Geographical Routing for Delay Tolerant Encounter Networks

Kurhinen, J.; Janatuinen, J.

doi:10.1109/iscc.2007.4381560

Cited by 10 publications

(8 citation statements)

References 6 publications

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“…The existing routing methods for DTNs are mainly classified into three categories: the probabilistic routings, 2–5 the social network–based routings, 6–11 and the location-based routings. 11–13…”

Section: Related Workmentioning

confidence: 99%

DRANS: Daily Routine Analysis for Node Searching in delay tolerant networks

Yang

Zhang

2017

International Journal of Distributed Sensor Networks

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In delay tolerant networks, the success rate and the transmission speed are restricted by limited social interaction and complex node mobility pattern analysis. To increase the success rate and reduce the transmission delay in delay tolerant networks, we propose Daily Routine Analysis for Node Searching in delay tolerant networks. In Daily Routine Analysis for Node Searching, each node is required to generate a Staying Probability Table and a Transiting Probability Table by analyzing its own daily routine, then to distribute its Staying Probability Table and Transiting Probability Table to the whole network with the help of other nodes having different mobility patterns. On the basis of the Staying Probability Table and Transiting Probability Table, Daily Routine Analysis for Node Searching further provides a node tracking strategy and an opportunistic routing strategy for delivering data from the source node to the destination node. Trace-driven experiments are performed to compare Daily Routine Analysis for Node Searching with previous node searching methods. The experimental results demonstrate that Daily Routine Analysis for Node Searching is able to promote the success rate and reduce the transmission delay effectively.

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Section: Related Workmentioning

confidence: 99%

DRANS: Daily Routine Analysis for Node Searching in delay tolerant networks

Yang

Zhang

2017

International Journal of Distributed Sensor Networks

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“…For instance, Predictive Graph Relay (PGR) [8] utilizes movement prediction and DTN techniques for routing packets in sparse mobile networks, while ORION [10] makes use of autoregressive moving average stochastic processes for contact prediction.…”

Section: Geographical Routingmentioning

confidence: 99%

GTA-m

Tan

Valera

2014

Proceedings of the Third ACM Workshop on Airborne Networks and Communications

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Airborne networks have potential applications in both civilian and military domains -such as passenger in-flight Internet connectivity, air traffic control and in intelligence, surveillance and reconnaissance (ISR) activities. However, airborne networks suffer from frequent disruptions due to high node mobility, ad hoc connectivity and line-of-sight blockages. These challenges can be alleviated through the use of disruption-tolerant networking (DTN) techniques. In this paper, we propose GTA-m, a multi-copy greedy trajectoryaware routing protocol for airborne networks. GTA-m employs DTN capabilities and exploits the use of flight information to forwarded bundles greedily to intended destination(s). To alleviate the local minima issues that are inherent in greedy algorithms, GTA-m allows m ≥ 1 copies of each bundle to be replicated throughout the entire network. We study the performance of GTA-m by simulating flights with varying numbers of aircraft and ground stations. Through simulations in OPNET, we show that GTA-m improves the average bundle delay by 34% and 52% as compared to conventional DTN routing protocols such as Sprayand-Wait and Epidemic respectively.

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“…Some mobility prediction schemes, Greedy Mobility Pattern agent [23], and the map matching algorithm [24] try to establish the mobility pattern in Figure 1 in an individual node by recording node-specific trajectories from GPS data. With trajectory history, a moving vehicle can compare its position to its previous trajectories and find out the most possible route.…”

Section: System Designmentioning

confidence: 99%

Mobility Crowdsourcing: Toward Zero-Effort Carpooling on Individual Smartphone

Liu

Feng

Wang

et al. 2013

International Journal of Distributed Sensor Networks

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In current carpooling systems, drivers and passengers offer and search for their trips through available mediums, for example, accessing carpool website by smartphone, for finding a possible match of the journey. While efforts have been made to achieve fast matching for known trips, the need for accurate mobile tracking for individual users still remains a bottleneck. For example, drivers feel impatient to input their routes before driving, or centralized systems haves difficulties to track a large number of vehicles in real time. In this paper, we present the idea of Mobility Crowdsourcing (MobiCrowd), which leverages private smartphone to collect individual trips for carpooling, without any explicit effort on the part of users. Our scheme generates daily trips and mobility models for each user, and then makes carpooling zero-effort by enabling travel data to be crowdsourced instead of tracking vehicles or asking users to input their trips. With prior mobility knowledge, one user's travel routes and positions for carpooling can be predicted according to the location of the time and other mobility context. Based on a realistic travel survey and simulation, we prove that our scheme can provide efficient and accurate position estimation for individual carpools.

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Geographical Routing for Delay Tolerant Encounter Networks

Cited by 10 publications

References 6 publications

DRANS: Daily Routine Analysis for Node Searching in delay tolerant networks

DRANS: Daily Routine Analysis for Node Searching in delay tolerant networks

GTA-m

Mobility Crowdsourcing: Toward Zero-Effort Carpooling on Individual Smartphone

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