DESYNC: Self-Organizing Desynchronization and TDMA on Wireless Sensor Networks

Degesys,; Rose,; Patel,; Nagpal,

doi:10.1109/ipsn.2007.4379660

Cited by 126 publications

(315 citation statements)

References 11 publications

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“…Cornejo and Kuhn [10] introduced the discrete (i.e., round-based) beeping model studied in this paper. They motivated this model by noting the continuous model in [11,12,17] was unrealistic and yielded trivial solutions to desynchronization, they then demonstrated how to solve desynchronization without these assumptions. Around this same time, Afek et al [3] described a maximal independent set (MIS) algorithm in a strong version of the discrete beeping model.…”

Section: Comparison To Existing Beep Resultsmentioning

confidence: 99%

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The Computational Power of Beeps

Gilbert

Newport

2015

Lecture Notes in Computer Science

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In this paper, we study the quantity of computational resources (state machine states and/or probabilistic transition precision) needed to solve specific problems in a single hop network where nodes communicate using only beeps. We begin by focusing on randomized leader election. We prove a lower bound on the states required to solve this problem with a given error bound, probability precision, and (when relevant) network size lower bound. We then show the bound tight with a matching upper bound. Noting that our optimal upper bound is slow, we describe two faster algorithms that trade some state optimality to gain efficiency. We then turn our attention to more general classes of problems by proving that once you have enough states to solve leader election with a given error bound, you have (within constant factors) enough states to simulate correctly, with this same error bound, a logspace TM with a constant number of unary input tapes: allowing you to solve a large and expressive set of problems. These results identify a key simplicity threshold beyond which useful distributed computation is possible in the beeping model.

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Section: Comparison To Existing Beep Resultsmentioning

confidence: 99%

“…Given probability ǫ ≤ 1/2 and parameter R = 4 logq(max(n, 1/ǫ)): after R silent iterations of the knockout loop (lines [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25], there remains exactly one active node, with probability at least 1 − ǫ/2.…”

Section: Fast Terminationmentioning

confidence: 99%

The Computational Power of Beeps

Gilbert

Newport

2015

Lecture Notes in Computer Science

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“…On the other hand, desynchronization is the logical opposite of synchronization; instead of nodes attempting to perform periodic tasks at the same time, nodes perform their tasks at moments in time equally spaced apart from each other. Desync [11] is such a self-maintaining desynchronization primitive and achieves desynchronization in a single-hop network. Other types of emergent algorithms, similar to ASH, exist as well.…”

Section: Related Workmentioning

confidence: 99%

ASH: tackling node mobility in large-scale networks

Pruteanu

Dulman

2012

Computing

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With the increased adoption of technologies like wireless sensor networks by real-world applications, dynamic network topologies are becoming the rule rather than the exception. Node mobility, however, introduces a range of problems (communication interference, path uncertainty, low quality of service and information loss, etc.) that are not handled well by periodically refreshing state information, as algorithms designed for static networks typically do. To address specifically this problem, the main contribution of this paper is the introduction of a novel mechanism (called ASH) for the creation of a quasi-static overlay on top of a mobile topology. It is powered by simple, local interactions between nodes and exhibits self-healing and self-organization capabilities with respect to failures and node mobility. We show that the overlay mechanism works without assumptions about position, orientation, speed, motion correlation, and trajectory prediction of the nodes. A preliminary evaluation by means of simulation shows that ASH succeeds in tackling node mobility, while consuming only minimal resources.

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“…After some further modifications to the theoretical model, they initial results were inconclusive but promising. Upon further refinement, they were able to implement a TDMA protocol which significantly outperforms existing TDMA protocols for sensor networks, according to their evaluation [105].…”

Section: Firefly Oscillatorsmentioning

confidence: 99%

A taxonomy of biologically inspired research in computer networking

2010

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a b s t r a c tThe natural world is enormous, dynamic, incredibly diverse, and highly complex. Despite the inherent challenges of surviving in such a world, biological organisms evolve, self-organize, self-repair, navigate, and flourish. Generally, they do so with only local knowledge and without any centralized control. Our computer networks are increasingly facing similar challenges as they grow larger in size, but are yet to be able to achieve the same level of robustness and adaptability. Many research efforts have recognized these parallels, and wondered if there are some lessons to be learned from biological systems. As a result, biologically inspired research in computer networking is a quickly growing field. This article begins by exploring why biology and computer network research are such a natural match. We then present a broad overview of biologically inspired research, grouped by topic, and classified in two ways: by the biological field that inspired each topic, and by the area of networking in which the topic lies. In each case, we elucidate how biological concepts have been most successfully applied. In aggregate, we conclude that research efforts are most successful when they separate biological design from biological implementation -that is to say, when they extract the pertinent principles from the former without imposing the limitations of the latter.

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DESYNC: Self-Organizing Desynchronization and TDMA on Wireless Sensor Networks

Cited by 126 publications

References 11 publications

The Computational Power of Beeps

The Computational Power of Beeps

ASH: tackling node mobility in large-scale networks

A taxonomy of biologically inspired research in computer networking

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