1,908 research outputs found
Relieving the Wireless Infrastructure: When Opportunistic Networks Meet Guaranteed Delays
Major wireless operators are nowadays facing network capacity issues in
striving to meet the growing demands of mobile users. At the same time,
3G-enabled devices increasingly benefit from ad hoc radio connectivity (e.g.,
Wi-Fi). In this context of hybrid connectivity, we propose Push-and-track, a
content dissemination framework that harnesses ad hoc communication
opportunities to minimize the load on the wireless infrastructure while
guaranteeing tight delivery delays. It achieves this through a control loop
that collects user-sent acknowledgements to determine if new copies need to be
reinjected into the network through the 3G interface. Push-and-Track includes
multiple strategies to determine how many copies of the content should be
injected, when, and to whom. The short delay-tolerance of common content, such
as news or road traffic updates, make them suitable for such a system. Based on
a realistic large-scale vehicular dataset from the city of Bologna composed of
more than 10,000 vehicles, we demonstrate that Push-and-Track consistently
meets its delivery objectives while reducing the use of the 3G network by over
90%.Comment: Accepted at IEEE WoWMoM 2011 conferenc
Transport poverty meets the digital divide : accessibility and connectivity in rural communities
Peer reviewedPublisher PD
Route Planning in Transportation Networks
We survey recent advances in algorithms for route planning in transportation
networks. For road networks, we show that one can compute driving directions in
milliseconds or less even at continental scale. A variety of techniques provide
different trade-offs between preprocessing effort, space requirements, and
query time. Some algorithms can answer queries in a fraction of a microsecond,
while others can deal efficiently with real-time traffic. Journey planning on
public transportation systems, although conceptually similar, is a
significantly harder problem due to its inherent time-dependent and
multicriteria nature. Although exact algorithms are fast enough for interactive
queries on metropolitan transit systems, dealing with continent-sized instances
requires simplifications or heavy preprocessing. The multimodal route planning
problem, which seeks journeys combining schedule-based transportation (buses,
trains) with unrestricted modes (walking, driving), is even harder, relying on
approximate solutions even for metropolitan inputs.Comment: This is an updated version of the technical report MSR-TR-2014-4,
previously published by Microsoft Research. This work was mostly done while
the authors Daniel Delling, Andrew Goldberg, and Renato F. Werneck were at
Microsoft Research Silicon Valle
Timely Data Delivery in a Realistic Bus Network
Abstract—WiFi-enabled buses and stops may form the backbone of a metropolitan delay tolerant network, that exploits nearby communications, temporary storage at stops, and predictable bus mobility to deliver non-real time information. This paper studies the problem of how to route data from its source to its destination in order to maximize the delivery probability by a given deadline. We assume to know the bus schedule, but we take into account that randomness, due to road traffic conditions or passengers boarding and alighting, affects bus mobility. We propose a simple stochastic model for bus arrivals at stops, supported by a study of real-life traces collected in a large urban network. A succinct graph representation of this model allows us to devise an optimal (under our model) single-copy routing algorithm and then extend it to cases where several copies of the same data are permitted. Through an extensive simulation study, we compare the optimal routing algorithm with three other approaches: minimizing the expected traversal time over our graph, minimizing the number of hops a packet can travel, and a recently-proposed heuristic based on bus frequencies. Our optimal algorithm outperforms all of them, but most of the times it essentially reduces to minimizing the expected traversal time. For values of deadlines close to the expected delivery time, the multi-copy extension requires only 10 copies to reach almost the performance of the costly flooding approach. I
Stochastic on-time arrival problem in transit networks
This article considers the stochastic on-time arrival problem in transit
networks where both the travel time and the waiting time for transit services
are stochastic. A specific challenge of this problem is the combinatorial
solution space due to the unknown ordering of transit line arrivals. We propose
a network structure appropriate to the online decision-making of a passenger,
including boarding, waiting and transferring. In this framework, we design a
dynamic programming algorithm that is pseudo-polynomial in the number of
transit stations and travel time budget, and exponential in the number of
transit lines at a station, which is a small number in practice. To reduce the
search space, we propose a definition of transit line dominance, and techniques
to identify dominance, which decrease the computation time by up to 90% in
numerical experiments. Extensive numerical experiments are conducted on both a
synthetic network and the Chicago transit network.Comment: 29 pages; 12 figures. This manuscript version is made available under
the CC-BY-NC-ND 4.0 license
https://creativecommons.org/licenses/by-nc-nd/4.0
A dynamic approach to rebalancing bike-sharing systems
Bike-sharing services are flourishing in Smart Cities worldwide. They provide a low-cost and environment-friendly transportation alternative and help reduce traffic congestion. However, these new services are still under development, and several challenges need to be solved. A major problem is the management of rebalancing trucks in order to ensure that bikes and stalls in the docking stations are always available when needed, despite the fluctuations in the service demand. In this work, we propose a dynamic rebalancing strategy that exploits historical data to predict the network conditions and promptly act in case of necessity. We use Birth-Death Processes to model the stations' occupancy and decide when to redistribute bikes, and graph theory to select the rebalancing path and the stations involved. We validate the proposed framework on the data provided by New York City's bike-sharing system. The numerical simulations show that a dynamic strategy able to adapt to the fluctuating nature of the network outperforms rebalancing schemes based on a static schedule
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