# Spectrum Based Power Management for Congested IoT Networks
## 1 Introduction  
The exponential growth in the interconnection of countless Internet of Things (IoT) applications has led to high density communications in IoT network [1], leading to network congestion problems. Furthermore, its high dependence on license-free bands have made spectrum resources increasingly scarce, and resulted in extremely busy channels [2]. This causes the battery power of IoT devices to degrade lot faster than expected; which is directly responsible for the longevity of the IoT network. In addition, due to lack of appropriate spectrum management, while sitting in a routing queue due to congestion delay, there is an increasing risk that the channel power may run out and important IoT data may get lostin transmission. Thus, it becomes of utmost important to pay attention to these problems; energy consumption and spectrum management. In IoT, Figure 1, generally a sensor is placed near or around the object to be monitored remotely to collect data. The collected data by sensor is included to form an Internet Protocol (IP) packet and sent to the Coordinator Node (CN) by using a transmitter in the sensor device [3; 4; 5]. This packet sending process should be carried out in a very short time. However, the huge amount of packet flow present in an IoT network environment is the main issue for longer sending time. This leads to a high consumption of power with busy channel problems in IoT networks. Currently, these problems have no effective solutions. The sensor device or data collector itself does not understand how to do spectrum management while in communication. Additionally, with a conventional network setup, the IoT network resources like battery life and communication channels usually do not get any special attention to improve overall power consumption and quality of network. Since IoT networks frequently become congested, the spectrum management and faster degradation of battery life issues for IoT devices must be addressed in order to increase the performance and network lifetime of these small devices. Different authors proposed some solutions to address these problems. Authors of the reference [6] proposed an intelligent opportunistic routing protocol (IOP) using a machine learning technique to select a relay node from the list of potential nodes to achieve energy efficiency and reliability in the network during packet transmission. The authors of [7] proposed a solution for large-scale heterogeneous WSN networks which consists of a robust clustering mechanism for energy optimization. In this mechanism, nodes declare themselves as a cluster head (CH) based on available resources such as residual power, available storage and computational capacity. The authors of [8] propose a software-defined wireless sensor networking architecture to improve energy efficiency. The purpose of the solution is to reduce the number of packets transmitted by the sensor nodes of the network with less energy resources available and thus increase the lifetime of the sensors. All of the above mentioned works use IoT devices to process there proposed method which by itself consumes a lot of energy, memory and processing capacity of IoT devices. However, our implementation runs entirely from the Freescale Freedom Development Board (FREDEVPLA). Therefore, in this paper we propose a different solution to manage communication channels in IoT networks, which outperforms the traditional way. We propose a spectrum based power management solution that scans channel, select the least congested channel and uses it for sending packets. It effectively manages power utilization by minimizing power used for retransmission at a connected state of the channel. Figure 1: Conventional IoT environment. Figure 1 shows a typical IoT scenario in a congested network. Data collected by different IoT sensors at different sources use network routing systems to be released to their end destination. The data packet's source could be from smart city, smart agriculture, social network data, healthcare data, etc.