# A Petri Net Model for Cognitive Radio Internet of Things Networks Exploiting GSM Bands +
## 1 Introduction  
Internet of Things (IoT) is a system of interconnected digital objects that provide things with the ability to transmit and receive data without human operation. IoT has rapidly evolved; nowadays, it touches most of our daily life. Smart lamps, smart switches, smart thermostats, smart opening drives, smart loudspeakers, smart microphones, and smart cameras are just some of the devices which can be automatically involved in managing specific scenarios of our life. Most IoT devices use a wireless link in order to communicate due to the increased versatility and ease of installation. The rapid growth of such IoT device installations is generating a high crowding of the spectrum due to the huge number of packets produced per unit time. Cognitive radio (CR) technology allows for these problems to be overcome thanks to the possibility of using the spectrum resource in a shared way. Machine learning approaches, usually based on deep reinforcement learning and federated learning, have been extensively investigated as of late for intelligent resource allocation in cognitive radio environments and IoT systems [1; 2; 3; 4]. The integration of IoT and CR technology is termed in the literature as Cognitive Radio Internet of Things (CRIoT) [5]. IoT wireless technologies can be classified in terms of the radio range:  
1. Long radio range: the device usually leverages a licensed band of cellular networks, and as such, it could be expensive; LoRa (short for long range) and satellite are also used technologies;2. Medium radio range (no greater than 100 m): the most common radio technologies include ZigBee, Wi-Fi, Z-Wave, and thread;
3. Short radio range (not exceeding 30 m): the most common radio technologies in use; it includes Bluetooth (or its evolution, Bluetooth LE) and RFID. The various states regulate wireless networks access for public radio communication by means of theirs governmental agencies. The spectrum is assigned to license holder companies on a long-term basis for large geographical regions. Specific portions of the radio spectrum are maintained as unlicensed all over the world thanks to international agreements, such as the industrial, scientific, and medical (ISM) radio bands. Except in the case of the long radio range, the use of unlicensed bands is the most typical case for wireless communications in the IoT environment. These unlicensed subbands are suitable for building services, having flexibility and low cost without any restrictions on the wireless coverage and network topology. Accordingly, these bands have often been overcrowded due to several wireless technologies. For instance, the ISM band at 2.4 GHz is used by many cordless telephones, baby monitors, WPANs (wireless personal area networks such as Bluetooth Low Energy), Bluetooth, ZigBee/IEEE 802.15.4 WSNs (wireless sensor networks), WLANs (wireless local area networks such as IEEE 802.11-based networks), WiMAX (worldwide interoperability for microwave access), and even microwave ovens, which operate by emitting a very high power signal in the same band. Conversely, as shown by various measurement campaigns conducted by agencies such as the Federal Communications Commission (FCC) in the United States and in the United Kingdom, the assigned frequency bands are not used at all times and in all places by primary users (PUs) [6; 7]. For example, as shown by the measurement results reported in [8], the uplink channel (UL) of the 850 MHz GSM band has, on average, a less than 10% duty cycle over a 24 h period measurement time [7]. CR technology can be used with the goal of achieving the dual purpose of both reducing the occupancy of overcrowded unlicensed ISM bands and increasing the utilization of licensed bands without disrupting the primary user [9]. CRs can be used, for instance, to gain more bandwidth for the bandwidth-hungry multimedia applications [10].