Experimental Energy Consumption of Frame Slotted ALOHA and Distributed Queuing for Data Collection Scenarios

Data collection is a key scenario for the Internet of Things because it enables gathering sensor data from distributed nodes that use low-power and long-range wireless technologies to communicate in a single-hop approach. In this kind of scenario, the network is composed of one coordinator that covers a particular area and a large number of nodes, typically hundreds or thousands, that transmit data to the coordinator upon request. Considering this scenario, in this paper we experimentally validate the energy consumption of two Medium Access Control (MAC) protocols, Frame Slotted ALOHA (FSA) and Distributed Queuing (DQ). We model both protocols as a state machine and conduct experiments to measure the average energy consumption in each state and the average number of times that a node has to be in each state in order to transmit a data packet to the coordinator. The results show that FSA is more energy efficient than DQ if the number of nodes is known a priori because the number of slots per frame can be adjusted accordingly. However, in such scenarios the number of nodes cannot be easily anticipated, leading to additional packet collisions and a higher energy consumption due to retransmissions. Contrarily, DQ does not require to know the number of nodes in advance because it is able to efficiently construct an ad hoc network schedule for each collection round. This kind of a schedule ensures that there are no packet collisions during data transmission, thus leading to an energy consumption reduction above 10% compared to FSA.

[1]  G. Campbell,et al.  A near perfect stable random access protocol for a broadcast channel , 1992, [Conference Record] SUPERCOMM/ICC '92 Discovering a New World of Communications.

[2]  Jesus Alonso-Zarate,et al.  A near-optimum cross-layered distributed queuing protocol for wireless LAN , 2008, IEEE Wireless Communications.

[3]  José Oriol Sallent Roig,et al.  A near-optimum medium access protocol based on the distributed queueing random access protocol (DQRAP) for a CDMA third generation mobile communication system , 1999 .

[4]  Jesus Alonso-Zarate,et al.  Performance Analysis of a Cluster-Based MAC Protocol for Wireless Ad Hoc Networks , 2010, EURASIP J. Wirel. Commun. Netw..

[5]  Oriol Sallent,et al.  A near-optimum MAC protocol based on the distributed queueing random access protocol (DQRAP) for a CDMA mobile communication system , 2000, IEEE Journal on Selected Areas in Communications.

[6]  Wenxin Xu,et al.  A Distributed Queueing Random Access Protocol for a Broadcast Channel , 1993, SIGCOMM.

[7]  Augustus J. E. M. Janssen,et al.  Analysis of contention tree algorithms , 2000, IEEE Trans. Inf. Theory.

[8]  Xavier Vilajosana,et al.  Bootstrapping smart cities through a self-sustainable model based on big data flows , 2013, IEEE Communications Magazine.

[9]  Zornitza Genova Prodanoff Optimal frame size analysis for framed slotted ALOHA based RFID networks , 2010, Comput. Commun..

[10]  Jesus Alonso-Zarate,et al.  Energy analysis of a contention tree-based access protocol for machine-to-machine networks with idle-to-saturation traffic transitions , 2014, 2014 IEEE International Conference on Communications (ICC).

[11]  Qin Wang,et al.  A Realistic Energy Consumption Model for TSCH Networks , 2014, IEEE Sensors Journal.

[12]  Vinod Namboodiri,et al.  An extensive study of slotted Aloha-based RFID anti-collision protocols , 2012, Comput. Commun..

[13]  Jesus Alonso-Zarate,et al.  Standardized Low-Power Wireless Communication Technologies for Distributed Sensing Applications , 2014, Sensors.

[14]  Andreas Willig,et al.  An energy consumption analysis of the Wireless HART TDMA protocol , 2013, Comput. Commun..

[15]  Wing Cheong Lau,et al.  Performance analysis of an adaptive, energy-efficient MAC protocol for wireless sensor networks , 2012, J. Parallel Distributed Comput..

[16]  Jesus Alonso-Zarate,et al.  Demonstrating Low-Power Distributed Queuing for active RFID communications at 433 MHz , 2014, 2014 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).

[17]  Cristina Cano,et al.  Low energy operation in WSNs: A survey of preamble sampling MAC protocols , 2011, Comput. Networks.

[18]  F. Jiang,et al.  Exploiting the capture effect for collision detection and recovery , 2005, The Second IEEE Workshop on Embedded Networked Sensors, 2005. EmNetS-II..

[19]  Kevin Weekly,et al.  OpenWSN: a standards‐based low‐power wireless development environment , 2012, Trans. Emerg. Telecommun. Technol..

[20]  Kin K. Leung,et al.  MAC Essentials for Wireless Sensor Networks , 2010, IEEE Communications Surveys & Tutorials.

[21]  Pere Tuset,et al.  On the suitability of the 433 MHz band for M2M low‐power wireless communications: propagation aspects , 2014, Trans. Emerg. Telecommun. Technol..

[22]  Gang Zhou,et al.  Models and solutions for radio irregularity in wireless sensor networks , 2006, TOSN.

[23]  Kwan-Wu Chin,et al.  A Survey and Tutorial of RFID Anti-Collision Protocols , 2010, IEEE Communications Surveys & Tutorials.

[24]  Jesus Alonso-Zarate,et al.  Cross-Layer Enhancement for WLAN Systems with Heterogeneous Traffic Based on DQCA , 2007, 2007 IEEE International Conference on Communications.