scholarly journals Resource allocation in heterogeneous cognitive radio sensor networks

2019 ◽  
Vol 15 (7) ◽  
pp. 155014771985194
Author(s):  
Mohammed Al-Medhwahi ◽  
Fazirulhisyam Hashim ◽  
Borhanuddin Mohd Ali ◽  
A Sali ◽  
Abdulsalam Alkholidi
Keyword(s):  
Resource Allocation ◽  
Cognitive Radio ◽  
Sensor Networks ◽  
Radio Channel ◽  
Radio Spectrum ◽  
Sensor Nodes ◽  
Medium Access ◽  
Access Protocol ◽  
Cognitive Radio Sensor Networks ◽  
Medium Access Protocol

Cognitive radio sensor networks offer a promising means of meeting rapidly expanding demand for wireless sensor network applications in new monitoring and objects tracking fields. Several challenges, particularly in terms of quality of service provisioning, arise because of the inherited capability-limitation of end-sensor nodes. In this article, an efficient resource allocation scheme, improved Pliable Cognitive Medium Access Protocol, is proposed to tackle multilevel of heterogeneity in cognitive radio sensor networks. The first level is the network’s application heterogeneity, and the second level is the heterogeneity of the radio environment. The proposed scheme addresses scheduling and radio channel allocation issues. Allocation-decision making is centralized, whereas spectrum sensing is distributed, thereby increasing efficiency and limiting interference. Despite the limited capabilities of the sensor’s networks, the effectiveness of the proposed scheme also includes increasing the opportunity to utilize a wider range of the radio spectrum. improved Pliable Cognitive Medium Access protocol is quite appropriate for critical communications that gain attention in the next 5G of wireless networks. Simulation results and the comparison of the proposed protocol with other protocols indicate the robust performance of the proposed scheme. The results reveal the significant effectiveness, with only a slight trade-off in terms of complexity.

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.

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.

Cognitive Radio Sensor Networks

2014 ◽  
pp. 160-195 ◽  
Author(s):  
Ejaz Ahmed ◽  
Salman Ali ◽  
Adnan Akhunzada ◽  
Ibrar Yaqoob
Keyword(s):  
Cognitive Radio ◽  
Sensor Networks ◽  
Sensor Network ◽  
Ad Hoc ◽  
Network Communication ◽  
Ad Hoc Routing ◽  
Medium Access ◽  
Cognitive Radio Sensor Networks ◽  
Cognitive Radio Sensor Network ◽  
Network Operation

This chapter provides a review of design practices in network communication for Cognitive Radio Sensor Networks. The basics of networking and Medium Access Control functionalities with focus on data routing and spectrum usage are discussed. Technical differences manifest in various network layouts, hence the role of various specialized nodes, such as relay, aggregator, or gateway in Cognitive Radio Sensor Networks need analysis. Optimal routing techniques suitable for different topologies are also summarized. Data delivery protocols are categorized under priority-based, energy-efficient, ad hoc routing-based, attribute-based, and location-aware routing. Broadcast, unicast, and detection of silence periods are discussed for network operation with slotted or unslotted time. Efficient spectrum usage finds the most important application here involving use of dynamic, opportunistic, and fixed spectrum usage. Finally, a thorough discussion on the open issues and challenges for Cognitive Radio Sensor Network communication and internetworking in Cognitive Radio Sensor Network-based deployments and methods to address them are provided.

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 Survey on Radio Resource Allocation in Cognitive Radio Sensor Networks

2015 ◽  
Vol 17 (2) ◽  
pp. 888-917 ◽  
Author(s):  
Ayaz Ahmad ◽  
Sadiq Ahmad ◽  
Mubashir Husain Rehmani ◽  
Naveed Ul Hassan
Keyword(s):  
Resource Allocation ◽  
Cognitive Radio ◽  
Sensor Networks ◽  
Radio Resource Allocation ◽  
Radio Resource ◽  
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.

Sign in / Sign up

Export Citation Format

Share Document