A distributed framework for supporting 3D swarming applications

In-flight wireless sensor networks (WSN) are of increased interest owing to efficiency gains in weight and operational lifetime of IP-enabled computers. High impact 3D swarming applications for such systems include autonomous mapping, surveying, servicing, environmental monitoring and disaster site management. For distributed robotic applications, such as quad copter swarms, it is critical that the robots are able to localise themselves autonomously with respect to other robots and to share information. The importance of fast and reliable dissemination of localised information in these elastic three-dimensional networks provides us sufficient reason to present a distributed framework and hardware settings for passing this information pervasively through the swarm. The research field of Internet of Things (IoT) have for several years been addressing issues around low-power, low-bandwidth wireless communication. By applying IoT technologies to the challenges around swarming, new opportunities are created. However, since IoT have been primarily used with stationary devices, the introduction of flying sensors will add more challenges to address.

[1]  H. Afzal,et al.  A hybrid routing protocol for wireless sensor networks with mobile sinks , 2012, ISWPC 2012 proceedings.

[2]  J.C. Barca,et al.  Augmenting the Human Entity Through Man/Machine Collaboration , 2006, 2006 IEEE International Conference on Computational Cybernetics.

[3]  Antonio J. Jara,et al.  Intra-mobility for Hospital Wireless Sensor Networks Based on 6LoWPAN , 2010, 2010 6th International Conference on Wireless and Mobile Communications.

[4]  Cristiane Rosul.  Message Passing Interface ( MPI ) Advantages and Disadvantages for applicability in the NoC Environment by , 2008 .

[5]  Olivier Bonaventure,et al.  Redundant Border Routers for Mission-Critical 6LoWPAN Networks , 2013, REALWSN.

[6]  Libor Preucil,et al.  Low-cost embedded system for relative localization in robotic swarms , 2013, 2013 IEEE International Conference on Robotics and Automation.

[7]  Luis Magdalena,et al.  An Open Localization and Local Communication Embodied Sensor , 2008, Sensors.

[8]  Fernando Boavida,et al.  A new approach for multi-sink environments in WSNs , 2009, 2009 IFIP/IEEE International Symposium on Integrated Network Management.

[9]  Christiaan J. J. Paredis,et al.  A Beacon System for the Localization of Distributed Robotic Teams , 1999 .

[10]  Hesham El-Rewini,et al.  Message Passing Interface (MPI) , 2005 .

[11]  A. Martinoli,et al.  A Fast Onboard Relative Positioning Module for Multirobot Systems , 2009, IEEE/ASME Transactions on Mechatronics.

[12]  Camillo J. Taylor,et al.  A vision-based formation control framework , 2002, IEEE Trans. Robotics Autom..

[13]  Y. Ahmet Sekercioglu,et al.  Swarm robotics reviewed , 2012, Robotica.

[14]  Najah AbuAli,et al.  Experimental evaluation of a Routing Protocol for WSNs: RPL robustness under study , 2013, 2013 IEEE 9th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob).

[15]  Rumen Kyusakov,et al.  Efficient XML Interchange in factory automation systems , 2011, IECON 2011 - 37th Annual Conference of the IEEE Industrial Electronics Society.

[16]  Vijay Kumar,et al.  The GRASP Multiple Micro-UAV Testbed , 2010, IEEE Robotics & Automation Magazine.

[17]  Spring Berman,et al.  A comparison of deterministic and stochastic approaches for allocating spatially dependent tasks in micro-aerial vehicle collectives , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[18]  Heikki Karvonen,et al.  Soft handover method for mobile wireless sensor networks based on 6LoWPAN , 2011, 2011 International Conference on Distributed Computing in Sensor Systems and Workshops (DCOSS).