The Trickle Algorithm: Issues and Solutions

To manage the various multi-purpose Internet of Things applications brought about by low-power and lossy networks, efficient methods of network configuration and administration; firmware installation and updates; neighbourhood, route and resource discovery are required. These requirements can be reduced to a basic data consistency maintenance problem, making the Trickle algorithm a powerful candidate solution. Trickle is shaped by the so-called short-listen problem, hence the imposition of a listen-only period. Such a period allows Trickle to robustly address the short-listen problem at the expense of increased latency. In this report, we revisit the Trickle rules, the short-listen problem and intervalsynchronisation, and hence introduce New-Trickle. New-Trickle is an optimisation to Trickle with virtually no extra cost in terms of communication overhead, computation demand and implementation effort, yet one that provides fast updates, yielding a propagation time more than 10 times faster than Trickle.

[1]  HyungJune Lee,et al.  Improving Wireless Simulation Through Noise Modeling , 2007, 2007 6th International Symposium on Information Processing in Sensor Networks.

[2]  Deborah Estrin,et al.  The Tenet architecture for tiered sensor networks , 2006, SenSys '06.

[3]  Tmm Thomas Meyfroyt Modeling and analyzing the trickle algorithm , 2013 .

[4]  David E. Culler,et al.  Design of an application-cooperative management system for wireless sensor networks , 2005, Proceeedings of the Second European Workshop on Wireless Sensor Networks, 2005..

[5]  Erol Gelenbe,et al.  Emergency response simulation using wireless sensor networks , 2008, Ambi-Sys '08.

[6]  JeongGil Ko,et al.  The Trickle Algorithm , 2011, RFC.

[7]  Adam Dunkels,et al.  Powertrace: Network-level Power Profiling for Low-power Wireless Networks , 2011 .

[8]  David E. Culler,et al.  The emergence of a networking primitive in wireless sensor networks , 2008, CACM.

[9]  Philip Levis,et al.  Collection tree protocol , 2009, SenSys '09.

[10]  Philip Levis,et al.  RPL: IPv6 Routing Protocol for Low-Power and Lossy Networks , 2012, RFC.

[11]  Antonio Iera,et al.  The Internet of Things: A survey , 2010, Comput. Networks.

[12]  Mun Choon Chan,et al.  Indriya: A Low-Cost, 3D Wireless Sensor Network Testbed , 2011, TRIDENTCOM.

[13]  Carmelita Görg,et al.  Modelling and Simulating the Trickle Algorithm , 2011, MONAMI.

[14]  David E. Culler,et al.  The dynamic behavior of a data dissemination protocol for network programming at scale , 2004, SenSys '04.

[15]  Wu-chi Feng,et al.  DHV: A Code Consistency Maintenance Protocol for Multi-hop Wireless Sensor Networks , 2009, EWSN.

[16]  Nabil Aouf,et al.  Towards efficient distributed service discovery in low-power and lossy networks , 2014, Wirel. Networks.

[17]  Adam Dunkels,et al.  The ContikiMAC Radio Duty Cycling Protocol , 2011 .

[18]  Philip Levis,et al.  Data Discovery and Dissemination with DIP , 2008, 2008 International Conference on Information Processing in Sensor Networks (ipsn 2008).

[19]  Badis Djamaa,et al.  Optimizing the Trickle Algorithm , 2015, IEEE Communications Letters.

[20]  Enzo Mingozzi,et al.  Trickle-F: Fair broadcast suppression to improve energy-efficient route formation with the RPL routing protocol , 2013, 2013 Sustainable Internet and ICT for Sustainability (SustainIT).

[21]  G. Tolle,et al.  Deluge : Data Dissemination for Network Reprogramming at Scale , 2022 .

[22]  Jonathan W. Hui,et al.  Multicast Protocol for Low-Power and Lossy Networks (MPL) , 2016, RFC.

[23]  Carles Gomez,et al.  Modeling the Message Count of the Trickle Algorithm in a Steady-State, Static Wireless Sensor Network , 2012, IEEE Communications Letters.

[24]  Emmanuel Baccelli,et al.  Reactive Discovery of Point-to-Point Routes in Low-Power and Lossy Networks , 2013, RFC.