Generic Routing Metric and Policies for WSNs

Saukh, Olga; Marrón, Pedro José; Lachenmann, Andreas; Gauger, Matthias; Minder, Daniel; Rothermel, Kurt

doi:10.1007/11669463_10

Cited by 10 publications

(7 citation statements)

References 13 publications

(20 reference statements)

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“…Energy-efficiency has pervaded nearly every aspect of wireless sensor network ("sensornet") research, from platform designs [11,3,6], to MAC layers [17,10], to routing protocols [12,13,16], to applications [18,5], and across a range of duty cycles [2,15,7]. What is missing is a method to empirically evaluate the energy-efficiency claims of this growing corpus of literature.…”

Section: Introductionmentioning

confidence: 99%

Micro power meter for energy monitoring of wireless sensor networks at scale

Jiang

Dutta

Culler

et al. 2007

Proceedings of the 6th International Conference on Information Processing in Sensor Networks - IPSN '07

111

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We present SPOT, a scalable power observation tool that enables in situ measurement of nodal power and energy over a dynamic range exceeding four decades or a temporal resolution of microseconds. Using SPOT, every node in a sensor network can now be instrumented, providing unparalleled visibility into the dynamic power profile of applications and system software. Power metering at every node enables previously impossible empirical evaluation of low power designs at scale. The SPOT architecture and design meet challenges unique to wireless sensor networks and other low power systems, such as orders of magnitude difference in current draws between sleep and active states, short-duration power spikes during periods of brief activity, and the need for minimum perturbation of the system under observation.

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Section: Introductionmentioning

confidence: 99%

Micro power meter for energy monitoring of wireless sensor networks at scale

Jiang

Dutta

Culler

et al. 2007

Proceedings of the 6th International Conference on Information Processing in Sensor Networks - IPSN '07

111

View full text Add to dashboard Cite

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“…(Akkaya and Younis, 2003) introduce a routing protocol that can find a least cost, delayconstrained path based on a clustered WSN. (Saukh et al, 2006) presents a generic metric for tree routing protocols by combing two QoS metrics: 1) end-to-end success rate and 2) resource demand. Experimental results show that it provides considerable energy saving with equivalent end-to-end packets success rate comparing to other metrics.…”

Section: Related Workmentioning

confidence: 99%

A User Configurable Metric for Clustering in Wireless Sensor Networks

Lillis

O’Hare

et al. 2014

Proceedings of the 3rd International Conference on Sensor Networks

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Wireless Sensor Networks (WSNs) are comprised of thousands of nodes that are embedded with limited energy resources. Clustering is a well-known technique that can be used to extend the lifetime of such a network. However, user adaption is one criterion that is not taken into account by current clustering algorithms. Here, the term "user" refers to application developer who will adjust their preferences based on the application specific requirements of the service they provide to application users. In this paper, we introduce a novel metric named Communication Distance (ComD), which can be used in clustering algorithms to measure the relative distance between sensors in WSNs. It is tailored by user configuration and its value is computed from real time data. These features allow clustering algorithms based on ComD to adapt to user preferences and dynamic environments. Through experimental and theoretical studies, we seek to deduce a series of formulas to calculate ComD from Time of Flight (ToF), Radio Signal Strength Indicator (RSSI), node density and hop count according to some user profile.

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“…A mixed entropy data compression algorithm based on interval wavelet transforming is proposed by literature [8] so as to prolong the life time of the entire network, but within this literature the energy consumption of the algorithm and multi-hop transmission effect are not considered. The method is discussed in the aspect of transmission quality and path energy consumption to prolong the life time, but data aggregation is neglected [9,10] . The theoretical analysis of aggregation shows that in the case of perfect aggregation, the task of routing with optimal data aggregation follows from the NP-completeness of the minimum Steiner problem on Graphs.…”

Section: Introductionmentioning

confidence: 99%

An Aggregation Tree Constructing Algorithm Based on Energy Consumption Assessment

Dong

Liu

Wang

2007

7th IEEE International Conference on Computer and Information Technology (CIT 2007)

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This paper first gives an analysis of Data Aggregationand Data Compression based on the energy consumption of sensor nodes, after which an approach is proposed to construct an aggregation tree in the case of non-perfect aggregation since GIT considers only the case of perfect aggregation and it does not work well if the aggregation is non-perfect. An assessment scheme that can get the information of hops from the aggregation point to the sink and the hops from the aggregation point to the source node is used to construct such an aggregation tree. Moreover, the energy consumption of the aggregation is also considered. This scheme can be used when perfect aggregation cannot be performed. In this paper, an approach to reduce the cost of reinforcement is also proposed, in which the reinforcement work is done by the source nodes themselves, not by the sink node. Simulation result shows that this approach can save more energy than GIT when the aggregation ratio is small.This result also provides a theoretical limit of aggregation to tell when GIT will lose its superiority and thus gives us a direction to choose among the aggregation algorisms.Another result shows that the further the sources are away from the sink, the less reinforcement messages are needed.

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Generic Routing Metric and Policies for WSNs

Cited by 10 publications

References 13 publications

Micro power meter for energy monitoring of wireless sensor networks at scale

Micro power meter for energy monitoring of wireless sensor networks at scale

A User Configurable Metric for Clustering in Wireless Sensor Networks

An Aggregation Tree Constructing Algorithm Based on Energy Consumption Assessment

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