© School of Engineering, Taylor’s University
FINDING OPTIMAL AND RELIABLE PATH IN MOBILE SINK
WIRELESS SENSOR NETWORK BY APPLYING GENATIC
OPTMIZATION CELLULER NEURAL NETWORK(GO-CNN)
TUKA K. JEBUER
Department of Business Management, College of Management and Economic, Al-Mustansiriyah University, Iraq.
*Corresponding Author: [email protected]
Abstract
Wireless sensors mobile sink(s) may either be homogenous or heterogeneous. Network sensor applications need not only longer network life but also quick and stable data transmission to decrease within a limited period to take preventive steps. High sink mobility in large dense networks could result in overhead for traffic, collision, loss of packet, delay, and fast energy consumption in nodes in such applications. Most routing schemes designed to be time-sensitive do not take all these problems into account in one single protocol and most protocols are built for homogeneous network sensor environments. Many such applications need heterogeneous sensors due to the benefits of node heterogeneity such as the enhanced suggest method called GO-CNN (Genetic optimization cellular neural network), where modified CNN by using GA to reducing high processing time and learned with minimum distance. has been developed to choose an energy-delay path that allows quick data transmission through the energy-efficient hop. Also, the proposed method increases the percentage of data packs that have been obtained successfully on a large network sink employing data collection and a reasonable network load balance simulation performance by applying Matlab 2016 in several parameters, such as packed, static or dynamic, link costs; the method proposed has demonstrated that data delivery by up to 96%, the limitation in this power consuming.
1. Introduction
Many types of research have recently focused on the use of sink mobility capacity. One of the most important problems facing the WSN is solving the problem of the power gap. Several methods have been suggested to decrease energy consumption and enhancement network performance. sink mobility often leads to extensive delay in the collection of data that can be led to maximum sink rapidity, there are several applications of the check that require accurate data collection processes such as health system monitoring and fire system monitoring, the collection and delivery of data are done within a specified period and therefore For me, finding the best path to send data with the least possible delay in the network is a big challenge [1].
One of the most important challenges facing this network is that it is limited by the quantity of energy in the node. So mobile drains were used, to conserve energy in the node and the load can be balanced on the sensor hold. In addition to finding a path to transmit data is another challenge facing the network, sink navigation may play an important role. Therefore, this network loses energy during the process of collecting data, finding the best path, transmitting data, and processing it [2].
Multiple individual Wireless Sensor Networks (WSNs) Nodes in which the node detects a certain physical environmental parameter and sends this sensitive information to the central government Commonly referred to as sink/base station. WSNs are basic applications The networks and their demands and problems differ from application to application to submit [3]. For finding the path from source to Bs, multiple routing protocols were suggested for the management of sink mobility. However, in an extensive network, high sink mobility has numerous shortcomings such as overhead traffic, increased packet collisions, reduced data supply, increased energy usage, and increased end-to-end delays. Consequently, all of these problems must be handled efficiently in massive, dense, unattended time-sensitive applications to provide fast and stable data delivery services and long network life [4, 5]
In this paper, we want to build a routing scheme that finds the optimal path of the network and delivers data quickly and reliably in a large, heterogeneous network with a mobile sink. The primary targets that need to be accomplished to take the required steps in due time, e.g. match the traffic load equally across the network. Redundant packets are reduced, and network collision reduced, order to improve network reliability.
2. Mobile Sink (Ms)
Mobile sink technology was used to decrease the amount of energy expended in wireless sensor nodes and increase the network life and reduce the data transmission distance between the network nodes which leads to reduce the time used in data transmission, as it is the best way to deal with the amount of unbalanced power distribution, there Some of the obstacles facing this strategy are the process of locating the mobile sink node by the rest of the network nodes as well as the nature of the traffic of this node must be well designed with the remaining local network nodes for data transmission [6].
3. System GO-CNN Design
new system. The system is implemented by using MATLAB software, and 64 bit Windows 8. The used computer has specifications of Intel CPU core i7 @ 2.10 GHz with RAM of 4 GB. The performance of the system is computed by analysing the results of the test. However, the new system is consisting of a hybrid of (a) Cellular Neural Network (CNN) (b) Genetic Algorithm as the following:
4. Multilayer Perceptron Neural Network
Is one of the types of neural networks feed-forward made up of three layers of the input layer, the hidden layer can be more than one a hidden layer depending on the type of problem processing and output layer is not any processing of the data entered in the input layer where you distribute data to the hidden layer for data processing Input [7, 8].
Cellular neural networks (topology optimization in cellular neural
networks)
One of the types of neural networks that apply non-linear systems and is a dynamic system, which performs the memory of the process of correlation and interactions are within the adjacent cells. This network is defined as a group of sub-connected to everyone via link communication. Therefore, the process of processing and flowing data through subnets is more expensive when comparing the same cost of data to communications within each subnet, Due to data transfer and processing [9-11]
Each neuron is independent and has a current source called Euij, an input, an independent source called I (bias) and several power controllers called Inuij, Inyij, and one power control source as Eyij, (output), In this network, the control sources of Inyij are connected to the cells that are adjacent to them. This connection is through feeding, which is called (feedback) of the output voltage of each neighbouring cell.
5. Material and Method
The important objective of this method to improve the efficiency of mobile sink wireless sensor network operations and services, which have affected semi-dynamic topology changes in the location of their nodes and which lead to time, storage, and computer power consumption which lead to network overhead traffic and reduce network life. This method was developed and implemented to prevent some of these problems.
algorithm was compared with one type of NN and with a genetic algorithm to get the best results for wireless network performance.
Algorithm 1 genetic optimization (GO-CNN) to discover the optimal path 1. Input: number of nodes, number of intra path p1, inter path p2, α, ν, size of
packed, (static or dynamic), costs of link. 2. Generate Matrices e, ê.
3. Create randomly the initial population of the CNN string ((with cells M, N = Number of rows and columns of the CNN equal to the nodes number in the mobile sink Wireless sensor network, get the input parameters, initial conditions, and learned templates.
4. Load all path information for the wireless networks.
5. load preferable parameters (packed size, dynamic, static, link costs) of wireless nodes Build a matrix of path cost representation between two nodes allocated the fitness to every chromosome in the population using fitness criteria measure.
6. Applying crossover operator from two existing chromosomes in the parent population to produce new offspring population
7. if required Mutate the resultant off-springs.
8. iterate step 7 and 8 until an optimal solution is found CNN (needs the training to operate and reducing high processing time and learned with minimum distance)
9. converge cells
10. while (converged-cells < total number of cells) 11. {for (i1=l; i1<=M1; i1++)
12. for (j1=l; j1<=N; j1++) 13. {if (convergences[i1] [j1])
14. continues; // the current cells were converged //
15. Activate the cells and get Q from all paths results as the short path whose is minimum Ei1j1 as Q = min(Ei1j1) the preferable parameters of wireless nodes (packed size, (Dynamic or static), costs of the link).
16. Calculate the next state using stored templates for the optimal path between p1 and p2.
17. xi1j1(t + 1) = xi1j1(t) + ∑k,l∈N ak−i1,l−j1 f�xkl (t)� +
i1j1
∑k,l∈Ni1j1bk−i1,l−j1 �ukl (t)� − Lc + I
18. where xij: the states of a cell at position(i1,j1), 19. Nij: the neighbors of the cell (i,j),
20. akl : the parameters of feedback templates (Links connection weights), 21. bkl : the feedforward template parameters,
23. I: is a bias value, the smallest’s Euclidean’s distances of Bij will select: 24. Bij = Q ∑∑ j,i=1,2..m ‖p1-p2‖
25. Update entire paths state values. 26. for (i1=l; i1<=M1; i1++) 27. for (j1=l; j1<=N1; j1++)
28. { if (convergences[i1][j1]) continue; 29. xij(tn) = xij(tn+1); }
30. iterations++;} /* end while */ 31. End
Table 1. Network parameter parameters scenario network size 100 × 100 m antenna type all-directional simulation time 700 seconds number of sinks 1-3 position of nodes random
6. Result and Desiccations
In our system efficiency is assessed with eminent metrics such as packet delivery ratio (PDR), performance. PDR: measure the sum of information that can be sent to the receiver. DR is the cumulative number of the nodes observed from overall network traffic (whether or not this is a black hole node). The method of measuring the number of data given in seconds represents the achievement. Where the Throughput represents the process of calculating the number of delivered data in seconds. Where the PDR was calculated as by Eq. (1):
PDR =∑ 𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛 𝑜𝑜𝑜𝑜 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑛𝑛𝑝𝑝 𝑛𝑛𝑛𝑛𝑝𝑝𝑟𝑟𝑟𝑟𝑛𝑛 ∑ 𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛 𝑜𝑜𝑜𝑜 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑛𝑛𝑝𝑝 𝑠𝑠𝑛𝑛𝑛𝑛𝑠𝑠 (1) where in the Throughput was calculated as by Eq. (2):
𝑇𝑇ℎ𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟ℎ𝑝𝑝𝑟𝑟𝑝𝑝 =𝑇𝑇𝑜𝑜𝑝𝑝𝑝𝑝𝑇𝑇 𝑠𝑠𝑝𝑝𝑝𝑝𝑝𝑝 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑛𝑛𝑝𝑝𝑠𝑠∑ 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑛𝑛𝑝𝑝𝑠𝑠 𝑠𝑠𝑛𝑛𝑛𝑛𝑝𝑝 (2) One-way delay: processing of calculating time to sending data from the sender to the recipient over the network.
One way delay = NL/R + ( P-1) L/R = (N+P-1)L/R (3) N = link, L = packet length, R = transmission rate.
Table 2. The packet delivery ratio evaluation values with 200 nodes.
Number of nodes GA MLP NN Proposed GO-CNN
10 80.18% 81.12% 83.93% 95.56%
25 80.36% 82.85% 86.53% 96.52%
50 80.25% 84.16% 87.28% 97.32%
100 80.78% 84.63% 90.15% 97.42%
200 80.86% 86.24% 90.028% 97.78%
Fig. 1. packet delivery ratio between different methods. Table 3. The throughput evaluation values.
Number of nodes GA MLP NN Proposed GO-CNN
10 70.21 61.22 66.288 78.51
50 71.76 62.12 66.97 79.41
100 73.31 63.02 67.71 80.31
150 74.86 63.92 68.55 81.21
200 76.41 64.82 68.75 82.11
Table 4. The one-way delay(s). with 200 nodes. Number of nodes MLP NN GA Proposed GO-CNN
10 0.07 0.35 0.25 0.03
50 0.09 0.39 0.23 0.045
100 0.11 0.43 0.22 0.06
150 0.13 0.47 0.21 0.075
200 0.15 0.51 0.20 0.09
Fig. 3. one –way delete(s) between different method with number of nodes (10-200).
Table 5. Comparative packet drop rate. Number of nodes GA MLP NN Proposed GO-CNN
10 2.31 3.98 4.3 0.1
70 3.12 4.55 4.79 0.18
100 3.93 5.12 5.28 0.26
180 4.74 5.69 5.77 0.34
200 5.55 6.26 6.26 0.42
Fig.4 . packet drop rate between different method with number of nodes (10-200)
7. Conclusions and Future Work
Reduce network energy consumption, packet loss, find out the optimal path from sink node to the destination node (BS). Thus, the suggested method, which has several strategies used in the first stage includes optimal path selection through obtaining input parameters, initial conditions, and learned templates. Download all route information for wireless networks. In the second phase of this protocol, multi-step compression is performed aimed at reducing the volume of data sent to the dump source node. The simulation results obtained from matlab2016 show that the proposed algorithm offers better performance in all parameters where the data delivery rate without loss is 97.78% compared to other similar methods presented, the genetic algorithm, NN, and the MLP, In the future, this work can further be modified to develop an energy-efficient routing for large scale dense network having multiple mobiles sinks.
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