Sensor Network Navigation without Locations Mo Li, Yunhao Liu, Jiliang Wang, and Zheng Yang Department of Computer Science and Engineering Hong Kong University.

Slides:



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

Integrated Coverage and Connectivity Configuration in Wireless Sensor Networks Xiaorui Wang, Guoliang Xing, Yuanfang Zhang*, Chenyang Lu, Robert Pless,
Beyond Trilateration: On the Localizability of Wireless Ad Hoc Networks Reported by: 莫斌.
Ranveer Chandra , Kenneth P. Birman Department of Computer Science
An Analysis of the Optimum Node Density for Ad hoc Mobile Networks Elizabeth M. Royer, P. Michael Melliar-Smith and Louise E. Moser Presented by Aki Happonen.
Beneficial Caching in Mobile Ad Hoc Networks Bin Tang, Samir Das, Himanshu Gupta Computer Science Department Stony Brook University.
1 Distributed Navigation Algorithms for Sensor Networks Chiranjeeb Buragohain, Divyakant Agrawal, Subhash Suri Dept. of Computer Science, University of.
T H E O H I O S T A T E U N I V E R S I T Y Computer Science and Engineering 1 Wenjun Gu, Xiaole Bai, Sriram Chellappan and Dong Xuan Presented by Wenjun.
Distributed Quad-Tree for Spatial Querying in Wireless Sensor Networks (WSNs) Murat Demirbas, Xuming Lu Dept of Computer Science and Engineering, University.
Before start… Earlier work single-path routing in sensor networks
1 A Distributed Delay-Constrained Dynamic Multicast Routing Algorithm Quan Sun and Horst Langendorfer Telecommunication Systems Journal, vol.11, p.47~58,
Dynamic Medial Axis Based Motion Planning in Sensor Networks Lan Lin and Hyunyoung Lee Department of Computer Science University of Denver
Distributed Quad-Tree for Spatial Querying in Wireless Sensor Networks (WSNs) Murat Demirbas, Xuming Lu Dept of Computer Science and Engineering, University.
11/14/ 2006ICNP Virtual Surrounding Face Geocasting with Guaranteed Message Delivery for Ad Hoc and Sensor Networks Jie Lian, Kshirasagar Naik University.
1 Distributed Algorithms for Guiding Navigation across a Sensor Network Qun Li, Michael De Rosa, and Daniela Rus Department of Computer Science Dartmouth.
Mobile Ad hoc Networks COE 549 Routing Protocols I
Yanyan Yang, Yunhuai Liu, and Lionel M. Ni Department of Computer Science and Engineering, Hong Kong University of Science and Technology IEEE MASS 2009.
Performance and Power Efficient On-Chip Communication Using Adaptive Virtual Point-to-Point Connections M. Modarressi, H. Sarbazi-Azad, and A. Tavakkol.
Roger ZimmermannCOMPSAC 2004, September 30 Spatial Data Query Support in Peer-to-Peer Systems Roger Zimmermann, Wei-Shinn Ku, and Haojun Wang Computer.
SOS: A Safe, Ordered, and Speedy Emergency Navigation Algorithm in Wireless Sensor Networks Andong Zhan ∗ †, Fan Wu ∗, Guihai Chen ∗ ∗ Shanghai Key Laboratory.
WAN technologies and routing Packet switches and store and forward Hierarchical addresses, routing and routing tables Routing table computation Example.
1 Routing. 2 Routing is the act of deciding how each individual datagram finds its way through the multiple different paths to its destination. Routing.
Message-Optimal Connected Dominating Sets in Mobile Ad Hoc Networks Paper By: Khaled M. Alzoubi, Peng-Jun Wan, Ophir Frieder Presenter: Ke Gao Instructor:
Rate-based Data Propagation in Sensor Networks Gurdip Singh and Sandeep Pujar Computing and Information Sciences Sanjoy Das Electrical and Computer Engineering.
CS-691 SEMINAR Dr. Omar Abdullah Batarfi
Boundary Recognition in Sensor Networks by Topology Methods Yue Wang, Jie Gao Dept. of Computer Science Stony Brook University Stony Brook, NY Joseph S.B.
Network and Communications Ju Wang Chapter 5 Routing Algorithm Adopted from Choi’s notes Virginia Commonwealth University.
RoamHBA : Maintaining Group Connectivity In Sensor Networks Qing Fang Jie Liu Leonidas Guibas Feng Zhao Department of Electrical Engineering, Stanford.
Quantitative Evaluation of Unstructured Peer-to-Peer Architectures Fabrício Benevenuto José Ismael Jr. Jussara M. Almeida Department of Computer Science.
Salah A. Aly,Moustafa Youssef, Hager S. Darwish,Mahmoud Zidan Distributed Flooding-based Storage Algorithms for Large-Scale Wireless Sensor Networks Communications,
Load-Balancing Routing in Multichannel Hybrid Wireless Networks With Single Network Interface So, J.; Vaidya, N. H.; Vehicular Technology, IEEE Transactions.
On Reducing Broadcast Redundancy in Wireless Ad Hoc Network Author: Wei Lou, Student Member, IEEE, and Jie Wu, Senior Member, IEEE From IEEE transactions.
Zibin Zheng DR 2 : Dynamic Request Routing for Tolerating Latency Variability in Cloud Applications CLOUD 2013 Jieming Zhu, Zibin.
1 Shape Segmentation and Applications in Sensor Networks Xianjin Xhu, Rik Sarkar, Jie Gao Department of CS, Stony Brook University INFOCOM 2007.
GPSR: Greedy Perimeter Stateless Routing for Wireless Networks EECS 600 Advanced Network Research, Spring 2005 Shudong Jin February 14, 2005.
Efficient Energy Management Protocol for Target Tracking Sensor Networks X. Du, F. Lin Department of Computer Science North Dakota State University Fargo,
MobiQuitous 2007 Towards Scalable and Robust Service Discovery in Ubiquitous Computing Environments via Multi-hop Clustering Wei Gao.
Dual-Region Location Management for Mobile Ad Hoc Networks Yinan Li, Ing-ray Chen, Ding-chau Wang Presented by Youyou Cao.
Efficient Computing k-Coverage Paths in Multihop Wireless Sensor Networks XuFei Mao, ShaoJie Tang, and Xiang-Yang Li Dept. of Computer Science, Illinois.
1 Presented by Jing Sun Computer Science and Engineering Department University of Conneticut.
GLIDER: Gradient Landmark-Based Distributed Routing for Sensor Networks Qing Fang, Jie Gao, Leonidas J. Guibas, Vin de Silva, Li Zhang Department of Electrical.
Po-Yu Chen, Zan-Feng Kao, Wen-Tsuen Chen, Chi-Han Lin Department of Computer Science National Tsing Hua University IEEE ICPP 2011 A Distributed Flow-Based.
SHORT: Self-Healing and Optimizing Routing Techniques for Mobile Ad Hoc Networks Presenter: Sheng-Shih Wang October 30, 2003 Chao Gui and Prasant Mohapatra.
Hole Detection and Boundary Recognition in Wireless Sensor Networks Kun-Ying Hsieh ( 謝坤穎 ) Dept. of Computer Science and Information Engineering National.
Location Privacy Protection for Location-based Services CS587x Lecture Department of Computer Science Iowa State University.
Localized Low-Power Topology Control Algorithms in IEEE based Sensor Networks Jian Ma *, Min Gao *, Qian Zhang +, L. M. Ni *, and Wenwu Zhu +
Energy Efficient Data Management for Wireless Sensor Networks with Data Sink Failure Hyunyoung Lee, Kyoungsook Lee, Lan Lin and Andreas Klappenecker †
Spring 2000CS 4611 Routing Outline Algorithms Scalability.
1 VLM 2 : A Very Lightweight Mobile Multicast System For Wireless Sensor Networks Anmol Sheth, Brian Shucker and Richard Han University of Colorado, Department.
Location-Centric Storage for Wireless Sensor Networks Kai Xingn 1, Xiuzhen Cheng 1, and Jiang Li 2 1 Department of Computer Science, The George Washington.
A Two-Tier Heterogeneous Mobile Ad Hoc Network Architecture and Its Load-Balance Routing Problem C.-F. Huang, H.-W. Lee, and Y.-C. Tseng Department of.
EASE: An Energy-Efficient In-Network Storage Scheme for Object Tracking in Sensor Networks Jianliang Xu Department of Computer Science Hong Kong Baptist.
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)
Dynamic Proxy Tree-Based Data Dissemination Schemes for Wireless Sensor Networks Wensheng Zhang, Guohong Cao and Tom La Porta Department of Computer Science.
1 Traffic Engineering By Kavitha Ganapa. 2 Introduction Traffic engineering is concerned with the issue of performance evaluation and optimization of.
Construction of Optimal Data Aggregation Trees for Wireless Sensor Networks Deying Li, Jiannong Cao, Ming Liu, and Yuan Zheng Computer Communications and.
Reliable Mobicast via Face- Aware Routing Qingfeng Huang,Chenyang Lu and Gruia-Catalin Roman Department of Computer Science and Engineering Washington.
VADD: Vehicle-Assisted Data Delivery in Vehicular Ad Hoc Networks Zhao, J.; Cao, G. IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 鄭宇辰
1 Along & across algorithm for routing events and queries in wireless sensor networks Tat Wing Chim Department of Electrical and Electronic Engineering.
Distributed Caching and Adaptive Search in Multilayer P2P Networks Chen Wang, Li Xiao, Yunhao Liu, Pei Zheng The 24th International Conference on Distributed.
A Place-based Model for the Internet Topology Xiaotao Cai Victor T.-S. Shi William Perrizo NDSU {Xiaotao.cai, Victor.shi,
COGNITIVE APPROACH TO ROBOT SPATIAL MAPPING
Virtual Domain and Coordinate Routing in Wireless Sensor Networks
A Study of Group-Tree Matching in Large Scale Group Communications
Analysis of Node Localizability in Wireless Ad-hoc Networks
Surviving Holes and Barriers in Geographic Data Reporting for
ECE 544 Protocol Design Project 2016
                                                                                                            Network Decoupling for Secure Communications.
                                                                                                            Network Decoupling for Secure Communications.
Speaker : Lee Heon-Jong
Presentation transcript:

Sensor Network Navigation without Locations Mo Li, Yunhao Liu, Jiliang Wang, and Zheng Yang Department of Computer Science and Engineering Hong Kong University of Science and Technology, Hong Kong Study group at 5/11 by Jason

Outline System introduction Design principles Implementation experience Performance evaluation

System introduction Traditional sensor network – data centric,efficiently collecting, routing, and processing in-network sensory data. Difference of human navigation network – no physically multicast or copied – Limited human speed – Frequent updating of emergency or dangerous situation changing

System introduction Human navigation system based on sensor network with two characteristics: – Release the necessity of utilizing location information – Address the dynamic leading to variations of dangerous area

System introduction Small-scale with 36 TelosB Motes on Objectives and requirements:  Safe: Be apart from dangerous area  Efficient: A shorter path is needed for rapid departure.  Scalable: Building and updating should be local and lightweight

Design principles 4 components of designing principles: – Building the road map – Guiding navigation on the road map – Reacting to emergency dynamics – Improving routing efficiency

Building the road map In 2D, the medial axis of a plane curve S is the locus of the centers of circles that are tangent to curve S in two or more points, where all such circles are contained in S. (It follows that the medial axis itself is contained in S.) Medial axis

Building the road map (a)Un-sensed place are defined as dangerous area (b)Preliminary information on boundary, like indoor environment, safely surrounded by walls or fences Medial axis are expressive and can capture the topological features of safe region R

Guiding navigation on the road map (1)Connecting the exit to the road map backbone - Defining potential field: p=1/d, extending each step on the most descending direction (2)Assigning directions on the road map - Flooding dc and dr from the gateway to all network (3)Exploring the routes for users - 3 stages, from cell to backbone, backbone routing and from gateway to exit (4)The safety of the navigation route -Guarantee maximizing the minimum distance from dangerous areas along the selected path

Reacting to emergency dynamics Expanding or shrinking of dangerous areas means points that switches in to or out from areas. Lemma 3.5. When the dangerous area in a cell c expands or shrinks continuously, only the points within c are affected Lemma 3.6. The emerging of a new dangerous point affects the points within the newly constructed cell and the diminishing of a dangerous point affects the points within the original cell. Theorem 3.7. The impact of the emergency dynamics in the field is local

Implementation experience s.danger is 0 when the node is out of dangerous area. s.border is a boolean variable that indicates whether the current node is on the boundary of the dangerous area s.mDist records the distance from the current node to the nearest dangerous area s.mSet records the set of nodes on the boundaries of dangerous areas that are of s.mDist to the current node s.road is a boolean variable that indicates whether the current node is on the road map backbone s.nextHop stores the ID of the next hop node along the path direction on the road. s.rDist records the minimum distance to the dangerous areas on the path from the current node to the exit

Implementation experience Emergency happening  deciding s.danger and generate danger ID. Confirming all boundary nodes(set s.border), and then flood to all network to decide s.mDist and s.mSet. Examine s.mSet of all nodes to decide if it contains boundary modes on two or more dangerous areas(setting s.road)

Implementation experience Calcculating s.potential=1/s.mDist Gate way node flooding the exit information through the road backbone: – dc, which records the minimum number of hops to the dangerous areas along the road from the current node to the gateway, – dr, which records the number of hops along theroad from the current node to the gateway.

Implementation experience Originally, every node sets its s.nextHop to be null, and s.rDist to be 0. IF(s.rDist < dc), switches its s.nextHop to be the ID of the node that forwards the message and sets its s.rDist to be dc. Assign dc as min(dc, s.mDist) and forward this message

Implementation experience

Performance evaluation

Simulating randomly deploying sensor nodes with average node degree of 28 Network size ranges from 1000 to internal users Number of randomly inserting dangerous areas is uniformly chosen from 3 to 6.

Performance evaluation SG=Skeleton Graph, PF=Potential Graph, RM=Road Map A. Minimum Distance to the Danger: performance ratio=d/dOPT B. Shortest Path: performance ratio= l/lOPT C. Minimum Exposure Path: S=sum(1/dist^2), performance ratio =S/SOPT D. Update Overhead

Performance evaluation