Performance Evaluation of Default Active Message Layer (AM) and TKN15.4 Protocol Stack in TinyOS 2.1.2

Wireless Sensor Networks (WSN) have become a leading solution to monitor and control smart buildings, health, industrial environments, and so on. Sensor nodes in a WSN have resource constraints, presenting low processing power and, in some cases, restrictions in power consumption. The resource constraints forced the researchers to develop Operating Systems (OS) for low-power wireless devices, and one of the most important and in active use is the TinyOS. This paper presents an experimental study to evaluate the performance of TinyOS default Active Message (AM) layer protocol in comparison to the fully 802.15.4 compliant protocol stack TKN15.4 developed for TinyOS. The AS-XM1000 802.15.4 mote modules were used to compare both protocols. The results showed that TKN15.4 protocol is better in both energy consumption and packet

[1]  Dirk Pesch,et al.  Experimental Evaluation of Beacon Scheduling Mechanisms for Multihop IEEE 802.15.4 Wireless Sensor Networks , 2010, 2010 Fourth International Conference on Sensor Technologies and Applications.

[2]  Andreas Willig,et al.  Passive discovery of IEEE 802.15.4-based body sensor networks , 2010, Ad Hoc Networks.

[3]  Giuseppe Anastasi,et al.  IEEE 802.15.4e: A survey , 2016, Comput. Commun..

[4]  Anna Hać,et al.  Wireless Sensor Network Designs , 2003 .

[5]  Muhammad Khalil Afzal,et al.  TinyOS-New Trends, Comparative Views, and Supported Sensing Applications: A Review , 2016, IEEE Sensors Journal.

[6]  Albert Y. Zomaya,et al.  A localized algorithm for Structural Health Monitoring using wireless sensor networks , 2014, Inf. Fusion.

[7]  Jason O. Hallstrom,et al.  Visualizing the runtime behavior of embedded network systems: A toolkit for TinyOS , 2009, Sci. Comput. Program..

[8]  Yeqiong Song,et al.  Measurement-based Analysis of the Effect of Duty Cycle in IEEE 802.15.4 MAC Performance , 2013, 2013 IEEE 10th International Conference on Mobile Ad-Hoc and Sensor Systems.

[9]  Majid Sarrafzadeh,et al.  Press the Cancel Button! A Performance Evaluation of Scalable In-Network Data Aggregation , 2009, 2009 International Conference on Information and Multimedia Technology.

[10]  David E. Culler,et al.  TinyOS: An Operating System for Sensor Networks , 2005, Ambient Intelligence.

[11]  Mikael Gidlund,et al.  Characterization of long term channel variations in industrial wireless sensor networks , 2014, 2014 IEEE International Conference on Communications (ICC).

[12]  David E. Culler,et al.  A wireless embedded sensor architecture for system-level optimization , 2002 .

[13]  Marcelo S. Alencar,et al.  Evaluation of Link Quality Estimators for Industrial Wireless Sensor Networks , 2016 .

[14]  Jaroslaw Domaszewicz,et al.  ProxyMotes: Linux-based TinyOS Platform for Non-TinyOS Sensors and Actuators , 2012, 2012 IEEE 10th International Symposium on Parallel and Distributed Processing with Applications.

[15]  David E. Culler,et al.  Wireless Sensor Networks - Introduction , 2004, Commun. ACM.

[16]  Matt Welsh,et al.  Simulating the power consumption of large-scale sensor network applications , 2004, SenSys '04.

[17]  Ilangko Balasingham,et al.  Wireless Sensor Networks - An Introduction , 2010 .

[18]  Vlado Handziski,et al.  Flexible hardware abstraction for wireless sensor networks , 2005, Proceeedings of the Second European Workshop on Wireless Sensor Networks, 2005..

[19]  Djamel Djenouri,et al.  DZ50: Energy-efficient Wireless Sensor Mote Platform for Low Data Rate Applications , 2014, EUSPN/ICTH.

[20]  Dirk Pesch,et al.  InRout - A QoS aware route selection algorithm for industrial wireless sensor networks , 2012, Ad Hoc Networks.

[21]  Giuseppe Anastasi,et al.  A Comprehensive Analysis of the MAC Unreliability Problem in IEEE 802.15.4 Wireless Sensor Networks , 2011, IEEE Transactions on Industrial Informatics.