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Статті в журналах з теми "Energy-Efficient Localization"

Автор: Grafiati

Опубліковано: 16 листопада 2024

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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 (March 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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Анотація:

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 also consumes less energy. In this paper, we present an energy-efficient localization strategy for smartphone applications. On one hand, it can dynamically estimate the next localization time point to avoid unnecessary localization operations. On the other hand, it can also automatically select the energy-optimal localization method. We evaluate the strategy through a series of simulations. Experimental results show that it can significantly reduce the localization energy consumption of smartphones while ensuring a good satisfaction degree.

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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 (November 22, 2016): 71–79. http://dx.doi.org/10.1002/tee.22346.

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7

Wang, Wendong, Teng Xi, Edith Ngai, and Zheng Song. "Energy-Efficient Collaborative Outdoor Localization for Participatory Sensing." Sensors 16, no. 6 (May 25, 2016): 762. http://dx.doi.org/10.3390/s16060762.

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8

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 (November 2017): 2468–71. http://dx.doi.org/10.1109/lcomm.2017.2731966.

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9

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 (August 29, 2017): 1–31. http://dx.doi.org/10.1145/3085575.

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10

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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11

Taheri, Mostafa, and Seyed Ahmad Motamedi. "Transceiver Optimization for ToA-Based Localization of Mobile WSN." Journal of Circuits, Systems and Computers 25, no. 09 (June 21, 2016): 1650100. http://dx.doi.org/10.1142/s0218126616501000.

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Анотація:

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-efficient. Optimum selection of the number of the transceiver nodes is obtained by the lowest possible energy consumption, the existent localization error, and speed of nodes. Mathematical relation between energy consumption and localization of mobile nodes is presented and then verified by simulation. VMIMO decreases power of transmitters and this in turn will result in decreasing destructive effects of electromagnetic sensitivity (EMS) on body. Furthermore, optimized localization parameters will increase the efficiency of the system and network lifetime.

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12

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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Анотація:

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 opportunistic wakeup probability sent from the user. Through utilizing this method, the number of active sensors in the sensing range of the user is decreased, and the power consumption is significantly reduced. Theoretical analysis has been presented to evaluate the performance of EEOL. Simulation results show that EEOL confirms our theoretical analysis.

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13

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 (January 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 topology in tunnels, it is a challenge for existing range-free localization methods to localize targets. In this paper, we propose a localization-MAC codesign approach for the monitoring system under the environment of coal mine. With the proposed approach, the system can achieve higher localization accuracy with low energy consumption and transmission delay, compared with existing range-free localization methods for sensor nodes.

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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 (June 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 (January 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 (January 12, 2018): e21238. http://dx.doi.org/10.1002/mmce.21238.

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17

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 (August 19, 2019): 3603. http://dx.doi.org/10.3390/s19163603.

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Анотація:

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 information directly to the unknown nodes, but a common beacon node is selected for communication which reduces overall energy consumption during transmission in indirect communication. Finally, a correction factor is also introduced, and localized unknown nodes are upgraded into helper nodes for reducing the localization error. To analyze the efficiency and effectiveness of the proposed algorithm, various simulations are conducted and compared with the existing algorithms.

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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 (January 9, 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. Some researchers focused on object detection accuracy while achieving energy efficiency by limiting the detection to a predefined target trajectory. In particular, some researchers only focused on node clustering and node scheduling for energy efficiency. In this study, we proposed a mobile object detection and tracking framework named the Energy Efficient Object Detection and Tracking Framework (EEODTF) for heterogeneous WSNs, which minimizes energy consumption during tracking while not affecting the object detection and localization accuracy. It focuses on achieving energy efficiency via node optimization, mobile node trajectory optimization, node clustering, data reporting optimization and detection optimization. We compared the performance of the EEODTF with the Energy Efficient Tracking and Localization of Object (EETLO) model and the Particle-Swarm-Optimization-based Energy Efficient Target Tracking Model (PSOEETTM). It was found that the EEODTF is more energy efficient than the EETLO and PSOEETTM models.

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19

Molla, Jahir Pasha, Dharmesh Dhabliya, Satish R. Jondhale, Sivakumar Sabapathy Arumugam, Anand Singh Rajawat, S. B. Goyal, Maria Simona Raboaca, Traian Candin Mihaltan, Chaman Verma, and George Suciu. "Energy Efficient Received Signal Strength-Based Target Localization and Tracking Using Support Vector Regression." Energies 16, no. 1 (January 3, 2023): 555. http://dx.doi.org/10.3390/en16010555.

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Анотація:

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-based model is named as the SVR+KF algorithm. The proposed localization solutions do not require computing distances using field measurements; rather, they need only three RSSI measurements to locate the mobile target. This paper also discussed the energy consumption associated with traditional Trilateration and the proposed SVR-based target-localization approaches. The impact of four kernel functions, namely, linear, sigmoid, RBF, and polynomial were evaluated with the proposed SVR-based schemes on the target-localization accuracy. The simulation results showed that the proposed schemes with linear and polynomial kernel functions were highly superior to trilateration-based schemes.

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20

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 (May 31, 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 localization while adapting to the dynamic environment changes. Adaptive EELA takes a fuzzy game-theoretic approach, whereby the Stackelberg game is used to model the interactions among sensor and anchor nodes in UWSNs and employs the adaptive neuro-fuzzy method to set the appropriate utility functions. We prove that a socially optimal Stackelberg–Nash equilibrium is achieved in Adaptive EELA. Through extensive numerical simulations under various environmental scenarios, the evaluation results show that our proposed algorithm accomplishes a significant energy reduction, e.g., 66% lower compared to baselines, while achieving a desired performance level in terms of localization coverage, error, and delay.

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21

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 (December 17, 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 tracing is applied to compensate for the variation of sound speed, this is one of the contributions of this article. Another contribution of this article is the iteration process which is used to improve the accuracy of localization and time synchronization. Simulation results show that the proposed joint approach outperforms the existing approaches in both energy efficiency and accuracy. This study also calculates Cramer-Rao lower bound to prove the convergence of the proposed technique along with the calculation of complexity of the proposed algorithm to show that the provided study takes less running time compared to the existing techniques.

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22

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 (April 1, 2017): 1–8. http://dx.doi.org/10.17485/ijst/2017/v10i15/93919.

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23

Elavarasan, R., and K. Chitra. "Efficient Localization Based Optimal Energy Routing for Wireless Sensor Networks." Journal of Computational and Theoretical Nanoscience 14, no. 6 (June 1, 2017): 2968–75. http://dx.doi.org/10.1166/jctn.2017.6369.

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24

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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25

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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26

Liu, Xi, Yiju Zhan, and Jian Cen. "An Energy-Efficient Crowd-Sourcing-Based Indoor Automatic Localization System." IEEE Sensors Journal 18, no. 14 (July 15, 2018): 6009–22. http://dx.doi.org/10.1109/jsen.2018.2842239.

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27

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

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28

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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Анотація:

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 that the proposed algorithm approximates the unknown node location and provides minimum localization error.

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29

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 (September 11, 2020): 10LT01. http://dx.doi.org/10.1088/1361-665x/abaa95.

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30

Alhmiedat, Tareq. "Fingerprint-Based Localization Approach for WSN Using Machine Learning Models." Applied Sciences 13, no. 5 (February 27, 2023): 3037. http://dx.doi.org/10.3390/app13053037.

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Анотація:

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, through establishing an indoor fingerprinting dataset and adopting four tailored ML models, is presented. The proposed system was validated by real experiments conducted in complex indoor environments with several obstacles and walls and achieves an efficient localization accuracy with an average of 1.4 m. In addition, through real experiments, we analyze and discuss the impact of reference point density on localization accuracy.

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31

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 (November 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 proposes a low-energy acoustic-based sensing scheme for target recognition and localization to be implemented in a cluster-based sensing approach designed to appropriately balance energy consumption and local processing performed by sensor nodes. A reduced set of low-complexity feature extraction methods in the time domain signal are used in the recognition process. The scheme uses the received energy of the acoustic signals for the target localization. This article details the network architecture, the scheme specification, and its implementation. The results show that the scheme can classify targets with 81.34% accuracy. It requires 3.2 mJ of energy when executed in MICAz, achieving 99% energy savings compared to streaming 3 s of an acoustic signal to a remote server.

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32

Suresh Limkar, Et al. "Energy-Efficient Localization Techniques for Wireless Sensor Networks in Indoor IoT Environments." Journal of Electrical Systems 19, no. 2 (January 25, 2024): 47–57. http://dx.doi.org/10.52783/jes.690.

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Анотація:

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 cases where there are limitations on line of sight, technologies based on ultrasound measure signal travel durations. Although calibration and sensitivity to interference are taken into account, magnetic field-based approaches use magnetic field anomalies to determine positions. Accuracy, energy usage, scalability, robustness, and calibration effort are some of the factors that these techniques are evaluated against in order to fulfil the demands of indoor IoT environments. A thoughtful choice of localization methods can increase energy efficiency, increase the lifespan of sensor networks, and enable precise location-aware IoT applications. In order to meet the increasing demand for energy-efficient localisation in Indoor IoT environments, more research in this field is still being conducted.

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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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Анотація:

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-efficient inertial measurement unit (IMU) sensors are routinely called to provide the displacement of a smartphone user in the manner of PDR, whereas a fuzzy inference system is employed to adaptively schedule energy-hungry WiFi scans to fulfill WiFi fingerprint localization according to a coarse metric for fusion localization errors and the remaining energy of the smartphone, so as to achieve a trade-off between localization accuracy and energy consumption. Moreover, in order to mitigate the effect of drift errors induced by PDR, straight trajectories of the user are further identified using a series of WiFi localization results to calibrate heading estimates of PDR. Extensive experimental results demonstrate that the proposed scheme achieves the same accuracy as the complementary filter, but consumes 38.02% energy than the complementary filter, confirming that the proposed scheme can effectively balance the localization accuracy and energy consumption.

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34

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 (August 2018): 999–1012. http://dx.doi.org/10.1631/fitee.1700598.

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35

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 (July 25, 2014): 571–77. http://dx.doi.org/10.19026/rjaset.8.1007.

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36

Yuan, Yazhou, Cailian Chen, Xinping Guan, and Qiuling Yang. "An Energy-Efficient Underground Localization System Based on Heterogeneous Wireless Networks." Sensors 15, no. 6 (May 26, 2015): 12358–76. http://dx.doi.org/10.3390/s150612358.

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37

Wei, Chun-Yi, and Hsuan-Yi Pan. "Adaptive Zone–Assisted Iterative Localization in Energy-Efficient Wireless Sensor Networks." IEEE Sensors Journal 21, no. 23 (December 1, 2021): 27186–94. http://dx.doi.org/10.1109/jsen.2021.3120883.

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38

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

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39

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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40

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 (June 2013): 1368–79. http://dx.doi.org/10.1109/tkde.2011.241.

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41

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 (June 2010): 897–910. http://dx.doi.org/10.1109/tmc.2010.39.

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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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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 (May 2016): 3450–61. http://dx.doi.org/10.1109/tvt.2015.2441733.

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44

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 (May 21, 2018): 1646. http://dx.doi.org/10.3390/s18051646.

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

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Анотація:

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 called ROCRSSI appears to be a suitable solution; however, it is affected by several inefficiencies that limit its effectiveness in real case scenarios. This paper proposes a refined version of this algorithm, called ROCRSSI++, which resolves such inefficiencies using and storing information gathered by the sensors in a more efficient manner. Several experiments on actual devices have been performed. The results show a reduction of the localization error with respect to the original algorithm. This paper investigates energy consumption and localization time required by the proposed approach.

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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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Анотація:

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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Kim, Yungeun, Songhee Lee, Seokjoon Lee, and Hojung Cha. "A GPS Sensing Strategy for Accurate and Energy-Efficient Outdoor-to-Indoor Handover in Seamless Localization Systems." Mobile Information Systems 8, no. 4 (2012): 315–32. http://dx.doi.org/10.1155/2012/109129.

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Анотація:

Indoor localization systems typically locate users on their own local coordinates, while outdoor localization systems use global coordinates. To achieve seamless localization from outdoors to indoors, a handover technique that accurately provides a starting position to the indoor localization system is needed. However, existing schemes assume that a starting position is known a priori or uses a naïve approach to consider the last location obtained from GPS as the handover point. In this paper, we propose an accurate handover scheme that monitors the signal-to-noise ratio (SNR) of the effective GPS satellites that are selected according to their altitude. We also propose an energy-efficient handover mechanism that reduces the GPS sampling interval gradually. Accuracy and energy efficiency are experimentally validated with the GPS logs obtained in real life.

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Jurdak, Raja, Branislav Kusy, and Alban Cotillon. "Group-based Motion Detection for Energy-Efficient Localisation." Journal of Sensor and Actuator Networks 1, no. 3 (October 19, 2012): 183–216. http://dx.doi.org/10.3390/jsan1030183.

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Long-term outdoor localization remains challenging due to the high energy profiles of GPS modules. Duty cycling the GPS module combined with inertial sensors can improve energy consumption. However, inertial sensors that are kept active all the time can also drain mobile node batteries. This paper proposes duty cycling strategies for inertial sensors to maintain a target position accuracy and node lifetime. We present a method for duty cycling motion sensors according to features of movement events, and evaluate its energy and accuracy profile for an empirical data trace of cattle movement. We further introduce the concept of group-based duty cycling, where nodes that cluster together can share the burden of motion detection to reduce their duty cycles. Our evaluation shows that both variants of motion sensor duty cycling yield up to 78% improvement in overall node power consumption, and that the group-based method yields an additional 20% power reduction during periods of low mobility.

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Bouhdid, Badia, Wafa Akkari, and Sofien Gannouni. "Low Cost Recursive Localization scheme for High Density Wireless Sensor Networks." International Journal on Semantic Web and Information Systems 13, no. 3 (July 2017): 68–88. http://dx.doi.org/10.4018/ijswis.2017070104.

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Анотація:

While existing localization approaches mainly focus on enhancing the accuracy, particular attention has recently been given to reducing the localization algorithm implementation costs. To obtain a tradeoff between location accuracy and implementation cost, recursive localization approaches are being pursued as a cost-effective alternative to the more expensive localization approaches. In the recursive approach, localization information increases progressively as new nodes compute their positions and become themselves reference nodes. A strategy is then required to control and maintain the distribution of these new reference nodes. The lack of such a strategy leads, especially in high density networks, to wasted energy, important communication overhead and even impacts the localization accuracy. In this paper, the authors propose an efficient recursive localization approach that reduces the energy consumption, the execution time, and the communication overhead, yet it increases the localization accuracy through an adequate distribution of reference nodes within the network.

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Akcan, Huseyin, and Cem Evrendilek. "Complexity of Energy Efficient Localization With the Aid of a Mobile Beacon." IEEE Communications Letters 22, no. 2 (February 2018): 392–95. http://dx.doi.org/10.1109/lcomm.2017.2772876.

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