Relevant bibliographies by topics / Location-based networks

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

Academic literature on the topic 'Location-based networks'

Author: Grafiati
Published: 4 June 2021
Last updated: 10 February 2022

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Journal articles on the topic "Location-based networks"

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Baydar, Mucahit, and Songul Albayrak. "Location prediction in location-based social networks." Global Journal of Information Technology: Emerging Technologies 7, no. 3 (December 24, 2017): 149–56. http://dx.doi.org/10.18844/gjit.v7i3.2835.

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AbstractDevelopments in mobile devices and wireless networks have led to the increasing popularity of location-based social networks. These networks allow users to explore new places, share their location, videos and photos and make friends. They give information about the mobility of users, which can be used to improve the networks. This paper studies the problem of predicting the next check-in of users of location-based social networks. For an accurate prediction, we first analyse the datasets that are obtained from the social networks, Foursquare and Gowalla. Then we obtain some features like place popularity, place popular time range, place distance to user’s home, user’s past visits, category preferences and friendships ,which are used for prediction and deeper understanding of the user behaviours. We use each feature individually, and then in combination, using the new method. Finally, we compare the acquired results and observe the improvement with the new method.Keywords: Location prediction, location-based social network, check-in data.
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Yan, Gongjun, Wu He, Hui Shi, and Dazhi Chong. "Rank and Location Based Security Mechanism for Mobile Networks." International Journal of Signal Processing Systems 7, no. 2 (March 2019): 43–48. http://dx.doi.org/10.18178/ijsps.7.2.43-48.

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Rahimi, Seyyed Mohammadreza, Behrouz Far, and Xin Wang. "Behavior-based location recommendation on location-based social networks." GeoInformatica 24, no. 3 (May 25, 2019): 477–504. http://dx.doi.org/10.1007/s10707-019-00360-3.

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Guo, Lei, Haoran Jiang, and Xinhua Wang. "Location Regularization-Based POI Recommendation in Location-Based Social Networks." Information 9, no. 4 (April 9, 2018): 85. http://dx.doi.org/10.3390/info9040085.

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Yu, Fei, and Shouxu Jiang. "Mining Location Influence for Location Promotion in Location-Based Social Networks." IEEE Access 6 (2018): 73444–56. http://dx.doi.org/10.1109/access.2018.2882057.

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Omar, Asma. "LOCATION BASED PROTOCOL FOR WIRELESS NETWORKS." Engineering Science & Technology Journal 2, no. 1 (March 29, 2020): 26–34. http://dx.doi.org/10.51594/estj.v2i1.120.

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The current networks such as GPSR have its own limitations such as data transmission delay and node errors. To overcome such problems, new protocols are proposed in various studies. In current study, a location-based protocol for wireless network is proposed by using the MAC interception. The algorithm is such that if best possible node is not possible, the data can be transferred using the second-best possible node thus introducing the intermediary nodes as well in the model. Additionally, based on simulation, the results are compared for the performance of the proposed protocol with two other protocols. The results show that the proposed protocol perform better compare to the other protocols in terms of data transmission efficiency and reliability.
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Likhyani, Ankita, Srikanta Bedathur, and Deepak P. "Location-Specific Influence Quantification in Location-Based Social Networks." ACM Transactions on Intelligent Systems and Technology 10, no. 3 (May 31, 2019): 1–28. http://dx.doi.org/10.1145/3300199.

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Mengual, Luis, Oscar Marbán, and Santiago Eibe. "Clustering-based location in wireless networks." Expert Systems with Applications 37, no. 9 (September 2010): 6165–75. http://dx.doi.org/10.1016/j.eswa.2010.02.111.

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Chorley, Martin J., Roger M. Whitaker, and Stuart M. Allen. "Personality and location-based social networks." Computers in Human Behavior 46 (May 2015): 45–56. http://dx.doi.org/10.1016/j.chb.2014.12.038.

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Guo, Lei, Yufei Wen, and Fangai Liu. "Location perspective-based neighborhood-aware POI recommendation in location-based social networks." Soft Computing 23, no. 22 (January 10, 2019): 11935–45. http://dx.doi.org/10.1007/s00500-018-03748-9.

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Dissertations / Theses on the topic "Location-based networks"

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Saleem, Muhammad. "Location Analytics for Location-Based Social Networks." Doctoral thesis, Universite Libre de Bruxelles, 2018. https://dipot.ulb.ac.be/dspace/bitstream/2013/271131/5/contratMS.pdf.

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The popularity of location empowered devices such as GPS enabled smart-phones has immensely amplified the use of location-based services in social networks. This happened by allowing users to share Geo-tagged contents such as current locations/check-ins with their social network friends. These location-aware social networks are called Location-based Social Networks (LBSN), and examples include Foursquare and Gowalla. The data of LBSNs are being used for providing different kinds of services such as the recommendation of locations, friends, activities, and media contents, and the prediction of user's locations. To provide such services, different queries are utilized that exploit activity/check-in data of users. Usually, LBSN data is divided into two parts, a social graph that encapsulates the friendships of users and an activity graph that maintains the visit history of users at locations. Such a data separation is scalable enough for processing queries that directly utilize friendship information and visit history of users. These queries are called user and activity analytic queries. The visits of users at locations create relationships between those locations. Such relationships can be built on different features such as common visitors, geographical distance, and mutual location categories between them. The process of analysing such relationships for optimizing location-based services is termed Location Analytics. In location analytics, we expose the subjective nature of locations that can further be used for applications in the domain of prediction of visitors, traffic management, route planning, and targeted marketing.In this thesis, we provide a general LBSN data model which can support storage and processing of queries required for different applications, called location analytics queries. The LBSN data model we introduce, segregates the LBSN data into three graphs: the social graph, the activity graph, and the location graph. The location graph maintains the interactions of locations among each other. We define primitive queries for each of these graphs. In order to process an advanced query, we express it as a combination of these primitive queries and process them on corresponding graphs in parallel. We further provide a distributed data processing framework called GeoSocial-GraphX (GSG). GSG implements the aforementioned LBSN data model for efficient and scalable processing of the queries. We further exploit the location graph for providing novel location analytics queries in the domain of influence maximization and visitor prediction. We introduce a notion of location influence. Such influence can capture the interactions of locations based on their visitors and can be used for propagation of information between them. The applications of such a query lie in the domain of outdoor marketing, and simulation of virus and news propagation. We also provide a unified system IMaxer that can evaluate and compare different information propagation mechanisms. We further exploit the subjective nature of locations by analysing the mobility behaviour of their visitors. We use such information to predict the individual visitors as well as the groups of visitors (cohorts) in future for those locations. The prediction of visitors can be used for better event planning, traffic management, targeted marketing, and ride-sharing services.In order to evaluate the proposed frameworks and approaches, we utilize data from four real-life LBSNs: Foursquare, Brightkite, Gowalla, and Wee Places. The detailed LBSN data mining and statistically significant experimental evaluation results show the effectiveness, efficiency, and scalability of our proposed methods. Our proposed approaches can be employed in real systems for providing life-care services.
Doctorat en Sciences de l'ingénieur et technologie
The portal is not showing my complete name. The name (my complete name), I want to have on the diploma is "Muhammad Aamir Saleem". Please correct this issue.
info:eu-repo/semantics/nonPublished
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NETO, JOSE ANTONIO CASEMIRO. "LOCATION BASED ROUTING IN AD-HOC NETWORKS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2007. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=11484@1.

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CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO
Um avanço importante gerado pela tecnologia de TV digital é a possibilidade de interatividade com os usuários, realizada por meio do assim chamado canal de retorno. As redes ad hoc têm um grande potencial para atender esse tipo de serviço, pois podem ser empregadas em diversas áreas geográficas e idealmente de forma independente de infra- estrutura. Isso diminui o seu custo e propícia o aumento da velocidade de implantação deste tipo de rede. Uma das principais questões técnicas a serem resolvidas no contexto das redes móveis ad hoc é a necessidade de algoritmos eficientes para a realização do roteamento dos pacotes. O projeto Terminodes, desenvolvido pelo Instituto Federal de Tecnologia da Suíça, desenvolveu um protocolo de roteamento que utiliza a informação de localização. Este método de roteamento é freqüentemente proposto como um meio para prover escalabilidade em redes ad hoc distribuídas sobre áreas geográficas extensas. O roteamento baseado em localização é difícil quando há áreas de exclusão na topologia da rede e os nós são móveis ou freqüentemente desconectados para fins de economia de bateria. Portanto, a investigação da robustez do protocolo para esses casos é fundamental para avaliar seu uso em redes que podem servir como canal de retorno de TV digital.
An important advance generated by the technology of digital TV is the possibility of interactivity with the users, what is done by means of the return channel. The mobile ad hoc networks have a great potential to provide this type of service, because it can ideally be used in diverse geographic areas and independent of any infrastructure. This minimizes the costs and the time needed to implement the network for this canal. One of the main questions techniques in the context of the mobile ad hoc networks is the necessity of efficient routing algorithms. The Terminodes project, developed by the Federal Institute of Technology of Switzerland, developed a routing protocol that is based in location information. This routing method frequently is a way to provide scalability in large ad hoc networks. The routing based on location is difficult when it has areas of exclusion in the topology of the network and the nodes are mobile or they are frequently disconnected to save battery. Therefore, assess the robustness of the protocol for these cases is basic to evaluate its use in networks for the digital TV return channel.
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Rebaza, Jorge Carlos Valverde. "Mining user behavior in location-based social networks." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-16112017-085356/.

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Online social networks (OSNs) are Web platforms providing different services to facilitate social interaction among their users. A particular kind of OSNs is the location-based social network (LBSN), which adds services based on location. One of the most important challenges in LBSNs is the link prediction problem. Link prediction problem aims to estimate the likelihood of the existence of future friendships among user pairs. Most of the existing studies in link prediction focus on the use of a single information source to perform predictions, i.e. only social information (e.g. social neighborhood) or only location information (e.g. common visited places). However, some researches have shown that the combination of different information sources can lead to more accurate predictions. In this sense, in this thesis we propose different link prediction methods based on the use of different information sources naturally existing in these networks. Thus, we propose seven new link prediction methods using the information related to user membership in social overlapping groups: common neighbors within and outside of common groups (WOCG), common neighbors of groups (CNG), common neighbors with total and partial overlapping of groups (TPOG), group naïve Bayes (GNB), group naïve Bayes of common neighbors (GNB-CN), group naïve Bayes of Adamic-Adar (GNB-AA) and group naïve Bayes of Resource Allocation (GNB-RA). Due to that social groups exist naturally in networks, our proposals can be used in any type of OSN.We also propose new eight link prediction methods combining location and social information: Check-in Observation (ChO), Check-in Allocation (ChA), Within and Outside of Common Places (WOCP), Common Neighbors of Places (CNP), Total and Partial Overlapping of Places (TPOP), Friend Allocation Within Common Places (FAW), Common Neighbors of Nearby Places (CNNP) and Nearby Distance Allocation (NDA). These eight methods are exclusively for work in LBSNs. Obtained results indicate that our proposals are as competitive as state-of-the-art methods, or better than they in certain scenarios. Moreover, since our proposals tend to be computationally more efficient, they are more suitable for real-world applications.
Redes sociais online (OSNs) são plataformas Web que oferecem serviços para promoção da interação social entre usuários. OSNs que adicionam serviços relacionados à geolocalização são chamadas redes sociais baseadas em localização (LBSNs). Um dos maiores desafios na análise de LBSNs é a predição de links. A predição de links refere-se ao problema de estimar a probabilidade de conexão futura entre pares de usuários que não se conhecem. Grande parte das pesquisas que focam nesse problema exploram o uso, de maneira isolada, de informações sociais (e.g. amigos em comum) ou de localização (e.g. locais comuns visitados). Porém, algumas pesquisas mostraram que a combinação de diferentes fontes de informação pode influenciar o incremento da acurácia da predição. Motivado por essa lacuna, neste trabalho foram desenvolvidos diferentes métodos para predição de links combinando diferentes fontes de informação. Assim, propomos sete métodos que usam a informação relacionada à participação simultânea de usuários en múltiples grupos sociais: common neighbors within and outside of common groups (WOCG), common neighbors of groups (CNG), common neighbors with total and partial overlapping of groups (TPOG), group naïve Bayes (GNB), group naïve Bayes of common neighbors (GNB-CN), group naïve Bayes of Adamic-Adar (GNB-AA), e group naïve Bayes of Resource Allocation (GNB-RA). Devido ao fato que a presença de grupos sociais não está restrita a alguns tipo de redes, essas propostas podem ser usadas nas diversas OSNs existentes, incluindo LBSNs. Também, propomos oito métodos que combinam o uso de informações sociais e de localização: Check-in Observation (ChO), Check-in Allocation (ChA), Within and Outside of Common Places (WOCP), Common Neighbors of Places (CNP), Total and Partial Overlapping of Places (TPOP), Friend Allocation Within Common Places (FAW), Common Neighbors of Nearby Places (CNNP), e Nearby Distance Allocation (NDA). Tais propostas são para uso exclusivo em LBSNs. Os resultados obtidos indicam que nossas propostas são tão competitivas quanto métodos do estado da arte, podendo até superá-los em determinados cenários. Ainda mais, devido a que na maioria dos casos nossas propostas são computacionalmente mais eficientes, seu uso resulta mais adequado em aplicações do mundo real.
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Li, Yafei. "Efficient group queries in location-based social networks." HKBU Institutional Repository, 2015. https://repository.hkbu.edu.hk/etd_oa/203.

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Nowadays, with the rapid development of GPS-equipped mobile devices, location-based social networks have been emerging to bridge the gap between the physical world and online social networking services. Various types of data, such as personal locations, check-ins, microblogs and social relations, have been available in location-based social networks. Efficiently managing and analyzing such data to meet users' daily query requirements become a challenging task. Among all the existing works in location-based social networks, group query is one of the most important research topics. In this thesis, we investigate query techniques for location-based services in social networking applications. Specifically, considering a location-based social network, we study spatial-aware interest group queries, geo-social {dollar}k{dollar}-cover group queries, and social-aware ridesharing group queries. Firstly, we study the spatial-aware interest group queries in location-based social networks. Recently, most of the location-based social networks release check-in services that allow users to share their visiting locations with their friends. These locations, considered as spatial objects, are usually associated with a few tags that describe the features of those locations. Utilizing such information, we propose a new type of \emph{Spatial-aware Interest Group} (SIG) query that retrieves a user group of size {dollar}k{dollar} where each user is interested in the query keywords and the users are close to each other in the Euclidean space. We prove this query problem is NP-complete, and develop two efficient algorithms IOAIR and DOAIR based on the IR-tree for the processing of SIG queries. We also validate the performance efficiency of the proposed query processing algorithms by empirical evaluation. Secondly, we study the problem of geo-social {dollar}k{dollar}-cover group queries for collaborative spatial computing. In this problem, we propose a novel type of geo-social queries, called \emph{Geo-Social K-Cover Group} (GSKCG) query, which is based on spatial containment and a new modeling of social relationships. Intuitively, given a set of spatial query points and an underlying social network, a GSKCG query finds a minimum user group in which the members satisfy certain social relationship and their associated regions can jointly cover all the query points. Albeit its practical usefulness, the GSKCG query problem is NP-complete. We consequently explore a set of effective pruning strategies to derive an efficient algorithm for finding the optimal solution. Moreover, we design a novel index structure tailored to our problem to further accelerate query processing. Extensive experiments demonstrate that our algorithm achieves desirable performance on real-life datasets. Thirdly, we study the problem of social-aware ridesharing group queries. With the deep penetration of smartphones and geo-locating devices, ridesharing is envisioned as a promising solution to transportation-related problems such as congestion and air pollution for metropolitan cities. Despite the potential to provide significant societal and environmental benefits, ridesharing has not so far been as popular as expected. Notable barriers include the social discomfort and safety concerns when traveling with strangers. To overcome these barriers, in this thesis, we propose a new type of \emph{Social-aware Ridesharing Group} (SaRG) query which retrieves a group of riders by taking into account their social connections besides traditional spatial proximities. Because the SaRG query problem is NP-hard, we design an efficient algorithm with a set of powerful pruning techniques to tackle this problem. We also present several incremental strategies to accelerate the search speed by reducing the repeated computations. Moreover, we propose a novel index tailored to the proposed problem to further speed up the query processing. Experimental results on real datasets show that our proposed algorithms achieve desirable performance. The works of this thesis show that the group query processing techniques are effective, which would facilitate the wider deployment of such query services in real applications
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Nilsson, Joackim. "Dynamic Personal Networks for Location-Based Applications : Within MediaSense." Thesis, Mid Sweden University, Department of Information Technology and Media, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-11782.

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As the development of context aware applications has evolved, there has been a corresponding increase in need for more sophisticated system. The aim for this thesis is the development of a dynamical P2P network system which is based on locations. The P2P network is self organizing and in a lightweight format. Modern technical solutions including   AGPS have facilitated the work associated with the ability to position users and modern mathematical solutions such as spherical trigonometry provides the P2P system with the necessary accuracy even for short distances. The P2P system works on different Java platforms including JSE, JME and Android. Unfortunately the 3G network distributor has not yet solved a NAT traversal problem, which means that the P2P network self organization and architecture has only been proved by means of simulations. Another problem is that certain mathematical formulas are required for the spherical trigonometry and the limitation for JME is that it is unable to handle inverse trigonometry. However, the Android and JSE versions can form a correct P2P network, under the condition that the Android device uses a WIFI connection point outside the 3G distributor network system. This thesis reports the successful testing of the locations-based P2P network.


MediaSense
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Chen, Chi. "Location-based data aggregation in mobile ad hoc networks." [S.l. : s.n.], 2003. http://www.bsz-bw.de/cgi-bin/xvms.cgi?SWB11144241.

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Siebert, Matthias. "Interworking of wireless and mobile networks based on location information /." Aachen : Mainz, 2006. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=016147498&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA.

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Wong, Hak Lim. "Signal strength-based location estimation in two different mobile networks." HKBU Institutional Repository, 2006. http://repository.hkbu.edu.hk/etd_ra/700.

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Ciurana, Adell Marc. "Contributions to TOA-based location with wlan." Doctoral thesis, Universitat Politècnica de Catalunya, 2010. http://hdl.handle.net/10803/77902.

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Location techniques that satisfy the requirements of advanced Location-Based Services (LBS) in environments where GPS fails are needed, therefore accurate indoor positioning is becoming increasingly important. This PhD Thesis is devoted to the research on location of mobile devices employing WLAN (IEEE 802.11). The use of this kind of wireless networks infrastructures for positioning enables a powerful synergy between communications and location and allows solutions with good performances at moderated costs. However the adopted WLAN location methods suffer from important limitations that prevents from applying them to some fields that need more flexible and robust solutions. The main objective of this PhD is exploring precise WLAN location methods that allow overcoming these limitations. The researched methods here are based on measuring the Time Of Arrival (TOA), which is the time that takes the signal propagating from the transmitter to the receiver. TOA-based location works in two stages: ranging and positioning. The ranging consists of estimating the distances between the targeted terminal and several WLAN access points, each distance obtained measuring the TOA and then multiplying it by the speed of the WLAN signal. After that, the positioning takes as inputs the estimated distances and the known coordinates of the involved access points and calculates the position of the terminal by means of a trilateration or tracking algorithm. The key problem is that the characteristics of the IEEE 802.11 protocols difficult to perform accurate TOA measurements. The main challenge that faces the research work reported here is demonstrating the feasibility of achieving this while keeping the modifications over standard WLAN consumer equipment at minimum. The objective of this work can be understood as exploring the current limits of TOA-based methods over WLAN, making contributions that form a complete TOA-based location method that goes a step forward with respect to the other existing proposals. First, research on TOA-based ranging -the key component of TOA-based location methods- is reported. The general adopted approach consists of performing Round Trip Time (RTT) measurements employing IEEE 802.11 MAC frames, taking the maximum advantage of the combination of IEEE 802.11 protocol and WLAN consumer devices mechanisms. After that, the performed research on trilateration/tracking -the second stage of TOA-based location methods- is explained. Finally some performed studies about the achieved location method are presented. Lloc i data Signatura
Actualment existeix la necessitat de disposar de tècniques de localització que satisfacin els requeriments de serveis avançats basats en localització en entorns on GPS no està disponible, de manera que el posicionament precís en interiors d’edificis és cada vegada més important. Aquesta tesi doctoral està dedicada a la investigació sobre la localització de dispositius mòbils que utilitzen WLAN (IEEE 802.11). L'ús d'aquest tipus de xarxes sense fils per al posicionament permet una profitosa sinèrgia entre les comunicacions i la localització i permet solucions amb un bon rendiment a un cost moderat. No obstant això, els mètodes basats WLAN proposats fins el moment pateixen de limitacions importants que impedeix la seva aplicació a alguns camps que requereixen solucions més flexibles i robustes. L'objectiu principal d'aquesta tesi és explorar mètodes de localització precisa WLAN que permetin superar aquestes limitacions. Els mètodes que s’han investigat durant la tesi es basen en la mesura del time of arrival (TOA), que és el temps que tarda el senyal en propagar-se des del transmissor fins al receptor. En les tècniques de posicionament basades en TOA s’hi poden diferenciar dues fases: ranging i posicionament. El ranging consisteix en l’estimació de distàncies entre el terminal a localitzar i diversos punts d'accés WLAN; cada estimació de distància s’obté mesurant el TOA i multiplicant-lo després per la velocitat de propagació del senyal IEEE 802.11. Un cop fet això, el posicionament pren com a inputs les distàncies estimades per a, conegudes les coordenades dels punts d'accés involucrats, calcular la posició del terminal per mitjà d'un algoritme de tracking o trilateració. El problema clau és que les característiques dels protocols IEEE 802.11 a dia d’avui fan difícil la realització de mesures precises de TOA d’una manera senzilla. El principal repte que afronta el present treball de recerca és demostrar la viabilitat d’això darrer, minimitzant en la major mesura possible les modificacions sobre els equips WLAN comercials. L'objectiu d'aquest treball pot ser entesa com l'exploració dels límits actuals dels mètodes de posicionament basats en TOA sobre WLAN, realitzant contribucions que conformen un mètode complet de localització basat en TOA que pretén anar un pas endavant respecte a les propostes existents. En primer lloc, la investigació sobre ranging basat en TOA -el component clau dels mètode de localització TOA- és explicada en detall. El mètode general adoptat per a calcular el TOA consisteix en la mesura del temps d'anada i tornada del senyal, round trip time (RTT), utilitzant trames MAC IEEE 802.11 per tal de treure el màxim profit de la combinació del protocol IEEE 802.11 i els mecanismes dels dispositius WLAN comercials. Després d'això, es detalla la investigació realitzada sobre trilateració i tracking, la segona etapa dels mètodes de localització basats en TOA. Finalment es descriuen alguns estudis realitzats sobre les prestacions, possibles millores i encaix en futurs estàndars del mètode de localització explorat.
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Ayyagari, Suhaas Bhargava. "ARTIFICIAL NEURAL NETWORK BASED FAULT LOCATION FOR TRANSMISSION LINES." UKnowledge, 2011. http://uknowledge.uky.edu/gradschool_theses/657.

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This thesis focuses on detecting, classifying and locating faults on electric power transmission lines. Fault detection, fault classification and fault location have been achieved by using artificial neural networks. Feedforward networks have been employed along with backpropagation algorithm for each of the three phases in the Fault location process. Analysis on neural networks with varying number of hidden layers and neurons per hidden layer has been provided to validate the choice of the neural networks in each step. Simulation results have been provided to demonstrate that artificial neural network based methods are efficient in locating faults on transmission lines and achieve satisfactory performances.
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Books on the topic "Location-based networks"

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Symeonidis, Panagiotis, Dimitrios Ntempos, and Yannis Manolopoulos. Recommender Systems for Location-based Social Networks. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-0286-6.

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Strout, Aaron. Location-based marketing for dummies. Hoboken, NJ: John Wiley & Sons, 2011.

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Strout, Aaron. Location-based marketing for dummies. Hoboken, NJ: John Wiley & Sons, 2011.

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Zhao, Shenglin, Michael R. Lyu, and Irwin King. Point-of-Interest Recommendation in Location-Based Social Networks. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1349-3.

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Espejo, Roman. Location-based social networking and services. Edited by Thomson Gale (Firm). Detroit: Greenhaven Press, 2014.

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Liu, Yunhao. Location, localization, and localizability: Location-awareness technology for wireless networks. New York: Springer, 2014.

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Zheng, Yang, and SpringerLink (Online service), eds. Location, Localization, and Localizability: Location-awareness Technology for Wireless Networks. New York, NY: Springer Science+Business Media, LLC, 2011.

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Germany) Wireless Communication and Information (Conference) (7th 2010 Berlin. Wireless communication and information: Car to car, sensor networks and location based services. Boizenburg: VWH, Verlag Werner Hülsbusch, Fachverlag für Medientechnik und -wirtschaft, 2010.

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N, Plataniotis Konstantinos, and Venetsanopoulos, A. N. (Anastasios N.), 1941-, eds. WLAN positioning systems: Principles and applications in location-based services. Cambridge: Cambridge University Press, 2012.

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Miodrag, Potkonjak, and Slijepcevic Sasha, eds. Localization in wireless networks: Foundations and applications. New York: Springer, 2012.

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Book chapters on the topic "Location-based networks"

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Lee, Wang-Chien, and Mao Ye. "Location-Based Social Networks." In Encyclopedia of Social Network Analysis and Mining, 821–33. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-6170-8_319.

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Symeonidis, Panagiotis, Dimitrios Ntempos, and Yannis Manolopoulos. "Location-Based Social Networks." In SpringerBriefs in Electrical and Computer Engineering, 35–48. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-0286-6_4.

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Lee, Wang-Chien, and Mao Ye. "Location-Based Social Networks." In Encyclopedia of Social Network Analysis and Mining, 1221–34. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-7131-2_319.

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Lee, Wang-Chien, and Mao Ye. "Location-Based Social Networks." In Encyclopedia of Social Network Analysis and Mining, 1–14. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4614-7163-9_319-1.

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Ardagna, Claudio A., Sushil Jajodia, Pierangela Samarati, and Angelos Stavrou. "Privacy Preservation over Untrusted Mobile Networks." In Privacy in Location-Based Applications, 84–105. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03511-1_4.

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Rahimi, Seyyed Mohammadreza, Xin Wang, and Behrouz Far. "Behavior-Based Location Recommendation on Location-Based Social Networks." In Advances in Knowledge Discovery and Data Mining, 273–85. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57529-2_22.

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Zheng, Yu. "Location-Based Social Networks: Users." In Computing with Spatial Trajectories, 243–76. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-1629-6_8.

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Zheng, Yu, and Xing Xie. "Location-Based Social Networks: Locations." In Computing with Spatial Trajectories, 277–308. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-1629-6_9.

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Markkula, Jouni, Artem Katasonov, and Artem Garmash. "Developing MLS Location-Based Service Pilot System." In Smart Networks, 229–44. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-0-387-35584-9_14.

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Lian, Defu, Yin Zhu, Xing Xie, and Enhong Chen. "Analyzing Location Predictability on Location-Based Social Networks." In Advances in Knowledge Discovery and Data Mining, 102–13. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-06608-0_9.

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Conference papers on the topic "Location-based networks"

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Ye, Mao, Peifeng Yin, and Wang-Chien Lee. "Location recommendation for location-based social networks." In the 18th SIGSPATIAL International Conference. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1869790.1869861.

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Saleem, Muhammad Aamir, Rohit Kumar, Toon Calders, Xike Xie, and Torben Bach Pedersen. "Location Influence in Location-based Social Networks." In WSDM 2017: Tenth ACM International Conference on Web Search and Data Mining. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3018661.3018705.

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Xu, Qianfang, Jiachun Wang, and Bo Xiao. "Personalized location recommendation for location-based social networks." In 2017 IEEE/CIC International Conference on Communications in China (ICCC). IEEE, 2017. http://dx.doi.org/10.1109/iccchina.2017.8330459.

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Gao, Huiji, Jiliang Tang, and Huan Liu. "Personalized location recommendation on location-based social networks." In the 8th ACM Conference. New York, New York, USA: ACM Press, 2014. http://dx.doi.org/10.1145/2645710.2645776.

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Manotumruksa, Jarana. "Users Location Prediction in Location-based Social Networks." In Sixth BCS-IRSG Symposium on Future Directions in Information Access (FDIA 2015). BCS Learning & Development, 2015. http://dx.doi.org/10.14236/ewic/fdia2015.11.

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Lin, Kunhui, Jingjin Wang, Zhongnan Zhang, Yating Chen, and Zhentuan Xu. "Adaptive location recommendation algorithm based on location-based social networks." In 2015 10th International Conference on Computer Science & Education (ICCSE). IEEE, 2015. http://dx.doi.org/10.1109/iccse.2015.7250231.

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Kosmides, Pavlos, Chara Remoundou, Konstantinos Demestichas, Ioannis Loumiotis, Evgenia Adamopoulou, and Michael Theologou. "A Location Recommender System for Location-Based Social Networks." In 2014 International Conference on Mathematics and Computers in Sciences and in Industry (MCSI). IEEE, 2014. http://dx.doi.org/10.1109/mcsi.2014.39.

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Richter, Stefanie, and Andreas Bohm. "A location and privacy service enabler for context-aware and location-based services in NGN." In Networks 2006. 12th International Telecommunications Network Strategy and Planning Symposium. IEEE, 2006. http://dx.doi.org/10.1109/netwks.2006.300408.

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lu, Huang, Gan Xingli, Li shuang, Zhang heng, Li Yaning, and Zhu Ruihui. "Indoor Positioning Technology based on Deep Neural Networks." In 2018 Ubiquitous Positioning, Indoor Navigation and Location-Based Services (UPINLBS). IEEE, 2018. http://dx.doi.org/10.1109/upinlbs.2018.8559721.

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Luomala, Jari, and Ismo Hakala. "Towards adaptive localization in wireless sensor networks." In 2012 Ubiquitous Positioning Indoor Navigation and Location Based Service (UPINLBS). IEEE, 2012. http://dx.doi.org/10.1109/upinlbs.2012.6409778.

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Reports on the topic "Location-based networks"

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Blackman, Donna K., and John A. Orcutt. Hydroacoustic Arrays for Seismic Detection, Location and Discrimination: A Comparison With Land-based Seismic Arrays and Networks. Fort Belvoir, VA: Defense Technical Information Center, January 1998. http://dx.doi.org/10.21236/ada336229.

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Vo, Jacqueline. Check-In Frequency with Friends on Location-Based Social Networks: A Look at Homophily and Relational Closeness. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.2626.

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Farrar, C. R., S. W. Doebling, M. B. Prime, P. Cornwell, M. Kam, E. G. Straser, and B. C. Hoerst. Neural network based system for damage identification and location in structural and mechanical systems. Office of Scientific and Technical Information (OSTI), November 1998. http://dx.doi.org/10.2172/674673.

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Young, Craig. Problematic plant monitoring in Lincoln Boyhood National Memorial: 2006–2019. Edited by Tani Hubbard. National Park Service, July 2021. http://dx.doi.org/10.36967/nrr-2286660.

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Managers are challenged with the impact of problematic plants, including exotic, invasive, and pest plant species. Information on the cover, distribution, and location of these plants is essential for developing risk-based approaches to managing these species. Based on surveys conducted in 2006, 2011, 2015, and 2019, Heartland Network staff and contractors identified a cumulative total of 45 potentially problematic plant taxa in Lincoln Boyhood National Memorial. Of the 34 species found in 2019, we characterized 4 as very low frequency, 14 as low frequency, 9 as medium frequency, and 7 as high frequency. A single species exceeded a 10-acre threshold based on a mid-point estimate. Because of the number of problematic plant taxa, as well as the extent and cover of problematic plants in the park and the small park size, control efforts should focus on treating high priority species across the entire park. High priority species may include plant species capable of rapid spread, species at low population levels, and species which can effectively be controlled.
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Young, Craig. Problematic plant monitoring in Arkansas Post National Memorial: 2006–2019. Edited by Tani Hubbard. National Park Service, July 2021. http://dx.doi.org/10.36967/nrr-2286657.

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Managers are challenged with the impact of problematic plants, including exotic, invasive, and pest plant species. Information on the cover, distribution, and location of these plants is essential for developing risk-based approaches to managing these species. Based on surveys conducted in 2006, 2011, 2015, and 2019, Heartland Network staff and contractors identified a cumulative total of 28 potentially problematic plant taxa in Arkansas Post National Memorial. Of the 23 species found in 2019, we characterized 9 as very low frequency, 7 as low frequency, 5 as medium frequency, and 2 as high frequency. Cover of all species was low with a single species slightly exceeding a 1-acre threshold based on a midpoint estimate. Efforts to control the woody invasive black locust, Chinese privet, and hardy orange appear to have successfully reduced the cover of these plants across the Memorial Unit. Japanese stiltgrass may have been increasing as recently as 2015, but a combination of recent flooding and control efforts may have stemmed the spread of this invasive grass. Efforts to control localized patches of Chinaberry tree also appear to have reduced the cover of this species. Outside of the problematic species currently subject
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