Statistical Analysis of the Main Configuration Parameters of the Network Dynamic and Adaptive Radio Protocol (DARP)

The present work analyses the wireless sensor network protocol (DARP) and the impact of different configuration parameter sets on its performance. Different scenarios have been considered, in order to gain a better understanding of the influence of the configuration on network protocols. The developed statistical analysis is based on the method known as Analysis of Variance (ANOVA), which focuses on the effect of the configuration on the performance of DARP. Three main dependent variables were considered: number of control messages sent during the set-up time, energy consumption and convergence time. A total of 20,413 simulations were carried out to ensure greater robustness in the statistical conclusions. The main goal of this work is to discover the most critical configuration parameters for the protocol, with a view to potential applications in Smart City type scenarios.

[1]  Charles E. Perkins,et al.  Ad hoc On-Demand Distance Vector (AODV) Routing , 2001, RFC.

[2]  Dajin Wang.  Clustering mesh-like wireless sensor networks with an energy-efficient scheme , 2010, Int. J. Sens. Networks.

[3]  Jian Wan,et al.  PMSW: a passive monitoring system in wireless sensor networks , 2011, Int. J. Netw. Manag..

[4]  Steven Furnell,et al.  D-FICCA: A density-based fuzzy imperialist competitive clustering algorithm for intrusion detection in wireless sensor networks , 2014 .

[5]  C. Wietfeld,et al.  A segmentation-based Radio Tomographic Imaging approach for interference reduction in hostile industrial environments , 2012, Proceedings of the 2012 IEEE/ION Position, Location and Navigation Symposium.

[6]  Stamatis Karnouskos,et al.  Simulation of a Smart Grid City with Software Agents , 2009, 2009 Third UKSim European Symposium on Computer Modeling and Simulation.

[7]  Margaret J. Robertson,et al.  Design and Analysis of Experiments , 2006, Handbook of statistics.

[8]  Rutvij H. Jhaveri,et al.  Mobile Ad-hoc Networking with AODV: A Review , 2015, Int. J. Next Gener. Comput..

[9]  Jesús González,et al.  DARAL: A Dynamic and Adaptive Routing Algorithm for Wireless Sensor Networks , 2016, Sensors.

[10]  Andrew Rutherford,et al.  Introducing Anova and Ancova: A Glm Approach , 2000 .

[11]  Xuxun Liu,et al.  A Survey on Clustering Routing Protocols in Wireless Sensor Networks , 2012, Sensors.

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

[13]  Carles Gomez,et al.  On the Network Convergence Process in RPL over IEEE 802.15.4 Multihop Networks: Improvement and Trade-Offs , 2014, Sensors.

[14]  Jesús González,et al.  DARP: dynamic and adaptive radio protocol for wireless sensor networks , 2014 .

[15]  A. Varga,et al.  THE OMNET++ DISCRETE EVENT SIMULATION SYSTEM , 2003 .

[16]  Yezid Donoso,et al.  Delay/Disruption Tolerant Network-Based Message Forwarding for a River Pollution Monitoring Wireless Sensor Network Application , 2016, Sensors.

[17]  Julian F. Thayer,et al.  Introduction to analysis of variance : design, analysis, and interpretation , 2001 .

[18]  Evangelos Theodoridis,et al.  SmartSantander: IoT experimentation over a smart city testbed , 2014, Comput. Networks.

[19]  Yasushi Yamao,et al.  High-throughput time group access MCR-SS-CSMA/CA for wireless ad hoc networks with layered-tree topology , 2013, Telecommun. Syst..

[20]  Mário Alves,et al.  mRPL+: A mobility management framework in RPL/6LoWPAN , 2017, Comput. Commun..