Network warehouses: Efficient information distribution to mobile users

Motskin, Arik; Downes, Ian; Kusý, Branislav; Gnawali, Omprakash; Guibas, Leonidas J.

doi:10.1109/infcom.2011.5935015

Cited by 9 publications

(8 citation statements)

References 10 publications

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“…Constant-doubling networks have been widely used as an appropriate model of a sensor network metric, e.g. see [7,11,12,27,40]. We use a distributed maximal independent set algorithm due to Luby [24] to select the nodes of G to include in HS; some recent algorithms [15,29] can also be used to construct HS.…”

Section: Construction Of Overlay Structurementioning

confidence: 99%

“…We use a (O(log n), O(log n)) partition scheme of [4,15,33] to maintain an overlay structure HS. A similar scheme is used by [27] to solve the problem of distributing information from a collection of sources to mobile users in a wireless mesh network. This scheme can also be computed through a distributed algorithm in a message efficient manner following the technique presented in Gao et al [12].…”

Section: Extensions To General Networkmentioning

confidence: 99%

“…MOT essentially maintains a data structure as we mentioned above for the tracking task. When applied to constant-doubling networks, which have been widely used as a model of a sensor network in the literature (e.g., [7,11,12,27,40]), MOT provides a cost ratio of O(min{log n, log D}) in maintaining the data structure for any arbitrary set of maintenance operations comparing its communication cost to the optimal cost, where n and D, respectively, are the number of nodes and the diameter of the network. This result assumes the case where each maintenance operation of an object arrives only after the previous maintenance operation for that object is finished.…”

Section: Introductionmentioning

confidence: 99%

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Near-Optimal Location Tracking Using Sensor Networks

Sharma

Krishnan

Busch

et al. 2015

IJNC

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We consider the problem of tracking mobile objects using a sensor network. We present a distributed tracking algorithm, called Mobile Object Tracking using Sensors (MOT), that scales well with the number of sensors and also with the number of mobile objects. MOT maintains a hierarchical structure of detection lists of objects that can efficiently track mobile objects and resolve object queries at any time. MOT guarantees that the cost to update (or maintain) its data structures will be at most O(min{log n, log D}) times the optimal update cost and query cost will be within O(1) of the optimal query cost in the constant-doubling graph model, where n and D, respectively, are the number of nodes and the diameter of the network. MOT achieves polylogarithmic approximations for both costs in the general graph model. MOT balances the object and bookkeeping information load at each node in the expense of only O(log n) increase in the update and query costs. The experimentation evaluation in both one by one and concurrent execution situations shows that MOT performs well in practical scenarios. To the best of our knowledge, MOT is the first algorithm for this problem in a distributed setting that is traffic-oblivious, i.e. agnostic to a priori knowledge of objects movement patterns, mobility and query rate, etc., and is load balanced. All previous solutions for this problem assumed traffic-consciousness in constructing the tracking data structure.

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Section: Construction Of Overlay Structurementioning

confidence: 99%

Section: Extensions To General Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Near-Optimal Location Tracking Using Sensor Networks

Sharma

Krishnan

Busch

et al. 2015

IJNC

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“…We propose a scalable query-optimal distributed NNQ algorithm for a set of m mobile objects with asymptotically optimal update cost in constant-doubling networks, including low-dimensional Euclidean and growth-restricted networks, which have been used as models of sensor network metrics in several prior works [31,11,32,12,9]. Our NNQ algorithm incurs a query competitive ratio of O(1) which is optimal within a constant factor, i.e.…”

Section: Contributionsmentioning

confidence: 99%

“…In the literature, various problems were solved in sensor networks using hierarchical structures providing good approximations, e.g. [32,11,12,9,31,28,10,8]. The solutions for those various problems and the hierarchical structures used in them are not ideal for NNQ as some of the approaches are probabilistic and others cannot handle the multiple objects case like ours.…”

Section: Approachmentioning

confidence: 99%

Optimal nearest neighbor queries in sensor networks

Sharma

Busch

2015

Theoretical Computer Science

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Given a set of m mobile objects in a sensor network, we consider the problem of finding the nearest object among them from any node in the network at any time. These mobile objects are tracked by nearby sensors called proxy nodes. This problem requires an object tracking mechanism which typically relies on two basic operations: query and update. A query is invoked by a node each time when there is a need to find the closest object from it in the network. Updates of an object's location are initiated when the object moves from one location (proxy node) to another. We present a scalable distributed algorithm for tracking these mobile objects such that both the query cost and the update cost are small. The main idea in our algorithm is to maintain a virtual tree of downward paths pointing to the objects. Our algorithm guarantees an asymptotically optimal O(1) approximation for query cost and an O(min{log n, log D}) approximation for update cost in the constantdoubling graph model, where n and D, respectively, are the number of nodes and the diameter of the network. We also give polylogarithmic approximations for both query and update cost in the general graph model. Our algorithm requires only polylogarithmic bits of memory per node. To the best of our knowledge, this is the first algorithm that is asymptotically optimal in handling nearest neighbor queries with low update cost in a distributed setting.

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Resilient and Low Stretch Routing through Embedding into Tree Metrics

Gao

Zhou

2011

Lecture Notes in Computer Science

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Abstract. Given a network, the simplest routing scheme is probably routing on a spanning tree. This method however does not provide good stretch -the route between two nodes can be much longer than their shortest distance, nor does it give good resilience -one node failure may disconnect quadratically many pairs. In this paper we use two trees to achieve both constant stretch and good resilience. Given a metric (e.g., as the shortest path metric of a given communication network), we build two hierarchical well-separated trees using randomization such that for any two nodes u, v, the shorter path of the two paths in the two respective trees gives a constant stretch of the metric distance of u, v, and the removal of any node only disconnect the routes between O(1/n) fraction of all pairs. Both bounds are in expectation and hold true as long as the metric follows certain geometric growth rate (the number of nodes within distance r is a polynomial function of r), which holds for many realistic network settings such as wireless ad hoc networks and Internet backbone graphs. The algorithms have been implemented and tested on real data.

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Network warehouses: Efficient information distribution to mobile users

Cited by 9 publications

References 10 publications

Near-Optimal Location Tracking Using Sensor Networks

Near-Optimal Location Tracking Using Sensor Networks

Optimal nearest neighbor queries in sensor networks

Resilient and Low Stretch Routing through Embedding into Tree Metrics

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