View of ENERGY EFFICIENT PROTOCOL DESIGN & IMPLEMENTATION BASED ON AI TECHNIQUE

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VOLUME: 07, Issue 07, Paper id-IJIERM-VII-VII, September 2020

ENERGY EFFICIENT PROTOCOL DESIGN & IMPLEMENTATION BASED ON AI TECHNIQUE

1Sahiba Parveen, (M.Tech Scholar)

Infinity Management & Engineering College (IMEC), Sagar, (M.P.)

2Guide- Sarvesh Rai, Asst. Professor

Infinity Management & Engineering College (IMEC) Sagar, (M.P.)

Abstract:- In the recent times, many routing protocols have been proposed increasing the network lifetime, stability in short proposing a reliable and robust routing protocol. In this paper we study the impact of hierarchical clustered network with sensor nodes of two-level heterogeneity. Wireless sensor network (WSNs) are network of Sensor Nodes One of current concerns is developing a stable clustered heterogeneous protocol prolonging the network lifetime with minimum consumption of battery power.

The main approach in this research is to develop an enhanced multi-hop DEEC routing protocol unlike DEEC. Simulation results show the proposed protocol is better than DEEC in terms of FDN (First Dead Node), energy consumption and Packet transmission. We propose an energy efficient adaptive scheme for transmission in high speed networks.. In this approach, the open loop is used for estimation and compensation of the quality of the link as a function of temperature. For each region and the current number of neighboring nodes helping to adapt the transmission power according to the quality of the link changes due to temperature variations.

Keyword:- DEEC, FDN (First Dead Node) Wireless sensor network (WSNs).

1. INTRODUCTION

Evaluating network lifetime: The number of half dead nodes in DEEC and proposed DEEC i.e.

IDEEC at round 2000. The number of half dead nodes is 0 in DEEC and 28 in IDEEC. the number of normal half dead and the number of advance half dead nodes in both DEECs. Initially the number of normal nodes was 80 in DEEC and 70 in IDEEC. The number of normal half dead is 0 in DEEC and 28 in IDEEC.

The number of advance half dead are 0 in both DEECs showing zero improvement in number of advance half dead nodes Assumed number of nodes was 100 in which we have take m= 0.2 showing number of normal nodes are 80 and number of advance nodes are 20 in DEEC and m1= 0.3 showing number of normal nodes are 70 and number of advance nodes are 30 in IDEEC. Additional energy for DEEC and IDEEC is 300% i.e. a= 3 been considered for both DEECs.

Table 1 Simulation Results of Network Lifetime Performance measure for network

lifetime DEEC IDEEC Improvement

(%) (In number of nodes)

Half Dead Nodes (Normal half dead and 0 28 28 advance half dead nodes)

Normal Half Dead Nodes 0 28 40

Advance Half Dead Nodes 0 0 0

Dead Nodes (Normal dead nodes and 80 42 38

advance dead nodes)

Normal Dead Nodes 80 42 40

Advance Dead Nodes 0 42 0

Alive Nodes (Normal alive nodes and 20 58 38 advance alive nodes)

Normal Alive Nodes 0 28 40

Advance Alive Nodes 20 30 0

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Table 2 Simulation Results of Stability and Residual Energy

2. PERFORMANCE MEASURES

 Network lifetime- This is based on number of half dead nodes, dead nodes and alive nodes. Half dead nodes are defined as nodes which are half dead and still half alive in comparison to dead nodes which are fully dead. Alive nodes are defined as nodes which are still alive after processing and further will participate in network operations.

So there has to be increment in number of alive nodes and half dead nodes to make network alive for longer extent and decrement in number of dead nodes is required.

 Stability (FND) - This is time till first sensor node died. The more the time interval, more is stability of network.

 Residual Energy - This defines the amount of battery power consumed by sensor nodes perprocessing round.

Lower the consumption and more the residual energy, better the network.

 Packets to CH - Amount of data transmitted from nodes to CH.

 Packets to BS - Amount of data from CH, nodes or INs to BS.

2.1 Introduction to AI Levels

Narrow AI: A artificial intelligence is said to be narrow when the machine can perform a specific task better than a human. The current research of AI is here now General AI: An artificial intelligence reaches the general state when it can perform any intellectual task with the same accuracy level as a human would Nowadays; AI is used in almost all industries, giving a technological edge to all companies integrating AI at scale.

According to McKinsey, AI has the potential to create 600 billions of dollars of value in retail, bring 50 percent more incremental value in banking compared with other analytics techniques. In transport and logistic, the potential revenue jump is 89 percent more.

2.2 Type of Artificial Intelligence

Artificial intelligence can be divided into three subfields:-

1. Artificial intelligence 2. Machine learning 3. Deep learning

3. ENERGY-EFFICIENT ROUTING PROTOCOLS In WSN all the nodes have power source which provide energy to participate in the network. In the basic WSN the power source is batteries. As we know, the amount of energy that can be stored by battery is limited and cannot be rechargeable. So we should try to not waste this energy and use it in an optimized way. The aim of routing protocols is to use the battery energy in an efficient way to increase the network lifetime.

Therefore target of the proposed routing algorithm should be energy consumption minimization and network lifetime maximization. For evaluating the performance of routing Energy aware Multi-hop Multi-Path Hierarchical routing protocol was developed by inducing the features of energy aware routing and multi-hop intra cluster routing [10]. The operation of the Energy aware Multi-hop Multi- Path Hierarchical protocol is broken up into rounds where each round begins with a set-up phase, when the clusters are organized, followed by a steady- state phase, when data transfers to the base station occur.

The below flow chart describes the overview of the protocol initially the user has to give the input which is in the form of number of nodes. For the nodes generated, their positions are randomly assigned and displayed. Once the nodes are deployed, every node uses the neighbor discovery algorithm to discover its neighbor nodes. Using the cluster head selection algorithm cluster heads are selected among the nodes.

These cluster heads broadcasts the advertisement message to all its neighboring nodes and thus clusters are formed with a fixed bound size. Each node in the cluster maintains Performance measures DEEC IDEEC Improvement (%)

Stability (First node dead) 1189 1465 13.8 in round number

Residual Energy (joules) at 0 0.09 18 1500 round

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routing table in which routing information of the nodes are updated. Distributed randomized time slot assignment algorithm method is used; it allows several nodes to share the same frequency channel by dividing the signal into different time slots. Protocol there is some concept related to energy efficiency.

3.1 GUI simulation comparison

GUI (Guided user interface) showing the comparison in number of alive nodes, dead nodes in DEEC and proposed DEEC (IDEEC) routing scheme. The number of alive nodes shows increment of 38 nodes showing increase in lifetime of network. BS of DEEC was at center

whereas it lies outside network field in IDEEC.

The first node died at 1189 whereas it dies at 1485 in IDEEC showing increment in stability.

GUI been created just to show the processing results of simulation performed in MATLAB. The network consists of 100 sensor nodes deployed randomly in 100m*100m sensing field. The inference due to signal collisions id been avoided for performing simulation of proposed protocol IDEEC. The title of the GUI is “Advance approach of lifetime enhancement in DEEC”. Two axes were considered for differentiating between DEEC and proposed IDEEC. GUI here contains push buttons, axes, static texts and variable texts.

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3.2 Flow chart the algorithm Setup phase:-

1. CN ⇒ r

2. if r> T(n) then CH = CN else, goto step1

3. CH ⇒ G: id(CH) , join adv

4. A(i) → CH(j): id(A(i)), id(CH(j)), join 5. req CH(j) → A(i): id(CH(j)), <t(i), id(A(i))>

Steady phase:-

1. A(i) → CH(j): id(A(i)), id(CH(j)), info 2. CH → BS: id(CH), id(BS), aggr info

The various symbols used here are:-

1. CN: candidate node to become the cluster head.

2. A: normal node

3. T(n): threshold value 4. join adv: request to join the cluster 5. CH: cluster head

6. join adv: advertisement to join the cluster

7. G: all nodes in the network 8. id: identification number

Figure 1 GUI simulation comparisons 4. PERFORMANCE EVALUATION

4.1 Field Area (X, Y)

We have taken Field Area X, Y, which is 100 x l00meter squares for number of node

covered in that area. This is taken in response of base paper so that comparison can be made on same area field for given number nodes.

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Figure 2 counts of no .of dead nodes / alive node /packet sent to base station at 500 rounds in the network with cluster head count

 Graph 1 represents dead node =0 on 200/400/600 rounds

 Graph 2 represents Alive node =100 on 200/400/600 rounds

 Graph 3 represents packet sent with energy 1.8 ×10⁴

 Graph 4 represents total number of cluster head selected on average 1-3

 Graph 1 represents dead node =0 on 500/1000 rounds

 Graph 2 represents Alive node =100 on 500/1000 rounds

 Graph 3 represents packet sent with energy 3 ×10⁴

 Graph 4 represents total number of cluster head selected on average 3-4

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 Graph 1 represents dead node =50 on 500/1000/2000 rounds

 Graph 2 represents Alive node =50 on 500/1000/2000 rounds

 Graph 3 represents packet sent with energy 6×10⁴

 Graph 4 represents total number of cluster head selected on average 7-10 5. RESULT ANALYSIS

Rounds Dead

node Alive

Node Packet sent to BS Cluster Head count

500 0 100 1.8 ×10⁴ 1-3

1000 0 100 3 ×10⁴ 3-4

2000 50 50 6×10⁴ 7-10

Base paper Proposed work

It defines only run time of IDEEC It defines run time as well as Number of nodes dead of IDEEC It worked upon reducing stability period,

number of messages, mean and variance of cluster heads (CHs),

It worked upon reducing intermediate nodes between CHs and

BS 1. The proposed work has many features,

which can be summarized as it is a hybrid clustering protocol.

2. The two scenarios are single-hop intra- cluster & multi-hop inter-cluster communications.

3. In the proposed protocols, CH (Cluster Heads) selection depends on fuzzy logic approach.

4. The proposed protocols are centralized clustering algorithms

6. CONCLUSIONS

In this research a new protocol is proposed named IDEEC using intermediate nodes

between CHs and BS. The number of half dead nodes, alive nodes, and dead nodes showed 28%, 38%, 38% improvement respectively. FND showed improvement of 13.8 % and 18%

improvement in residual energy. The lifetime of network increased by 34.66% on an average of three parameters i.e. half dead nodes, dead nodes and alive nodes. The BS is placed out of network field and rechargeable intermediate nodes inside the field with other nodes.

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