Energy-aware medium access control for energy-harvesting machine-to-machine networks

Energy-harvesting is being actively researched for the Machine-to-Machine networks. Without replacement of battery, energy-harvesting enables nodes (or machines) to perform their work permanently by recharging energy store periodically from an external source. After performing given tasks, in many applications, each energy-harvesting node transmits data to the gateway node. Here, the difference in harvested/consumed energy could lead to sub-optimal communication due to depletion of energy. In this paper, we design an energy-aware medium access control scheme for energy-harvesting machine-to-machine networks. The proposed algorithm controls delivery error rate due to energy depletion through limited contention among energy-exhausting nodes, and maximize slot efficiency to minimize overall communication duration. Maximizing slot efficiency is implemented in two ways: utility-based and learning-based. Simulation studies have shown that the proposed schemes effectively minimize delivery error rate and communication period, outperforming the existing strategies in the literature.

[1]  T. G. Venkatesh,et al.  Performance Analysis of M2M Data Collection Networks Using Dynamic Frame-Slotted ALOHA , 2018, IEEE Transactions on Green Communications and Networking.

[2]  Deborah Estrin,et al.  An energy-efficient MAC protocol for wireless sensor networks , 2002, Proceedings.Twenty-First Annual Joint Conference of the IEEE Computer and Communications Societies.

[3]  Umberto Spagnolini,et al.  Energy group-based dynamic framed ALOHA for wireless networks with energy harvesting , 2012, 2012 46th Annual Conference on Information Sciences and Systems (CISS).

[4]  Peter Dayan,et al.  Technical Note: Q-Learning , 2004, Machine Learning.

[5]  Gianluigi Liva,et al.  Graph-Based Analysis and Optimization of Contention Resolution Diversity Slotted ALOHA , 2011, IEEE Transactions on Communications.

[6]  L. Kleinrock,et al.  Packet Switching in Radio Channels: Part I - Carrier Sense Multiple-Access Modes and Their Throughput-Delay Characteristics , 1975, IEEE Transactions on Communications.

[7]  Jesus Alonso-Zarate,et al.  Performance Evaluation of Frame Slotted-ALOHA with Succesive Interference Cancellation in Machine-to-Machine Networks , 2014 .

[8]  Jaeho Kim,et al.  M2M Service Platforms: Survey, Issues, and Enabling Technologies , 2014, IEEE Communications Surveys & Tutorials.

[9]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.

[10]  Jesus Alonso-Zarate,et al.  Duty-Cycle Optimization for Machine-to-Machine Area Networks Based on Frame Slotted-ALOHA with Energy Harvesting Capabilities , 2014 .

[11]  F. Vázquez Gallego,et al.  Energy and delay analysis of contention resolution mechanisms for machine-to-machine networks based on low-power WiFi , 2013, 2013 IEEE International Conference on Communications (ICC).

[12]  Chun-Yi Wang,et al.  A Grouping-Based Dynamic Framed Slotted ALOHA Anti-Collision Method with Fine Groups in RFID Systems , 2010, 2010 5th International Conference on Future Information Technology.

[13]  David E. Culler,et al.  Versatile low power media access for wireless sensor networks , 2004, SenSys '04.

[14]  Bhaskar Krishnamachari,et al.  An adaptive energy-efficient and low-latency MAC for data gathering in wireless sensor networks , 2004, 18th International Parallel and Distributed Processing Symposium, 2004. Proceedings..

[15]  Boris Bellalta,et al.  Energy efficiency of MAC protocols in low data rate wireless multimedia sensor networks: A comparative study , 2017, Ad Hoc Networks.

[16]  Lei Tang,et al.  PW-MAC: An energy-efficient predictive-wakeup MAC protocol for wireless sensor networks , 2011, 2011 Proceedings IEEE INFOCOM.

[17]  Peter Dayan,et al.  Q-learning , 1992, Machine Learning.

[18]  F. Schoute,et al.  Dynamic Frame Length ALOHA , 1983, IEEE Trans. Commun..

[19]  Renjie Huang,et al.  TreeMAC: Localized TDMA MAC protocol for real-time high-data-rate sensor networks , 2009, Pervasive Mob. Comput..

[20]  John S. Heidemann,et al.  Ultra-low duty cycle MAC with scheduled channel polling , 2006, SenSys '06.

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

[22]  Carsten Bormann,et al.  Terminology for Constrained-Node Networks , 2014, RFC.

[23]  Jesus Alonso-Zarate,et al.  Contention Tree-Based Access for Wireless Machine-to-Machine Networks With Energy Harvesting , 2017, IEEE Transactions on Green Communications and Networking.

[24]  Katia Obraczka,et al.  Energy-efficient collision-free medium access control for wireless sensor networks , 2003, SenSys '03.

[25]  Lawrence G. Roberts,et al.  ALOHA packet system with and without slots and capture , 1975, CCRV.

[26]  Biplab Sikdar,et al.  A Survey of MAC Layer Issues and Protocols for Machine-to-Machine Communications , 2015, IEEE Internet of Things Journal.

[27]  Umberto Spagnolini,et al.  Medium Access Control Protocols for Wireless Sensor Networks with Energy Harvesting , 2011, IEEE Transactions on Communications.

[28]  Chin Keong Ho,et al.  Markovian models for harvested energy in wireless communications , 2010, 2010 IEEE International Conference on Communication Systems.

[29]  J. J. Garcia-Luna-Aceves,et al.  A new approach to channel access scheduling for Ad Hoc networks , 2001, MobiCom '01.

[30]  Riccardo De Gaudenzi,et al.  Contention Resolution Diversity Slotted ALOHA (CRDSA): An Enhanced Random Access Schemefor Satellite Access Packet Networks , 2007, IEEE Transactions on Wireless Communications.

[31]  Richard S. Sutton,et al.  Introduction to Reinforcement Learning , 1998 .

[32]  Purushottam Kulkarni,et al.  Energy Harvesting Sensor Nodes: Survey and Implications , 2011, IEEE Communications Surveys & Tutorials.

[33]  Deborah Estrin,et al.  Medium access control with coordinated adaptive sleeping for wireless sensor networks , 2004, IEEE/ACM Transactions on Networking.