A crisis can occur with little or no warning, anywhere, anytime and it can happen either locally or globally. The SmartRescue project will explore how communication technology can be used in acute crisis situations.

What is the SmartRescue Project? 

Mobile wireless devices such as smartphones have become a widespread and typical asset. At the same time, such devices are equipped with ever more advanced sensor technology, including accelerometer, digital compass, gyroscope, GPS, microphone, and camera. This has enabled an entirely new class of mobile device based applications that connects low-level sensor input with high-level events.
The SmartRescue project at University of Agder, Norway, have explored how this kind of communication technology can be used in acute crisis situations, where individuals need to be alerted about immediate threats, and be supported with plans for evacuating the affected area in the safest possible way. Our focus has been on the first phase of an acute and severe emergency situation in which human life and health are endangered, and where individuals for the moment are partially left to themselves, for example, because the traditional response apparatus is suppressed, delayed or paralyzed by the crisis.

Scenario: Fire in building

Our focus has been on fire in building, forecasting the extent, impact and development of the fire. 

SmartRescue App Technologies

  • Android app based
  • Smartphone based sensing
  • Data sharing through Publish-Subscribe Notification System
  • Artificial intelligence techniques (Bayesian Network, K-Nearest neighbor algorithm) for threat assessment and forecasting
  • Ant-based algorithms for evacuation planning

SmartRescue App Features

The app user can share sensor information through a publish-subscribe system, meaning that in a crisis situation, users can filter the sensor information they wish to know, such as peers’ locations or temperature surrounding them. It has simple activity recognition that detects someone’s movement, and senses the environment surrounding the users, for instance, if they are inside in office light, in a dark area, or outdoor. 
By exploiting the sensor readings from the devices (temperature, humidity, pressure) in a fire hazard scenario, the user app can assess and predict the fire development based on a color-coding scheme. Besides, the SmartRescue app has an indoor localization feature that can track people within a building during a fire, such as victims trapped in a room. Although the app is prototyped for building fire, its applicability can be extended into other fire scenarios such as a ship or tunnel fire.

Cooperation with Local Authorities

Smartphone sensor data collection in the Mandal Drill 24 September 2013. 

Serious Game for Testing SmartRescue App

1. Testing the fire development feature in ISCRAM Summer School, University of Tilburg, the Netherlands, 18 August 2014
2. Testing indoor localization, fire assessment and prediction features  of SmartRescue App in University of Agder, Campus Grimstad, 20 November 2014

Workshops and Dissemination

The SmartRescue Project has been involved in various workshops and dissemination events, such as:
  • SmartRescue Workshop, Grimstad, June 2012
  • Forskningsdagene, Grimstad, 22 September 2012
  • Evaluation Workshop for Mandal Terrorist Attack Drill, Kristiansand, 23 October 2013 
  • CIEM Workshop, Kristiansand, 27 November 2013 
  • ISCRAM Summer School, University of Tilburg, the Netherlands, 13-22 August 2014
  • CIEM Workshop, Kristiansand, 4 November 2014
  • Digitalkonferansen Change IT, Kristiansand, 18 March 2015
  • Kristiansandskonferansen, Kristiansand, 21 April 2015

Publications and Teaching 

In the SmartRescue project we have published 16 peer-reviewed international conference papers and 5 scientific journal papers and 1 book chapter.
In teaching, the SmartRescue project has been used as a case for student projects, Master’s theses and PhD projects. The topics covered span a wide range of challenges, including simulating fire based on Bayesian networks, publish-subscribe systems for real-time emergency management, hazard visualization, evacuation planning, as well as fire detection and prediction using machine learning techniques such as Naïve Bayes Classifier and Decision Trees.
Jaziar Radianti, Ole-Christoffer Granmo, Parvaneh Sarshar, Morten Goodwin, Julie Dugdale, Jose. J. Gonzalez (2015). A spatio-temporal probabilistic model of hazard- and crowd dynamics for evacuation planning in disasters. Applied Intelligence. Volume 42, Issue 1, pp 3-23. First online: 04 September 2014.
Morten Goodwin, Ole-Christoffer Granmo, Jaziar Radianti (2015). Escape Planning in Realistic Fire Scenarios with Ant Colony Optimisation. Applied Intelligence. Volume 42, Issue 1, pp 24-35. First online: 03 May 2014.
Jaziar Radianti, Ole-Christoffer Granmo (2014). A Framework for Assessing the Condition of Crowds Exposed to a Fire Hazard Using a Probabilistic Model. International Journal of Machine Learning and Computing (IJMLC) Vol 4. No 1, pp. 14-20.
Jaziar Radianti, Ole-Christoffer Granmo, Noureddine Bouhmala, Parvaneh Sarshar, Jose J. Gonzalez (2014). Comparing Different Crowd Emergency Evacuation Models Based on Human Centered Sensing Criteria. IJISCRAM.International Journal of Information Systems for Crisis Response and Management. Volume 6, Issue 3.
Simin Rasouli, Ole-Christoffer Granmo, and Jaziar Radianti (2015). “A Methodology for Fire Data Analysis based on Pattern Recognition towards the Disaster Management”. the 2nd International Conference on Information and Communication Technologies for Disaster Management (ICT-DM’2015) to be held in Rennes (France), 30 November - 2 December 2015.
Vimala Nunavath, Jaziar Radianti, Martina Comes, Andreas Prinz (2015) . The impact of ICT support on information distribution, task assignment and teams' situational awareness in search and rescue operations. The 2015 International Conference on Computing and Network Communications (CoCoNet'15). December 16-19, 2015, Trivandrum, India.
Sarshar, Parvaneh, Vimala Nunavath, Jaziar Radianti (2015). A Study on the Usage of Smartphone Apps in Fire Scenarios - Comparison between GDACSmobile and SmartRescue Apps. ICEIS (2) 2015: 469-474
Sarshar, Parvaneh, Vimala Nunavath, and Jaziar Radianti (2015). "On the Usability of Smartphone Apps in Emergencies." Human-Computer Interaction: Interaction Technologies. Springer International Publishing, 765-774.
Mehdi Ben Lazreg, Jaziar Radianti, Ole-Christoffer Granmo (2015). A Bayesian Network Model for Fire Assessment and Prediction. The First International Workshop on Machine learning, Optimization and big Data - MOD 2015 Volume Editor(s):  Giuseppe Nicosia, Panos Pardalos, Mario Pavone, Giovanni Maria Farinella and V. Cutello.
Mehdi Ben Lazreg, Jaziar Radianti, Ole-Christoffer Granmo (2015). “SmartRescue: Architecture for Fire Crisis Assessment and Prediction”. ISCRAM Conference, Kristiansand, Norway, May 2015.
Vimala Nunavath, Jaziar Radianti, Martina Comes, Andreas Prinz (2015). “Visualization of Information Flows and Exchanged Information: Evidence from an indoor fire game”. ISCRAM Conference, Kristiansand, Norway.
Parvaneh Sarshar, Jaziar Radianti, Jose J. Gonzalez (2015). “On the Impacts of Utilizing Smartphones on Organizing Rescue Teams and Evacuation Procedures”, ISCRAM Conference, Kristiansand, Norway.
Jaziar Radianti, Julie Dugdale, Jose. J. Gonzalez, Ole-Christoffer Granmo (2014). Smartphone Sensing Platform for Emergency Management. ISCRAM Conference, Pennsylvania State University, Pennsylvania, USA.
Jaziar Radianti, Jose. J. Gonzalez, Ole-Christoffer Granmo (2014). Publish-Subscribe Smartphone Sensing Platform for the Acute Phase of a Disaster. Accepted for the Pervasive Network (PerNem) Workshop, in a Pervasive Computing Conference, Budapest, Hungary.
Parvaneh Sarshar, Jaziar Radianti, Ole-Christoffer Granmo, and Jose J. Gonzalez (2013) . A Dynamic Bayesian Network Model for Predicting Congestion During a Ship Fire Evacuation. Proceedings of The World Congress on Engineering and Computer Science , pp29-34
Sarshar Parvaneh, Radianti Jaziar, Gonzalez Jose J (2013), Modeling Panic in Ship Fire Evacuation Using Dynamic Bayesian Network. 2013 Third International Conference on Innovative Computing Technology (INTECH).
Radianti, J., Granmo, O., Bouhmala, N., Sarshar, P., Yazidi, A., Gonzalez, J. (2013)Crowd Models for Emergency Evacuation: A Review Targeting Human-Centered Sensing, 2013 46th Hawaii International Conference on System Sciences, 2013 Pages: 156-165 , DOI: 10.1109/HICSS.2013.155 (Nominated for the best paper award)
Morten Goodwin, Ole-Christoffer Granmo, Jaziar Radianti, Parvaneh Sarshar, Sondre Glimsdal (2013)"Ant Colony Optimisation for Planning Safe Escape Routes", Recent Trends in Applied Artificial Intelligence. Lecture Notes in Computer Science Volume 7906, 2013, pp 53-62. 
Parvaneh Sarshar, Jaziar Radianti, Ole-Christoffer Granmo, Jose J. Gonzalez (2013). "A Bayesian network model for evacuation time analysis during a ship fire," Computational Intelligence in Dynamic and Uncertain Environments (CIDUE), 2013 IEEE Symposium pp.100-107. doi: 10.1109/CIDUE.2013.6595778
Ole-Christoffer Granmo, Jaziar Radianti, Morten Goodwin, Julie Dugdale, Parvaneh Sarshar, Sondre Glimsdal, Jose J. Gonzalez (2013)A Spatio-temporal Probabilistic Model of Hazard and Crowd Dynamics in Disasters for Evacuation Planning, Recent Trends in Applied Artificial Intelligence, Lecture Notes in Computer Science Volume 7906, 2013, pp 63-72. 
Parvaneh Sarshar, Jaziar Radianti, Jose. J. Gonzalez (2014).Predicting Congestions in a Ship Fire Evacuation: A Dynamic Bayesian Netwoks Simulation”, in Transactions on Engineering Technologies: Special Issue of the World Congress on Engineering and Computer Science 2013 Haeng-Kon Kim; Sio-Iong Ao; Mahyar A Amouzegar, Editor. Dordrecht : Springer.



Project Info

Project Manager: Ole-Christoffer Granmo
Researchers: | Jaziar Radianti | Vimala Nunavath | Mehdi Ben Lazreg | Parvaneh Sarshar | Morten Goodwin | Sondre Glimsdal | José J. Gonzalez
Project Period: August 2012 - August 2015
Funding: Aust-Agder kompetanse- og utviklingsfond and UIA