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Author(s): Manisha Chandrakar, V. K. Patle

Email(s): manishachandrkar00@gmail.com , patlevinod@gmail.com

Address: School of Studies in Computer Science and IT, Pt. Ravishankar Shukla University Raipur, Chhattisgarh, India.
School of Studies in Computer Science and IT, Pt. Ravishankar Shukla University Raipur, Chhattisgarh, India.
*Corresponding Author: manishachandrkar00@gmail.com

Published In:   Volume - 36,      Issue - 2,     Year - 2023


Cite this article:
Manisha Chandrakar; V. K. Patle (2023). Enhanced ACO in clustering algorithm for QoS in WSN enabled IoT. Journal of Ravishankar University (Part-B: Science), 36(2), pp. 19-34.



Enhanced ACO in clustering algorithm for QoS in WSN enabled IoT

Manisha Chandrakar1, V. K. Patle2

1,2School of Studies in Computer Science and IT, Pt. Ravishankar Shukla University Raipur, Chhattisgarh, India.

manishachandrkar00@gmail.com, patlevinod@gmail.com

 

Corresponding Author: manishachandrkar00@gmail.com

Abstract:

Various sensor nodes whose deployment is done in a random way are included in Wireless sensor networks (WSN) within a region for gathering the information out of the respective atmospheres as well as forwards it towards the Base Station (BS). Constrained sources of energy are allotted to the WSN nodes. The consumption of energy of nodes must be reduced for obtaining the enhanced lifetime of the network. Clustering is referred as a technique that reduced the consumption of energy in WSNs. By transmitting the gathered data to the sink through the Cluster Head (CH) nodes located within the clustered networks, the energy of the node can be saved. In WSNs, fault tolerance is a significant issue to consider. The entire data communication system can be rendered inoperable by the failure of just one cluster head. Within the scope of this research, a cluster-based routing method with fault tolerance is provided. The purpose of this study is to present an innovative technique for increasing tolerance of failure and data accumulation in clustered WSN by making use of backup CHs and improving the relay node selection mechanism by modifying ACO. The selection of the backup cluster head is what allows for fault tolerance in this case (BKCH). The method can be broken down into two distinct stages. In the first step, the network is categorized into clusters; in the second step, CHs and BCHs are chosen from the pool of candidates. Communication within the cluster takes place between the member nodes and the CH nodes. Aggregator nodes (AG) are utilised for the purpose of inter-cluster communication, and the modified ACO is utilised to determine which node serves as the most effective relay between CH and AG.  When compared with other energy-conserving protocols, this technique uses less energy while increasing fault resilience and PDR.

Keywords: WSN, IoT, Data aggregation, Modified ACO, Cluster head selection, Fault tolerance.

1.     Introduction

In the 21st century, WSNs play an important part [1]. WSN is capable of running an enormous number of applications that are becoming increasingly important. In order to physically collect useful data from a region and then send it to sink nodes via wireless links, an implementation of low-cost, battery-energized sensor nodes is carried out in the region. This deployment takes place physically. Direct or indirect sensor-sink communication occurs. Energy resources that are limited are distributed among the nodes in WSNs that are responsible for representing the most significant problems. By taking into account important aspects including energy efficiency, reliable self-association, clustering, and routing protocols, guaranteeing desirable network functionalities, it is possible to save energy while also extending the lifespan of the network. This achieves an extended lifetime for the network. Similar to how WSNs exhibit some imperfections because to their own restricted buffering features and computing sources.

The constrained energy and communication capabilities of a sensor node make it difficult for longer distances [2, 3], [4], [5], and [6] to be covered directly by the sink node in direct communication. [2], [3], [4], [5], and [6] As a result, a communication model consisting of multiple hops is utilised during the data transfer between the source sensor node and the sink node. The most important challenge in this predicament is to create a novel energy-optimized routing path that will result in the network lifespan to improve. A quality of service (QoS) assurance [7–11], [12], and [13] is necessary for a number of mission-critical as well as real-time implementations in addition to the extension of the network's lifespan. In addition, security is a significant obstacle in WSNs because the unattended activities and the unreliable sensor nodes leave the network vulnerable to assault [14], [15], [12], [3], and [13].

Nevertheless, the implementation of WSN is degenerated out of the significant factors like constrained storage ability, non-rechargeable as well as lean batteries, restricted computing ability, in addition to insignificant safety. The consumption of power within WSNs relies on the implementations [16–18]. The installation of such networks is done in an intermittent way within a forbidding atmosphere in which the power unit of a sensor node couldn’t be restored. Therefore, the WSN’s lifetime is indicated by the batteries. The usage of energy is done while transmitting the data within the wireless situations [19–21]. Thus, designing of energy-efficient routing is a necessary thing here.

During the course of the last few decades, a number of different strategies for conserving power have been presented. On the basis of data-accessing techniques, the authors started their analysis at the physical layer and moved up to the link layer before moving on to the network routing layer [22]. Several clustering procedures have been developed by a number of writers [23]. The two strategies that result from dividing the data sensing, aggregation, and transmission stages are setup stage and steady state stage. The Cluster member nodes methodically receives and sends the data to a CH. The cluster's several nodes are used to distribute wireless media. The CH determines the timing for data aggregation and communicates it to all participant nodes. The aggregation process is implemented using a TDMA access mechanism. The CH does the data reduction and makes use of the compression approaches to reduce the data size in addition to advance to the BS.

The limitations of Dependable & Energy-Efficient Multi-Hop LEACH-Based Clustering Protocol [38] for WSNs include higher re-clustering due to greater communication as well as increased routing overhead caused by the multi-hop routing technique. The limitations faced by the Ant Colony Optimization Based Quality of Service Aware Energy Balancing Secure Routing process [39] for WSNs are that it does not call for the use of a path failure repair method and that routing overhead is raised due to the use of many metrics in route selection. The shortcomings of this method include the need for a recovery methodology in the event of a CH failure and an increase in overhead due to the inclusion of many criteria when choosing a CH in the Energy-Efficient QoS-Aware Intelligent Hybrid Clustered Routing Protocol [40]. The limitations of fuzzy multipath routing and probability-based cluster head selection for prolonging the life of WSNs [41] include the incidence of path failure due to disregarding the building of the routing route and the lack of method of reparation if a path outage has occurred.

In this article, we present an innovative fault tolerance approach that features enhanced data aggregation and relay selection methodology as a solution to the problems that were discussed earlier. The proposed method includes two stages: fault tolerance clustering and fault tolerance routing. Both of these phases are independent from one another. During the first stage of the process, candidate CHs are selected using criteria such as the amount of residual energy, the distance to the BS, the probability value, the delay between the nodes, and the number of neighbours. During the setup phase, both CHs and BKCHs are put into consideration for selection. The second phase of the process, known as fault-tolerant routing, comprises of two levels: intra-cluster routing and inter-cluster routing. The CH is charged with aggregating the data obtained from the member nodes when intra-cluster routing is being used. Additionally, in inter-cluster routing, the ACO functions are utilized for finding the most resourceful path among CHs that leads to the Aggregator node.

Finally, the goal of this research is to provide WSNs with lightweight and effective fault tolerance through the introduction of backup clustering hierarchy (CH) nodes. The data communication is controlled by ACO, and these nodes are responsible for aggregating data and reducing overhead at the SINK level. During the process of data aggregation, TDMA is utilised to carry out subsequent data scheduling between the nodes.

2.     Literature Survey

The development of various routing protocols [4-6], [24] is done to maximize the energy effectiveness or to lessen consumption of energy within WSN. The issues of excessive use of certain sensors particularly the nodes, which are close to the sink node could not be handled by such methods within a static network using a single sink node. The energy is exhausted during the network collapsing while the sensor nodes are near to sink nodes. Whereas, 90% of the initial energy [25] is possessed by the nodes which remain far away from sink node. The issues irregular consumption of energy within sensor networks is investigated by Li and Mohapatra [26] and a verification is made that the nearest nodes towards the sink depletes the respective energy rapidly.

On behalf of a three-tiered network, Kumar et al. [27] investigate the benefits of node energy heterogeneousness within WSN via the construction of the EEHC (Energy- Efficient Heterogonous Clustered) protocol. Via the use of a probability threshold operation, it chooses a CH based on the amount of energy, which is still present in the sensor node. EEHC outperforms LEACH through the use of a heterogeneous methodology when compared with LEACH with regard to the increase in the network's lifespan. In addition, Sharma et al. [28] suggest an energy model and present a traffic and energy-aware routing (TEAR) for the purpose of refining the stability interval when the sensor nodes are supposed to possess arbitrary initial energy. The restrictions of the systems complexity are overcome by the fluctuations in the rate at which traffic originates.

On behalf of heterogeneous clustering networks, the techniques of CREEP (cluster-head restricted energy-efficient protocol), LA-MHR (automata-based multilevel heterogeneous routing), and EDCS (efficient & dynamic clustering scheme) were proposed by Dutt et al. [29], Tanwar et al. [30], and Hong et al. [31] respectively. CREEP is used to make the network lifespan improved by utilising the 2-stage heterogeneity. This is accomplished by reducing the number of CHs needed for every round. The findings of the comparison, when compared to both the mobile and the stationary HWSN scenarios, result in the improvement in network longevity. The LA-MHR methodology presents a multilevel, heterogeneous node model that is dependent on autonomous learning. When running LA-MHR, it is necessary to use a dependent learning technique based on an S-model in order to choose a CH. In addition, the BS is responsible for allocating the cognitive radio spectrum before the CHs are selected to use it. In the end, it is the evaluation of the hole problem that determines how long the network will continue to function.

When several variables like PLR and connection reliability are taken into account, Hong et al. [32] construct a clustering-tree topology control depending on the energy prediction (CTEF) for network load balancing along with energy saved. The central theorem and log normal distribution approaches are used for precise network mean energy prediction, together with a traditional CH selection technique and cluster creation for fluctuation between the real and optimal average residual energy.

 A multi-objective as well as multi-constraint optimization routing procedure is introduced by Kavi et al. [33]. The quality of the routing protocol is estimated by considering the performance metrics including traffic load, link quality, and residual energy. Speedy packet delivery along with the reliability of the link is guaranteed by it.

A hard real-time protocol is proposed by Chen [34] referred as SHE (Self-stabilizing Hop-constrained Energy efficient). Various paths are used for routing the traffic packets from CHs towards the BS later to the cluster-formation stage. The requirement of QoS is acquired by specifying AT (Aging Tag).

Faheem and Gungor [35] suggest an energy-aware QoS routing protocol (EQRP) clustering approach. This method was based on the trustworthy framework's foundation, which was the result of the efficient bird mating optimisation (BMO). By using this routing protocol, the network's throughput and reliability are improved, excess packet retransmissions are decreased, the PDR is increased, and the end-to-end time is minimized.

By Mishra et al. [36], a smart modified chain model is provided. The primary goal of this strategy, and it is accomplished utilising PEGASIS and the election of a CH closer to a BS, is to extend the network's lifespan. Also, the members of the overlapping chain mechanism are used by BS to transmit data.

An average threshold energy-efficient routing approach (ATEER) is proposed by Singh and Verma [37]. The comparison of the outcomes is done in this approach with the earlier heterogeneous techniques. Moreover, this method describes the suitableness of this technique on behalf of proactive- and reactive-based networks.

A multi-objective clustering approach was proposed by Parvinder Singh and his colleagues [38] to optimise the energy usage, the network lifespan, and the throughput of a network along with the delay in the network. On behalf of both heterogeneous and homogenous WSNs, the derivation of a fitness function is completed. Besides the CH balance, the fitness function is used in the service of reducing energy consumption to elect the best possible CH. The implementation of an innovative hybrid clustered routing protocol may or may not be feasible depending on the fitness function.

3.     Proposed System

Due to the restrictions of WSNs, delivering fault tolerance-based routing has several issues. Limited battery capacity, ill-defined positions, large numbers, and arbitrary dissemination of nodes are all taken into account when building the routing protocol. The two stages of this approach are fault-tolerant clustering and fault-tolerant routing. At this initial step, CHs are chosen based on a number of criteria, including residual energy, distance to the BS, probability value, delay between nodes, and number of neighbours. During the setup phase, BKCHs are chosen together with CHs. The fault tolerant routing in the second stage includes both intra-cluster and inter-cluster routing. Within the intra-cluster routing, CH aggregates the data from the participant nodes. The optimal path between CHs in the inter-cluster routing is also determined using the ACO.

Clustering phase:

The major goal of this phase is to divide the network nodes into clusters and allocate a CH on behalf of each CH recipient. The nodes later aggregate the data before transmitting it to its final destination. The criteria used to choose a CH include residual energy, proximity to the BS, probability value, delay between nodes, and the number of neighbours. As a result, CH is picked if an individual node meets all of the aforementioned criteria. The distance between the nodes and BS are determined using the following equation:

Where  and  denote the location of BS and node, respectively.

Based on the nodes' centroid and a probability value between 0 and 1, the BKCH nodes are chosen. The nodes (other than the CH nodes that were selected previously) that are chosen to be BKCH are the ones that satisfy these conditions.

The centroid of the nodes is computed applying the formula given

To be more specific, we have  and  as the X and Y coordinates of nodes i and j, correspondingly; N refers the overall number of network nodes; and  as the distance between node i and the BS.

In the first phase, the nodes use GPS to determine their separate positions, and nodes within range of communication receive notification by way of the delivery of a Welcome packet that contains the user's ID and location (Xi, Yi). After receiving Hello packets from neighbours, the nodes store the corresponding neighbor's data in a table called the neighbours table. The number of packets received is afterwards counted and stored in variable n for use in identifying the various neighbours. With the aid of GPS, the nodes are informed about the relevant individuals' places, and the BS locations are also known. In light of this, the distance between the BS and the nodes is calculated. By probing the links, the nodes determine the present latency between the nodes. The nodes calculate several factors and send the individual estimation results to their respective neighbours via an advertisement packet. The nodes are aware of the specific estimation values for each of their neighbours. As a result, when a node meets the aforementioned requirements, they compare each estimation value with other values before choosing the CH. The nodes begin to estimate the centroid and the probability values for BKCH selection in the following cycle. The estimates are compared by the nodes after they have been shared with their neighbors who do not participate in CH. The BKCH nodes are chosen based on whether or not they fit the requirements.

Node coverage:

Node coverage is an important aspect of Wireless Sensor Networks (WSN) as it determines the quality of sensing and monitoring in the network. Node coverage refers to the extent to which the sensor nodes present in the network can detect and monitor their surrounding environment.

There are two distinct forms of node coverage that may be distinguished in a WSN: area coverage and target coverage.

  1. Area coverage: It refers to the extent to which the sensor nodes cover a particular geographical area. In area coverage, the goal is to ensure that the entire area considered is monitored by the sensor nodes.
  2. Target coverage: It indicates the ability of the sensor nodes to detect and monitor specific targets or events in the environment. In target coverage, the goal is to guarantee that the sensor nodes can find and monitor the targets of interest with a certain level of accuracy.

To achieve node coverage, the placement and deployment of the sensor nodes present in the network play a critical part. The distribution of the sensor nodes should be done in such a way that guarantees full coverage of the region of interest or the targets of interest, whichever comes first.

Different algorithms and approaches have been introduced to optimize node coverage in WSN, such as the Genetic Algorithm, Particle Swarm Optimization, and Voronoi Diagram-based techniques. These algorithms work towards optimizing the positioning of nodes to increase the coverage when reducing the number of sensor nodes required, thereby reducing the cost and energy dissipation of the network.

Top of Form

Routing phase:

Intra-cluster routing: The next step, which follows the selection of the CHs and BKCHs, is the routing. The data that the sensor nodes have sensed has to be transmitted to BS regardless. Typically in clustered scenarios, the information obtained from the member nodes is collated by the CHs and later forwarded towards the BS.  This strategy results in a significant drop in both the volume of traffic within the network and the level of congestion. In the protocol, the selected CHs perform the aggregation of the data obtained from the member nodes applying this aggregation mechanism, employing the path that the routing protocol has identified.

Intra-cluster routing: During inter-cluster routing, the CH nodes send the collated data that they have collected to the BS node so that it can be analysed further. In conventionally clustered setups, the CH node is the one that, regardless of the circumstances, is responsible for directly establishing the connection to the BS. There is an  increase in the BS overhead there is simultaneous connection between several CH nodes and the BS. We have devised the AGGREGATOR nodes (AG) in order to circumvent this problem. These nodes gather the data taken from the CH nodes and then exchange it with the BS. This results in a reduction of the overhead in BS. The technique for selecting relay nodes inside the routing protocol is adjusted by PSO functions in order to facilitate the establishment of the connection and the selection of the most suitable relay nodes between AG and CH nodes. This PSO approach computes the fitness of the nodes and chooses the relay node that is the greatest fit for the network based on the values for Particle Best (PBEST) and Global Best (GBEST).

ACO:

Ant Colony Optimization, or ACO for short, is a type of metaheuristic algorithm that takes its cues from the activities of ants as they look for food. ACO has found its application in different problems of optimization, including wireless sensor networks (WSN).

In WSN, ACO can be used to optimize the routing of data packets from the sensors to the sink node (the node that collects and processes the data). ACO algorithms for WSN usually operate in a distributed fashion, where each sensor node acts as an ant and searches for the optimum path to the sink node.

The following is an outline of the primary steps involved in an ACO algorithm for WSN:

  1. Initialization: The ant colony is initialized with a group of paths between every sensor node and the sink node. The paths are assigned pheromone levels based on their initial quality.
  2. Ant behavior: Each sensor node acts as an ant and probabilistically chooses a path based on the pheromone levels of the paths and the heuristic information (such as distance, energy consumption, and link quality).
  3. Pheromone update: Once all of the ants have traversed their paths, the pheromone concentrations on those paths are adjusted such that they are proportional to the quality of those paths.
  4. Termination: The algorithm terminates when a certain termination condition is met, including the highest number of iterations or a convergence criterion.

ACO algorithms for WSN have been shown to improve network performance in terms of energy efficacy, network lifespan, and throughput. Nonetheless, there are still some challenges in applying ACO to WSN, such as dealing with dynamic network conditions, scalability issues, and the need for efficient distributed implementations.

Top of Form

Algorithm for CH selection

= distance between the nodes and BS;   = residual energy of node i

 = delay of the node i;

 = probability of node i;

CH[i] = cluster head list

##

 

For all the nodes

Calculate

Calculate

Estimate

If ((n) < n+1) && (n) >  (n+1))

            If ( <)

            CH[i] = n

Else

CH[i] = n+1

End for

For all the nodes

If n ε CH[i]

Informs about its CH election to other nodes

            Else

Joins with the nearest CH as cluster member

End for

 

Algorithm for BKCH selection

= distance between the nodes and BS; 

= distance between the nodes I and j;

  = centroid of node I;

 = probability of node i;

BKCH[i] = Backup cluster head list

##

 

For all the nodes

Calculate

Estimate

If ((n) +  (n)) < ((n) +  (n+1))

            If ((n) > (n+1))

            CH[i] = n

Else

CH[i] = n+1

End for

 

4.     RESULT AND DISCUSSION

The evaluation of the proposed FT-BKCH-ACO technique is done in this section and the validation of the respective efficiency is done with the comparison of the existing method such as QOS-IHC [38]. Similar simulation environment is used for implementing the abovementioned techniques. NETWORK SIMULATOR-2 is used to simulate the energy parameters. The presented routing techniques within WSNs are compared by using the parameters given below:  

1)     Network Delay: the average delay sustained with a packet for reaching from the source towards the sink node is done using this parameter.

2)     Energy Consumption: This metric evaluates the average energy usage in the nodes for the entire network activity.

3)     Throughput: This metric describes the efficacy of proposed technique in terms of data transmission and the data receiving rate at the receiver end.

4)     Routing Overhead:  It evaluates the amount of additional workload incurred to the network by the proposed algorithm.

5)     PDR: the proportion of the received packets at sink towards the packets forwarded towards the sink is presented in this parameter.

 

The performance of this method is evaluated by finalizing parameters such as energy balancing, QoS, and security upon every problem mentioned within this paper.

 4.1 Network Environment

During the simulation, a square area measuring 1000 m x 500 m is taken into consideration, and the placement of the sensors is done in a manner that is completely at random. The unintentional placement of nodes takes place at the geographic centre of a sensing area, which is also where sink nodes are found to have their locations. The many aspects of the network are detailed in Table 1.

Table1: Network parameters

PARAMETER

VALUE

Traffic protocol

 Constant bit rate

Data rate

1024 B/s

Average communication range

250m

Size of packet

1024 bytes

Routing Protocol

AODV

Time

100 sec

Network size

50 nodes

Network area

1000 m x 500 m

Communication Protocol

UDP

Initial Energy of Node

100 joules

BKPCH

4

Number of Clusters

4

AGGREGATOR nodes

2

 

 

 

 

 

4.2 Performance evolution of results

The network is divided as multiple unequal clusters. The list shows the node ids with respect to their clusters

The proposed approach considers multiple parameters for CH selection. Each node calculates these selection parameters and the node which meet the required condition will be selected as CHs

Fig: Analysis of Delay

It is important that the data be consolidated and sent on time to the intended recipients so that the situation can be escalated further. The data transportation between the nodes is not negatively impacted by extraneous elements thanks to the protocol that is being presented. These factors include a high number of hops and significant data traffic. The findings indicate that the newly proposed protocol exhibits a shorter delay compared to any of the protocols that have been proposed before in the allotted amount of time.

Fig: Analysis of energy consumption variant of time

Energy is absolutely necessary for the sensor nodes to be able to function continuously. Energy consumption is a limiting factor in sensor networks. The most efficient use of available energy results in an increased lifespan. The fault tolerance mechanism in our proposed method alleviates the factors such as retransmission, inappropriate path and improves the overall energy consumption. The findings prove that the rate of energy consumption is considerably lower when matched with the standard operating procedures. 

Fig: Analysis of network performance variant of time

The cluster heads (CHs) are made more easily reachable to the member nodes thanks to the split of clusters and the judicious election of CHs utilising several factors of relevance. The utilisation of Aggregator nodes and ACO guarantees the seamless delivery of data over the network, which aids in the distribution of fair data with relatively little interruptions. The findings show that the proposed method has the capability of delivering the data in a more dependable manner than the protocols that were proposed previously.

Fig: Routing overhead

Overhead is determined by how many control packets are needed to run the network. When packets need to be resent frequently because of network problems, this increases the overhead because more control packets must be used. Our suggested protocol minimises the requirement for extra control packets and overhead by relying on a fault tolerance mechanism to guarantee seamless communication and provide a backup method for continuous transmission.

Fig: Packet Delivery Ratio

The network's data transmission efficiency is enhanced when the shortest and most direct routing path is used. Our method involves sending data packets along the paths determined to be the best on a global scale by the ACO algorithm. The findings prove the remarkable performance that the proposed technique achieves over the standard procedures.

Delay

Energy consumption

Throughput

Routing overhead

PDR

QOS-IHC

FT-BKCH-ACO

QOS-IHC

FT-BKCH-ACO

QOS-IHC

FT-BKCH-ACO

QOS-IHC

FT-BKCH-ACO

 

FT-BKCH-ACO

50

0.062

0.011

4.72

3.14

119.43

131.81

1.43

1.29

0.8719

0.9310

100

0.084

0.025

5.01

3.92

127.93

144.13

1.58

1.26

0.9026

0.9795

200

0.146

0.068

5.93

4.93

133.98

151.42

2.42

1.40

0.9103

0.9991

300

0.191

0.148

6.8

5.8

142.88

165.75

3.77

2.30

0.9256

0.9726

400

0.338

0.259

8.20

7.067

154.68

171.01

4.59

 3.01

0.9370

0.9739

                                                                                                             

5.     Conclusion

In a system where the energy sources of sensor nodes are limited, providing fault resilience can be a challenging and complicated task. Clustering and routing are the two phases that are covered in this article's offering of fault tolerance, which is offered by the paper. Within the first phase of the project, the choice of CHs and BKPCHs is made based on a few indicators that are deemed appropriate. In the 2nd phase, the data is transferred from cluster member nodes towards the CH. Within the context of inter-cluster routing, the CH nodes distribute the data to the AGGREGATORs prior to transferring it on to the BS. Clustering and routing both play an important part in the network's ability to tolerate failures, and together they make up a crucial component of fault tolerance. The results of the simulation reveal that this strategy is superior to the ones that came before it in terms of end-to-end delay, throughput, average energy usage, routing overhead, and the proportion of packets that are successfully delivered.

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