An Energy-Efficient Routing Algorithm for Software-Defined Wireless Sensor Networks

Wang, Xiang; Wang, Ning; Zhou, Yuan

doi:10.1109/jsen.2016.2585019

Cited by 130 publications

(116 citation statements)

References 38 publications

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“…An SDN‐based sleep scheduling algorithm is proposed in Wang et al for prolonging the network lifetime by dividing the interval time into two slices: a beacon slice and an execution slice. Xiang et al use a MATLAB tool to demonstrate SDN over WSN by selecting the control devices using the particle swarm optimization technique. Sood et al review network management paradigms for integrating SDN into the IoT network and describe security and hybrid/optical network issues of SDN.…”

Section: Related Workmentioning

confidence: 99%

“…Wireless sensor and actuator networks (WSANs) struggle due to energy constraints since each device in the network has limited battery capacity and, most of the time, a device battery is unlikely to be replaced after being deployed . In the literature, few researchers have studied the inclusion of Internet of Things (IoT) applications on WSAN, which require a flexible network architecture and efficient energy management strategies .…”

Section: Introductionmentioning

confidence: 99%

“…The forwarding mechanisms that are used in the aforementioned topologies should consider the energy consumption ratio, quality of service (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 …”

Section: Introductionmentioning

confidence: 99%

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A novel energy‐aware routing mechanism for SDN‐enabled WSAN

Al-Hubaishi

Çeken

Al-Shaikhli

2018

Int J Communication

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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...

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A novel energy‐aware routing mechanism for SDN‐enabled WSAN

Al-Hubaishi

Çeken

Al-Shaikhli

2018

Int J Communication

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“…Through a mobile application, it can interact with humans and other robots. A social robot owns a virtual assistant and has the mechanical ability, such as the ability to move its body parts [14] [15] [16]. Current social robots focus on four major applications: health care and treatment, education, public and work environment, and home environment [17] [18] [19].…”

Section: Introductionmentioning

confidence: 99%

Wearable Affective Robot

et al. 2018

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With the development of the artificial intelligence (AI), the AI applications have influenced and changed people's daily life greatly. Here, a wearable affective robot that integrates the affective robot, social robot, brain wearable, and wearable 2.0 is proposed for the first time. The proposed wearable affective robot is intended for a wide population, and we believe that it can improve the human health on the spirit level, meeting the fashion requirements at the same time. In this paper, the architecture and design of an innovative wearable affective robot, which is dubbed as Fitbot, are introduced in terms of hardware and algorithm's perspectives. In addition, the important functional component of the robot-brain wearable device is introduced from the aspect of the hardware design, EEG data acquisition and analysis, user behavior perception, and algorithm deployment, etc. Then, the EEG based cognition of user's behavior is realized. Through the continuous acquisition of the in-depth, in-breadth data, the Fitbot we present can gradually enrich user's life modeling and enable the wearable robot to recognize user's intention and further understand the behavioral motivation behind the user's emotion. The learning algorithm for the life modeling embedded in Fitbot can achieve better user's experience of affective social interaction. Finally, the application service scenarios and some challenging issues of a wearable affective robot are discussed.Index Terms-emotion cognition, social robot, RNN, EEG, Wearable 2.0 M. Chen is with the Wuhan National

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“…PSO algorithm was also found to optimize energy efficiency exclusively for software-defined aspects in sensory application. Xiang et al [13] have presented a technique for energy conservation for software-defined sensor network. Issues of maintaining higher degree of fault tolerance while scheduling the allocation process of task can be also handled by PSO as seen in the work carried out by Guo et al [14].…”

Section: Introductionmentioning

confidence: 99%

Novel Scheme for Minimal Iterative PSO Algorithm for Extending Network Lifetime of Wireless Sensor Network

Hemavathi¹,

Nandakumar²

2018

IJECE

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Clustering is one of the operations in the wireless sensor network that offers both streamlined data routing services as well as energy efficiency. In this viewpoint, Particle Swarm Optimization (PSO) has already proved its effectiveness in enhancing clustering operation, energy efficiency, etc. However, PSO also suffers from a higher degree of iteration and computational complexity when it comes to solving complex problems, e.g., allocating transmittance energy to the cluster head in a dynamic network. Therefore, we present a novel, simple, and yet a cost-effective method that performs enhancement of the conventional PSO approach for minimizing the iterative steps and maximizing the probability of selecting a better clustered. A significant research contribution of the proposed system is its assurance towards minimizing the transmittance energy as well as receiving energy of a cluster head. The study outcome proved proposed a system to be better than conventional system in the form of energy efficiency. [5] etc. There has been a voluminous amount of investigation carried out in this area for addressing such issues, but still majority of the issues are yet to meet its full-proof solution. The primary cause of this problem is basically the sensor node, which is very small in size, posses low computational capabilities, and powered by a battery that has limited lifetime. The biggest challenge for the researchers to prove the applicability of their presented system in real-time sensor node that is not found to be discussed in majority of the existing research work. However, some of the researchers avoid such problems by considering either benchmark test bed, or adopt the configuration of some real-time motes e.g. Berkley Mote, MicaZ mote [6], etc. Hence, optimization is the best possibility in such scenario of node resources constraints. There are various methods by which optimizations have been carried out towards improving the performance of sensor node e.g. neural network, genetic algorithm, swarm intelligences etc [7]. Particle Swarm Optimization (PSO) is one such technique that uses many numbers of iterations in order to explore the best solution against the problems posed [8], [9]. From computational viewpoint, PSO enhances the candidate solution in the perspective of the anticipated outcomes and given problem in wireless sensor network. The problem is optimized by considering the candidate solution and its population (also called as particles) and this form of the candidate solution is subjected to iterative processing in order to obtain personal and global best outcome from the position and velocity of the particles. The primary reason behind the adoption of PSO is basically its intelligence-based approach that can be possibly implemented on any of Keyword: Clustering

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An Energy-Efficient Routing Algorithm for Software-Defined Wireless Sensor Networks

Cited by 130 publications

References 38 publications

A novel energy‐aware routing mechanism for SDN‐enabled WSAN

A novel energy‐aware routing mechanism for SDN‐enabled WSAN

Wearable Affective Robot

Novel Scheme for Minimal Iterative PSO Algorithm for Extending Network Lifetime of Wireless Sensor Network

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