Throughput-Optimal Routing in Unreliable Networks

We demonstrate the feasibility of throughput-efficient routing in a highly unreliable network. Modeling a network as a graph with vertices representing nodes and edges representing the links between them, we consider two forms of unreliability: unpredictable edge-failures, and deliberate deviation from protocol specifications by corrupt nodes. The first form of unpredictability represents networks with dynamic topology, whose links may be constantly going up and down; while the second form represents malicious insiders attempting to disrupt communication by deliberately disobeying routing rules, by e.g. introducing junk messages or deleting or altering messages. We present a robust routing protocol for end-to-end communication that is simultaneously resilient to both forms of unreliability, achieving provably optimal throughput performance. Our proof proceeds in three steps: 1) We use competitive-analysis to find a lower-bound on the optimal throughput-rate of a routing protocol in networks susceptible to only edge-failures (i.e. networks with no malicious nodes); 2) We prove a matching upper bound by presenting a routing protocol that achieves this throughput rate (again in networks with no malicious nodes); and 3) We modify the protocol to provide additional protection against malicious nodes, and prove the modified protocol performs (asymptotically) as well as the original

Providing End-to-End Delay Guarantees for Multi-hop Wireless Sensor Networks over Unreliable Channels

Wireless sensor networks have been increasingly used for real-time surveillance over large areas. In such applications, it is important to support end-to-end delay constraints for packet deliveries even when the corresponding flows require multi-hop transmissions. In addition to delay constraints, each flow of real-time surveillance may require some guarantees on throughput of packets that meet the delay constraints. Further, as wireless sensor networks are usually deployed in challenging environments, it is important to specifically consider the effects of unreliable wireless transmissions. In this paper, we study the problem of providing end-to-end delay guarantees for multi-hop wireless networks. We propose a model that jointly considers the end-to-end delay constraints and throughput requirements of flows, the need for multi-hop transmissions, and the unreliable nature of wireless transmissions. We develop a framework for designing feasibility-optimal policies. We then demonstrate the utility of this framework by considering two types of systems: one where sensors are equipped with full-duplex radios, and the other where sensors are equipped with half-duplex radios. When sensors are equipped with full-duplex radios, we propose an online distributed scheduling policy and proves the policy is feasibility-optimal. We also provide a heuristic for systems where sensors are equipped with half-duplex radios. We show that this heuristic is still feasibility-optimal for some topologies

Low Power, Low Delay: Opportunistic Routing meets Duty Cycling

Traditionally, routing in wireless sensor networks consists of two steps: First, the routing protocol selects a next hop, and, second, the MAC protocol waits for the intended destination to wake up and receive the data. This design makes it difficult to adapt to link dynamics and introduces delays while waiting for the next hop to wake up. In this paper we introduce ORW, a practical opportunistic routing scheme for wireless sensor networks. In a dutycycled setting, packets are addressed to sets of potential receivers and forwarded by the neighbor that wakes up first and successfully receives the packet. This reduces delay and energy consumption by utilizing all neighbors as potential forwarders. Furthermore, this increases resilience to wireless link dynamics by exploiting spatial diversity. Our results show that ORW reduces radio duty-cycles on average by 50% (up to 90% on individual nodes) and delays by 30% to 90% when compared to the state of the art

A Survey on Delay-Aware Resource Control for Wireless Systems --- Large Deviation Theory, Stochastic Lyapunov Drift and Distributed Stochastic Learning

In this tutorial paper, a comprehensive survey is given on several major systematic approaches in dealing with delay-aware control problems, namely the equivalent rate constraint approach, the Lyapunov stability drift approach and the approximate Markov Decision Process (MDP) approach using stochastic learning. These approaches essentially embrace most of the existing literature regarding delay-aware resource control in wireless systems. They have their relative pros and cons in terms of performance, complexity and implementation issues. For each of the approaches, the problem setup, the general solution and the design methodology are discussed. Applications of these approaches to delay-aware resource allocation are illustrated with examples in single-hop wireless networks. Furthermore, recent results regarding delay-aware multi-hop routing designs in general multi-hop networks are elaborated. Finally, the delay performance of the various approaches are compared through simulations using an example of the uplink OFDMA systems.Comment: 58 pages, 8 figures; IEEE Transactions on Information Theory, 201

Rapid Convergecast on Commodity Hardware: Performance Limits and Optimal Policies

ReSensePromo

From Dumb Wireless Sensors to Smart Networks using Network Coding

The vision of wireless sensor networks is one of a smart collection of tiny, dumb devices. These motes may be individually cheap, unintelligent, imprecise, and unreliable. Yet they are able to derive strength from numbers, rendering the whole to be strong, reliable and robust. Our approach is to adopt a distributed and randomized mindset and rely on in network processing and network coding. Our general abstraction is that nodes should act only locally and independently, and the desired global behavior should arise as a collective property of the network. We summarize our work and present how these ideas can be applied for communication and storage in sensor networks.Comment: To be presented at the Inaugural Workshop of the Center for Information Theory and Its Applications, University of California - San Diego, La Jolla, CA, February 6 - 10, 200