Multidimensional Routing Protocol in Human-Associated Delay-Tolerant Networks
TL;DR: A multidimensional routing protocol (M-Dimension) for the human-associated delay-tolerant networks which uses local information derived from multiple dimensions to identify a mobile node more accurately and significantly increases the average success ratio with a competitive end-to-end delay when compared with other multicast DTNs routing protocols.
Abstract: Human-associated delay-tolerant networks (HDTNs) are new networks where mobile devices are associated with humans and can be viewed from multiple dimensions including geographic and social aspects. The combination of these different dimensions enables us to comprehend delay-tolerant networks and consequently use this multidimensional information to improve overall network efficiency. Alongside the geographic dimension of the network, which is concerned with geographic topology of routing, social dimensions such as social characters can be used to guide the routing message to improve not only the routing efficiency for individual nodes, but also efficiency for the entire network. We propose a multidimensional routing protocol (M-Dimension) for the human-associated delay-tolerant networks which uses local information derived from multiple dimensions to identify a mobile node more accurately. The importance of each dimension has been measured by the weight function and it is used to calculate the best route. The greedy routing strategy is applied to select an intermediary node to forward message. We compare M-Dimension to the existing benchmark routing protocols via MIT reality Data Set and INFOCOM 2006 Data Set, which are real human-associated mobile network trace files. The results of our simulations show that M-Dimension significantly increases the average success ratio with a competitive end-to-end delay when compared with other multicast DTNs routing protocols.
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TL;DR: This paper decomposes the Fog computing network with two planes, where the cloud is a control plane to process content update queries and organize data flows, and the geometrically distributed Fog servers form a data plane to disseminate data among Fog servers with a DTN technique.
Abstract: Fog computing, known as “cloud closed to ground,” deploys light-weight compute facility, called Fog servers, at the proximity of mobile users. By precatching contents in the Fog servers, an important application of Fog computing is to provide high-quality low-cost data distributions to proximity mobile users, e.g., video/live streaming and ads dissemination, using the single-hop low-latency wireless links. A Fog computing system is of a three tier Mobile–Fog–Cloud structure; mobile user gets service from Fog servers using local wireless connections, and Fog servers update their contents from Cloud using the cellular or wired networks. This, however, may incur high content update cost when the bandwidth between the Fog and Cloud servers is expensive, e.g., using the cellular network, and is therefore inefficient for nonurgent, high volume contents. How to economically utilize the Fog–Cloud bandwidth with guaranteed download performance of users thus represents a fundamental issue in Fog computing. In this paper, we address the issue by proposing a hybrid data dissemination framework which applies software-defined network and delay-tolerable network (DTN) approaches in Fog computing. Specifically, we decompose the Fog computing network with two planes, where the cloud is a control plane to process content update queries and organize data flows, and the geometrically distributed Fog servers form a data plane to disseminate data among Fog servers with a DTN technique. Using extensive simulations, we show that the proposed framework is efficient in terms of data-dissemination success ratio and content convergence time among Fog servers.
63 citations
TL;DR: Proximity-Interest-Social (PIS) as discussed by the authors is a multi-dimensional routing protocol in which the three different social dimensions are integrated into a unified distance function in order to select optimal intermediate data carriers.
Abstract: Socially-aware networking is an emerging paradigm for intermittently connected networks consisting of mobile users with social relationships and characteristics. In this setting, humans are the main carriers of mobile devices. Hence, their connections, social features, and behaviors can be exploited to improve the performance of data forwarding protocols. In this paper, we first explore the impact of three social features, namely physical proximity, user interests, and social relationship on users’ daily routines. Then, we propose a multi-dimensional routing protocol called Proximity-Interest-Social (PIS) protocol in which the three different social dimensions are integrated into a unified distance function in order to select optimal intermediate data carriers. PIS protocol utilizes a time slot management mechanism to discover users’ movement similarities in different time periods during a day. We compare the performance of PIS to Epidemic, PROPHET, and SimBet routing protocols using SIGCOMM09 and INFOCOM06 data sets. The experiment results show that PIS outperforms other benchmark routing protocols with the highest data delivery ratio with a low communication overhead.
52 citations
TL;DR: A Social-based Watchdog system in which watchdog nodes analyze messages received from their encountered nodes with respect to their social tie information to identify the nodes’ selfish behavior in message relaying and outperforms a benchmark contact-based watchdog system in terms of detection time and detection ratio.
48 citations
Cites methods from "Multidimensional Routing Protocol i..."
...We employ a unification process [36] to identify the importance of each feature....
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TL;DR: A provenance-based trust framework, namely PROVEST (PROVEnance-baSed Trust model), that aims to achieve accurate peer-to-peer trust assessment and maximize the delivery of correct messages received by destination nodes while minimizing message delay and communication cost under resource-constrained network environments is proposed.
Abstract: Delay tolerant networks (DTNs) are often encountered in military network environments where end-to-end connectivity is not guaranteed due to frequent disconnection or delay. This work proposes a provenance-based trust framework, namely PROVEST (PROVEnance-baSed Trust model) that aims to achieve accurate peer-to-peer trust assessment and maximize the delivery of correct messages received by destination nodes while minimizing message delay and communication cost under resource-constrained network environments. Provenance refers to the history of ownership of a valued object or information. We leverage the interdependency between trustworthiness of information source and information itself in PROVEST. PROVEST takes a data-driven approach to reduce resource consumption in the presence of selfish or malicious nodes while estimating a node's trust dynamically in response to changes in the environmental and node conditions. This work adopts a model-based method to evaluate the performance of PROVEST (i.e., trust accuracy and routing performance) using Stochastic Petri Nets. We conduct a comparative performance analysis of PROVEST against existing trust-based and non-trust-based DTN routing protocols to analyze the benefits of PROVEST. We validate PROVEST using a real dataset of DTN mobility traces.
46 citations
TL;DR: The results show that the proposed algorithm significantly improves routing performances compared to Epidemic, Prophet and First Contact, especially SPBR is lower by about 55.1% in overhead ratio and higher by about 22.2% in delivery rate when there are 40 nodes in the networks.
Abstract: Due to node’s mobility, Delay Tolerant Networks (DTNs) feature the nonexistence of end-toend path between source and destination, frequent topology partitions and extremely high delivery latency, thus posing great challenges to successful message transmission. To improve routing performance and provide high quality communication service, nodes’ social characteristics are exploited to routing design recently. Hence, a social popularity based routing algorithm is proposed, named SPBR which takes the inter-contact time and multi-hop neighbor information into consideration. In this paper, we first introduce a method to detect the quality of relation between pair of nodes accurately. Used the reliable relationships, social popularity is proposed to evaluate the social power of node in the network. SPBR makes the routing decisions based on the popularity, leading message closer to destinations with low hops of routing and network resources. Extensive simulations are conducted and the results show that the proposed algorithm significantly improves routing performances compared to Epidemic, Prophet and First Contact (FC), especially SPBR is lower by about 55.1% in overhead ratio and higher by about 22.2% in delivery rate than Epidemic when there are 40 nodes in the networks.
44 citations
Cites background from "Multidimensional Routing Protocol i..."
...Many researchers find that some DTNs like mobile social networks (MSNs) [8-10] exhibit human behaviors, where mobile users move around, communicate and share data with each other via their mobile devices such as smartphones, laptops, and tablet PCs....
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References
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TL;DR: Simple models of networks that can be tuned through this middle ground: regular networks ‘rewired’ to introduce increasing amounts of disorder are explored, finding that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs.
Abstract: Networks of coupled dynamical systems have been used to model biological oscillators, Josephson junction arrays, excitable media, neural networks, spatial games, genetic control networks and many other self-organizing systems. Ordinarily, the connection topology is assumed to be either completely regular or completely random. But many biological, technological and social networks lie somewhere between these two extremes. Here we explore simple models of networks that can be tuned through this middle ground: regular networks 'rewired' to introduce increasing amounts of disorder. We find that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs. We call them 'small-world' networks, by analogy with the small-world phenomenon (popularly known as six degrees of separation. The neural network of the worm Caenorhabditis elegans, the power grid of the western United States, and the collaboration graph of film actors are shown to be small-world networks. Models of dynamical systems with small-world coupling display enhanced signal-propagation speed, computational power, and synchronizability. In particular, infectious diseases spread more easily in small-world networks than in regular lattices.
39,297 citations
"Multidimensional Routing Protocol i..." refers background in this paper
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TL;DR: The homophily principle as mentioned in this paper states that similarity breeds connection, and that people's personal networks are homogeneous with regard to many sociodemographic, behavioral, and intrapersonal characteristics.
Abstract: Similarity breeds connection. This principle—the homophily principle—structures network ties of every type, including marriage, friendship, work, advice, support, information transfer, exchange, comembership, and other types of relationship. The result is that people's personal networks are homogeneous with regard to many sociodemographic, behavioral, and intrapersonal characteristics. Homophily limits people's social worlds in a way that has powerful implications for the information they receive, the attitudes they form, and the interactions they experience. Homophily in race and ethnicity creates the strongest divides in our personal environments, with age, religion, education, occupation, and gender following in roughly that order. Geographic propinquity, families, organizations, and isomorphic positions in social systems all create contexts in which homophilous relations form. Ties between nonsimilar individuals also dissolve at a higher rate, which sets the stage for the formation of niches (localize...
15,738 citations
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...First, social user relationships dictate the Probe Message frequency, which means nodes in the same social group receive much more information than other nodes....
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01 Aug 2000
TL;DR: Greedy Perimeter Stateless Routing is presented, a novel routing protocol for wireless datagram networks that uses the positions of routers and a packet's destination to make packet forwarding decisions and its scalability on densely deployed wireless networks is demonstrated.
Abstract: We present Greedy Perimeter Stateless Routing (GPSR), a novel routing protocol for wireless datagram networks that uses the positions of routers and a packet's destination to make packet forwarding decisions. GPSR makes greedy forwarding decisions using only information about a router's immediate neighbors in the network topology. When a packet reaches a region where greedy forwarding is impossible, the algorithm recovers by routing around the perimeter of the region. By keeping state only about the local topology, GPSR scales better in per-router state than shortest-path and ad-hoc routing protocols as the number of network destinations increases. Under mobility's frequent topology changes, GPSR can use local topology information to find correct new routes quickly. We describe the GPSR protocol, and use extensive simulation of mobile wireless networks to compare its performance with that of Dynamic Source Routing. Our simulations demonstrate GPSR's scalability on densely deployed wireless networks.
7,384 citations
"Multidimensional Routing Protocol i..." refers background or methods in this paper
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...Index Terms—Multiple dimensions, delay-tolerant network, multicast, social aware routing Ç...
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...AT the time of writing this paper, most routing protocolsfor DTNs used either geographic aspects or social aspects, while M-Dimension routing protocol utilized the characters from the multiple dimensions including geographic and social dimensions....
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TL;DR: A hierarchical agglomeration algorithm for detecting community structure which is faster than many competing algorithms: its running time on a network with n vertices and m edges is O (md log n) where d is the depth of the dendrogram describing the community structure.
Abstract: The discovery and analysis of community structure in networks is a topic of considerable recent interest within the physics community, but most methods proposed so far are unsuitable for very large networks because of their computational cost. Here we present a hierarchical agglomeration algorithm for detecting community structure which is faster than many competing algorithms: its running time on a network with n vertices and m edges is O (md log n) where d is the depth of the dendrogram describing the community structure. Many real-world networks are sparse and hierarchical, with m approximately n and d approximately log n, in which case our algorithm runs in essentially linear time, O (n log(2) n). As an example of the application of this algorithm we use it to analyze a network of items for sale on the web site of a large on-line retailer, items in the network being linked if they are frequently purchased by the same buyer. The network has more than 400 000 vertices and 2 x 10(6) edges. We show that our algorithm can extract meaningful communities from this network, revealing large-scale patterns present in the purchasing habits of customers.
6,599 citations
"Multidimensional Routing Protocol i..." refers methods in this paper
...It demonstrates two significances: The attributes from the multiple dimensions are used to identify nodes comprehensively, and the weight function is used to measure the importance of the individual dimension in calculating the route....
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TL;DR: After defining a set of new characteristic quantities for the statistics of communities, this work applies an efficient technique for exploring overlapping communities on a large scale and finds that overlaps are significant, and the distributions introduced reveal universal features of networks.
Abstract: A network is a network — be it between words (those associated with ‘bright’ in this case) or protein structures. Many complex systems in nature and society can be described in terms of networks capturing the intricate web of connections among the units they are made of1,2,3,4. A key question is how to interpret the global organization of such networks as the coexistence of their structural subunits (communities) associated with more highly interconnected parts. Identifying these a priori unknown building blocks (such as functionally related proteins5,6, industrial sectors7 and groups of people8,9) is crucial to the understanding of the structural and functional properties of networks. The existing deterministic methods used for large networks find separated communities, whereas most of the actual networks are made of highly overlapping cohesive groups of nodes. Here we introduce an approach to analysing the main statistical features of the interwoven sets of overlapping communities that makes a step towards uncovering the modular structure of complex systems. After defining a set of new characteristic quantities for the statistics of communities, we apply an efficient technique for exploring overlapping communities on a large scale. We find that overlaps are significant, and the distributions we introduce reveal universal features of networks. Our studies of collaboration, word-association and protein interaction graphs show that the web of communities has non-trivial correlations and specific scaling properties.
5,217 citations