Internet of Things (IoT) is an emerging domain that promises ubiquitous connection to the Internet, turning common objects into connected devices. The IoT paradigm is changing the way people interact with things around them. It paves the way for creating pervasively connected infrastructures to support innovative services and promises better flexibility and efficiency. Such advantages are attractive not only for consumer applications, but also for the industrial domain. Over the last few years, we have been witnessing the IoT paradigm making its way into the industry marketplace with purposely designed solutions. In this paper, we clarify the concepts of IoT, Industrial IoT, and Industry 4.0. We highlight the opportunities brought in by this paradigm shift as well as the challenges for its realization. In particular, we focus on the challenges associated with the need of energy efficiency, real-time performance, coexistence, interoperability, and security and privacy. We also provide a systematic overview of the state-of-the-art research efforts and potential research directions to solve Industrial IoT challenges.

Industrial Internet of Things: Challenges, Opportunities, and Directions

The ever-increasing number of resource-constrained machine-type communication (MTC) devices is leading to the critical challenge of fulfilling diverse communication requirements in dynamic and ultra-dense wireless environments. Among different application scenarios that the upcoming 5G and beyond cellular networks are expected to support, such as enhanced mobile broadband (eMBB), massive machine type communications (mMTCs), and ultra-reliable and low latency communications (URLLCs), the mMTC brings the unique technical challenge of supporting a huge number of MTC devices in cellular networks, which is the main focus of this paper. The related challenges include quality of service (QoS) provisioning, handling highly dynamic and sporadic MTC traffic, huge signalling overhead, and radio access network (RAN) congestion. In this regard, this paper aims to identify and analyze the involved technical issues, to review recent advances, to highlight potential solutions and to propose new research directions. First, starting with an overview of mMTC features and QoS provisioning issues, we present the key enablers for mMTC in cellular networks. Along with the highlights on the inefficiency of the legacy random access (RA) procedure in the mMTC scenario, we then present the key features and channel access mechanisms in the emerging cellular IoT standards, namely, LTE-M and narrowband IoT (NB-IoT). Subsequently, we present a framework for the performance analysis of transmission scheduling with the QoS support along with the issues involved in short data packet transmission. Next, we provide a detailed overview of the existing and emerging solutions toward addressing RAN congestion problem, and then identify potential advantages, challenges, and use cases for the applications of emerging machine learning (ML) techniques in ultra-dense cellular networks. Out of several ML techniques, we focus on the application of low-complexity $Q$ -learning approach in the mMTC scenario along with the recent advances toward enhancing its learning performance and convergence. Finally, we discuss some open research challenges and promising future research directions.

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Toward Massive Machine Type Communications in Ultra-Dense Cellular IoT Networks: Current Issues and Machine Learning-Assisted Solutions

The advancement of technologies over years has poised Internet of Things (IoT) to scoop out untapped information and communication technology opportunities. It is anticipated that IoT will handle the gigantic network of billions of devices to deliver plenty of smart services to the users. Undoubtedly, this will make our life more resourceful but at the cost of high energy consumption and carbon footprint. Consequently, there is a high demand for green communication to reduce energy consumption, which requires optimal resource availability and controlled power levels. In contrast to this, IoT devices are constrained in terms of resources—memory, power, and computation. Low power wide area (LPWA) technology is a response to the need for efficient utilization of power resource, as it evinces characteristics such as the capability to proffer low power connectivity to a huge number of devices spread over wide geographical areas at low cost. Various LPWA technologies, such as LoRa and SigFox, exist in the market, offering a proficient solution to the users. However, in order to abstain the need of new infrastructure (like base station) that is required for proprietary technologies, a new cellular-based licensed technology, narrowband IoT (NBIoT), is introduced by 3GPP in Rel-13. This technology presents a good candidature to handle LPWA market because of its characteristics like enhanced indoor coverage, low power consumption, latency insensitivity, and massive connection support towards NBIoT. This survey presents a profound view of IoT and NBIoT, subsuming their technical features, resource allocation, and energy-efficiency techniques and applications. The challenges that hinder the NBIoT path to success are also identified and discussed. In this paper, two novel energy-efficient techniques “zonal thermal pattern analysis” and energy-efficient adaptive health monitoring system have been proposed towards green IoT.

A Survey on Energy Efficient Narrowband Internet of Things (NBIoT): Architecture, Application and Challenges

Fast and reliable time and frequency synchronization is crucial for packet-based orthogonal frequency division multiplex (OFDM) systems. In this paper we compare and analyze preambles for OFDM systems based on repeated OFDM data symbols or repeated short pseudonoise sequences (PN-sequences). We make an analytical evaluation for AWGN channels and simulate the performance in oneand two-tap Rayleigh fading channels. The PN-based preamble gives better detection properties in terms of lower false detection probability and lower probability of missing the synchronization signal. The PN-based preamble has low peak-to-average power ratio and it makes it possible to use an ADC with one-bit quantization in stand-by mode. For frequency offset estimation both preambles give similar performance, but the PN-based preamble allows for a greater reduction in stand-by mode power consumption.

Time and Frequency Synchronization for BRAN using PN-sequence Preambles

The rapid growth of Internet-of-Things (IoT) in the current decade has led to the development of a multitude of new access technologies targeted at low-power, wide area networks (LP-WANs). However, this has also created another challenge pertaining to technology selection. This paper reviews the performance of LP-WAN technologies for IoT, including design choices and their implications. We consider Sigfox, LoRaWAN, WavIoT, random phase multiple access (RPMA), narrowband IoT (NB-IoT), as well as LTE-M and assess their performance in terms of signal propagation, coverage and energy conservation. The comparative analyses presented in this paper are based on available data sheets and simulation results. A sensitivity analysis is also conducted to evaluate network performance in response to variations in system design parameters. Results show that each of RPMA, NB-IoT, and LTE-M incurs at least 9 dB additional path loss relative to Sigfox and LoRaWAN. This paper further reveals that with a 10% improvement in receiver sensitivity, NB-IoT 882 MHz and LoRaWAN can increase coverage by up to 398% and 142%, respectively, without adverse effects on the energy requirements. Finally, extreme weather conditions can significantly reduce the active network life of LP-WANs. In particular, the results indicate that operating an IoT device in a temperature of −20 °C can shorten its life by about half; 53% (WavIoT, LoRaWAN, Sigfox, NB-IoT, and RPMA) and 48% in LTE-M compared with environmental temperature of 40 °C.

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Low-Power Wide Area Network Technologies for Internet-of-Things: A Comparative Review

LPWAN is a type of wireless telecommunication network designed to allow long range communications with relaxed requirements on data rate and latency between the core network and a high-volume of battery-operated devices This article first reviews the leading LPWAN technologies on both unlicensed spectrum (SIGFOX, and LoRa) and licensed spectrum (LTE-M and NB-IoT) Although these technologies differ in many aspects, they do have one thing in common: they all utilize the narrow-band transmission mechanism as a leverage to achieve three fundamental goals, that is, high system capacity, long battery life, and wide coverage This article introduces an effective bandwidth concept that ties these goals together with the transmission bandwidth, such that these contradicting goals are balanced for best overall system performance

Narrowband Wireless Access for Low-Power Massive Internet of Things: A Bandwidth Perspective

Physical layer control signals in cellular communications serve the purpose of delivering physical layer control messages in a timely fashion to support cell radio resource management and data transmissions between the network and the mobile users. This article describes the evolution of 3GPP LTE in control channel design. The legacy LTE control channels (Release 8) have proven to be well-designed robust channels for signaling physical layer messages. However, emerging wireless communication transmission technologies continue to challenge the traditional design of LTE control channels. We have reached a point where a totally new design of the control channels has to be provided in order for the new transmission features to be introduced into LTE. In this article we first give an overview of the legacy LTE control channel, and present the new challenges. We then describe in depth the new solutions provided by LTE (Release 11). Finally, we address the limitations of the new design, which are recently identified in certain newly-emerging applications, in particular, the machine-type communications. A new round of evolution is hence imperative. New solutions provided by next release of LTE (Release 13) are presented.

The Evolution of LTE Physical Layer Control Channels

The Internet of Things (IoT), which is a network that enables devices in the network to exchange information, has rapidly developed in recent years. Wireless communication technologies on the IoT are the best way to solve the “last mile” problem and gain substantial attention. The low power consumption and low cost are notably important for the IoT devices that are called massive IoT (mIoT). Narrow-band communication is one of the solutions to reduce the cost and power consumption and is considered the solution to large coverage without an available spectrum. Large coverage also implies a long transmission time interval, which makes the random access process in the current LTE system unsuitable for mIoT applications. The random access process in the current LTE system is notably complex with some handshakes to exchange information between the base station and the device. We address the issue in this paper and propose a packet-based preamble to simplify the random access process to decrease battery consumption.

Packet-Based Preamble Design for Random Access in Massive IoT Communication Systems

Machine-type communication (MTC) is the key technology to support data transfer among devices (sensors and actuators) in Internet of Things (IoT). Although cellular communication technologies are developed mainly for “human-type” communications, enabling MTC with cellular networks not only improves the connectivity, accessibility, and availability of an MTC network but also has the potential to further drive down the operation cost. However, cellular MTC, especially when applied to low-power massive IoT (mIoT), poses some unique challenges due to the low-cost and low-power nature of an mIoT device. One of the most challenging issues is providing a robust way for an mIoT device to acquire the network under a large frequency offset due to the use of low-cost crystal oscillators and under extended coverage. Although differentiation is a well-known technique for removing impairments caused by frequency offset, its “noise amplification” effect limits its applications in cellular communications due to the fact that cellular communication is typically interference limited. Matched-filter-based detection is, therefore, almost unexceptionally used. We show that the differential technique can actually benefit system acquisition in mIoT, where the use of low-cost crystals is a default. Although the existing system acquisition design in a cellular mIoT system, i.e., NB-IoT, facilitates both techniques, there still remain issues that need to be solved in order to take full advantage of the design. We provide a comprehensive analysis on the performance of two most common techniques when applied in a typical NB-IoT environment based on two factors, the geometry factor and the frequency offset factor. Finally, we derive the operating regions for matched-filter-based detection and differentiation using these two factors, in which the system acquisition performance of the two types of techniques is maximized for NB-IoT.

Jun Zou

Papers

Narrowband Wireless Access for Low-Power Massive Internet of Things: A Bandwidth Perspective

The Evolution of LTE Physical Layer Control Channels

Packet-Based Preamble Design for Random Access in Massive IoT Communication Systems

Enhanced System Acquisition for NB-IoT

Coverage Analysis on NB-IoT and LoRa in Power Wireless Private Network