Branch-Based Centralized Data Collection for Smart Grids Using Wireless Sensor Networks

A smart grid is one of the most important applications in smart cities. In a smart grid, a smart meter acts as a sensor node in a sensor network, and a central device collects power usage from every smart meter. This paper focuses on a centralized data collection problem of how to collect every power usage from every meter without collisions in an environment in which the time synchronization among smart meters is not guaranteed. To solve the problem, we divide a tree that a sensor network constructs into several branches. A conflict-free query schedule is generated based on the branches. Each power usage is collected according to the schedule. The proposed method has important features: shortening query processing time and avoiding collisions between a query and query responses. We evaluate this method using the ns-2 simulator. The experimental results show that this method can achieve both collision avoidance and fast query processing at the same time. The success rate of data collection at a sink node executing this method is 100%. Its running time is about 35 percent faster than that of the round-robin method, and its memory size is reduced to about 10% of that of the depth-first search method.

[1]  Robert J. McEliece,et al.  Packets distribution algorithms for sensor networks , 2003, IEEE INFOCOM 2003. Twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Cat. No.03CH37428).

[2]  David Flynn,et al.  A Comprehensive WSN-Based Approach to Efficiently Manage a Smart Grid , 2014, Sensors.

[3]  Hyung Seok Kim,et al.  Hybrid Distributed Stochastic Addressing Scheme for ZigBee/IEEE 802.15.4 Wireless Sensor Networks , 2011 .

[4]  Wei Hong,et al.  Proceedings of the 5th Symposium on Operating Systems Design and Implementation Tag: a Tiny Aggregation Service for Ad-hoc Sensor Networks , 2022 .

[5]  Sunshin An,et al.  Slotted Beacon Scheduling Using ZigBee Cskip Mechanism , 2008, 2008 Second International Conference on Sensor Technologies and Applications (sensorcomm 2008).

[6]  Bhaskar Krishnamachari,et al.  Fast Data Collection in Tree-Based Wireless Sensor Networks , 2012, IEEE Transactions on Mobile Computing.

[7]  Ian F. Akyildiz,et al.  Wireless sensor networks , 2007 .

[8]  Tadatoshi Babasaki,et al.  Smart Power Supply Systems for Mission Critical Facilities , 2012, IEICE Trans. Commun..

[9]  Suxiang Zhang,et al.  Wireless sensor network in smart grid: Applications and issue , 2012, 2012 World Congress on Information and Communication Technologies.

[10]  Kwangsoo Kim,et al.  Efficient data collection for smart grid using wireless sensor networks , 2013, 2013 IEEE 2nd Global Conference on Consumer Electronics (GCCE).

[11]  Ju Wang,et al.  Scheduling for information gathering on sensor network , 2009, Wirel. Networks.

[12]  Philip Levis,et al.  Collection tree protocol , 2009, SenSys '09.

[13]  Ivan Stojmenovic,et al.  Depth first search and location based localized routing and QoS routing in wireless networks , 2000, Proceedings 2000 International Conference on Parallel Processing.

[14]  Janelle J. Harms,et al.  ENCAST: energy-critical node aware spanning tree for sensor networks , 2005, 3rd Annual Communication Networks and Services Research Conference (CNSR'05).

[15]  Gerhard P. Hancke,et al.  Opportunities and Challenges of Wireless Sensor Networks in Smart Grid , 2010, IEEE Transactions on Industrial Electronics.

[16]  Won Cheol Lee,et al.  An Optimal Power Scheduling Method Applied in Home Energy Management System Based on Demand Response , 2013 .

[17]  Eitan Altman,et al.  NS Simulator for Beginners , 2012, NS Simulator for Beginners.

[18]  Chenyang Lu,et al.  Dynamic Conflict-free Query Scheduling for Wireless Sensor Networks , 2006, Proceedings of the 2006 IEEE International Conference on Network Protocols.