SOS: A Safe, Ordered, and Speedy Emergency Navigation Algorithm in Wireless Sensor Networks

1. SOS: A Safe, Ordered, and Speedy Emergency Navigation Algorithm in Wireless Sensor Networks Andong Zhan∗†,Fan Wu∗,Guihai Chen∗ ∗Shanghai Key Laboratory of Scalable Computing and Systems, Department of Computer Science and Engineering, Shanghai Jiao Tong University, China †Department of Computer Science, Johns Hopkins University, USA IEEE ICCCN (Accepted rate:29.6%)

2. Outline • Introduction • Related Works • Problem Specification • Design Principles • Simulations • Conclusions

3. Introduction • More people were killed by natural disasters worldwide past. • Evacuation techniques are highly needed to navigate the personnel out of danger quickly. • Wireless sensor networks can play an important role in detecting disasters and navigating the personnel out of dangerous areas.

4. Introduction • Objective • The objective of a successful navigation is to schedule all the users to bypass the dangerous areas safely, and finally evacuate to pre-known exits as soon as possible. • Requirements • All the escape paths given by the navigation algorithm should be safe paths. • All the users should evacuate from the emergency area orderlywithout congestion. • The navigation algorithm should minimize the total evacuation time. • To lower the cost, sensor nodes do not require geographical location information.

5. Problem Specification

6. Problem Specification • Assumptions • a wireless sensor network can detect dangerous areas • Users carry wireless communication devices • which enable them to “talk” with nearby sensors • e.g., 802.15.4 compatible PDA or smart phone

7. Problem Specification • Safe Path: A path P = {s1, s2, . . . , sn} is a safe path if and only if ∀ si ∈ P, di ≥ dΓ • di is the distance between sensor node si and its nearest alarming neighbor node. • dΓ is the safe distance threshold. • Safety Capacity: For each sensor node si on a safe path, its safety capacity is the maximum number of people passing through it safely per unit time.

8. Design Principles • Constructing the Medial Axis Graph • Formulating the Navigation Schedule Problem • Designing the Distributed Algorithm

9. Design Principles • Constructing the Medial Axis Graph • Formulating the Navigation Schedule Problem • Designing the Distributed Algorithm

10. Constructing the Medial Axis Graph Design Principles

11. Design Principles • Constructing the Medial Axis Graph • Formulating the Navigation Schedule Problem • Designing the Distributed Algorithm

12. Formulating the Navigation Schedule Problem Design Principles • Navigating users to the closest gateway, choosing the safest path for every user, or taking both safety and distance into account • When the number of users is large and the capacities of safe paths are low, congestion may occur, and greatly increases the evacuation time resulting in more casualties. • Time should be considered. • Waiting is inevitable when the number of users is larger than the maximum safety capacity of the network. It can not guarantee the optimal scheduling

13. Formulating the Navigation Schedule Problem Design Principles Safety Capacity Time cost (between two nodes) = 1 Time cost=2 Time cost=3

14. Formulating the Navigation Schedule Problem Design Principles The number of usersis larger than the safety capacity Time cost (between two nodes) = 1 Time cost=2 Time cost=3

15. Formulating the Navigation Schedule Problem Design Principles • We create a graph G(V, E) and denote vertex it ∈ V, i ∈ N as the state of sensor node si at time t, and directed edge edge (i, j, t) ∈ E as the arc from vertex it to vertex jt+1 • A sensor node may have several vertices so as to represent the number of users in different time units. Traveling: Waiting: it jt+1 it it+1 edge (i, j, t) edge (i, i, t)

16. Formulating the Navigation Schedule Problem Design Principles

17. Formulating the Navigation Schedule Problem Design Principles • The navigation schedule problem can be formulated as a linear program: the flow from vertex itto jt +1 Minimize time cost Subject to: the number of users in the whole safe area safety capacity

18. Design Principles • Constructing the Medial Axis Graph • Formulating the Navigation Schedule Problem • Designing the Distributed Algorithm

19. Designing the Distributed Algorithm Design Principles

20. Designing the Distributed Algorithm Design Principles hopd=10 i i hopd=3 u(i) u(i)

21. Designing the Distributed Algorithm Design Principles height: 8 7 6 5 4 3 2 1 Exit in out in out in out in out S4 S3 S2 S1 gateway u(i) u(4)=10 u(3)=5 u(2)=10 u(1)=15 di: 4-hop 3-hop 2-hop 1-hop

22. Designing the Distributed Algorithm Design Principles • Local Minimum Problem height 4 8 S4 S1 4+1=5 2 S2 6 S3 • Record the navigation schedule, i.e, the time and the number of users to certain neighbor node

23. Simulations • Simulate randomly deploying sensor nodes in a square field. • The number of nodes is from 1000 to 8000. • We randomly create 1 to 5 groups of users in each run. • The number of users in a group is created randomly between 1 and 50. • In each run, we also randomly setup 1 to 5 exits and 3 to 6 dangerous areas.

24. Simulations • Uniform capacity: the capacity of every sensor node is equal. • Linear capacity: the capacity of a node is linear with the number of hops from the node to the closest alarming node. Linear capacity:

25. Simulations Potential Feld (PF) Skeleton Graph (SG) Road Map (RM) • Average evacuation time

26. Simulations • Last evacuation time

27. Simulations • Network overhead

28. Conclusions • We have proposed SOS emergency navigation algorithm in WSNs. • To minimize users’ evacuation time, we have converted the emergency evacuation problem to a traditional network flow problem and used push-relabel algorithm to solve it. • In simulations, SOS is better than existing approaches in terms of average evacuation time, last evacuation time, and network overhead.

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