Sensor Networks." IEEE Access 6 (2018): 17165–77. http://dx.doi.org/10.1109/access.2018.2809692.

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48

Kianoush, Sanaz, Anna Vizziello, and Paolo Gamba. "Energy-Efficient and Mobile-Aided Cooperative Localization in Cognitive Radio Networks." IEEE Transactions on Vehicular Technology 65, no. 5 (2016): 3450–61. http://dx.doi.org/10.1109/tvt.2015.2441733.

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49

Yan, Yongsheng, Haiyan Wang, Xiaohong Shen, Bing Leng, and Shuangquan Li. "Efficient Convex Optimization for Energy-Based Acoustic Sensor Self-Localization and Source Localization in Sensor Networks." Sensors 18, no. 5 (2018): 1646. http://dx.doi.org/10.3390/s18051646.

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50

Kashyap, Varun, and Hadi Ghasemi. "Solar heat localization: concept and emerging applications." Journal of Materials Chemistry A 8, no. 15 (2020): 7035–65. http://dx.doi.org/10.1039/d0ta01004a.

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Recently, the solar heat localization concept has provided an appealing route for efficient utilization of solar thermal energy. A detailed study is conducted on this concept highlighting the figures of merit for various applications.
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