Lifeguard: Local Health Awareness for More Accurate Failure Detection

SWIM is a peer-to-peer group membership protocol with attractive scaling and robustness properties. However, slow message processing can cause SWIM to mark healthy members as failed (so called false positive failure detection), despite inclusion of a mechanism to avoid this. We identify the properties of SWIM that lead to the problem, and propose Lifeguard, a set of extensions to SWIM which consider that the local failure detector module may be at fault, via the concept of local health. We evaluate this approach in a precisely controlled environment and validate it in a real-world scenario, showing that it drastically reduces the rate of false positives. The false positive rate and detection time for true failures can be reduced simultaneously, compared to the baseline levels of SWIM

Reliable routing scheme for indoor sensor networks

Indoor Wireless sensor networks require a highly dynamic, adaptive routing scheme to deal with the high rate of topology changes due to fading of indoor wireless channels. Besides that, energy consumption rate needs to be consistently distributed among sensor nodes and efficient utilization of battery power is essential. If only the link reliability metric is considered in the routing scheme, it may create long hops routes, and the high quality paths will be frequently used. This leads to shorter lifetime of such paths; thereby the entire network's lifetime will be significantly minimized. This paper briefly presents a reliable load-balanced routing (RLBR) scheme for indoor ad hoc wireless sensor networks, which integrates routing information from different layers. The proposed scheme aims to redistribute the relaying workload and the energy usage among relay sensor nodes to achieve balanced energy dissipation; thereby maximizing the functional network lifetime. RLBR scheme was tested and benchmarked against the TinyOS-2.x implementation of MintRoute on an indoor testbed comprising 20 Mica2 motes and low power listening (LPL) link layer provided by CC1000 radio. RLBR scheme consumes less energy for communications while reducing topology repair latency and achieves better connectivity and communication reliability in terms of end-to-end packets delivery performance

Models and Algorithms for Addressing Travel Time Variability: Applications from Optimal Path Finding and Traffic Equilibrium Problems

An optimal path finding problem and a traffic equilibrium problem are two important, fundamental, and interrelated topics in the transportation research field. Under travel time variability, the road networks are considered as stochastic, where the link travel times are treated as random variables with known probability density functions. By considering the effect of travel time variability and corresponding risk-taking behavior of the travelers, this dissertation proposes models and algorithms for addressing travel time variability with applications from optimal path finding and traffic equilibrium problems. Specifically, two new optimal path finding models and two novel traffic equilibrium models are proposed in stochastic networks. To adaptively determine a reliable path with the minimum travel time budget required to meet the user-specified reliability threshold α, an adaptive α-reliable path finding model is proposed. It is formulated as a chance constrained model under a dynamic programming framework. Then, a discrete-time algorithm is developed based on the properties of the proposed model. In addition to accounting for the reliability aspect of travel time variability, the α-reliable mean-excess path finding model further concerns the unreliability aspect of the late trips beyond the travel time budget. It is formulated as a stochastic mixed-integer nonlinear program. To solve this difficult problem, a practical double relaxation procedure is developed. By recognizing travelers are not only interested in saving their travel time but also in reducing their risk of being late, a α-reliable mean-excess traffic equilibrium (METE) model is proposed. Furthermore, a stochastic α-reliable mean-excess traffic equilibrium (SMETE) model is developed by incorporating the travelers’ perception error, where the travelers’ route choice decisions are determined by the perceived distribution of the stochastic travel time. Both models explicitly examine the effects of both reliability and unreliability aspects of travel time variability in a network equilibrium framework. They are both formulated as a variational inequality (VI) problem and solved by a route-based algorithm based on the modified alternating direction method. In conclusion, this study explores the effects of the various aspects (reliability and unreliability) of travel time variability on travelers’ route choice decision process by considering their risk preferences. The proposed models provide novel views of the optimal path finding problem and the traffic equilibrium problem under an uncertain environment, and the proposed solution algorithms enable potential applicability for solving practical problems

Efficient fault-tolerant routing in multihop optical WDM networks

This paper addresses the problem of efficient routing in unreliable multihop optical networks supported by Wavelength Division Multiplexing (WDM). We first define a new cost model for routing in (optical) WDM networks that is more general than the existing models. Our model takes into consideration not only the cost of wavelength access and conversion but also the delay for queuing signals arriving at different input channels that share the same output channel at the same node. We then propose a set of efficient algorithms in a reliable WDM network on the new cost model for each of the three most important communication patterns - multiple point-to-point routing, multicast, and multiple multicast. Finally, we show how to obtain a set of efficient algorithms in an unreliable WDM network with up to f faulty optical channels and wavelength conversion gates. Our strategy is to first enhance the physical paths constructed by the algorithms for reliable networks to ensure success of fault-tolerant routing, and then to route among the enhanced paths to establish a set of fault-free physical routes to complete the corresponding routing request for each of the communication patterns.published_or_final_versio

Unified Power Management in Wireless Sensor Networks, Doctoral Dissertation, August 2006

Radio power management is of paramount concern in wireless sensor networks (WSNs) that must achieve long lifetimes on scarce amount of energy. Previous work has treated communication and sensing separately, which is insufficient for a common class of sensor networks that must satisfy both sensing and communication requirements. Furthermore, previous approaches focused on reducing energy consumption in individual radio states resulting in suboptimal solutions. Finally, existing power management protocols often assume simplistic models that cannot accurately reflect the sensing and communication properties of real-world WSNs. We develop a unified power management approach to address these issues. We first analyze the relationship between sensing and communication performance of WSNs. We show that sensing coverage often leads to good network connectivity and geographic routing performance, which provides insights into unified power management under both sensing and communication performance requirements. We then develop a novel approach called Minimum Power Configuration that ingegrates the power consumption in different radio states into a unified optimization framework. Finally, we develop two power management protocols that account for realistic communication and sensing properties of WSNs. Configurable Topology Control can configure a network topology to achieve desired path quality in presence of asymmetric and lossy links. Co-Grid is a coverage maintenance protocol that adopts a probabilistic sensing model. Co-Grid can satisfy desirable sensing QoS requirements (i.e., detection probability and false alarm rate) based on a distributed data fusion model