Relevant bibliographies by topics / Mobile opportunistic networks

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

Academic literature on the topic 'Mobile opportunistic networks'

Author: Grafiati
Published: 4 June 2021
Last updated: 7 September 2023

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Journal articles on the topic "Mobile opportunistic networks"

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Ciobanu, Radu Ioan, and Ciprian Dobre. "Opportunistic Networks." International Journal of Virtual Communities and Social Networking 5, no. 2 (2013): 11–26. http://dx.doi.org/10.4018/jvcsn.2013040102.

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When mobile devices are unable to establish direct communication, or when communication should be offloaded to cope with large throughputs, mobile collaboration can be used to facilitate communication through opportunistic networks. These types of networks, formed when mobile devices communicate only using short-range transmission protocols, usually when users are close, can help applications still exchange data. Routes are built dynamically, since each mobile device is acting according to the store-carry-and-forward paradigm. Thus, contacts are seen as opportunities to move data towards the destination. In such networks data dissemination is usually based on a publish/subscribe model. Opportunistic data dissemination also raises questions concerning user privacy and incentives. In this the authors present a motivation of using opportunistic networks in various real life use cases, and then analyze existing relevant work in the area of data dissemination. The authors present the categories of a proposed taxonomy that captures the capabilities of data dissemination techniques used in opportunistic networks. Moreover, the authors survey relevant techniques and analyze them using the proposed taxonomy.
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Vidhya Lakshmi, Vimitha R., and Gireesh Kumar T. "Opportunistic mobile social networks: architecture, privacy, security issues and future directions." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 2 (2019): 1145. http://dx.doi.org/10.11591/ijece.v9i2.pp1145-1152.

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Mobile Social Networks and its related applications have made a very great impact in the society. Many new technologies related to mobile social networking are booming rapidly now-a-days and yet to boom. One such upcoming technology is Opportunistic Mobile Social Networking. This technology allows mobile users to communicate and exchange data with each other without the use of Internet. This paper is about Opportunistic Mobile Social Networks, its architecture, issues and some future research directions. The architecture and issues of Opportunistic Mobile Social Networks are compared with that of traditional Mobile Social Networks. The main contribution of this paper is regarding privacy and security issues in Opportunistic Mobile Social Networks. Finally, some future research directions in Opportunistic Mobile Social Networks have been elaborated regarding the data's privacy and security.
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Patra, Tapas Kumar, and Albert Sunny. "Forwarding in Heterogeneous Mobile Opportunistic Networks." IEEE Communications Letters 22, no. 3 (2018): 626–29. http://dx.doi.org/10.1109/lcomm.2018.2790393.

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Abouaroek, Musaeed, and Khaleel Ahmad. "Node Authentication Using NTRU Algorithm in Opportunistic Network." Scalable Computing: Practice and Experience 20, no. 1 (2019): 83–92. http://dx.doi.org/10.12694/scpe.v20i1.1481.

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The demand for using wireless paradigms for performing various information and communication operations has been exploded. The opportunistic networks is a special type of delay tolerant networks proposed to operate in an emergency manner to facilitate mobile connectivity between the nodes when there is no connectivity. These emergencies are caused either by human-made or natural disasters. Opportunistic Networks depend on mobile phones and other mobile devices that carry wireless technology. This paper is an attempt to expand the opportunistic network through the authentication nodes. We propose an NTRU algorithm for node authentication in opportunistic networks .NTRU algorithm is an asymmetric post-quantum cryptosystem. This algorithm is unbreakable and robust compared to RSA and ECC cryptosystem.
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Zhao, Yan, and Farzana Akter. "Adaptive Clustering Algorithm for IIoT Based Mobile Opportunistic Networks." Security and Communication Networks 2022 (May 6, 2022): 1–11. http://dx.doi.org/10.1155/2022/3872214.

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The clustering algorithms play a crucial role for energy saving solutions in mobile opportunistic networks. If the selection of cluster head is made appropriately, then the energy can be consumed optimally. The existing clustering algorithms do not consider the optimal selection of the cluster head resulting in low survival rates and high energy consumption rates in nodes. The adaptive clustering is required in Industrial Internet of Things (IIoT) based sophisticated networks where seamless connectivity is imperative for rapid communication. In order to meet this research gap, an adaptive clustering algorithm for mobile opportunistic networks is proposed within military bases that uses a heuristic algorithm (GA) for adaptive clustering. An analysis of the opportunity network at the connected military base is carried out and the mobile opportunity model is constructed using the adaptive clustering for the similar traffic. In a mobile machine network, the next hop node is determined by the node clustering principle. A LEACH clustering protocol enables communication between cluster heads and base stations based on single-hop and multihop cluster nodes. In order to perform adaptive clustering of mobile network nodes based on network partitioning and scheduling of clusters, genetic algorithms are used. The proposed approach can be applied to the IIoT systems in places where adaptive clustering is required to optimize energy consumption, to reduce latency rates, and to enhance the throughput of mobile networks. The experimental findings suggest that the proposed adaptive strategy is capable of optimizing energy consumption rates, reducing network latency, and boosting efficiency by increasing throughput in mobile opportunistic networks.
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Yan, Yeqing, Zhigang Chen, Jia Wu, and Leilei Wang. "An Effective Data Transmission Algorithm Based on Social Relationships in Opportunistic Mobile Social Networks." Algorithms 11, no. 8 (2018): 125. http://dx.doi.org/10.3390/a11080125.

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With the popularization of mobile communication equipment, human activities have an increasing impact on the structure of networks, and so the social characteristics of opportunistic networks become increasingly obvious. Opportunistic networks are increasingly used in social situations. However, existing routing algorithms are not suitable for opportunistic social networks, because traditional opportunistic network routing does not consider participation in human activities, which usually causes a high ratio of transmission delay and routing overhead. Therefore, this research proposes an effective data transmission algorithm based on social relationships (ESR), which considers the community characteristics of opportunistic mobile social networks. This work uses the idea of the faction to divide the nodes in the network into communities, reduces the number of inefficient nodes in the community, and performs another contraction of the structure. Simulation results show that the ESR algorithm, through community transmission, is not only faster and safer, but also has lower transmission delay and routing overhead compared with the spray and wait algorithm, SCR algorithm and the EMIST algorithm.
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Dash, Sanjit Kumar, Aiswaryalaxmi Pradhan, Sasmita Mishra, and Jibitesh Mishra. "Lightweight Opportunistic Mobile Data Offloading." International Journal of Mobile Devices, Wearable Technology, and Flexible Electronics 9, no. 1 (2018): 1–15. http://dx.doi.org/10.4018/ijmdwtfe.2018010101.

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Current cellular networks are overloaded due to the increasing number of smartphones and demands for bandwidth-eager multimedia content. Upgrading the existing infrastructure of the cellular system is the most straight forward solution to meet the growing demand. Apart from this, offloading mobile data through Wi-Fi can be a feasible solution. Mobile offloading via Wi-Fi is the latest emerging trend in research and industry. In this article, the authors have proposed a framework for mobile data offloading for both cellular and Wi-Fi networks. The authors have introduced a daemon process-based approach to make the entire process lightweight by using a suitable offloading decision algorithm. This article then formulates a mathematical model to evaluate its feasibility and accuracy for achieving optimum performance.
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Zhang, Junbao, Haojun Huang, Geyong Min, Wang Miao, and Dapeng Wu. "Social-Aware Routing in Mobile Opportunistic Networks." IEEE Wireless Communications 28, no. 2 (2021): 152–58. http://dx.doi.org/10.1109/mwc.001.2000189.

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Mtibaa, Abderrahmen. "Towards Node Cooperation in Mobile Opportunistic Networks." Qatar Foundation Annual Research Forum Proceedings, no. 2011 (November 2011): CSO12. http://dx.doi.org/10.5339/qfarf.2011.cso12.

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Li, He, Kaoru Ota, Mianxiong Dong, and Minyi Guo. "Mobile Crowdsensing in Software Defined Opportunistic Networks." IEEE Communications Magazine 55, no. 6 (2017): 140–45. http://dx.doi.org/10.1109/mcom.2017.1600719.

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Dissertations / Theses on the topic "Mobile opportunistic networks"

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Erramilli, Vijay. "Forewarding in Mobile Opportunistic Networks." Boston University Computer Science Department, 2009. https://hdl.handle.net/2144/1722.

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Recent advances in processor speeds, mobile communications and battery life have enabled computers to evolve from completely wired to completely mobile. In the most extreme case, all nodes are mobile and communication takes place at available opportunities – using both traditional communication infrastructure as well as the mobility of intermediate nodes. These are mobile opportunistic networks. Data communication in such networks is a difficult problem, because of the dynamic underlying topology, the scarcity of network resources and the lack of global information. Establishing end-to-end routes in such networks is usually not feasible. Instead a store-and-carry forwarding paradigm is better suited for such networks. This dissertation describes and analyzes algorithms for forwarding of messages in such networks. In order to design effective forwarding algorithms for mobile opportunistic networks, we start by first building an understanding of the set of all paths between nodes, which represent the available opportunities for any forwarding algorithm. Relying on real measurements, we enumerate paths between nodes and uncover what we refer to as the path explosion effect. The term path explosion refers to the fact that the number of paths between a randomly selected pair of nodes increases exponentially with time. We draw from the theory of epidemics to model and explain the path explosion effect. This is the first contribution of the thesis, and is a key observation that underlies subsequent results. Our second contribution is the study of forwarding algorithms. For this, we rely on trace driven simulations of different algorithms that span a range of design dimensions. We compare the performance (success rate and average delay) of these algorithms. We make the surprising observation that most algorithms we consider have roughly similar performance. We explain this result in light of the path explosion phenomenon. While the performance of most algorithms we studied was roughly the same, these algorithms differed in terms of cost. This prompted us to focus on designing algorithms with the explicit intent of reducing costs. For this, we cast the problem of forwarding as an optimal stopping problem. Our third main contribution is the design of strategies based on optimal stopping principles which we refer to as Delegation schemes. Our analysis shows that using a delegation scheme reduces cost over naive forwarding by a factor of O(√N), where N is the number of nodes in the network. We further validate this result on real traces, where the cost reduction observed is even greater. Our results so far include a key assumption, which is unbounded buffers on nodes. Next, we relax this assumption, so that the problem shifts to one of prioritization of messages for transmission and dropping. Our fourth contribution is the study of message prioritization schemes, combined with forwarding. Our main result is that one achieves higher performance by assigning higher priorities to young messages in the network. We again interpret this result in light of the path explosion effect.<br>Thomson Research, Paris; National Science Foundation (CCR-0325701, ANI-0322990); HAGGLE FET Project; Erramilli family.
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Pietiläinen, Anna-Kaisa. "Opportunistic mobile social networks at work." Paris 6, 2010. http://www.theses.fr/2010PA066587.

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Les réseaux mobiles opportunistes ad-hoc se forment lorsque des terminaux mobiles portés par des utilisateurs communiquent entre eux sans aucune infrastructure. Ils diffèrent ainsi des réseaux classiques comme Internet dont l'architecture suppose la disponibilité instantanée de chemins reliant les hôtes et dont les délais de propagation, et le taux de perte des paquets, sont faibles. Dans les réseaux opportunistes, la mobilité des individus induit de nombreuses déconnexions et de grandes variations des délais. Dans cette thèse nous adoptons une démarche expérimentale pour concevoir et analyser les réseaux opportunistes. D'abord l'étude détaillée des communications opportunistes via Bluetooth dans des environnements contrôlés et réels, montre qu'en dépit des limitations pratiques de cette technologie, la communication opportuniste ad-hoc constitue un paradigme de communication efficace et attractif. Nous avons ensuite conçu et implémenté MobiClique, un middleware de communication destiné aux réseaux opportunistes qui s'appuie sur la mobilité et les relations sociales des utilisateurs pour le routage opportuniste des messages. Enfin, la réalisation d'une expérience grandeur nature mobilisant 80 personnes nous a permis de collecter les informations concernant leurs réseaux sociaux, leurs contacts ad-hoc et les traces de leurs communications. Nous proposons une méthodologie d'analyse des structures des communautés temporelles dans le réseau opportuniste. Nous étudions également de quelle façon ces structures et les interactions sociales caractérisent les chemins de dissémination du contenu.
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Heinemann, Andreas. "Collaboration in Opportunistic Networks." Phd thesis, Saarbrücken VDM Verlag Dr. Müller, 2007. http://tuprints.ulb.tu-darmstadt.de/834/1/heinemann07-diss.pdf.

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Motivation. With the increasing integration of wireless short-range communication technologies (Bluetooth, 802.11b WiFi) into mobile devices, novel applications for spontaneous communication, interaction and collaboration are possible. We distinguish between active and passive collaboration. The devices help users become aware of each other and stimulate face-to-face conversation (active collaboration). Also, autonomous device communication for sharing information without user interaction is possible, i.e., devices pass information to other devices in their vicinity (passive collaboration). Both, active and passive collaboration requires a user to specify what kind of information he offers and what kind of information he is interested in. Object of Research: Opportunistic Networks. Spontaneous communication of mobile devices leads to so-called opportunistic networks, a new and promising evolution in mobile ad-hoc networking. They are formed by mobile devices which communicate with each other while users are in close proximity. There are two prominent characteristics present in opportunistic networks: 1) A user provides his personal device as a network node. 2) Users are a priori unknown to each other. Objectives. Due to the fact that a user dedicates his personal device as a node to the opportunistic network and interacts with other users unknown to him, collaboration raises questions concerning two important human aspects: user privacy and incentives. The users’ privacy is at risk, since passive collaboration applications may expose personal information about a user. Furthermore, some form of incentive is needed to encourage a user to share his personal device resources with others. Both issues, user privacy and incentives, need to be taken into account in order to increase the user acceptability of opportunistic network applications. These aspects have not been addressed together with the technical tasks in prior opportunistic network research. Scientific Contribution and Evaluation. This thesis investigates opportunistic networks in their entirety, i.e., our technical design decisions are appropriate for user privacy preservation and incentive schemes. In summary, the proposed concepts comprise system components, a node architecture, a system model and a simple one-hop communication paradigm for opportunistic network applications. One focus of this work is a profile-based data dissemination mechanism. A formal model for this mechanism will be presented. On top of that, we show how to preserve the privacy of a user by avoiding static and thus linkable data and an incentive scheme that is suitable for opportunistic network applications. The evaluation of this work is twofold. We implemented two prototypes on off-the-shelf hardware to show the technical feasibility of our opportunistic network concepts. Also, the prototypes were used to carry out a number of runtime measurements. Then, we developed a novel two-step simulation method for opportunistic data dissemination. The simulation combines real world user traces with artificial user mobility models, in order to model user movements more realistically. We investigate our opportunistic data dissemination process under various settings, including different communication ranges and user behavior patterns. Our results depict, within the limits of our model and assumptions, a good performance of the data dissemination process.
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Panidis, Panteleimon. "Middleware for Context-Aware Opportunistic Networks." Thesis, KTH, Kommunikationssystem, CoS, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-92261.

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Mobile devices such as palmtops and cell phones are continuously increasing in capabilities and popularity. At the same time, due to their decreasing price they are becoming more and more attractive and available to the average customer. This has lead to the development of many new applications for such portable electronic devices. Mobile devices tend to have increasing local resources in terms of memory/storage and CPU [2]. Despite these improvements in hardware attributes, there are still limitations that characterize these portable devices and which have not improved as quickly as the increase of the local computational power. These limitations mostly concern the network resources and battery power that are both still rather poor. Thus the main barriers for mobile nodes are network resources and limiting the power consumption of the device itself. Today, wireless networks provide limited reliability and less bandwidth than fixed networks. Moreover, all mobile nodes are highly energy dependent as they use batteries with a limited capacity. Additionally, roaming is a feature that increasingly must be supported for such wireless devices, as their physical portability leads to users to use them even as they move about. This may require the utilization of different wireless networks while the node is on the move. Therefore, for all the above reasons there is a demand for the development of intelligent mechanisms and techniques for optimizing the management of these limited resources, while exploiting the local resources, thus providing users with the best possible performance within the available resources. At the present, there are operating systems, such as the Symbian OS [22], specially designed for supporting advanced features in mobile computing. However, there is still development to be done. Moreover, although there are many new applications for mobile computing, they are not yet sophisticated enough to cope with changes in the wireless environment, these changes occur due to the node’s change in context. This creates a gap that should be filled by software between the applications and the operating system; this is frequently called middleware. This middleware provides a collaborative partnership between the operating system and the applications, assisting and making both more sophisticated, in terms of scheduling and managing traffic in a wireless environment. The focus of this project is how to utilize such middleware to best serve the needs of the mobile user.<br>Mobila enheter som handdatorer och mobiltelefoner har kontinuerligt utökat sina användningsområden och popularitet. Samtidigt har de i och med det fallande priset blivit mer och mer attraktiva och tillgängliga för den allmänna marknaden. Detta har lett till utveckling av nya tillämpningar för sådana portabla elektriska enheter. Mobila enheter tenderar att få ökade lokala resurser som exempelvis större minne och CPU [2]. Fastän en förbättring av dessa hårdvaror har gjorts så karaktäriseras enheterna av begränsningar som inte har utvecklats i lika rask takt som de lokala resurserna. Dessa begränsningar handlar till större delen om nätverks resurser och tillförsel av energi via batteri, som båda fortfarande är relativt dåliga. De huvudsakliga barriärerna för de mobila noderna är alltså nätverks resurser och enheternas energikonsumtion. I dagens läge förser de trådlösa närverken en begränsad pålitlighet och lägre bandbredd än de fasta nätverken. Alla mobila noder är även väldigt energiberoende eftersom de använder sig av ett energibegränsat batteri. Förutom detta så är roaming en aspekt som måste öka för sådana trådlösa enheter eftersom deras transportabla egenskaper medför att användaren kan använda sig av enhetens funktioner även vid mobilitet. Detta leder till att det behövs tillgång till olika trådlösa nätverk i och med att enheten omplaceras. På grund av alla dessa orsaker som beskrivits ovan finns det en efterfrågan på utveckling av intelligenta mekanismer och tekniker för användningsoptimering av dessa begränsande faktorer, samtidigt som man maximerar de lokala resurserna och på detta sätt ger användaren bästa möjliga prestanda inom det tillgängliga området. I dagens läge finns det operativ system, som Symbian OS [22], speciellt designade för att stödja avancerade drag inom mobile computing. Det finns däremot utrymme för utveckling av dessa och fastän det finns många nya tillämpningar för mobile computing så är de inte tillräckligt sofistikerade för att klara av ett smidigt byte mellan trådlösa nätverk när noderna omplaceras. Detta medför ett glapp som borde åtgärdas med mjukvara kallad mellanvara, ett program som förmedlar arbetsuppgifter mellan användarnas tillämpningar och datornätets resurser. Mellanvaran gör att operativ systemet samarbetar med användarens tillämpningar och gör att hela systemet blir mer sofistikerat i termer av trafik hantering i den trådlösa miljön. Tyngdpunkten i detta projekt ligger i hur man ska utnyttja sådan mellanvara för att optimera systemet i de mobila enheterna utifrån användarens behov.
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Yang, Zhimin. "Opportunistic Computing in Wireless Networks." The Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1267743144.

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Huang, Rui. "Providing Location-Privacy in Opportunistic Mobile Social Networks." Thesis, Université d'Ottawa / University of Ottawa, 2018. http://hdl.handle.net/10393/37353.

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Users face location-privacy risks when accessing Location-Based Services (LBSs) in an Opportunistic Mobile Social Networks (OMSNs). In order to protect the original requester's identity and location, we propose two location privacy obfuscation protocols utilizing social ties between users. The first one is called Multi-Hop Location-Privacy Protection (MHLPP) protocol. To increase chances of completing obfuscation operations, users detect and make contacts with one-hop or multi-hop neighbor friends in social networks. Encrypted obfuscation queries avoid users learning important information especially the original requester's identity and location except for trusted users. Simulation results show that our protocol can give a higher query success ratio compared to its existing counterpart. The second protocol is called Appointment Card Protocol (ACP). To facilitate the obfuscation operations of queries, we introduce the concept called Appointment Card (AC). The original requesters can send their queries to the LBS directly using the information in the AC, ensuring that the original requester is not detected by the LBS. Also, a path for reply message is kept when the query is sent, to help reduce time for replying queries. Simulation results show that our protocol preserves location privacy and has a higher query success ratio than its counterparts. We have also developed a new OMSN simulator, called OMSN Routing Simulator (ORS), for simulating OMSN protocols more efficiently and effectively for reliable performance.
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Zhang, Boying. "Large Scale Message Dissemination in Mobile Opportunistic Networks." The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1338398822.

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Kouyoumdjieva, Sylvia T. "System Design for Opportunistic Networks." Doctoral thesis, KTH, Kommunikationsnät, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-176479.

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Device-to-device communication has been suggested as a complement to traditional cellular networks as a means of offloading cellular traffic. In this thesis we explore a solution for device-to-device communication based on opportunistic content distribution in a content-centric network. Communication opportunities arise as mobile nodes roam around in an area and occasionally enter in direct communication range with one another. We consider a node to be a pedestrian equipped with a mobile device and explore the properties of opportunistic communication in the context of content dissemination in urban areas. The contributions of this thesis lie in three areas. We first study human mobility as one of the main enablers of opportunistic communication. We introduce traces collected from a realistic pedestrian mobility simulator and demonstrate that the performance of opportunistic networks is not very sensitive to the accurate estimation of the probability distributions of mobility parameters. However, capturing the space in which mobility occurs may be of high importance. Secondly, we design and implement a middleware for opportunistic content-centric networking, and we evaluate it via a small-scale testbed, as well as through extensive simulations. We conclude that energy-saving mechanisms should be part of the middleware design, while caching should be considered only as an add-on feature. Thirdly, we present and evaluate three different energy-saving mechanisms in the context of opportunistic networking: a dual-radio architecture, an asynchronous duty-cycling scheme, and an energy-aware algorithm which takes into account node selfishness. We evaluate our proposals analytically and via simulations. We demonstrate that when a critical mass of participants is available, the performance of the opportunistic network is comparable to downloading contents directly via the cellular network in terms of energy consumption while offloading large traffic volumes from the operator.<br><p>QC 20151120</p>
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Phe-Neau, Tiphaine. "Properties and Impact of Vicinity in Mobile Opportunistic Networks." Phd thesis, Université Pierre et Marie Curie - Paris VI, 2014. http://tel.archives-ouvertes.fr/tel-00957864.

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The networking paradigm uses new information vectors consisting of human carried devices is known as disruption-tolerant networks (DTN) or opportunistic networks. We identify the binary assertion issue in DTN. We notice how most DTNs mainly analyze nodes that are in contact. So all nodes that are not in contact are in intercontact. Nevertheless, when two nodes are not in contact, this does not mean that they are topologically far away from one another. We propose a formal definition of vicinities in DTNs and study the new resulting contact/intercontact temporal characterization. Then, we examine the internal organization of vicinities using the Vicinity Motion framework. We highlight movement types such as birth, death, and sequential moves. We analyze a number of their characteristics and extract vicinity usage directions for mobile networks. Based on the vicinity motion outputs and extracted directions, we build the TiGeR that simulates how pairs of nodes interact within their vicinities. Finally, we inquire about the possibilities of vicinity movement prediction in opportunistic networks. We expose a Vicinity Motion-based heuristic for pairwise shortest distance forecasting. We use two Vicinity Motion variants called AVM and SVM to collect vicinity information. We find that both heuristics perform quite well with performances up to 99% for SVM and around 40% for AVM.
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Chen, Yuan. "Opportunistic Overlays: Efficient Content Delivery in Mobile Environments." Diss., Available online, Georgia Institute of Technology, 2005, 2005. http://etd.gatech.edu/theses/available/etd-04082005-002659/unrestricted/chen%5Fyuan%5F200505%5Fphd.pdf.

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Thesis (Ph. D.)--Computing, Georgia Institute of Technology, 2005.<br>Riley, George, Committee Member ; Zhou, Dong, Committee Member ; Pu, Calton, Committee Member ; Ahamad, Mustaque, Committee Member ; Schwan, Karsten, Committee Chair. Vita. Includes bibliographical references.
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Books on the topic "Mobile opportunistic networks"

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Misra, Sudip, Barun Kumar Saha, and Sujata Pal. Opportunistic Mobile Networks. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-29031-7.

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Mobile opportunistic networks: Architectures, protocols and applications. CRC Press, 2011.

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Machinery, Association for Computing, and International Conference on Mobile Systems, Applications and Services (5th : 2007 : San Juan, P.R.), eds. MobiOpp '07: Proceedings of the First International MobiSys Workshop on Mobile Opportunistic Networking : San Juan, Puerto Rico, June 11, 2007. Association for Computing Machinery, 2007.

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Denko, Mieso K., ed. Mobile Opportunistic Networks. Auerbach Publications, 2016. http://dx.doi.org/10.1201/b10904.

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Denko, Mieso K. Mobile Opportunistic Networks. Taylor & Francis Group, 2019.

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Denko, Mieso K. Mobile Opportunistic Networks. Taylor & Francis Group, 2011.

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Wu, Jie, and Yunsheng Wang, eds. Opportunistic Mobile Social Networks. CRC Press, 2014. http://dx.doi.org/10.1201/b17231.

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Wu, Jie, and Yunsheng Wang. Opportunistic Mobile Social Networks. Taylor & Francis Group, 2014.

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Wu, Jie, and Yunsheng Wang. Opportunistic Mobile Social Networks. Taylor & Francis Group, 2019.

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Wu, Jie, and Yunsheng Wang. Opportunistic Mobile Social Networks. Taylor & Francis Group, 2014.

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Book chapters on the topic "Mobile opportunistic networks"

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Chen, Yuanzhu, Cheng Li, and Zehua Wang. "Opportunistic Routing in Mobile Networks." In Opportunistic Networks. CRC Press, 2021. http://dx.doi.org/10.1201/9781003132585-13.

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Iglesias, Carlos Borrego. "Mobile-Code-Based Opportunistic Networking." In Opportunistic Networks. CRC Press, 2021. http://dx.doi.org/10.1201/9781003132585-1.

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Talukdar, Md Ibrahim, and Md Sharif Hossen. "Reactive and Proactive Routing Strategies in Mobile Ad Hoc Network." In Opportunistic Networks. CRC Press, 2021. http://dx.doi.org/10.1201/9781003132585-3.

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Mir, Mohd Yaseen, and Chih-Lin Hu. "Message Forwarding and Relay Selection Strategies in Mobile Opportunistic Networks." In Opportunistic Networks. CRC Press, 2021. http://dx.doi.org/10.1201/9781003132585-8.

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Naznin, Rukhsana, and Md Sharif Hossen. "Performance Analysis of AODV and DSDV Routing Protocols in Mobile Ad Hoc Network Using OMNeT++." In Opportunistic Networks. CRC Press, 2021. http://dx.doi.org/10.1201/9781003132585-7.

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Lee, Kyunghan, Pan Hui, and Song Chong. "Mobility Models in Opportunistic Networks." In Mobile Ad Hoc Networking. John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118511305.ch10.

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Boldrini, Chiara, and Andrea Passarella. "Data Dissemination in Opportunistic Networks." In Mobile Ad Hoc Networking. John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118511305.ch12.

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Mitra, Pramita, and Christian Poellabauer. "Opportunistic Routing in Mobile Ad Hoc Networks." In Routing in Opportunistic Networks. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-3514-3_6.

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Misra, Sudip, Barun Kumar Saha, and Sujata Pal. "Opportunistic Mobile Networks: Toward Reality." In Computer Communications and Networks. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-29031-7_9.

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Goswami, Antriksh, Ruchir Gupta, and Gopal Sharan Parashari. "Infrastructure in Mobile Opportunistic Networks." In Emerging Wireless Communication and Network Technologies. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0396-8_8.

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Conference papers on the topic "Mobile opportunistic networks"

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Jun Tao, Yifan Xu, Chengwei Tan, and Xiaoxiao Wang. "Location-aware opportunistic forwarding in mobile opportunistic networks." In 2015 IEEE Wireless Communications and Networking Conference (WCNC). IEEE, 2015. http://dx.doi.org/10.1109/wcnc.2015.7127749.

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Xiong, Yongping, Limin Sun, Wenbo He, and Jian Ma. "Anycast routing in mobile opportunistic networks." In 2010 IEEE Symposium on Computers and Communications (ISCC). IEEE, 2010. http://dx.doi.org/10.1109/iscc.2010.5546790.

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Visca, Jorge, Raul Fuentes, Ana R. Cavalli, and Javier Baliosian. "Opportunistic media sharing for mobile networks." In NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium. IEEE, 2016. http://dx.doi.org/10.1109/noms.2016.7502902.

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Lakkakorpi, Jani, Mikko Pitkänen, and Jörg Ott. "Adaptive routing in mobile opportunistic networks." In the 13th ACM international conference. ACM Press, 2010. http://dx.doi.org/10.1145/1868521.1868539.

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Chaintreau, Augustin, and Laurent Massoulie. "Phase Transition in Opportunistic Mobile Networks." In 2008 IEEE International Zurich Seminar on Communications (IZS). IEEE, 2008. http://dx.doi.org/10.1109/izs.2008.4497269.

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Martyna, Jerzy. "Multicast modelling in mobile opportunistic networks." In 2014 9th International Symposium on Communication Systems, Networks & Digital Signal Processing (CSNDSP). IEEE, 2014. http://dx.doi.org/10.1109/csndsp.2014.6923832.

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Kosmides, Pavlos, and Lambros Lambrinos. "Intelligent Routing in Mobile Opportunistic Networks." In 2018 Global Information Infrastructure and Networking Symposium (GIIS). IEEE, 2018. http://dx.doi.org/10.1109/giis.2018.8635592.

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Chaintreau, Augustin, Abderrahmen Mtibaa, Laurent Massoulie, and Christophe Diot. "The diameter of opportunistic mobile networks." In the 2007 ACM CoNEXT conference. ACM Press, 2007. http://dx.doi.org/10.1145/1364654.1364670.

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Leedham, James, and Thanassis Tiropanis. "Opportunistic social networks for academia." In 2011 Workshop on Mobile and Online Social Networks (MOSN). IEEE, 2011. http://dx.doi.org/10.1109/mosn.2011.6060785.

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Tsolkas, Dimitris, Dionysis Xenakis, Nikos Passas, and Lazaros Merakos. "Opportunistic spectrum access over mobile WiMAX networks." In 2010 IEEE 15th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD). IEEE, 2010. http://dx.doi.org/10.1109/camad.2010.5686976.

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