Contiki-Based IEEE 802.15.4 Channel Capacity Estimation and Suitability of Its CSMA-CA MAC Layer Protocol for Real-Time Multimedia Applications

Real-time multimedia applications require quality of service (QoS) provisioning in terms of bounds on delay and packet loss along with soft bandwidth guarantees. The shared nature of the wireless communication medium results in interference. Interference combined with the overheads, associated with a medium access control (MAC) protocol, and the implementation of a networking protocol stack limit the available bandwidth in IEEE 802.15.4-based networks and can result in congestion, even if the transmission rates of nodes are well below the maximum bandwidth supported by an underlying communication technology. Congestion degrades the performance of admitted real-time multimedia flow(s). Therefore, in this paper, we experimentally derive the IEEE 802.15.4 channel capacity using an unslotted CSMA-CA MAC protocol. We experimentally derive channel capacity for two cases, that is, when the CSMA-CA protocol is working without ACKs and when it is working with ACKs. Moreover, for both cases, we plot the relationship of offered data load with delay and packet loss rate. Simulation results demonstrate that the parameters that affect the choice of a CSMA-CA MAC layer protocol are end-to-end delay and packet loss requirements of a real-time multimedia flow, data load within the interference range of transmitters along the forwarding path, and length of the forwarding path.

[1]  Eduardo Tovar,et al.  A Simulation Model for the IEEE 802.15.4 protocol: Delay/Throughput Evaluation of the GTS Mechanism , 2007, 2007 15th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems.

[2]  Marc St-Hilaire,et al.  Comparison of ns2.34's ZigBee/802.15.4 implementation to Memsic's IRIS Motes , 2011, 2011 7th International Wireless Communications and Mobile Computing Conference.

[3]  Solomon W. Golomb,et al.  Mathematical Models: Uses and Limitations , 1971 .

[4]  Adam Dunkels,et al.  Contiki - a lightweight and flexible operating system for tiny networked sensors , 2004, 29th Annual IEEE International Conference on Local Computer Networks.

[5]  Giuseppe Lipari,et al.  Wireless line sensor network for distributed visual surveillance , 2009, PE-WASUN '09.

[6]  Insup Lee,et al.  Cyber-physical systems: The next computing revolution , 2010, Design Automation Conference.

[7]  Min Young Chung,et al.  MAC throughput limit analysis of slotted CSMA/CA in IEEE 802.15.4 WPAN , 2006, IEEE Commun. Lett..

[8]  Rui Wang,et al.  Throughput analysis of GTS allocation in beacon enabled IEEE 802.15.4 , 2010, 2010 3rd International Conference on Computer Science and Information Technology.

[9]  Thomas Kunz,et al.  Operating Systems for Wireless Sensor Networks: A Survey , 2011, Sensors.

[10]  A. Kumar,et al.  Testbed based throughput analysis in a Wireless Sensor Network , 2012, 2012 National Conference on Communications (NCC).

[11]  Bo Yan,et al.  Design and implementation of a sensor-based wireless camera system for continuous monitoring in assistive environments , 2010, Personal and Ubiquitous Computing.

[12]  Ashok Koujalagi.  Bandwidth Estimation For IEEE 802.11 Based Ad Hoc Networks , 2014 .

[13]  Ian F. Akyildiz,et al.  A survey on wireless multimedia sensor networks , 2007, Comput. Networks.

[14]  Adam Dunkels,et al.  Cross-Level Sensor Network Simulation with COOJA , 2006, Proceedings. 2006 31st IEEE Conference on Local Computer Networks.