# Combined Access Barring Scheme for IoT Devices Using Bayesian Estimation
## 1 Introduction  
Internet of things (IoT) is one of the technologies to shape the future in communications [1]. Huge number of devices are expected to communicate autonomously to each other, or to the server. It is shown in Figure 1 that according to [2], the count of devices that desire to connect to the network will increase to in excess of 75 billion by the end of 2025. IoT finds its role in various applications that are to be used in near future, for example smart grids, smart hospitals and even smart cities. The key idea behind all these applications is that the generated data, either from sensors, or other smart devices, is transmitted to the central stations. Similarly in some applications, the devices may be required to send data to their peer devices for cooperation or decision making. Thus, the nature of traffic is shifting from human type communications (HTC) to machine type communication (MTC) or IoT. HTC is generally characterized with sparse traffic and long traffic duration whereas in IoT, communicating devices are in large number and the nature of traffic is bursty. The devices generally come in bursts and the duration of traffic is short. Long term evolution (LTE/LTE-A) promises to overcome the problems of the bursty nature of IoT traffic. Recognizing different requirements of IoT, LTE has maintained a separate procedure to deal with this traffic. First step in dealing with IoT devices is the random access (RA) process. The devices have to undergo RA process to get scheduled service from the network. Since the resources to deal with RA process are limited and the number of devices attempting RA process is large, serious congestion can take place. So, the devices will be unable to complete RA process successfully and hence cannot get served through scheduled access by the network. In literature, many solutions are proposed for this problem [3; 4; 5; 6; 7]. In order to address the issue of congestion, access barring (AB) is considered as a accepted technique by third generation partnership project (3 GPP). Access barring is the process in which some devices are barred from transmission based on some pre-defined procedure. In LTE-A, the most sought solutions for congestion control are access class barring (ACB) [8] and extended access barring (EAB) [9]. While EAB is designed only for MTC/IoT traffic, ACB can handle both IoT and HTC traffic. In ACB, the reduction in congestion is proportional to the reduction in number of collisions, or in other words, to the probability of success. The success probability in turn depends upon the transmission probability of the devices. The process of ACB, in short, can be explained as follows: In ACB, a transmission probability called ACB factor is broadcast by eNodeB. Then, every device generates a random number and then transmits if the random number is less than the probability broadcast by the eNodeB, otherwise the device refrains from transmission. The devices that become eligible for transmission are said to have passed ACB check. This ACB factor is of prime importance in controlling congestion in IoT traffic.If we take the value of ACB factor low, very less number of IoT devices pass the ACB check and resources may go idle. On the other hand if the ACB factor is high, large number of devices may pass the ACB check, increasing the number of collisions. So the ACB factor needs to be optimal. In [8], the authors propose the methodology to obtain the optimal ACB factor that rely on the number of active devices in the network. In [10], the authors used fixed ACB factor, i.e., they assume fixed number of devices in the network. However, we can observe that the number of IoT devices is always changing. In [11], the authors use the information on number of idle, success and collided preambles (channels), and maximum likelihood to find the number of devices in the network. Then the authors use Kalman filter to fine tune their estimation. But the maximum likelihood is an expansive operation and not good to use in real time scenarios.