In the telecom sector, a huge volume of data is being generated on a daily basis due to a vast client base. Decision makers and business analysts emphasized that attaining new customers is costlier than retaining existing ones. Business analysts and Customer Relationship Management (CRM) analyzers need to know the reasons for churn customers as well as behavior patterns from existing churn customers data. This study proposes a churn prediction model that uses classification as well as clustering techniques to identify churn customers and provides the factors behind the churning of customers in the telecom sector. Feature selection is performed by using information gain and correlation attribute ranking filter. The proposed model first classifies churn customers data using classification algorithms in which the Random Forest (RF) algorithm performed well with 88.63% correctly classified instances. Creating effective retention policies is an essential task of the CRM to prevent churners. After classification, the proposed model segments the churning customer's data by categorizing the churn customers in groups using cosine similarity to provide group-based retention offers. This study also identified churn factors, that are essential in determining the root causes of churn. By knowing the significant churn factors from customers data, CRM can improve the productivity, recommend relevant promotions to the group of likely churn customers based on similar behavior patterns and excessively improve marketing campaigns of the company. The proposed churn prediction model is evaluated using metrics such as accuracy, precision, recall, f-measure, and Receiving Operating Characteristics (ROC) area. Results reveal that our proposed churn prediction model produced better churn classification using RF algorithm and customer profiling using k-means clustering. Further, it also provides factors behind the churning of churn customers through rules generated by using the attribute selected classifier algorithm.

Dealing with air pollution is one of the major environmental challenges in a smart city environment. Real-time monitoring of pollution data enables the metropolitans to analyze the current traffic situation of the city and take their decisions accordingly. Deployment of the Internet of things (IoT) based sensors has considerably changed the dynamics of predicting air quality. Existing research has used different machine learning tools for pollution prediction; however, comparative analysis of these techniques is often required to have a better understanding of their processing time for multiple datasets. In this paper, we have performed the pollution prediction using four advanced regression techniques and have presented a comparative study to analyze the best model for accurately predicting the air quality with reference to data size and processing time. We have used Apache Spark for conducting experiments and performing pollution estimation using multiple available data sets. Mean Absolute error (MAE) and Root Mean Square Error (RMSE) have been used as evaluation criteria for the comparison of regression models. Furthermore, the processing time of each technique by standalone learning and by fitting the hyperparameter tuning on Apache Spark has also been calculated to find the best-fitted model in terms of processing time and least error rate.

Fog computing is a promising technology that can provide storage and computational services to future 6G networks. To support the massive Internet-of-Things (IoT) applications in 6G, fog computing will play a vital role. IoT devices and fog nodes have energy limitations and hence, energy-efficient techniques are needed for storage and computation services. We present an overview of massive IoT and 6G-enabling technologies. We discuss different energy-related challenges that arise while using fog computing in 6G-enabled massive IoT. We categorize different energy-efficient fog computing solutions for IoT and describe the recent work done in these categories. Finally, we discuss future opportunities and open challenges in designing energy-efficient techniques for fog computing in the future 6G massive IoT network.

Human emotions are strongly coupled with physical and mental health of any individual. While emotions exbibit complex physiological and biological phenomenon, yet studies reveal that physiological signals can be used as an indirect measure of emotions. In unprecedented circumstances alike the coronavirus (Covid-19) outbreak, a remote Internet of Things (IoT) enabled solution, coupled with AI can interpret and communicate emotions to serve substantially in healthcare and related fields. This work proposes an integrated IoT framework that enables wireless communication of physiological signals to data processing hub where long short-term memory (LSTM)-based emotion recognition is performed. The proposed framework offers real-time communication and recognition of emotions that enables health monitoring and distance learning support amidst pandemics. In this study, the achieved results are very promising. In the proposed IoT protocols (TS-MAC and R-MAC), ultralow latency of 1 ms is achieved. R-MAC also offers improved reliability in comparison to state of the art. In addition, the proposed deep learning scheme offers high performance ( $f$ -score) of 95%. The achieved results in communications and AI match the interdependency requirements of deep learning and IoT frameworks, thus ensuring the suitability of proposed work in distance learning, student engagement, healthcare, emotion support, and general wellbeing.

Fog computing has emerged as a revolutionary paradigm to serve the massive data generated in the Internet of Things (IoT) environments. It can be considered a derivative of cloud computing that provides cloud-like services at the edge of the network. As such, it helps address the, often significant, issue of delays encountered when using cloud systems for the IoT. According to the literature, inefficient scheduling of user tasks in fog computing can actually result in higher delays than cloud computing. Hence, the real benefits of fog computing can only be obtained by applying effective job scheduling strategies. In fact, task scheduling is an NP-hard problem and requires optimal and efficient techniques to address issues of latency, response time, and the efficient resource utilization of resources available at the edge of the network. Given this, we propose a novel bio-inspired hybrid algorithm (NBIHA) which is a hybrid of modified particle swarm optimization (MPSO) and modified cat swarm optimization (MCSO). In the proposed scheme, the MPSO is used to schedule the tasks among fog devices and the hybrid of the MPSO and MCSO is used to manage resources at the fog device level. In the proposed approach, the resources are assigned and managed on the basis of the demand of incoming requests. The main objective of the proposed work is to reduce the average response time and to optimize resource utilization by efficiently scheduling the tasks and managing the fog resources available. The simulations are performed using iFogSim. The evaluation results show that the proposed approach (NBIHA) shows promising results in terms of energy consumption, execution time, and average response time in comparison to the state-of-the-art scheduling techniques.

Internet of Things-enabled Intelligent Transportation Systems (ITS) are gaining significant attention in academic literature and industry, and are seen as a solution to enhancing road safety in smart cities. Due to the ever increasing number of vehicles, a significant rise in the number of road accidents has been observed. Vehicles embedded with a plethora of sensors enable us to not only monitor the current situation of the vehicle and its surroundings but also facilitates the detection of incidents. Significant research, for example, has been conducted on accident rescue, particularly on the use of Information and Communication Technologies (ICT) for efficient and prompt rescue operations. The majority of such works provide sophisticated solutions that focus on reducing response times. However, such solutions can be expensive and are not available in all types of vehicles. Given this, we present a novel Internet of Things-based accident detection and reporting system for a smart city environment. The proposed approach aims to take advantage of advanced specifications of smartphones to design and develop a low-cost solution for enhanced transportation systems that is deployable in legacy vehicles. In this context, a customized Android application is developed to gather information regarding speed, gravitational force, pressure, sound, and location. The speed is a factor that is used to help improve the identification of accidents. It arises because of clear differences in environmental conditions (e.g., noise, deceleration rate) that arise in low speed collisions, versus higher speed collisions). The information acquired is further processed to detect road incidents. Furthermore, a navigation system is also developed to report the incident to the nearest hospital. The proposed approach is validated through simulations and comparison with a real data set of road accidents acquired from Road Safety Open Repository, and shows promising results in terms of accuracy.