Erratum: Minimum-Energy Broadcast in Static Ad Hoc Wireless Networks

Wan, Peng‐Jun; Călinescu, Gruia; Li, X.-Y.

doi:10.1007/s11276-005-1774-z

Cited by 32 publications

(46 citation statements)

References 1 publication

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“…In [18], the authors propose a global knowledge scheme called broadcast incremental power (BIP) algorithm. A simple improvement to the BIP algorithm called broadcast average incremental power (BAIP) was proposed in [12]. In this method, the metric considered when adding a new node to the tree is the average incremental cost, which is defined as the ratio of the minimum additional power increased by some node in the current tree to reach some new nodes to the number of these new nodes.…”

Section: Previous Workmentioning

confidence: 99%

“…This allows us controlling the energy consumption, in order to avoid situation of wasting unnecessary battery charge. In addition we apply the following strategy previously described at [12]. At node u, our strategy finds the nodes that lie within u's transmission range, but are currently not child nodes of u. Assigning such nodes (except those being upstream nodes of u) to be child nodes of u may result in power saving at the current parents of these nodes.…”

Section: Improved Bip Algorithmmentioning

confidence: 99%

“…We can learn from the Figure 6 below that the number of transmissions is almost the same while comparing the two methods. This can be explained by the fact that the sweep strategy does not improve the energy performance of the broadcast protocol for random networks [12], even of improved time complexity that can produce better solution in terms of energy waste. Figure 6.…”

Section: Mesh Topologymentioning

confidence: 99%

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Improving lifetime of wireless sensor networks

Segal¹

2010

NPA

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A wireless sensor network consists from a collection of transceivers positioned in the plane. Each transceiver is equipped with a limited battery charge. The battery charge is reduced after each transmission, depending on the transmission distance. One of the major problems in wireless ad hoc network is designing a route network traffic algorithm that will maximize the lifetime of the network i.e., the number of successful transmissions. Our objective is to construct a broadcast routing tree rooted at the source node with the longest possible lifetime. We perform a simulative study of the problem and show an improvement to currently existing BIP algorithm [18].

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Section: Previous Workmentioning

confidence: 99%

Section: Improved Bip Algorithmmentioning

confidence: 99%

Section: Mesh Topologymentioning

confidence: 99%

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Improving lifetime of wireless sensor networks

Segal¹

2010

NPA

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“…It was shown in [3,7,36] that broadcasting based on MST consumes energy within a constant factor of the optimum.…”

Section: Model and Problemmentioning

confidence: 99%

“…Thus in addition to the traditional time and message complexity, it is also relevant to consider energy complexity that accounts for the total energy associated with the messages exchanged among the nodes in a distributed algorithm. This paper addresses the minimum spanning tree (MST) problem, which is an important primitive in many applications in wireless networks, e.g., broadcasting, data aggregation and topology control [33,25,36,3,7]. Data aggregation paradigms commonly use trees to schedule the transmission of data from all nodes in the graph at a source [24]; minimum cost spanning trees help optimize the energy usage in this process.…”

Section: Introductionmentioning

confidence: 99%

Energy-Optimal Distributed Algorithms for Minimum Spanning Trees

Choi

Pandurangan

Khan

et al. 2009

IEEE J. Select. Areas Commun.

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Traditionally, the performance of distributed algorithms has been measured in terms of time and message complexity. Message complexity concerns the number of messages transmitted over all the edges during the course of the algorithm.However, in energy-constrained ad hoc wireless networks (e.g., sensor networks), energy is a critical factor in measuring the ef ciency of a distributed algorithm. Transmitting a message between two nodes has an associated cost (energy) and moreover this cost can depend on the two nodes (e.g., the distance between them among other things). Thus in addition to the time and message complexity, it is important to consider energy complexity that accounts for the total energy associated with the messages exchanged among the nodes in a distributed algorithm. This paper addresses the minimum spanning tree (MST) problem, a fundamental problem in distributed computing and communication networks. We study energy-ef cient distributed algorithms for the Euclidean MST problem assuming random distribution of nodes. We show a non-trivial lower bound of Ω(log n) on the energy complexity of any distributed MST algorithm. We then give an energy-optimal distributed algorithm that constructs an optimal MST with energy complexity O(log n) on average and O(log n log log n) with high probability. This is an improvement over the previous best known bound on the average energy complexity of Ω(log 2 n). Our energy-optimal algorithm exploits a novel property of the giant component of sparse random geometric graphs. All the above results assume that nodes do not know their geometric coordinates. If the nodes know their own coordinates, then we give an algorithm with O(1) energy complexity (which is the best possible) that gives an O(1) approximation to the MST.

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Balls and Funnels: Energy Efficient Group-to-Group Anycasts

Iglesias

Rajaraman

Ravi

et al. 2016

Lecture Notes in Computer Science

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Abstract. We introduce group-to-group anycast (g2g-anycast), a network design problem of substantial practical importance and considerable generality. Given a collection of groups and requirements for directed connectivity from source groups to destination groups, the solution network must contain, for each requirement, an omni-directional down-link broadcast, centered at any node of the source group, called the ball; the ball must contain some node from the destination group in the requirement and all such destination nodes in the ball must aggregate into a tree directed towards the source, called the funnel-tree. The solution network is a collection of balls along with the funnel-trees they contain. g2g-anycast models DBS (Digital Broadcast Satellite), Cable TV systems and drone swarms. It generalizes several well known network design problems including minimum energy unicast, multicast, broadcast, Steiner-tree, Steiner-forest and Group-Steiner tree. Our main achievement is an O(log 4 n) approximation, counterbalanced by an log (2− ) n hardness of approximation, for general weights. Given the applicability to wireless communication, we present a scalable and easily implemented O(log n) approximation algorithm, Cover-and-Grow for fixeddimensional Euclidean space with path-loss exponent at least 2.

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Erratum: Minimum-Energy Broadcast in Static Ad Hoc Wireless Networks

Cited by 32 publications

References 1 publication

Improving lifetime of wireless sensor networks

Improving lifetime of wireless sensor networks

Energy-Optimal Distributed Algorithms for Minimum Spanning Trees

Balls and Funnels: Energy Efficient Group-to-Group Anycasts

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