The rising number of technological advanced devices making network coverage planning very challenging tasks for network operators. The transmission quality between the transmitter and the end users has to be optimum for the best performance out of any device. Besides, the presence of coverage hole is also an ongoing issue for operators which cannot be ignored throughout the whole operational stage. Any coverage hole in network operators' coverage region will hamper the communication applications and degrade the reputation of the operator's services. Presently, there are techniques to detect coverage holes such as drive test or minimization of drive test. However, these approaches have many limitations. The extreme costs, outdated information about the radio environment and high time consumption do not allow to meet the requirement competently. To overcome these problems, we take advantage of Unmanned aerial vehicle (UAV) and Q-learning to autonomously detect coverage hole in a given area and then deploy UAV based base station (UAV-BS) by considering wireless backhaul with the core network and users demand. This machine learning mechanism will help the UAV to eliminate human-in-the-loop (HiTL) model. Later, we formulate an optimisation problem for 3D UAV-BS placement at various angular positions to maximise the number of users associated with the UAV-BS. In summary, we have illustrated a cost-effective as well as time saving approach of detecting coverage hole and providing on-demand coverage in this article.

Whether by habit or necessity, people tend to spend most of their time indoors. Built-up Carbon dioxide (CO2) can lead to a series of negative health effects such as nausea, headache, fatigue, and so on. Thus, indoor air quality must be monitored for a variety of health reasons. Various air quality monitoring systems are available on the market. However, since they are expensive and difficult to obtain, they are not commonly employed by the general population. With the advent of the Internet of Things (IoT), the Indoor Air Quality (IAQ) monitoring system has been simplified, and a number of studies have been conducted in order to monitor the IAQ using IoT. In this paper, we propose an improved IoT-based, low-cost IAQ monitoring system using Artificial Intelligence (AI) to provide recommendations. In our proposed system, the IoT sensors transmit data via Message Queuing Telemetry Transport (MQTT) protocol which can be visualised in real time on a user-friendly dashboard. Furthermore, the AI technique referred to as Long Short-Term Memory (LSTM) is applied to the collected CO2 data for the purpose of predicting future CO2 concentrations. Based on the predicted CO2 concentration, our system can compute CO2 steady state in advance with an error margin of 5.5%.

Internet of Things (IoT) is not new in the market; however, they are becoming more dominant in various operations and applications. These pervasive infrastructures can collect different data in a given environment such as temperature, pressure, light sensitivity, and so on to enable remote condition monitoring. Subsequently, these collected data can be used for many purposes depending on the user's requirement. Currently, there are colossal interests from researchers to know how to use these infrastructures to collect and send data over different protocols to the cloud for efficient remote handling from anywhere in the world. In this paper, we design and implement an infrastructure based on Pycom development board FiPy and sensor shield Pysense to collect and send data to the remote cloud over Wi-Fi and Long Range (LoRa) protocols. The intelligible set up is beneficial for observing and managing data in the cloud.

Ultra reliable and low latency communications (uRLLC) is one of the most significant requirements for future wireless networks. The conventional terrestrial base stations cannot always provide the required uRLLC for emerging applications and scenarios, e.g., Tactile Internet services or when a large number of users get connected during an event. Therefore, multilayer airborne networks with low/medium/high altitude platforms can be deployed as an effective solution to offer capacity and coverage along with required latency and reliability for wireless networks. In this article, we propose a three layers airborne network to support the uRLLC requirement in wireless networks. Optimized link selection schemes have been provided based on Polychromatic Sets (PSets) theory to focus on the uRLLC. With the optimized link selection algorithm, multiple properties of the airborne platforms are exploited and the links are selected based on the multi-constrained requirements to support the desired performance of the airborne network. Moreover, two links distribution schemes have been proposed as distributed greedy scheme and centralized greedy scheme to demonstrate the deployment of proposed airborne network. Numerical results show that both PSets-based links distribution schemes outperform the general distribution schemes on average latency and overall reliability also known as unassociated ratio, which strongly supports the uRLLC in considered airborne networks.

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