Lightweight time synchronization for sensor networks

Greunen, J. van; Rabaey, Jan M.

doi:10.1145/941351.941353

Cited by 117 publications

(163 citation statements)

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“…Unfortunately, continuous synchronization turns out to be quite a difficult problem for WANETs because of hardware clock skew and other factors [6][7][8][9]. However, in many situations, such as in the case of a packet transmission, which exemplifies the occurrence of a commonly observable event, a common signal can be observed by many nearby nodes.…”

Section: The Self-selection Algorithmmentioning

confidence: 99%

A Self-selection Technique for Flooding and Routing in Wireless Ad-hoc Networks

2006

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There is a fundamental difference between wireless and wired networks, since the latter employ point-to-point communication while the former use broadcast transmission as the communication primitive. In this paper, we describe an algorithm, called self-selection, which takes advantage of broadcast communication to efficiently implement the basic operation of selecting a node possessing some desired properties among all the neighbors of the requestor. Self-selection employs a prioritized transmission back-off delay scheme in which each node's delay of transmitting a signal is dependent on the probability of the node's ability to best perform a pertinent task and in turn, enables the node to autonomously select itself for the task. We demonstrate the benefits of self-selection in two basic wireless ad hoc network communication algorithms: flooding and routing. By relating back-off delay to the signal strength of a received packet, we design an efficient variant of conventional flooding named Signal Strength Aware Flooding. By using distance-to-destination to derive back-off delay, we design a novel and fault-tolerant wireless ad hoc network routing protocol named Self-Selective Routing.Keywords -Wireless ad hoc networks, wireless networks, ad hoc routing, ad hoc flooding, leader election INTRODUCTIONWireless ad hoc networks (WANETs) [1] are used to provide on-demand network infrastructures. They are usually composed of small portable clients (or nodes), but they lack consistent topologies. They range in size from small Bluetooth piconets [2] used for device synchronization to large-scale networks used for supporting military and emergency-response applications. Perhaps the most popular WANET applications recently have been wireless sensor networks [3], which are most often used for the pervasive and remote monitoring of environmental or spatial phenomena.To date, a plethora of research efforts have addressed some significant challenges introduced by various aspects of WANET operation, perhaps the most fundamental being fault-tolerant and energy-efficient communication. Since communication is typically the most costly operation in WANETs because of the overhead of transceiver operation, innovative and efficient approaches to basic communication algorithms such as flooding and routing are in strong demand. Fortunately, the communication primitive in WANETs, broadcast, offers some interesting possibilities.As an instructive example, let us consider a lecture hall with a lecturer and student audience. If the lecturer wants to identify a student with a certain property (e.g., the least number of credits taken at the university) in a lecture hall with n students, it seems that n communications (verbal in this case) would be needed. However, if the lecturer and students use the broadcast nature of verbal communication, they can accomplish this task in just two communications. In the first broadcast, the lecturer asks all students to announce their total number of completed credits after a delay (in seconds, start...

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Section: The Self-selection Algorithmmentioning

confidence: 99%

A Self-selection Technique for Flooding and Routing in Wireless Ad-hoc Networks

2006

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“…Many practical distributed algorithms for localization in space (e.g., [1,7,14,16,20,21]) and time (e.g., [5,8,13,23,24]) are based on a few common structural elements. In this section we point out these structural elements and discuss various concrete instances of these elements found in existing algorithms.…”

Section: Distributed Algorithms For Localization In Spacetimementioning

confidence: 99%

“…A round-trip message exchange between client and reference can be used to derive lower and upper bounds on the temporal distance (e.g., [18]). The average of these bounds can be used as a point estimate of the temporal distance (e.g., [8,24]). A broadcast message is usually received almost concurrently by a set of receivers.…”

Section: Obtaining Constraintsmentioning

confidence: 99%

“…For each anchor, a spanning tree of the network is constructed with the anchor at the root. A client node becomes active as soon as its parent has estimated its location and can thus act as a reference (e.g., [5,8,24]). Another typical overlay topology are clusters, where nodes in a cluster establish a local coordinate system and estimate their locations in terms of this reference grid.…”

Section: Selecting Constraintsmentioning

confidence: 99%

“…Hence, the maximum precision over time is also limited. Alternatively, if a certain target precision is requested by the application, the execution fre-quency may be calculated to be just high enough to provide the requested precision (see, e.g., [24]). For localization in space it is also possible to limit re-execution to nodes that have changed their location (e.g., [20]) in the meantime.…”

Section: Maintaining Localization Over Timementioning

confidence: 99%

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Towards a unified view on space and time in sensor networks

Römer

Mattern

2005

Computer Communications

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In wireless sensor networks, many applications rely on the ability of sensor nodes to estimate their physical location and time with respect to a common reference scale. This necessity is reflected by the development of a significant number of algorithms for localization and time synchronization for sensor networks in the recent past. However, research on location estimation on the one hand and on time synchronization on the other hand has been largely separated. Despite this, models, requirements, techniques, and algorithms of the two domains are rather similar and in some respects closely related. The purpose of this paper is to make this affinity explicit, with the hope of stimulating a mutual fertilization and enabling a better understanding of both domains.

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