Towards High Energy Efficiency in the Internet of Things

Internet of Things (IoT) protocols provide the fundamental mechanisms to collect data from low power devices and lossy networks. IoT protocols collect data blocks from the devices in messages that have one header and a single payload, regardless the size of the payload. This paper presents a solution to collect small size data blocks from low power devices in an efficient way, carrying these data blocks in the payload of a single message. Current solutions do not offer manners to gather many small blocks of data and reduce the overhead of the communication. The proposed solution is a light-weight layer designed to operate with the standard IoT protocol stack aiming to reduce the energy consumption of the energy constrained devices without lowering the data accuracy. The proposed solution was developed in Contiki devices and the measurements conducted on a testbed showed up to 14% energy savings.

[1]  Stefano Avallone,et al.  Combining multi-path forwarding and packet aggregation for improved network performance in wireless mesh networks , 2014, Comput. Networks.

[2]  Lusheng Ji,et al.  A first look at cellular machine-to-machine traffic: large scale measurement and characterization , 2012, SIGMETRICS '12.

[3]  G. Priyanka Reddy,et al.  Message Queuing Telemetry Transport , 2017 .

[4]  Marimuthu Palaniswami,et al.  Internet of Things (IoT): A vision, architectural elements, and future directions , 2012, Future Gener. Comput. Syst..

[5]  Leen Stougie,et al.  Latency-constrained aggregation in sensor networks , 2006, TALG.

[6]  Carsten Bormann,et al.  The Constrained Application Protocol (CoAP) , 2014, RFC.

[7]  Akbar Rahman,et al.  Group Communication for the Constrained Application Protocol (CoAP) , 2014, RFC.

[8]  Sneha A. Dalvi,et al.  Internet of Things for Smart Cities , 2017 .

[9]  Ingrid Moerman,et al.  Observing CoAP groups efficiently , 2016, Ad Hoc Networks.

[10]  Daji Qiao,et al.  Lifetime balanced data aggregation for the internet of things , 2017, Comput. Electr. Eng..

[11]  Dimitris Tsitsipis,et al.  Data merge: A data aggregation technique for wireless sensor networks , 2011, ETFA2011.

[12]  Raphaël Couturier,et al.  Comparison of Different Data Aggregation Techniques in Distributed Sensor Networks , 2017, IEEE Access.

[13]  Torsten Braun,et al.  On the Accuracy of Software-Based Energy Estimation Techniques , 2011, EWSN.

[14]  Edmundo Monteiro,et al.  A Two-Tier Adaptive Data Aggregation Approach for M2M Group-Communication , 2016, IEEE Sensors Journal.