# Energy Efficient Routing Protocol in Novel Schemes for Performance Evaluation

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## Abstract

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## 1. Introduction

#### 1.1. Related Work

#### 1.2. Review of Related Work of Energy Balancing

#### 1.3. Problem Definition

#### 1.4. Research Contribution

- The serious issues that have been addressed with energy and consumption will affect the dependable solutions.
- Wireless sensor network have to go with Clustering to save energy. As the Contribution will go with adaptive clustering, low energy and leach for data efficiency, energy, delivery rate and longer stay of network.
- We went with two-function notations to improve the development of WSNs, i.e., CH using a single fitness function for accountable of data information, sending the data to the base station (BS) in the forwarded node.
- The collection of nodes of all data reach out to the CH and nodes of other members. The collection of CH nodes from the total numbers of nodes are transferred to the base station.
- Battery unit power will rely on the WSN. Operations are deployed to optimize the WSN operations for performance and life-span.

#### 1.5. Research Objective

- Residual power and possibility cost values for the sink and function depend on optimal CH.
- The idea of efficient routing and dependent nodes is to improve performance of the modified gravitational search algorithm (MGSA).
- To increase stronger adaptive multi-objective fuzzy clustering set of rules (EAMOFC) is to optimize the strength intake for multi-objective strong CH choice and improvement in records aggregation.
- Balancing the energy improvement function in developing the energy levels.

#### 1.6. Research Methodlogy

## 2. Materials and Methods

#### 2.1. GSA—Gravitational Search Algorithm

#### 2.1.1. Position

#### 2.1.2. Effective Gravitation

#### 2.1.3. In Effective Gravitation

_{a,l}and M

_{p,k}denote effective and non-effective gravitational;

_{k,l}(t) denote about Euclidean length of two “k” and “l”.

#### 2.1.4. Evaluation of Fitness Function

#### 2.2. Single Fitness Function of GSA Algorithm

- The lifetime of a sensor network is broadened, in view of further developed choice of transfer hub and utilization of refreshed GSA.
- Information moving among the sink and CH is improved, despite the fact that CHs are chosen as contingent upon sink closeness to save energy.
- Since hub postponement is an urgent measure for the ideal choice of forwarder hub in MGSA, start to finish, the information transmission time (delay) is reduced.

#### 2.3. Modified Gravitational Search Algorithm—ORS

- (1)
- The selection of cluster heads relies upon the sink distance, likelihood, and leftover energy of hubs. It describes the proposed convention’s presentation.
- (2)
- The inter- and intra-cluster multi-hop station transmission favored the least energy escalated multi-bounce course.

#### 2.4. Cluster Head Selection

**xpos**is the sink node’s x-status,

_{sink}**xpos**is the node’s

_{node}**x**-position,

**ypos**is the node’s

_{node}**y**-position, and

**ypos**is the node’s

_{node}**y**-position.

**E**—sensor node;

_{initial}**E**—energy utilized in 1 round.

_{consumed}Algorithm 1. Selection of Cluster head |

${\mathit{d}}_{\mathit{i},\mathit{s}\mathit{i}\mathit{n}\mathit{k}}=\mathit{S}\mathit{i}\mathit{n}\mathit{k}\mathit{D}\mathit{i}\mathit{s}\mathit{t}\mathit{a}\mathit{n}\mathit{c}\mathit{e}$ ${\mathit{E}}_{\mathit{r}\mathit{e}\mathit{s},}=\mathit{i}\mathit{t}\mathit{h}\mathit{N}\mathit{o}\mathit{d}\mathit{e}\mathit{r}\mathit{e}\mathit{m}\mathit{a}\mathit{n}\mathit{i}\mathit{n}\mathit{g}\mathit{e}\mathit{n}\mathit{e}\mathit{r}\mathit{g}\mathit{y}$ $\mathit{P}\mathit{r}{\mathit{o}}_{\mathit{i}}=\mathit{i}\mathit{t}\mathit{h}\mathit{N}\mathit{o}\mathit{d}\mathit{e}\mathit{o}\mathit{f}\mathit{P}\mathit{r}\mathit{o}\mathit{b}\mathit{a}\mathit{b}\mathit{i}\mathit{l}\mathit{i}\mathit{t}\mathit{y}$ CH [i] = cluster heads list As the total nodes of Sink(S) = {S1, S2, S3…….Sn} Start: $\mathit{F}\mathit{o}\mathit{r}\mathit{i}=1:\mathit{i}\le \mathit{n};i++)$ While (CH selection) For cluster node Si Determining the sink distance${\mathit{d}}_{\mathit{i},\mathit{s}\mathit{i}\mathit{n}\mathit{k}}$ Compute the sensor node probability Proi Compute${E}_{\mathit{i},\mathit{r}\mathit{e}\mathit{s}}$(remaining energy) If ((${\mathit{d}}_{\mathit{i},\mathit{s}\mathit{i}\mathit{n}\mathit{k}}$<${\mathit{d}}_{\mathit{i}+1,\mathit{s}\mathit{i}\mathit{n}\mathit{k}})$&& (${\mathit{E}}_{\mathit{i},\mathit{s}\mathit{i}\mathit{n}\mathit{k}}$>${\mathit{E}}_{\mathit{i}+1,\mathit{r}\mathit{e}\mathit{s}})$&& ($\mathit{P}\mathit{r}{\mathit{o}}_{\mathit{i},}$>$\mathit{P}\mathit{r}{\mathit{o}}_{\mathit{i}+1}))$ CH [i] = Si; Else $\mathit{C}\mathit{H}\left[\mathit{I}\right]={\mathit{S}}_{\mathit{i}}+1;$ End If n connects to CH [i] End |

_{n−CH}tells approximation of ‘nth ‘node and respective CH,

_{res}(n) represents ‘nth’ node balanced power, and

## 3. Distance between Nodes to CHs

- xpos
_{CH}denotes the cluster head’s X-position; - X-position→XPOS
_{node}is node’s X-position; - Y-position denotes→ypos
_{CH}denotes the CH node’s; - Y-position denotes→ypos
_{node}.

## 4. Delay (D(n))

- Expected transmission count;
- Propagation delay;
- Network’s communication delay.

_{n}→denotes the nth node expected transmission count;

Algorithm 2. MGSA for choosing relay node |

P_{i} = total particles in WSN |

f_{I =} particle’s fitness; |

B_{fit =}best fitness of the node |

E_{res} = residual energy |

Dist_{i,CH} = the proximity, together with sensor nodes and cluster head |

OFN = relay node or optimal forwarder |

For ∀ nodes ‘n’ |

Evaluate f–i of total particles P |

Calculate E_{res} |

Estimate dist_{I,CH} |

Determine D_{n} |

${\mathrm{F}}_{\mathrm{i}}=\left\{{\mathrm{dist}}_{\mathrm{i}-\mathrm{CH}}+{\mathrm{E}}_{\mathrm{res}}+{\mathrm{D}}_{\mathrm{n}}\right\}$ |

End |

${B}_{fit}=\left\{\mathrm{max}\left[{f}_{i}\right]\right\}$ |

For ∀ nodes ‘n’ |

$\mathrm{if}({\mathrm{B}}_{\mathrm{fit}}{\mathrm{P}}_{\mathrm{i}}{\mathrm{B}}_{\mathrm{fit}}{\mathrm{P}}_{\mathrm{i}+1})$ |

OFN = P_{i} |

Else |

$\mathrm{OFN}={\mathrm{P}}_{\mathrm{i}}+1$ |

End for |

#### 4.1. Derivation of a Cluster Head Selection Fitness Function

- (a)
- Energy: All part hubs impart information to important CH. Bunch head consolidates the approaching information and converts it into a solitary transmission parcel. Later, the bundles are directed to the BS. Therefore, CH utilizes more energy than other sensor hubs. Thus, a sensor hub with most elevated leftover energy should be picked as CH, proposing that energy utilization is lower for low-energy hubs, rather than higher for high-energy hubs. In this way, our underlying object is to limit F1, as we go with the following Equation (8)$$\mathrm{F}1={{\displaystyle \sum}}_{i=1}^{m}\frac{1}{{E}_{CHi}}$$
_{CHi}= E_{intinal}− E_{consumed},- E
_{intial}→ energy of sensor nodes - E
_{consumed}→ consumed energy of sensor nodes

- (b)
- Distance: utilized to decide the typical distance between the BS and sensor hub. It will be brought down when the energy utilization of the CH hubs is at its most minimal. Thus, CH has a more drawn-out life expectancy. The second parameter, F2, can be diminished to Equation (9).$${\mathrm{F}}_{2}={{\displaystyle \sum}}_{\mathrm{j}=1}^{\mathrm{m}}({{\displaystyle \sum}}_{\mathrm{i}=1}^{\mathrm{i}\mathrm{j}}\mathrm{dis}\left(\mathrm{Si},\mathrm{BS}\right))$$$$\mathrm{dis}\left({\mathrm{S}}_{\mathrm{i}},{\mathrm{B}}_{\mathrm{s}}\right)=\mathrm{Sink}\mathrm{Distance}=\sqrt[2]{({\mathrm{xpos}}_{\mathrm{sin}\mathrm{k}}}-{\mathrm{xpos}}_{\mathrm{node}}{)}^{2}+{({\mathrm{ypos}}_{\mathrm{sin}\mathrm{k}}-{\mathrm{ypos}}_{\mathrm{node}})}^{2}$$
- xpos
_{sink}and xpos_{node}—here, the sink and node are denoted; - ypos
_{sink}and ypos_{node}tells y position of sink and node.

- (c)
- Probability Value: during CH determination, the sensor hubs make an irregular number. In a few strange occasions, the ‘F1’ and ‘F2’ upsides of at least two sensor hubs might be indistinguishable. In those conditions, the CH’s not set in stone by the likelihood esteem. As an outcome, the third boundary, ‘F3’, can be limited utilizing Equation (10).$${\mathrm{F}}_{3}={{\displaystyle \sum}}_{i=1}^{m}\mathrm{Pr}({S}_{i})$$

_{1,}β

_{2}, and β

_{3}are weighted numbers apportioned to each boundary.

Algorithm 3. Selection of Cluster head |

Applied excitation: Total sensor nodes set S = (s _{1}, s_{2}, s_{3} …, s_{n}) Number of agents: N _{p}; dimension K; Response: CHs set Set up the agents A _{j}, where 1 ≤ j ≤ N_{p}While (j! = N _{p}) doDetermine fitness (A _{j})End While (j! = N _{p}) dobest = max of (fitness(A _{j}))worst = min of (fitness(A _{j}))End While (j! = N _{p}) doEvaluate mass(A _{j})End While (j! = N _{p}) doFind force (A _{j}), with the help of Equation (3)Evaluate acceleration (A _{j}), with the help of Equation (4)Updating coordinates CH _{j} with the help of Equations (5) and (6)End Assign sensor to CHs End |

#### 4.2. Derivation of Optimal Relay Selection Fitness Function

- (a)
- Distance between cluster member and CH:

_{1}’

_{i}, cluster head) is distance between cluster member and respective CH

_{CH}and xpos

_{n}

_{ode}are x position of the cluster head and node;

_{CH}and ypos

_{node}are y position of the cluster head and node.

_{SI}): through relay nodes, total cluster members provide data to respective CHs. Due to insufficient energy or exhaustion; the minimal energy node can expire or stop operating during data transmission. Equation (13) can be used to calculate the minimizing of the second parameter, ‘r

_{2}’

_{Si}= E

_{initial}− E

_{consumed};

_{initial}denotes initial energy of sensor nodes;

_{consumed}represents consumed energy of sensor nodes.

_{3}’ can all be minimized as Equation (14)

_{I}→ denotes the ith node expected transmission count;

_{i}→ represents the ith node propagation delay.

_{I}of node is determined by the RPDR of sensor node to FPDR at time ’t’., and Equation (16) is used to compute it.

_{i}(t) denotes the ith node’s RPDR (received packet delivery ratio);

_{i}(t) denotes to the ith node’s FPDR (forward packet delivery ratio) at time ‘t’.

_{1}, β

_{2}, and β

_{3}are weighted numbers allocated to every parameter.

Algorithm 4. Selection of optimal relay node |

Applied excitation: Total sensor nodes set S = (s _{1}, s_{2}, s_{3} …, s_{n}) Number of agents: N _{p}; dimension K; Response: optimal forwarder or relay nodes set Set up the agents A _{j}, where 1 ≤ j ≤ N_{p}While (j! = N _{p}) doEvaluate fitness (A _{j})End While (j! = N _{p}) dobest = max of (fitness(A _{j}))worst = min of (fitness(A _{j}))End While (j! = N _{p}) doEvaluate mass (A _{j})End While (j! = N _{p}), doFind force (A _{j}), with the help of Equation (3)Evaluate acceleration (A _{j}), with the help of Equation (4)Updating coordinates CH _{j}, with the help of Equations (5) and (6)End Maximum (fitness (A j)) is used to choose forwarder or relay nodes for every route End |

## 5. Experimental Setup

^{2}, the placement of each sensing node was random for easier access and placed in the middle of the network.

#### 5.1. MGSA—ORS

#### 5.2. Modified Gravitational Search Algorithm with Two Fitness Functions

## 6. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Abbreviations

CH | Cluster head |

LEACH | Low-energy adaptive clustering hierarchy |

DMEERP | Dynamic multi-hop energy-efficient routing protocol |

MMBCR | Min–max battery cost routing |

CMDR | Conditional minimum drain rate |

DCFR | Double cost function-based route |

EAMOFC | Adaptive multi-objective fuzzy clustering |

RPDR | Received packet delivery ratio |

Bs | Base station |

EESRA | Energy efficient scalable routing algorithm |

MTE | Minimum total energy |

CMMBCR | Conditional min–max battery cost routing |

ESCFR | Exponential and sine cost function-based route |

MGSA | Modified gravitational search algorithm |

ORS | Optimal relay selection |

PDR | Packet delivery ratio |

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**Figure 3.**Block diagram representation with MGSA, node selection, and data transmission process for proposed method.

Authors and Reference | Category | Brief Description | Limitations |
---|---|---|---|

S Kole .S [28] | Article survey | The performance of the LEACH protocol was improved, and the network lifetime was increased, according to a study on the distance-based construction of cluster approach. | The cluster head and relay node selection with fitness function for optimal routing was not covered in this paper because it only focused on the distance-based CH selection. |

Sai Krishna Mothku [29] | Article survey | An investigation was made on the methods used by fuzzy-based energy-aware and delay-intelligent routing to select effective routes. | Higher percentage of inactive nodes. There are fewer communications in the WSN as a result of these inactive nodes. |

Abu Salem [30] | Article survey | To solve the shortcomings of the LEACH (low energy adaptive clustering hierarchy) protocol, a survey was carried out. Cluster heads should be selected using EN-LEACH approach, taking into account their proximity to the BS and degree of expertise. | This idea did not encompass multi-hop routing and not reacted to the relay of nodes. |

Authors and Reference | Brief Description | Cluster Head Selection | Limitations |
---|---|---|---|

Sateesh [31] | Proposed an efficient directing by choosing legitimate courses, based on the probability values appointed. | Residual energy. | Different boundaries, such as deferral and battery limit, were not considered for bunch head determination; subsequently, it results in wasteful steering. |

Han and Zhang [32] | An energy-effective bunching system was executed by choosing suitable courses, in light of the distance among BS and CH. | R.E. and node degree. | This article was specific only to the distance-based CH selection and did not cover cluster head and relay node selection with fitness function for optimal routing. |

Arivubrak an and Sundari [33] | With the procedures of clear to send and request to send, a new multicast directing convention was presented that chooses the data transfer capacity and multi-bounce distance from CH to group individuals (RTS). With the decreased postponement, this convention builds data transmission and parcel conveyance proportion. | --- | Less number of nodes used. |

Elsmany Eyman F. Ahmed, Omar [34] | The energy-efficient scalable routing algorithm, which is described in this study, is an energy-efficient clustering and hierarchical routing technique (EESRA). The suggested technique seeks to maximise network longevity despite growing network size. To reduce the stress on the cluster heads and randomise the selection of cluster heads, the method utilises a three-layer structure. | The LEACH protocol’s stochastic rotation technique was used. | There is extension for additional likelihood to diminish energy utilization of the organization. |

V. Nivedhit ha [35] | The dynamic multi-bounce energy-productive directing convention (DMEERP) picks a course founded on the channel limit model, which is carried out on both the transmitter and beneficiary sides. | Residual energy, delay, and bandwidth. | The creators did not consider mixing secure steering with information total methodology. |

Cluster No. | Cluster Member Nodes |
---|---|

1 | 7 13 17 22 26 27 31 34 36 48 |

2 | 10 11 12 20 30 32 33 37 45 47 |

3 | 5 6 14 15 16 18 21 23 28 35 40 43 |

4 | 1 2 3 4 8 9 19 24 25 29 38 39 41 42 44 46 49 |

Sink Node | Node | Node (x_pos, y_pos) | Sink (x_pos,y_pos) | Sink Distance (m) | R.E(J) | Probability |
---|---|---|---|---|---|---|

0 | 7 | (260,223) | (511,344) | 278.64314100 | 99.0325030 | 0.3680650 |

0 | 13 | (400,235) | (511,344) | 155.56992000 | 98.9487660 | 0.5082040 |

0 | 17 | (484,26) | (511,344) | 319.14416800 | 98.9577950 | 0.9023470 |

0 | 22 | (472,142) | (511,344) | 207.69448700 | 98.9097170 | 0.5728850 |

0 | 27 | (472,244) | (511,344) | 130.59862200 | 99.8801300 | 0.912580 |

0 | 31 | (341,16) | (511,344) | 369.43741000 | 99.0735020 | 0.5851150 |

0 | 34 | (25,68) | (511,344) | 558.90249600 | 99.3250840 | 0.2356530 |

0 | 36 | (61,103) | (511,344) | 510.47135100 | 99.2608170 | 0.1036330 |

0 | 48 | (408,25) | (511,344) | 335.21634800 | 99.0299930 | 0.8960380 |

TIME | FEARM | EN-LEACH | PROPOSED—ONE FF | PROPOSED—TWO FFs |
---|---|---|---|---|

300 | 0.04800 | 0.03600 | 0.02900 | 0.02200 |

600 | 0.05100 | 0.03700 | 0.0300 | 0.02400 |

900 | 0.0500 | 0.03600 | 0.0300 | 0.02400 |

1200 | 0.05200 | 0.03900 | 0.03200 | 0.02600 |

1500 | 0.05200 | 0.04100 | 0.03500 | 0.02900 |

TIME | FEARM | EN-LEACH | PROPOSED—ONE FF | PROPOSED—TWO FFs |
---|---|---|---|---|

300 | 5.8900 | 4.1300 | 3.3600 | 2.600 |

600 | 5.9600 | 4.5600 | 3.8400 | 3.1200 |

900 | 6.1100 | 5.2600 | 4.2100 | 3.3600 |

1200 | 6.1500 | 5.800 | 4.9500 | 4.100 |

1500 | 6.1900 | 6.0100 | 5.1300 | 4.2500 |

TIME | FEARM | EN—LEACH | PROPOSED—ONE FF | PROPOSED—TWO FFs |
---|---|---|---|---|

300 | 90.00 | 87.00 | 85.00 | 81.00 |

600 | 96.00 | 91.00 | 86.00 | 82.00 |

900 | 94.00 | 90.00 | 86.00 | 82.00 |

1200 | 95.00 | 89.00 | 83.00 | 82.00 |

1500 | 95.00 | 89.00 | 85.00 | 81.00 |

TIME | FEARM | EN—LEACH | PROPOSED—ONE FF | PROPOSED—TWO FFs |
---|---|---|---|---|

300 | 2.900 | 2.500 | 2.0300 | 1.5600 |

600 | 2.900 | 2.300 | 1.9300 | 1.5600 |

900 | 2.700 | 2.300 | 1.9100 | 1.5200 |

1200 | 2.600 | 2.200 | 1.8600 | 1.5300 |

1500 | 2.600 | 2.200 | 1.8600 | 1.5200 |

TIME | FEARM | EN—LEACH | PROPOSED—ONE FF | PROPOSED—TWO FFs |
---|---|---|---|---|

300 | 0.9400 | 0.9100 | 0.8900 | 0.8600 |

600 | 0.9800 | 0.9500 | 0.9200 | 0.8800 |

900 | 0.9500 | 0.9300 | 0.9100 | 0.8600 |

1200 | 0.9700 | 0.9400 | 0.9100 | 0.8700 |

1500 | 0.9700 | 0.9400 | 0.9200 | 0.8600 |

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## Share and Cite

**MDPI and ACS Style**

Pradeep, S.; Sharma, Y.K.; Verma, C.; Dalal, S.; Prasad, C.
Energy Efficient Routing Protocol in Novel Schemes for Performance Evaluation. *Appl. Syst. Innov.* **2022**, *5*, 101.
https://doi.org/10.3390/asi5050101

**AMA Style**

Pradeep S, Sharma YK, Verma C, Dalal S, Prasad C.
Energy Efficient Routing Protocol in Novel Schemes for Performance Evaluation. *Applied System Innovation*. 2022; 5(5):101.
https://doi.org/10.3390/asi5050101

**Chicago/Turabian Style**

Pradeep, S., Yogesh Kumar Sharma, Chaman Verma, Surjeet Dalal, and Cvpr Prasad.
2022. "Energy Efficient Routing Protocol in Novel Schemes for Performance Evaluation" *Applied System Innovation* 5, no. 5: 101.
https://doi.org/10.3390/asi5050101