Bootstrapping a hop-optimal network in the weak sensor model

Journal of Parallel and Distributed Computing

2013

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In this paper, we study algebraic aggregate computations in Sensor Networks. The main contribution is the presentation of an early-stopping protocol that computes the average function under a harsh model of the conditions under which sensor nodes operate. This protocol is shown to be time-optimal in presence of unfrequent failures. The approach followed saves time and energy by relying the computation on a small network of delegate nodes that can be rebuilt fast in case of node failures and communicate using a collisionfree schedule. Delegate nodes run simultaneously two protocols, namely, a collection/dissemination tree-based algorithm, which is shown to be optimal, and a mass-distribution algorithm. Both algorithms are analyzed under a model where the frequency of failures is a parameter. Other aggregate computation algorithms can be easily derived from this protocol. To the best of our knowledge, this is the first optimal early-stopping algorithm for aggregate computations in Sensor Networks.

“…The following lemma establishes formally the efficiency of these phases. Further details can be found in [6]- [8]…”

Section: A Preprocessing and Maintenancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An early-stopping protocol for computing aggregate functions in Sensor Networks

Journal of Parallel and Distributed Computing

2013

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“…Regarding network topology and connectivity, and node constraints, we use the restrictive model in [24,25,23] summarized here as follows.…”

Section: Model and Problems Definitionmentioning

confidence: 99%

Deterministic Recurrent Communication and Synchronization in Restricted Sensor Networks

Algorithms for Sensor Systems

2010

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Monitoring physical phenomena in Sensor Networks requires guaranteeing permanent communication between nodes. Moreover, in an effective implementation of such infrastructure, the delay between any two consecutive communications should be minimized. The problem is challenging because, in a restricted Sensor Network, the communication is carried out through a single and shared radio channel without collision detection. Dealing with collisions is crucial to ensure effective communication between nodes. Additionally, minimizing them yields energy consumption minimization, given that sensing and computational costs in terms of energy are negligible with respect to radio communication. In this work, we present a deterministic recurrent-communication protocol for Sensor Networks. After an initial negotiation phase of the access pattern to the channel, each node running this protocol reaches a steady state, which is asymptotically optimal in terms of energy and time efficiency. As a by-product, a protocol for the synchronization of a Sensor Network is also proposed. Furthermore, the protocols are resilient to an arbitrary node power-up schedule and a general node failure model.

“…Regarding the strong resource-limitations of sensor nodes, we use the comprehensive Weak Sensor Model [11] unless otherwise stated. The following assumptions are included in this model.…”

Section: Modelmentioning

confidence: 99%

Deterministic Communication in the Weak Sensor Model

Lecture Notes in Computer Science

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In Sensor Networks, the lack of topology information and the availability of only one communication channel has led research work to the use of randomization to deal with collision of transmissions. However, the scarcest resource in this setting is the energy supply, and radio communication dominates the sensor node energy consumption. Hence, redundant trials of transmission as used in randomized protocols may be counter-effective. Additionally, most of the research work in Sensor Networks is either heuristic or includes unreallistic assumptions. Hence, provable results for many basic problems still remain to be given. In this paper, we study upper and lower bounds for deterministic communication primitives under the harsh constraints of sensor nodes.