Vol.04, Issue 10, October 2019, Available Online: www.ajeee.co.in/index.php/AJEEE ELECTRIC POWER SAVING PROTOCOL IN WIRELESS SENSOR NETWORKS
1Abhinav Dubey, M.Tech, (Electrical), RKDF Bhopal
2Pramod Kumar Rathore, Guide, RKDF, Bhopal
3Dr. A. K. Jhala, Co- Guide, RKDF, Bhopal
Abstract:- 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. 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. 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 neighbouring 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
Table.2 Simulation Results of Stability and Residual Energy Performance measures DEEC IDEEC Improvement (%) Stability (First node 1189 1465 13.8
Vol.04, Issue 10, October 2019, Available Online: www.ajeee.co.in/index.php/AJEEE
2 dead)
in round number
Residual Energy (joules)
at 0 0.09 18
1500 round
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 per processing 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.
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 neighbour discovery algorithm to discover its neighbour nodes.
Using the cluster head selection algorithm cluster heads are selected among the nodes. These cluster heads broadcasts the advertisement message to all its neighbouring nodes and thus clusters are formed with a fixed bound size. Each node in the cluster maintains 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 centre 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
Vol.04, Issue 10, October 2019, Available Online: www.ajeee.co.in/index.php/AJEEE 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.
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 l00 meter 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.
Vol.04, Issue 10, October 2019, Available Online: www.ajeee.co.in/index.php/AJEEE
4
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
Figure 3 counts of no .of dead nodes / alive node /packet sent to base station at 1000 rounds in the network with cluster head count
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
Vol.04, Issue 10, October 2019, Available Online: www.ajeee.co.in/index.php/AJEEE
Figure 4 counts of no .of dead nodes / alive node /packet sent to base station at 2000 rounds in the network with cluster head count
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
The proposed work has many features, which can be summarized as it is a hybrid clustering protocol. b. The two scenarios are single-hop intra-cluster & multi-hop inter-cluster communications. c. In the proposed protocols, CH (Cluster Heads) selection depends on fuzzy logic approach. d. 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.
REFERENCES
1. Najam ul Hasan. COOPERATIVE Spectrum sensing IN COGNITIVE RADIO NETWORKS,
2006-NUST-MS PhD-ComE
http://enpub.fulton.asu.edu/PowerZone/Fu zzyLogic/chapter%201/frame1.htm.
Vol.04, Issue 10, October 2019, Available Online: www.ajeee.co.in/index.php/AJEEE
6
2. L. Giupponi, Ana I. Pérez-Neira. Fuzzy-based Spectrum Handoff in Cognitive radio network, Centre Tecnològic de Telecomunicacions de Catalunya(CTTC), Universitat Politècnica de Catalunya (UPC).
3. Dong Li, Xianhua Dai, Han Zhang. Joint Adaptive Modulation and Power Control in Cognitive Radio Networks, School of Information and Scienc Technology, Sun Yat-Sen University Guangzhou 510275, P.
R. China.
4. Nouha Baccour, Anis Koubˆaa, Habib Youssef, Maissa Ben Jamˆaa1,Denis do Ros´ario, M´ario Alves, and Leandro B.
Becker.F-LQE: A Fuzzy Link Quality Estimator for Wireless Sensor Networks.
5. Miao Ma and Danny H. K. Tsan. Cross-Layer Throughput Optimization in Cognitive Radio Networks with SINR Constraints, Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology.
6. T.charles Clancy, William A Arbaugh, Measuring Interference temperature, Department of computer Science, University
of Maryland
http://www.scribd.com/doc/23492277/85/
Major-advantages-of-SINR-based-
lgorithmover-RSS-based Paul j kolodzy.
Interference temperature: A metric for dynamic spectrum, March 2006,phD member, IEEE.
7. http://
www.electronics.dit.ie/staff/amoloney/...2/d ig-comms-ii-lecture-11-12.pdf.
8. http://www.csgnetwork.com/tvfreqtable.ht ml.
9. HJ Cheng, ZHSu R Guo, NX Xiong, WZ Guo, Service-oriented node scheduling schemes with energy efficiency in wireless sensor networks. Int. J. Distributed Sensor Netw.2014 (2014).
10. H Zhang, Z Zhang, H Dai, R Yin, XM Chen, in IEEE Global Telecommunications Conference Distributed spectrum-aware clustering in cognitive radio sensor networks, (2011), pp. 1–6.
11. RM Eletreby, HM Elsayed, MM Khairy, in International Conference on Cognitive Radio Oriented Wireless Networks and Communications Cog LEACH: a spectrum aware clustering protocol for cognitive radio sensor networks, (2014), pp. 179–184.
12. M Ozger, OB Akan, in Proceedings IEEE INFOCOM. Event-driven spectrum-aware clustering in cognitive radio sensor networks, (2013),pp. 1483–1491.
13. CF Li, M Ye, GH Chen, J Wu, in IEEE International Conference on Mobile Adhoc and Sensor Systems. An energy-efficient unequal clustering mechanism for wireless sensor networks, (2005), pp. 604–612.
14. AO Bicen, OB Akan, Reliability and congestion control in cognitive radio sensor networks. Ad Hoc Netw. J. (Elsevier).9(7), 1154–1164 (2011).
15. B Gulbahar, OB Akan. Information theoretical optimization gains in energy adaptive data gathering and relaying in cognitive radio sensor networks. IEEE Trans. Wireless Commun.11 (5), 1788–1796 (2012).
16. GA Shah, VC Gungor, OB Akan, A cross- layer QoS-aware communication framework in cognitive radio sensor networks for smart grid applications. IEEE Trans. Ind. Inf.9 (3), 1477–1485 (2013).
17. GA Shah, OB Akan, Performance analysis of CSMA-based opportunistic medium access protocol in cognitive radio sensor networks.
Ad HocNetw. J. (Elsevier).15 4–13 (2014).
18. GA Shah, F Alagoz, E Fadel, OB Akan, A spectrum-aware clustering for efficient multimedia routing in cognitive radio sensor networks. IEEE Trans. Vehicular Technol.63 (7), 3369–3380 (2014).
19. GA Shah, OB Akan, Cognitive adaptive medium access control in cognitive radio sensor networks. IEEE Trans. vehicular Technol.64 (2), 757–767 (2015).
20. V Esmaeelzadeha, R Berangia, ES Hosseinia, OB Akanb, Stochastic backlog and delay bounds of generic rate-based AIMD congestion control scheme in cognitive radio sensor networks, (2015). Pervasive and Mobile Computing.
21. W Heinzelman, A Chandrakasan, H Balakrishnan, application specific protocol architecture for wireless micro sensor networks. IEEE Trans. Wireless Commun.1 (4), 660–670 (2002).
22. GA Shah, OB Akan, in IEEE International Conference on Communications Spectrum- aware cluster-based routing for cognitive radio sensor networks, (2013), pp. 2885–
288.
23. Preon VM - Virtual maschine for wireless sensor devicesmArchived 2017-11-11 at the Wayback Machine Retrieved 2017-11- 10.
24. Silva, D.; Ghanem, M.; Guo, Y. (2012). "Wiki Sensing: An Online Collaborative Approach
for Sensor Data
Management". Sensors. 12 (10): 13295–332.
25. Niazi, Muaz; Hussain, Amir (2011). "A Novel Agent-Based Simulation Framework for Sensing in Complex Adaptive Environments" (PDF). IEEE Sensors Journal. 11 (2):
26. Li, Wangyan; Wang, Zidong; Wei, Guoliang;
Ma, Lifeng; Hu, Jun; Ding, Derui (2015). "A Survey on Multisensor Fusion and Consensus Filtering for Sensor Networks". Discrete Dynamics in Nature and
Society. 2015: 1–
12. doi:10.1155/2015/683701. ISSN 1026- 0226.
27. Bosman, H. H. W. J.; Iacca, G; Tejada, A.;
Wörtche, H. J.; Liotta, A. (2015-12-01).
"Ensembles of incremental learners to detect anomalies in ad hoc sensor networks". Ad Hoc Networks. Special Issue on Big Data Inspired Data Sensing, Processing and Networking Technologies.
28. Bosman, H. H. W. J.; Liotta, A.; Iacca, G.;
Wörtche, H. J. (October 2013). "Anomaly Detection in Sensor Systems Using Lightweight Machine Learning". 2013 IEEE International Conference on Systems, Man, and Cybernetics:
29. Bosman, H. H. W. J.; Liotta, A.; Iacca, G.;
Wörtche, H. J. (December 2013). "Online Extreme Learning on Fixed-Point Sensor Networks". 2013 IEEE 13th International Conference on Data Mining Workshops:
Vol.04, Issue 10, October 2019, Available Online: www.ajeee.co.in/index.php/AJEEE 30. Bosman, H. H. W. J.; Iacca, G.; Wörtche, H.
J.; Liotta, A. (December 2014). "Online Fusion of Incremental Learning for Wireless Sensor Networks". 2014 IEEE International Conference on Data Mining Workshop:
31. Iacca, G. (2013-12-01). "Distributed optimization in wireless sensor networks: an island-model framework". Soft Computing. 17 (12): 2257 2277.
32. Bosman, H. H. W. J.; Iacca, G.; Tejada, A.;
Wörtche, H. J.; Liotta, A. (2017-01-01).
"Spatial anomaly detection in sensor networks using neighbourhood information". Information Fusion.
33. Cam, H; Ozdemir, S Nair, P Muthuavinashiappan, D (October 2003). ESPDA: Energy-efficient and Secure Pattern-based Data Aggregation for wireless sensor networks. Proceedings of IEEE Sensors 2003. 2. pp. 732–736.
34. Rowayda, A. Sadek (May 2018). "Hybrid energy aware clustered protocol for IoT heterogeneous network". Future Computing and Informatics Journal. 3 (2): 166–
177. doi:10.1016/j.fcij.2018.02.003.
35. Jump up to:ab Hu, Lingxuan; David Evans (January 2003). "Secure aggregation for wireless networks". Workshop on Security and Assurance in Ad Hoc Networks.
36. Przydatek, Bartosz; Dawn Song; Adrian Perrig (2003). SIA: secure information aggregation in sensor networks. SenSys.
pp. 255–265.
37. Kumar, Vimal; Sanjay K. Madria (August 2012). "Secure Hierarchical Data Aggregation in Wireless Sensor Networks:
Performance Evaluation and Analysis". MDM 12.
