NDLI: Lacas: learning automata-based congestion avoidance scheme for healthcare wireless sensor networks

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IEEE Journal on Selected Areas in Communications - Front cover

IEEE Communications society

Guest editorial wireless and pervasive communications for healthcare

Sage: a strong privacy-preserving scheme against global eavesdropping for ehealth systems

An integrated communications framework for context aware continuous monitoring with body sensor networks

A secure mobile healthcare system using trust-based multicast scheme

Pervasive, secure access to a hierarchical sensor-based healthcare monitoring architecture in wireless heterogeneous networks

Remote patient monitoring service using heterogeneous wireless access networks: architecture and optimization

Battery-dynamics driven tdma mac protocols for wireless body-area monitoring networks in healthcare applications

Bridging between ieee 802.15.4 and IEEE 802.11b networks for multiparameter healthcare sensing

Congestion-aware, loss-resilient bio-monitoring sensor networking for mobile health applications

Lacas: learning automata-based congestion avoidance scheme for healthcare wireless sensor networks

Angelah: a framework for assisting elders at home

Comparative analysis of quality of service and memory usage for adaptive failure detectors in healthcare systems

Context-aware middleware for pervasive elderly homecare

Power- and delay-awareness of health telemonitoring services: the mobihealth system case study

Novel methods of faster cardiovascular diagnosis in wireless telecardiology

Highly reliable energy-saving mac for wireless body sensor networks in healthcare systems

Medical QoS provision based on reinforcement learning in ultrasound streaming over 3.5G wireless systems

IEEE Communications society - 2009 board of governors

Table of contents - (continued from Front cover)

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Lacas: learning automata-based congestion avoidance scheme for healthcare wireless sensor networks

Content Provider	IEEE Xplore Digital Library
Author	Misra, S. Tiwari, V. Obaidat, M.
Copyright Year	1983
Abstract	One of the major challenges in wireless sensor network (WSN) research is to curb down congestion in the network's traffic, without compromising with the energy of the sensor nodes. Congestion affects the continuous flow of data, loss of information, delay in the arrival of data to the destination and unwanted consumption of significant amount of the very limited amount of energy in the nodes. Obviously, in healthcare WSN applications, particularly in the ones that cater to medical emergencies or in the ones that closely monitor critically ailing patients, it is desirable in the first place to avoid congestion from occurring and even if it occurs, to reduce the loss of data due to congestion. In this work, we address the problem of congestion in the nodes of healthcare WSN using a learning automata (LA)-based approach. Our primary objective in using this approach is to adaptively make the processing rate (data packet arrival rate) in the nodes equal to the transmitting rate (packet service rate), so that the occurrence of congestion in the nodes is seamlessly avoided. We maintain that the proposed algorithm, named as learning automata-based congestion avoidance algorithm in sensor networks (LACAS), can counter the congestion problem in healthcare WSNs effectively. An important feature of LACAS is that it intelligently' learns' from the past and improves its performance significantly as time progresses. Our proposed LA based model was evaluated using simulations representing healthcare WSNs. The results obtained through the experiments with respect to performance criteria having important implications in the healthcare domain, for example, the number of collisions, the energy consumption at the nodes, the network throughput, the number of unicast packets delivered, the number of packets delivered to each node, the signals received and forwarded to the medium access control (MAC) layer, and the change in energy consumption with variation in transmission range, have shown that the proposed algorithm is capable of successfully avoiding congestion in typical healthcare WSNs requiring a reliable congestion control mechanism.
File Size	890564
File Format	PDF
ISSN	07338716
Volume Number	27
Issue Number	4
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-05-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Learning automata Medical services Wireless sensor networks Intelligent sensors Energy consumption Telecommunication traffic Delay Patient monitoring Biomedical monitoring Counting circuits Congestion control, healthcare applications, learning automata, performance evaluation, wireless sensor networks (WSNs)
Content Type	Text
Resource Type	Article
Subject	Computer Networks and Communications Electrical and Electronic Engineering

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