Repeated game theory-based reducer selection strategy for energy management in SDWSN
Introduction
The Internet of Things (IoT) is altering everything on the earth and will continue to spark innovations in the future [1]. Today, social and commercial interactions among humans, between machines and people produce a continuous flow of data for monitoring and analysis. While IoT is now emerging as a mandate in every industry, including transportation, healthcare, building, and so on, several barriers exist (the deployment of IPV6, agreement on standards, and power of sensors) that slows down the growth of IoT [2]. A research conducted by Andy Noronha [3] revealed that IoT is not about things — It is all about data. The sensor network, which is the heart of the IoT, produces an unpredictable amount of data that becomes a major source to big data. As a result, the organizations are posed with significant challenges in building infrastructure that can handle big data. A report by Cisco says that sensors are producing 5 Quintilian data bytes every day. It is expected to generate 79.4 zettabytes (ZB) of data by the year 2025 according to international data corporation forecast. Therefore, reducing the volume of data before transmitting it to the cloud (in-network processing) is found to be essential. Also, when data are processed even before they are transferred to the sink node, the burden imposed on the tiny sensor nodes will be diminished [4], [5]. This leads to a great reduction in overall communication cost in the network. This reduced cost, in terms of communication, in turn reduces network latency and, hence, paves way for better real-time performance.
The software-defined networks (SDN) paradigm is fascinating the interest of both the academicians and industrial researchers. It is worthy of attention as it remarkably simplifies network management and control [6]. As shown in Fig. 1, SDN can help to alleviate many complications of wireless sensor network (WSN). The elements of WSN are typically energy constrained by nature. Thus, running all the protocols and functionalities within the tiny sensors makes them prone to early failure. Using SDN, the energy-intensive functionalities are pulled out from the physical sensor node to a logically centralized or distributed SDN controller. The SDN controller plays the role of an operating system of the network. SDN also enables flexible network management, which is also a key element in IoT.
The application of SDN in WSN promoted the growth of a software-defined wireless sensor network (SDWSN) [7]. SDWSN is a good approach to improve the efficiency and sustainability of WSN and to foster interoperability with other networks. It eases network management by adding it as another application on the application layer that lies on top of the control layer, which most of the network administrators prefer [8], [9]. It is a boon to device vendors as it is easy to enforce policy changes throughout the entire network which was a tedious task in traditional WSN.
For carrying out in-network processing, appropriate nodes are selected as reducers among the several nodes that are distributed randomly in the network. In multi-tasking environment, by setting a node as a reducer, all other nodes in the network which sense the same task will forward the data towards that reducer, exacerbating the bandwidth limitation. The authors in [10] focused on conserving the energy of the nodes by accompanying in-network processing in SDWSN network, but there are chances of selecting a particular node as reducer more than once. As computation consumes energy, though not as much as communication, selecting a particular node as reducer more than once will shorten the lifespan of nodes [11]. Many researchers [12], [13] till date have used game theory for optimal selection of cluster head in WSN for its efficiency in decision making. Repeated games, an important type of dynamic games uses the history of players’ behavior to change their own strategies accordingly and is repeated over time [14]. In this paper, the utility function of repeated game has been formulated, which the sensor nodes try to maximize. The game is played in the SDN controller for efficient selection of reducers. Each reducer runs reducer functions which are loaded into them on the fly dynamically and sends the resultant data to the sink node, naturally conserving the energy depleted in the network. The main contributions of this work are as follows:
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A game model has been formulated through which appropriate reducers are chosen for various tasks in the network, to operate in-network processing;
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Based on the selection of reducers, a routing algorithm which routes the packets to the sink node via the respective reducer has been proposed;
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Through extensive results, this work proves to be efficient than any other existing works.
The rest of this paper is organized as follows. Section 2 reviews the related works on the basis of energy consumption in SDWSN and game theory in WSN. Section 3 describes the network model along with the energy consumption model. Reducer selection using game theory and the routing protocol are described in Section 4 and Section 5 respectively. Section 6 furnish the results and analysis of the experiment. Finally, Section 7 concludes this work.
Section snippets
Related works
Several works were proposed to curtail the energy depleted by nodes in SDWSN, and in-network processing has been consistently a great method to reduce the traffic in traditional WSN. In this section, a few of the works from the aspect of energy management in WSN and energy management in SDWSN is explored.
Network model
A typical SDWSN architecture which consists of one or more logically centralized controller(s) and a set of sensor nodes, , where is the number of sensor nodes is considered. As per SDN paradigm, the data layer and control layer are distinctly separated. More definitely, the data layer consists of sensor nodes and sink node(s), that are responsible for packet forwarding and in-network processing. Whereas, the control layer which holds the SDN controller, in principle, is outside
Reducer selection using repeated game theory
In this section, the problem of reducer selection is formulated as a repeated game model. This provides a solution to help in-network processing in SDWSN. The key–value pairs are forwarded to the respective reducers that perform analytical operation to reduce the amount of data. The purpose of this section is to select the most suitable node as reducer dynamically at run-time, which performs reducing operation, such as max, sum, top-k, and so on, for a particular sensor type in the given
Routing via respective reducer protocol
In this section, a solution to optimally route packets to sink node so as to minimize the overall communication cost in the network is discussed. In traditional WSN, the data is aggregated in the sink node or the nearest aggregator node or the most convenient aggregator node. But in routing via respective reducer (RVRR) protocol, data must be forwarded to the appropriate reducer responsible for the corresponding sensor type, even though reducers of other sensor types exist near them.
Performance evaluation
In this section, the performance of RVRR protocol is evaluated with regard to communication cost, network lifetime, energy consumption, and average end-to-end delay using the network simulator (ns-3). RVRR is compared to reducer selection using integer linear programming (ILP) method [10], a SDN based framework that leverages SDN-WISE protocol to employ appropriate nodes as reducers using ILP, SDN-WISE [28], a SDN based routing protocol that carries chain based in-network processing and R-LEACH
Conclusion
In this paper, an energy efficient routing protocol, routing via respective reducers (RVRR) has been introduced for software defined wireless sensor networks (SDWSN) that does in-network processing to significantly reduce the overall communication cost of the network. To this context, repeated game model is proposed to dynamically select the appropriate node as reducer responsible for executing the reducer function and send the resultant data to the sink node. Furthermore, latency is
CRediT authorship contribution statement
S. Suja Golden Shiny: Methodology, Software, Validation, Investigation, Data curation, Writing - original draft. S. Sathya Priya: Formal analysis, Writing - review & editing. K. Murugan: Formal analysis, Resources, Conceptualization, Supervision.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
S. Suja Golden Shiny received her B.E. degree from Anna University, Chennai, India, in 2011, and the M.E. degree from St. Peter’s University, Chennai, India, in 2014. She is currently pursuing the Ph.D. degree with the Department of Computer Science and Engineering, Anna University, Chennai, India. Her research interests include wireless sensor networks, software defined networks and Internet of Things.
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S. Suja Golden Shiny received her B.E. degree from Anna University, Chennai, India, in 2011, and the M.E. degree from St. Peter’s University, Chennai, India, in 2014. She is currently pursuing the Ph.D. degree with the Department of Computer Science and Engineering, Anna University, Chennai, India. Her research interests include wireless sensor networks, software defined networks and Internet of Things.
S. Sathya Priya is an Associate Professor in School of Computing Sciences, Hindustan Institute of Technology and Science, Chennai, India. She received her Ph.D in Information and Communication Engineering from Anna University, Chennai, India, M.E. in Computer Science and Engineering from Annamalai University,Chidambaram, Tamil Nadu, India. She has a professional experience of around 13 years and her area of expertise include IoT, Wireless Networks, Machine Learning and Blockchain. She has seven International Journal Publications and more than 10 International Conference Publications to her credit.
K. Murugan completed his Masters in Computer Science, at National Institute of Technology, Tamil Nadu, India. He received his Ph.D degree from Anna University, Chennai, India. He is currently working as Professor at Ramanujan Computing Centre, Anna University, Chennai, India. He has published and presented papers in highly reputed journals and conferences. He is a life member of IETE, ISTE, and CSI. His area of interest includes Wireless Networks, MANET Routing, Internet of Things (IoT), SDN and Cognitive networks.