The vision of Internet of Things (IoT) is to enable systems across the globe to share data using advanced communication technologies. With the recent technological advancements, IoT-based solutions are no longer a challenging vision. IoT will offer numerous, and potentially revolutionary, benefits to today’s digital world. Future personalized and connected heathcare is one of the promising area to see the benefits of IoT. This paper surveys emerging healthcare applications, including detailed technical aspects required for the realization of a complete endto- end solution for each application. The survey explores the key application-specific requirements from the perspective of communication technologies. Furthermore, a detailed exploration from the existing to the emerging technologies and standards that would enable such applications is presented, highlighting the critical consideration of short-range and long-range communications. Finally, the survey highlights important open research challenges and issues specifically related to IoT-based future healthcare systems.

In this survey a new application paradigm life and safety for critical operations and missions using wearable Wireless Body Area Networks (WBANs) technology is introduced. This paradigm has a vast scope of applications, including disaster management, worker safety in harsh environments such as roadside and building workers, mobile health monitoring, ambient assisted living and many more. It is often the case that during the critical operations and the target conditions, the existing infrastructure is either absent, damaged or overcrowded. In this context, it is envisioned that WBANs will enable the quick deployment of ad-hoc/on-the-fly communication networks to help save many lives and ensuring people’s safety. However, to understand the applications more deeply and their specific characteristics and requirements, this survey presents a comprehensive study on the applications scenarios, their context and specific requirements. It explores details of the key enabling standards, existing state-of-the-art research studies, and projects to understand their limitations before realizing aforementioned applications. Application-specific challenges and issues are discussed comprehensively from various perspectives and future research and development directions are highlighted as an inspiration for new innovative solutions. To conclude, this survey opens up a good opportunity for companies and research centers to investigate old but still new problems, in the realm of wearable technologies, which are increasingly evolving and getting more and more attention recently.

Gait is the locomotion attained through the movement of limbs and gait analysis examines the patterns (normal/abnormal) depending on the gait cycle. It contributes to the development of various applications in the medical, security, sports, and fitness domains to improve the overall outcome. Among many available technologies, two emerging technologies that play a central role in modern day gait analysis are: A) wearable sensors which provide a convenient, efficient, and inexpensive way to collect data and B) Machine Learning Methods (MLMs) which enable high accuracy gait feature extraction for analysis. Given their prominent roles, this paper presents a review of the latest trends in gait analysis using wearable sensors and Machine Learning (ML). It explores the recent papers along with the publication details and key parameters such as sampling rates, MLMs, wearable sensors, number of sensors, and their locations. Furthermore, the paper provides recommendations for selecting a MLM, wearable sensor and its location for a specific application. Finally, it suggests some future directions for gait analysis and its applications.

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.

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.

Narrowband Internet of Things (NB-IoT) is the prominent technology that fits the requirements of future IoT networks. However, due to the limited spectrum (i.e., 180 kHz) availability for NB-IoT systems, one of the key issues is how to efficiently use these resources to support massive IoT devices? Furthermore, in NB-IoT, to reduce the computation complexity and to provide coverage extension, the concept of time offset and repetition has been introduced. Considering these new features, the existing resource management schemes are no longer applicable. Moreover, the allocation of frequency band for NB-IoT within LTE band, or as a standalone, might not be synchronous in all the cells, resulting in intercell interference (ICI) from the neighboring cells’ LTE users or NB-IoT users (synchronous case). In this paper, first a theoretical framework for the upper bound on the achievable data rate is formulated in the presence of control channel and repetition factor. From the conducted analysis, it is shown that the maximum achievable data rates are 89.2 Kbps and 92 Kbps for downlink and uplink, respectively. Second, we propose an interference aware resource allocation for NB-IoT by formulating the rate maximization problem considering the overhead of control channels, time offset, and repetition factor. Due to the complexity of finding the globally optimum solution of the formulated problem, a sub-optimal solution with an iterative algorithm based on cooperative approaches is proposed. The proposed algorithm is then evaluated to investigate the impact of repetition factor, time offset and ICI on the NB-IoT data rate, and energy consumption. Furthermore, a detailed comparison between the non-cooperative, cooperative, and optimal scheme (i.e., no repetition) is also presented. It is shown through the simulation results that the cooperative scheme provides up to 8% rate improvement and 17% energy reduction as compared with the non-cooperative scheme.