ICEMAN: A Practical Architecture for Situational Awareness at the Network Edge

Part of the Lecture Notes in Computer Science book series (LNCS, volume 9200)


Situational awareness applications used in disaster response and tactical scenarios require efficient communication without support from a fixed infrastructure. As commercial off-the-shelf mobile phones and tablets become cheaper, they are increasingly deployed in volatile ad-hoc environments. Despite wide use, networking in an efficient and distributed way remains as an active research area, and few implementation results on mobile devices exist. In these scenarios, where users both produce and consume sensed content, the network should efficiently match content to user interests without making any fixed infrastructure assumptions. We propose the ICEMAN (Information CEntric Mobile Ad-hoc Networking) architecture which is designed to support distributed situational awareness applications in tactical scenarios. We describe the motivation, features, and implementation of our architecture and briefly summarize the performance of this novel architecture.


Data Object Network Code Situational Awareness Bloom Filter Cache Strategy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



Apart from building on results from earlier projects, such those funded in the context of the DARPA Disruption Tolerant Networking program and our NSF- and ONR- funded projects on Networked Cyber Physical Systems at SRI, the ENCODERS project has also leveraged some results and ideas developed by International Fellows in their own research while visiting SRI. Here we would like to use the opportunity to thank Hasnain Lakhani, Jong-Seok Choi, Dawood Tariq, Rizwan Asghar, Je-Min Kim, Francoise Sailhan, Sathiya Kumar, and Sylvain Lefebvre for their valuable ideas and contributions. We would like to thank our team member SET Corp. (David Anhalt, Ralph Costantini, Dr. Hua Li, and Dr. Rafael Alonso) for their support and collaboration on interest modeling. At the beginning of the program we selected SAIC as our Mobile Systems Integrator. Hence we would like to thank the entire SAIC team (led by Dr. William Merrill and George Weston) for providing the development platform and successfully demonstrating an integrated CBMEN system based on the SRI ENCODERS architecture at Ft. AP Hill, VA, and MIT Lincoln Labs (the team led by Dr. Andrew Worthen) for their independent evaluation of the performance in testbed experiments and in the field.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.UC Santa CruzSanta CruzUSA
  2. 2.SUNS-tech, Inc.Santa CruzUSA
  3. 3.UC Los AngelesLos AngelesUSA
  4. 4.SRI InternationalMenlo ParkUSA

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