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37
Nearly Constant Approximation for Data Aggregation Scheduling
 in Wireless Sensor Networks”, IEEE INFOCOM 2007
"... Abstract — Data aggregation is a fundamental yet timeconsuming task in wireless sensor networks. We focus on the latency part of data aggregation. Previously, the data aggregation algorithm of least latency [1] has a latency bound of ( ∆ − 1)R, where ∆ is the maximum degree and R is the network rad ..."
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Cited by 44 (14 self)
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Abstract — Data aggregation is a fundamental yet timeconsuming task in wireless sensor networks. We focus on the latency part of data aggregation. Previously, the data aggregation algorithm of least latency [1] has a latency bound of ( ∆ − 1)R, where ∆ is the maximum degree and R is the network radius. Since both ∆ and R could be of the same order of the network size, this algorithm can still have a rather high latency. In this paper, we designed an algorithm based on maximal independent sets which has an latency bound of 23R +∆ − 18. Here ∆ contributes to an additive factor instead of a multiplicative one; thus our algorithm is nearly constant approximation and it has a significantly less latency bound than earlier algorithms especially when ∆ is large. I.
Distributed minimal time convergecast scheduling in wireless sensor networks
 in ICDCS ’06: Proceedings of the 26th IEEE International Conference on Distributed Computing Systems, 2006
"... Abstract — Many applications of sensor networks require the base station to collect all the data generated by sensor nodes. As a consequence manytoone communication pattern, referred to as convergecast, is prevalent in sensor networks. In this paper, we address the challenge of fast and reliable c ..."
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Cited by 32 (0 self)
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Abstract — Many applications of sensor networks require the base station to collect all the data generated by sensor nodes. As a consequence manytoone communication pattern, referred to as convergecast, is prevalent in sensor networks. In this paper, we address the challenge of fast and reliable convergecast on top of the collisionprone CSMA MAC layer. More specifically, we extend previous work by considering the following two situations: (1) the length of the packets generated by nodes is much smaller than the maximum length of a data frame that can be transmitted in one time slot and (2) not every node in the network has data to transmit and for those that have, may have lots of data that require more than one packet. The first situation leads to the possibility of improvement by data piggybacking/aggregation; the second scenario arises in networks where nodes locally store the data and serves query request ondemand. We present distributed minimal time scheduling algorithms for both the cases. Simulation results have shown significant performance improvements of our new approaches over existing solutions. I.
TimeEfficient Broadcast in Radio Networks
, 2010
"... Broadcasting is a basic network communication task, where a message initially held by a source node has to be disseminated to all other nodes in the network. Fast algorithms for broadcasting in radio networks have been studied in a wide variety of different models and under different requirements. S ..."
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Cited by 16 (0 self)
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Broadcasting is a basic network communication task, where a message initially held by a source node has to be disseminated to all other nodes in the network. Fast algorithms for broadcasting in radio networks have been studied in a wide variety of different models and under different requirements. Some of the main parameters giving rise to the different variants of the problem are the accessibility of knowledge about the network topology, the availability of collision detection mechanisms, the wakeup mode, the topology classes considered, and the use of randomness. This chapter introduces the problem, reviews the literature on timeefficient broadcasting algorithms for radio networks under a variety of models and assumptions, and illustrates some of the basic techniques.
An EnergyEfficient Distributed Algorithm for MinimumLatency Aggregation Scheduling in Wireless Sensor Networks
"... Abstract—Data aggregation is an essential yet timeconsuming task in wireless sensor networks (WSNs). This paper studies the wellknown MinimumLatency Aggregation Schedule (MLAS) problem and proposes an energyefficient distributed scheduling algorithm named CluDDAS based on a novel clusterbased a ..."
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Cited by 14 (2 self)
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Abstract—Data aggregation is an essential yet timeconsuming task in wireless sensor networks (WSNs). This paper studies the wellknown MinimumLatency Aggregation Schedule (MLAS) problem and proposes an energyefficient distributed scheduling algorithm named CluDDAS based on a novel clusterbased aggregation tree. Our approach differs from all the previous schemes where Connected Dominating Sets or Maximal Independent Sets are employed. We prove that CluDDAS has a latency bound of 4R ′ + 2 ∆ − 2, where ∆ is the maximum degree and R ′ is the inferior network radius which is smaller than the network radius R. CluDDAS has comparable latency as the previously best centralized algorithm EPAS, while CluDDAS consumes 78 % less energy as shown by the simulation results. CluDDAS outperforms the previously best distributed algorithm DAS whose latency bound is 16R ′ +∆−14 on both latency and energy consumption. On average, CluDDAS transmits 67 % fewer total messages than DAS does. We also propose an adaptive strategy for updating the schedule to accommodate dynamic network topology.
An improved approximation algorithm for data aggregation in multihop wireless sensor networks
 In Proc. of FOWANC’09. ACM
, 2009
"... Data aggregation is an efficient primitive in wireless sensor network (WSN) applications. This paper focuses on data aggregation scheduling problem to minimize the latency. We propose an efficient distributed method that produces a collisionfree schedule for data aggregation in WSNs. We prove that ..."
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Cited by 12 (5 self)
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Data aggregation is an efficient primitive in wireless sensor network (WSN) applications. This paper focuses on data aggregation scheduling problem to minimize the latency. We propose an efficient distributed method that produces a collisionfree schedule for data aggregation in WSNs. We prove that the latency of the aggregation schedule generated by our algorithm is at most 16R+Δ−14 timeslots. Here R is the network radius and Δ is the maximum node degree in the communication graph of the original network. Our method significantly improves the previously known best data aggregation algorithm [3], that has a latency bound of 24D +6Δ+16timeslots, where D is the network diameter (Note that D can be as large as 2R). We conduct extensive simulations to study the practical performances of our proposed data aggregation method. Our simulation results corroborate our theoretical results
Complexity of Data Collection, Aggregation, and Selection for Wireless Sensor Networks
"... Processing the gathered information efficiently is a key functionality for wireless sensor networks. In this paper, we study the time complexity, message complexity, and energy cost complexity of various processing operations for a multihop wireless sensor network of n nodes. For most of the operat ..."
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Cited by 12 (6 self)
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Processing the gathered information efficiently is a key functionality for wireless sensor networks. In this paper, we study the time complexity, message complexity, and energy cost complexity of various processing operations for a multihop wireless sensor network of n nodes. For most of the operations studied in this paper, we first present a lowerbound on the complexity for the optimal methods, then we provide an (asymptotically matching) upperbound on the complexity by presenting efficient distributed algorithms to solve these problems. Let ϱT, ϱM, and ϱE be the approximation ratio of an algorithm in terms of time complexity, message complexity, and energy complexity respectively for a certain operation, such as data collection, data aggregation, or data selection. Specifically, we show that, for data collection, there are networks of n nodes and maximum degree ∆, such that ϱM ϱE = Ω(∆) for any algorithm. We then present an efficient algorithm for data collection with ϱT = O(1), ϱM = O(1), and ϱE = O(∆). For data aggregation, we show that there are networks of n nodes and maximum degree ∆, such that ϱT ϱE = Ω(∆) for any algorithm. We then present an efficient algorithm for data aggregation with ϱT = O(1), ϱM = O(1), and ϱE = O(∆). For data selection, we show that any deterministic distributed algorithm needs Ω( ∆ + D logD N) time to find the median of all data items, where N is the number of total elements collected by sensors. We then present a randomized algorithm that achieves this lowerbound with high probability. In terms of the message complexity, there is a graph G, such that Ω(n log h) messages are required to compute the kth smallest element in G in expectation and with probability at least 1/nδ for every constant δ < 1/2, where h = min(k, N − k). We also present a randomized algorithm that achieves this bound with high probability.
Broadcasting in UDG radio networks with unknown topology
, 2009
"... The paper considers broadcasting in radio networks, modeled as unit disk graphs (UDG). Such networks occur in wireless communication between sites (e.g., stations or sensors) situated in a terrain. Network stations are represented by points in the Euclidean plane, where a station is connected to al ..."
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Cited by 12 (2 self)
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The paper considers broadcasting in radio networks, modeled as unit disk graphs (UDG). Such networks occur in wireless communication between sites (e.g., stations or sensors) situated in a terrain. Network stations are represented by points in the Euclidean plane, where a station is connected to all stations at distance at most 1 from it. A message transmitted by a station reaches all its neighbors, but a station hears a message (receives the message
Tradeoffs between energy and latency for convergecast
 In Proceedings of the Second International Workshop on Networked Sensing Systems (INSS
, 2005
"... We focus on the problem of energyefficient convergecast in sensor networks. This problem identifies the energylatency tradeoff during convergecast. Whenever a group of sensors communicate an event of interest, the latency involved in delivering such messages to the base station should be minimized ..."
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Cited by 9 (0 self)
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We focus on the problem of energyefficient convergecast in sensor networks. This problem identifies the energylatency tradeoff during convergecast. Whenever a group of sensors communicate an event of interest, the latency involved in delivering such messages to the base station should be minimized. Since the sensors are constrained by limited power and are mostly idle, it is important that the sensors conserve energy. We show how time division multiple access (TDMA) can be effectively used to provide energyefficient convergecast. This solution allows the sensors to save energy when the network is idle and to switch to active mode when the network observes an event. Furthermore, for a typical application where the event probability is less than 10 − 15%, our solution improves the network lifetime by approximately 3 fold. 1
TCPJersey for Wireless
 IP Communications,” IEEE JSAC
, 2004
"... Abstract—Data aggregation is a key functionality in wireless sensor networks (WSNs). This paper focuses on data aggregation scheduling problem to minimize the delay (or latency). We propose an efficient distributed algorithm that produces a collisionfree schedule for data aggregation in WSNs. We th ..."
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Cited by 5 (0 self)
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Abstract—Data aggregation is a key functionality in wireless sensor networks (WSNs). This paper focuses on data aggregation scheduling problem to minimize the delay (or latency). We propose an efficient distributed algorithm that produces a collisionfree schedule for data aggregation in WSNs. We theoretically prove that the delay of the aggregation schedule generated by our algorithm is at most 16R þ 14 time slots. Here, R is the network radius and is the maximum node degree in the communication graph of the original network. Our algorithm significantly improves the previously known best data aggregation algorithm with an upper bound of delay of 24D þ 6 þ 16 time slots, where D is the network diameter (note that D can be as large as 2R). We conduct extensive simulations to study the practical performances of our proposed data aggregation algorithm. Our simulation results corroborate our theoretical results and show that our algorithms perform better in practice. We prove that the overall lower bound of delay for data aggregation under any interference model is maxflog n; Rg, where n is the network size. We provide an example to show that the lower bound is (approximately) tight under the protocol interference model when rI r, where rI is the interference range and r is the transmission range. We also derive the lower bound of delay under the protocol interference model when r<rI < 3r and rI 3r.
Broadcasting in UDG radio networks with missing and inaccurate information
 IN: PROC. 22ND INTERNATIONAL SYMPOSIUM ON DISTRIBUTED COMPUTING, DISC 2008
, 2008
"... We study broadcasting time in radio networks, modeled as unit disk graphs (UDG). Emek et al. showed that broadcasting time depends on two parameters of the UDG network, namely, its diameter D (in hops) and its granularity g. The latter is the inverse of the density d of the network which is the mi ..."
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Cited by 4 (1 self)
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We study broadcasting time in radio networks, modeled as unit disk graphs (UDG). Emek et al. showed that broadcasting time depends on two parameters of the UDG network, namely, its diameter D (in hops) and its granularity g. The latter is the inverse of the density d of the network which is the minimum Euclidean distance between any two stations. They proved that the minimum broadcasting time is Θ ` min ˘ D + g 2,Dlog g ¯ ´ , assuming that each node knows the density of the network and knows exactly its own position in the plane. In many situations these assumptions are unrealistic. Does removing them influence broadcasting time? The aim of this paper is to answer this question, hence we assume that density is unknown and nodes perceive their position with some unknown error margin ɛ. It turns out that this combination of missing and inaccurate information substantially changes the problem: the main new challenge becomes fast broadcasting in sparse networks (with constant density), when optimal time is O(D). Nevertheless, under our very weak scenario, we construct a broadcasting algorithm that maintains optimal time O ` min ˘ D + g 2,Dlog g ¯ ´ for all networks with at least 2 nodes, of diameter D and granularity g, ifeachnodeperceives its position with error margin ɛ = αd, for any (unknown) constant α<1/2. Rather surprisingly, the minimum time of an algorithm stopping if the source is alone, turns out to be Θ(D + g 2). Thus, the mere stopping requirement for the special case of the lonely source causes an exponential increase in broadcasting time, for networks of any density and any small diameter. Finally, broadcasting is impossible if ɛ ≥ d/2.