Improving the energy efficiency for the WBSN bottleneck zone based on random linear network coding

The reduction of energy consumption and the successful delivery of data are important for the Wireless Body Sensor Network (WBSN). Many studies have been proposed to improve energy efficiency, but most of them have not focussed on the biosensor nodes in the WBSN bottleneck zone. Energy consumption is a critical issue in WBSNs, as the nodes that are placed next to the sink node consume more energy. All biomedical packets are aggregated through these nodes forming a bottleneck zone. This paper proposes a novel mathematical model for body area network (BAN) topology to explain the deployment and connection between biosensor nodes, simple relay nodes, network coding relay nodes and the sink node. Therefore, this paper is dedicated to researching both the energy saving and delivery of data if there is a failure in one of the links of the transmission, which relates to the proposed Random Linear Network Coding (RLNC) model in the WBSN. Using a novel mathematical model for a WBSN, it is apparent that energy consumption is reduced and data delivery achieved with the proposed mechanism. This paper details the stages of the research work

Energy saving and reliability for Wireless Body Sensor Networks (WBSN)

In healthcare and medical applications, the energy consumption of biosensor nodes affects the collection of biomedical data packets, which are sensed and measured from the human body and then transmitted toward the sink node. Nodes that are near to the sink node consume more energy as all biomedical packets are aggregated through these nodes when communicated to sink node. Each biosensor node in a wireless body sensor networks (WBSNs) such as ECG (Electrocardiogram), should provide accurate biomedical data due to the paramount importance of patient information. We propose a technique to minimise energy consumed by biosensor nodes in the bottleneck zone for WBSNs, which applies the Coordinated Duty Cycle Algorithm (CDCA) to all nodes in the bottleneck zone. Superframe order (SO) selection in CDCA is based on real traffic and the priority of the nodes in the WBSN. Furthermore, we use a special case of network coding, called Random Linear Network coding (RLNC), to encode the biomedical packets to improve reliability through calculating the probability of successful reception (PSR) at the sink node. It can be concluded that CDCA outperforms other algorithms in terms of energy saving as it achieves energy savings for most biosensor nodes in WBSNs. RLNC employs relay nodes to achieve the required level of reliability in WBSNs and to guarantee that the biomedical data is delivered correctly to the sink nod

Cooperative Hyper-Scheduling based improving Energy Aware Life Time Maximization in Wireless Body Sensor Network Using Topology Driven Clustering Approach

The Wireless Body Sensor Network (WBSN) is an incredible developing data transmission network for modern day communication especially in Biosensor device networks. Due to energy consumption in biomedical data transfer have impacts of sink nodes get loss information on each duty cycle because of Traffic interruptions. The reason behind the popularity of WBSN characteristics contains number of sensor nodes to transmit data in various dense regions. Due to increasing more traffic, delay, bandwidth consumption, the energy losses be occurred to reduce the lifetime of the WBSN transmission. So, the sensor nodes are having limited energy or power, by listening to the incoming signals, it loses certain amount of energy to make data losses because of improper route selection. To improve the energy aware lifetime maximization through Traffic Aware Routing (TAR) based on scheduling. Because the performance of scheduling is greatly depending on the energy of nodes and lifetime of the network. To resolve this problem, we propose a Cooperative Hyper-scheduling (CHS) based improving energy aware life time maximization (EALTM) in Wireless Body sensor network using Topology Driven Clustering Approach (TDCA).Initially the method maintains the traces of transmission performed by different Bio-sensor nodes in different duty cycle. The method considers the energy of different nodes and history of earlier transmission from the Route Table (RT) whether the transmission behind the Sink node. Based on the RT information route discovery was performed using Traffic Aware Neighbors Discovery (TAND) to estimate Data Transmission Support Measure (DTSM) on each Bio-sensor node which its covers sink node. These nodes are grouped into topology driven clustering approach for route optimization. Then the priority is allocated based on The Max-Min DTSM, the Cooperative Hyper-scheduling was implemented to schedule the transmission with support of DTSM to reduce the energy losses in WBSN. This improves the energy level to maximization the life time of data transmission in WBSN than other methods to produce best performance in throughput energy level

SPARC 2018 Internationalisation and collaboration : Salford postgraduate annual research conference book of abstracts

Welcome to the Book of Abstracts for the 2018 SPARC conference. This year we not only celebrate the work of our PGRs but also the launch of our Doctoral School, which makes this year’s conference extra special. Once again we have received a tremendous contribution from our postgraduate research community; with over 100 presenters, the conference truly showcases a vibrant PGR community at Salford. These abstracts provide a taster of the research strengths of their works, and provide delegates with a reference point for networking and initiating critical debate. With such wide-ranging topics being showcased, we encourage you to take up this great opportunity to engage with researchers working in different subject areas from your own. To meet global challenges, high impact research inevitably requires interdisciplinary collaboration. This is recognised by all major research funders. Therefore engaging with the work of others and forging collaborations across subject areas is an essential skill for the next generation of researchers

A Flexible, Low-Power, Programmable Unsupervised Neural Network Based on Microcontrollers for Medical Applications

We present an implementation and laboratory tests of a winner takes all (WTA) artificial neural network (NN) on two microcontrollers (μC) with the ARM Cortex M3 and the AVR cores. The prospective application of this device is in wireless body sensor network (WBSN) in an on-line analysis of electrocardiograph (ECG) and electromyograph (EMG) biomedical signals. The proposed device will be used as a base station in the WBSN, acquiring and analysing the signals from the sensors placed on the human body. The proposed system is equiped with an analog-todigital converter (ADC), and allows for multi-channel acquisition of analog signals, preprocessing (filtering) and further analysis