Wireless Sensor Networks (WSN) comprise numerous sensor nodes for monitoring specific areas. Great deals of efforts have been achieved to obtain effective routing approaches using clustering methods. Clustering is considered an effective way to provide a better route for transmitting the data, but cluster head selection and route generation is considered as a complicated task. To manage such complex issues and to enhance network lifetime and energy consumption, an energy-effective cluster-based routing approach is proposed. As the major intention of this paper is to select an optimal cluster head, this paper proposes a modified golden eagle optimization (M-GEO) algorithm to figure out the most significant issue of choosing an optimal cluster head in every cluster. The M-GEO algorithm selects an optimal cluster head among all the sensors by employing diverse factors namely the residual energy, node degree, distance among the nearby sensors, centrality of sensor nodes as well as distance between the cluster head and sink node. Additionally, the yellow saddle goatfish (YSG) optimization algorithm is employed in generatingan optimal routing path from the cluster head to the base station. Also, the YSG optimization algorithm detectsthe shortest routing path thereby minimizing the energy consumption. Then later, the performance analyses for various parameters are performed to evaluate the performance of the proposed approach.

Wireless sensor networks (WSNs) are largely been reviewed in a ubiquitous computational environment due to their extensive usage [

Numerous sensor network models permit the sensors for transmitting the data to the base station or sink using a multi-hop routing process. In general, the WSN routing protocol determines the path among the source node and the base station (i.e., destination node) to transmit the sensed data. The WSN efficiency relies mainly on routing that promptly influences the network lifetime [

The cluster-based routing protocol provides an effective way of minimizing the energy consumption and the total number of messages transmitted to the base station or the sink node. The clustering approach also effectively manages the scalability and energy consumption of the network to enhance the network lifetime [

Proposing modified golden eagle optimization (M-GEO) algorithm for optimal selection of cluster head from every cluster.

Utilizing yellow saddle goatfish (YSG) optimization algorithm to generate the optimal routing path from the cluster head to the base station thereby detecting the shortest routing path and minimizing the energy consumption.

Enhancing the network lifetime and reducing the energy consumption of the nodes during data packet transmission.

The remaining of the paper is structured as follows: In Section 2, the latest surveys based on clustering approaches are presented. Section 3 section portrays the ongoing challenges of WSN and objective function. The system model containing network design, energy model and distance model is described in Section 4. Section 5 depicts the proposed approach for optimal CH selection and route generation. The experimental evaluations are discussed in Section 6. Section 7 concludes the article.

Numerous research papers have been implemented to obtain energy-efficient cluster-based routing protocols for wireless sensor networks. A detailed review of few relevant research works is discussed in

Authors & Reference no. | Optimization techniques involved | Objective of the study | Parameters utilized | Achievements | Limitations |
---|---|---|---|---|---|

Maheshwari et al. [ |
Butterfly optimization algorithm and ant colony optimization algorithm | To maximize network lifetime and to reduce energy consumption | Routing overhead, packet ratio, throughput, average energy consumption | High throughput and network lifetime | Performances while determining packet ratio, routing overhead are ineffective. |

Sharma et al. [ |
Flower pollination algorithm | To extend the stability period of the network | Total energy consumption, network lifetime residual energy | Minimum network lifetime and energy consumption | Low efficiency and complex |

Qureshi et al. [ |
Gateway clustering energy effective Centroid | To attain low management cost | Cluster head percentage, average data transmission, energy consumption, scalability | High throughput, high network lifetime and minimum energy consumption | Failed to analyze sensor-based transportation system |

Vaiyapuri et al. [ |
Black widow optimization algorithm | To effectively select optimal cluster headset | Scalability, throughput, network lifetime | Enhanced energy efficiency and network lifetime | Increased routing overhead |

Chauhan et al. [ |
Nature-inspired firefly optimization algorithm | To maximize the network lifetime | Packet delivery ratio, packet drop ratio, packet delay, network lifetime | Effective load balancing and congestion control | Delay in evaluating energy |

Nagarajan et al. [ |
Hybrid grey wolf optimization algorithm based sunflower optimization algorithm | To select optimal cluster head and to enhance the network lifetime | Network survivability index, throughput, residual energy | Enhanced efficiency and network lifetime | Large communication overhead |

Ahmad et al. [ |
Artificial bee colony optimization algorithm | To minimize energy consumption and to select optimal cluster head | Total energy consumption, network lifetime, throughput, scalability | High performances with minimum energy consumption | Less effective |

Balaji et al. [ |
Fuzzy logic based clustering protocol | To minimize overhead and to enhance network lifetime | Energy consumption, network lifetime, throughput | Prolonged network lifetime | Rule fixation issues while selecting cluster head |

Lee et al. [ |
Sampling-based spider monkey optimization algorithm | To select optimal cluster head and to enhance the network lifetime | Network lifetime, Total energy consumption | Enhanced computation, high selection accuracy | Not applicable for small networks |

Sharma et al. [ |
Trusted moth flame optimization algorithm and genetic algorithm | To select the most trustworthy head node | Network lifetime, average energy consumption ratio, number of active and dead nodes | High efficiency and network stability | Energy hole issues |

Maheshwari et al. [

An energy-efficient stable clustering approach for WSNsemploying flower pollination optimization algorithm is proposed by Sharma et al. [

Qureshi et al. [

A Novel Hybrid black widow optimization algorithm for Cluster-Based Routing Protocol in IoT Based Mobile Edge Computing was proposed by Vaiyapuri et al. [

Chauhan et al. [

An energy-efficient cluster head selection in wireless sensor networks for lifetime enhancement using hybrid grey wolf optimization algorithm based sunflower optimization algorithm was demonstrated by Nagarajan et al. [

An energy-efficient cluster head selection using artificial bee colony optimization for wireless sensor networks was developed by Ahmad et al. [

Balaji et al. [

Energy-Efficient cluster-head selection for wireless sensor networks utilizing sampling-based spider monkey optimization was proposed by Lee et al. [

Sharma et al. [

This section portrays the ongoing challenges of WSN and in what way the proposed approach addresses these issues. Also, the objective function to provide an energy-efficient approach is discussed.

A suitable selection of fitness functions must be taken into consideration to create optimal energy efficiency in WSN. Two different energy approaches namely weighted energy-efficient clustering-based routing protocol and aware cluster-based routing protocol approach provide great importance to the residual energy of the sensor node. The network energy consumption minimizes only if these approaches provide the same level of priority regarding distance and energy. In few other routing and clustering techniques, the WSN performances were influenced when numerous non-cluster head members are present in the cluster head. Furthermore, the performances are also affected due to nodal density. It is also worth noting that in WSN, the direct transmission of data among the cluster head as well as sink or base station consumes more amount of energy that further results in packet loss among the networks. The sensor node turns to be faulty and unreliable because of nodal deployment in the challenging environment. During packet transmission, the average energy consumption is considered a challenging problem and due to insufficient energy, there occurs packet drop in data transmission.

This paper considers both distance and energy to develop energy-efficient WSN since the energy consumption of nodes depends significantly on the distance among the nodes. Thus, the energy consumption is in direct proportion with the distance among the sensor nodes. Furthermore, every nodal energy in WSN is taken into consideration to minimize packet loss. Here, the M-GEO algorithm is employed in selecting the optimal cluster head and the YSG algorithm is utilized to develop an optimal route to the destination node from the source node. The objective functions discussed in this routing process are residual energy, distance among the sensor nodes, distance between the base station and cluster head, node centrality and node degree. Thus, an energy-efficient approach is applicable for both small and large-scale applications by considering the above-mentioned parameters. This further minimizes the packet loss among the networks.

This section depicts the mathematical formulation of five objective functions namely residual energy, distance among the sensor nodes, distance between base station and cluster head, node degree and node centrality.

In WSN, several tasks are performed by the CH (i.e., data collection from ordinary sensor nodes and transmitting data to the sink). To accomplish the above-mentioned tasks, high energy is required by the CH. Therefore, a CH prefers a sensor node containing high residual energy._{J} signifies the residual energy of

Node degree refers to the total number of node that belongs to the corresponding CH. The CH containing the minimum number of sensor nodes is selected. This is because more energy is lost when the total number of CH is increased.

From _{J} [

The distance among the nodes refers to the distances among the CH and ordinary sensor nodes. The dissipation of nodal energy depends mainly on the transmission path distance. When the transmission distance towards the sink is less, then the energy consumption of the sensor nodes is small. Thus,

_{J}, _{J}) indicates the distance between the

Node centrality refers to, what extend a node is located centrally from the neighboring sensor nodes. Therefore, the mathematical formula to determine the centrality of the sensor nodes is obtained as follows.

From

It defines the total distance between the CH and sink. The energy consumption of the sensor node relies mainly on distance utilizing a transmitting path. Let us consider an illustration; if both the CH and sink are far away from each other, more energy is required for transmitting data. This results in an abrupt drop and large energy consumption of CH. Therefore, the sensor nodes containing less distance from the sink are chosen while transmitting the data [_{K},

This section illustrates the comprehensive description regarding three different designs namely the network design, energy design and distance design.

See

A 2D Cartesian system is employed for the deployment of random sensors

Well-equipped non-rechargeable power resources are utilized for all sensor nodes

The sensor nodes are not feasible to vary their location after they are deployed

The processing and communication abilities of all sensor nodes are analogous

The sensor nodes comprising initial energy are identical

The transmission link between sensor nodes is bi-directional (i.e., capable of diffusing data in both directions).

In general, the selected CH of a particular network transmits messages to a state wherein they operate as CH [

From ^{th}^{th}

In this paper, the receiver and the transmitter energy are computed using a basic first-order ratio design [

The receiving and the transmitting energy dissipated is denoted by ε_{E}. _{0} indicates the threshold distance. The following equation determining the threshold value is stated in

From the above _{F} and ε_{M} denotes the amplification energy of free space and multipath. It is also noted that the transmitter amplification model is dependent on both ε_{F} and ε_{M} [

This paper proposes a modified golden eagle optimization (M-GEO) algorithm to figure out the most significant issue of choosing an optimal CH in every cluster. In WSN, every sensor node is considered as an individual golden eagle and their prey selection process depends upon the distance among an individual sensor node and the nearby sensors. Here, the sensor nodes are attracted by the prey selection process of the golden eagle. The performance behavior and the characteristics of the golden eagle are based on search algorithmic processes. The M-GEO algorithm selects an optimal CH among all the sensors by employing node degree, residual energy, the distance among the nearby sensors, centrality of sensor nodes as well as distance among the CH and sink node. The steps involved in the M-GEO algorithm for optimal CH selection are discussed in the following section.

The generic name of the golden eagle is Aquila and the specific name is chrysaetos belongs to the family of Accipitridae and species of hawks and eagles. Generally, the golden eagles are specialized hunters with extraordinary vision capability and influential talons. The golden eagles can fly at a speed of 190 km per hour and are distributed widely in the northern hemisphere of the earth. Hunting and cruising are the two unique characters of golden eagles that are performed in a spiral path. Additionally, the golden eagles cleverly establish a balance for snatching the best kill at a reasonable amount of energy and time. One of the special features of this eagles are it lower the altitude gradually and at the same time, it approaches the prey. The mathematical modeling and the step-by-step procedure involved in the M-GEO algorithm are discussed as follows [

The M-GEO is based on the circular movement of golden eagles. Each golden eagle remembers the best spot it has traveled previously. Simultaneously, the eagle attacks the prey and cruise for searching for better foodstuff. For every iteration, every golden eagle

The golden eagle optimization algorithm utilizes the prey selection process of the chameleon optimization algorithm [

From ^{th}_{1} and A_{2} is the two different positive numbers for controlling the exploration capability. The best and the global best position scored by the chameleon with respect to ^{th}^{th}

The attack phase is designed in such a way that the present golden eagle's position begins with the vector and terminates with the best spot decided by the eagle after memorizing. Therefore, the following equation provides the computation equation of the attack vector [

From

In accordance with the attack vector, the cruise vector is computed. The cruise vector is perpendicular to the attack vector and considered as a tangent vector. Therefore, initially, it is necessary to determine the hyperplane

Then the cruise vector with respect to the

From

The golden eagle displacement generally comprises of two different factors namely the vector and attack. Therefore accordingly the step vector for a golden angle with respect to

From the above

Then the eagle position with respect to

The best spot in the memory is worse than the fitness of the new position, and then the memory of the golden eagle is updated with the new position.

It is well known that the golden eagles reveal their higher tendency for cruising and hunting at both initial and final stages. In addition to this, the M-GEO algorithm utilizes both _{a} and _{c} for exploitation and exploration. Thus,

From

After CH selection using M-GEO, the CH allocates the sensor nodes by employing the potential function stated in

From _{p} and

YSG algorithm is a metaheuristic algorithm stimulated by the performance behavior of the goatfish. In general, the goatfish discovers a hunting strategy to obtain its prey (i.e., small fishes). The YSG algorithm is applied to solve diverse discrete problems that are illustrated in the graphical form containing the number of links and nodes. During the initialization process, each node comprises goatfishes and every individual is accompanied to the weight. Initially, the weights of the links are computed concerning the random value or by employing mathematical expressions.

The collective hunting model of a goatfish is regarded as a guiding principle for developing a bio-inspired algorithm. The YSG algorithm concerns two distinctive types of search agents namely chaser as well as a blocker. One fish in every sub-population is considered as a chaser and the rest are the blockers. The hunting designs of YSG with their necessary steps are discussed as follows [

Every individual goat fish among the population are generated randomly and distributed uniformly containing lower and upper boundaries of M-dimensional search space area.

From

During the process of hunting, the prey tries to hide behind the cervices and escape in between the corals. The tactics of the chaser fish involve inserting the barbels into the cervices to catch the prey. Here, the levy flight algorithm is employed in random walk generation. The chaser fish attempts in finding the cervices by varying its position containing random walk. Therefore, the new position of the chaser fish is stated in

From the above

From _{MAX} respectively. Every group generally ignores the other sub-populations to obtain the best prey, then.

From _{S} signifies the random step.

Once the chase fishes are selected for every cluster, the rest goat fishes are considered as the blocker. During the blocking process, the hunter strategy is employed to encircle the corals thereby block the escaping prey. The blocker fish moves in a circular motion when the chaser fish approaches the prey. Therefore, the new position of the blocker fish is evaluated based on the logarithmic spiral path stated below [

From _{g}.

The significant intention of the blocker fish prevents the perspective of the prey. The prey progresses to the hunting location during hunting. Hence, the blocker fish which is near to the prey show the way to hunt thus become the new chaser fish and the present chaser fish becomes the blocker fish. Such a technique is referred to as the swapping of roles.

The group changes its position for identifying the new prey once the area has been exploited completely. Under such circumstances, the YSG algorithm utilizes an exploitation parameter and hence for each cluster, if pre-determined iterations exceed without determining the optimal solution, then it is regarded as successful hunting. Therefore zone transformation is performed for entire goat fishes.

From

In this paper, the maintenance of clusters is the most significant phase for balancing the load among the cluster. Due to inter-cluster congestion, the neighboring cluster present near the sink node or the base station consumes more energy. Hence, maintaining clusters is necessary for eliminating the failure of nodes. This results in the enhancement of network lifetime during the transmission of data from the source node to the base station. Furthermore, if the residual energies of the CH exceed the threshold level, then M-GEO algorithm is initialized for network clustering. Later, the clustering M-GEO algorithm selects the CH and YSG algorithm is employed in the route generation process.

In the proposed approach, the CH is selected effectively using the M-GEO algorithm. The CH selection is based on employing node degree, the distance among the nearby sensors, residual energy, centrality of sensor nodes as well as distance between the CH and sink node. The base station or the sink node frequently monitors the residual energy to prevent nodal failure while transmitting the data. Consequently, the YSG algorithm is employed to generate an optimal routing path from the CH to the base station. It is capable of detecting the shortest routing path to minimize energy consumption. Therefore, the M-GEO and YSG algorithm for optimal selection of CH and route generation is designed to obtain energy-efficient WSN. Thus, energy-effective WSN is employed in enhancing the total number of packets and network lifetime while transmitting the data.

This section evaluates the performance analysis of the proposed approach. The major intention of this paper is to maximize the network life and to reduce the overall energy consumption of the network. The evaluation results and comparative analysis to achieve an energy-efficient proposed approach are discussed in the following section.

The proposed energy-efficient routing protocol approach is investigated and executed under the platform of MATLAB R2018a, windows 8 OS containing 4 GB RAM and Intel core i3 processor. The significant intention of utilizing MATLAB is to obtain simple mathematical formulations and appropriate analysis of data.

Parameters | Values utilized |
---|---|

Area of deployment | 200 m*200 m |

Clusters | Differs |

Nodes | 100 to 500 |

Nodal ranges | 30–40 m |

Maximum number of rounds | 3500 |

Maximum throughput of the network | 1 Mbps |

Size of the packet | 10000 bits |

Initial network energy | Changes with respect to number of nodes |

Parameters | Ranges |
---|---|

Size of the population | 50 |

Maximum number of iterations | 100 |

Total number of replications | 30 |

Attack propensity | (0.5 to 2) |

Cruise propensity | (1 to 0.5) |

Total number of clusters | 4 |

In this section, various parameters namely the network lifetime, number of alive nodes, energy consumption, throughput, number of dead nodes, packet delivery ratio as well as routing overhead is described.

The network lifetime signifies the total rounds and total time duration of the network for performing the operations. In other words, it defines the rounds that the nodes will die during the task processing.

From the above _{M}, _{J(SN)} and _{K} signifies the coverage matrix, source node lifetime as well as the total number of nodes. From

It refers to the sensor node in the network containing adequate energy to process its task. The mathematical expression involved in determining the total number of alive nodes is stated in

From the above ^{th}_{N(J)} and _{E}(

It refers to the sensor node in the network that doesn't contain adequate energy to process its task. The mathematical expression involved in determining the total number of alive nodes is formulated in the below

From _{N(J)}.

The mathematical formulation to determine the total amount of consumed energy by the sensor nodes is formulated in

From the above _{C}, _{(CH)} and _{(CM)} signifies the energy consumption, energy employed by the cluster head and cluster member.

Throughput is the ratio of the product of total number of packets and the size of the packet to the total time taken during transmission of data. The mathematical formula to depict the throughput value is determined in

From the above _{sent} _{size} denotes the total number of packets sent and packet size respectively. The throughput value and the time was taken are denoted by _{R} and

The term packet delivery ratio refers to the fraction of the total number of received packets by the total sent packet. Thus,

The term routing overhead refers to the ratio among the total packet received by the sink node to the total number of packets generated.

The comparative graphical analysis of alive node present in the network concerning the diverse routing approaches namely ACI-GWO, BACO, FPA, HGWS as well as the proposed approach is presented in

In WSN, the selection of cluster head and generation of the optimal route is considered as a challenging task. This study considers both distance and energy to develop energy-efficient WSN since the energy consumption of every node depends significantly on the distance among the nodes. Thus the energy consumption is in direct proportion with the distance among the sensor node. The cluster-based routing protocol provides an effective way to minimize the energy consumption and the total number of messages transmitted to the base station or the sink node. In the proposed approach, the CH is selected effectively using M-GEO algorithm. The selection of CH is based on employing node degree, residual energy, the distance among the nearby sensors, centrality of sensor nodes as well as distance among CH and sink nodes. Additionally, a yellow saddle goatfish (YSG) optimization algorithm is employed in generating the optimal routing path from the cluster head to the base station that detects the short routing path to minimize energy consumption. Finally, the proposed energy-efficient routing protocol approach is investigated by employing various parameters and the experimental analyses are conducted and the comparative analysis is made concerning the diverse routing approaches namely ACI-GWO, BACO, FPA, HGWS as well as the proposed approach. Thus the obtained outcome revealed that the proposed approach achieved high performance than other routing-based approaches. The conclusion of the proposed approach also directs the future study to enhance the robustness of the system.