A Dynamic Topology Reformation Algorithm for Power Saving in ZigBee Sensor Networks

Wang, Reen-Cheng; Chang, Ruay-Shiung; Yen, Jei-Hsiang; Lee, Pu-I

doi:10.1155/2013/801284

Cited by 4 publications

(5 citation statements)

References 20 publications

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“…The radio of the CC2530 chip can easily be built on top of the IEEE 802.15.4 based standard protocols such as RemoTI, TIMAC, and Z-Stack. However, take the power consumption of these protocols into consideration, we finally choose the Z-Stack protocol as our application platform [11]. For the design of the antenna, a small 2.4 GHz antenna was made in the printed circuit board which largely reduced the size of the node compared with the whip antenna.…”

Section: Wireless Communication Modulementioning

confidence: 99%

Design and Implementation of a High Integrated Noncontact ECG Monitoring Belt

Sun¹

2015

JFBI

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The ECG plays a key role in the rapid diagnosis of heart diseases such as coronary heart disease, ischemic heart disease, myocardial infarction, arrhythmias, etc. Unfortunately, the adhesion of conventional electrodes to the skin sometimes is difficult if not impossible due to the wet skin caused by higher perspiration. Besides, for the application of long term ECG monitoring, the wet adhesive ECG electrodes are easy to cause infection of the wearers' skin. Moreover, in certain situations it is difficult or simply too time-consuming to undress the patient for an ECG. Therefore, novel ECG monitoring techniques are urgently needed. In this paper, an easy-to-use noncontact ECG (NCECG) monitoring node for wireless body sensor network was designed and tested. The NCECG node introduced in this paper use the doubled shielded active ECG electrodes, compared with many other two electrodes noncontact ECG monitoring node, it also added a right-leg-drive circuit to reduce the common mode noise. The experiment result shows that it could accurately monitor the ECG signals while insulated by one layer of clothes. Furthermore, the NCECG monitoring node we proposed is high compact and easy to use. Besides ECG monitoring function, it also integrates with temperature, respiration and motion state monitoring function, while the size of the circuit board is just 93×40 mm. And the elastic belt architecture makes the ECG monitoring much more convenient and easy to use.

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Section: Wireless Communication Modulementioning

confidence: 99%

Design and Implementation of a High Integrated Noncontact ECG Monitoring Belt

Sun¹

2015

JFBI

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“…Many work in this area of energy saving have shown that energy is consumed in sensing and monitoring the surrounding area as compared to the data packet exchange activities [1]. Excluding, energy consumption in the nodes such as forwarding nodes and gateway nodes in cluster are higher than other sensing nodes in the cluster.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…WSNs were originally deployed in military, heavy industrial applications and, later extended to the lighter applications such as consumer WSN applications [1][2][3][4][5][6].…”

mentioning

confidence: 99%

“…The most important issue considered when planning WSN Cluster is the energy consumption per node and how the next protocol can improve this. By switching node activities the power consumed is optimized leads to the increased network life time [1].In section 2 we discussed some of the related cluster based protocols, the proposed Distributed Self-organizing Load Balancing Clustering Algorithm in section 3, and in section 4 we analysed the new proposed algorithm with the standard clustering algorithms and finally concluded in the section 5. …”

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confidence: 99%

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Distributed Clustering Based on Node Density and Distance in Wireless Sensor Networks

2016

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Wireless Sensor Networks (WSNs) Keywords: wireless sensor network, clustering, distributed computing, node density, distanceCopyright © 2016 Universitas Ahmad Dahlan. All rights reserved. IntroductionA WSN of randomly deployed self-operable sensor nodes to monitor physical conditions of the environment, as thermal, acoustic, imaging, etc., as measurements, and collaboratively work together to take the data sensed to the base station. WSNs were originally deployed in military, heavy industrial applications and, later extended to the lighter applications such as consumer WSN applications [1][2][3][4][5][6].WSN devices are battery operated; saving power is the biggest challenge. Many work in this area of energy saving have shown that energy is consumed in sensing and monitoring the surrounding area as compared to the data packet exchange activities [1]. Excluding, energy consumption in the nodes such as forwarding nodes and gateway nodes in cluster are higher than other sensing nodes in the cluster. The dynamic topology reformation algorithm method [1] is expected to reduce and balance the energy consumption in WSN. The main purpose of deploying these gateway nodes is to share and balance the energy consumption for demanding jobs in the network.The battery operated nodes have its own limitation with the respect to the distance it can communicate. Hence they can't directly communicate to the base station, looking for intermediate nodes to share link. The idea of hierarchical architecture is used to help these nodes by introducing multihop communication strategy. In the later part the clustering algorithms are focussed to give additional features to the multihop communication such as dynamic network management and, data collection and forwarding with limited contention. These features motivated to propose a new clustering algorithm to study the Distributed and Selforganizing Load Balancing Clustering for WSN. The most important issue considered when planning WSN Cluster is the energy consumption per node and how the next protocol can improve this. By switching node activities the power consumed is optimized leads to the increased network life time [1].In section 2 we discussed some of the related cluster based protocols, the proposed Distributed Self-organizing Load Balancing Clustering Algorithm in section 3, and in section 4 we analysed the new proposed algorithm with the standard clustering algorithms and finally concluded in the section 5.

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