Presentation by Stephanie Reese
Authors Peng Zhuang, Qingguo Wang, Yi Shang,
Honchi Shi, and Bei Hua
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.”
How it Relates

Similarities:
 The search and rescue algorithms
 Most scenarios are assumed to be “non-
moving” accidents
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Differences:
 Our sensors will be scattered randomly
 More broad scope
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
Finding a Probable Location
“The lost case is assumed to be [a] nonmoving 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
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)
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
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
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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
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
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
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**
Single Air SaR Agent (S-ASA)

Minimizing search cost:

When:
 EiT is the set of all edges (including dummy
edges) traveled
Single Air SaR Agent (S-ASA), con’t

Minimizing the expected search cost:

Similar to expected search cost of S-GSA
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