Complexity in geometric SINR

Goussevskaia, Olga; Oswald, Yvonne Anne; Wattenhofer, Rogert

doi:10.1145/1288107.1288122

Cited by 345 publications

(377 citation statements)

References 35 publications

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“…It was the first time a scaling law that describes the achievable data rate in worst-case sensor networks was derived. Goussevskaia et al [8] make the milestone contribution of proving the NP-completeness of a special case of the MLS problem.…”

Section: B Link Scheduling Under the Physical Interference Modelmentioning

confidence: 99%

Minimum-latency aggregation scheduling in wireless sensor networks under physical interference model

Hua

et al. 2010

Proceedings of the 13th ACM International Conference on Modeling, Analysis, and Simulation of Wireless and Mobile Systems

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Abstract-Minimum-Latency Aggregation Scheduling (MLAS)is a problem of fundamental importance in wireless sensor networks. There however has been very little effort spent on designing algorithms to achieve sufficiently fast data aggregation under the physical interference model which is a more realistic model than traditional protocol interference model. In particular, a distributed solution to the problem under the physical interference model is challenging because of the need for globalscale information to compute the cumulative interference at any individual node. In this paper, we propose a distributed algorithm that solves the MLAS problem under the physical interference model in networks of arbitrary topology in O(K) time slots, where K is the logarithm of the ratio between the lengths of the longest and shortest links in the network. We also give a centralized algorithm to serve as a benchmark for comparison purposes, which aggregates data from all sources in O(log 3 (n)) time slots (where n is the total number of nodes). This is the current best algorithm for the problem in the literature. The distributed algorithm partitions the network into cells according to the value K, thus obviating the need for global information. The centralized algorithm strategically combines our aggregation tree construction algorithm with the non-linear power assignment strategy in [13]. We prove the correctness and efficiency of our algorithms, and conduct empirical studies under realistic settings to validate our analytical results.

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Section: B Link Scheduling Under the Physical Interference Modelmentioning

confidence: 99%

Minimum-latency aggregation scheduling in wireless sensor networks under physical interference model

Hua

et al. 2010

Proceedings of the 13th ACM International Conference on Modeling, Analysis, and Simulation of Wireless and Mobile Systems

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“…In [14], the scheduling problem without power control under the physical interference model, where nodes are arbitrarily distributed in Euclidean space, has been shown to be NPcomplete. A greedy scheduling algorithm with approximation ratio of 0(n l -2 / (\}J (a )+ €) (Iogn)"}, where \lJ(a) is a constant that depends on the path-loss exponent a, is proposed in [3].…”

Section: B Link Scheduling Under the Physical Interference Modelmentioning

confidence: 99%

Efficient data aggregation in multi-hop wireless sensor networks under physical interference model

Wang

et al. 2009

2009 IEEE 6th International Conference on Mobile Adhoc and Sensor Systems

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Abstract-Efficient aggregation of data collected by sensors is crucial for a successful application of wireless sensor networks (WSNs). Both minimizing the energy cost and reducing the time duration (or called latency) of data aggregation have been extensively studied for WSNs. Algorithms with theoretical performance guarantees are only known under the protocol interference model, or graph-based interference models generally. In this paper, we study the problem of designing time efficient aggregation algorithm under the physical interference model. To the best of our knowledge, no algorithms with theoretical performance guarantees are known for this problem in the literature. We propose an efficient algorithm that produces a data aggregation tree and a collision-free aggregation schedule. We theoretically prove that the latency of our aggregation schedule is bounded by O(R+~) time-slots. Here R is the network radius and~is the maximum node degree in the communication graph of the original network. In addition, we derive the lower-bound of latency for any aggregation scheduling algorithm under the physical interference model. We show that the latency achieved by our algorithm asymptotically matches the lower-bound for random wireless networks. Our extensive simulation results corroborate our theoretical analysis.

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“…In fact, it has been argued that the performance of graph based algorithms is inferior to algorithms in the SINR model [GOW07]. In [MWW06,MWZ06] a logarithmic ratio between the throughput in the SINR model and the throughput in the graph model is presented.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Video Streaming in Multi-hop Wireless Static Ad Hoc Networks

Even

Fais

Medina

et al. 2012

Algorithms for Sensor Systems

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We deal with the problem of streaming multiple video streams between pairs of nodes in a multi-hop wireless ad hoc network. The nodes are static, know their locations, and are synchronized (via GPS). We introduce a new interference model that uses variable interference radiuses. We present an algorithm for computing a frequency assignment and a schedule whose goal is to maximize throughput over all the video streams. In addition, we developed a localized flow-control mechanism to stabilize the queue lengths.We simulated traffic scheduled by the algorithm using OMNET++/MixiM (i.e., physical SINR interference model with 802.11g) to test whether the computed throughput is achieved. The results of the simulation show that the computed solution is SINR-feasible and achieves predictable stable throughputs.

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Complexity in geometric SINR

Cited by 345 publications

References 35 publications

Minimum-latency aggregation scheduling in wireless sensor networks under physical interference model

Minimum-latency aggregation scheduling in wireless sensor networks under physical interference model

Efficient data aggregation in multi-hop wireless sensor networks under physical interference model

Real-Time Video Streaming in Multi-hop Wireless Static Ad Hoc Networks

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