Sustainable Load-Balancing Scheme for Inter-Sensor Convergence Processing of Routing Cooperation Topology

Recent advancements in Information Technology (IT) have sparked the creation of numerous and diverse types of devices and services. Manual data collection measurement methods have been automated through the use of various wireless or wired sensors. Single sensor devices are included in smart devices such as smartphones. Data transmission is critical for big data collected from sensor nodes, such as Mobile Sensor Nodes (MSNs), where sensors move dynamically according to sensor mobility, or Fixed Sensor Nodes (FSNs), where sensor locations are decided by the users. False data transfer processing of big data results in topology lifespan reduction and data transfer delays. Hence, a variety of simulators and diverse load-balancing algorithms have been developed as protocol verification tools for topology lifespan maximization and effective data transfer processing. However, those previously developed simulators have limited functions, such as an event function for a specific sensor or a battery consumption rate test for sensor deployment. Moreover, since the previous load-balancing algorithms consider only the general traffic distribution and the number of connected nodes without considering the current topology condition, the sustainable load-balancing technique that takes into account the battery consumption rate of the dispersed sensor nodes is required. Therefore, this paper proposes the Sustainable Load-balancing Scheme (SLS), which maximizes the overall topology lifespan through effective and sustainable load-balancing of data transfer among the sensors. SLS is capable of maintaining an effective topology as it considers both the battery consumption rate of the sensors and the data transfer delay.

[1]  Yoo Sang-Jo,et al.  Power, mobility and wireless channel condition aware connected dominating set construction algorithm in the wireless ad-hoc networks , 2005 .

[2]  Xiuzhen Cheng,et al.  Connected Dominating Set in Sensor Networks and MANETs , 2004 .

[3]  Anuradha Pughat,et al.  A review on stochastic approach for dynamic power management in wireless sensor networks , 2015, Human-centric Computing and Information Sciences.

[4]  Young-Sik Jeong,et al.  Visual Scheme for the Detection of Mobile Attack on WSN Simulator , 2013, Int. J. Distributed Sens. Networks.

[5]  Young-Sik Jeong,et al.  Large-Scale Middleware for Ubiquitous Sensor Networks , 2010, IEEE Intelligent Systems.

[6]  Youn-Sik Hong,et al.  A load-balanced topology maintenance with partial reconstruction of connected dominating sets , 2011, Proceedings of the International Conference on Wireless Information Networks and Systems.

[7]  Young-Sik Jeong,et al.  Parallel Processing Simulator for Separate Sensor of WSN Simulator with GPU , 2015, 2015 IEEE 29th International Conference on Advanced Information Networking and Applications.

[8]  Young-Sik Jeong,et al.  Visual Scheme Monitoring of Sensors for Fault Tolerance on Wireless Body Area Networks with Cloud Service Infrastructure , 2014, Int. J. Distributed Sens. Networks.

[9]  Young-Sik Jeong,et al.  Adaptive resource management scheme for monitoring of CPS , 2013, The Journal of Supercomputing.

[10]  Manoj Misra,et al.  An Energy Efficient Distributed Approach-Based Agent Migration Scheme for Data Aggregation in Wireless Sensor Networks , 2015, J. Inf. Process. Syst..

[11]  Bin He,et al.  Big Data Reduction and Optimization in Sensor Monitoring Network , 2014, J. Appl. Math..

[12]  Hongke Zhang,et al.  A Distributed Energy-Efficient Topology Control Routing for Mobile Wireless Sensor Networks , 2007, Networking.

[13]  Ivan Stojmenovic,et al.  On calculating power-aware connected dominating sets for efficient routing in ad hoc wireless networks , 2002, J. Commun. Networks.

[14]  Young-Sik Jeong,et al.  Integrated Validation System for the Simulation of Diverse Sensors in WSNs , 2013, Int. J. Distributed Sens. Networks.