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Energy consumption is one of the most important design constraints when building a wireless sensor and actuator network since each device in the network has a limited battery capacity, and prolonging the lifetime of the network depends on saving energy. Overcoming this challenge requires a smart and reconfigurable network energy management strategy. The Software-Defined Networking (SDN) paradigm aims at building a flexible and dynamic network structure, especially in wireless sensor networks. In this study, we propose an SDN-enabled wireless sensor and actuator network architecture that has a new routing discovery mechanism. To build a flexible and energyefficient network structure, a new routing decision approach that uses a fuzzy-based Dijkstra's algorithm is developed in the study. The proposed architecture can change the existing path during data transmission, which is the key property of our model and is achieved through the adoption of the SDN approach. All the components and algorithms of the proposed system are modeled and simulated using the Riverbed Modeler software for more realistic performance evaluation. The results indicate that the proposed SDN-enabled structure with fuzzy-based Dijkstra's algorithm outperforms the one using the regular Dijkstra's and the ZigBee-based counterpart, in terms of the energy consumption ratio, and the proposed architecture can provide an effective cluster routing while prolonging the network lifetime. KEYWORDSInternet of Things (IoT), routing discovery, Software-Defined Networking, wireless sensor and actuator networks, WSANFlow (QoS), and application-specific requirements when determining the optimum path between the source and destination devices. Since prolonging the network lifetime is a crucial design constraint for WSAN, building an energy-aware routing discovery algorithm is a major challenge. 3,8,9 Another problem that must be faced when dealing with a WSAN structure is lack of flexibility in such network operations as establishment, management, and configuration. In the last few years, Software-Defined Networking (SDN) has attracted growing interest in the computer network communities as a new solution for a simpler, more flexible, and less overworked network control. 1,4 In the SDN architecture, which aims at separating data and control planes, all the control functions of the network are performed by a centralized device, which is referred to as the SDN Controller (SDNC). Network devices in the data plane are responsible only for forwarding the data using the related entries in their flow tables.The primary goal of this research study is to implement a new energy-aware and flexible WSAN structure with an effective routing discovery mechanism. To find effective solutions to the abovementioned problems of the WSAN structure, we proposed an SDN-enabled WSAN architecture that has a new routing discovery mechanism. To be able to build a flexible and energy-efficient network structure, a new routing decision approach that uses a fuzzy-based Dijkstra's algorithm is...

SUMMARY The medium access control protocol determines system throughput in wireless mobile ad hoc networks following the ieee 802.11 standard. Under this standard, asynchronous data transmissions have a defined distributed coordination function that allows stations to contend for channel usage in a distributed manner via the carrier sensing multiple access with collision avoidance protocol. In distributed coordination function, a slotted binary exponential backoff (BEB) algorithm resolves collisions of packets transmitted simultaneously by different stations. The BEB algorithm prevents packet collisions during simultaneous access by randomizing moments at stations attempting to access the wireless channels. However, this randomization does not eliminate packet collisions entirely, leading to reduced system throughput and increased packet delay and drop. In addition, the BEB algorithm results in unfair channel access among stations. In this paper, we propose an enhanced binary exponential backoff algorithm to improve channel access fairness by adjusting the manner of increasing or decreasing the contention window based on the number of the successfully sent frames. We propose several configurations and use the NS2 simulator to analyze network performance. The enhanced binary exponential backoff algorithm improves channel access fairness, significantly increases network throughput capacity, and reduces packet delay and drop. Copyright © 2013 John Wiley & Sons, Ltd.

The importance of big data analytics has become apparent with the increasing volume of data on the Internet. The amount of data will increase even more with the widespread use of Internet of Things (IoT). One of the most important application areas of the IoT is healthcare. This study introduces new real‐time data analytics architecture for an IoT‐based smart healthcare system, which consists of a wireless sensor network and a radio‐frequency identification technology in a vertical domain. The proposed platform also includes high‐performance data analytics tools, such as Kafka, Spark, MongoDB, and NodeJS, in a horizontal domain. To investigate the performance of the system developed, a diagnosis of Wolff–Parkinson–White syndrome by logistic regression is discussed. The results show that the proposed IoT data analytics system can successfully process health data in real‐time with an accuracy rate of 95% and it can handle large volumes of data. The developed system also communicates with a riverbed modeler using Transmission Control Protocol (TCP) to model any IoT‐enabling technology. Therefore, the proposed architecture can be used as a time‐saving experimental environment for any IoT‐based system.

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