TinyNode

Introducing TinyNode

Shockfish SA has developed the TinyNode platform with real industrial application in mind. Our mission is to bridge results from academic research with industrial needs in the area of wireless sensor networks.

 

The design philosophy of TinyNode is to provide a platform for both academic projects and industrial applications. The TinyNode 584 and TinyNode 184 core module are versatile lowest-power sensor nodes and come with an array of extension hardware offering a wide set of connectivity, storage, energy and interfacing options.

TinyNode 584

The TinyNode 584 is an ultra-low power OEM module that provides a simple and reliable way to add wireless communication to sensors, actuators and controllers.

 

TinyNode 584 is optimized to run TinyOS and packaged as a complete wireless subsystem with 19 configurable I/O pins offering up to 6 analog inputs, up to 2 analog outputs as well as serial interface.

Key Features

	Ultra Low Power design
	TI MSP430 microcontroller
	Fast wake-up from sleep (<6us)
	Semtech radio transceiver XE1205
	European 868MHz ISM band operation
	Adjustable datarates up to 153kbit/s
	Range up to 2km
	Compact 30 x 40mm
	On-board temperature sensor
	On-board 512kB flash chip
	30 pin board-to-board connector
	Analog, digital and serial interfaces
	On-board or external antenna options
	Out-of-the-box TinyOS support TinyNode 18 The TinyNode 184 is a state of the art ultra-low power OEM module that provides a simple and reliable way to add wireless communication to sensors, actuators, and controllers. Indeed the 184 serie is the first wireless sensor consuming only 3mA in receive mode   TinyNode 184 is optimized to run TinyOS and packaged as a complete wireless subsystem with 19 configurable I/O pins offering up to 6 analog inputs, up to 2 analog outputs as well as serial interface.     Key features Ultra Low Power 2.1 V design: > 10 years battery life on 2/3AA Lithium batteries (using 2uA sleep modes) 16MHz Texas Instruments MSP430 microcontroller (MSP430F2417) 868 / 915MHz Semtech SX1211 ultra-low power wireless transceiver (3mA in receive mode, 25mA in transmit mode at +10dBm) 512kB ST m25p40 external flash On-board 8-pin expansion connector with power supply, 2 ADC channels, interrupt and UART pins On-board battery connector Small: 30x40 mm RF Receiver sensitivity down to -107 dBm Transmit output power programmable up to +10 dBm, on-board SAW filter Bit rates up to 200kbit/s, NRZ coding On-board chip antenna, footprint for SMA/MMBX connector Fast wakeup from sleep (<1µs) Pin compatible to TinyNode584

Mobile Sensor Networks

Cooperative mapping & location. Multisensor fusion for distributed fields. Wireless & underwater networking.  Deployment experiments are carried out in our lab using a large network of integrated mobile sensor platforms.  Energy harvesting from multiple sources (vibration, light and temperature) augment the available on-board mobile node power. Objective: Development of algorithms and prototype vehicles for wide-area surveillance and reconnaissance using mobile sensor networks (MWSN). Monitoring on land, water and air using large numbers of mobile sensor nodes is demonstrated at our Distributed Intelligence and Autonomy Lab (DIAL). Land-based mobile robot fleet at DIAL consisting of inexpensive mobile robots carrying wireless sensors is used to study deployment algorithms. Bandwidth Maximization using Potential Fields: A potential field is used to reposition sensor nodes to maximize network bandwidth. Approach: Deployment of large numbers of sensors using heterogeneous robotic platforms including inexpensive ARRI-Bots. Use of a Potential Fields (PF) approach to achieve mobility subject to communication bandwidth, energy and navigation/collision. Use of Extended Kalman Filter (EKF) for information gathering, localization and navigation. Use of a Discrete Event Controller (DEC) for resource allocation and mission planning. Data logging and supervisory control using Labview for managing and visualization of sensor networks. Platform independent algorithms: Land, Aerial, Underwater. Adaptive Sampling (AS): Efficient, information-driven sensor deployment. Adaptive Sampling (AS)aims to reconstruct a distributed sensor field from multiple measurements.   AS is much more efficient than conventional Raster Scan Sampling. types of Deployment Algorithms: Communication-Aware deployment consisting of dynamic sensor repositioning in the presence of network communication constraints. Information-Aware deployment consisting of optimal placement of mobile sensors to gather the most information. Mission-Aware deployment consisting of resource coordination to accomplish a common mission. Energy-Aware deployment consisting of conservation and harvesting of energy for sensor network sustainability. Publications: [1] V. Giordano, P. Ballal, F.L. Lewis, B. Turchiano, J.B. Zhang, “Supervisory control of mobile sensor networks: math formulation, simulation, implementation,” IEEE Trans. Systems, Man, Cybernetics Part B, 2006. [2] V. Giordano, F.L. Lewis, P. Ballal, and B. Turchiano, “Supervisory controller for task management and resource dispatching in mobile wireless sensor networks,” in Cutting Edge Robotics, ed. V. [3] V. Giordano, F.L. Lewis, B. Turchaino, P. Ballal, V. Yeshala, “Matrix computational framework for discrete event control of wireless sensor networks with some mobile agents,” Proc. Mediterranean Conf. Control & Automation, Limassol, Cyprus, June 2005. This paper won an award at MED 05. [4] Ankit Tiwari, Prasanna Ballal, Frank L. Lewis, "Energy-Efficient Wireless Sensor Network Design & Implementation for Condition Based Maintenance," Submitted to ACM Trans. on Sensor Networks, Aug 2005. [5] Prasanna Ballal, Vincenzo Giordano, Frank Lewis, "Deadlock free dynamic resource assignment in multi-robot systems with multiple missions: a matrix-based approach", submitted to the Mediterranean Conference on Control, 2006. [6] Prasanna Ballal, Vincenzo Giordano, Pritpal Dang, Sankar Gorthi, Frank Lewis, "A LabView based test-bed with off-the-shelf components for research in mobile sensor networks," submitted to ISIC 06 Munich, Commuri special session, 2006. [7] D.O. Popa and F.L. Lewis, “Algorithms for robotic deployment of WSN in adaptive sampling applications,” in Wireless Sensor Networks and Applications, ed. Y. Li, M. Thai, and W. Wu, Springer-Verlag, Berlin, 2006, to appear. [8] P. Dang, F. L. Lewis, D. O. Popa, “Dynamic Localization of Air-Ground Wireless Sensor Networks,” in Advances in Unmanned Aerial Vehicles, ed. Kimon Valavanis, Springer Verlag, Berlin, 2007, to appear. [9] K. Sreenath, F. L. Lewis, D. O. Popa, “Simultaneous Adaptive Localization of a Wireless Sensor Network,” in ACM SIGMOBILE Mobile Computing and Communications Review, 2007, to appear. [10] Das A.N., Popa D.O., Ballal P., Lewis F.L., “Data-logging and Supervisory Control in Wireless Sensor Networks”, in ACIS Int’l Journal of Wireless and Mobile Computing, 2007, to appear. [11] D.O. Popa, M.F. Mysorewala, F.L. Lewis, "Deployment Algorithms and In-Door Experimental Vehicles for Studying Mobile Wireless Sensor Networks", in ACIS Int’l Journal of Wireless and Mobile Computing, 2007, to appear. [12] D. O. Popa, M. F. Mysorewala, F. L. Lewis, "EKF-based Adaptive Sampling with Mobile Robotic Sensor Nodes", to appear in Proceedings of International Conference on Intelligent Robots and Systems (IROS), Beijing, China, Oct 2006. [13] Priya, S.; Popa, D. O.; Lewis, F.L.; "Energy efficient Mobile Wireless Sensor Networks," to Appear in Proc. of 2006 ASME International Mechanical Engineering Congress & Exposition November 5 – 10, Chicago, Illinois. [14] Das A.N., Lewis F.L., Popa D.O., "Data-logging and Supervisory Control in Wireless Sensor Networks" in proceedings of the 2nd ACIS Workshop on Self-Assembly Wireless Networks (SAWN), Las Vegas, USA, 19-20 June 2006, pp.330-338. [15] M.F. Mysorewala, D.O. Popa, V. Giordano, F.L. Lewis, "Deployment Algorithms and In-Door Experimental Vehicles for Studying Mobile Wireless Sensor Networks", in Proceedings of 2nd ACIS International Workshop on Self-Assembling Wireless Networks (SAWN), Las Vegas, Nevada, USA, June 2006. [16] Popa, D.O., Sanderson A., Hombal, V. et. al., “Optimal Sampling using Singular Value Decomposition of the Parameter Variance Space”, in Proc. of IEEE IROS ’05, Edmonton, CA, August 2005. [17] D.O. Popa, K. Sreenath, and F.L. Lewis, “Robotic deployment for environmental sampling applications,” Proc. Int. Conf. Control and Applics., pp. 197-202, Budapest, June 2005. [18] Popa, D.O., A. Sanderson, R. Komerska, S. Mupparapu, R. Blidberg, S. Chappel, “Adaptive Sampling Algorithms for Multiple Autonomous Underwater Vehicles”, in Proc. of 2004 Workshop on Underwater Vehicles, Sebasco Estates, ME, June 2004. [19] Popa, D.O., A. Sanderson, R. Komerska, S. Mupparapu, R. Blidberg, S. Chappel, “Autonomous Monitoring and Control (ASMAC) - An AUV Fleet Controller”, in Proc. of 2004 Workshop on Underwater Vehicles, Sebasco Estates, ME, June 2004. [20] Popa, D.O., Helm, C., Stephanou, H.E., Sanderson, A,“Robotic Deployment of Sensor Networks using Potential Fields”, in Proc. Of International Robotics and Automation Conference, April 2004. [21] D. O. Popa, M. F. Mysorewala, and F. L. Lewis, "Adaptive Sampling using Nonlinear EKF with Mobile Robotic Wireless Sensor Nodes", to appear in Proceedings of the Ninth International Conference on Control, Automation, Robotics and Vision (ICARCV), Singapore, Dec 2006. [22] D. O. Popa, F. Lewis, "Robotic Deployment of Sensors in Environmental Monitoring Applications", invited presentation at 2006 US Navy Workshop on Transduction Materials and Devices, State College, PA, May 2006., Texas A&M, September 2004. [23] Priya S, Chen CT, Fye D, et al. Piezoelectric windmill: A novel solution to remote sensing Japanese Journal of Applied Physics Part 2-Letters & Express Letters 44 (1-7): L104-L107 2005.