MEMS-Based Smart Gas Metering for Internet of Things

Utilities have traditionally employed or contracted meter readers to collect natural gas usage data, which is expensive and time consuming, and thus necessitates the need of smart natural gas metering. Existing gas metering systems mainly focus on measuring the amount of gas flowing through an microelectro mechanical system (MEMS) thermal gas flow sensor and simply ignore the detailed gas composition. From computational fluid mechanics simulations, however, we discover that gases with different compositions will cause different effects on the reading of an MEMS sensor. Based on a thorough analysis of the working principle of MEMS thermal gas flow sensor, we propose an innovative mechanism to compensate the errors caused by different types of natural gases on the sensor’s reading. The proposed solution first measures the physical property of metered gas to derive the composition correction coefficient that will then be used to correct the meter’s reading errors, considering the relation between the calorific value and physical property of natural gases. In this way, the proposed solution realizes a real-time multicomposition gas metering via thermal gas flow sensors. We implement and evaluate the proposed gas metering technique in various Internet of Things systems, including industrial flow metering, gas metering in smart home, and gas metering in low-power wide-area networks. Experiment results verify the innovative design and confirm that the proposed solution features high sensitivity, high precision, and high range ratio.

[1]  Jiming Chen,et al.  Smart community: an internet of things application , 2011, IEEE Communications Magazine.

[2]  Zhihua Xia,et al.  A Secure and Dynamic Multi-Keyword Ranked Search Scheme over Encrypted Cloud Data , 2016, IEEE Transactions on Parallel and Distributed Systems.

[3]  F. Kohl,et al.  MEMS Thermal Flow Sensor With Smart Electronic Interface Circuit , 2012, IEEE Sensors Journal.

[4]  Jan H. J. Fluitman,et al.  Micro-liquid flow sensor , 1993 .

[5]  Xingming Sun,et al.  Achieving Efficient Cloud Search Services: Multi-Keyword Ranked Search over Encrypted Cloud Data Supporting Parallel Computing , 2015, IEICE Trans. Commun..

[6]  Artur Jachimowicz,et al.  Dynamic thermal sensor - principles in MEMS for fluid characterization , 2001 .

[7]  Lida Xu,et al.  The internet of things: a survey , 2014, Information Systems Frontiers.

[8]  Jiming Chen,et al.  A Survey on Demand Response in Smart Grids: Mathematical Models and Approaches , 2015, IEEE Transactions on Industrial Informatics.

[9]  Fredrik Wallin,et al.  A Comprehensive Review of Smart Energy Meters in Intelligent Energy Networks , 2016, IEEE Internet of Things Journal.

[10]  M. Tewolde,et al.  High-resolution meter reading system for gas utility meter , 2010, 2010 IEEE Sensors.

[11]  Xingming Sun,et al.  Enabling Personalized Search over Encrypted Outsourced Data with Efficiency Improvement , 2016, IEEE Transactions on Parallel and Distributed Systems.

[12]  Xiaodong Liu,et al.  A speculative approach to spatial-temporal efficiency with multi-objective optimization in a heterogeneous cloud environment , 2016, Secur. Commun. Networks.

[13]  W. Marsden.  I and J , 2012 .

[14]  Andrea Zanella,et al.  The challenges of M2M massive access in wireless cellular networks , 2015, Digit. Commun. Networks.

[15]  Raj Jain,et al.  An Internet of Things Framework for Smart Energy in Buildings: Designs, Prototype, and Experiments , 2015, IEEE Internet of Things Journal.

[16]  Zhangbing Zhou,et al.  An energy efficient hierarchical clustering index tree for facilitating time-correlated region queries in the Internet of Things , 2014, J. Netw. Comput. Appl..

[17]  Hichem Besbes,et al.  Radio resource management scheme and outage analysis for network-assisted multi-hop D2D communications , 2016 .

[18]  Li Wang,et al.  Fast Pairing of Device-to-Device Link Underlay for Spectrum Sharing With Cellular Users , 2014, IEEE Communications Letters.

[19]  Minjie Zhang,et al.  A belief propagation-based method for task allocation in open and dynamic cloud environments , 2017, Knowl. Based Syst..

[20]  Yiqing Zhou,et al.  A super base station based centralized network architecture for 5G mobile communication systems , 2015 .

[21]  Li Wang,et al.  Resource Allocation for D2D Communications Underlay in Rayleigh Fading Channels , 2017, IEEE Transactions on Vehicular Technology.

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

[23]  M. Zorzi,et al.  The challenges of M 2 M massive access in wireless cellular networks , 2015 .

[24]  Sai Ji,et al.  Towards efficient content-aware search over encrypted outsourced data in cloud , 2016, IEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications.