Maximizing Routing Throughput with Applications to Delay Tolerant Networks

abstract: 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.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Review of Optimization Problems in Wireless Sensor Networks

International audienc

Improved Bi-criteria Approximation for the All-or-Nothing Multicommodity Flow Problem in Arbitrary Networks

This paper addresses the following fundamental maximum throughput routing problem: Given an arbitrary edge-capacitated $n$-node directed network and a set of $k$ commodities, with source-destination pairs $(s_i,t_i)$ and demands $d_i> 0$, admit and route the largest possible number of commodities -- i.e., the maximum {\em throughput} -- to satisfy their demands. The main contributions of this paper are two-fold: First, we present a bi-criteria approximation algorithm for this all-or-nothing multicommodity flow (ANF) problem. Our algorithm is the first to achieve a {\em constant approximation of the maximum throughput} with an {\em edge capacity violation ratio that is at most logarithmic in $n$}, with high probability. Our approach is based on a version of randomized rounding that keeps splittable flows, rather than approximating those via a non-splittable path for each commodity: This allows our approach to work for {\em arbitrary directed edge-capacitated graphs}, unlike most of the prior work on the ANF problem. Our algorithm also works if we consider the weighted throughput, where the benefit gained by fully satisfying the demand for commodity $i$ is determined by a given weight $w_i>0$. Second, we present a derandomization of our algorithm that maintains the same approximation bounds, using novel pessimistic estimators for Bernstein's inequality. In addition, we show how our framework can be adapted to achieve a polylogarithmic fraction of the maximum throughput while maintaining a constant edge capacity violation, if the network capacity is large enough. One important aspect of our randomized and derandomized algorithms is their {\em simplicity}, which lends to efficient implementations in practice

Resource Allocation in Relay Enhanced Broadband Wireless Access Networks

The use of relay nodes to improve the performance of broadband wireless access (BWA) networks has been the subject of intense research activities in recent years. Relay enhanced BWA networks are anticipated to support multimedia traffic (i.e., voice, video, and data traffic). In order to guarantee service to network users, efficient resource distribution is imperative. Wireless multihop networks are characterized by two inherent dynamic characteristics: 1) the existence of wireless interference and 2) mobility of user nodes. Both mobility and interference greatly influence the ability of users to obtain the necessary resources for service. In this dissertation we conduct a comprehensive research study on the topic of resource allocation in the presence of interference and mobility. Specifically, this dissertation investigates the impact interference and mobility have on various aspects of resource allocation, ranging from fairness to spectrum utilization. We study four important resource allocation algorithms for relay enhanced BWA networks. The problems and our research achievements are briefly outlined as follows. First, we propose an interference aware rate adaptive subcarrier and power allocation algorithm using maximum multicommodity flow optimization. We consider the impact of the wireless interference constraints using Signal to Interference Noise Ratio (SINR). We exploit spatial reuse to allocate subcarriers in the network and show that an intelligent reuse of resources can improve throughput while mitigating the impact of interference. We provide a sub-optimal heuristic to solve the rate adaptive resource allocation problem. We demonstrate that aggressive spatial reuse and fine tuned-interference modeling garner advantages in terms of throughput, end-to-end delay and power distribution. Second, we investigate the benefits of decoupled optimization of interference aware routing and scheduling using SINR and spatial reuse to improve the overall achievable throughput. We model the routing optimization problem as a linear program using maximum concurrent flows. We develop an optimization formulation to schedule the link traffic such that interference is mitigated and time slots are reused appropriately based on spatial TDMA (STDMA). The scheduling problem is shown to be NP-hard and is solved using the column generation technique. We compare our formulations to conventional counterparts in the literature and show that our approach guarantees higher throughput by mitigating the effect of interference effectively. Third, we investigate the problem of multipath flow routing and fair bandwidth allocation under interference constraints for multihop wireless networks. We first develop a novel isotonic routing metric, RI3M, considering the influence of interflow and intraflow interference. Second, in order to ensure QoS, an interference-aware max-min fair bandwidth allocation algorithm, LMX:M3F, is proposed where the lexicographically largest bandwidth allocation vector is found among all optimal allocation vectors while considering constraints of interference on the flows. We compare with various interference based routing metrics and interference aware bandwidth allocation algorithms established in the literature to show that RI3M and LMX:M3F succeed in improving network performance in terms of delay, packet loss ratio and bandwidth usage. Lastly, we develop a user mobility prediction model using the Hidden Markov Model(HMM) in which prediction control is transferred to the various fixed relay nodes in the network. Given the HMM prediction model, we develop a routing protocol which uses the location information of the mobile user to determine the interference level on links in its surrounding neighborhood. We use SINR as the routing metric to calculate the interference on a specific link (link cost). We minimize the total cost of routing as a cost function of SINR while guaranteeing that the load on each link does not exceed its capacity. The routing protocol is formulated and solved as a minimum cost flow optimization problem. We compare our SINR based routing algorithm with conventional counterparts in the literature and show that our algorithm reinforces routing paths with high link quality and low latency, therefore improving overall system throughput. The research solutions obtained in this dissertation improve the service reliability and QoS assurance of emerging BWA networks

Adaptive Routing Algorithm for Priority Flows in a Network

This research presents the development of an Adaptive Routing Algorithm for Priority (ARAP) flows in a Network. Devices in today\u27s battle space require information to function. Additional bandwidth requirements for such devices place an increased burden on the already congested networks in the battle space. Some devices require real time information (high priority) and other devices will not require real time information (low priority). Existing protocols treat the network like an opaque entity and have little knowledge of user requirements. User requirement information is available in tactical networks and we can take advantage of the known requirements to better optimize network behavior. One such optimization is during times of congestion ARAP will enable better Quality of Service (QoS) for higher priority information. Mechanisms such as Network Tasking Order (NTO) and Network Weatherman (NWM) can provide this information to facilitate improved network behavior. The NTO gives advance knowledge of network state and NWM provides future estimates on utilization of specific network queues allowing for improved QoS guarantees

Fair Resource Allocation in Macroscopic Evacuation Planning Using Mathematical Programming: Modeling and Optimization

Evacuation is essential in the case of natural and manmade disasters such as hurricanes, nuclear disasters, fire accidents, and terrorism epidemics. Random evacuation plans can increase risks and incur more losses. Hence, numerous simulation and mathematical programming models have been developed over the past few decades to help transportation planners make decisions to reduce costs and protect lives. However, the dynamic transportation process is inherently complex. Thus, modeling this process can be challenging and computationally demanding. The objective of this dissertation is to build a balanced model that reflects the realism of the dynamic transportation process and still be computationally tractable to be implemented in reality by the decision-makers. On the other hand, the users of the transportation network require reasonable travel time within the network to reach their destinations. This dissertation introduces a novel framework in the fields of fairness in network optimization and evacuation to provide better insight into the evacuation process and assist with decision making. The user of the transportation network is a critical element in this research. Thus, fairness and efficiency are the two primary objectives addressed in the work by considering the limited capacity of roads of the transportation network. Specifically, an approximation approach to the max-min fairness (MMF) problem is presented that provides lower computational time and high-quality output compared to the original algorithm. In addition, a new algorithm is developed to find the MMF resource allocation output in nonconvex structure problems. MMF is the fairness policy used in this research since it considers fairness and efficiency and gives priority to fairness. In addition, a new dynamic evacuation modeling approach is introduced that is capable of reporting more information about the evacuees compared to the conventional evacuation models such as their travel time, evacuation time, and departure time. Thus, the contribution of this dissertation is in the two areas of fairness and evacuation. The first part of the contribution of this dissertation is in the field of fairness. The objective in MMF is to allocate resources fairly among multiple demands given limited resources while utilizing the resources for higher efficiency. Fairness and efficiency are contradicting objectives, so they are translated into a bi-objective mathematical programming model and solved using the ϵ-constraint method, introduced by Vira and Haimes (1983). Although the solution is an approximation to the MMF, the model produces quality solutions, when ϵ is properly selected, in less computational time compared to the progressive-filling algorithm (PFA). In addition, a new algorithm is developed in this research called the θ progressive-filling algorithm that finds the MMF in resource allocation for general problems and works on problems with the nonconvex structure problems. The second part of the contribution is in evacuation modeling. The common dynamic evacuation models lack a piece of essential information for achieving fairness, which is the time each evacuee or group of evacuees spend in the network. Most evacuation models compute the total time for all evacuees to move from the endangered zone to the safe destination. Lack of information about the users of the transportation network is the motivation to develop a new optimization model that reports more information about the users of the network. The model finds the travel time, evacuation time, departure time, and the route selected for each group of evacuees. Given that the travel time function is a non-linear convex function of the traffic volume, the function is linearized through a piecewise linear approximation. The developed model is a mixed-integer linear programming (MILP) model with high complexity. Hence, the model is not capable of solving large scale problems. The complexity of the model was reduced by introducing a linear programming (LP) version of the full model. The complexity is significantly reduced while maintaining the exact output. In addition, the new θ-progressive-filling algorithm was implemented on the evacuation model to find a fair and efficient evacuation plan. The algorithm is also used to identify the optimal routes in the transportation network. Moreover, the robustness of the evacuation model was tested against demand uncertainty to observe the model behavior when the demand is uncertain. Finally, the robustness of the model is tested when the traffic flow is uncontrolled. In this case, the model's only decision is to distribute the evacuees on routes and has no control over the departure time

Journal of Telecommunications and Information Technology, 2004, nr 2

kwartalni

Design issues in quality of service routing

The range of applications and services which can be successfully deployed in packet-switched networks such as the Internet is limited when the network does nor provide Quality of Service (QoS). This is the typical situation in today's Internet. A key aspect in providing QoS support is the requirement for an optimised and intelligent mapping of customer traffic flows onto a physical network topology. The problem of selecting such paths is the task of QoS routing QoS routing algorithms are intrinsically complex and need careful study before being implemented in real networks. Our aim is to address some of the challenges present m the deployment of QoS routing methods. This thesis considers a number of practical limitations of existing QoS routing algorithms and presents solutions to the problems identified. Many QoS routing algorithms are inherently unstable and induce traffic fluctuations in the network. We describe two new routing algorithms which address this problem The first method - ALCFRA (Adaptive Link Cost Function Routing Algorithm) - can be used in networks with sparse connectivity, while the second algorithm - CAR (Connectivity Aware Routing) - is designed to work well in other network topologies. We also describe how to ensure co-operative interaction of the routing algorithms in multiple domains when hierarchial routing is used and also present a solution to the problems of how to provide QoS support m a network where not all nodes are QoS-aware. Our solutions are supported by extensive simulations over a wide range of network topologies and their performance is compared to existing algorithms. It is shown that our solutions advance the state of the art in QoS routing and facilitate the deployment of QoS support in tomorrow's Internet

Networks, Communication, and Computing Vol. 2

Networks, communications, and computing have become ubiquitous and inseparable parts of everyday life. This book is based on a Special Issue of the Algorithms journal, and it is devoted to the exploration of the many-faceted relationship of networks, communications, and computing. The included papers explore the current state-of-the-art research in these areas, with a particular interest in the interactions among the fields