Relevant bibliographies by topics / Energy-Efficient Localization

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'Energy-Efficient Localization'

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Published: 16 November 2024

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Journal articles on the topic "Energy-Efficient Localization"

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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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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 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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Energy-Efficient Localization"

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Vecchia, Davide. "Energy-efficient, Large-scale Ultra-wideband Communication and Localization." Doctoral thesis, Università degli studi di Trento, 2022. http://hdl.handle.net/11572/349081.

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Among the low-power wireless technologies that have emerged in recent years, ultra-wideband (UWB) has successfully established itself as the reference for accurate ranging and localization, both outdoors and indoors. Due to its unprecedented performance, paired with relatively low energy consumption, UWB is going to play a central role in the next wave of location-based applications. As the trend of integration in smartphones continues, UWB is also expected to reach ordinary users, revolutionizing our lives the same way GPS and similar technologies have done. But the impact of UWB may not be limited to ranging and localization. Because of its considerable data rate, and its robustness to obstacles and interference, UWB communication may hold untapped potential for sensing and control applications. Nevertheless, several research questions still need to be answered to assess whether UWB can be adopted widely in the communication and localization landscapes. On one hand, the rapid evolution of UWB radios and the release of ever more efficient chips is a clear indication of the growing market for this technology. However, for it to become pervasive, full-fledged communication and localization systems must be developed and evaluated, tackling the shortcomings affecting current prototypes. UWB systems are typically single-hop networks designed for small areas, making them impractical for large-scale coverage. This limitation is found in communication and localization systems alike. Specifically for communication systems, energy-efficient multi-hop protocols are hitherto unexplored. As for localization systems, they rely on mains-powered anchors to circumvent the issue of energy consumption, in addition to only supporting small areas. Very few options are available for light, easy to deploy infrastructures using battery-powered anchors. Nonetheless, large-scale systems are required in common settings like industrial facilities and agricultural fields, but also office spaces and museums. The general goal of enabling UWB in spaces like these entails a number of issues. Large multi-hop infrastructures exacerbate the known limitations of small, single-hop, networks; notably, reliability and latency requirements clash with the need to reduce energy consumption. Finally, when device mobility is a factor, continuity of operations across the covered area is a challenge in itself. In this thesis, we design energy-efficient UWB systems for large-scale areas, supporting device mobility across multi-hop infrastructures. As our opening contribution, we study the unique interference rejection properties of the radio to inform our design. This analysis yields a number of findings on the impact of interference in communication and distance estimation, that are directly usable by developers to improve UWB solutions. These findings also suggest that concurrent transmissions in the same frequency channel are a practical option in UWB. While the overlapping of frames is typically avoided to prevent collisions, concurrent transmissions have counter-intuitively been used to provide highly reliable communication primitives for a variety of traffic patterns in narrowband radios. In our first effort to use concurrent transmissions in a full system, we introduce the UWB version of Glossy, a renowned protocol for efficient network-wide synchronization and data dissemination. Inspired by the success of concurrency-based protocols in narrowband, we then apply the same principles to define a novel data collection protocol, Weaver. Instead of relying on independent Glossy floods like state-of-the-art systems, we weave multiple data flows together to make our collection engine faster, more reliable and more energy-efficient. With Glossy and Weaver supporting the communication aspect in large-scale networks, we then propose techniques for large-scale localization systems. We introduce TALLA, a TDoA solution for continuous position estimation based on wireless synchronization. We evaluate TALLA in an UWB testbed and in simulations, for which we replicate accurately the behavior of the clocks in our real-world platforms. We then offer a glimpse of what TALLA can be employed for, deploying an infrastructure in a science museum to track visitors. The collected movement traces allow us to analyze fine-grained stop-move mobility patterns and infer the sequence of visited exhibits, which is only possible because of the high spatio-temporal granularity offered by TALLA. Finally, with SONAR, we tackle the issue of large-scale ranging and localization when the infrastructure cannot be mains-powered. By blending synchronization and scheduling operations into neighbor discovery and ranging, we drastically reduce energy consumption and ensure years-long system lifetime. Overall, this thesis enhances UWB applicability in scenarios that were previously precluded to the technology, by providing the missing communication and localization support for large areas and battery-powered devices. Throughout the thesis, we follow an experiment-driven approach to validate our protocol models and simulations. Based on the evidence collected during this research endeavor, we develop full systems that operate in a large testbed at our premises, showing that our solutions are immediately applicable in real settings.
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Robles, Jorge Juan [Verfasser]. "Energy-Efficient Indoor Localization Based on Wireless Sensor Networks / Jorge Juan Robles." München : Verlag Dr. Hut, 2015. http://d-nb.info/1075408962/34.

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Oztarak, Hakan. "An Energy-efficient And Reactive Remote Surveillance Framework Using Wireless Multimedia Sensor Networks." Phd thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614328/index.pdf.

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With the introduction of Wireless Multimedia Sensor Networks, large-scale remote outdoor surveillance applications where the majority of the cameras will be battery-operated are envisioned. These are the applications where the frequency of incidents is too low to employ permanent staffing such as monitoring of land and marine border, critical infrastructures, bridges, water supplies, etc. Given the inexpensive costs of wireless resource constrained camera sensors, the size of these networks will be significantly larger than the traditional multi-camera systems. While large number of cameras may increase the coverage of the network, such a large size along with resource constraints poses new challenges, e.g., localization, classification, tracking or reactive behavior. This dissertation proposes a framework that transforms current multi-camera networks into low-cost and reactive systems which can be used in large-scale remote surveillance applications. Specifically, a remote surveillance system framework with three components is proposed: 1) Localization and tracking of objects
2) Classification and identification of objects
and 3) Reactive behavior at the base-station. For each component, novel lightweight, storage-efficient and real-time algorithms both at the computation and communication level are designed, implemented and tested under a variety of conditions. The results have indicated the feasibility of this framework working with limited energy but having high object localization/classification accuracies. The results of this research will facilitate the design and development of very large-scale remote border surveillance systems and improve the systems effectiveness in dealing with the intrusions with reduced human involvement and labor costs.
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Shah, Ghalib Asadullah. "Energy-efficient Real-time Coordination And Routing Framework For Wireless Sensor And Actor Networks." Phd thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/12608239/index.pdf.

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In Wireless Sensor Actor Networks (WSANs), sensor nodes perform the sensing task and actor nodes take action based on the sensed phenomenon. The presence of actors in this configuration can not be benefited from, unless they are able to execute actions at right place and right time in the event region. The right place can be related to the accurate position of the sensor nodes. While, the right time is related to delivering the packets directly to the appropriate actors within the event specific response times. Hence, the efficient localization of sensor nodes, sensor-actor/actor-actor coordination and real-time routing is indispensable in WSANs. Furthermore, the limited energy levels and bandwidth of the state of art sensor nodes currently impose stringent requirements for low-complexity, low-energy, distributed coordination and cooperation protocols and their implementation. In this study, we propose an integrated framework which addresses the issues of sensors localization, network configuration, data aggregation, real-time data delivery, sensor-actor/actor-actor coordination and energy saving mechanisms. The proposal incorporates novel approaches on three fronts
(1) timing-based sensors localization (TSL) algorithm to localize the sensor nodes relative to actors, (2) real-time coordination and routing protocols and (3) energy conservation. The distributed real-time coordination and routing is implemented in addressing and greedy modes routing. A cluster-based real-time coordination and routing (RCR) protocol operates in addressing mode. The greedy mode routing approach (Routing by Adaptive Targeting, RAT) is a stateless shortest path routing. In dense deployment, it performs well in terms of delay and energy consumption as compared to RCR. To keep the traffic volume under control, the framework incorporates a novel real-time data aggregation (RDA) approach in RCR such that the packets deadlines are not affected. RDA is adaptive to the traffic conditions and provides fairness among the farther and nearer cluster-heads. Finally, framework incorporates a power management scheme that eliminates data redundancy by exploiting the spatial correlation of sensor nodes. Simulation results prove that the framework provides the real-time guarantees up to 95 % of the packets with lesser energy consumption of up to 33 % achieved using MEAC as compared to LEACH and SEP. The packet delivery ratio is also 60 % higher than that of semi-automated architecture. Furthermore the action accuracy is supported by TSL which restricts the localization errors less than 1 meter by tuning it according to the expected velocity of nodes and required accuracy.
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Jouni, Zalfa. "Analog spike-based neuromorphic computing for low-power smart IoT applications." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPAST114.

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Avec l'expansion de l'Internet des objets (IoT) et l'augmentation des appareils connectés et des communications complexes, la demande de technologies de localisation précises et économes en énergie s'est intensifiée. Les techniques traditionnelles de machine learning et d'intelligence artificielle (IA) offrent une haute précision dans la localisation par radiofréquence (RF), mais au prix d'une complexité accrue et d'une consommation d'énergie élevée. Pour relever ces défis, cette thèse explore le potentiel de l'informatique neuromorphique, inspirée par les mécanismes du cerveau, pour permettre une localisation RF basée sur l'IA et économe en énergie. Elle présente un système neuromorphique analogique à base de pointes (RF NeuroAS), avec une version simplifiée entièrement implémentée en technologie BiCMOS 55 nm. Ce système identifie les positions des sources dans une plage de 360 degrés sur un plan bidimensionnel, en maintenant une haute résolution (10 ou 1 degré) même dans des conditions bruyantes. Le cœur de ce système, un réseau de neurones à impulsions basé sur l'analogique (A-SNN), a été formé et testé sur des données simulées (SimLocRF) à partir de MATLAB et des données expérimentales (MeasLocRF) provenant de mesures en chambre anéchoïque, tous deux développés dans cette thèse. Les algorithmes d'apprentissage pour l'A-SNN ont été développés selon deux approches: l'apprentissage profond (DL) et la plasticité dépendante du timing des impulsions (STDP) bio-plausible.RF NeuroAS atteint une précision de localisation de 97,1% avec SimLocRF et de 90,7% avec MeasLoc à une résolution de 10 degrés, tout en maintenant une haute performance avec une faible consommation d'énergie de l'ordre du nanowatt. Le RF NeuroAS simplifié consomme seulement 1.1 nW et fonctionne dans une plage dynamique de 30 dB. L'apprentissage de l'A-SNN, via DL et STDP, a démontré des performances sur les problèmes XOR et MNIST.Le DL dépend de la non-linéarité des fonctions de transfert post-layout des neurones et des synapses de l'A-SNN, tandis que le STDP dépend du bruit aléatoire dans les circuits neuronaux analogiques. Ces résultats marquent des avancées dans les applications IoT économes en énergie grâce à l'informatique neuromorphique, promettant des percées dans l'IoT intelligent à faible consommation d'énergie inspirées par les mécanismes du cerveau
As the Internet of Things (IoT) expands with more connected devices and complex communications, the demand for precise, energy-efficient localization technologies has intensified. Traditional machine learning and artificial intelligence (AI) techniques provide high accuracy in radio-frequency (RF) localization but often at the cost of greater complexity and power usage. To address these challenges, this thesis explores the potential of neuromorphic computing, inspired by brain functionality, to enable energy-efficient AI-based RF localization. It introduces an end-to-end analog spike-based neuromorphic system (RF NeuroAS), with a simplified version fully implemented in BiCMOS 55 nm technology. RF NeuroAS is designed to identify source positions within a 360-degree range on a two-dimensional plane, maintaining high resolution (10 or 1 degree) even in noisy conditions. The core of this system, an analog-based spiking neural network (A-SNN), was trained and tested on a simulated dataset (SimLocRF) from MATLAB and an experimental dataset (MeasLocRF) from anechoic chamber measurements, both developed in this thesis.The learning algorithms for A-SNN were developed through two approaches: software-based deep learning (DL) and bio-plausible spike-timing-dependent plasticity (STDP). RF NeuroAS achieves a localization accuracy of 97.1% with SimLocRF and 90.7% with MeasLoc at a 10-degree resolution, maintaining high performance with low power consumption in the nanowatt range. The simplified RF NeuroAS consumes just over 1.1 nW and operates within a 30 dB dynamic range. A-SNN learning, via DL and STDP, demonstrated performance on XOR and MNIST problems. DL depends on the non-linearity of post-layout transfer functions of A-SNN's neurons and synapses, while STDP depends on the random noise in analog neuron circuits. These findings highlight advancements in energy-efficient IoT through neuromorphic computing, promising low-power smart edge IoT breakthroughs inspired by brain mechanisms
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Lin, Wen-Chieh, and 林文傑. "Energy Efficient Localization Schemes in Wireless Sensor Networks." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/94697077125583010580.

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碩士
逢甲大學
資訊工程所
97
In wireless sensor networks, localization play an important role in data gathering, data reporting and object tracking. However if each sensor equips with GPS receiver, it is high cost and not energy efficient. Besides, sensors also unable to obtain the GPS signal in the indoor environment due to the shielding effect. In general, there are several anchors deployed in wireless sensor networks and the anchors broadcast beacons with their locations to help sensor nodes to estimate their locations; another way is that the mobile anchor moves along the planned path and broadcasts location information to improve localization accuracy and reduce cost. However, the path of a mobile anchor has obvious effect on accuracy, overhead and convergence time. This thesis focuses on path planning of a mobile anchor. By modifying Hilbert curve, it proposes several appropriate paths for different applications, such as time concern, intrusion detection, the low density environment, and the large scale networks. Simulation results show the proposed paths can reduce the energy consumption and time during localization and keep the accuracy.
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Lin, Tsung-Han, and 林宗翰. "Energy-Efficient Boundary Detection for RF-Based Indoor Localization Systems." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/96629098938075670811.

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碩士
國立臺灣大學
電機工程學研究所
96
Boundary detection is a form of location-aware services that aims at detecting targets crossing certain critical regions. Typically, a lower location sampling rate contributes to a lower level of energy consumption but, in the meantime, delays the detection of boundary crossing events. Opting to enable energy-efficient boundary detection services, we propose a mobility-aware mechanism that adapts the location sampling rate to the target mobility. Results from our simulations and live experiments confirm that the proposed adaptive sampling mechanism is effective. In particular, when experimented with realistic errors measured from a live RF-based localization system, the energy consumption can be reduced significantly to 20%.
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Lin, Tsung-Han. "Energy-Efficient Boundary Detection for RF-Based Indoor Localization Systems." 2008. http://www.cetd.com.tw/ec/thesisdetail.aspx?etdun=U0001-1807200814365200.

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You, Chuang-Wen, and 游創文. "Enabling Energy-Efficient Localization Services on Sensor Network Positioning Systems." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/40648502526364542762.

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博士
臺灣大學
資訊工程學研究所
96
One of the most important performance objectives for a localization system is positional accuracy. It is fundamental and essential to general location-aware services. Energy efficiency and positional accuracy, however, are often contradictive goals. We propose to decrease energy consumption without sacrificing significant accuracy by developing an energy-aware localization that adapts the sampling rate to target''s mobility level and current estimation error. As an energy-aware adaptive localization system, our system actively adapts its sampling rate to conserve energy without sacrificing significant accuracy according to target''s mobility level which is estimated with the help of an additional sensor. Moreover, in order to be more adhering to application''s requirements, we improve the radio interferometric positioning (RIP) method to estimate positional error more accurately. Because the positional error is highly related to not only user mobility level but also current estimation error, we designed an estimation error model to estimate the estimation error of the RIP algorithm and applied it in the design of our energy-efficient localization system. Furthermore, building upon this estimation error model, we devise an adaptive RIP method that selects the optimal sender-pair combination (SPC) according to the locations of targets relative to anchor nodes. Promising to satisfy an application''s requirements on positional accuracy, our system actively tries to adapt its sampling rate to reduce its energy consumption. In this thesis, energy-aware adaptive localization systems based on different sensor network localization systems, i.e. Zigbee-based fingerprinting positioning system or adaptive RIP system, are designed, implemented, and evaluated.
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Cheng, Li-Wen, and 鄭理文. "Energy-Efficient Indoor Localization and Tracking for Internet of Things." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/mjasxg.

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碩士
國立臺灣大學
電信工程學研究所
106
With the rapid rise of Internet of Things (IoT), it is convenient to connect the physical world to the Internet through wireless communication. IoT is composed of many sensors and wireless networks. IoT has been applied to a lot of applications such as environmental sensing, factory automation, healthcare monitoring, etc. In the hospital, nurses and doctors are interested in the health status of patients and, of course, the location of them. In the factory, the managers want to monitor not only the robot operations but also the locations of them. Among these various applications, knowing the locations of the sensors and users is great importance. Although Global Positioning System (GPS) is a popular localization technology, it does not work in most of indoor environments. A common method for locating and tracking objects in indoor environments is to use known positions of anchors with the radio frequency (RF) signal. In short, objects to be located or tracked transmit beacons and then anchors estimate the distances based on the signal strength of beacons. A well-known problem of using the RF signal is its large variation as the received signal strength is often influenced by the multipath and shadowing. Some other signal signals such as ultrasound and the laser can be used to obtain better distance measurement but doing so incurs additional hardware and installation cost. Regardless of the signal used to measure the distance, two other challenges need to be resolved in order to enable localization/tracking in a real environment. First, tracked objects are usually battery powered due to mobility. Thus, the power of it is limited and must be well managed. How to save the energy while maintaining tracking accuracy then becomes an important issue. Second, the distance info usually goes through a large and potentially multi-hop networks for process at a local or cloud server in many practical usage scenarios. Therefore, end-to-end reliability of transporting a very large number of distance messages in a short period of time is also a critical design issue. In this thesis, we focus on these two challenges and propose a feasible locating/tracking solution. We adopt a duty-cycling mechanism that takes MCU wake-up interval (MWI) and Maximum Beacon Transmission Interval (MBTI) into consideration. G-sensor is also used to make tradeoff between energy consumption and tracking accuracy. For end-to-end reliability, we take network congestion into account. By using directional and prioritized forwarding, distance messages can be delivered on time. The proposed solution is implemented to evaluate its performance. In our test bed in the NTU BL building, a total of 48 anchors are installed while up to 12 tags are deployed. An end-to-end delivery rate of 85% can be reached in case with 8 tags. The rates even increase to 96% in case with 1 tag. In addition, the average tracking error is 1.1 meters when the tags are static and is 5.1 meters when the tags are motion. The lifetime of mobile tags is almost two years with a 300 mAH battery. The results show the feasibility of our solution in real-world environments.
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Book chapters on the topic "Energy-Efficient Localization"

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Sutagundar, A. V., S. S. Shirabur, and V. S. Bennur. "Energy Efficient Localization in Wireless Sensor Networks." In Lecture Notes in Electrical Engineering, 139–46. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-03692-2_11.

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Constandache, Ionut, Shravan Gaonkar, Matt Sayler, Romit Roy Choudhury, and Landon Cox. "Energy-Efficient Localization via Personal Mobility Profiling." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 203–22. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12607-9_14.

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Liu, Minmin, Baoqi Huang, Qing Miao, and Bing Jia. "An Energy-Efficient DV-Hop Localization Algorithm." In Algorithms and Architectures for Parallel Processing, 175–86. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-05054-2_13.

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Gu, Yu, Wei Zhang, HengChang Liu, Baohua Zhao, and Yugui Qu. "Energy-Efficient Target Localization Based on a Prediction Model." In Embedded and Ubiquitous Computing – EUC 2005 Workshops, 1178–90. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11596042_120.

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Shikoska, Ustijana Rechkoska, and Danco Davcev. "An Energy-Efficient Approach for Time-Space Localization in Wireless Sensor Networks." In Advances in Intelligent and Soft Computing, 107–18. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28664-3_10.

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Kumar, Sunil, Prateek Raj Gautam, Swati Verma, and Arvind Kumar. "An Energy-Efficient Localization Scheme Using Beacon Nodes for Wireless Sensor Networks." In Lecture Notes in Electrical Engineering, 145–55. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6840-4_12.

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Hao, Kun, Haifeng Shen, Yonglei Liu, and Beibei Wang. "An Energy-Efficient Localization-Based Geographic Routing Protocol for Underwater Wireless Sensor Networks." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 365–73. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-90802-1_32.

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Bhairavi, R., and Gnanou Florence Sudha. "Energy Efficient Advancement-Based Dive and Rise Localization for Underwater Acoustic Sensor Networks." In Inventive Computation and Information Technologies, 241–55. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-6723-7_18.

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Kalina, Jan, and Patrik Janáček. "Robustness Aspects of Optimized Centroids." In Studies in Classification, Data Analysis, and Knowledge Organization, 193–201. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-09034-9_22.

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AbstractCentroids are often used for object localization tasks, supervised segmentation in medical image analysis, or classification in other specific tasks. This paper starts by contributing to the theory of centroids by evaluating the effect of modified illumination on the weighted correlation coefficient. Further, robustness of various centroid-based tools is investigated in experiments related to mouth localization in non-standardized facial images or classification of high-dimensional data in a matched pairs design. The most robust results are obtained if the sparse centroid-based method for supervised learning is accompanied with an intrinsic variable selection. Robustness, sparsity, and energy-efficient computation turn out not to contradict the requirement on the optimal performance of the centroids.
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Sandnes, Frode Eika. "An Energy Efficient Localization Strategy for Outdoor Objects Based on Intelligent Light-Intensity Sampling." In Ubiquitous Intelligence and Computing, 192–204. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16355-5_17.

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Conference papers on the topic "Energy-Efficient Localization"

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Cheng, Wei, Jindan Zhu, Prasant Mohapatra, and Jie Wang. "Time and energy efficient localization." In 2014 Eleventh Annual IEEE International Conference on Sensing, Communication, and Networking (SECON). IEEE, 2014. http://dx.doi.org/10.1109/sahcn.2014.6990330.

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Dai, We nhan, Yuan Shen, and Moe Z. Win. "Energy efficient cooperative network localization." In ICC 2014 - 2014 IEEE International Conference on Communications. IEEE, 2014. http://dx.doi.org/10.1109/icc.2014.6884108.

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Jurdak, Raja, Peter Corke, Dhinesh Dharman, Guillaume Salagnac, Chris Crossman, Philip Valencia, and Greg-Bishop Hurley. "Energy-efficient localization for virtual fencing." In the 9th ACM/IEEE International Conference. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1791212.1791268.

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You, Xudong, Zefang Lv, Yuzhen Ding, Wei Su, and Liang Xiao. "Reinforcement Learning Based Energy Efficient Underwater Localization." In 2020 International Conference on Wireless Communications and Signal Processing (WCSP). IEEE, 2020. http://dx.doi.org/10.1109/wcsp49889.2020.9299789.

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Wang, Yufeng, Yuanting Bu, Qun Jin, and Athanasios V. Vasilakos. "Energy-Efficient Localization and Tracking on Smartphones." In CFI '16: The 11th International Conference on Future Internet Technologies. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2935663.2935675.

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Constandache, I., S. Gaonkar, M. Sayler, R. R. Choudhury, and L. Cox. "EnLoc: Energy-Efficient Localization for Mobile Phones." In 2009 Proceedings IEEE INFOCOM. IEEE, 2009. http://dx.doi.org/10.1109/infcom.2009.5062218.

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Dhumal, Sujata, and B. S. Shetty. "Energy Efficient Coverage and Sensor Localization for Scheduling." In 2019 International Conference on Communication and Electronics Systems (ICCES). IEEE, 2019. http://dx.doi.org/10.1109/icces45898.2019.9002075.

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You, Chuang-wen, Yi-chao Chen, Ji-rung Chiang, Polly Huang, Hao-hua Chu, and Seng-yong Lau. "Sensor-Enhanced Mobility Prediction for Energy-Efficient Localization." In 2006 3rd Annual IEEE Communications Society on Sensor and Ad Hoc Communications and Networks. IEEE, 2006. http://dx.doi.org/10.1109/sahcn.2006.288513.

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Xi, Teng, Wendong Wang, Edith C. H. Ngai, Zheng Song, Ye Tian, and Xiangyang Gong. "Energy-Efficient Collaborative Localization for Participatory Sensing System." In GLOBECOM 2015 - 2015 IEEE Global Communications Conference. IEEE, 2014. http://dx.doi.org/10.1109/glocom.2014.7416983.

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Xi, Teng, Wendong Wang, Edith C. H. Ngai, Zheng Song, Ye Tian, and Xiangyang Gong. "Energy-Efficient Collaborative Localization for Participatory Sensing System." In GLOBECOM 2015 - 2015 IEEE Global Communications Conference. IEEE, 2015. http://dx.doi.org/10.1109/glocom.2015.7416983.

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Reports on the topic "Energy-Efficient Localization"

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Oliynyk, Kateryna, and Matteo Ciantia. Application of a finite deformation multiplicative plasticity model with non-local hardening to the simulation of CPTu tests in a structured soil. University of Dundee, December 2021. http://dx.doi.org/10.20933/100001230.

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Abstract:
In this paper an isotropic hardening elastoplastic constitutive model for structured soils is applied to the simulation of a standard CPTu test in a saturated soft structured clay. To allow for the extreme deformations experienced by the soil during the penetration process, the model is formulated in a fully geometric non-linear setting, based on: i) the multiplicative decomposition of the deformation gradient into an elastic and a plastic part; and, ii) on the existence of a free energy function to define the elastic behaviour of the soil. The model is equipped with two bonding-related internal variables which provide a macroscopic description of the effects of clay structure. Suitable hardening laws are employed to describe the structure degradation associated to plastic deformations. The strain-softening associated to bond degradation usually leads to strain localization and consequent formation of shear bands, whose thickness is dependent on the characteristics of the microstructure (e.g, the average grain size). Standard local constitutive models are incapable of correctly capturing this phenomenon due to the lack of an internal length scale. To overcome this limitation, the model is framed using a non-local approach by adopting volume averaged values for the internal state variables. The size of the neighbourhood over which the averaging is performed (characteristic length) is a material constant related to the microstructure which controls the shear band thickness. This extension of the model has proven effective in regularizing the pathological mesh dependence of classical finite element solutions in the post-localization regime. The results of numerical simulations, conducted for different soil permeabilities and bond strengths, show that the model captures the development of plastic deformations induced by the advancement of the cone tip; the destructuration of the clay associated with such plastic deformations; the space and time evolution of pore water pressure as the cone tip advances. The possibility of modelling the CPTu tests in a rational and computationally efficient way opens a promising new perspective for their interpretation in geotechnical site investigations.
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