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Narrowband internet of things (NB-IoT) is a recent cellular radio access technology based on Long-Term Evolution (LTE) introduced by Third-Generation Partnership Project (3GPP) for Low-Power Wide-Area Networks (LPWAN). The main aim of NB-IoT is to support massive machine-type communication (mMTC) and enable low-power, low-cost, and low-data-rate communication. NB-IoT is based on LTE design with some changes to meet the mMTC requirements. For example, in the physical (PHY) layer only single-antenna and low-order modulations are supported, and in the Medium Access Control (MAC) layers only one physical resource block is allocated for resource scheduling. The aim of this survey is to provide a comprehensive overview of the design changes brought in the NB-IoT standardization along with the detailed research developments from the perspectives of Physical and MAC layers. The survey also includes an overview of Evolved Packet Core (EPC) changes to support the Service Capability Exposure Function (SCEF) to manage both IP and non-IP data packets through Control Plane (CP) and User Plane (UP), the possible deployment scenarios of NB-IoT in future Heterogeneous Wireless Networks (HetNet). Finally, existing and emerging research challenges in this direction are presented to motivate future research activities.

With advances in Fog and edge computing, various problems such as data processing for large Internet of things (IoT) systems can be solved in an efficient manner. One such problem for the next generation smart grid IoT system comprising of millions of smart devices is the data aggregation problem. Traditional data aggregation schemes for smart grids incur high computation and communication costs, and in recent years there have been efforts to leverage fog computing with smart grids to overcome these limitations. In this paper, a new fog-enabled privacy-preserving data aggregation scheme (FESDA) is proposed. Unlike existing schemes, the proposed scheme is resilient to false data injection attacks by filtering out the inserted values from external attackers. To achieve privacy, a modified version of Paillier crypto-system is used to encrypt consumption data of the smart meter users. In addition, FESDA is fault-tolerant, which means, the collection of data from other devices will not be affected even if some of the smart meters malfunction. We evaluate its performance along with three other competing schemes in terms of aggregation, decryption and communication costs. The findings demonstrate that FESDA reduces the communication cost by 50%, when compared with the PPFA aggregation scheme.

The growing network density and unprecedented increase in network traffic, caused by the massively expanding number of connected devices and online services, require intelligent network operations. Machine Learning (ML) has been applied in this regard in different types of networks and networking technologies to meet the requirements of future communicating devices and services. In this article, we provide a detailed account of current research on the application of ML in communication networks and shed light on future research challenges. Research on the application of ML in communication networks is described in: i) the three layers, i.e., physical, access, and network layers; and ii) novel computing and networking concepts such as Multi-access Edge Computing (MEC), Software Defined Networking (SDN), Network Functions Virtualization (NFV), and a brief overview of ML-based network security. Important future research challenges are identified and presented to help stir further research in key areas in this direction. INDEX TERMS Communication networks, machine learning, physical layer, MAC layer, network layer, SDN, NFV, MEC, security, artificial intelligence (AI) I. INTRODUCTION T HE security, availability and performance demands of new applications, services and devices are increasing at a pace higher than anticipated. Real-time responsiveness in application areas like e-health, traffic, and industry requires communication networks to make real-time decisions autonomously. Such real-time autonomous decision-making requires that the network must react and learn from the environment, and control itself without human interventions. However, communication networks have until now taken a different path. Traditional networks rely on human involvement to respond manually to changes such as traffic variation, updates in network functions and services, security breaches, and faults. Human-machine interactions have resulted in network downtime [1], have opened the network to security vulnerabilities [2], and lead to many other challenges in current communication networks [3], [4]. The requirement for human interaction or manual configuration constitutes a major hindrance for a network to use its past experiences to adapt to changing requirements. The general idea is to predict the (future) behavior of a service, network segment, user or User Equipment (UE), and tune the network at run-time based on this information. For instance, the movement trajectory of a user can be predicted using