Implementation Of New Fault Tolerance Solution In Wireless Sensor Networks In A Multi-Channel Context
Abstract
Wireless sensor network is application specific, which is deployed in an interested area like about hundred or thousands of sensor nodes. All the sensor nodes communicate via a wireless medium and works cooperatively to sense the environment in order to achieve the required task.
Such sensor nodes which is application specific needs a good fault tolerance scheme to keep the system working. Since this sensor nodes are battery operated, have a small memory, deployed in harsh environmental condition and can easily be depleted.
So we improved on distributed fault tolerance algorithm which enable the detection status of a node with fewer neighbors to be accurate and can also detect the status of a node in a multi-channel context.
Our distributed fault tolerance algorithm uses majority voting with priority. Although in literature fault tolerance detection in multi-channel channel context has not really been put into practice. Chapters one of Implementation Of New Fault Tolerance Solution In Wireless Sensor Networks In A Multi-Channel Context
1.0 INTRODUCTION1.1
Introduction Wireless sensor network consists of a number of sensor nodes and a base station (BS). A wireless sensor network is a collection of nodes organized into a cooperative networks [1], Sensors contain an on-board processor, perform simple computations within itself by using their processing capabilities to process their raw data before transmitting it to the central node sent to the BS.
Wireless sensor nodes are low power, battery operated devices with limited computation and transmission ability [2].Sensor node deployment in wireless sensor network architecture is shown in fig 1 and the component of sensor node its self is shown in fig 2.Figure 1.1 Wireless sensor network Architecture
Figure 1.2: component of a sensor node1In wireless sensor network (WSN) communication using the multi-hop mode where each node communicate with the base station using an intermediate node, thus the node closer to the base station of course become the only source through which all other node send their data to the base station which give rise to interference and low throughput since all other node compete to use node closer to the base station.
The WSN technology tremendous improvement has gained application in so many areas of our daily routine such as medical care, environmental monitoring, smart buildings, banks, telecommunication industry, many other industry and military application. Most of these sensor application are in harsh environment which can cause WSNs to be prone to failure as compared tooter wireless networks, this involve safe mobility and performance, data quality and energy consumption.
Data quality is defined as the number of readings received by the user divided by total number of readings generated by the network during an observation period [3]. To preserve resource and achieve high quality of data, we identify the following as key requirements for FT in WSNs [3]:
1. Awareness of the network main operation and the status of the network resources.2. Adaptability to the frequent changes in WSNs conditions.
The Physical harsh environmental condition that can affect WSNs deployment are fire, rain, humidity, floods and any other physical thing that can affect it. All these can actually cause the sensor nodes to fail or transmit error messages.
To guarantee the network quality of service and performance, it is essential for the wireless sensor networks to be able to detect faults, and to perform something akin to healing and recovering from events that might cause faults in the network, hence fault tolerance should be seriously considered in many wireless sensor network deployments [7].
Fault tolerance is the mechanism put in place for sensor nodes to keep working after a failure occurred. fault tolerance is a need in this type of networks due to sensor node characteristics, radiocommunications and hostile environments in which these networks are deployed [4].
To successful2utilize full deployment of WSNs, fault tolerance should be put in place. One approach to achieve fault tolerance in wireless sensor networks is to deploy a small number of additional relay nodes to provide
k (k ≥ 1) vertex-disjoint paths between every pair of functioning devices (including sensors, data sinks, and other wireless equipment, all termed target nodes in this paper) so that the network can survive the failure of fewer than k nodes [6].
1.2 Problem Statement
Most of the implementation of wireless sensor networks are done in environment with harsh conditions and as such the batteries of the sensor nodes prove very difficult to charge or replace immediately when the batteries run off. The mode of communication in WSN include single-hop and multi-hop. In multi-hop wireless sensor networks bandwidth is a critical issue, because of interference between successive hops on the same path as well as that between neighboring paths[5].
Hence in order to solve this problem of interference and maximize throughput and also to overcome fault thus several fault tolerance scheme are been diverse. Using a single channel for communication causes a heavy work load on it and many packets do not get to their destination due to collision,
so when a node fail the entire network stop functioning. Due to this disadvantage a CSMA/CA or CD are been used to enable parallel communication but because of the heavy workload, this solution do not provide high channel utilization.
Another cost effective solution is to use multiple channels that works for parallel data transmission based on the current WSN hardware, such as MICAz and Telos that provide multiple channels with single radio [5]. Thus in WSNs, there are issue of node failure and transmission failure which are the two main fault in Wireless sensor networks.
Fault is one of the major impact in building a Sensor networks, the ability to identify node fault in sensor network deployment is one of the major issue. Retransmission problem for the detected faulty node also account for in WSNs deployment.
A faulty sensor node can send incorrect message to the base station. Imagine in military deployment of sensor nodes where sensors are expected to3detect the enemy, if such sensor failed then the whole military are in serious danger.
1.3 Objectives
The main objective of these thesis is to provide an adequate solution to fault tolerance in wireless sensor networks in a multi-channel context. The general objectives are:1. To carry out state of the art of multi-channel communication and fault tolerance in wireless sensor networks2. To implement a proposed algorithm for decentralized fault tolerance detection in wireless sensor network.3. To Simulate the implemented algorithm4. To compare implemented algorithm with already existing algorithm
1.4 Motivation of work
From our research and findings we discovered that most of the papers in literature never care about fault tolerance in multi-channel context. Their algorithm never put into consideration sensor nodes with all faulty neighbors.
Some of these papers just focus on finding faulty nodes without applying the algorithm in a gradual process. That means making sure that nodes with fewer neighbors are treated last and nodes with large neighbors are treated first. Doing this ensure that sensor nodes are not mis-diagnose.
1.5 Significance of Study
There are several methods in literature for solving the problem of node failure in wireless sensor network deployment. Our improved distributed fault tolerance algorithm will enable the researchers and wireless sensor network application deployment to be able to detect node failure as fast as possible and at the same time use an alternative channel in transmitting data to the base station using the one hop neighbors
.41.6 Thesis Organization
The remaining part of these thesis is organized as follows, chapter two gives state of the art of multi-channel communication and fault tolerance in WSNs, chapter three gives the materials and methods of the thesis, chapter four gives the implementation of the algorithm and simulation and comparison with existing work and chapter five gives conclusion and future work.
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