Energy-Efficient Computing for Wildlife Tracking: Design Tradeoffs and Early Experiences with ZebraNet Presented by Eric Arnaud Makita 1232036006

Slides:



Advertisements
Similar presentations
What Is an Ad Hoc Network?
Advertisements

ZebraNet Rolf Kristensen & Torben Jensen s s
Minimum Energy Mobile Wireless Networks IEEE JSAC 2001/10/18.
A Mobile Ad hoc Biosensor Network Muzammil KP S7,ECE Govt. Engg. College, Wayanad.
TOPOLOGIES FOR POWER EFFICIENT WIRELESS SENSOR NETWORKS ---KRISHNA JETTI.
Improvement on LEACH Protocol of Wireless Sensor Network
Presented By- Sayandeep Mitra TH SEMESTER Sensor Networks(CS 704D) Assignment.
Presentation on “WSN Application” (ZebraNet) Presented by: Belal Abdullah Abdulmaged 28\11\2011.
Sensor Network Platforms and Tools
Impala: A Middleware System for Managing Autonomic, Parallel Sensor Systems Ting Liu and Margaret Martonosi Princeton University.
1 An Approach to Real-Time Support in Ad Hoc Wireless Networks Mark Gleeson Distributed Systems Group Dept.
Highly-Resilient, Energy-Efficient Multipath Routing in Wireless Sensor Networks Computer Science Department, UCLA International Computer Science Institute,
Mobile and Wireless Computing Institute for Computer Science, University of Freiburg Western Australian Interactive Virtual Environments Centre (IVEC)
What is a Wireless Sensor Network (WSN)? An autonomous, ad hoc system consisting of a collective of networked sensor nodes designed to intercommunicate.
By Libo Song and David F. Kotz Computer Science,Dartmouth College.
1-1 Topology Control. 1-2 What’s topology control?
1 Energy-Efficient localization for networks of underwater drifters Diba Mirza Curt Schurgers Department of Electrical and Computer Engineering.
The Impact of Spatial Correlation on Routing with Compression in WSN Sundeep Pattem, Bhaskar Krishnamachri, Ramesh Govindan University of Southern California.
Location Systems for Ubiquitous Computing Jeffrey Hightower and Gaetano Borriello.
Extending Network Lifetime for Precision-Constrained Data Aggregation in Wireless Sensor Networks Xueyan Tang School of Computer Engineering Nanyang Technological.
Online Data Gathering for Maximizing Network Lifetime in Sensor Networks IEEE transactions on Mobile Computing Weifa Liang, YuZhen Liu.
Hardware Design Experiences in ZebraNet [ZSLM04] - Princeton University Sensys ‘04 Presented By: Jay Taneja.
1 Challenging the Modeling Assumptions of Mobile Networks Seminar 266 Michalis Faloutsos.
Mobile and Wireless Computing Institute for Computer Science, University of Freiburg Western Australian Interactive Virtual Environments Centre (IVEC)
Energy Saving In Sensor Network Using Specialized Nodes Shahab Salehi EE 695.
Presented by Amira Ahmed El-Sharkawy Ibrahim.  There are six of eight turtle species in Ontario are listed as endangered, threatened or of special concern.
A Mobile Sensor Network Using Autonomously Controlled Animals Yihan Li, Shivendra S. Panwar and Srinivas Burugupalli New York State Center for Advanced.
Tom Chao Zhou, CUHK 1 Wireless Sensor Network Speaker: Tom Chao Zhou Feb, Study Group Subtopic: Sensor Technology.
Secure Cell Relay Routing Protocol for Sensor Networks Xiaojiang Du, Fengiing Lin Department of Computer Science North Dakota State University 24th IEEE.
ZebraNet Hardware Design Experiences in ZebraNet Presented by Zuhal Tepecik.
Gathering Data in Wireless Sensor Networks Madhu K. Jayaprakash.
College of Engineering Non-uniform Grid- based Coordinated Routing Priyanka Kadiyala Major Advisor: Dr. Robert Akl Department of Computer Science and Engineering.
Demo. Overview Overall the project has two main goals: 1) Develop a method to use sensor data to determine behavior probability. 2) Use the behavior probability.
SoftCOM 2005: 13 th International Conference on Software, Telecommunications and Computer Networks September 15-17, 2005, Marina Frapa - Split, Croatia.
Mobile Relay Configuration in Data-Intensive Wireless Sensor Networks.
Patch Based Mobile Sink Movement By Salman Saeed Khan Omar Oreifej.
TRICKLE: A Self-Regulating Algorithm for Code Propagation and Maintenance in Wireless Sensor Networks Philip Levis, Neil Patel, Scott Shenker and David.
1/30 Energy-Efficient Forwarding Strategies for Geographic Routing in Lossy Wireless Sensor Networks Wireless and Sensor Network Seminar Dec 01, 2004.
한국기술교육대학교 컴퓨터 공학 김홍연 Habitat Monitoring with Sensor Networks DKE.
ENERGY-EFFICIENT FORWARDING STRATEGIES FOR GEOGRAPHIC ROUTING in LOSSY WIRELESS SENSOR NETWORKS Presented by Prasad D. Karnik.
Load-Balancing Routing in Multichannel Hybrid Wireless Networks With Single Network Interface So, J.; Vaidya, N. H.; Vehicular Technology, IEEE Transactions.
Energy-Efficient Signal Processing and Communication Algorithms for Scalable Distributed Fusion.
Rushing Attacks and Defense in Wireless Ad Hoc Network Routing Protocols ► Acts as denial of service by disrupting the flow of data between a source and.
Efficient Energy Management Protocol for Target Tracking Sensor Networks X. Du, F. Lin Department of Computer Science North Dakota State University Fargo,
Differential Ad Hoc Positioning Systems Presented By: Ramesh Tumati Feb 18, 2004.
11/25/2015 Wireless Sensor Networks COE 499 Localization Tarek Sheltami KFUPM CCSE COE 1.
By Naeem Amjad 1.  Challenges  Introduction  Motivation  First Order Radio Model  Proposed Scheme  Simulations And Results  Conclusion 2.
The ZebraNet Wild Life Tracker Department of Electrical Engineering Princeton University.
Algorithms for Energy-Efficient Multicasting in Static Ad Hoc Wireless Networks Mobile Networks and Applications 6, ,2001 Author : JEFFREY E. WIESELTHIER.
Performance of Adaptive Beam Nulling in Multihop Ad Hoc Networks Under Jamming Suman Bhunia, Vahid Behzadan, Paulo Alexandre Regis, Shamik Sengupta.
A Wakeup Scheme for Sensor Networks: Achieving Balance between Energy Saving and End-to-end Delay Xue Yang, Nitin H.Vaidya Department of Electrical and.
Tufts Wireless Laboratory School Of Engineering Tufts University Paper Review “An Energy Efficient Multipath Routing Protocol for Wireless Sensor Networks”,
An Efficient Localization Algorithm Focusing on Stop-and-Go Behavior of Mobile Nodes IEEE PerCom 2011 Takamasa Higuchi, Sae Fujii, Hirozumi Yamaguchi and.
Wireless sensor and actor networks: research challenges
Energy-Efficient Signal Processing and Communication Algorithms for Scalable Distributed Fusion.
Hierarchical Trust Management for Wireless Sensor Networks and Its Applications to Trust-Based Routing and Intrusion Detection Wenhai Sun & Ruide Zhang.
Toward Reliable and Efficient Reporting in Wireless Sensor Networks Authors: Fatma Bouabdallah Nizar Bouabdallah Raouf Boutaba.
On Mobile Sink Node for Target Tracking in Wireless Sensor Networks Thanh Hai Trinh and Hee Yong Youn Pervasive Computing and Communications Workshops(PerComW'07)
Mobile Node for Wireless Sensor Network to Detect Landmines Presented by : Jameela Hassan.
1 Effectiveness of Physical and Virtual Carrier Sensing in IEEE Wireless Ad Hoc Networks Fu-Yi Hung and Ivan Marsic WCNC 2007.
I owa S tate U niversity Laboratory for Advanced Networks (LAN) Coverage and Connectivity Control of Wireless Sensor Networks under Mobility Qiang QiuAhmed.
VADD: Vehicle-Assisted Data Delivery in Vehicular Ad Hoc Networks Zhao, J.; Cao, G. IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 鄭宇辰
A Spatial-based Multi-resolution Data Dissemination Scheme for Wireless Sensor Networks Jian Chen, Udo Pooch Department of Computer Science Texas A&M University.
Overview of Wireless Networks:
Wireless Sensor Network Architectures
Energy-Efficient Communication Protocol for Wireless Microsensor Networks by Wendi Rabiner Heinzelman, Anantha Chandrakasan, and Hari Balakrishnan Presented.
Energy-Efficient Computing for Wildlife Tracking
Protocols.
Header Store & Haul Improving Mobile Ad-Hoc Network Connectivity through Repeated Controlled Flooding Thesis Presentation Robert Tyson Thedinger Department.
Protocols.
Presentation transcript:

Energy-Efficient Computing for Wildlife Tracking: Design Tradeoffs and Early Experiences with ZebraNet Presented by Eric Arnaud Makita

ABSTRACT  This paper examines the research decisions and design tradeoffs that arise when applying wireless peer-to-peer networking techniques in a mobile sensor network designed to support wildfire tracking for biology research.  The ZebraNet: System which includes custom tracking collars (nodes) carried by animals under study across a large, wild area.  Targeted Problem: To use the least energy, storage, and other resources necessary to maintain reliable system with a very high data homing success rate.

INTRODUCTION The main focus of mobile computing has been on system such as PDAs and telephones intended for direct human use. Research attention is increasingly focused, however, on systems with more limited human intervention; wireless sensor networks are a key example. ZebraNet, a wireless sensor network aimed at wildfire tracking. We focus here on issue related to dynamic sensor networks with mobile nodes and wireless communication between them. An increasing focus of biology and biocomplexity research has been on gathering data and observations on a range of species, with goal of understanding their interactions and influences on each other. Current wildfire tracking studies rely on fairly simple technology. Many studies rely on collaring a sample subset of animals with simple VHF transmitters. Data collection is infrequent and may miss many “interesting event”. Data collection is often limited to daylight hours…

We do not assume the presence of fixed antenna towers or cellular telephone service. The system therefore uses peer-to-peer data swaps to move the data around. Contribution of this paper: First, we believe we are the first to study protocols for mobile sensor networks in which the “ base” station is also mobile. Second, zebra-tracking is a domain in which the node mobility models are largely unknown, and in fact are ultimately the research goal. We examine energy tradeoffs in detail, using real system energy measurements for ZebraNet prototype hardware in operation. In considering ZebraNet, some questions arise such: o How to make the communication protocol both effective and power- efficient? o To what extent can we rely on ad hoc, peer-to-peer transfers in sparsely- connected spatially-huge sensors network? o How can we provide comprehensive tracking of a collection of animals?

ZEBRANET DESIGN GOALS GPS position sample taken every three minutes Detailed activity logs taken for 3minutes every hour 1 year of operation without direct human intervention Operation over a wide range No fixed base station, antennas, or cellular service Overall, the key goal is to deliver back to the researchers a very high fraction of the data collected over the months or years that the system is in operation.

ZebraNet Problem Statement The engineering research problems arise from several issues: Weight limits on each node translate almost directly to computational energy limits. Our collar and protocol design decisions must manage the number and size of data transmissions required. We must also make system design choices that limit the range of transmissions, since the required transmitter energy increase dramatically with the distance transmitted. Some of the key challenges in ZebraNet come from the spatial and temporal scale of the system.

A DAY IN THE LIFE OF ZEBRA Mobility models are at the core of design decisions for many mobile networks. To design ZebraNet, we also need to understand how the nodes will move, as this critically affects hardware, protocol and overall system design.

Social structure and Collaring Understanding how they use the landscape requires collaring representative individuals and characterizing their fine-grained movements and behavior over large scale. Females typically initiate movements but the male often adjust the direction and speed of movement of the group. By collaring only the male we can effectively track the movement of individuals, vastly reducing the number of collaring required as we try to characterize the movements of entire plains zebra populations.

Movement Patterns

Distance Moved The net distance from the beginning of the three-minutes interval to the end

Turning Angle Absolute value of the angle between the start of the time interval and the end of the time interval

COLLAR DESIGN

Energy Issues and Power Supply

PROTOCOL DESIGN The goal in ZebraNet is to gather data collected at each back to the base station. In ZebraNet, all nodes except the base station are data sources, while the base station alone is a data sink.

Flooding Protocol

History-based protocol

ZnetSim ZnetSim( user-defined storage & bandwidth constraints ) (success rate, energy comsuption) We compared ZnetSim`s mobility model with that observed by biologists and found our distribution to match almost exactly with Figures 2 and 3.

Network connectivity Direct connectivity: This counts neighbors encountered by each node. Indirect connectivity: in addition to direct neighbors, indirect con nectivity includes nodes that are reachable via multihop relay th rough neighbors and neighbors` neighbors.

Average percentage of distinct neighbors encountered directly and indirectly

Protocol Evaluations

Storage Constraints

Bandwidth Constraints

Energy Tradeoffs

Final Design Choices Simulating a flooding protocol for short range and a direct protocol for long range, our simulations show an 83% success rate.

Related work Sensor networks in general, and environmental sensing in particular, are areas of considerable research interest.

Conclusion This paper discusses the design tradeoffs and early experiences in building a low-power wireless system for position tracking of wildfire. By using peer-to-peer networking techniques, our system can forward data to a researcher`s mobile base station without assuming the presence of any cellular phone service or widely available telecommunication support.