Towards a commodity solution for the internet of things

Embedded-class processors found in commodity palmtop computers continue to become increasingly capable. Moreover, wireless connectivity in these systems provides new opportunities in designing flexible and smarter wireless sensor networks (WSNs). In this paper, we present Lynx, a self-organizing wireless sensor network framework. Leveraging palmtop systems as sensor hubs, Lynx provides fundamental functionality to make a distributively managed, customizable WSN system implementation. Second, we describe Ocelot, a mobile distributed grid-like computing engine for commodity palmtop platforms. The combination of Lynx and Ocelot provides sensor nodes that are capable of collecting, recording, processing, and communicating data without any central server support. Significant energy savings can be achieved for light to medium weight tasks through the Lynx and Ocelot combined system compared to traditional server-class grid-solutions such as BOINC. We demonstrate Lynx and Ocelot in the context of life-cycle building energy usage.

[1]  Mohammad Abdul Azim,et al.  Energy-Efficient Methods for Highly Correlated Spatio-Temporal Environments in Wireless Sensor Network Communications , 2014 .

[2]  Wei Zhang,et al.  A survey on intelligent routing protocols in wireless sensor networks , 2014, J. Netw. Comput. Appl..

[3]  Qiuwei Yang,et al.  Survey of Security Technologies on Wireless Sensor Networks , 2015, J. Sensors.

[4]  Mohamed A. Sharaf,et al.  TiNA: a scheme for temporal coherency-aware in-network aggregation , 2003, MobiDe '03.

[5]  Vidushi Sharma,et al.  A survey on dynamic power management approach in wireless sensor networks , 2014, 2014 6th IEEE Power India International Conference (PIICON).

[6]  Daniel Camps-Mur,et al.  Device-to-device communications with Wi-Fi Direct: overview and experimentation , 2013, IEEE Wireless Communications.

[7]  Yang Yang,et al.  Self-configuration and self-optimization for LTE networks , 2010, IEEE Communications Magazine.

[8]  Prabal Dutta,et al.  AudioDAQ: turning the mobile phone's ubiquitous headset port into a universal data acquisition interface , 2012, SenSys '12.

[9]  Haifeng Xu,et al.  Ocelot: A wireless sensor network and computing engine with commodity palmtop computers , 2013, 2013 International Green Computing Conference Proceedings.

[10]  Carles Gomez,et al.  Overview and Evaluation of Bluetooth Low Energy: An Emerging Low-Power Wireless Technology , 2012, Sensors.

[11]  Haifeng Xu,et al.  Improving efficiency of wireless sensor networks through lightweight in-memory compression , 2015, 2015 Sixth International Green and Sustainable Computing Conference (IGSC).

[12]  Sahin Albayrak,et al.  An Android Application Sandbox system for suspicious software detection , 2010, 2010 5th International Conference on Malicious and Unwanted Software.

[13]  Christos Douligeris,et al.  Energy Efficient Routing in Wireless Sensor Networks Through Balanced Clustering , 2013, Algorithms.

[14]  Haifeng Xu,et al.  Enabling dynamic life cycle assessment of buildings with wireless sensor networks , 2011, Proceedings of the 2011 IEEE International Symposium on Sustainable Systems and Technology.

[15]  Yanchun Zhang,et al.  Fault Tolerance in Data Gathering Wireless Sensor Networks , 2011, Comput. J..

[16]  David P. Anderson,et al.  BOINC: a system for public-resource computing and storage , 2004, Fifth IEEE/ACM International Workshop on Grid Computing.

[17]  Qi Han,et al.  A Survey of Fault Management in Wireless Sensor Networks , 2007, Journal of Network and Systems Management.