scholarly journals A Self-Learning MAC Protocol for Energy Harvesting and Spectrum Access in Cognitive Radio Sensor Networks

2016 ◽  
Vol 2016 ◽  
pp. 1-18 ◽  
Author(s):  
Mohammed Hawa ◽  
Khalid A. Darabkh ◽  
Raed Al-Zubi ◽  
Ghazi Al-Sukkar
Keyword(s):  
Cognitive Radio ◽  
Sensor Networks ◽  
Energy Harvesting ◽  
Mac Protocol ◽  
Sensor Nodes ◽  
Distributed Mac ◽  
Data Packets ◽  
Cognitive Radio Sensor Networks ◽  
Energy Needs ◽  
The Cost

The fusion of Wireless Sensor Networks (WSNs) and Cognitive Radio Networks (CRNs) into Cognitive Radio Sensor Networks (CRSNs) is quite an attractive proposal, because it allows a distributed set of low-powered sensor nodes to opportunistically access spectrum bands that are underutilized by their licensed owners (called primary users (PUs)). In addition, when the PUs are actively transmitting in their own bands, sensor nodes can switch to energy harvesting mode to obtain their energy needs (for free), to achieve almost perpetual life. In this work, we present a novel and fully distributed MAC protocol, calledS-LEARN, that allows sensor nodes in a CRSN to entwine their RF energy harvesting and data transmission activities, while intelligently addressing the issue of disproportionate difference between the high power necessary for the node to transmit data packets and the small amount of power it can harvest wirelessly from the environment. The presented MAC protocol can improve both the network throughput and total harvested energy, while being robust to changes in the network configuration. Moreover, S-LEARN can keep the cost of the system low, and it avoids the pitfalls from which centralized systems suffer.

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.

A Short Preamble Cognitive MAC Protocol in Cognitive Radio Sensor Networks

2019 ◽  
Vol 19 (15) ◽  
pp. 6530-6538 ◽  
Author(s):  
Meng Zheng ◽  
Chuqing Wang ◽  
Manyi Du ◽  
Lin Chen ◽  
Wei Liang ◽  
...  
Keyword(s):  
Cognitive Radio ◽  
Sensor Networks ◽  
Mac Protocol ◽  
Cognitive Radio Sensor Networks

scholarly journals Outage Probability Minimization for Energy Harvesting Cognitive Radio Sensor Networks

Sensors ◽  
2017 ◽  
Vol 17 (2) ◽  
pp. 224 ◽  
Author(s):  
Fan Zhang ◽  
Tao Jing ◽  
Yan Huo ◽  
Kaiwei Jiang
Keyword(s):  
Cognitive Radio ◽  
Sensor Networks ◽  
Energy Harvesting ◽  
Outage Probability ◽  
Cognitive Radio Sensor Networks

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.

Sign in / Sign up

Export Citation Format

Share Document