Eileen Balci, Stephanie Reese, and Shannon Seefeld

1. CREU Undergraduate Research:Rescue Support System with Wireless Sensor Networks In an Unsafe Territory Eileen Balci, Stephanie Reese, and Shannon Seefeld

2. Abstract • Goal: • To design a rescue support system using wireless sensor networks in an unsafe territory. • How to achieve this? • The rescue system finds stationary victims using deployed sensor sensors, mobile rescue agents, and a single base station which are all sensory devices. 

3. About Our Research • A wireless sensor network (WSN) is a wireless network that consists of distributed sensors to monitor certain conditions at different locations. • A sensor is a tiny computer with extremely limited computational power, memory, transmission power, and battery power. • Energy efficient routing is essential in WSN due to the limited battery power in each sensor. • A WSN is useful for a rescues support system because of the ability to be deployed in an unsafe territory, preventing the harm of more people.

4. The Problem • A sensors in the network is no longer useful when its battery dies • In order prolong lifespan of the sensors, it is essential to allow only the minimum work needed to transmit data • The lifespan of the network is crucial because the ability to save as many victims as possible is very time sensitive.

5. WSN limitations • Sensor energy • Each sensor has limited energy supply • sensors may not be rechargeable • Eventually sensors may be self-powered • Energy consumption in sensing, data processing, and communication • Communication often the most energy-intensive • For some sensors, sensing may also be energy-intensive • Must use energy-conserving protocols

6. WSN Limitations Cont. • Communication • Bandwidth is limited and must be shared among all the sensors in the sensor network • Spatial reuse essential • Efficient local use of bandwidth needed

7. Direct v. Minimum Transmission • Energy usage is at least d2 therefore longer transmission is not recommended • Fig. A requires the first node to expend all the energy whereas Fig. B shares the workload with neighboring sensors

8. Energy Efficient Routing Protocols • To help assist this project, the following previously proposed routing protocols have been researched to be used for communication between sensors. • These protocols are GPSR, HEED, and LEACH.

9. Authors: B. Karp, H. T. Kung Presentation By Eileen Balci GPSR: Greedy Perimeter Stateless Routing for Wireless Networks

10. About GPSR • GPSR allows sensors to figure out who its closest neighbors are (using beacons) that are also close to the final destination the information is supposed to travel to • To calculate a path, GPSR uses a greedy forwarding algorithm that will send the information to the final destination using the most efficient path possible. If the greedy forwarding fails, perimeter forwarding will be used which routes around the perimeter of the region.

11. About the Greedy Forwarding Algorithm • Assuming the wireless sensors know their own locations the Greedy forwarding algorithm will try to find the closest sensors which is also the closest to the final destination • Benefits • A sensors just has to remember the location of neighbors within one-hop

12. Greedy Forwarding Algorithm Drawback • If the network is dense enough that each interior sensors has a neighbor in every 2/3 angular sector, Greedy Forwarding will always succeed. However, the greedy forwarding algorithm can fail: Greedy Forwarding fails

13. Perimeter Forwarding Algorithm Benefits • When the Greedy Forwarding algorithm fails, the Perimeter Forwarding algorithm will be used • Applying the right-hand rule to traverse the edges of the void will find a path using the topology’s perimeter

14. Perimeter Forwarding Algorithm Drawback • The Perimeter Forwarding Algorithm uses a longer path to the destination so the perimeter forwarding algorithm less efficient and cannot be used alone

15. greedy fails Greedy Perimeter Stateless Routing - GPSR • Putting Greedy Forwarding and Perimeter Forwarding together makes the final GPSR which will use the necessary algorithm(s) to find the best path in a given topology Greedy Forwarding Perimeter Forwarding have left local maxima greedy works greedy fails

16. HEED: Hybrid Energy Efficient Distributed Clustering Author: O. Younis & S. Fahmy Presentation by Shannon Seefeld

17. What is clustering? • Clustering plays a dominant role in delaying the first sensors death, while aggregation plays a dominant role in delaying the last sensors death. • In each cluster one sensor acts as a cluster head which is in charge of coordinating with other cluster heads.

18. About HEED • HEED was designed to select different cluster heads in a field according to the amount of energy that is distributed in relation to a neighboring sensor. • Four primary goals: • Prolonging network life-time by distributing energy consumption • Terminating the clustering process within a constant number of iterations/steps • Minimizing control overhead • Producing well distributed cluster heads and compact clusters

19. Advantages • HEED distribution of energy extends the lifetime of the sensors within the network thus stabilizing the neighboring sensors. • Does not require special sensor capabilities, such as location awareness. • Does not make assumptions about sensor distribution • Operates correctly, even when sensors are not synchronized.

20. Advantages cont. • Creates well distributed clusters • Terminates in constant time • Requires only local communication • Reduces energy load • Extends network lifetime • Sensors only require local(neighborhood) information to form clusters • Algorithm will guarantee that every sensor is part of just one cluster • Cluster Heads are well distributed.

21. Disadvantages • The random selection of the cluster heads may cause higher communication overhead for: • The ordinary member sensors in communicating with their corresponding cluster head • Cluster heads in establishing the communication amoung them, or • Between a cluster head and a base station • The periodic cluster head rotation or election needs extra energy to rebuild clusters.

22. LEACH PROTOCOLFOR WIRELESS SENSORY NETWORKS Authors: M. J. Handy, M. Haas, D. Timmermann Presentation by Stephanie Reese

23. An Introduction • LEACH stands for Low-Energy Adaptive Clustering Hierarchy • This WSN is considered to be a dynamic clustering method • LEACH has two phases

24. The Cluster-Head • The LEACH Network is made up of sensors, some of which are called cluster-heads • The job of the cluster-head is to collect data from their surrounding sensors and pass it on to the base station • LEACH is dynamic because the job of cluster-head rotates

25. LEACH’s Two Phases • The LEACH network has two phases: the set-up phase and the steady-state • The Set-Up Phase • Where cluster-heads are chosen • The Steady-State • The cluster-head is maintained • When data is transmitted between sensors

26. Stochastic & Dynamic Threshold Algorithm • Clusterheads can be chosen stochastically (randomly based) • The algorithm is designed so that each sensors becomes a clusterhead at least once • A modified version of this protocol is known as LEACH-C (or LEACH Centralized) • This version has a deterministic threshold algorithm, which takes into account the amount of energy in the sensors and/or whether or not the sensors was recently a cluster-head.

27. What’s the Difference? • REMEMBER: The goal of these protocol is to increase the life of the network • The changes between the LEACH stochastic algorithm and the LEACH-C deterministic algorithm alone is proven to increase the FND (First sensors Dies) lifetime by 30% and the HND (Half sensors Dies) lifetime by 20%

28. An Example of a LEACH Network • While neither of these diagrams is the optimum scenario, the second is better because the cluster-heads are spaced out and the network is more properly sectioned

29. Rescue Systems • The following are examples of existing WSN rescue systems that are helpful in designing this new rescue system. • They are Fire Detection and Rescue and WSN Aided Search and Rescue in Trails

30. Fire Detection and Rescue Applications Authors: Yeon-sup Lim, Sangsoon Lim, Jaehyuk Choi, Seongho Cho, Chong-kwon Kim, Yong-Woo Lee Researched By: Eileen Balci

31. Fire Detection and Rescue Support Framework with WSN • Low Latency • Management at a Distance • Indirect remote control management system • Pre arranged sensors • Middleware and Monitoring Software • Multi-hop routing protocol based on link quality indicator (LQI)

32. sensors Operating Algorithm • The sensors operating algorithm is an algorithm that receives messages from other sensors and if it gets an event alarm, it notifies that the path is not safe by having a blinking LED light and forwards the event to other sensors. • This could be useful to an extent. It is used to detect fire and mark unsafe paths, it would be nice if there was a way for it to “detect” the victim though other means such as sound, and mark a path to the victim.

33. Conclusion • This group is using fire detection software and they are placing the sensors in specific places marking unsafe paths rather than paths to a specific location.

34. Wireless Sensor Network Aided Search and Rescue in Trails Authors: Peng Zhuang, Qingguo Wang, Yi Shang, Honchi Shi, and Bei Hua Presentation by Stephanie Reese

35. Focus & How it Relates • “The 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.” • 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

36. Finding a Probable Location • 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 • “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

37. 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)

38. Single Ground SaR Agent (S-GSA) and Multiple Ground SaR Agents (M-GSA) • S-GSA takes into account if only one search and rescue agent is searching. • M-GSA takes into account if more than one search and rescue agent is searching. • The effort of each agent is minimized in the M-GSA scenario.

39. ROBOT SENSOR NETWORKS Authors: Presentation By Shannon Seefeld

40. Goals • Map the space in 3D • Identify targets within the space • Long term goal is to deploy a physical system in a urban search and rescue test area.

41. Challenge • A challenge in working with the network is to reconfigure the network automatically but still focusing on the main goals. • Network must possess reliability and complete full network connectivity • Need to maintain consistent and reliable network communication amongst remote rescuers (human or robot) • Sensor networks may provide broader and more dynamic perspectives if placed strategically around an environment, delivering small snapshots over time. • Later combine snapshots to create full image

42. Non Mobile • Traditional non mobile sensor networks possess potential yet face challenges • Cant take an active role in manipulating and interacting with their environment • Cant physically reconfigure themselves for more efficient area coverage, reliable wireless connectivity or protection against the elements

43. Mobile Robots • Mobile robots provide the ability to explore and interact with the environment in a dynamic and decentralized way. • Robots with sensor capabilities offer new solutions to localization and navigation

44. Robot Behavior • Rescue Agent will be based on a robot • Each robot to make a independent decision • Avoid computational costs associated with sophisticated decision making • Each robot has a hierarchy of behaviors • Target has been detected • Robot searches for the target until: the robot finds the target, robot discovers another robot has gotten there or the robot knows the target signal • Target signal is present but some sensor is in range • Robot will traverse the network towards a target some hops away Robot conducts a blind search looking first for target signals and for sensor signals

45. Robot Behavior, Con’t. • Once target is found and surroundings explored, sensors close enough to receive the target signal should be marked by the network • Sensors may mark the passage of robots with a time stamp • Doing so robots may decide to avoid sensors visited often or recently Robots may leave trails in order to facilitate quick transference of information back to the base station • The sensor robot systems perform badly when some of the targets have several sensors nearby while others have few or no nearby sensors

46. Rescue Support System with Wireless Sensor Networks In an Unsafe Territory • Using a simulation that is currently in development, it is our goal to use an energy efficient path algorithm that will traverse through the network topology finding victims in the quickest time possible taking into consideration that the victims and sensors life spans are very time sensitive

47. Basic Rescue Scenario idea • 1. Each sensor is attempting to sense the existence of victim • 2. The sensor(s) that sense a victim sends the sensed data to the base station using an energy efficient routing algorithm. • 3. Base station calculates a path to visit all the sensed victims’ locations before the victims’ lifetime ends. • (Currently, the path will be a shortest distance path based on the victims’ remaining lifetime and shortest move to the next sensor) • 4. Base Station sends the path to the mobile rescue agent using an energy efficient routing protocol • 5. The rescue agent visits each victim based on the pre-calculated path. • 6. If the rescue agent senses a new victim while it is moving, it sends the information to Base Station using the energy efficient routing algorithm and keeps moving to the next victim. • 7.  In the meantime, the Base Station calculates an updated path and sends it to the mobile rescue agent using the energy efficient routing algorithm. • 8. It will continue to do these steps from 1 to 7 until all the sensed victims are visited.

48. Simulation Screenshot

49. IRIS Wireless Module • Some design features: • Outdoor line-of-sight tests have yeilded ranges as far as 500 meters between nodes without amplification. • IEEE 802.15.4 compliant RF transceiver. • 2.4 to 2.48 GHz, a globally compatible ISM band. • Direct sequence spread spectrum radio which is resistant to RF interference and provides inherent data security. • Expansion connector for light, temperature, RH, barometric pressure, acceleration/seismic, acoustic, magnetic and other Crossbow sensor boards. • 250 kbps data rate. • Supported by MoteWorks™ wireless sensor network platform for reliable, ad-hoc mesh networking. • Plug and play with Crossbow’s sensor boards, data acquisition boards, gateways, and software From xbow.com

50. Questions?