Sara Ghanavati

A Data Fusion Method in Wireless Sensor Networks

The success of a Wireless Sensor Network (WSN) deployment strongly depends on the quality of service (QoS) it provides regarding issues such as data accuracy, data aggregation delays and network lifetime maximisation. This is especially challenging in data fusion mechanisms, where a small fraction of low quality data in the fusion input may negatively impact the overall fusion result. In this paper, we present a fuzzy-based data fusion approach for WSN with the aim of increasing the QoS whilst reducing the energy consumption of the sensor network. The proposed approach is able to distinguish and aggregate only true values of the collected data as such, thus reducing the burden of processing the entire data at the base station (BS). It is also able to eliminate redundant data and consequently reduce energy consumption thus increasing the network lifetime. We studied the effectiveness of the proposed data fusion approach experimentally and compared it with two baseline approaches in terms of data collection, number of transferred data packets and energy consumption. The results of the experiments show that the proposed approach achieves better results than the baseline approaches.

An Alternative Clustering Scheme in WSN

Despite significant advancements in wireless sensor networks (WSNs), energy conservation in the networks remains one of the most important research challenges. One approach commonly used to prolong the network lifetime is through aggregating data at the cluster heads (CHs). However, there is possibility that the CHs may fail and function incorrectly due to a number of reasons such as power instability. During the failure, the CHs are unable to collect and transfer data correctly. This affects the performance of the WSN. Early detection of failure of CHs will reduce the data loss and provide possible minimal recovery efforts. This paper proposes a self-configurable clustering mechanism to detect the disordered CHs and replace them with other nodes. Simulation results verify the effectiveness of the proposed approach.

An Alternative Node Deployment Scheme for WSNs

Sensing coverage is a fundamental design problem in wireless sensor networks (WSNs). This is because there is always a possibility that the sensor nodes may function incorrectly due to a number of reasons, such as failure, power, or noise instability, which negatively influences the coverage of the WSNs. In order to address this problem, we propose a fuzzy-based self-healing coverage scheme for randomly deployed mobile sensor nodes. The proposed scheme determines the uncovered sensing areas and then select the best mobile nodes to be moved to minimize the coverage hole. In addition, it distributes the sensor nodes uniformly considering Euclidean distance and coverage redundancy among the mobile nodes. We have performed an extensive performance analysis of the proposed scheme. The results of the experiment show that the proposed scheme outperforms the existing approaches.

A new energy efficient cluster-head and backup selection scheme in WSN

Despite significant advancements in wireless sensor networks (WSNs), energy conservation remains one of the most important research challenges. Proper organization of nodes (clustering) is one of the major techniques to expand the lifespan of the whole network through aggregating data at the cluster head. The cluster head is the backbone of the entire cluster. That means if a cluster head fails to accomplish its function, the received and collected data by cluster head can be lost. Moreover, the energy consumption following direct communications from sources to base stations will be increased. In this paper, we propose a type-2 fuzzy based self-configurable cluster head selection (SCCH) approach to not only consider the selection criterion of the cluster head but also present the cluster backup approach. Thus, in case of cluster failure, the system still works in an efficient way. The novelty of this protocol is the ability of handling communication uncertainty, which is an inherent operational aspect of sensor networks. The experiment results indicate SCCH performs better than other recently developed methods.

A Congestion Control Scheme Based on Fuzzy Logic in Wireless Body Area Networks

One of the major challenges in healthcare wireless body area network (WBAN) applications is to control congestion. Unpredictable traffic load, many-to-one communication nature and limited bandwidth occupancy are among major reasons that can cause congestion in such applications. Congestion has negative impacts on the overall network performance such as packet losses, increasing end-to-end delay and wasting energy consumption due to a large number of retransmissions. In life-critical applications, any delay in transmitting vital signals may lead to death of a patient. Therefore, in order to enhance the network quality of service (QoS), developing a solution for congestion estimation and control is imperative. In this paper, we propose a new congestion detection and control protocol for remote monitoring of patients health status using WBANs. The proposed system is able to detect congestion by considering local information such as buffer capacity and node rate. In case of congestion, the proposed system differentiates between vital signals and assigns priorities to them based on their level of importance. As a result, the proposed approach provides a better quality of service for transmitting highly important vital signs.

Cloud-assisted IoT-based health status monitoring framework

Internet of Things (IoT), leveraging interconnected sensors (i.e., wireless body area network (WBAN), can offer opportunities for real time monitoring of patient health status and manage patients and treatment. Therefore, it is expected that IoT will play a significant role in the next-generation healthcare provisioning. Although IoT-based patient health status monitoring has become very popular, monitoring patients remotely outside of hospital settings requires augmenting the capabilities of IoT with other resources for health data storage and processing. In this paper, we propose an IoT-based service-oriented framework for continuous patient health status monitoring framework that connects WBAN through smartphones to cloud computing. We theoretically model the proposed framework, which is the first attempt in this direction, and study the behaviour of the proposed IoT–WBANs-based framework. Experimental evaluation based on the sensor lifetime, existing cost and energy consumption shows that the proposed framework substantially outperforms the baseline WBANs.

A fuzzy technique to control congestion in WSN

Congestion in wireless sensor networks (WSNs) is a crucial issue. That is due to the relatively high node density and source-to-sink communication pattern. Congestion not only causes packet loss, but also leads to excessive energy consumption as well as delay. Therefore, in order to prolong network lifetime and improve fairness and provide better quality of service, developing a novel solution for congestion estimation and control is important to be considered. To address this problem, we propose a type-2 fuzzy logic based algorithm to detect and control congestion level in WSNs. The proposed algorithm considers local information such as packet loss rate and delay to control congestion in the network. Simulation results show that our protocol performs better than a recently developed protocol in prolonging network lifetime as well as decreasing packet loss.

ECG rate control scheme in pervasive health care monitoring system

One of the major challenges in healthcare wireless body area network (WBAN) applications is to control congestion. Unpredictable traffic load, many-to-one communication nature and limited bandwidth occupancy are among major reasons that can cause congestion in such applications. Congestion has negative impacts on the overall network performance such as increasing end-to-end delay and wasting energy consumption due to a large number of retransmissions. In life-critical applications, any delay in transmitting vital signals may lead to a serious consequences including patient death. Therefore, an approach for congestion estimation and control is imperative to enhance the network quality of service (QoS). In this paper, we propose a new fuzzy based congestion detection and control protocol for a WBAN application that monitors patient ECG signals remotely. The proposed system is able to detect link congestion by considering local information such as available bandwidth (BW) and end to end delay. The proposed protocol changes the rate of transferred ECG information and assigns priorities to patients based on their level of urgency. As a result, the proposed approach provides a better QoS for transmitting highly important ECG signs.

Fuzzy logic optimized wireless sensor network routing protocol

Wireless sensor networks WSNs are used in health monitoring, tracking and security applications. Such networks transfer data from specific areas to a nominated destination. In the network, each sensor node acts as a routing element for other sensor nodes during the transmission of data. This can increase energy consumption of the sensor node. In this paper, we propose a routing protocol for improving network lifetime and performance. The proposed protocol uses type-2 fuzzy logic to minimize the effects of uncertainty produced by the environmental noise. Simulation results show that the proposed protocol performs better than a recently developed routing protocol in terms of extending network lifetime and saving energy and also reducing data packet lost.

An alternative data collection scheduling scheme in wireless sensor networks

Despite significant advancements in wireless sensor networks (WSNs), energy conservation remains one of the most important research challenges. Recently, the problem of energy conservation has been addressed by applying mobile sink as an effective technique that can enhance efficiency of energy consumption in the networks. In this paper, the energy conservation problem is firstly formulated to maximize the lifetime of WSN subject to delay and node energy constraints. Then, to solve the defined energy conservation problem, a data collection scheduling with a mobile sink scheme is proposed. In the proposed approach, the sink movement is governed by a type-2 fuzzy controller to be located at the best location and time to collect sensory data. We conducted extensive experiments to study the effectiveness of the proposed protocol and compared it against the streaming data delivery (SDD) and virtual circle combined straight routing (VCCS) protocols. We observed that the proposed protocol outperforms both SDD and VCCS approaches by reducing energy consumption, minimize delays and enhance data collection quality.