Power Modeling and Power Analysis for IEEE 802.15.4: a Concurrent State Machine Approach

Mura, Marcello; Paolieri, Marco; Fabbri, F.; Negri, Luca; Sami, M.

doi:10.1109/ccnc.2007.135

Cited by 12 publications

(3 citation statements)

References 5 publications

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“…Experiments carried out through a measurement campaign [9] show that results of the model approximate real consumption within an interval of less than five percent. Moreover all the main components of the nodes are included and this allows giving a real estimation of the node consumption and may help isolating some inefficiency of the protocol.…”

Section: Higher-level Modelsmentioning

confidence: 98%

Optimal Design of Wireless Sensor Networks

Mura

Campanoni

Fornaciari

et al. 2012

Lecture Notes in Computer Science

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Since their introduction,Wireless SensorNetworks(WSN) have been proposed as a powerful support for environment monitoring, ranging from monitoring of remote or hard-to-reach locations to fine-grained control of cultivations. Development\ud of a WSN-based application is a complex task and challenging issues must be tackled starting from the first phases of the design cycle.We present here a tool supporting the DSE phase to perform architectural choices for the nodes and network\ud topology, taking into account target performance goals and estimated costs. When designing applications based onWSN, the most challenging problem is energy shortage.\ud Nodes are normally supplied through batteries, hence a limited amount of energy is available and no breakthroughs are foreseen in a near future. In our design cycle we approach this issue through a methodology that allows analysing and optimising\ud the power performances in a hierarchical fashion, encompassing various abstraction levels

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Section: Higher-level Modelsmentioning

confidence: 98%

Optimal Design of Wireless Sensor Networks

Mura

Campanoni

Fornaciari

et al. 2012

Lecture Notes in Computer Science

Self Cite

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“…When suitably validated and characterised, a power model simplifies design procedures as simulation can be used instead of experimental measurements; a model is an instrumental tool for Design Space Exploration. We introduced Protocol Level Modelling (Negri et al, 2004;Mura et al, 2007), extending the concept of functional breakdown to the networking context. Such methodology, further revised and extended in order to be used in a wider set of analysis, is used for this RF-Wipe activity.…”

Section: Fsm-based Protocol-level Power Modellingmentioning

confidence: 99%

Wireless sensor networks for planetary exploration: Experimental assessment of communication and deployment

Sanz

Barrientos

Garzón

et al. 2013

Advances in Space Research

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“…1. In Beacon Enabled mode, nodes follow Superframe structure which is composed of two parts: Contention Access Period (CAP) without Guaranteed Time Slots (GTS) and Contention Free Period (CFP) with GTS [6].…”

Section: Ieee 802154 Power Modelsmentioning

confidence: 99%

Performance Analysis of LEACH with Machine Learning Algorithms in Wireless Sensor Networks

Randhawa¹,

Jain²

2016

IJCA

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Wireless Sensor Networks consist of thousands of power constrained micro sensors whose main task is to sense and report the target phenomena to the base station. Hierarchical routing plays an important role for transmitting the aggregated data to the sink. Sensor nodes are organized into number of clusters and within each cluster, cluster head is responsible for collecting the data and to report that data to the Base Station. Machine learning algorithms play an important role while selecting the cluster head based on various QoS parameters. In this paper, a hierarchical protocol LEACH is chosen for analyzing the impact of machine learning algorithms -KMeans and modified K-Means clustering on energy consumption of nodes by varying the type of input parameters. This paper covers the brief introduction of 802.15.4 based Wireless Sensor Networks, power models, machine learning algorithms for sensor clustering and simulation environment using NetSim.

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Power Modeling and Power Analysis for IEEE 802.15.4: a Concurrent State Machine Approach

Cited by 12 publications

References 5 publications

Optimal Design of Wireless Sensor Networks

Optimal Design of Wireless Sensor Networks

Wireless sensor networks for planetary exploration: Experimental assessment of communication and deployment

Performance Analysis of LEACH with Machine Learning Algorithms in Wireless Sensor Networks

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