Relevant bibliographies by topics / Wireless Data Collection

Contents

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

Academic literature on the topic 'Wireless Data Collection'

Author: Grafiati
Published: 4 June 2021
Last updated: 5 February 2022

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Journal articles on the topic "Wireless Data Collection"

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Dohare, Anand, Tulika, and Mallikarjuna B. "Data Collection in Wireless Sensor Networks Using Prediction Method." Journal of Advanced Research in Dynamical and Control Systems 11, no. 0009-SPECIAL ISSUE (September 25, 2019): 815–20. http://dx.doi.org/10.5373/jardcs/v11/20192637.

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Kumar, N. B. S. Vijay, D. Venkatesh D. Venkatesh, and K. Ramesh K. Ramesh. "Rapid Data Collection in Wireless Sensor Networks Organized as Trees." International Journal of Scientific Research 2, no. 5 (June 1, 2012): 223–26. http://dx.doi.org/10.15373/22778179/may2013/74.

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Tapia, Andrea, and Carleen Maitland. "WIRELESS DEVICES FOR HUMANITARIAN DATA COLLECTION." Information, Communication & Society 12, no. 4 (June 2009): 584–604. http://dx.doi.org/10.1080/13691180902857637.

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patel, Shubham. "DATA COLLECTION IN WIRELESS SENSOR NETWORKS." International Journal of Recent Advancement in Engineering & Research 2, no. 5 (May 14, 2017): 23. http://dx.doi.org/10.24128/ijraer.2017.ij9de.

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Li, Kai, Wei Ni, Lingjie Duan, Mehran Abolhasan, and Jianwei Niu. "Wireless Power Transfer and Data Collection in Wireless Sensor Networks." IEEE Transactions on Vehicular Technology 67, no. 3 (March 2018): 2686–97. http://dx.doi.org/10.1109/tvt.2017.2772895.

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A.Dharmadhikari, Pramod, B. M. Patil, and V. M. Chandode. "Reliable Data Collection in Wireless Sensor Networks." International Journal of Computer Applications 61, no. 18 (January 18, 2013): 32–37. http://dx.doi.org/10.5120/10031-5075.

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Ayars, Eric, and Estella Lai. "Using XBee transducers for wireless data collection." American Journal of Physics 78, no. 7 (July 2010): 778–81. http://dx.doi.org/10.1119/1.3427415.

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Han, Xiao Wei, Jing Lin Duan, and Jian Zhang. "Design of Environmental Monitoring Data Collection Repeater." Advanced Materials Research 955-959 (June 2014): 1112–15. http://dx.doi.org/10.4028/www.scientific.net/amr.955-959.1112.

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A data collection repeater based on ARM Cortex-M3 core for environmental monitoring is introduced in this paper. The chip STM32 is used as CPU processor, CC2530 module as a sink node of Wireless Sensor Networks, the collected data is sent to monitoring center by GPRS network. Integration of WSN, ARM and GPRS, the collection of environmental parameters and capability of wireless transmission are achieved in low-power conditions. Hardware structure and application program of the repeater are given.
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Nandha kumar, R. "An Efficient Data Collection Protocol in Wireless Networks." International Journal of Ad hoc, Sensor & Ubiquitous Computing 3, no. 4 (August 31, 2012): 11–19. http://dx.doi.org/10.5121/ijasuc.2012.3402.

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B, Nandhini, and Srie Vidhya Janani E. "ENERGY EFFICIENT DATA COLLECTION IN WIRELESS SENSOR NETWORK." ICTACT Journal on Communication Technology 04, no. 03 (September 1, 2013): 796–801. http://dx.doi.org/10.21917/ijct.2013.0113.

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Dissertations / Theses on the topic "Wireless Data Collection"

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Rasul, Aram Mohammed. "Data collection in wireless sensor networks." Thesis, University of Leicester, 2016. http://hdl.handle.net/2381/37606.

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This thesis is principally concerned with effcient energy consumption in wireless sensor networks from two distinct aspects from a theoretical point of view. The thesis addresses the issue of reducing idle listening states in a restricted tree topology to minimise energy consumption by proposing an optimisation technique: the extra-bit technique. This thesis also focuses on showing lower bounds on the optimal schedule length, which are derived for some special cases of the tree, such as a single chain, balanced chains, imbalanced chains, three and four level k-ary trees and Rhizome trees. Then, we propose an algorithm which can exactly match the lower bound for a single chain, balanced chains and Rhizome trees individually and which is a few steps away from the optimal solution for imbalanced chains. Finally, we propose the use of two frequencies to further save energy and minimize latency. Recent research has shown that significant energy improvements can be achieved in WSNs by exploiting a mobile sink for data collection via single hop communications. A mobile sink approaches the transmission range of sensors to receive their data and deposit the data at the base station. The thesis, as a second problem, focuses on the design issues of an energy efficient restricted tour construction for sink mobility. We propose two different techniques. The first one is heuristic and uses a criterion based on maximum coverage and minimum energy consumption called the "max-ratio". Although its time complexity is polynomial, this heuristic algorithm cannot always produce a good solution. As a result, we propose the sec- ond algorithm. Despite the time complexity of the second algorithm being pseudo polynomial, the optimal solution can be found if one exists. For each algorithm men- tioned, two scenarios are taken into account with regard to the transmission. In the first scenario, one assumes that there is no upper bound on the transmission range while in the second setting the nodes can adjust their transmission range between 0 and the maximum range. The algorithms have been implemented and simulated in Matlab.
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Hassanzadeh, Navid. "Scalable Data Collection for Mobile Wireless Sensor Networks." Thesis, KTH, Kommunikationsnät, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-98818.

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In the near future WSNs (wireless sensor networks) which consist of tiny wireless embedded systems will be an inseparable part of our daily lives. Data collection, collecting data from a large number of sources to one or more base stations, is a typical application for WSNs. A substantial number of data collection algorithms have been specifically designed for static scenarios while there are some scenarios in which sensor nodes are attached to intrinsically mobile objects. Generally, in such scenarios delay tolerant networking approaches have been exploited for offline data analysis. However, in-situ dta collection from mobile scenarios has received little attention. We propose Mobile Collect to address the limitations of static data collection protocols in mobile scenarios. For this purpose, Collection Tree Protocol (CTP), a de facto standard for data collection, which is implemented in Contiki-OS (Contiki Collect), has been optimized to avoid loops and to react quickly to topology changes which occur frequently in mobile scenarios. The MAC (Medium Access Control) layer in WSNs has a decisive impact on the overall performance of mobile networks in terms of power consumption, and packet delivery rate. We have evaluated Mobile Collect protocol with a receiver-initiated (A-MAC that we implemented in Contiki-OS) and a sender-initiated (Contiki-MAC) MAC protocol. Compared to the Contiki Collect and the recently proposed DYMO (Dynamic MANET On-demand) protocol, MObile Collect with Contiki-MAC shows a significant improvement in reliability while it has a slight increase in power consumption. A-MAC slightly improves reliability for sparse topologies, but has higher power consumption.
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Hung, Ka-Lok. "The fair data collection problem in wireless sensor networks /." View abstract or full-text, 2006. http://library.ust.hk/cgi/db/thesis.pl?COMP%202006%20HUNG.

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Tall, Hamadoun. "Load balancing in multichannel data collection wireless sensor networks." Thesis, Université Clermont Auvergne‎ (2017-2020), 2018. http://www.theses.fr/2018CLFAC006/document.

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Les Réseaux de Capteurs Sans Fil (RCSF) sont de plus en plus exploités par des applications diverses grâce à leur facilité de déploiement et d’auto-configuration. Les applications de collecte de données qui utilisent les RCSF ont souvent un profil convergecast : l’ensemble des données récoltées par tous les capteurs du réseau sont acheminées vers un puits de collecte, grâce à une communication multi-saut. Pendant l’acheminement des données des nœuds de collecte vers le puits, des goulots d’étranglement sont fréquemment observés, principalement au voisinage du puits. Cela est du à la congestion et au phénomène d’entonnoir couramment observé sur le trafic de données ayant un profile convergecast. Outre un risque accru de collision, cela entraîne le débordement des files d’attente des nœuds concernés conduisant à des pertes de données. Cette perte réduit le taux de livraison au puits entraînant une baisse du débit du réseau. Afin de réduire ces pertes et de permettre un meilleur taux de livraison au puits, le trafic doit être équitablement réparti au niveau de chaque saut pendant l’acheminement. Dans cette thèse, nous avons d’une part proposé S-CoLBA (Single channel Collaborative Load Balancing Algorithm), un protocole mono-canal de routage dynamique avec équilibrage de la charge. Sa métrique de routage est basée sur le délais moyen d’accès au medium radio par nœud. Chaque nœud choisit comme prochain saut à destination du puits, un de ses voisins ayant le délais d’accès le plus court. S-CoLBA intègre également une surveillance permanente des files d’attente des nœuds afin de prévenir la congestion et d’éviter le débordement de ces files. D’autre part, nous avons adapté S-CoLBA pour le rendre utilisable dans un réseau multicanal. Cette version du protocole s’appelle M-CoLBA (pour Mulitchannel CoLBA). M-CoLBA évite la congestion en équilibrant la charge grâce à une répartition du trafic au niveau de chaque saut du réseau. Dans un réseau multicanal, le problème de support de diffusion se pose. M-CoLBA introduit des périodes de synchronisations où tous les nœuds utilisent le même canal pour échanger les informations de routage. Ces périodes de synchronisation contribuent à allonger les délais de bout en bout des paquets. Nous avons ainsi optimisé M-CoLBA en "surchargeant" les acquittements des trames avec les informations de routage ( piggybacking) et les états des files d’attente. Cela évite de passer par des périodes de synchronisation pour diffuser ces informations. Cette version optimisée s’appelle ABORt ( Acknowledgement-Based opportunistic Routing protocol). Dans un cas de trafic de type convergecast, ABORt induit une diversité des routes prises par les données collectées, ce qui est bénéfique à la quantité de données transportées et à la robustesse de la solution. Les contributions ont été évaluées par simulation et expérimentation dans un réseau monocanal et multicanal. Les résultats montrent que nos contributions améliorent le taux de livraison des données au puits, optimisent le délais de bout en bout et réduisent la quantité de trafic de contrôle comparé à des solutions déjà existantes
The popularity of wireless sensor networks (WSNs) is increasing due to their ease ofdeployment and auto-configuration capabilities. They are used in different applica-tion domains including data collection with convergecast scenarios. In convergecast,all data collected in the network is destined to one common node usually called thesink. In case of high carried traffic load and depending on the used routing policy,this many-to-one data collection leads to congestion and queue overflow mainly innodes located near the sink. Congestion and queue overflow reduce delivery ratiothat negatively affects the network efficiency.Wireless sensor nodes are resource constrained devices with limited buffers sizeto store and forward data to the sink. Introducing multichannel communication inWSNs helps to increase the carried traffic load thanks to allowing parallel data trans-mission and reduction of contention and interference. With high traffic load, thenumber of data packets travelling from leaf nodes towards the sink becomes higher.In case the routing scheme does not balance the traffic load, it will be unfairly dis-tributed between forwarding nodes. Thus, nodes that are in part of the routing will beoverloaded while others are less used. Overloaded nodes increase the risk of conges-tion and queue overflow leading to data loss that reduces the throughput. Therefore,we need to couple the routing protocols with traffic load balancing scheme in hightraffic load network scenarios.The goal of this thesis is to propose an efficient routing solution to prevent con-gestion and queue overflow in high data rate convergecast WSNs, in such a way, tooptimize data delivery ratio at the sink node.On the one hand, we proposed a single channel traffic load balancing routingprotocol, named S-CoLBA (Single channel Collaborative Load balancing routing).It relies on data queueing delay metric and best score (according to the value of themetric) next hop neighbors to fairly distribute traffic load in per hop basis in the net-work. Since the carried traffic load increases in multichannel communication, onthe other hand, we adapted our contribution to cope with multichannel WSNs andwe named it as Multichannel CoLBA (M-CoLBA). As broadcasting information isnot straightforward in multichannel, we optimize M-CoLBA to use piggybackingscheme for routing information sharing in the network. This enhanced version iscalled ABORt for Acknowledgement-Based opportunistic Routing protocol and re-lies on ACK frames to share routing information. Doing so helps to optimize dataframe end-to-end delay and to reduce the transmitted beacons in the network. ABORtfairly distributes traffic load in the network and avoids congestion and queue over-flow.We evaluated the performance of our contributions in both simulation using Con-tiki OS Cooja simulator and experiment (only for S-CoLBA) on TelosB motes. Ob-tained results in both simulation and experiment confirm the efficiency of our routingprotocols in term of packet delivery ratio and queue overflow compared to some ex-isting routing protocols in the literature
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Rodhe, Ioana. "Secure and Privacy-Aware Data Collection in Wireless Sensor Networks." Doctoral thesis, Uppsala universitet, Avdelningen för datorteknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-180087.

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A wireless sensor network is a collection of numerous sensors distributed on an area of interest to collect and process data from the environment. One particular threat in wireless sensor networks is node compromise attacks, that is, attacks where the adversary gets physical access to a node and to the programs and keying material stored on it. Only authorized queries should be allowed in the network and the integrity and confidentiality of the data that is being collected should be protected. We propose a layered key distribution scheme together with two protocols for query authentication and confidential data aggregation. The layered key distribution is more robust to node and communication failures than a predefined tree structure. The protocols are secure under the assumption that less than n sensor nodes are compromised. n is a design parameter that allows us to trade off security for overhead. When more than n sensor nodes are compromised, our simulations show that the attacker can only introduce unauthorized queries into a limited part of the network and can only get access to a small part of the data that is aggregated in the network. Considering the data collection protocol we also contribute with strategies to reduce the energy consumption of an integrity preserving in-network aggregation scheme to a level below the energy consumption of a non-aggregation scheme. Our improvements reduce node congestion by a factor of three and the total communication load by 30%. Location privacy of the users carrying mobile devices is another aspect considered in this thesis. Considering a mobile sink that collects data from the network, we propose a strategy for data collection that requires no information about the location and movement pattern of the sink. We show that it is possible to provide data collection services, while protecting the location privacy of the sink. When mobile phones with built-in sensors are used as sensor nodes, location information about where the data has been sensed can be used to trace users and infer other personal information about them, like state of health or personal preferences. Therefore, location privacy preserving mechanisms have been proposed to provide location privacy to the users. We investigate how a location privacy preserving mechanism influences the quality of the collected data and consider strategies to reconstruct the data distribution without compromising location privacy.
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Garcia, Juliette. "Opportunistic data collection and routing in segmented wireless sensor networks." Thesis, Toulouse 3, 2020. http://www.theses.fr/2020TOU30153.

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La surveillance régulière des opérations dans les aires de manoeuvre (voies de circulation et pistes) et aires de stationnement d'un aéroport est une tâche cruciale pour son fonctionnement. Les stratégies utilisées à cette fin visent à permettre la mesure des variables environnementales, l'identification des débris (FOD) et l'enregistrement des statistiques d'utilisation de diverses sections de la surface. Selon un groupe de gestionnaires et contrôleurs d'aéroport interrogés, cette surveillance est un privilège des grands aéroports en raison des coûts élevés d'acquisition, d'installation et de maintenance des technologies existantes. Les moyens et petits aéroports se limitent généralement à la surveillance de quelques variables environnementales et des FOD effectuée visuellement par l'homme. Cette dernière activité impose l'arrêt du fonctionnement des pistes pendant l'inspection. Dans cette thèse, nous proposons une solution alternative basée sur les réseaux de capteurs sans fil (WSN) qui, contrairement aux autres méthodes, combinent les propriétés de faible coût d'installation et maintenance, de déploiement rapide, d'évolutivité tout en permettant d'effectuer des mesures sans interférer avec le fonctionnement de l'aéroport. En raison de la superficie d'un aéroport et de la difficulté de placer des capteurs sur des zones de transit, le WSN se composerait d'une collection de sous-réseaux isolés les uns des autres et du puits. Pour gérer cette segmentation, notre proposition s'appuie sur l'utilisation opportuniste des véhicules circulants dans l'aéroport considérés alors comme un type spécial de nœud appelé Mobile Ubiquitous LAN Extension (MULE) chargé de collecter les données des sous-réseaux le long de son trajet et de les transférer vers le puits. L'une des exigences pour le déploiement d'un nouveau système dans un aéroport est qu'il cause peu ou pas d'interruption des opérations régulières. C'est pourquoi l'utilisation d'une approche opportuniste basé sur des MULE est privilégiée dans cette thèse. Par opportuniste, nous nous référons au fait que le rôle de MULE est joué par certains des véhicules déjà existants dans un aéroport et effectuant leurs déplacements normaux. Et certains nœuds des sous- réseaux exploiteront tout moment de contact avec eux pour leur transmettre les données à transférer ensuite au puits. Une caractéristique des MULEs dans notre application est qu'elles ont des trajectoires structurées (suivant les voies de circulation dans l'aéroport), en ayant éventuellement un contact avec l'ensemble des nœuds situés le long de leur trajet (appelés sous-puits). Ceci implique la nécessité de définir une stratégie de routage dans chaque sous-réseau, capable d'acheminer les données collectées des nœuds vers les sous-puits et de répartir les paquets de données entre eux afin que le temps en contact avec la MULE soit utilisé le plus efficacement possible. [...]
The regular monitoring of operations in both movement areas (taxiways and runways) and non-movement areas (aprons and aircraft parking spots) of an airport, is a critical task for its functioning. The set of strategies used for this purpose include the measurement of environmental variables, the identification of foreign object debris (FOD), and the record of statistics of usage for diverse sections of the surface. According to a group of airport managers and controllers interviewed by us, the wide monitoring of most of these variables is a privilege of big airports due to the high acquisition, installation and maintenance costs of most common technologies. Due to this limitation, smaller airports often limit themselves to the monitoring of environmental variables at some few spatial points and the tracking of FOD performed by humans. This last activity requires stopping the functioning of the runways while the inspection is conducted. In this thesis, we propose an alternative solution based on Wireless Sensor Network (WSN) which, unlike the other methods/technologies, combines the desirable properties of low installation and maintenance cost, scalability and ability to perform measurements without interfering with the regular functioning of the airport. Due to the large extension of an airport and the difficulty of placing sensors over transit areas, the WSN might result segmented into a collection of subnetworks isolated from each other and from the sink. To overcome this problem, our proposal relies on a special type of node called Mobile Ubiquitous LAN Extension (MULE), able to move over the airport surface, gather data from the subnetworks along its way and eventually transfer it to the sink. One of the main demands for the deployment of any new system in an airport is that it must have little or no interference with the regular operations. This is why the use of an opportunistic approach for the transfer of data from the subnetworks to the MULE is favored in this thesis. By opportunistic we mean that the role of MULE will be played by some of the typical vehicles already existing in an airport doing their normal displacements, and the subnetworks will exploit any moment of contact with them to forward data to the sink. A particular characteristic of the MULEs in our application is that they move along predefined structured trajectories (given by the layout of the airport), having eventual contact with the set of nodes located by the side of the road (so-called subsinks). This implies the need for a data routing strategy to be used within each subnetwork, able to lead the collected data from the sensor nodes to the subsinks and distribute the data packets among them so that the time in contact with the MULE is used as efficiently as possible. In this thesis, we propose a routing protocol which undertakes this task. Our proposed protocol is named ACME, standing for ACO-based routing protocol for MULE-assisted WSNs.[...]
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Chippa, Mukesh Kumar. "Performance of Tree-Based Data Collection in Wireless Sensor Systems." University of Akron / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=akron1312209206.

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Basheer, Al-Qassab. "Reliability of Data Collection and Transmission in Wireless Sensor Networks." Youngstown State University / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1377472863.

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Pinjala, Mallikarjuna Rao. "Adhoc routing based data collection application in wireless sensor networks." Kansas State University, 2010. http://hdl.handle.net/2097/6235.

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Master of Science
Department of Computing and Information Sciences
Gurdip Singh
Ad hoc based routing protocol is a reactive protocol to route messages between mobile nodes. It allows nodes to pass messages through their neighbors to nodes which they cannot directly communicate. It uses Route Request (RREQ) and Route Reply (RREP) messages for communication. Wireless sensor networks consist of tiny sensor motes with capabilities of sensing, computation and wireless communication. This project aims to implement data collector application to collect the temperature data from the set of wireless sensor devices located within a building, which will help in gathering the information by finding the route with minimum number of hops to reach destination and generates low message traffic by not encouraging the duplicate message within the network. Using this application, wireless devices can communicate effectively to provide the network information to the user. This system consists of a mobile wireless sensor device called base station which is connected to a PC to communicate and is the root of the network. It also consists of set of client sensor devices which are present in different parts of the building. This project has been evaluated by determining how well the ad hoc protocol performs by measuring the number of messages and time consumed in learning about the complete topology. This application will eventually find the path with minimum number of hops. Simple Network Management Protocol (SNMP) is also used to monitor the sensor nodes remotely. This project was developed using nesC and C programming languages with TinyOS and UNIX based operating systems. It has been tested with a sufficient number of motes and evaluated based on the number of messages generated and number of hops traveled for each route request.
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Xu, Gang. "QoI-Aware Data Collection for Mobile Users in Wireless Sensor Networks." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-196385.

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Ubiquitous data collection enables mobile users to collect data from the surrounding wireless sensors along their walks. However, the limited contact time and the wireless capacity constrain the amount of data that can be collected by the mobile users. Quality of Service (QoS) becomes very important for mobile users to collect sensing data that can maximize their information value. To the best of our knowledge, we are the first to propose a distributed algorithm that can support QoS ubiquitous data collection for multiple mobile users. Our distributed algorithm constructs the data collection trees adaptively to the dynamic moving speeds and the available capacity of the mobile users. It allocates capacity for receiving high priority data to maximize the information value with low communication overheads. Our algorithm supports smooth data collection for multiple mobile users with independent movements. We provide analysis and extensive simulations to evaluate the information value, energy efficiency and scalability of our distributed solution. The results showed that our distributed algorithm can improve information value up to 50% and reduce energy consumption to half compared with the existing approach. Our algorithm also scales perfectly well with increasing number of mobile users and moving speeds.
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Books on the topic "Wireless Data Collection"

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Latona, John. Tetherless trucking: Mobile data opportunities in the trucking industry. Alexandria, VA: Telecom Pub. Group, 1994.

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H.R. ________ [sic]: A discussion draft addressing broadband mapping and data collection : hearing before the Subcommittee on Telecommunications and the Internet of the Committee on Energy and Commerce, House of Representatives, One Hundred Tenth Congress, first session, May 17, 2007. Washington: U.S. G.P.O., 2008.

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Book chapters on the topic "Wireless Data Collection"

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Suhonen, Jukka, Mikko Kohvakka, Ville Kaseva, Timo D. Hämäläinen, and Marko Hännikäinen. "Sensor Data Collection." In Low-Power Wireless Sensor Networks, 61–70. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4614-2173-3_6.

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Qin, Zhan. "Privacy-Preserving Data Collection." In Encyclopedia of Wireless Networks, 1106–9. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-319-78262-1_302.

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Qin, Zhan. "Privacy-Preserving Data Collection." In Encyclopedia of Wireless Networks, 1–3. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-32903-1_302-1.

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Zhang, Kuan, and Xuemin Shen. "Secure Health Data Collection in MHN." In Wireless Networks, 21–45. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24717-5_3.

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Kuznetsov, Roman, and Valeri Chipulis. "Wireless Data Collection in Power System." In Lecture Notes in Electrical Engineering, 21–26. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-41671-2_4.

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Ji, Shouling, Jing (Selena)He, and Yingshu Li. "Optimizing Data Collection Capacity in Wireless Networks." In Handbook of Combinatorial Optimization, 2503–47. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-7997-1_78.

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Li, Kai, and Kien A. Hua. "Mobile Data Collection Networks for Wireless Sensors." In Communications in Computer and Information Science, 200–211. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-30721-8_20.

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Ifzarne, Samir, Imad Hafidi, and Nadia Idrissi. "Secure Data Collection for Wireless Sensor Network." In Emerging Trends in ICT for Sustainable Development, 241–48. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-53440-0_26.

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Vasani, Meet J., and Satish Maurya. "Efficient Data Collection in Wireless Sensor Network." In Computer Networks and Inventive Communication Technologies, 17–31. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3728-5_2.

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Fouquet, Marc, Christian Hoene, Morten Schläger, and Georg Carle. "Data Collection for Heterogeneous Handover Decisions in beyond 3G Networks." In Wireless and Mobile Networking, 347–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03841-9_31.

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Conference papers on the topic "Wireless Data Collection"

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Tzung-Cheng Chen, Tzung-Shi Chen, and Ping-Wen Wu. "Data collection in wireless sensor networks assisted by mobile collector." In 2008 1st IFIP Wireless Days (WD). IEEE, 2008. http://dx.doi.org/10.1109/wd.2008.4812895.

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Yin, Kaicheng, and Chaosheng Zhong. "Data collection in wireless sensor networks." In 2011 IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS). IEEE, 2011. http://dx.doi.org/10.1109/ccis.2011.6045040.

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Gul, Omer Melih. "Fair Data Collection in Wireless Networks." In 2021 29th Signal Processing and Communications Applications Conference (SIU). IEEE, 2021. http://dx.doi.org/10.1109/siu53274.2021.9477894.

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Martincic, Fernando, and Loren Schwiebert. "Adaptive data collection in sensor networks." In 2009 2nd IFIP Wireless Days (WD). IEEE, 2009. http://dx.doi.org/10.1109/wd.2009.5449655.

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Fotiou, Nikos, Vasilios A. Siris, Alexandros Mertzianis, and George C. Polyzos. "Smart IoT Data Collection." In 2018 IEEE 19th International Symposium on "A World of Wireless, Mobile and Multimedia Networks" (WoWMoM). IEEE, 2018. http://dx.doi.org/10.1109/wowmom.2018.8449766.

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Ponnusamy, Vasaki, Low Tang Jung, and Anang Hudaya. "Mobile data collection in Wireless Sensor Network." In 2013 IEEE Malaysia International Conference on Communications (MICC). IEEE, 2013. http://dx.doi.org/10.1109/micc.2013.6805812.

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Wang, Chao, Huadong Ma, Yuan He, and Shuguang Xiong. "Approximate Data Collection for Wireless Sensor Networks." In 2010 IEEE 16th International Conference on Parallel and Distributed Systems (ICPADS). IEEE, 2010. http://dx.doi.org/10.1109/icpads.2010.32.

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Romdhani, Bilel, Dominique Barthel, and Fabrice Valois. "Data collection in heterogeneous wireless sensor networks." In the Asian Internet Engineeering Conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2402599.2402603.

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Zhu, Yuting, and Jeff Kilby. "Wireless Data Collection of Surface Electromyography Signals." In 2011 7th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM). IEEE, 2011. http://dx.doi.org/10.1109/wicom.2011.6040369.

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Coutinho, Rodolfo W. L., and Azzedine Boukerche. "Data Collection in Underwater Wireless Sensor Networks." In MSWiM '17: 20th ACM Int'l Conference on Modelling, Analysis and Simulation of Wireless and Mobile Systems. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3127540.3134267.

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Reports on the topic "Wireless Data Collection"

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Kim, David. Wireless Data Collection System for Real-Time Arterial Travel Time Estimates: Final Report. Portland State University Library, March 2011. http://dx.doi.org/10.15760/trec.23.

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Nidal, Jodeh M. Optimal UAS Assignments and Trajectories for Persistent Surveillance and Data Collection from a Wireless Sensor Network. Fort Belvoir, VA: Defense Technical Information Center, December 2015. http://dx.doi.org/10.21236/ad1003575.

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Krogmeier, J., and Darcy Bullock. Statewide Wireless Communications Project,Volume 3 - Data Collection and Signal Processing for Improvement of Road Profiling and Proof of Concept of a Vehicle-Inftrastructure Based Road Surface Monitoring Application. West Lafayette, IN: Purdue University, 2008. http://dx.doi.org/10.5703/1288284314220.

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You might also be interested in the extended bibliographies on the topic 'Wireless Data Collection' for particular source types:
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