scholarly journals Energy-Efficient Cognitive Radio Sensor Networks: Parametric and Convex Transformations

2021 ◽  
Author(s):  
Muhammad Naeem ◽  
Kandasamy Illanko ◽  
Ashok Karmokar ◽  
Alagan Anpalagan ◽  
Muhammad Jaseemuddin
Keyword(s):  
Cognitive Radio ◽  
Sensor Networks ◽  
Constrained Optimization ◽  
Power Allocation ◽  
Energy Efficient ◽  
Optimization Problem ◽  
Numerical Solutions ◽  
Sensor Nodes ◽  
Constrained Optimization Problem ◽  
Cognitive Radio Sensor Networks

Designing energy-efficient cognitive radio sensor networks is important to intelligently use battery energy and to maximize the sensor network life. In this paper, the problem of determining the power allocation that maximizes the energy-efficiency of cognitive radio-based wireless sensor networks is formed as a constrained optimization problem, where the objective function is the ratio of network throughput and the network power. The proposed constrained optimization problem belongs to a class of nonlinear fractional programming problems. Charnes-Cooper Transformation is used to transform the nonlinear fractional problem into an equivalent concave optimization problem. The structure of the power allocation policy for the transformed concave problem is found to be of a water-filling type. The problem is also transformed into a parametric form for which a ε-optimal iterative solution exists. The convergence of the iterative algorithms is proven, and numerical solutions are presented. The iterative solutions are compared with the optimal solution obtained from the transformed concave problem, and the effects of different system parameters (interference threshold level, the number of primary users and secondary sensor nodes) on the performance of the proposed algorithms are investigated.

scholarly journals Energy-Efficient Cognitive Radio Sensor Networks: Parametric and Convex Transformations

2021 ◽  
Author(s):  
Muhammad Naeem ◽  
Kandasamy Illanko ◽  
Ashok Karmokar ◽  
Alagan Anpalagan ◽  
Muhammad Jaseemuddin
Keyword(s):  
Cognitive Radio ◽  
Sensor Networks ◽  
Constrained Optimization ◽  
Power Allocation ◽  
Energy Efficient ◽  
Optimization Problem ◽  
Numerical Solutions ◽  
Sensor Nodes ◽  
Constrained Optimization Problem ◽  
Cognitive Radio Sensor Networks

Designing energy-efficient cognitive radio sensor networks is important to intelligently use battery energy and to maximize the sensor network life. In this paper, the problem of determining the power allocation that maximizes the energy-efficiency of cognitive radio-based wireless sensor networks is formed as a constrained optimization problem, where the objective function is the ratio of network throughput and the network power. The proposed constrained optimization problem belongs to a class of nonlinear fractional programming problems. Charnes-Cooper Transformation is used to transform the nonlinear fractional problem into an equivalent concave optimization problem. The structure of the power allocation policy for the transformed concave problem is found to be of a water-filling type. The problem is also transformed into a parametric form for which a ε-optimal iterative solution exists. The convergence of the iterative algorithms is proven, and numerical solutions are presented. The iterative solutions are compared with the optimal solution obtained from the transformed concave problem, and the effects of different system parameters (interference threshold level, the number of primary users and secondary sensor nodes) on the performance of the proposed algorithms are investigated.

scholarly journals Energy-Efficient Medium Access Control Protocols for Cognitive Radio Sensor Networks: A Comparative Survey

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3781 ◽  
Author(s):  
Subash Luitel ◽  
Sangman Moh
Keyword(s):  
Cognitive Radio ◽  
Sensor Networks ◽  
Access Control ◽  
Medium Access Control ◽  
Frequency Spectrum ◽  
Energy Efficient ◽  
Mac Protocols ◽  
Sensor Nodes ◽  
Medium Access ◽  
Cognitive Radio Sensor Networks

The increase of application areas in wireless sensor networks demands novel solutions in terms of energy consumption and radio frequency management. Cognitive radio sensor networks (CRSNs) are key for ensuring efficient spectrum management, by making it possible to use the unused licensed frequency spectrum together with the unlicensed frequency spectrum. Sensor nodes powered by energy-constrained batteries necessarily require energy-efficient protocols at the routing and medium access control (MAC) layers. In CRSNs, energy efficiency is more important because the sensor nodes consume additional energy for spectrum sensing and management. To the best of authors’ knowledge, there is no survey on “energy-efficient” MAC protocols for CRSNs in the literature, even though a conceptual review on MAC protocols for CRSNs was presented at a conference recently. In this paper, energy-efficient MAC protocols for CRSNs are extensively surveyed and qualitatively compared. Open issues, and research challenges in the design of MAC protocols for CRSNs, are also discussed.

scholarly journals An Energy-Efficient Spectrum-Aware Reinforcement Learning-Based Clustering Algorithm for Cognitive Radio Sensor Networks

Sensors ◽  
2015 ◽  
Vol 15 (8) ◽  
pp. 19783-19818 ◽  
Author(s):  
Ibrahim Mustapha ◽  
Borhanuddin Ali ◽  
Mohd Rasid ◽  
Aduwati Sali ◽  
Hafizal Mohamad
Keyword(s):  
Reinforcement Learning ◽  
Cognitive Radio ◽  
Sensor Networks ◽  
Energy Efficient ◽  
Clustering Algorithm ◽  
Cognitive Radio Sensor Networks

Integration of Cognitive Radio Sensor Networks and Cloud Computing

2014 ◽  
pp. 288-312 ◽  
Author(s):  
Yasir Saleem ◽  
Farrukh Salim ◽  
Mubashir Husain Rehmani
Keyword(s):  
Cloud Computing ◽  
Cognitive Radio ◽  
Sensor Networks ◽  
Resource Limitation ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Future Directions ◽  
Cognitive Radio Sensor Networks ◽  
Computational Speed ◽  
Is Integration

Cognitive Radio Sensor Networks (CRSNs) are composed of sensor nodes equipped with Cognitive Radio (CR) technology with limited resources (e.g., storage, computational speed, bandwidth, security, etc.). In order to overcome resource limitation, cognitive radio sensor nodes are integrated with cloud computing, which provides computing resources (e.g., storage, computation, security, etc.) to sensor nodes. Therefore, the focus of this chapter is integration of cognitive radio sensor networks with cloud computing. In this chapter, the authors first provide background on cloud computing, cognitive radio networks, wireless sensor networks, and cognitive radio sensor networks. This chapter also describes benefits of this integration to both cognitive radio sensor networks and cloud computing, followed by advantages of using cloud computing in cognitive radio sensor networks. Furthermore, it provides applications of cloud-based cognitive radio sensor networks. In the end, the authors provide some issues, challenges, and future directions for such integration.

Integration of Cognitive Radio Sensor Networks and Cloud Computing

2015 ◽  
pp. 1025-1048
Author(s):  
Yasir Saleem ◽  
Farrukh Salim ◽  
Mubashir Husain Rehmani
Keyword(s):  
Cloud Computing ◽  
Cognitive Radio ◽  
Sensor Networks ◽  
Resource Limitation ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Future Directions ◽  
Cognitive Radio Sensor Networks ◽  
Computational Speed ◽  
Is Integration

Cognitive Radio Sensor Networks (CRSNs) are composed of sensor nodes equipped with Cognitive Radio (CR) technology with limited resources (e.g., storage, computational speed, bandwidth, security, etc.). In order to overcome resource limitation, cognitive radio sensor nodes are integrated with cloud computing, which provides computing resources (e.g., storage, computation, security, etc.) to sensor nodes. Therefore, the focus of this chapter is integration of cognitive radio sensor networks with cloud computing. In this chapter, the authors first provide background on cloud computing, cognitive radio networks, wireless sensor networks, and cognitive radio sensor networks. This chapter also describes benefits of this integration to both cognitive radio sensor networks and cloud computing, followed by advantages of using cloud computing in cognitive radio sensor networks. Furthermore, it provides applications of cloud-based cognitive radio sensor networks. In the end, the authors provide some issues, challenges, and future directions for such integration.

Nature-Inspired Algorithms in Wireless Sensor Networks

2019 ◽  
pp. 246-275
Author(s):  
Ajay Kaushik ◽  
S. Indu ◽  
Daya Gupta
Keyword(s):  
Wireless Sensor Networks ◽  
Big Data ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Energy Efficient ◽  
Sensor Node ◽  
Optimization Problem ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Nature Inspired Algorithms

Wireless sensor networks (WSNs) are becoming increasingly popular due to their applications in a wide variety of areas. Sensor nodes in a WSN are battery operated which outlines the need of some novel protocols that allows the limited sensor node battery to be used in an efficient way. The authors propose the use of nature-inspired algorithms to achieve energy efficient and long-lasting WSN. Multiple nature-inspired techniques like BBO, EBBO, and PSO are proposed in this chapter to minimize the energy consumption in a WSN. A large amount of data is generated from WSNs in the form of sensed information which encourage the use of big data tools in WSN domain. WSN and big data are closely connected since the large amount of data emerging from sensors can only be handled using big data tools. The authors describe how the big data can be framed as an optimization problem and the optimization problem can be effectively solved using nature-inspired algorithms.

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