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Maximizing Routing Throughput with Applications to Delay Tolerant Networks

Description

Many applications require efficient data routing and dissemination in Delay Tolerant Networks (DTNs) in order to maximize the throughput of data in the network, such as providing healthcare to remote

Many applications require efficient data routing and dissemination in Delay Tolerant Networks (DTNs) in order to maximize the throughput of data in the network, such as providing healthcare to remote communities, and spreading related information in Mobile Social Networks (MSNs). In this thesis, the feasibility of using boats in the Amazon Delta Riverine region as data mule nodes is investigated and a robust data routing algorithm based on a fountain code approach is designed to ensure fast and timely data delivery considering unpredictable boat delays, break-downs, and high transmission failures. Then, the scenario of providing healthcare in Amazon Delta Region is extended to a general All-or-Nothing (Splittable) Multicommodity Flow (ANF) problem and a polynomial time constant approximation algorithm is designed for the maximum throughput routing problem based on a randomized rounding scheme with applications to DTNs. In an MSN, message content is closely related to users’ preferences, and can be used to significantly impact the performance of data dissemination. An interest- and content-based algorithm is developed where the contents of the messages, along with the network structural information are taken into consideration when making message relay decisions in order to maximize data throughput in an MSN. Extensive experiments show the effectiveness of the above proposed data dissemination algorithm by comparing it with state-of-the-art techniques.

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Created

Date Created
  • 2018

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Stochastic simulation framework for a data mule network in the Amazon delta

Description

This report investigates the improvement in the transmission throughput, when fountain codes are used in opportunistic data routing, for a proposed delay tolerant network to connect remote and isolated communities

This report investigates the improvement in the transmission throughput, when fountain codes are used in opportunistic data routing, for a proposed delay tolerant network to connect remote and isolated communities in the Amazon region in Brazil, to the main city of that area. To extend healthcare facilities to the remote and isolated communities, on the banks of river Amazon in Brazil, the network [7] utilizes regularly schedules boats as data mules to carry data from one city to other.

Frequent thunder and rain storms, given state of infrastructure and harsh geographical terrain; all contribute to increase in chances of massages not getting delivered to intended destination. These regions have access to medical facilities only through sporadic visits from medical team from the main city in the region, Belem. The proposed network uses records for routine clinical examinations such as ultrasounds on pregnant women could be sent to the doctors in Belem for evaluation.

However, due to the lack of modern communication infrastructure in these communities and unpredictable boat schedules due to delays and breakdowns, as well as high transmission failures due to the harsh environment in the region, mandate the design of robust delay-tolerant routing algorithms. The work presented here incorporates the unpredictability of the Amazon riverine scenario into the simulation model - accounting for boat mechanical failure in boats leading to delays/breakdowns, possible decrease in transmission speed due to rain and individual packet losses.

Extensive simulation results are presented, to evaluate the proposed approach and to verify that the proposed solution [7] could be used as a viable mode of communication, given the lack of available options in the region. While the simulation results are focused on remote healthcare applications in the Brazilian Amazon, we envision that our approach may also be used for other remote applications, such as distance education, and other similar scenarios.

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Created

Date Created
  • 2015