Improving the lifetime of multichannel cognitive radio sensor networks via new spectrum sensing method

Abstract

Cognitive Radio Sensor Networks (CRSNs) are wireless sensor networks implemented with cognitive radio capability to provide spectrum resources for sensors in an opportunistic manner. The application of CRSNs can be found in the paradigm of Internet of Things (IoT) where each sensor represents an object in the IoT paradigm. In order to prevent simultaneous transmissions of Primary Users (PUs) and CRSN nodes, sensors should be aware of the presence of PUs. One of the most common methods for making the sensors aware of the presence of PUs is consecutive spectrum sensing. Since the sensors have the duty of both sensing environment and transmitting the results, their battery lives are highly prone to rapid drain. In addition, the lack of effective management of sensor operations for spectrum sensing may lead to short longevity of the network. In this paper, the energy consumption process of sensors in such networks has been analyzed and an energy‐based algorithm is proposed to appropriately select sensors based on their detection probabilities for spectrum sensing. The analytical and numerical results confirm the improved lifetime and performance based on our proposed algorithm in comparison with the conventional ones. Moreover, the computational complexity of the proposed algorithm is significantly lower than that of the existing methods.

Trading-Based Dynamic Spectrum Access and Allocation in Cognitive Internet of Things

“…The fitness value is directly related to the objective function, and the minimal fitness value what the pattern search algorithm aims to find. In this paper, we adopt equation ( 14) which depends on equation (7) as the fitness function for pattern search algorithm. As shown in equation (7), our ultimate objective is to find proper B 1 , B 2 , B 3 , B N for cognitive secondary users.…”

“…In this paper, we adopt equation ( 14) which depends on equation (7) as the fitness function for pattern search algorithm. As shown in equation (7), our ultimate objective is to find proper B 1 , B 2 , B 3 , B N for cognitive secondary users. Thus, there are many variables rather than one variable to be addressed.…”

“…In the next-generation wireless communication networks, the density of wireless terminal blows up which will inevitably lead to the shortage of spectrum resource especially in distributed IoT networks. As a result, the extensive application of IoT may be constrained by crowned wireless spectrum [7]- [10]. To solve this problem, dynamic spectrum…”

Energy-Efficient Transmission Range and Duration for Cognitive Radio Sensor Networks

“…where the first term in (8) is the probability of having k PUs in the area S illustrated in Fig. 2 according to Poisson distribution, and the second term is the probability that all of these k PUs remain silent at least for a duration of τ t as per the PU activity model from Section II.…”

“…Recent research on CRSNs focused mostly on spectrum sensing [7], [8], [9], spectrum decision and handoff [10], [11], clustering [12], physical layer studies on adaptive modulation [14], CR-enabled Internet of Things [15] and channel bonding for CRSN [16] and attempts on MAC layer [17], network layer [18] and transport layer [19] protocols and solutions. In addition, full-duplex communication with non-orthogonal multiple access is also investigated for security and reliability of in CR-enabled networks [20], [21].…”