Predicting the energy consumption in software defined wireless sensor networks: a probabilistic Markov model approach

The smart world is connecting all universe more than ever thought possible, benefiting from the significant advances of the Internet of Things (IoT) applications using wireless sensor networks (WSN) as the core technology. A challenging issue in the IoT paradigm is the heterogeneity in different parts of the network. The network developers need to use resources belonging to different platforms for their applications, and the software defined network (SDN) approach is a mainly considered solution. In this paper, a software defined wireless sensor network (SDWSN) with an energy predictor model (SDWSN-EPM) based on the Markov probabilistic model is proposed to reduce the energy consumption and the network latency. The energy consumption rate (ECR) of the sensor nodes is modeled using the Markov model and the states of the sensor nodes. The ECR is used by the SDN controller to predict the residual energy level of the nodes and consequently, the energy consumption of the network. The cumulative distribution functions (CDF) of the delay, power consumption, and the network lifetime in both SDWSN and SDWSN-EPM schemes are compared. The results confirm that the SDWSN-EPM model significantly improves the performance of the sensor networks.

[1]  Sylvia Richardson,et al.  Markov Chain Monte Carlo in Practice , 1997 .

[2]  B. R. Badrinath,et al.  Prediction-based energy map for wireless sensor networks , 2003, Ad Hoc Networks.

[3]  Nick McKeown,et al.  OpenFlow: enabling innovation in campus networks , 2008, CCRV.

[4]  Lynne E. Parker,et al.  Energy and Buildings , 2012 .

[5]  Hwee Pink Tan,et al.  Sensor OpenFlow: Enabling Software-Defined Wireless Sensor Networks , 2012, IEEE Communications Letters.

[6]  Shengwei Wang,et al.  Development of prediction models for next-day building energy consumption and peak power demand using data mining techniques , 2014 .

[7]  Zhi-jie Han,et al.  A Novel Wireless Sensor Networks Structure Based on the SDN , 2014, Int. J. Distributed Sens. Networks.

[8]  Laura Galluccio,et al.  Towards a software-defined Network Operating System for the IoT , 2015, 2015 IEEE 2nd World Forum on Internet of Things (WF-IoT).

[9]  Min Chen,et al.  Software-defined internet of things for smart urban sensing , 2015, IEEE Communications Magazine.

[10]  Laura Galluccio,et al.  SDN-WISE: Design, prototyping and experimentation of a stateful SDN solution for WIreless SEnsor networks , 2015, 2015 IEEE Conference on Computer Communications (INFOCOM).

[11]  Ke Xu,et al.  Toward software defined smart home , 2016, IEEE Communications Magazine.

[12]  Wu Muqing,et al.  Energy-efficient algorithm based on multi-dimensional energy space for software-defined wireless sensor networks , 2016, 2016 International Symposium on Wireless Communication Systems (ISWCS).

[13]  Ning Wang,et al.  An Energy-Efficient Routing Algorithm for Software-Defined Wireless Sensor Networks , 2016, IEEE Sensors Journal.

[14]  Giacomo Morabito,et al.  An SDN-Assisted Framework for Optimal Deployment of MapReduce Functions in WSNs , 2016, IEEE Transactions on Mobile Computing.

[15]  Lei Shu,et al.  An energy-efficient SDN based sleep scheduling algorithm for WSNs , 2016, J. Netw. Comput. Appl..

[16]  Antonio F. Gómez-Skarmeta,et al.  Data driven modeling for energy consumption prediction in smart buildings , 2017, 2017 IEEE International Conference on Big Data (Big Data).

[17]  Pedro Furtado,et al.  Planning for Heterogeneous IoT with Time Guaranties , 2017, ANT/SEIT.

[18]  Rui Wang,et al.  ETMRM: An Energy-efficient Trust Management and Routing Mechanism for SDWSNs , 2018, Comput. Networks.

[19]  Giacomo Morabito,et al.  Toward Unified Control of Networks of Switches and Sensors Through a Network Operating System , 2018, IEEE Internet of Things Journal.

[20]  Lin Yang,et al.  A methodology for reliability of WSN based on software defined network in adaptive industrial environment , 2018, IEEE/CAA Journal of Automatica Sinica.

[21]  Xiaohong Huang,et al.  A Load Balancing Routing Mechanism Based on SDWSN in Smart City , 2019, Electronics.

[22]  Alimorad Mahmoudi,et al.  A markov model for investigating the impact of IEEE802.15.4 MAC layer parameters and number of clusters on the performance of wireless sensor networks , 2019, Wirel. Networks.

[23]  Reza Malekian,et al.  Flexible network management and application service adaptability in software defined wireless sensor networks , 2018, Journal of Ambient Intelligence and Humanized Computing.

[24]  Yezid Donoso,et al.  A Prediction Algorithm based on Markov Chains for finding the Minimum Cost Path in a Mobile WSNs , 2019, Int. J. Comput. Commun. Control.

[25]  Sung Won Kim,et al.  Proposition and Real-Time Implementation of an Energy-Aware Routing Protocol for a Software Defined Wireless Sensor Network , 2019, Sensors.

[26]  V. R. Sarma Dhulipala,et al.  Improved energy efficient design in software defined wireless electroencephalography sensor networks (WESN) using distributed architecture to remove artifact , 2020, Comput. Commun..

[27]  Yonghong Ma,et al.  Hybridized Intelligent Home Renewable Energy Management System for Smart Grids , 2020 .

[28]  Masayuki Murata,et al.  Anomaly Detection in Smart Home Operation From User Behaviors and Home Conditions , 2020, IEEE Transactions on Consumer Electronics.

[29]  Hongyang Chen,et al.  Energy-Efficient Relay-Selection-Based Dynamic Routing Algorithm for IoT-Oriented Software-Defined WSNs , 2020, IEEE Internet of Things Journal.

[30]  Nan Zhou,et al.  An Effective Edge-Assisted Data Collection Approach for Critical Events in the SDWSN-Based Agricultural Internet of Things , 2020, Electronics.

[31]  Mohammad R. Khosravi,et al.  An IoT-enabled intelligent automobile system for smart cities , 2020, Internet Things.