Wireless Communications and Mobile Computing

Volume 2018, Article ID 9469106, 11 pages

https://doi.org/10.1155/2018/9469106

## EESS: An Energy-Efficient Spectrum Sensing Method by Optimizing Spectrum Sensing Node in Cognitive Radio Sensor Networks

^{1}School of Computer and Software, Nanjing University of Information Science and Technology, 219 Ningliu Rd, Nanjing 210044, China^{2}Dept. of Computer Science and Engineering, Michigan State University, 428 S Shaw Ln, MI 48824, USA^{3}College of Information Engineering, Yangzhou University, 196 Huayang West Rd, Yangzhou 225127, China

Correspondence should be addressed to Zilong Jin; moc.361@58nijlz

Received 4 May 2018; Accepted 26 June 2018; Published 11 July 2018

Academic Editor: Naixue Xiong

Copyright © 2018 Zilong Jin et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

In cognitive radio sensor networks (CRSNs), the sensor devices which are enabled to perform dynamic spectrum access have to frequently sense the licensed channel to find idle channels. The behavior of spectrum sensing will consume a lot of battery power of sensor devices and reduce the network lifetime. In this paper, we aim to answer the question of how many spectrum sensing nodes (SSNs) are required. In order to achieve this, SSN ratio effects on the accuracy of spectrum sensing from the perspective of network energy efficiency are analyzed first. Based on these analyses, the optimal SSN ratio is derived for maximizing the network lifetime by optimizing the cooperative detection probability (CDP). Simulation results show that the optimal SSN ratio can guarantee the spectrum sensing performance in terms of detection and false alarm probabilities and effectively extend the network lifetime.

#### 1. Introduction

Traditional wireless sensor networks (WSNs) have been an attractive area of research since last decade and usually operate on the license-free industrial, scientific, and medical (ISM) band. However, as the number of wireless devices and applications has seen explosive growth over the past few decades, there has been increasing pressure on the limited spectrum resources [1, 2]. Moreover, the improvement in throughput of wireless networks largely depends on the utilization of channels [3]. In this situation, the concept of cognitive radio (CR) has emerged to alleviate the scarcity of limited radio spectrum resources by improving the utilization of spectrum resources [4]. The CR is a dynamically programmable and configurable radio that opportunistically uses an idle licensed channel in its vicinity to address the problem of unlicensed spectrum resources shortage and the underutilization of the licensed spectrum resources [5, 6]. Such a radio can be applied in the existing network technologies, like wireless sensor networks, machine to machine networks, wireless body area networks, etc., enabling the PHY (PHYsic) and MAC (Media Access) layers of the devices to automatically and dynamically detect available channels and then accordingly change their transmission or reception parameters to allow more concurrent wireless communications in a given spectrum band. As a specific application of the CR technology, cognitive radio sensor network (CRSN) is recently regarded as one of the most attractive topics in IoT paradigms [7].

Not the same as the traditional WSNs [8–12], CRSNs operate on licensed bands, periodically sense the spectrums, and determine vacant channels. In order to achieve this, CRSDs (cognitive radio-enabled sensor devices) in CRSNs have to frequently sense the licensed channels to identify an idle one and detect the active state of primary users (PUs) signal with strictly limited interference to PUs [13]. On the other hand, different from CR networks [14, 15], CRSNs inherit the basic limitations of traditional WSNs of which the lifetime is strictly constraint due to energy limitations. In addition, spectrum sensing (SS) [16, 17] is a crucial element in the implementation of a CRSN, and energy consumption is also a major consideration in spectrum sensing. The more SUs that participate in spectrum sensing will result in higher energy consumption of the network and shorter network lifetime. For this reason, our goal is to improve the network energy efficiency by optimizing the number of spectrum sensing nodes (SSNs) that participate in spectrum sensing while still guaranteeing the spectrum sensing accuracy.

Moreover, it has been shown that cooperative spectrum sensing (CSS) can not only deal with multipath fading and shadow effects but also improve the accuracy of spectrum sensing [18–21]. The idea of CSS scheme is to use multiple SUs and combine their sensing results at a fusion center (FC). There are two possible CSS strategies: the first one is that all nodes perform CSS, and the second one is that some nodes sense spectrums. But if their performance is similar, the second one is obviously more suitable. To this end, there are some existing node selection methods in various works [3, 22–27]. Specifically, an optimal hard fusion strategy was proposed to maximize the energy efficiency in [22]. In [23], an optimal number of multihop-based SUs was derived. To minimize the total energy consumption, a closed-form equation and optimal conditions due to* KKT* were proposed in [24] to determine the SUs which sense the spectrum. An energy-efficient CSS was also proposed in [25] to solve the problem of sensing node selection. Taking into consideration the scenario when only partial information of SUs and PUs is available in [26], an energy-efficient SUs selection algorithm has been proposed to save energy and improve the detection performance. In [3], a correlation-aware node selection scheme was proposed to adaptively select uncorrelated nodes for CSS, because of the openness, dynamics, and uncertainty of wireless environment. Moreover, in [27], general criteria for decision-approach selection were analyzed and derived when there are actual channel propagation effects.

However, when the environment of network changes dynamically, fewer nodes cannot guarantee the accuracy of spectrum sensing, and more nodes involving spectrum sensing will increase the energy consumption of the network. Therefore, all of the above existing work cannot ensure the accuracy of spectrum sensing and less energy consumption at the same time. In addition, there is no efficient mathematical model which quantitatively describes the relationship between the number of SSNs and spectrum sensing performance.

In this paper, we analyze the optimal SSN selection strategy from the following three aspects. The first is to explore the relationship between the SSN ratio and the received signal power of PU, i.e., signal-to-noise ratio (SNR), in randomly deployed networks (which means that both of CRSDs and PUs are randomly deployed) for analyzing the number of SSNs impacts on the performance of individual spectrum sensing. Secondly, a mathematical formula which describes the relationship between the ratio of SSN and cooperative detection probability (CDP) is explored to ensure the accuracy of cooperative spectrum sensing. Finally, an optimization function is proposed to derive the optimal SSN ratio which can prolong the network lifetime and guarantee the accuracy of cooperative spectrum sensing.

The remainder of the paper is organized as follows. Section 2 gives the system model and problem definition. Analysis of optimal SSN ratio is formulated in Section 3. Simulation results are discussed in Section 4. Finally, Section 5 concludes the paper and discusses the future work.

#### 2. System Model and Problem Definition

This section first describes the network model for CRSN and then presents the spectrum sensing. Afterwards, this section gives the definition of the problem for the optimal SSN ratio selection.

##### 2.1. Network Model

###### 2.1.1. The Channel Model

The network environment of a CRSN under consideration, where a set of CRSDs, , are distributed to monitor the area of interest, includes a PU and a FC, as shown in Figure 1. The CRSDs can be regarded as the secondary users (SUs) in traditional CR networks that can access idle channels opportunistically. According to the practical requirements, CRSDs periodically sense the environment with different sampling rates and then report their sensed data to the FC [28]. In general, the available licensed spectrum consists of multiple primary channels whose active state follows different traffic patterns. For simplicity, in this paper, a single primary channel is assumed, and the PU traffic pattern follows a stationary exponential ON/OFF random process [29].