An ACO and Mobile Sink based Algorithm for Improvement of Ml-Mac for Wsns using Compressive Sensing

Table of contents

1. Introduction

Wireless Sensor Networks (WSN) is an arrangement of hundreds or many small scale sensor hubs that have capacities of detecting, building up remote correspondence between each other and doing computational and preparing operations. Wireless sensor networks are used in many applications.Multi-layer Mac Protocol is an effective technique used in WSNs. It is designed with two main features: less active time and lesser collisions. Sensor hubs in ML-MAC have a very short active time which would lessen the vitality required to communicate with other nodes. Eventually, the number of collisions in cases where two or more nodes try to send at the same time is minimized in ML-MAC. This spares the vitality required to re-send the corrupted packets along these lines expanding system lifetime.ML-MAC demonstrate much better execution of the vitality utilization contrasted and the current MAC conventions. In this paper we further try to optimize the ML-MAC protocol by applying the techniques of Compressive sensing and ACO( Ant Colony Optimization). ACO: ACO calculation depends on the conduct of genuine ants. While moving a few ants discover food store pheromones while in transit to their homes, and alternate ants take after pheromones saved before by different ants. Over the long haul, pheromones dissipate, opening up new conceivable outcomes, and ants coordinate to pick a way with vigorously laid pheromones. Along these lines, ants meet to most optimum path from their home to a food deposits with just pheromone data [1]. ACO depends on swarm intelligence. In swarm knowledge complex aggregate conduct rises up out of the conduct of numerous basic specialists. ACO has taking after qualities. The correspondences in the WSN have the many to-one property in that information from an extensive number of sensor hubs have a tendency to be amassed into a few sinks. Since multi-hop routing is by and large required for far off sensor hubs from the sinks to save energy, the hubs close to a sink can be loaded with transferring a lot of activity from other hubs. This problem is called the "crowded centre effect" [8] or the "energy hole problem" .It results in vitality consumption at the hubs close to the sink too early, prompting the partition of the sink from whatever is left of hubs that even now have a lot of vitality. In any case, by moving the sink in the sensor field, one can maintain a strategic distance from or moderate the energy hole problem and expect an expanded system lifetime. Comptressive sensing: Compressive sensing (CS) is recent technique of simultaneously sensing and compressing that is highly appealing for fully distributed compression in wireless sensor networks (WSNs). WSNs observing ecological marvels over expansive geographic territories gather estimations from an extensive number of circulated sensors. Compressive Sensing gives a viable method for revelation and remaking of capacities with just a subset of tests. The issue of information examining and accumulation in remote sensor systems (WSNs) is getting to be basic as bigger systems are being sent. Expanding system size stances noteworthy information gathering challenges, for what concerns examining and transmission coordination and system lifetime. To handle these issues, in-system in-network compression techniques are getting to be vital answers for develop network lifetime.

II.

2. Related Work

Z. Li and Q. Shi [3] proposed another vitality successful QoS routing convention. The calculation is to speeds up the joining of ant colony algorithm by using SNGF to optimize routing candidate nodes; the pheromone is characterized as a blend of connection burden and transmission capacity delay.

S. Okdem and D. Karaboga [4] acquaints another methodology with routing operations in remote sensor systems (WSNs).

Compressive Sensing gives a powerful method for revelation and remaking of capacities with only a subset of samples.

Customary CS depends on consistently circulated tests which limits reasonableness of CS based recuperation. To improve the adaptability of sampling and implementation, D. C. Dhanapala et.al [5] proposed approach utilizes irregular walk based examples.

3. III.

4. Proposed Alogithm

The proposed algorithm follows following steps:

i. Initialize ML-MAC based remote sensor system.

ii.

Check if "a" every single current nodes '1/b' ideal percentages end up being dead if yes then exhibit no. of utilized bees speaking to any arrangement of hub equal to zero else proceed next stride.In the event that a % 1/b== 0 ??. (1)

iii. "X" is no. of appointed bees "j" is any hub that needs to end up the CH in that round "Y" is the set of hubs that previously chosen as CHs Cluster head in past '1/p' round. An ACO and Mobile Sink based Algorithm for Improvement of Ml-Mac for Wsns using Compressive Sensing W. Yan et.al [6] introduced a very simple deterministic measurement matrix design algorithm (SDMMDA), based on which the data gathering and reconstruction in wireless sensor networks (WSNs) are greatly enhanced. C.Caione et.al [7] compared Distributed compressed sensing (DCS) and Kronecker compressive sensing (KCS) two structures against a typical arrangement of artificial signals legitimately worked to typify the primary attributes of characteristic signs. J.Wang et.al [9] separates the system into a few groups and cluster heads are chosen inside every group. At that point, a mobile sink speaks with every cluster head to gather information specifically through short range correspondences. The ACO calculation has been used in this work keeping in mind the end goal to locate the ideal mobility direction for the mobile sink.B.Nazir and H.Hasbullah provide a mobile sink based routing protocol for prolonging network lifetime [10]. N.Vlajic and D.Stenvanoic performed analysis of zigbee-based wireless sensor networks with path constrained mobile sink [11]. Y.Nizhamudong et.al [12] evaluated the cost of route wireless sensor network with a mobile sink. Manish Kumar Jha et al. [13] gives an enhanced time synchronized relay node based ML-MAC convention for WSNs. Manish Kumar Jha et al. [ S. Singh et al. [2] proposed a ACO method and discovered the sink area for which the quantity of sensors is least among every accessible area in the matrix. In their calculation, they process aggregate of separations of the objectives from that sensor, which are in its reach. At that point they include these totals for all sensors in the network. This separation compares to the given sink area. Then rehash same procedure for registering the separation by changing the sink area in the lattice. That sink area for which the separation is least is picked and this sink area requires least number of sensors to cover all objectives. Check whether "rnd" is less than threshold value if yes than set as cluster head (CH) and report all hubs else wait and join with mean set cluster head.

5. X (j)

Note: .

6. IF rnd ? TH(n)

vi.

Find relay hub from Cluster Head. vii.

Apply Ant Colony Optimization (ACO) on CHs to discover short routes way amongst CHs and sink. viii.

Apply compressive sensing and Communicate information and update vitality dissemination. ix.

Check whether dead is equivalent to no. of hubs "n" if yes then Join with mean set (CH)cluster head else go to step 2. Is dead == n IV.

7. Experimental Setup

For performing the simulation we are using MATLAB 2010a version 7.10.0.499 32-bit.We are using windows 7 core i5 processor with 64 bit operating system and 4GB RAM.

V.

8. Experimental Results

The main objective of simulation is to evaluate the performance of proposed algorithm .In the simulations we refer to network with nodes varying from 100 to 600.we get the following results which the effectiveness of algorithm.

Figure 1.
Figure 1 v.Check whether "rnd" is less than threshold value if yes than set as cluster head (CH) and report all hubs else wait and join with mean set cluster head.
Figure 2. Table 2 :
2
Exiting ACO based ml-mac Mobile sink and aco baesd ML-mac
100 19.4779 24.4513 24.4516
150 29.9045 37.7087 37.9928
200 40.6416 51.7361 51.9828
250 51.0113 65.2385 65.5300
300 62.0976 79.0836 79.2515
350 71.2310 92.5709 92.8383
400 83.0392 105.8739 106.5069
450 92.3126 120.0419 120.3627
500 103.9739 133.6166 133.9290
600 124.2635 160.6876 161.1973
Figure 3. Table 3 :
3
Exiting ACO based ml -mac Mobile sink and aco baesd ML-mac
100 3620 4980 4964
150 5565 7682 7790
200 7570 10586 10652
250 9509 13367 13419
300 11572 16803 16238
350 13226 19032 19028
400 15459 21801 21880
450 17206 24655 24733
500 19325 27435 27537
600 23144 32994 33127
Figure 4. Table 4 :
4
No. of nodes ACO based ml-
Year 2018
32
Volume XVIII Issue II Version I No. of nodes
( ) E
Global Journal of Computer Science and Technology 100 No. of nodes No. of nodes Exiting 0.6436 ACO based ml-mac 0.1084 Mobile sink and aco baesd ML-mac 0.1201
150 0.9438 0.1041 0.0984
200 1.2171 0.1371 0.7141
250 1.6492 0.1637 0.3470
300 1.7300 0.1816 0.1746
350 2.0176 0.1938 0.1898
400 2.1084 0.2368 0.2278
450 2.5062 0.2529 0.3223
500 2.8251 0.3051 0.3415
600 3.2361 0.4563 0.4070
© 2018 Global Journals 1
Figure 5. Table 5 :
5
Exiting ACO based ml-mac Mobile sink and aco baesd ML-mac
100 128 10.2000 19.3600
150 129 2.7867 6.0667
200 121.5000 8.0700 17.7400
250 109.6400 12.2800 27.3240
300 104.2667 17.0633 16.8733
350 112.1143 18.6229 16.6343
400 103.5250 33.4975 17.3000
450 107.6444 16.2111 16.0378
500 103.5000 16.1300 15.9260
600 104.2667 28.0100 30.7883

Appendix A

Appendix A.1

Appendix B

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Date: 2018 2018-01-15