Energy Hole Analysis for Energy Efficient Routing in Body Area Networks K. Latif, N. Javaid Kamran. Latif Senior System Analyst, National Institute of.

Slides:



Advertisements
Similar presentations
RESEARCH POSTER PRESENTATION DESIGN © QUICK DESIGN GUIDE (--THIS SECTION DOES NOT PRINT--) This PowerPoint 2007 template.
Advertisements

BY: TAUSEEF SHAH REG. NO. FA11-REE-049 SUPERVISOR DR. SAFDAR H. BOUK CO-SUPERVISOR DR. NADEEM JAVAID MVC: Modified VIKOR Model based Clustering Protocol.
An Energy Efficient Routing Protocol for Cluster-Based Wireless Sensor Networks Using Ant Colony Optimization Ali-Asghar Salehpour, Babak Mirmobin, Ali.
Tufts Wireless Laboratory Tufts University School Of Engineering Energy-Efficient Structuralized Clustering for Sensor-based Cyber Physical Systems Jierui.
Improvement on LEACH Protocol of Wireless Sensor Network
Low-Energy Adaptive Clustering Hierarchy An Energy-Efficient Communication Protocol for Wireless Micro-sensor Networks M. Aslam hayat.
Kai Li, Kien Hua Department of Computer Science University of Central Florida.
Ubiquitous Healthcare Using MAC Protocols in Wireless Body Area Sensor Networks (WBASNs)
An Adaptive Energy-Efficient MAC Protocol for Wireless Sensor Network
1 Mohammad Ariful Huq Supervisor : Eryk Dutkiewicz Minimizing Channel Access Delay for Emergency Traffic in IEEE  Wireless Body Area Network.
1 An Energy-Efficient Unequal Clustering Mechanism for Wireless Sensor Networks Chengfa Li, Mao Ye, Guihai Chen State Key Laboratory for Novel Software.
By Areeba Rao with Dr. Nadeem Javaid COMSATS, Institute of Information Technology, Islamabad, Pakistan AM-DisCNT: Angular Multi-hop DIStance based Circular.
A Novel Cluster-based Routing Protocol with Extending Lifetime for Wireless Sensor Networks Slides by Alex Papadimitriou.
NCKU CSIE CIAL1 Principles and Protocols for Power Control in Wireless Ad Hoc Networks Authors: Vikas Kawadia and P. R. Kumar Publisher: IEEE JOURNAL ON.
Avoiding Energy Holes in Wireless Sensor Network with Nonuniform Node Distribution Xiaobing Wu, Guihai Chen and Sajal K. Das Parallel and Distributed Systems.
A Survey on Energy Efficient MAC Protocol for Wireless Sensor Networks Huma Naushad.
1 MOBMAC - An Energy Efficient and low latency MAC for Mobile Wireless Sensor Networks Proceedings of the 2005 Systems Communications (ICW ’ 05)
Talha Naeem Qureshi Joint work with Tauseef Shah and Nadeem Javaid
1 Algorithms for Bandwidth Efficient Multicast Routing in Multi-channel Multi-radio Wireless Mesh Networks Hoang Lan Nguyen and Uyen Trang Nguyen Presenter:
1 Energy Efficient Communication in Wireless Sensor Networks Yingyue Xu 8/14/2015.
CS 712 | Fall 2007 Using Mobile Relays to Prolong the Lifetime of Wireless Sensor Networks Wei Wang, Vikram Srinivasan, Kee-Chaing Chua. National University.
Vikramaditya. What is a Sensor Network?  Sensor networks mainly constitute of inexpensive sensors densely deployed for data collection from the field.
Spatial Correlation-Based Collaborative Medium Access Control in Wireless Sensor Networks Authors : Mehmet C. Vuran, Ian F. Akyildiz Georgia Institute.
M-GEAR: Gateway-Based Energy-Aware Multi-Hop Routing Protocol
A Framework for Energy- Saving Data Gathering Using Two-Phase Clustering in Wireless Sensor Networks Wook Chio, Prateek Shah, and Sajal K. Das Center for.
Multimedia & Networking Lab
Grammati Pantziou 1, Aristides Mpitziopoulos 2, Damianos Gavalas 2, Charalampos Konstantopoulos 3, and Basilis Mamalis 1 1 Department of Informatics, Technological.
Wireless Sensor Networks COE 499 Energy Aware Routing
Efficient Deployment Algorithms for Prolonging Network Lifetime and Ensuring Coverage in Wireless Sensor Networks Yong-hwan Kim Korea.
Wireless Sensor Network Protocols Dr. Monir Hossen ECE, KUET Department of Electronics and Communication Engineering, KUET.
VAPR: Void Aware Pressure Routing for Underwater Sensor Networks
Lan F.Akyildiz,Weilian Su, Erdal Cayirci,and Yogesh sankarasubramaniam IEEE Communications Magazine 2002 Speaker:earl A Survey on Sensor Networks.
Optimal Selection of Power Saving Classes in IEEE e Lei Kong, Danny H.K. Tsang Department of Electronic and Computer Engineering Hong Kong University.
An Energy Efficient Hierarchical Clustering Algorithm for Wireless Sensor Networks Seema Bandyopadhyay and Edward J. Coyle Presented by Yu Wang.
A Low-Latency and Energy-Efficient Algorithm for Convergecast in Wireless Sensor Networks Authors Sarma Upadhyayula, Valliappan Annamalai, Sandeep Gupta.
REECH ME: Regional Energy Efficient Cluster Heads based on Maximum Energy Routing Protocol Prepared by: Arslan Haider. 1.
SIMPLE: Stable Increased Throughput Multi-hop Link Efficient Protocol For WBANs Qaisar Nadeem Department of Electrical Engineering Comsats Institute of.
Secure and Energy-Efficient Disjoint Multi-Path Routing for WSNs Presented by Zhongming Zheng.
Residual Energy Aware Channel Assignment in Cognitive Radio Sensor Networks Wireless Communications and Networking Conference (WCNC), 2011 IEEE Xiaoyuan.
A Power Assignment Method for Multi-Sink WSN with Outage Probability Constraints Marcelo E. Pellenz*, Edgard Jamhour*, Manoel C. Penna*, Richard D. Souza.
Network and Systems Laboratory nslab.ee.ntu.edu.tw Branislav Kusy, Christian Richter, Wen Hu, Mikhail Afanasyev, Raja Jurdak, Michael Brunig, David Abbott,
By Naeem Amjad 1.  Challenges  Introduction  Motivation  First Order Radio Model  Proposed Scheme  Simulations And Results  Conclusion 2.
S& EDG: Scalable and Efficient Data Gathering Routing Protocol for Underwater Wireless Sensor Networks 1 Prepared by: Naveed Ilyas MS(EE), CIIT, Islamabad,
Authors: N. Javaid, M. Aslam, K. Djouani, Z. A. Khan, T. A. Alghamdi
AEDG:AUV aided Efficient Data Gathering Routing Protocol for UWSNs Prepared by: Mr. Naveed Ilyas CIIT, Islamabad, Pakistan 1.
Cross-Layer Network Planning and Performance Optimization Algorithms for WLANs Yean-Fu Wen Advisor: Frank Yeong-Sung Lin 2007/4/9.
Simulation of Sensor Clustering in WBAN Networks
Tufts Wireless Laboratory School Of Engineering Tufts University Paper Review “An Energy Efficient Multipath Routing Protocol for Wireless Sensor Networks”,
Energy-aware Node Placement in Wireless Sensor Networks Global Telecommunications Conference 2004 (Globecom 2004) Peng Cheng, Chen-Nee Chuah Xin Liu UCDAVIS.
Variable Bandwidth Allocation Scheme for Energy Efficient Wireless Sensor Network SeongHwan Cho, Kee-Eung Kim Korea Advanced Institute of Science and Technology.
Rami Melhem Sameh Gobriel & Daniel Mosse Modeling an Energy-Efficient MAC Layer Protocol.
MCEEC: MULTI-HOP CENTRALIZED ENERGY EFFICIENT CLUSTERING ROUTING PROTOCOL FOR WSNS N. Javaid, M. Aslam, K. Djouani, Z. A. Khan, T. A. Alghamdi.
Energy-Efficient Wake-Up Scheduling for Data Collection and Aggregation Yanwei Wu, Member, IEEE, Xiang-Yang Li, Senior Member, IEEE, YunHao Liu, Senior.
Turkmen Canli ± and Ashfaq Khokhar* Electrical and Computer Engineering Department ± Computer Science Department* The University of Illinois at Chicago.
Centralized Transmission Power Scheduling in Wireless Sensor Networks Qin Wang Computer Depart., U. of Science & Technology Beijing Edward Y. Hua Wireless.
Prolonging the Lifetime of Wireless Sensor Networks via Unequal Clustering Stanislava Soro Wendi B. Heinzelman University of Rochester IPDPS 2005.
FERMA: An Efficient Geocasting Protocol for Wireless Sensor Networks with Multiple Target Regions Young-Mi Song, Sung-Hee Lee and Young- Bae Ko Ajou University.
Critical Area Attention in Traffic Aware Dynamic Node Scheduling for Low Power Sensor Network Proceeding of the 2005 IEEE Wireless Communications and Networking.
“LPCH and UDLPCH: Location-aware Routing Techniques in WSNs”. Y. Khan, N. Javaid, M. J. Khan, Y. Ahmad, M. H. Zubair, S. A. Shah.
Toward Reliable and Efficient Reporting in Wireless Sensor Networks Authors: Fatma Bouabdallah Nizar Bouabdallah Raouf Boutaba.
A Bit-Map-Assisted Energy- Efficient MAC Scheme for Wireless Sensor Networks Jing Li and Georgios Y. Lazarou Department of Electrical and Computer Engineering,
Abstract 1/2 Wireless Sensor Networks (WSNs) having limited power resource report sensed data to the Base Station (BS) that requires high energy usage.
Wireless Access and Networking Technology (WANT) Lab. An Efficient Data Aggregation Approach for Large Scale Wireless Sensor Networks Globecom 2010 Lutful.
MAC Protocols for Sensor Networks
Ing-Ray Chen, Member, IEEE, Hamid Al-Hamadi Haili Dong Secure and Reliable Multisource Multipath Routing in Clustered Wireless Sensor Networks 1.
How to minimize energy consumption of Sensors in WSN Dileep Kumar HMCL 30 th Jan, 2015.
MAC Protocols for Sensor Networks
On Achieving Maximum Network Lifetime Through Optimal Placement of Cluster-heads in Wireless Sensor Networks High-Speed Networking Lab. Dept. of CSIE,
Distributed Minimum-Cost Clustering for Underwater Sensor Networks
Edinburgh Napier University
Presentation transcript:

Energy Hole Analysis for Energy Efficient Routing in Body Area Networks K. Latif, N. Javaid Kamran. Latif Senior System Analyst, National Institute of electronics, Pakistan PhD. Scholar, COMSATS, Pakistan Presented In: The 28 th IEEE International Conference on Advanced Information Networking and Applications (AINA-2014) Victoria, Canada, 1

Motivations Energy Hole Analysis of routing techniques: – Direct Transmission Technique (DTT) – Cluster Based routing Technique (CBT) – Intermediate node Transmission Technique (ITT) in Wireless Body Area Networks (WBANs) under different packet size, distance, and effect of overhearing. 2

Communication Architecture in DTT, CBT, and ITT 3

Sensor Nodes Deployment Sensor Nodes deployment with sequence numbers as depicted in plots. 4 Seq. No.Sensor Node 1EEG 2Hearing Aid 3Position 4ECG 5Glucose 6SPO2 7Insulin pump 8EMG Lactic Acid 9Motion 10Pressure

WBAN Radio Energy Consumption 5

Energy Consumption in DTT 6

Energy Consumption in IBT Two types of nodes – Originator node – Intermediate node Transmit energy of originator node (eq. 6) Transmit energy of intermediate node (eq. 7) Where ɸ is the data aggregation factor 7

If there are m intermediate nodes then total energy consumption of all intermediate nodes for the whole network lifetime is given by the following equation: (eq. 8) Receive Energy of Listener nodes: (eq. 9) Energy Consumption in IBT 8

Energy Consumption in CBT Two types of nodes: – Normal nodes – CH nodes Transmit energy of normal nodes will be consumed according to eq. 1 Transmit energy of CH nodes (eq. 8) 9

Energy Consumption in CBT Receive energy of CH nodes Receive Energy of listener nodes – If there are m listener nodes each with probability of existence p then, total receive energy of all these nodes is : 10

DISCUSSION AND RESULTS 11

Effect of Distance on Energy Consumption Due to small distances in WBAN, effect of distance on DTT and ITT is minimum. High energy consumption of CBT is because of packet size, because packet size of CH is greater than normal nodes. 12

Effect of Distance on Lifetime of nodes Nodes near to coordinator have longer life time in DTT and ITT Nodes away from coordinator have almost equal effect on lifetime. In CBT, nodes nearer to coordinator has smaller lifetime because of large packet size. 13

Effect of Packet size on Energy Consumption of nodes in ITT & DTT Effect of packet size on energy consumption is minimal on the nodes near to coordinator Whereas it appeared as multiplicative shift on distant nodes. 14

Effect of Packet size on Energy Consumption of nodes in CBT Effect of packet size appeared as multiplicative shift in the energy consumption of nodes 15

Effect of Over-hearing on Energy Consumption of nodes in CBT In CBT, effect of over- hearing is minimal as compared to DTT and ITT, reason behind minimal effect is the distance between node and CH is minimum Nodes with in the cluster are affected due to over hearing, therefore energy consumption is increased 16

Effect of Over-hearing on Energy Consumption of nodes in DTT & ITT Energy consumption is largely affected due to over-hearing in DTT and ITT However in DTT, over- hearing is creating almost double effect on energy consumption. 17

Conclusion We analysed DTT, ITT, and CBT in WBAN We found that distance does not effect transmission energy in DTT, because of small distances. Over-hearing effects DTT energy consumption because a far distance node when transmits data to coordinator, its transmission radius increases and maximum nodes are affected. This may become a reason for creation of energy hole if over-hearing is controlled at MAC or routing layer then DTT provides better result. 18

Conclusion In ITT, reduction in transmission distance reduces overhearing effect. However increased data size at forwarding node causes more consumption of energy. Therefore ITT is more suitable for applications in which life time of nodes with more critical data is to be increased with reduced overhearing effects. 19

Conclusion In CBT, CHs energy is badly affected due to large size of data CH at far distance from coordinator then its energy consumed very quickly CBT produces less overhearing effects with in the cluster Therefore CBT technique is more suitable for applications where periodic forwarding of data is to be required. 20

Future Work we are intended to propose an intelligent energy efficient technique which takes benefits of DTT, ITT, and CBT depending on nature of traffic (critical, emergency, normal) and optimize throughput and lifetime of network. 21