Presentation is loading. Please wait.

Presentation is loading. Please wait.

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

Similar presentations


Presentation on theme: "Sensor Network Navigation without Locations Mo Li, Yunhao Liu, Jiliang Wang, and Zheng Yang Department of Computer Science and Engineering Hong Kong University."— Presentation transcript:

1 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

2 Outline System introduction Design principles Implementation experience Performance evaluation

3 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

4 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

5 System introduction Small-scale with 36 TelosB Motes on 802.15.4 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

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

7 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

8 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

9 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

10 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

11 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

12 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)

13 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.

14 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

15 Implementation experience

16 Performance evaluation

17

18 Simulating randomly deploying sensor nodes with average node degree of 28 Network size ranges from 1000 to 16000 10 internal users Number of randomly inserting dangerous areas is uniformly chosen from 3 to 6.

19 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

20 Performance evaluation


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

Similar presentations


Ads by Google