ABSTRACT
Indoor localization or zonification in disaster affected settings is a challenging research problem. Existing studies encompass localization and tracking of first-responders or fire fighters using wireless sensor networks. In addition to that, fast evacuation, routing, and planning have also been proposed. However, the problem of locating survivors or victims is yet to be explored to the full potential. State-of-the-art literature often employ infrastructure dependent solutions, for example, WiFi localization using WiFi access points exploiting fingerprinting techniques, Pedestrian Dead Reckoning (PDR) starting from known locations, etc. Owing to unpredictable and dynamic nature of disaster affected environments, infrastructure dependent solutions are seldom useful. Therefore, in this study, we propose an ad hoc WiFi zonification technique (named as AWZone) that is independent of any infrastructural settings. AWZone attempts to perform localization through exploiting commodity smartphones as a beaconing device and successively searching and narrowing down the search space. We perform two testbed experiments. The results reveal that, for a single survivor or victim, AWZone can identify the search space and estimate a location with an approximate 1.5m localization error through eliminating incorrect zones from a set of possible results.
- J. T. Biehl, M. Cooper, G. Filby, and S. Kratz. Loco: a ready-to-deploy framework for efficient room localization using wi-fi. In Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pages 183--187. ACM, 2014. Google ScholarDigital Library
- A. Bose and C. H. Foh. A practical path loss model for indoor wifi positioning enhancement. In Information, Communications & Signal Processing, 2007 6th International Conference on, pages 1--5. IEEE, 2007.Google ScholarCross Ref
- M. Carli, S. Panzieri, and F. Pascucci. A joint routing and localization algorithm for emergency scenario. Ad Hoc Networks, 13:19--33, 2014. Google ScholarDigital Library
- I. Constandache, R. R. Choudhury, and I. Rhee. Compacc: Using mobile phone compasses and accelerometers for localization. In IEEE INFOCOM, pages 1--9, 2010.Google Scholar
- B. Cook, G. Buckberry, I. Scowcroft, J. Mitchell, and T. Allen. Indoor location using trilateration characteristics. In Proc. London Communications Symposium, pages 147--150. Citeseer, 2005.Google Scholar
- D. B. Faria et al. Modeling signal attenuation in ieee 802.11 wireless lans-vol. 1. Computer Science Department, Stanford University, 1, 2005.Google Scholar
- S. Feese, B. Arnrich, G. Tröster, M. Burtscher, B. Meyer, and K. Jonas. Sensing group proximity dynamics of firefighting teams using smartphones. In Proceedings of the 2013 international symposium on wearable computers, pages 97--104. ACM, 2013. Google ScholarDigital Library
- C. Fischer and H. Gellersen. Location and navigation support for emergency responders: A survey. IEEE Pervasive Computing, 9(1):38--47, 2010. Google ScholarDigital Library
- E. Gelenbe and F.-J. Wu. Large scale simulation for human evacuation and rescue. Computers & Mathematics with Applications, 64(12):3869--3880, 2012. Google ScholarDigital Library
- T. A. Khan, T. Chakraborty, and A. Islam. Poster: Infrastructure independent indoor localization for post-disaster rescue mission. In Proceedings of the 14th Annual International Conference on Mobile Systems, Applications, and Services Companion, pages 42--42. ACM, 2016. Google ScholarDigital Library
- H. Liu, H. Darabi, and P. Banerjee. A new rapid sensor deployment approach for first responders. Int. J. Intell. Control Syst, 10(2):131--142, 2005.Google Scholar
- Y. Liu and G. Nejat. Robotic urban search and rescue: A survey from the control perspective. Journal of Intelligent & Robotic Systems, 72(2):147--165, 2013. Google ScholarDigital Library
- K. Nagatani, K. Otake, and K. Yoshida. Three-dimensional thermography mapping for mobile rescue robots. In Field and Service Robotics, pages 49--63. Springer, 2014.Google Scholar
- A. Rai, K. K. Chintalapudi, V. N. Padmanabhan, and R. Sen. Zee: zero-effort crowdsourcing for indoor localization. In Proceedings of the 18th annual international conference on Mobile computing and networking, pages 293--304. ACM, 2012. Google ScholarDigital Library
- T. S. Rappaport et al. Wireless communications: principles and practice, volume 2. Prentice Hall PTR New Jersey, 1996. Google ScholarDigital Library
- S. Y. Seidel and T. S. Rappaport. 914 mhz path loss prediction models for indoor wireless communications in multifloored buildings. IEEE transactions on Antennas and Propagation, 40(2):207--217, 1992.Google Scholar
- Sparkfun. Wifi module - esp8266. {Available online at https://www.sparkfun.com/products/13678; accessed September 07, 2016}.Google Scholar
Recommendations
A weight-based clustering multicast routing protocol for mobile ad hoc networks
In mobile ad hoc networks, the mobile nodes can move arbitrarily without any centralised management mechanism. The topology of these networks can be very dynamic due to the mobility of mobile nodes. Under such changeable network topology, multicasting ...
A Polygonal Method for Ranging-Based Localization in an Indoor Wireless Sensor Network
In this paper, we propose an indoor localization method in a wireless sensor network based on IEEE 802.15.4 specification. The proposed method follows a ranging-based approach using not only the measurements of received signal strength (RSS) but also ...
Neighborhood-Based Route Discovery Protocols for Mobile Ad Hoc Networks
Network-wide broadcasting is used extensively in mobile ad hoc networks for route discovery and for disseminating data throughout the network. Flooding is a common approach to performing network-wide broadcasting. Although it is a simple mechanism that ...
Comments