# Device Centric Throughput and QoS Optimization for IoTsin a Smart Building Using CRN-Techniques
## 1 Introduction  
With the recent advancements in sensor and communication technologies, the Internet of Things (IoT)-based network has become one of the most promising technologies for future applications. IoT offers a diverse range of applications from healthcare to surveillance [1; 2; 3; 4]. IoT is a network of smart physical objects--devices, vehicles, buildings and other items--armed with different kinds of micro-controllers, transceivers and protocols for the dissemination of sensing and control information [5]. Therefore, IoT aims at provisioning an immersive, pervasive and ubiquitous connectivity of these smart objects. The current state of the art technologies to connect these heterogeneous objects include RFID, UWB, Bluetooth, ZigBee, WiFi Direct, etc. However, these technologies operate using the unlicensed spectrum band [6]. Therefore, the QoS over different established links is uncontrollable. However, the QoS is of prime importance for the IoT; hence, an innovative solution is needed to ensure the desired QoS. For this purpose, the cognitive radio (CR) technology is a promising solution in this scenario because it can exploit both the unlicensed and the licensed spectra for transmission [7; 8]. Therefore, in this paper, our major focus is tooptimize the throughput and ensure desired QoS to IoT devices using CR technology in a smart building environment. The smart city is an emerging concept that brings benefits in the management and optimization of public services, such as transport and parking, lighting on roads and inside buildings, the surveillance and maintenance of public areas, the salubrity of hospitals and schools, etc. To achieve this, extensive and prevalent data availability and computing compatibilities are needed for different IoT devices [9; 10]. The concept of the smart city starts with the idea of a smart building. A smart building consists of several control systems, such as ventilation, heating, air conditioning, communication, and many more [11; 12; 13; 14; 15; 16; 17]. To make intelligent decisions and effective optimizations of the available building resources, the data collected from IoT devices of different systems are all needed together. For instance, to make intelligent energy management decisions, the data collected from IoT devices belonging to different systems, such as heating, air conditioning and ventilation, need to be analyzed together. Therefore, a communication mechanism is required to provide the ubiquitous and pervasive availability of data collected by IoT devices of one system to the IoT devices of other systems within the building [2; 4; 18]. Other important concerns for IoT in a smart building are energy efficiency [19; 20], admission control [21], naming and addressing [22], privacy and security [23], mobile sensing [24; 25; 26], etc. The smart building is the convergence of construction, electrical and information technology [11; 15]. It is a new concept of building construction with the aggregation of network systems and devices. These devices belong to different systems. Therefore, they have different QoS requirements in terms of data rate, latency and bit error rate. Additionally, these IoT systems consist of massive numbers of IoT devices [19; 22]. Therefore, the integration of these devices must be done with higher spectral efficiency. One of the key technologies to improve spectral efficiency is cognitive radio (CR) technology [27; 28]. Thus, we employ CR technology for these IoT devices. CR is an intelligent communication device that can learn, decide and reconfigure itself based on the available portion of the spectrum [29]. In a CR-based network (CRN), there are two kinds of users: (1) primary users (PUs) and (2) CR users or secondary users (SUs). CRs are only allowed to use the spectrum unused by the PU [30; 31; 32].