CS230 Project Mobility in Energy Harvesting Wireless Sensor Network Nga Dang, Henry Nguyen, Xiujuan Yi.

Slides:

Advertisements

Similar presentations

Multicasting in Mobile Ad hoc Networks By XIE Jiawei.

Advertisements

Advisor : Prof. Yu-Chee Tseng Student : Yi-Chen Lu 12009/06/26.

Maximum Battery Life Routing to Support Ubiquitous Mobile Computing in Wireless Ad Hoc Networks By C. K. Toh.

Decentralized Reactive Clustering in Sensor Networks Yingyue Xu April 26, 2015.

Target Tracking Algorithm based on Minimal Contour in Wireless Sensor Networks Jaehoon Jeong, Taehyun Hwang, Tian He, and David Du Department of Computer.

Energetic Sustainability of Routing Algorithms in Energy Harvesting Wireless Sensor Networks Edoardo Regini Emanuele Lattanzi Andrea Acquaviva Alessandro.

Semester Copyright USM EEE442 Computer Networks The Data Link / Network Layer Functions: Switching En. Mohd Nazri Mahmud MPhil (Cambridge, UK)

1 On Handling QoS Traffic in Wireless Sensor Networks 吳勇慶.

REAL TIME COMMUNICATION IN WIRELESS SENSOR NETWORKS BY ZILLE HUMA KAMAL.

Agent-friendly aggregation 1 On agent-friendly aggregation in networks ATSN 2008 (at AAMAS 2008) Christian Sommer and Shinichi Honiden National Institute.

1 Rendezvous Design Algorithms for Wireless Sensor Networks with a Mobile Station Guoliang Xing; Tian Wang; Weijia Jia; Minming Li Department of Computer.

A Survey on Energy Efficient MAC Protocol for Wireless Sensor Networks Huma Naushad.

Rendezvous Planning in Mobility- assisted Wireless Sensor Networks Guoliang Xing; Tian Wang; Zhihui Xie; Weijia Jia Department of Computer Science City.

LPT for Data Aggregation in Wireless Sensor networks Marc Lee and Vincent W.S Wong Department of Electrical and Computer Engineering, University of British.

Online Data Gathering for Maximizing Network Lifetime in Sensor Networks IEEE transactions on Mobile Computing Weifa Liang, YuZhen Liu.

CS 441: Charles Durran Kelly.  What are Wireless Sensor Networks?  WSN Challenges  What is a Smartphone Sensor Network?  Why use such a network? 

On the Energy Efficient Design of Wireless Sensor Networks Tariq M. Jadoon, PhD Department of Computer Science Lahore University of Management Sciences.

Power saving technique for multi-hop ad hoc wireless networks.

Wireless Video Sensor Networks Vijaya S Malla Harish Reddy Kottam Kirankumar Srilanka.

Kick-off meeting 3 October 2012 Patras. Research Team B Communication Networks Laboratory (CNL), Computer Engineering & Informatics Department (CEID),

Delay-aware Routing in Low Duty-Cycle Wireless Sensor Networks Guodong Sun and Bin Xu Computer Science and Technology Department Tsinghua University, Beijing,

Energy Harvesting and Wireless Sensor Networks Adam Skelton.

Switching Techniques Student: Blidaru Catalina Elena.

Energy Saving In Sensor Network Using Specialized Nodes Shahab Salehi EE 695.

Special Topics on Algorithmic Aspects of Wireless Networking Donghyun (David) Kim Department of Mathematics and Computer Science North Carolina Central.

CS 712 | Fall 2007 Using Mobile Relays to Prolong the Lifetime of Wireless Sensor Networks Wei Wang, Vikram Srinivasan, Kee-Chaing Chua. National University.

Fault Tolerant and Mobility Aware Routing Protocol for Mobile Wireless Sensor Network Name : Tahani Abid Aladwani ID :

Vikramaditya. What is a Sensor Network?  Sensor networks mainly constitute of inexpensive sensors densely deployed for data collection from the field.

Steady and Fair Rate Allocation for Rechargeable Sensors in Perpetual Sensor Networks Zizhan Zheng Authors: Kai-Wei Fan, Zizhan Zheng and Prasun Sinha.

IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2007 (TPDS 2007)

EShare: A Capacitor-Driven Energy Storage and Sharing Network for Long-Term Operation(Sensys 2010) Ting Zhu, Yu Gu, Tian He, Zhi-Li Zhang Department of.

An Introduction Table Of Context Sensor Network PreviewRouting in Sensor NetworksMobility in Sensor Networks Structure and characteristics of nodes and.

TRUST, Spring Conference, April 2-3, 2008 Taking Advantage of Data Correlation to Control the Topology of Wireless Sensor Networks Sergio Bermudez and.

“Intra-Network Routing Scheme using Mobile Agents” by Ajay L. Thakur.

Grammati Pantziou 1, Aristides Mpitziopoulos 2, Damianos Gavalas 2, Charalampos Konstantopoulos 3, and Basilis Mamalis 1 1 Department of Informatics, Technological.

SoftCOM 2005: 13 th International Conference on Software, Telecommunications and Computer Networks September 15-17, 2005, Marina Frapa - Split, Croatia.

On Renewable Sensor Networks with Wireless Energy Transfer IEEE INFOCOM 2011 Yi Shi, Liguang Xie, Y. Thomas Hou, Hanif D. Sherali.

KAIS T CS712 병렬처리 특강 - 차세대 무선네트워크 응용 및 보안 - Syllabus Network & Security Lab.

Data and Computer Communications Circuit Switching and Packet Switching.

Maximum Lifetime Routing in Wireless Sensor Networks by Collins Adetu Nicole Powell Course: EEL 5784 Instructor: Dr. Ming Yu.

SENSOR NETWORKS BY Umesh Shah Mayuresh Patil G P Reddy GUIDES Prof U.B.Desai Prof S.N.Merchant.

AD-HOC NETWORK SUBMITTED BY:- MIHIR GARG A B.TECH(E&T)/SEC-A.

Mobile Agent Migration Problem Yingyue Xu. Energy efficiency requirement of sensor networks Mobile agent computing paradigm Data fusion, distributed processing.

Wireless Sensor Network (WSN). WSN - Basic Concept WSN is a wireless network consisting of spatially distributed autonomous devices using sensors to cooperatively.

1 Dynamic Sleeping Scheduling for Real-time Wireless Sensor Networks Department of EECS University of Tennessee, Knoxville Xiaodong Wang, Yanjun Yao.

Node Reclamation and Replacement for Long-lived Sensor Networks Bin Tong, Wensheng Zhang, and Chuang Wang Department of Computer Science, Iowa State University.

Evaluating Wireless Network Performance David P. Daugherty ITEC 650 Radford University March 23, 2006.

Mobility assistance in Wireless Sensor Network Xiujuan Yi Nalini Venkatasubramanian University of California, Irvine.

Simulation of Sensor Clustering in WBAN Networks

1/8/2016 Wireless Sensor Networks COE 499 Sleep-based Topology Control I Tarek Sheltami KFUPM CCSE COE

An Energy-Efficient Geographic Routing with Location Errors in Wireless Sensor Networks Julien Champ and Clement Saad I-SPAN 2008, Sydney (The international.

McGraw-Hill©The McGraw-Hill Companies, Inc., 2000 CH. 8: SWITCHING & DATAGRAM NETWORKS 7.1.

Data Gathering in Wireless Sensor Networks with Mobile Collectors Ming Ma and Yuanyuan Yang State University of New York, Stony Brook 1 IEEE Parallel and.

Self-stabilizing energy-efficient multicast for MANETs.

Toward Reliable and Efficient Reporting in Wireless Sensor Networks Authors: Fatma Bouabdallah Nizar Bouabdallah Raouf Boutaba.

Repairing Sensor Network Using Mobile Robots Y. Mei, C. Xian, S. Das, Y. C. Hu and Y. H. Lu Purdue University, West Lafayette ICDCS 2006 Speaker ： Shih-Yun.

1 Minimum Interference Algorithm for Integrated Topology Control and Routing in Wireless Optical Backbone Networks Fangting Sun Mark Shayman University.

Wireless Sensor Network: A Promising Approach for Distributed Sensing Tasks.

-1/16- Maximum Battery Life Routing to Support Ubiquitous Mobile Computing in Wireless Ad Hoc Networks C.-K. Toh, Georgia Institute of Technology IEEE.

Energy Efficiency Energy consumption is the most important factor to determine the life of sensor network. since sensors networks has low power resources,

A Secure Routing Protocol with Intrusion Detection for Clustering Wireless Sensor Networks International Forum on Information Technology and Applications.

Ing-Ray Chen, Member, IEEE, Hamid Al-Hamadi Haili Dong Secure and Reliable Multisource Multipath Routing in Clustered Wireless Sensor Networks 1.

Data and Computer Communications Eighth Edition by William Stallings Lecture slides by Lawrie Brown Chapter 10 – Circuit Switching and Packet Switching.

Jan 2017 report.

Switching Techniques In large networks there might be multiple paths linking sender and receiver. Information may be switched as it travels through various.

Net 435: Wireless sensor network (WSN)

Switching Techniques.

Differences between WSN & MANET By: Dana Alotaibi.

LEACH Protocol for Wireless Sensor Networks

Presentation transcript:

CS230 Project Mobility in Energy Harvesting Wireless Sensor Network Nga Dang, Henry Nguyen, Xiujuan Yi

What is our project? Motivation: Wireless Sensor Network - Sensor nodes are powered by batteries - High maintenance cost - Unreliability: network is disconnected when nodes are out of battery Energy Harvesting WSN - Powered by a centralized energy harvesting source whose energy is delivered to sensor nodes by robot - Advantage: + Green computing + Autonomous system + Low maintenance cost Energy Harvesting System Model Battery System Model

What have other groups done? Energy-Efficient Approaches in WSN – Hardware layer: energy-efficient circuit, redundant deployment _ Network layer: energy-efficient routing protocol and network topology _ Operating system: dynamic voltage scheduling, duty cycling _ Application layer: energy-efficient quality-aware data collection, multi-version applications Use robot mobility as data collector – Robot is scheduled to visit sensor nodes, collecting data in close range – Goal: prolong system’s lifetime reduce transmission energy for sensor nodes (shorter range) Find a shortest path to minimize travelling energy Avoid buffer flow at sensor node’s data buffer, deliver data in time Usually modeled as Travelling Salesman Problem with additional constraints Hardware layer Network layer Operating system Application

What have other groups done? (cont.) Use robot mobility as energy deliverer – Robot is equipped with a large capacity battery – Sensors’ nodes batteries are monitoring periodically – Every hour k nodes with least remaining energy are chosen and robot will visit and charge these nodes through wireless transfer – Prolong system lifetime by charging extra battery – Disadvantage: System lifetime extension is limited by robot’s battery capacity Maintenance cost: changing robot battery

How does our system work? Sensor Nodes Base Station Send Energy Requests Collect Energy Requests &Run algorithm to schedule charging activity Robot Send schedule to robot Execute plan: Visit nodes and recharge batteries Report charging status Sensor Nodes

The charging algorithm If the robot can’t visit all the node. - It should find the maximum subset of nodes it can visit and give the shortest path of that subset. Find a starting time satisfy both energy and timing constraints Input: Energy request queue: sensor deadline Input: Robot charging status Robot speed & power consumption & energy harvesting profile Travelling Salesman Problem Input to TSP: D[i]: Deadline of each sensor node C[i,j]: Time to travel from node i to node j W[i]: Waiting time at each node i to charge Output: A sequence of sensor nodes which robot had to visit

6:10 8:00 9:30 13:15 7:30 leave base station at 5:00 get back at 23:40 12:00 21:00 23: hour charging 1 hour 2 hours 1.5 hours 2 hours 4 hours 2 hours Charging algorithm example 0.5 hour charging