GPS free coordinate assignment and routing in wireless sensor networks

Caruso, Antonio; Chessa, Stefano; De, Swades; Urpi, A.

doi:10.1109/infcom.2005.1497887

Cited by 199 publications

(209 citation statements)

References 29 publications

(30 reference statements)

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“…Moreover, we would work on new routing protocols that work better in non-uniformly distriuted networks. We will also explore the use of the modified GFG protocol proposed in [21] and the opportunity to use virtual coordinates systems [22] instead of GPS-based positioning systems.…”

Section: Discussionmentioning

confidence: 99%

Q-NiGHT: Adding QoS to Data Centric Storage in Non-Uniform Sensor Networks

Albano¹,

Chessa²,

Nidito³

et al. 2007

2007 International Conference on Mobile Data Management

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Storage of sensed data in wireless sensor networks is essential when the sink node is unavailable due to failure and/or disconnections, but it can also provide efficient access to sensed data to multiple sink nodes. Recent approaches to data storage rely on Geographic Hash Tables for efficient data storage and retrieval. These approaches however do not support different QoS levels for different classes of data as the programmer has no control on the level of redundancy of data (and thus on data dependability). Moreover, they result in a great unbalance in the storage usage in each sensor, even when sensors are uniformly distributed. This may cause serious data losses, waste energy and shorten the overall lifetime of the sensornet. In this paper, we propose a novel protocol, Q-NiGHT, which (1) provides a direct control on the level of QoS in the data dependability, and (2) uses a strategy similar to the rejection method to build a hash function which scatters data approximately with the same distribution as sensors. The benefits of Q-NiGHT are assessed through a detailed simulation experiment, also discussed in the paper. Results show its good performance on different sensors distributions on terms of both protocol costs and load balance between sensors.

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Section: Discussionmentioning

confidence: 99%

Q-NiGHT: Adding QoS to Data Centric Storage in Non-Uniform Sensor Networks

Albano¹,

Chessa²,

Nidito³

et al. 2007

2007 International Conference on Mobile Data Management

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“…[1]) by other virtual localisation techniques (e.g. [2,3]). In distributed forwarding, a best relay node is decided at a transmitter from its local neighbours based on various criteria, such as the amount of energy a relay would consume, remaining energy at a candidate node, distance progress toward the destination, link quality between the transmitter and receiver, receiver buffer size etc.…”

Section: Motivationmentioning

confidence: 99%

“…The sensor nodes are assumed to have homogeneous coverage range and uniformly random distributed in a two-dimensional location space. Each node is aware of its own location information via some localization techniques, for example, [2,3], and the one-hop neighbourhood activity status is collected centrally by the relay or in a distributed fashion at the MAC layer (see e.g. [41]).…”

Section: System Model and Notationsmentioning

confidence: 99%

Network lifetime maximising distributed forwarding strategies in Ad Hoc wireless sensor networks

Panigrahi

Panda

et al. 2012

IET Commun.

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Abstract:The authors propose three variants of distributed and stateless forwarding strategies for wireless sensor networks, namely greedy minimum energy consumption forwarding protocol (GMFP), lifetime maximising GMFP (LM-GMFP) and variance minimising GMFP (VAR-GMFP), which aim at maximising the network lifetime while achieving a high forwarding success rate. GMFP selects a forwarding node that minimises per-packet energy consumption while maximising the forwarding progress. LM-GMFP extends the GMFP algorithm by also taking into account the remaining energy at the prospective one-hop forwarding nodes. In VAR-GMFP, on the other hand, the packet is forwarded to the next node that ensures a locally high mean and low variance of nodal remaining energy. Through simple probabilistic analysis the authors prove the intuition behind the optimum forwarding node selection for network lifetime maximisation. They then model the lifetime maximisation of a sensor network as an optimisation problem and compare the practical protocol-dependent network lifetime with the theoretical upper bound. Through extensive simulations the author demonstrate that the proposed protocols outperform the existing energy-aware protocols in terms of network lifetime and end-to-end delay.

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“…The authors of [6] introduce VCap (Virtual Coordinate assignment protocol ) to support geographic routing. A system of virtual coordinates based on three landmarks is proposed.…”

Section: Related Workmentioning

confidence: 99%

“…Firstly we compared RPT with MFR [28] and VCap [6]. MFR uses geographic coordinates provided by a satellite receiver like GPS.…”

Section: Routing Evaluationmentioning

confidence: 99%