Wireless Sensor Network Aided Search and Rescue in Trails

1. Presentation by Stephanie Reese Authors PengZhuang, Qingguo Wang, Yi Shang, Honchi Shi, and BeiHua Wireless Sensor Network Aided Search and Rescue in Trails

2. Focus • “[T]he issues involved in applying wireless sensor networks to search and rescue of lost hikers in trails and focus on the optimal placement of sensors and access points such that the cost of search and rescue is minimized.”

3. How it Relates • Similarities: • The search and rescue algorithms • Most scenarios are assumed to be “non-moving” accidents • Differences: • Our sensors will be scattered randomly • More broad scope

4. An Overview of CenWits • Connection-less Sensor-Based Tracking System Using Witnesses • Hikers wear sensors that have communication and GPS capabilities • Access Points (AP) are strategically placed around the trail • When any of these sensors come into range of another, their information is recorded as “witness” • Provides constant, dynamic information about the hiker and their movement

5. Finding a Probable Location • “The lost case is assumed to be [a] non-moving accident, such as being injured, sick, or stuck along the trail.” • The range of the hiker is established by the witness information held by APs • A probable path is determined • This section of the implementation is mostly irrelevant due to the fact that there are no trails in our scenarios and therefore no paths

6. Search and Rescue • There are four types of search and rescue (SaR) to consider: • Single Ground SaR Agent (S-GSA) • Multiple Ground SaR Agents (M-GSA) • Single Air SaR Agent (S-ASA) • Multiple Air SaR Agent (M-ASA)

7. Single Ground SaR Agent (S-GSA) • Minimizing the worst case scenario: • Where: • cM is maximum cost • Gi is a trail segment • c`(P) is the cost to travel along Gi on the shortest path P • n(e) is the number of times an edge is visited • c(e) is the cost to search each edge

8. Single Ground SaR Agent (S-GSA), con’t • Minimizing cost for the expected scenario: • Where: • cE is the expected cost • t is the specific numbered tour segment (path whose edges have not been visited before) • j is the specific numbered redundant segment (path whose edges have been visited at least once) • n is the number of total paths • lt is a list of all tour segments • rj is a list of all redundant segments • p(lt) is the probability of a hiker getting lost in segment (lt) • w(lt) is the weight of tour segment lt • w(rj) is the weight of redundant segment rj • wi is the total weight of the number of edges

9. Multiple Ground SaR Agents (M-GSA) • Minimizing the search effort of each agent: • When: • k is the number of agents • EiT(x) is the set of edges travelled by agent x in Gi

10. Multiple Ground SaR Agents (M-GSA), con’t • Minimizing cost for the expected scenario: • Where: • ltx are the tour segments by agent x • rjx are the redundant segments by agent x

11. Difference Between Air and Ground Rescue • Ground: • Strictly stick to the paths as defined • Air: • Can cross from one trail to another • Calls for an insertion of “dummy” edges in order to follow previous standard of defining paths **crossing can only happen at two vertices**

12. Single Air SaR Agent (S-ASA) • Minimizing search cost: • When: • EiT is the set of all edges (including dummy edges) traveled

13. Single Air SaR Agent (S-ASA), con’t • Minimizing the expected search cost: • Similar to expected search cost of S-GSA

14. Multiple Air SaR Agents (M-ASA) • Use the same Gi as the S-ASA equation • Maximal and expected cost are the same as M-GSA