Nano-CF: A coordination framework for macro-programming in Wireless Sensor Networks

Wireless Sensor Networks (WSN) are being used for a number of applications involving infrastructure monitoring, building energy monitoring and industrial sensing. The difficulty of programming individual sensor nodes and the associated overhead have encouraged researchers to design macro-programming systems which can help program the network as a whole or as a combination of subnets. Most of the current macro-programming schemes do not support multiple users seamlessly deploying diverse applications on the same shared sensor network. As WSNs are becoming more common, it is important to provide such support, since it enables higher-level optimizations such as code reuse, energy savings, and traffic reduction. In this paper, we propose a macro-programming framework called Nano-CF, which, in addition to supporting in-network programming, allows multiple applications written by different programmers to be executed simultaneously on a sensor networking infrastructure. This framework enables the use of a common sensing infrastructure for a number of applications without the users being concerned about the applications already deployed on the network. The framework also supports timing constraints and resource reservations using the Nano-RK operating system. Nano-CF is efficient at improving WSN performance by (a) combining multiple user programs, (b) aggregating packets for data delivery, and (c) satisfying timing and energy specifications using Rate-Harmonized Scheduling. Using representative applications, we demonstrate that Nano-CF achieves 90% reduction in Source Lines-of-Code (SLoC) and 50% energy savings from aggregated data delivery.

[1]  Gaurav S. Sukhatme,et al.  Designing Wireless Sensor Networks as a Shared Resource for Sustainable Development , 2006, 2006 International Conference on Information and Communication Technologies and Development.

[2]  Anthony Rowe,et al.  Rate-Harmonized Scheduling for Saving Energy , 2008, 2008 Real-Time Systems Symposium.

[3]  David E. Culler,et al.  Hood: a neighborhood abstraction for sensor networks , 2004, MobiSys '04.

[4]  David A. Maltz,et al.  DSR: the dynamic source routing protocol for multihop wireless ad hoc networks , 2001 .

[5]  Shih-Lin Wu,et al.  Wireless Ad Hoc Networking : Personal-Area, Local-Area, and the Sensory-Area Networks , 2007 .

[6]  Wei Hong,et al.  Proceedings of the 5th Symposium on Operating Systems Design and Implementation Tag: a Tiny Aggregation Service for Ad-hoc Sensor Networks , 2022 .

[7]  Michele Zorzi,et al.  SYNAPSE: A Network Reprogramming Protocol for Wireless Sensor Networks Using Fountain Codes , 2008, 2008 5th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks.

[8]  David E. Culler,et al.  Incremental network programming for wireless sensors , 2004, SECON.

[9]  Anthony Rowe,et al.  Sensor Andrew: Large-scale campus-wide sensing and actuation , 2011, IBM J. Res. Dev..

[10]  Amy L. Murphy,et al.  TinyLIME: bridging mobile and sensor networks through middleware , 2005, Third IEEE International Conference on Pervasive Computing and Communications.

[11]  Philip Levis,et al.  Maté: a tiny virtual machine for sensor networks , 2002, ASPLOS X.

[12]  Ramesh Govindan,et al.  TOSThreads: thread-safe and non-invasive preemption in TinyOS , 2009, SenSys '09.

[13]  Anthony Rowe,et al.  FireFly: A Time Synchronized Real-Time Sensor Networking Platform , 2007 .

[14]  Cecilia Mascolo,et al.  Demo abstract: A shared sensor network infrastructure , 2010 .

[15]  David E. Culler,et al.  Versatile low power media access for wireless sensor networks , 2004, SenSys '04.

[16]  Suman Nath,et al.  On-line sensing task optimization for shared sensors , 2010, IPSN '10.

[17]  Anthony Rowe,et al.  Nano-RK: an energy-aware resource-centric RTOS for sensor networks , 2005, 26th IEEE International Real-Time Systems Symposium (RTSS'05).

[18]  Sang Hyuk Son,et al.  Event Detection Services Using Data Service Middleware in Distributed Sensor Networks , 2003, Telecommun. Syst..

[19]  Adam Dunkels,et al.  Efficient Sensor Network Reprogramming through Compression of Executable Modules , 2008, 2008 5th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks.

[20]  Charles E. Perkins,et al.  Ad-hoc on-demand distance vector routing , 1999, Proceedings WMCSA'99. Second IEEE Workshop on Mobile Computing Systems and Applications.

[21]  Viktor K. Prasanna,et al.  Issues in designing middleware for wireless sensor networks , 2004, IEEE Network.

[22]  M. Welsh,et al.  The Regiment Macroprogramming System , 2007, 2007 6th International Symposium on Information Processing in Sensor Networks.

[23]  Carlos André Guimarães Ferraz,et al.  A message-oriented middleware for sensor networks , 2004, MPAC '04.

[24]  Yong Yao,et al.  The cougar approach to in-network query processing in sensor networks , 2002, SGMD.

[25]  Wei Hong,et al.  TinyDB: an acquisitional query processing system for sensor networks , 2005, TODS.

[26]  Matt Welsh,et al.  Programming Sensor Networks Using Abstract Regions , 2004, NSDI.

[27]  Todd Millstein,et al.  Kairos: a macro-programming system for wireless sensor networks , 2005, SOSP '05.

[28]  Anthony Rowe,et al.  RT-Link: A Time-Synchronized Link Protocol for Energy- Constrained Multi-hop Wireless Networks , 2006, 2006 3rd Annual IEEE Communications Society on Sensor and Ad Hoc Communications and Networks.