Fault-Tolerant Routing for Wireless Sensor Networks using a Gaussian Network

Abstract

Wireless Sensor Networks (WSNs) are utilised for sensing various physical and environmental variables like temperature, pressure, motion, and pollution. A component of a network to continue operating even when some sensor nodes fail is regarded as fault tolerance in wireless sensor networks. Due to battery life deadlines and the challenges associated with charging or replacing failing nodes, the deployment of sensor nodes in the target area is done in a dense manner to maximise coverage and connection. This paper implements a hybrid fault-tolerant routing methodology to address the problem of fault tolerance in hierarchical topology for wireless sensor networks (WSNs). The network location is partitioned into small square grids, with a Gaussian integer serving as each grid\'s cluster head. A Gaussian network is created through communicating these cluster heads together. Discovering the Gaussian network\'s shortest path and multi-path routing, the project implements a hybrid Fault-tolerant Clustering routing protocol based on Gaussian network for Wireless Sensor Network (FCGW). The purpose of FCGW is to improve fault tolerance and reduce energy consumption for Wireless Sensor Networks. Experimental results using MATLAB shows that FCGW protocol has high data reliability. In addition, the FCGW protocol consumes lesser energy in the network compared to other protocols namely FT-LEACH, HEED and PSO-UFC.

Introduction

I. INTRODUCTION

Wireless Sensor Networks (WSNs) development and use have accelerated in recent years. The fundamental purpose of a wireless sensor network is to monitor an environment devoid of infrastructure like a power supply or wired internet connection as well as without human contact. Sensor nodes are small, inexpensive sensing devices with wireless radio transceivers. Data dependability, ideal energy consumption, memory limit and data latency present significant difficulties in wireless sensor networks (WSN) deployment. The scarcity of sensor nodes combined with challenging communication settings like rain, wind, snow, and water constantly results in incorrect connections. As a result, increasing network fault tolerance will increase WSN availability and service quality.

A. Clustering

One important component for prolonging the network lifetime in Wireless Sensor Networks is Clustering. The Cluster Heads (CHs) for every cluster are chosen after the sensor nodes have been organised into groups called clusters.

B. Fault-tolerance

Fault tolerance is the ability of a network to continue operating even when certain sensor nodes in Wireless Sensor Networks. The deployment of sensor nodes in the target region is done in a dense way to maximise coverage and connection due to battery life limitations and the difficulties of charging or replacing failing nodes.

C. Gaussian Network

Combining clustering and labelling sensor nodes as Gaussian integers creates a hierarchical topology. As a result, the network area is divided into tiny square grids, with a Gaussian integer serving as the cluster head for each grid. A Gaussian network is built by connecting these cluster heads together.

D. Multi-path Routing

Over the past ten years, research on routing in wireless sensor networks has been seen as being of significant importance. In order to increase network performance by effectively utilizing the resources provided by the network, wireless sensor networks frequently use multi-path routing strategy.

This paper's major contributions are:

1. Formation of cluster nodes in the proper location.
2. Effective communication among nodes within a cluster.
3. Identification of fault in sensor nodes.

The rest of the part of the essay is organised as follows the section II presentation of related work. The preliminaries of selection cluster head in the cluster formation models are given in section III Section IV discuss the planned FCHW's details. Section explains the simulation results. V. Finally section VI concludes the paper.

II. RELATED WORK

The author in [1] summarizes various issues and challenges in WSNs.

A. Energy

Power is needed by sensors for its effective functioning. Data processing, data exchange, and data collection need energy. Even when they are inactive after being used up, batteries that provide electricity need to be replaced or recharged. Designing, developing, and implementing energy-efficient hardware and software protocols for WSNs is the most important area of research for WSN researchers.

B. Level of Service

The level of service given by sensor networks to its users is known as quality of service. Many real-time essential applications use wireless sensor networks, therefore, the network must deliver high-quality service.

C. Fault Tolerance

Sensor network should remain functional even if any node fails while the network is operational. Network should be able to adapt by changing its connectivity in case of any fault. In that case, well- efficient routing algorithm is applied to change the overall configuration of network.

Redundancy guarantees accurate facts for making decisions [2]. The analysis, monitoring, and forecasting of system behaviour depend heavily on reliable data, whereas poor quality data may lead to incorrect results in the decision-making process. Endpoints in Wireless Sensor Networks (WSNs) are widely dispersed around an area to gather data. Sensors gather comparable data and send it to the sink.

Often, redundant data at the sink results from these comparable data. Eliminating duplicated data improves accuracy, dependability, and security while using less energy because dealing with redundant data consumes most of the sink network node energy. Even if network costs and/or time increase, data accuracy in fig 1 show the  must still be guaranteed.

In order to give a more consistent, accurate and trustworthy data collection in an energy-efficient manner, a technique must be created to extract information from redundant data. The strategies for data fusion assist in preserving the same.

Energy depletion, node software or hardware issues [3], environmental occurrences, hostile attacks, and other factors are frequently to blame for failures in Wireless Sensor Networks (WSNs).

It is crucial to guarantee that a WSN application system is functional in the event of a failure or interruption. An acceptable topology can increase the robustness of WSN, according to recent topology control research. However, due to the restricted supply of sensor nodes, topology control finds it difficult to balance energy conservation with fault tolerance.

In order to solve this issue, this study offers a Regular Hexagonal-Based Clustering Scheme (RHCS) and a Scale-Free Topology Evolution Mechanism (SFTEM) for WSNs. These techniques improve network survivability and maintain energy balance. For clustering sensor nodes, RHCS employs a regular hexagonal topology that provides at least 1-coverage fault-tolerance.

SFTEM takes advantage of the synergy between a reliable clustering scheme and topology evolution to connect clusters and create a resilient WSN that can withstand a wide range of defects, including random failure and energy failure. Additionally, simulated experiments were conducted to compare three factors, including fault-tolerance, intrusion-tolerance, and energy balance, with other approaches described in the literature in order to assess the performance of SFTEM. The simulation results ef [3] demonstrate that SFTEM performs well and is depicted in figure 2.2 & figure 2.3  

Topology control improves network scalability and longevity while balancing the load on sensor nodes[4]. Sensor node clustering is a successful topology control strategy. For long-lived ad hoc sensor networks, we provide an unique distributed clustering approach is proposed. Other than the presence of several power levels in sensor nodes, neither the existence of infrastructure nor the capabilities of nodes are assumed in our proposed solution.

This paper presents HEED (Hybrid Energy-Efficient Distributed clustering) protocol, which regularly chooses cluster heads based on a hybrid of the node residual energy and a secondary parameter, such as the node degree or node proximity to its neighbours.

HEED may asymptotically virtually certainly ensure connection of clustered networks with the right restrictions on node density, intra-cluster, and inter-cluster transmission ranges.

The four main goals of HEED are

1. Spreading out energy use to extend network lifespan.
2. Ending the clustering procedure after a predetermined number of repetitions.
3. Reducing control overhead.
4. Generating evenly spaced cluster heads.

Network nodes can rarely be visited or even recharged after being planted in the environment, energy constraints are a significant barrier for wireless sensor networks. The nodes quickly grow flaws since they are positioned in hostile and challenging environments. As a result, it is crucial to manage these networks to increase their fault-tolerance capacity in the face of scarce resources.

Wherein each node, in its acting as a cluster member, must report its most recent energy level to the head cluster upstream. Additionally, the node will make sure that it only communicates sensed data to the cluster head when it differs from the sensed data from the prior period in order to avoid rework and energy loss (CH)[5].

The first hierarchical routing protocol for wireless sensor networks, LEACH, served as the prototype for numerous subsequent hierarchical protocols.      

Cluster formation and stable phase are its two primary phases. A Round is the name of the two phases cycle of execution.

Following the selection of the CHs, NCH nodes join the CH nodes. In the CH selection process, each sensor node should produce a number between 0 and 1, and if that number is less than a threshold (Tn), that particular node will be designated as a CH node and will broadcast its status to the other nodes.

Using TDMA timing, the member nodes deliver ambient data to the appropriate CH. In order to prevent sending unnecessary data, CH nodes evaluate incoming data before delivering it to the sink. Then they send the aggregated data to the sink.

Leach protocol is a well-known hierarchical clustering algorithm for wireless sensor networks. It served as the inspiration for numerous other protocols that branched off from it and enhanced specific fields, but none of them took the aforementioned fault-tolerance techniques into account.

Wireless sensor networks, clustering are one of the most effective energy-saving approaches for maximising network lifetime. Due to the high inter-cluster relay traffic load in the multi-hop strategy, cluster heads (CHs) near to the base station soon run out of energy, creating the hot spot issue. A clustering protocol must be fault-tolerant and energy-efficient. a particle swarm optimization (PSO)-based unequal and fault tolerant clustering protocol referred as PSO-UFC[6].

Protocol addresses imbalanced clustering and fault tolerance issues in the existing energy-balanced unequal clustering (EBUC) protocol for the long-run operation of the network.

The PSO-UFC protocol uses an unequal clustering technique to balance the Master CHs intra-cluster and inter-cluster energy consumption in order to address the imbalanced clustering issue (MCHs).

a. PSO based clustering mechanism to solve hot spot problem in WSN.

b. Derivation of the cost functions for unequal clustering mechanism to balance the intra-cluster and inter-cluster energy consumption.

c. Construction of a multi-hop routing tree to ensure the network connectivity among the MCHs.

d. Election of Surrogate Cluster Head in each cluster to address the fault tolerance issue.

Conclusion

A hierarchical topology for a Wireless Sensor Network utilizing clustering routing and the Gaussian network connection features is presented. This method places the sensors in a rectangle region at random and groups them into various square grids. Through symmetric links and multi-path routing, this technique has increased fault tolerance because the CH nodes are joined to form a Gaussian network. The routing protocol uses the representation of the CH nodes as Gaussian integers to simplify the routing algorithms. Experimental results show significant reduction in the cost of multi-path routing maintenance and routing. It is a significant difficulty that increases packet delay to connect long-distance sensor nodes as a Gaussian network in a wireless environment. The characteristics of the Gaussian network and its Hamiltonian cycles will be used to address broadcast problems in Wireless Sensor Networks in the future.

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Copyright

Copyright © 2023 M. Velmurugan, A. Meenakowshalya. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.