In current days, the domain of Internet of Things (IoT) and Wireless Sensor Networks (WSN) are combined for enhancing the sensor related data transmission in the forthcoming networking applications. Clustering and routing techniques are treated as the effective methods highly used to attain reduced energy consumption and lengthen the lifetime of the WSN assisted IoT networks. In this view, this paper presents an Ensemble of Metaheuristic Optimization based QoS aware Clustering with Multihop Routing (EMO-QoSCMR) Protocol for IoT assisted WSN. The proposed EMO-QoSCMR protocol aims to achieve QoS parameters such as energy, throughput, delay, and lifetime. The proposed model involves two stage processes namely clustering and routing. Firstly, the EMO-QoSCMR protocol involves cross-entropy rain optimization algorithm based clustering (CEROAC) technique to select an optimal set of cluster heads (CHs) and construct clusters. Besides, oppositional chaos game optimization based routing (OCGOR) technique is employed for the optimal set of routes in the IoT assisted WSN. The proposed model derives a fitness function based on the parameters involved in the IoT nodes such as residual energy, distance to sink node, etc. The proposed EMO-QoSCMR technique has resulted to an enhanced NAN of 64 nodes whereas the LEACH, PSO-ECHS, E-OEERP, and iCSHS methods have resulted in a lesser NAN of 2, 10, 42, and 51 rounds. The performance of the presented protocol has been evaluated interms of energy efficiency and network lifetime.

Internet of Things (IoT) is globally suggested to use in various applications for interlinking various networks. In recent days IoTs are used in various heterogeneous networks such as medical networks, vehicular networks, mobile networks as well as sensor networks [

WSN provides extremely flexible control and monitoring at an efficient cost since they are infrastructure-less and autonomous [

In general, the dynamic nature of the WSN environments is because of the absence/presence of the hurdle, exhaustion of sensor battery, mobility of the sensor, sink nodes, and unstable weather situations between the nodes in the network. Because of the fact, that there is a continuous variation in the routes among the nodes, that require reacting and tracing via efficient routing protocols [

Shende et al. [

Chouhan et al. [

In Sunitha et al. [

Jaiswal et al. [

Shafiq et al. [

This paper presents an Ensemble of Metaheuristic Optimization based QoS aware Clustering with Multihop Routing (EMO-QoSCMR) Protocol for IoT assisted WSN. The proposed EMO-QoSCMR protocol aims to achieve QoS parameters such as energy, throughput, delay, and lifetime. The proposed model involves two stage processes namely clustering and routing. Firstly, the EMO-QoSCMR protocol involves cross-entropy rain optimization algorithm based clustering (CEROAC) technique to select an optimal set of cluster heads (CHs) and construct clusters. Besides, oppositional chaos game optimization based routing (OCGOR) technique is employed for the optimal set of routes in the IoT assisted WSN. The proposed model derives a fitness function based on the parameters involved in the IoT nodes. The performance of the presented protocol has been evaluated interms of energy efficiency and network lifetime.

In this study, the EMO-QoSCMR protocol is designed to accomplish QoS in WSN by accomplishing energy efficiency and maximizing network lifetime. The EMO-QoSCMR protocol involves a two stage process namely CEROAC based clustering and OCGOR based routing. The detailed operations of these modules are given in the following.

At this stage, the CEROAC technique is derived to select the CHs and organize clusters. In ROA, the rain behaviours are inspired as it is determined in the traditional subsection. All the solutions to a problem can be referred to as raindrops. Based on this issue, few points in the answer space is determined in an arbitrary manner as raindrop falls on the ground. The main feature of a drop of rain is the radius. The radius of all the raindrops might be constrained as time passes and it is improved as raindrops are connected to alternative drops. Once the primary answer population is made, the radius of all droplets is assigned in a random fashion to a constraint range. In addition, every droplet validates the neighbourhood according to the size. Individual droplet which isn't yet connected just verify the end limits of the position that has covered. To solve the issue in dimension space, all the droplets are composed of n variables. Therefore, in the first phase, the minimum and maximum limits of the parameter are validated as the limit is calculated by the radius of the droplets [

Once 2 droplets using radius r_{1} & r_{2}, they are closer to one another with the general field and they connect to develop large droplets of radius R:

Let _{1} isn't moved, according to the soil features, which is shown as

Apparently, _{min}, whereas droplets with the least radius of that r_{min} will be reduced.

As abovementioned, the population values can be decreased afterward few iterations, and maximal droplet is placed with a large area of analysis. By improving the analyses method, the local possible search of drop is proportionally maximized to the diameter of droplet. Hence, by increasing the amount of rounds, weak droplets get vanished or are linked to strong drops using the maximal area of analysis, and the primary population will be intensively decreased and discover the accurate answer (s). It is supposed that there are some variants between the recently proposed optimization method in ROA and the newly presented search models placed RFA approach, i.e., given below:

In ROA, the early population numbers are adapted afterward each iteration due to the link of neighbouring drop. It leads to enhance the search ability of a method and considerably reduce the optimization cost.

When the size of droplets is altered, the connecting of adjacent droplet or adsorption with the soils are carried out. Such performances modify the possible search of all the droplets and classify the droplet.

In RFA, and alternative searching methods, each population is made up of neighbour points and the droplets are improved one-step in an arbitrary manner. Likewise, all the populations identify the optimum path to the least points. When the path is established, it is moved in downwards iteratively using step, and the cost function is decreased in an individual iteration.

Based on the idealization and approximation of the models, the rain methods are described. In depth, tuning parameters of these methods such as basic raindrops radius, initial raindrops amount (population amount), etc. Followed by, the values are assigned to each droplet based on the cost function. Next, all the droplets are shifted in downwards direction. Therefore, nearer droplet is integrated with each other, that results in enhanced result. When droplets are ended at the lowest points, the radius begins to reduce gradually caused the precision of the answer to be improved. Subsequently, it is relevant for identifying an extremal point of the objective function.

In order to improve the performance of the ROA, CEROAC technique is derived by the inclusion of the CE concept. The CE approach for optimization could be determined in the following equation.

The process of CE could be summarised into 3 major stages:

Produce an arbitrary instance from Gaussian distribution using mean

Choose a certain amount of optimal samples from the entire sample.

Upgrade

To increase the network lifespan of a clustered based WSN, the CEROAC technique is derived to choose an optimal set of best positions CH. To satisfy this aim, a multiobjective FF is created that has 4 variables like degree of node, residual node energy, coverage ratio, and intracluster distance. The derivation and definition of this parameter can be expressed in the following:

Node Energy

Now

Degree of node

Intracluster distance

Coverage of CH

The last multi-objective FF

Linear programming equations for an optimum location CH election problems are given below:

At this stage, the OCGOR based routing technique is designed to elect an optimal set of routes to sink nodes. The CGO technique has been presented dependent upon the projected rules of chaos theory. The fundamental models of fractals and chaos games were employed for formulating a mathematical method to the CGO technique. Due to the fact that several natural evolution techniques continue a population of solutions that are progressed with arbitrary alteration as well as selection. The CGO technique assumes the amount of solution candidates (S) during this determination that signifies few suitable seeds inside a Sierpinski triangle. The Sierpinski triangle has been assumed as search space to solution candidates from the optimized technique. The mathematical model of these features is as follows:

A schematic demonstration of seeds 3^{rd} and 4^{th} has been explained as under:

To boost the convergence rate of the CGO algorithm, OBL concept is employed. OBL concepts are utilized for enhancing the quality of initial population solutions with the divergence of the solution. The OBL scheme searches in each direction in the search space, namely opposite and original solution directions. At last, the OBL concepts consider the appropriate solution from every solution.

The opposite amount

The aforementioned formula could be normalized to apply in a search space with multiple dimensions. So, for normalization, each search agent and the corresponding opposite positions can be defined using

The value of each individual component in

Now, the fitness function is

The process included in the CGO algorithm is listed as follows.

Population initiation X as

Compute the opposite position of individuals OX as

Elect the

In routing, the FF of the OCGOR technique implies the data forwarding route in CH to sink node. The importance of FF is related to CH being reachable from the network, and further locations are added in the sink. The superiority of FF is interrelated to

Distance is represented as distance between CH to next hop & sink. While the distance is minimum afterward the energy utilization rate is also diminished. The next objectives to minimize the distance amongst CHs to sink is estimated by:

Node degree represents the number of vehicles in next hop. When the next hop is comprised of limited CH members, then it employs minimum energy in attained data in neighbouring members and remains active for a long period. Later, the next hop using limited node degree is prominently selected. Lastly, node degree is determined based on node degree of

Afterward, the weighted sum model is executed for each sub objective and transformed as single objective as shown in

This section investigates the performance analysis of the EMO-QoSCMR with existing techniques under different dimensions. The proposed model is simulated using MATLAB.

Total energy consumption (J) | |||||
---|---|---|---|---|---|

No. of rounds | LEACH | PSO-ECHS | E_OEERP | iCSHS | EMO-QoSCMR |

Sink location (100, 100) | |||||

100 | 10.17 | 8.72 | 3.69 | 3.58 | 2.43 |

200 | 16.09 | 14.42 | 9.00 | 6.60 | 5.99 |

300 | 23.36 | 19.56 | 13.86 | 10.40 | 9.79 |

400 | 29.28 | 26.82 | 19.00 | 13.75 | 13.25 |

500 | 37.10 | 34.30 | 25.70 | 16.88 | 16.23 |

Sink location (200, 200) | |||||

100 | 58.27 | 35.53 | 16.15 | 11.10 | 5.20 |

200 | 108.82 | 69.22 | 32.16 | 18.68 | 9.41 |

300 | 166.00 | 97.02 | 45.64 | 27.94 | 13.62 |

400 | 220.00 | 140.83 | 58.27 | 35.53 | 17.84 |

500 | 269.72 | 187.16 | 75.12 | 43.95 | 24.58 |

An overall TEC analysis of the EMO-QoSCMR technique with existing techniques takes place in

Total energy consumption (J) | |||||
---|---|---|---|---|---|

Position of sink node | LEACH | PSO-ECHS | E_OEERP | iCSHS | EMO-QoSCMR |

100 * 100 | 37.10 | 34.30 | 25.70 | 16.88 | 16.23 |

150 * 150 | 71.17 | 62.18 | 37.65 | 22.93 | 17.35 |

200 * 200 | 269.72 | 187.16 | 75.12 | 43.95 | 24.58 |

Number of alive nodes | |||||
---|---|---|---|---|---|

No. of rounds | LEACH | PSO-ECHS | E_OEERP | iCSHS | EMO-QoSCMR |

Sink location (100, 100) | |||||

200 | 139 | 160 | 188 | 196 | 197 |

400 | 110 | 129 | 163 | 168 | 174 |

600 | 88 | 101 | 146 | 150 | 155 |

800 | 61 | 79 | 132 | 141 | 145 |

1000 | 46 | 56 | 114 | 122 | 128 |

1200 | 31 | 41 | 99 | 111 | 116 |

1400 | 21 | 36 | 85 | 94 | 103 |

1600 | 12 | 23 | 79 | 85 | 94 |

1800 | 9 | 19 | 65 | 73 | 84 |

2000 | 6 | 16 | 54 | 65 | 79 |

Sink location (200, 200) | |||||

200 | 129 | 154 | 179 | 186 | 192 |

400 | 98 | 123 | 156 | 162 | 168 |

600 | 78 | 93 | 136 | 143 | 151 |

800 | 50 | 72 | 125 | 132 | 142 |

1000 | 38 | 47 | 108 | 116 | 127 |

1200 | 23 | 38 | 90 | 100 | 109 |

1400 | 14 | 25 | 84 | 90 | 98 |

1600 | 8 | 18 | 66 | 78 | 86 |

1800 | 5 | 13 | 53 | 62 | 71 |

2000 | 2 | 10 | 42 | 51 | 64 |

In this study, the EMO-QoSCMR protocol is designed to accomplish QoS in WSN by accomplishing energy efficiency and maximizing network lifetime. The EMO-QoSCMR protocol involves a two stage process namely CEROAC based clustering and OCGOR based routing. The proposed EMO-QoSCMR protocol aims to achieve QoS parameters such as energy, throughput, delay, and lifetime. In addition, the EMO-QoSCMR protocol involves OCGOR for the optimal set of routes in the IoT assisted WSN. The proposed model derives a fitness function based on the parameters involved in the IoT nodes. The proposed EMO-QoSCMR technique has resulted to an enhanced NAN of 64 nodes whereas the LEACH, PSO-ECHS, E-OEERP, and iCSHS methods have resulted in a lesser NAN of 2, 10, 42, and 51 rounds. The performance of the presented protocol has been evaluated interms of energy efficiency and network lifetime. As a part of future scope, the data aggregation and MAC scheduling techniques can be designed to improve the overall performance of the WSN.