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23
Spatiotemporal Delay Control for LowDutyCycle Sensor Networks
"... Abstract—Data delivery is a major function of sensor network applications. Many applications, such as military surveillance, require the detection of interested events to be reported to a command center within a specified time frame, and therefore impose a realtime bound on communication delay. On ..."
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Abstract—Data delivery is a major function of sensor network applications. Many applications, such as military surveillance, require the detection of interested events to be reported to a command center within a specified time frame, and therefore impose a realtime bound on communication delay. On the other hand, to conserve energy, one of the most effective approaches is to keep sensor nodes in the dormant state as long as possible while satisfying application requirements. Obviously a node can not communicate if it is not active. Therefore, to deliver data in a timely manner for such extremely low dutycycle sensor networks, communication needs to be carefully managed among sensor nodes. In this work, we introduce three different approaches to provide realtime guarantee of communication delay. First, we present a method for increasing dutycycle at individual node. Then we describe a scheme on placement of sink nodes. Based on previous two methods, we discuss a hybrid approach that shows better balance between cost and efficiency on bounding communication delay. Our solution is global optimal in terms of minimizing the energy consumption for bounding pairwise endtoend delay. For manytoone and manytomany cases, which are NPhard, we propose corresponding heuristic algorithms for them. To our knowledge, these are the most generic and encouraging results to date in this new research direction. We evaluate our design with an extensive simulation of 5,000 nodes as well as with a smallscale running testbed on TinyOS/Mote platform. Results show the effectiveness of our approach and significant improvements over an existing solution. I.
Anytime Search in Dynamic Graphs
"... Agents operating in the real world often have limited time available for planning their next actions. Producing optimal plans is infeasible in these scenarios. Instead, agents must be satisfied with the best plans they can generate within the time available. One class of planners wellsuited to this ..."
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Cited by 25 (5 self)
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Agents operating in the real world often have limited time available for planning their next actions. Producing optimal plans is infeasible in these scenarios. Instead, agents must be satisfied with the best plans they can generate within the time available. One class of planners wellsuited to this task are anytime planners, which quickly find an initial, highly suboptimal plan, and then improve this plan until time runs out. A second challenge associated with planning in the real world is that models are usually imperfect and environments are often dynamic. Thus, agents need to update their models and consequently plans over time. Incremental planners, which make use of the results of previous planning efforts to generate a new plan, can substantially speed up each planning episode in such cases. In this paper, we present an A*based anytime search algorithm that produces significantly better solutions than current approaches, while also providing suboptimality bounds on the quality of the solution at any point in time. We also present an extension of this algorithm that is both anytime and incremental. This extension improves its current solution while deliberation time allows and is able to incrementally repair its solution when changes to the world model occur. We provide a number of theoretical and experimental results and demonstrate the effectiveness of the approaches in a robot navigation domain involving two physical systems. We believe that the simplicity, theoretical properties, and generality of the presented methods make them well suited to a range of search problems involving large, dynamic graphs.
Expected shortest paths for landmarkbased robot navigation
 International Journal of Robotics Research
, 2004
"... Abstract. In this paper we address the problem of planning reliable landmarkbased robot navigation strategies in the presence of significant sensor uncertainty. The navigation environments are modeled with directed weighted graphs in which edges can be traversed with given probabilities. To construc ..."
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Abstract. In this paper we address the problem of planning reliable landmarkbased robot navigation strategies in the presence of significant sensor uncertainty. The navigation environments are modeled with directed weighted graphs in which edges can be traversed with given probabilities. To construct robust and efficient navigation plans, we compute expected shortest paths in such graphs. We formulate the expected shortest paths problem as a Markov decision process and provide two algorithms for its solution. We demonstrate the practicality of our approach using an extensive experimental analysis using graphs with varying sizes and parameters. 1
Fast paths in largescale dynamic road networks
, 2007
"... Efficiently computing fast paths in largescale dynamic road networks (where dynamic traffic information is known over a part of the network) is a practical problem faced by several traffic information service providers who wish to offer a realistic fast path computation to GPS terminal enabled vehi ..."
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Cited by 5 (1 self)
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Efficiently computing fast paths in largescale dynamic road networks (where dynamic traffic information is known over a part of the network) is a practical problem faced by several traffic information service providers who wish to offer a realistic fast path computation to GPS terminal enabled vehicles. The heuristic solution method we propose is based on a highway hierarchybased shortest path algorithm for static largescale networks; we maintain a static highway hierarchy and perform each query on the dynamically evaluated network, using a simple algorithm to propagate available dynamic traffic information over a larger part of the road network. We provide computational results that show the efficacy of our approach.
Finding Topk Shortest Path Distance Changes in an Evolutionary Network
"... Abstract. Networks can be represented as evolutionary graphs in a variety of spatiotemporal applications. Changes in the nodes and edges over time may also result in corresponding changes in structural garph properties such as shortest path distances. In this paper, we study the problem of detectin ..."
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Abstract. Networks can be represented as evolutionary graphs in a variety of spatiotemporal applications. Changes in the nodes and edges over time may also result in corresponding changes in structural garph properties such as shortest path distances. In this paper, we study the problem of detecting the topk most significant shortestpath distance changes between two snapshots of an evolving graph. While the problem is solvable with two applications of the allpairs shortest path algorithm, such a solution would be extremely slow and impractical for very large graphs. This is because when a graph may contain millions of nodes, even the storage of distances between all node pairs can become inefficient in practice. Therefore, it is desirable to design algorithms which can directly determine the significant changes in shortest path distances, without materializing the distances in individual snapshots. We present algorithms that are up to two orders of magnitude faster than such a solution, while retaining comparable accuracy. 1
Integrated Transit Priority and Rail/Emergency Preemption in RealTime Traffic Adaptive Signal Control
 J. Intelligent Transportation Systems
"... The article discusses a strategy, referred to as Categorized Arrivalsbased Phase Reoptimization at Intersections (CAPRI), which integrates transit signal priority and rail/emergency preemption within a dynamic programmingbased realtime traffic adaptive signal control system. The system takes as i ..."
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Cited by 4 (0 self)
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The article discusses a strategy, referred to as Categorized Arrivalsbased Phase Reoptimization at Intersections (CAPRI), which integrates transit signal priority and rail/emergency preemption within a dynamic programmingbased realtime traffic adaptive signal control system. The system takes as input sensor data, from detectors, automatic vehicle locators, transponders, etc., for realtime predictions of traffic flow, and “optimally ” controls the flow through the network using signal phasing. The system utilizes a traffic adaptive signal control architecture that (1) decomposes the traffic control problem into several subproblems that are interconnected in a hierarchical fashion, (2) predicts traffic flows, at appropriate resolution levels (individual vehicles, platoons of vehicles, transit vehicles, emergency response units, and trains) to enable proactive control, (3) supports various optimization modules for solving the hierarchical subproblems, and (4) utilizes data structure and computer/communication approaches that allow for fast solution of the subproblems, so that each decision can be implemented in the field within an appropriate rolling time horizon of the corresponding subproblem. Simulationbased analyses illustrate the effectiveness of the CAPRI system.
Shortest paths on dynamic graphs
 International Transactions in Operational Research
"... All intext references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately. ..."
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All intext references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately.
Shortest Paths in TimeDependent FIFO Networks Using Edge Load Forecasts
, 2009
"... We study the problem of finding shortest paths in timedependent networks with edge load forecasts where the behavior of each edge is modeled as a timedependent arrival function with FIFO property. Here, we present a new algorithm that computes for a given start node s and destination node d, the sh ..."
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We study the problem of finding shortest paths in timedependent networks with edge load forecasts where the behavior of each edge is modeled as a timedependent arrival function with FIFO property. Here, we present a new algorithm that computes for a given start node s and destination node d, the shortest paths and earliest arrival times for all possible starting times. Our algorithm runs in time O((Fd + λ)(E  + V log V )) where Fd is the output size (number of linear pieces needed to represent the earliest arrival time function) and λ is the input size (number of linear pieces needed to represent the local earliest arrival time functions for all edges in the network). Our method improves significantly on the best previously known algorithm which requires time O(FmaxV E) where Fmax ≥ Fd is the maximum number of linear pieces needed to represent the earliest arrival time function between the start node s to any node in the network. It has been conjectured that there are cases where Fmax is of superpolynomial size; however, even in such cases, Fd might still be of linear size. In such cases, our algorithm would take polynomial time to find the solution, while other methods require superpolynomial time. Both of the above methods are not useful in practice for graphs where Fd is of superpolynomial size. For such graphs, we present the first approximation method to compute for all possible starting times at s, the earliest arrival times at d within error at most ǫ. Our algorithm runs in time O ( ∆ (E  + V log V )) where ∆ is the difference beǫ tween the earliest arrival times at d for the latest and earliest starting times at s.
Bounding Communication Delay in Energy Harvesting Sensor Networks
"... Abstract—In energyharvesting sensor networks, limited ambient energy from environment necessitates sensor nodes to operate at a lowdutycycle, i.e., they communicate briefly and stay asleep most of time. Such lowdutycycle operation leads to orders of magnitude longer communication delays in comp ..."
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Abstract—In energyharvesting sensor networks, limited ambient energy from environment necessitates sensor nodes to operate at a lowdutycycle, i.e., they communicate briefly and stay asleep most of time. Such lowdutycycle operation leads to orders of magnitude longer communication delays in comparison with traditional alwaysactive networks, imposing a new challenge in many timesensitive sensor network applications (e.g., tracking and alert). In this paper, we introduce novel solutions for bounding sinktonode communications in energyharvesting sensor networks. We first present an optimal solution for the sinktoone case and its distributed implementation. For the sinktomany case, we theoretically prove its NPHardness and inapproximability property, followedbyan efficientheuristicsolution.We have evaluated our design with both extensive simulation and a TinyOS/Mote based implementation. Compared with an improved version of a stateoftheart design, our delay maintenance design effectively provides E2E delay guarantees while consuming as much as 60% less energy. I.
TIMEDEPENDENT STOCHASTIC SHORTEST PATH(S) ALGORITHMS FOR A SCHEDULED TRANSPORTATION NETWORK
"... Following on from our work concerning travellers’ preferences in public transportation networks (Wu and Hartley, 2004), we introduce the concept of stochasticity to our algorithms. Stochasticity greatly increases the complexity of the route finding problem, so greater algorithmic efficiency becomes ..."
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Cited by 3 (0 self)
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Following on from our work concerning travellers’ preferences in public transportation networks (Wu and Hartley, 2004), we introduce the concept of stochasticity to our algorithms. Stochasticity greatly increases the complexity of the route finding problem, so greater algorithmic efficiency becomes imperative. Public transportation networks (buses, trains) have two important features: edges can only be traversed at certain points in time and the weights of these edges change in a day and have an uncertainty associated with them. These features determine that a public transportation network is a stochastic and timedependent network. Finding multiple shortest paths in a both stochastic and timedependent network is currently regarded as the most difficult task in the route finding problems (Loui, 1983). This paper discusses the use of kshortestpaths (KSP) algorithms to find optimal route(s) through a network in which the edge weights are defined by probability distributions. A comprehensive review of shortest path(s) algorithms with probabilistic graphs was conducted.