46 research outputs found

    A P2P Query Algorithm for Opportunistic Networks Utilizing betweenness Centrality Forwarding

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    Understanding information centric layer of adaptive collaborative caching framework in mobile disconnection-prone networks

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    Smart networks and services leverage in-network caching to improve transmission efficiency and support large amount of content sharing, decrease high operating costs and handle disconnections. In this paper, we investigate the complex challenges related to content popularity weighting process in collaborative caching algorithm in heterogeneous mobile disconnection prone environments. We describe a reputation-based popularity weighting mechanism built in information-centric layer of our adaptive collaborative caching framework CafRepCache which considers a realistic case where caching points gathering content popularity observed by nodes differentiates between them according to node's reputation and network's connectivity. We extensively evaluate CafRepCache with competitive protocols across three heterogeneous real-world mobility, connectivity traces and use YouTube dataset for different workload and content popularity patterns. We show that our collaborative caching mechanism CafRepCache balances the trade-off that achieves higher cache hit ratio, efficiency and success ratios while keeping lower delays, packet loss and caching footprint compared to competing protocols across three traces in the face of dynamic mobility of publishers and subscribers

    Cognitive privacy for personal clouds

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    This paper proposes a novel Cognitive Privacy (CogPriv) framework that improves privacy of data sharing between Personal Clouds for different application types and across heterogeneous networks. Depending on the behaviour of neighbouring network nodes, their estimated privacy levels, resource availability, and social network connectivity, each Personal Cloud may decide to use different transmission network for different types of data and privacy requirements. CogPriv is fully distributed, uses complex graph contacts analytics and multiple implicit novel heuristics, and combines these with smart probing to identify presence and behaviour of privacy compromising nodes in the network. Based on sensed local context and through cooperation with remote nodes in the network, CogPriv is able to transparently and on-the-fly change the network in order to avoid transmissions when privacy may be compromised. We show that CogPriv achieves higher end-to-end privacy levels compared to both noncognitive cellular network communication and state-of-the-art strategies based on privacy-aware adaptive social mobile networks routing for a range of experiment scenarios based on real-world user and network traces. CogPriv is able to adapt to varying network connectivity and maintain high quality of service while managing to keep low data exposure for a wide range of privacy leakage levels in the infrastructure

    Understanding information centric layer of adaptive collaborative caching framework in mobile disconnection-prone networks

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    Smart networks and services leverage in-network caching to improve transmission efficiency and support large amount of content sharing, decrease high operating costs and handle disconnections. In this paper, we investigate the complex challenges related to content popularity weighting process in collaborative caching algorithm in heterogeneous mobile disconnection prone environments. We describe a reputation-based popularity weighting mechanism built in information-centric layer of our adaptive collaborative caching framework CafRepCache which considers a realistic case where caching points gathering content popularity observed by nodes differentiates between them according to node's reputation and network's connectivity. We extensively evaluate CafRepCache with competitive protocols across three heterogeneous real-world mobility, connectivity traces and use YouTube dataset for different workload and content popularity patterns. We show that our collaborative caching mechanism CafRepCache balances the trade-off that achieves higher cache hit ratio, efficiency and success ratios while keeping lower delays, packet loss and caching footprint compared to competing protocols across three traces in the face of dynamic mobility of publishers and subscribers

    Adaptive real-time predictive collaborative content discovery and retrieval in mobile disconnection prone networks

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    Emerging mobile environments motivate the need for the development of new distributed technologies which are able to support dynamic peer to peer content sharing, decrease high operating costs, and handle intermittent disconnections. In this paper, we investigate complex challenges related to the mobile disconnection tolerant discovery of content that may be stored in mobile devices and its delivery to the requesting nodes in mobile resource-constrained heterogeneous environments. We propose a new adaptive real-time predictive multi-layer caching and forwarding approach, CafRepCache, which is collaborative, resource, latency, and content aware. CafRepCache comprises multiple multi-layer complementary real-time distributed predictive heuristics which allow it to respond and adapt to time-varying network topology, dynamically changing resources, and workloads while managing complex dynamic tradeoffs between them in real time. We extensively evaluate our work against three competitive protocols across a range of metrics over three heterogeneous real-world mobility traces in the face of vastly different workloads and content popularity patterns. We show that CafRepCache consistently maintains higher cache availability, efficiency and success ratios while keeping lower delays, packet loss rates, and caching footprint compared to the three competing protocols across three traces when dynamically varying content popularity and dynamic mobility of content publishers and subscribers. We also show that the computational cost and network overheads of CafRepCache are only marginally increased compared with the other competing protocols

    Understanding information centric layer of adaptive collaborative caching framework in mobile disconnection-prone networks

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    Smart networks and services leverage in-network caching to improve transmission efficiency and support large amount of content sharing, decrease high operating costs and handle disconnections. In this paper, we investigate the complex challenges related to content popularity weighting process in collaborative caching algorithm in heterogeneous mobile disconnection prone environments. We describe a reputation-based popularity weighting mechanism built in information-centric layer of our adaptive collaborative caching framework CafRepCache which considers a realistic case where caching points gathering content popularity observed by nodes differentiates between them according to node's reputation and network's connectivity. We extensively evaluate CafRepCache with competitive protocols across three heterogeneous real-world mobility, connectivity traces and use YouTube dataset for different workload and content popularity patterns. We show that our collaborative caching mechanism CafRepCache balances the trade-off that achieves higher cache hit ratio, efficiency and success ratios while keeping lower delays, packet loss and caching footprint compared to competing protocols across three traces in the face of dynamic mobility of publishers and subscribers

    Reputation aware obfuscation for mobile opportunistic networks

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    © 2013 IEEE. Current anonymity techniques for mobile opportunistic networks typically use obfuscation algorithms to hide node's identity behind other nodes. These algorithms are not well suited to sparse and disconnection prone networks with large number of malicious nodes and new opportunistic, adaptive. So, new, opportunistic, adaptive fully localized mechanisms are needed for improving user anonymity. This paper proposes reputation aware localized adaptive obfuscation for mobile opportunistic networks that comprises of two complementary techniques: opportunistic collaborative testing of nodes' obfuscation behaviour (OCOT) and multidimensional adaptive anonymisation (AA). OCOT-AA is driven by both explicit and implicit reputation building, complex graph connectivity analytics and obfuscation history analyses. We show that OCOT-AA is very efficient in terms of achieving high levels of node identity obfuscation and managing low delays for answering queries between sources and destinations while enabling fast detection and avoidance of malicious nodes typically within the fraction of time within the experiment duration. We perform extensive experiments to compare OCOT-AA with several other competitive and benchmark protocols and show that it outperforms them across a range of metrics over a one month real-life GPS trace. To demonstrate our proposal more clearly, we propose new metrics that include best effort biggest length and diversity of the obfuscation paths, the actual percentage of truly anonymised sources' IDs at the destinations and communication quality of service between source and destination

    On social and technical aspects of managing mobile Ad-hoc communities

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    Soziale Software beschreibt eine Klasse von Anwendungen, die es Benutzern erlaubt ueber das Internet mit Freunden zu kommunizieren und Informationen auszutauschen. Mit zunehmender Leistungsfaehigkeit mobiler Prozessoren verwandeln sich Mobiltelefone in vollwertige Computer und eroeffnen neue Moeglichkeiten fuer die mobile Nutzung sozialer Software. Da Menschen Mobiltelefone haeufig bei sich fuehren, koennen vergleichbare mobile Anwendungen staerker auf ihre unmittelbare Umgebungssituation zugeschnitten werden. Moegliche Szenarien sind die Unterstuetzung realer Treffen und damit verbundenen Mitgliederinteraktionen. Client-Server-Plattformen, die dabei haeufig zum Einsatz kommen wurden allerdings nie fuer solche hochflexiblen Gruppensituationen konstruiert. Mobile Encounter Netzwerke (MENe) verprechen hier mehr Flexibilitaet. Ein MEN stellt eine mobiler Peer-to-Peer-Plattformen dar, das ueber ein kurzreichweitiges Funknetz betrieben wird. Mit diesem Netzwerk werden Beitraege ueber einen raeumlichen Diffusionsprozess von einem mobilen Endgeraet zum naechsten verbreitet. Das hat zwei entscheidende Vorteile: Zunaechst ist der direkte Nachrichtenaustausch besser geeignet zur Verbreitung von situationsspezifischer Information, da die Informationsrelevanz mit ihrer Entfehrnung abnimmt. Gleichzeitig koennen aber auch Inhalte, die fuer einen breiten Interessenkreis bestimmt sind ueber Mitglieder mit herausragenden Mobilitaetscharakteristik in weit entfernte Gebiete transportiert werden. Ein Nachteil ist jedoch der hohe Ressourcenverbrauch. Zur Loesung dieses Problems entwickeln wir ein Rahmenwerk zur Unterstuetzung mobiler ad-hoc Gruppen, das es uns erlaubt, Gruppensynergien gezielt auszunutzen. Dieses Rahmenwerk bietet Dienstleistungen zur Verwaltung der Gruppendynamik und zur Verbreitung von Inhalten an. Mittels soziale Netzwerkanalyse wird die technische Infrastruktur ohne notwendige Benutzereingriffe kontinuierlich an die reale Umgebungssituation angepasst. Dabei werden moegliche Beziehungen zwischen benachbarten Personen anhand frueher Begegnungen analysiert, spontane Gruppenbildungen mit Clusterverfahren identifiziert und jedem Gruppenmitglied eine geeignete Rolle durch eine Positionsanalyse zugewiesen. Eine Grundvorraussetzung fuer eine erfolgreiche Kooperation ist ein effizienter Wissensaustausch innerhalb einer Gemeinschaft. Wie die Small World-Theorie zeigt, koennen Menschen Wissen auch dann effizient verbreiten, wenn ihre Entscheidung nur auf lokaler Umgebungsinformation basiert. Verschiedene Forscher machten sich das zu nutze, indem sie kurze Verbreitungspfade durch eine Verkettung hochvernetzter Mitglieder innerhalb einer Gemeinschaft konstruierten. Allerdings laesst sich dieses Verfahren nicht einfach auf MENe uebertragen, da die Transferzeit im Gegensatz zu dem drahtgebundenen Internet beschraenkt ist. Unser Ansatz beruht daher, auf der von Reagan et al. vorgestellten Least Effort Transfer-Hypothese. Diese Hypothese besagt, dass Menschen Wissen nur dann weitergeben, wenn sich der Aufwand zur Informationsuebertragung innerhalb bestimmter Grenzen bewegt. Eine erfolgreiche Wissensuebertragung haengt in diesem Fall vom Hintergrundwissen aller Beteiligter ab, was wiederum von unterschiedlichen kognitiven und sozialen Faktoren abhaengt. Entsprechend leiten wir ein Diffusionsverfahren ab, dass in der Lage ist, Inhalte in verschiedene Kompexitaetstufen einzuteilen und Datenuebertragungen an die vorgefundene soziale Situation anzupassen. Mit einem Prototyp evaluieren wir die Machbarkeit der Gruppen- und Informationsmanagementkomponente unseres Rahmenwerkes. Da Laborexperimente keinen ausreichenden Aufschluss ueber Diffusionseigenschaften im groesseren Massstab geben koennen, simulieren wir die Beitragsdiffusion. Dazu dient uns eine Verkehrsimulation, bei der Agenten zusaetzlich mit aktivitaetsbezogenen, sozialen und territorialen Modellen erweitern werden. Um eine realitaetsnahe Simulation zu gewaehrleisten, werden diese Modelle in Uebereinstimmung mit verschiedenen Studien zum Stadtleben generiert. Der technische Uebertragungsprozess wird anhand der Ergebnisse einer vorangegangenen Prototypuntersuchung parametrisiert. Waehrend eines Simulationslaufes bewegen sich Agenten auf einem Stadtplan und sammeln Kontakt- und Beitragsdaten. Analysiert man anschliessend die Netzwerktopologie auf Small World-Eigenschaften, so findet man eine Netzstruktur mit einer ausgepraegten Neigung zum Clustering (Freundschaftsnetzwerke) und einer ueberdurschnittlichen kurzen Weglaenge. Offensichtlich reicht die Alltagsmobilitaet aus, um ausreichend viele Verknuepfungen zwischen Gemeinschaftmitgliedern zu bilden. Die nachfolgende Diffusionsanalyse zeigt, dass vergleichbare Reichweiten wie bei einem flutungsbasierten Ansatz erzielt werden, allerdings mit anfaenglichen Verzoegerungen. Da unser Verfahren bei einem Ortswechsel die Anzahl der Informationsuebermittler auf zentrale Gruppenmitglieder begrenzt, steht mehr Bandbreite fuer den Datenaustausch zur Verfuegung. Herkoemliche Mitglieder (ohne Leitungsaufgaben) tauschen Inhalte vornehmlich in zeitunkritschen Situationen aus. Das hat den positiven Nebeneffekt, dass im Cache erheblich weniger Kopien aussortiert werden muessen. Wechselt man waehrend der Simulation die Beitragskategorie so erkennt man, dass zeitabhaengige Inhalte besser ueber regelmaessige Kontakte und zeitunabhaengig Inhalte durch zufaellige Kontakte verbreitet werden. Eine abschliessende Precision-Recall Analyse zeigt, dass herkoemmliche Gruppenmitglieder eine bessere Genauigkeit (Precision), und zentrale Mitglieder eine bessere Trefferquote (Recall) im Vergleich zu traditionellen Ansaetzen besitzen. Eine Erklaerung dafuer ist, dass der von uns gewaehlte gruppenbasierte Cacheansatz zu weniger Saeuberungszyklen aller Gruppenmitglieder fuehrt und somit nachhaltiger ausgerichtet ist.Social software encompasses a range of software systems that allow users to interact and share data. This computer-mediated communication has become very popular with social networking sites like Facebook and Twitter. The evolvement of smart phones toward mobile computers opens new possibilities to use social software also in mobile usage scenarios. Since mobile phones are permanently carried by their owners, the support focus is, however, much stronger set on promoting and augmenting real group gatherings. Traditional client-server platforms are not flexible enough to support complex and dynamic human encounter behavior. Mobile encounter networks (MENs) which represent a mobile peer-to-peer platform on top of a short range wireless network promise better flexibility. MENs diffuse content from neighbor-to-neighbor in a spatial diffusion process. For physical group gatherings this is advantageous for two reasons. Direct device-to-device interactions encourage sharing of situation-dependent content. Moreover, content is not necessarily locked within friend groups and may trigger networking effects by reaching larger audiences through user mobility. One disadvantage is, however, the high resource usage. We develop a social software framework for mobile ad-hoc groups, which partly solves this problem. This framework supports services for the management of group dynamics and content diffusion within and between groups. Social network analysis as an inherent part of the framework is used to adapt internal community states continuously with real world encounter situations. We hereby qualify interpersonal relationships based on encounter and communication statistics, identify social groups through incremental clustering and assign diffusion roles through position analysis. To achieve efficient content dissemination we make use of social diffusion phenomena. Other researchers have experimented extensively with the small world model as it proofs that people transfer knowledge based on local knowledge but are still capable of diffusing it efficiently on a global scale. Their approach is often based on identifying short paths through member connectivity. However, this scenario is not applicable in MENs as transfer time is limited in contrast to the wired Internet. Our approach is therefore based on the least effort transfer theory. Following Reagan et al., who first postulated this hypothesis, people transfer knowledge only if the transfer effort is within specific limits, which depends on different social and cognitive factors. We derive routing mechanisms, which are capable of distinguishing between different content complexities and apply information about peer's expertise and social network to identify advantageous paths and content transfers options. We evaluate the feasibility of the group management and content transfer component with prototypes. Since labor settings do not allow to obtain information about large scale diffusion experiences, we also conduct a multi-agent simulation to evaluate the diffusion capabilities of the system. Experiences from an earlier prototype implementation have been used to quantify the technical routing process. To emulate realistic community life, we assigned to each agent an individual daily agenda, social contacts and territory preferences specified according to outcomes from different urban city life surveys. During the simulation agents move on a city map according to these models and collect contact and content specific data. Analyzing the network topology according to small world characteristics shows a structure with a high tendency for clustering (friend networks) and a short average path length. Daily urban mobility creates enough opportunities to form shortcuts through the community. Content diffusion analysis shows that our approach reaches a similar amount of peers as network flooding but with delays in the beginning. Since our approach artificially limits the number of intermediates to central community peers more bandwidth is available during traveling and more content can be transferred as in the case of the flooding approach. Ordinary peers seem to have significantly fewer content replications if an unlimited cache is assumed proofing that our mechanism is more efficient. By varying the content type used during the simulation we recognize that time dependent content is better disseminated through frequent contacts and time independent content through random contacts. Performing a precision-recall analysis on peers caches shows that ordinary peers gain an overall better context precision, and central peers a better community recall. One explanation is that the shared cache approach leads to fewer content replacements in the cache as for instance the least recently used cache strategy
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