# Green Computing in Sensors-Enabled Internet of Things: Neuro Fuzzy Logic-Based Load Balancing

^{1}

^{2}

^{3}

^{*}

## Abstract

**:**

## 1. Introduction

- Firstly, in the system model, a first-order energy radio model was used to examine the energy consumption throughout the network.
- Secondly, we designed an adaptive fuzzy logic inference system (AFLIS) with the help of fuzzy rules, sets, and membership functions that were updated (rules of AFLIS were updated) using input–output mapping of the hybrid systems. The output of the AFLIS was used as input for the neural network.
- Thirdly, in ANFCA, the metrics were input into the fuzzy logic inference system and output was produced, which provided the information about the sensor nodes, whether it was capable or not of playing the role of cluster head. The output of the fuzzy logic inference system was handed to the neuro fuzzy logic inference system to elect the cluster head for the next round. The ANFCA used a supervised learning strategy to adjust the weight of the membership function of the AFLIS.
- Fourthly, we present an approach to form clusters in which cluster heads aggregate data and send that data to the base station.
- Finally, the proposed algorithm was simulated and the results were compared with LEACH, CHEF, and LEACH-ERE algorithms to shows the effectiveness of the ANFCA.

## 2. Related Works

#### 2.1. Green Computing without Heuristics

#### 2.2. Green Computing Using Fuzzy-Centric Heuristics

## 3. Adaptive Neuro-Fuzzy for Green Computing in IoT

#### 3.1. System Model

**the**packet be $m$ bits. The total energy consumed in transmitting a packet of $m$ bits across $l$ meter distance between the sender and receiver is given by

#### 3.2. Adaptive Neuro-Fuzzy Clustering Algorithm

#### 3.2.1. Adaptive Fuzzy Logic Inference System

^{3}= 27 rules that are shown in the Table 1. These rules were stored in the knowledge base component of the fuzzy inference system. There were two extreme rules: The first one was related to creating strong chances for assigning the responsibility of cluster head to a sensor node when the value of the residual energy of that sensor node was equal to top, the node density was compact in nature, and the node was adjacent to the base station. The second one was if the value of the residual energy was equal to below, the node was distant from the base station, and the node was deficient in node density, then a node would have a very weak chance to become a cluster head.

- Fuzzification or Input Crisp Value: In this step, we input the three metrics: residual energy, node density, and distance to base station as crisp values in the fuzzy inference system. In this step, the inference system creates membership functions for each metric that is the intersection points.
- Knowledge Base or If–Then Rules: The knowledge base consists of all 27 rules, which runs concurrently on inputs and generates output as chance values. There are multiple inputs (three membership values), but selection is done among the minimum membership values which use fuzzy AND operator.
- Aggregation: There are 27 rules in the fuzzy inference system, which give multiple outputs. In this step, we aggregate all the output to generate a single fuzzy output set using union fuzzy OR operator which choose maximum of rule evaluation.
- Defuzzification: In this step, whether a sensor node can act as a cluster head or not is computed. For this purpose, we use a centroid method in the defuzzification step under the fuzzy set to get from aggregation, which is given by Equation (3).

#### 3.2.2. Adaptive Neuro-Fuzzy Inference System

Rule 1 = If RE is below, NDBS is adjacent and ND is deficient | Then ${F}_{1}$ = S_{1}m + T_{1}n + U_{1}o + P_{1} |

Rule 2 = If RE is below, NDBS is adjacent and ND is medium | Then ${F}_{2}$ = S_{2}m + T_{2}n + U_{2}o + P_{2} |

Rule 3 = If RE is below, NDBS is adjacent and ND is compact | Then ${F}_{3}$ = S_{3}m + T_{3}n + U_{3}o + P_{3} |

_{…} | |

Rule 25 = If RE is top, NDBS is distant and ND is deficient | Then ${\mathrm{F}}_{25}$ = S_{25}m + T_{25}n + U_{25}o + P_{25} |

Rule 26 = If RE is top, NDBS is distant and ND is medium | Then ${\mathrm{F}}_{26}$ = S_{26}m + T_{26}n + U_{26}o + P_{26} |

Rule 27 = If RE is top, NDBS is distant and ND is compact | Then ${\mathrm{F}}_{27}$ = S_{27}m + T_{27}n + U_{27}o + P_{27} |

_{i}, T

_{j}, ${\mathrm{U}}_{k}$ are linear parameters of then part (consequent) of the Takagi–Sugeno fuzzy inference model. The linguistic variable for residual energy RE (M) = (below, fair, top) is represented as (M

_{1}, M

_{2}, M

_{3}), node distance to base station NDBS (N) = (adjacent, midway, distant) is represented as (N

_{1}, N

_{2}, N

_{3}) and node density ND (O) = (deficient, medium, compact) is represented as (O

_{1}, O

_{2}, O

_{3}).

**1.**- Fuzzy Layer: This section describes the nature of the node which is actually flexible according to backward pass (denoted by square adaptable node) that resembles each input variable relative to membership function. The membership function graph is plotted against each adaptable node to describe their output. Membership function follows Gaussian distribution as shown in Equation (4) or generalized bell-shaped membership function (see Equation (5)) which gives a value in the range of 0 and 1.$${\mu}_{M\alpha}\left(M\right)=exp\left[-{\left(\frac{m-{f}_{\alpha}}{2{d}_{\alpha}}\right)}^{2}\right]$$$${\mu}_{M\alpha}\left(M\right)=\frac{1}{1+{\left|\frac{m-{f}_{\alpha}}{d}\right|}^{2}{e}_{\alpha}}$$The output of the first layer is given by
- $\text{}{R}_{1,\alpha}={\mu}_{M\alpha}\left(M\right),\text{}\alpha =1,2,3$
- ${R}_{1,\alpha}={\mu}_{N\alpha}\left(N\right),\text{}\alpha =1,2,3$
- ${R}_{1,\alpha}={\mu}_{O\alpha}\left(O\right),\text{}\alpha =1,2,3$

where $M$ is the input node to α and ${\mu}_{Mi},\text{}{\mu}_{Ni},\text{}{\mu}_{Oi}$ are the degree of membership function cross-ponding to linguistic variables ${M}_{i}$, ${N}_{i}$, and ${O}_{i}$ and {${d}_{i},{e}_{i},{f}_{i}$} are referred to as a parameter set of the membership function or premise parameter. The bell-shaped membership function varies along with the values of the premise parameter set. In this layer, we can also use the triangular and trapezoidal membership function for the input node; they are also valid quantifiers for this node. **2.**- T-Norm Layer: In this layer, each node is non-adaptive in nature, and called as rule nodes which are depicted by the circle labeled with $\pi $ (see Figure 5). These nodes represent the firing strength of each rule connected to it. To determine the results of each node, multiply all the signals (membership function) coming to the node. The T-norm operator uses generalized AND to calculate the antecedents/outputs at second layer of the rule.$${R}_{2\alpha}={T}_{\alpha}={\mu}_{M\alpha}\left(M\right)\ast {\mu}_{N\alpha}\left(N\right)\ast {\mu}_{O\alpha}\left(O\right),\text{}\alpha =1,2,3$$
**3.**- Normalized Layer: Non-adaptive in nature nodes found in the normalized layer, which is known as normalized mode, are depicted by circles labeled as N (see Figure 5). The output of every node is an estimation of the proportion between the $\alpha $th rule’s firing strength to the summation of firing strength of all rules. The result at the third layer or normalized output can be expressed as$${R}_{3\alpha}={T}_{n\alpha}=\frac{{T}_{\alpha}}{{{\displaystyle \sum}}_{\alpha}{T}_{\alpha}},\text{}\alpha =1,2,3$$
**4.**- Defuzzy Layer: This layer consists of those nodes which have adaptive essence depicted by a square (see Figure 5). The output of the node is the product of normalized firing strength and individual rule. The output at the fourth layer can be given by$${R}_{4\alpha}={T}_{n\alpha}\text{}{f}_{\alpha}={T}_{n\alpha}\left({s}_{\alpha}m+{t}_{\alpha}n+{u}_{\alpha}o+{p}_{\alpha}\right)$$
**5.**- Aggregated Output Layer: This layer consists of a single consolidated node as an output which is specified as non-adaptive in nature. This non-adaptive node gives information about the complete system performance evaluated by adding up all the approaching signals arriving at this layer from the previous node. Summation sign $\sum$ is used inside a circle to represent this aggregated output node. The output of the fifth layer is computed as$${R}_{5\alpha}={\displaystyle \sum}_{\alpha}{T}_{n\alpha}\text{}{f}_{\alpha}=\frac{{{\displaystyle \sum}}_{\alpha}{W}_{\alpha}{f}_{\alpha}}{{{\displaystyle \sum}}_{\alpha}{w}_{\alpha}}$$

Algorithm 1- Adaptive Neuro Fuzzy Clustering Algorithm (ANFCA) | |

1. | Begin |

2. | Input: Given input training pattern, {RE, NDBS, ND} and maximum number of Epoch to E_{max}. // obtained from first modeling mamdani type fuzzy inference system. |

3. | Output {CH} |

4. | Process |

5. | for E=1 to E_{max}. |

6. | Input the training data into first layer of Takagi-sugeno inference engine. |

7. | Membership function ${\mu}_{M\alpha}\left(M\right)$ tuned using Equations (4) and (5). |

8. | Adjust the firing strength of each node (${T}_{\alpha}$), using Equation (6) in non-adaptive T-norm layer. |

9. | Normalize the firing strength of each node (${W}_{ni}$) using Equation (7) in normalized layer. |

10. | Defuzzification of each node using Equation (8). |

11. | Aggregated output is produced for each node using Equation (9) in fifth layer. |

12. | END |

#### 3.2.3. Phases of the Algorithm

#### Selection Phase

^{2}(in decibel meter 4 ≤ σ ≤ 12). The distance between the base station and a node is calculated using Equation (11). Where θ is the received signal strength in one meter distance from the base station without any obstacles. The base station triggers election procedures for cluster heads choosing some nodes as cluster heads randomly. These temporary cluster head nodes are gone through AFLIS to check the validity of each node whether they can play the role of cluster head or not. Afterwards, output of AFLIS is recorded and trained using ANFIS, and the final output is recorded. If the nodes have fulfilled the selection criteria for a cluster head, then these nodes are designated as permanent cluster heads for the present round. The selection procedure for a cluster head is rehashed in each cycle so it is potentially able for the sensor to get opportunity to become a part of cluster head group, therefore all nodes exhaust their energy relatively that upgrades the network lifetime.

#### Cluster Formation Phase

#### Transfer Phase

## 4. Simulation

#### 4.1. Simulation Environment

#### Evaluation Metrics

- ▪
- Network Lifetime: Lifetime definition of the network is application dependent, and it may be stated that as the tenure spans from the start, it is the functioning of the network to a moment when a certain percentage of the nodes have died or the network will be disconnected. In this paper, we considered the simulation time until 90% of the nodes were dead.
- ▪
- (First node death) FND, (Half node death) HND and (Last node death) LND: The round at which the death of the first node occurred was defined as FND. Similarly, the round at which half of the nodes had died was defined as HND. The round at which the last node death has occurred was taken as LND.
- ▪
- Average Residual Energy: Defined as the mean of residual energy of alive nodes in the network with respect to rounds.
- ▪
- Average Energy Consumption: Defined as the summation of overall energy consumption taken place during the sensing and transmission by each sensor node to the number of sensor nodes with respect to rounds.
- ▪
- Standard Deviation of Residual Energy: The standard deviation of residual energy is the square root of variance of residual energy of all the sensor nodes. It shows the variation of residual energy around the mean.

#### 4.2. Simulation Results

#### 4.2.1. Network Lifetime Over Rounds

#### 4.2.2. Energy Expenditure over Rounds

#### 4.2.3. Standard Deviation of Residual Energy

## 5. Conclusions and Future Perspectives

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

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**Figure 5.**Three input type-3 adaptive neuro fuzzy inference system (ANFIS) with 27 rules and one output.

Rule | IF THEN | Rule | IF THEN | ||||||
---|---|---|---|---|---|---|---|---|---|

RE | NDBS | ND | CH | RE | NDBS | ND | CH | ||

1. | below | adjacent | deficient | weakest | 15. | fair | midway | compact | medium |

2. | below | adjacent | medium | weaker | 16. | fair | distant | deficient | weakest |

3. | below | adjacent | compact | weak | 17. | fair | distant | medium | weaker |

4. | below | midway | deficient | weakest | 18. | fair | distant | compact | weaker |

5. | below | midway | medium | weakest | 19. | top | adjacent | deficient | strong |

6. | below | midway | compact | weaker | 20. | top | adjacent | Medium | stronger |

7. | below | distant | deficient | weaker | 21. | top | adjacent | compact | strongest |

8. | below | distant | medium | weakest | 22. | top | midway | deficient | medium |

9. | below | distant | compact | weakest | 23. | top | midway | medium | strong |

10. | fair | adjacent | deficient | weak | 24. | top | midway | compact | stronger |

11. | fair | adjacent | medium | medium | 25. | top | distant | deficient | strong |

12. | fair | adjacent | compact | strong | 26. | top | distant | medium | stronger |

13. | fair | midway | deficient | weak | 27. | top | distant | compact | stronger |

14. | fair | midway | medium | weak | - | - | - | - | - |

Metrics | Specification |
---|---|

Number of nodes | 200 |

Rectangular area | 200 × 200 m^{2} |

Node sensing range | 5 m |

Node transmission range | 25 m |

Base station location | (200,170) |

Packet size m | 512 bits |

Initial Energy | 2 J |

${E}_{TDA}$ | 5 nJ/bit/message |

${E}_{TNE}$ | 50 nJ/bit |

${\epsilon}_{fsp}$ | 10 pJ/bit/m^{2} |

${\epsilon}_{mpf}$ | 0.0013 pJ/bit/m^{4} |

Cycle time | 60 µs |

Death Percentage (%) | LEACH | CHEF | LEACH-ERE | ANFCA |
---|---|---|---|---|

FND | 96 | 150 | 205 | 260 |

20 | 600 | 827 | 1107 | 1224 |

40 | 934 | 1223 | 1536 | 1707 |

HND | 1167 | 1400 | 1665 | 1810 |

60 | 1386 | 1631 | 1885 | 1972 |

80 | 1600 | 1795 | 1994 | 2206 |

LND | 1970 | 2056 | 2140 | 2310 |

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**MDPI and ACS Style**

Kumar Kashyap, P.; Kumar, S.; Dohare, U.; Kumar, V.; Kharel, R.
Green Computing in Sensors-Enabled Internet of Things: Neuro Fuzzy Logic-Based Load Balancing. *Electronics* **2019**, *8*, 384.
https://doi.org/10.3390/electronics8040384

**AMA Style**

Kumar Kashyap P, Kumar S, Dohare U, Kumar V, Kharel R.
Green Computing in Sensors-Enabled Internet of Things: Neuro Fuzzy Logic-Based Load Balancing. *Electronics*. 2019; 8(4):384.
https://doi.org/10.3390/electronics8040384

**Chicago/Turabian Style**

Kumar Kashyap, Pankaj, Sushil Kumar, Upasana Dohare, Vinod Kumar, and Rupak Kharel.
2019. "Green Computing in Sensors-Enabled Internet of Things: Neuro Fuzzy Logic-Based Load Balancing" *Electronics* 8, no. 4: 384.
https://doi.org/10.3390/electronics8040384