Simultaneous spectrum sensing, data transmission and Energy harvesting in multi-channel cognitive sensor Networks with imperfect signal cancellation

2020 ◽  
Vol 33 (14) ◽  
pp. e4528
Author(s):  
Maryam Najimi
Keyword(s):  
Sensor Networks ◽  
Energy Harvesting ◽  
Spectrum Sensing ◽  
Data Transmission ◽  
Sensing Data ◽  
Signal Cancellation ◽  
Cognitive Sensor Networks

Utility Maximization of Temporally Correlated Sensing Data in Energy Harvesting Sensor Networks

2019 ◽  
Vol 6 (3) ◽  
pp. 5411-5422 ◽  
Author(s):  
Rongrong Zhang ◽  
Jian Peng ◽  
Wenzheng Xu ◽  
Weifa Liang ◽  
Zheng Li ◽  
...  
Keyword(s):  
Sensor Networks ◽  
Energy Harvesting ◽  
Utility Maximization ◽  
Sensing Data ◽  
Temporally Correlated

scholarly journals Spectrum Sensing in Opportunity-Heterogeneous Cognitive Sensor Networks: How to Cooperate?

2013 ◽  
Vol 13 (11) ◽  
pp. 4247-4255 ◽  
Author(s):  
Guoru Ding ◽  
Jinlong Wang ◽  
Qihui Wu ◽  
Fei Song ◽  
Yingying Chen
Keyword(s):  
Sensor Networks ◽  
Spectrum Sensing ◽  
Cognitive Sensor Networks

scholarly journals Survivable Deployments of Optical Sensor Networks against Multiple Failures and Disasters: A Survey

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4790 ◽  
Author(s):  
Yongjun Zhang ◽  
Jingjie Xin
Keyword(s):  
Sensor Networks ◽  
Optical Communication ◽  
Optical Sensor ◽  
Data Transmission ◽  
High Speed ◽  
Optical Sensing ◽  
Self Healing ◽  
Multiple Failures ◽  
Sensing Data ◽  
Potential Development

Optical sensing that integrates communication and sensing functions is playing a more and more important role in both military and civil applications. Incorporating optical sensing and optical communication, optical sensor networks (OSNs) that undertake the task of high-speed and large-capacity applications and sensing data transmissions have become an important communication infrastructure. However, multiple failures and disasters in OSNs can cause serious sensing provisioning problems. To ensure uninterrupted sensing data transmission, survivability has always been an important research emphasis. This paper focuses on the survivable deployment of OSNs against multiple failures and disasters. We first review and evaluate the existing survivability technologies developed for or applied to OSNs, such as fiber bus protection, self-healing architecture, and 1 + 1 protection. We then elaborate on the disaster-resilient survivability requirement of OSNs. Moreover, we propose a new k-node (edge) sensing connectivity concept, which ensures the connectivity between sensing data and users. Based on k-node (edge) sensing connectivity, the disaster-resilient survivability technologies are developed. The key technologies necessary to implement k-node (edge) sensing connectivity are also elaborated. Recently, artificial intelligence (AI) has developed rapidly. It can be used to improve the survivability of OSNs. This paper details potential development directions of survivability technologies of optical sensing in OSNs employing AI.

scholarly journals Multibit-Quantization-Based Collaborative Spectrum Sensing Scheme for Cognitive Sensor Networks

IEEE Access ◽  
2017 ◽  
Vol 5 ◽  
pp. 25207-25216 ◽  
Author(s):  
Hao Wu ◽  
Fuqiang Yao ◽  
Yong Chen ◽  
Yongxiang Liu ◽  
Tao Liang
Keyword(s):  
Sensor Networks ◽  
Spectrum Sensing ◽  
Collaborative Spectrum Sensing ◽  
Cognitive Sensor Networks

Joint Spectrum Sensing and Data Transmission Optimization for Energy Efficiency in Cognitive Radio Sensor Networks: A Dynamic Cooperative Method

2015 ◽  
Vol 19 (2) ◽  
pp. 197-204 ◽  
Author(s):  
Yi Li ◽  
◽  
Jun Peng ◽  
Fu Jiang ◽  
Kaiyang Liu ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Cognitive Radio ◽  
Sensor Networks ◽  
Spectrum Sensing ◽  
Data Transmission ◽  
Cooperative Spectrum Sensing ◽  
Optimization Method ◽  
Joint Optimization ◽  
Transmission Scheme ◽  
Cognitive Radio Sensor Networks

To address the inherent energy constraint in cognitive radio sensor networks, a novel joint optimization method of spectrum sensing and data transmission for energy efficiency is investigated in this paper. To begin with, a cooperative spectrum sensing scheme based on dynamic censoring is employed to shorten sensing time and save unnecessary spectrum sensing energy. Then to jointly optimize the energy efficiency, the distortion constrained probabilistic transmission scheme is utilized. Afterwards the sensing threshold solving issue can be formulated as a nonlinear minmax optimization problem with the detection probability and false alarm probability constraints. Solving by the Matlab software with the free OPTI toolbox, simulation results demonstrate that significant energy can be saved via the the proposed joint optimization method in various mobile cloud scenarios.

scholarly journals A joint optimization of energy harvesting and spectrum sensing for wireless sensor networks under Middleton Class A noise

2018 ◽  
Vol 14 (5) ◽  
pp. 155014771877766
Author(s):  
Enwei Xu ◽  
Shuo Shi ◽  
Dawei Chen ◽  
Xuemai Gu
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Energy Harvesting ◽  
Spectrum Sensing ◽  
Lower Order ◽  
Wireless Sensor ◽  
Joint Optimization ◽  
Order Moment ◽  
Average Throughput ◽  
Class A

With the growing popularity of wireless sensor networks, the environment in which the network is located becomes more undesirable. In addition, the problems of spectrum scarcity and the short sensor lifetime have become increasingly prominent. In this article, we incorporate the two technologies of cognitive radio and energy harvesting to solve the above problems of wireless sensor networks under impulsive noise. First, we use a Middleton Class A noise model to imitate the practical environment and the fractional lower order moments detector is employed to perform spectrum sensing for the sensors of wireless sensor networks, which are performing as the second users. Second, a new time-slots structure is proposed for the self-powered second user and the analytical expression of the second user’s average throughput is derived. Finally, we maximize the second user’s average throughput by a joint optimization of the sensing duration and data transmission duration while giving the primary user sufficient protection. Simulation shows that a much better performance can be achieved by fractional lower order moment detector than the traditional energy detector. Moreover, our optimization of the time-slots allocation is feasible and the maximum second user’s average throughput can be obtained.

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