Heterogeneous MAC duty-cycling for energy-efficient Internet of Things deployments

The Internet of Things (IoT) paradigm aims at connecting any object to the Internet (i.e. to the IP world). Due to the physical constraints (limited energy capacities) and deployment conditions (numerous autonomous devices scattered into an area) of such Things, power management and scalability are key issues in IoT deployments. While the problematics of the IP addressing have been successfully transposed to IoT networks, the dedicated IEEE 802.15.4 Medium Access Control standard lacks of scalability, provides insufficient energy-efficiency and thus fails to fulfill their needs. In this paper, we consider alternative MAC protocols, compatible with IoT specificities. These protocols realize energy gains by asynchronously alternating active and passive periods at the radio scale, thus allowing both energy-efficiency and scalability. For the time being, most real IoT deployments implement static and homogeneous duty-cycling (i.e. invariant and identical for each node in the network). Although preventing any node isolation, such method fails to address the dynamics of the network efficiently. We propose a strategy to enable heterogeneous MAC duty-cycle configurations among nodes in the network. We aim at granting each node a specific sleep-depth, according to criteria specific to the deployment (e.g. applicative criteria, location in the routing structure). To implement this idea, the nodes are divided into disjoint subsets, each of them standing for a given duty-cycle configuration and leading to a network performance managed at its best (e.g. energy consumption, loss-rate, delays). We detail to what extent our approach preserves network connectivity with coherent heterogeneous duty-cycling, thus reaching a compromise between energy consumption and reactivity. The presented experimental campaign was led over the IoT SensLAB testbed. It demonstrates that our solutions provide up to 61% energy saving, preserve the loss-rate below 10% and guarantee the connectivity of the network. They thus offer a better compromise between energy-efficiency and network performances than any homogeneous MAC configuration.

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

[2]  Ian F. Akyildiz,et al.  Wireless sensor networks: a survey , 2002, Comput. Networks.

[3]  Eric Anderson,et al.  X-MAC: a short preamble MAC protocol for duty-cycled wireless sensor networks , 2006, SenSys '06.

[4]  Thomas Noël,et al.  Medium access controlfacing the reality of WSN deployments , 2009, CCRV.

[5]  Deborah Estrin,et al.  Directed diffusion for wireless sensor networking , 2003, TNET.

[6]  Thomas Noël,et al.  From versatility to auto-adaptation of the medium access control in wireless sensor networks , 2011, J. Parallel Distributed Comput..

[7]  Dusit Niyato,et al.  Remote patient monitoring service using heterogeneous wireless access networks: architecture and optimization , 2009, IEEE Journal on Selected Areas in Communications.

[8]  Thomas Noël,et al.  Using SensLAB as a First Class Scientific Tool for Large Scale Wireless Sensor Network Experiments , 2011, Networking.

[9]  Adam Dunkels,et al.  Software-based on-line energy estimation for sensor nodes , 2007, EmNets '07.

[10]  Tahiry Razafindralambo,et al.  Performance Evaluation of Gradient Routing Strategies for Wireless Sensor Networks , 2009, Networking.

[11]  Antonio F. Gómez-Skarmeta,et al.  An internet of things–based personal device for diabetes therapy management in ambient assisted living (AAL) , 2011, Personal and Ubiquitous Computing.

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

[13]  Mani B. Srivastava,et al.  Power management in energy harvesting sensor networks , 2007, TECS.

[14]  Patrick J. Vincent,et al.  Energy conservation in wireless sensor networks , 2007 .

[15]  Hartmut Ritter,et al.  Utilizing solar power in wireless sensor networks , 2003, 28th Annual IEEE International Conference on Local Computer Networks, 2003. LCN '03. Proceedings..

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

[17]  Kamin Whitehouse,et al.  The hitchhiker's guide to successful residential sensing deployments , 2011, SenSys.

[18]  Fikret Sivrikaya,et al.  Time synchronization in sensor networks: a survey , 2004, IEEE Network.

[19]  Hongseok Yoo,et al.  Dynamic Duty-Cycle Scheduling Schemes for Energy-Harvesting Wireless Sensor Networks , 2012, IEEE Communications Letters.

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

[21]  Gregor Schiele,et al.  Energy-efficient cluster-based service discovery for Ubiquitous Computing , 2004, EW 11.

[22]  Mario Di Francesco,et al.  Energy conservation in wireless sensor networks: A survey , 2009, Ad Hoc Networks.

[23]  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.

[24]  Lothar Thiele,et al.  pTUNES: Runtime parameter adaptation for low-power MAC protocols , 2012, 2012 ACM/IEEE 11th International Conference on Information Processing in Sensor Networks (IPSN).