Next-generation Internet Of Things Research Articles

Optimal links selection procedure in buffer‐aided relaying for point‐to‐multipoints communications

The increasing diffusion of smart devices has triggered new Internet of Things (IoT) applications, where communications patterns are often in an autonomous mode. Key enabling technologies, such as relaying communications, cell size reduction and multicast communications have been identified to bring the next-generation IoT to a complete fruition. In particular, the use of relay nodes is expected to be useful for coverage and capacity improvements, as well as to support point-to-multipoint services for data disseminating to subscribers communities. To this end, this study proposes a dynamic quorum links selection (DQm) procedure to optimise the throughput for the case of buffer-aided relaying multicast communications. A theoretical analysis based on the Markov chain framework is provided and validated through simulation results in terms of multicast throughput and relay buffer occupancy for different sizes of the multicast group. The advantages of the proposed DQm procedure have been highlighted by comparing the achieved performance with respect to classical conventional relay scheme. Finally, a protocol for channel state estimation is presented and the impact of the requested overhead on the performance of DQm procedure is shown.

Client-Centric Access Device Selection for Heterogeneous QoS Requirements in Beyond 5G IoT Networks

The expected manifolds increase in the traffic and the massive number of connected Internet of Things (IoT) devices would be a challenge to ensure a certain Quality of Service (QoS) in Beyond 5G (B5G) networks. Due to the increased availability of processing and energy resources and heterogeneous QoS requirements in modern IoT nodes, a client-centric access device selection approach for QoS provisioning in multiple Radio Access Technologies (RATs) scenario is proposed in this paper. The proposed algorithm provides the ability to specify node-specific QoS requirements at each node, a better access device selection, and improved network scalability. For experimental evaluation, a hybrid indoor network consisting of Wireless Fidelity (WiFi) and Light Fidelity (LiFi) RATs has been considered. Experimental results show that the proposed technique outperforms several conventional client-based access device selection approaches by up to 32.66% in network emulation experiments and up to 50% in hardware experimentation. The contribution of this paper includes the proposed algorithm, its complexity and game-theory based convergence analyses, evaluation of the proposed algorithm using network emulation and hardware-based setups, and LiFi channel analysis. The analyses and results imply that the proposed algorithm could be utilized in next-generation IoT networks as it performs better than conventionally used access device selection techniques for QoS provisioning.

Human-Like Hybrid Caching in Software-Defined Edge Cloud

With the development of Internet of Things (IoT) and communication technology, the number of next-generation IoT devices has increased explosively, and the delay requirement for content requests is becoming progressively higher. Fortunately, the edge-caching scheme can satisfy users’ demands for low latency of content. However, the existing caching schemes are not smart enough. To address these challenges, we propose a human-like hybrid caching architecture based on the software-defined edge cloud, which simultaneously considers the content popularity and the fine-grained user characteristics. Then, an optimization problem with a caching hit ratio as an optimization objective is formulated. To solve this problem, using reinforcement learning, we design a human-like hybrid caching algorithm. The extensive experiments show that compared with popular caching schemes, human-like hybrid caching schemes can improve the cache hit ratio by 20%.

A 47.14-$\mu\text{W}$ 200-MHz MOS/MTJ-Hybrid Nonvolatile Microcontroller Unit Embedding STT-MRAM and FPGA for IoT Applications

The demand for energy-efficient, high-performance microcontroller units (MCUs) for the use in power-supply-critical Internet-of-Things (IoT) sensor-node applications has witnessed a substantial increase. In response, research concerning the development of several low-power-consuming MCUs has been actively pursued. The performance level of such MCUs, however, has not been sufficient, thereby rendering them non-feasible for the use in IoT sensor-node applications that process a large number of received signals immediately followed by extraction of valuable information from them to limit data transferred to a data center. To realize next-generation IoT systems based on intelligent sensor-node application, ultra-low-power high-performance MCUs need to be developed. This paper presents an ultra-low-power-consuming and high-performance MCU configuration based on the spintronics device technology, using which all modules are non-volatilized, and any wasteful power consumption is eliminated by controlling the power supplied independently to each module. By incorporating a reconfigurable accelerator module, for performing various signal-processing procedures in sensor-node applications, and a memory controller, which can speed up the entire system by relaxing the data-transfer bottleneck of logic and memory, the proposed MCU configuration achieves ultra-low power consumption and high-speed operation. As confirmed by the results obtained via measurements performed on a fabricated chip, the proposed MCU design, on average, consumed 47.14 $\mu \text{W}$ power at an operating frequency of 200 MHz. This corresponds to the world’s highest signal-processing performance and energy efficiency of highly functional IoT sensor nodes powered by harvested energy

Software-Defined architecture for QoS-Aware IoT deployments in 5G systems

Internet of Things (IoT), a ubiquitous network of interconnected objects, harvests information from the environments, interacts with the physical world, and uses the existing Internet infrastructure to provide services for information transfer and emerging applications. However, the scalability and Internet access fundamentally challenge the realization of a wide range of IoT applications. Based on recent developments of 5G system architecture, namely SoftAir, this paper introduces a new software-defined platform that enables dynamic and flexible infrastructure for 5G IoT communication. A corresponding sum-rate analysis is also carried out via an optimization approach for efficient data transmissions. First, the SoftAir decouples control plane and data plane for a software-defined wireless architecture and enables effective coordination among remote radio heads (RRHs), equipped with millimeter-wave (mmWave) frontend, for IoT access. Next, by introducing an innovative design of software-defined gateways (SD-GWs) as local IoT controllers in SoftAir, the wide diversity of IoT applications and the heterogeneity of IoT devices are easily accommodated. These SD-GWs aggregate the traffic from heterogeneous IoT devices and perform protocol conversions between IoT networks and radio access networks. Moreover, a cross-domain optimization framework is proposed in this extended SoftAir architecture concerning both upstream and downstream communication, where the respective sum-rates are maximized and system-level constraints are guaranteed, including (i) quality-of-service requirements of IoT transmissions, (ii) total power limit of mmWave RRHs, and (iii) fronthaul network capacities. Simulation results validate the efficacy of our solutions, where the extended SoftAir solution surpasses existing IoT schemes in spectral efficiency and achieves optimal data rates for next-generation IoT communication.

Toward Robust Crowdsourcing-Based Localization: A Fingerprinting Accuracy Indicator Enhanced Wireless/Magnetic/Inertial Integration Approach

The next-generation internet of things (IoT) systems have an increasingly demand on intelligent localization which can scale with big data without human perception. Thus, traditional localization solutions without accuracy metric will greatly limit vast applications. Crowdsourcing-based localization has been proven to be effective for mass-market location-based IoT applications. This paper proposes an enhanced crowdsourcing-based localization method by integrating inertial, wireless, and magnetic sensors. Both wireless and magnetic fingerprinting accuracy are predicted in real time through the introduction of fingerprinting accuracy indicators (FAI) from three levels (i.e., signal, geometry, and database). The advantages and limitations of these FAI factors and their performances on predicting location errors and outliers are investigated. Furthermore, the FAI-enhanced extended Kalman filter (EKF) is proposed, which improved the dead-reckoning (DR)/WiFi, DR/Magnetic, and DR/WiFi/Magnetic integrated localization accuracy by 30.2 %, 19.4 %, and 29.0 %, and reduced the maximum location errors by 41.2 %, 28.4 %, and 44.2 %, respectively. These outcomes confirm the effectiveness of the FAI-enhanced EKF on improving both accuracy and reliability of multi-sensor integrated localization using crowdsourced data.

Next-generation Internet of things and cloud security solutions

Next-generation Internet of things and cloud security solutions

Self-powered flexible electronics beyond thermal limits

Self-powered flexible electronics using high-performance inorganic materials have been studied and developed for the essence of future electronics due to the thing, lightweight, self-sustainable, and biocompatible characteristics, which can be applied to body sensor network and next generation Internet of Things (IoT). However, most of inorganic materials should be processed in the high-temperature processes such as the semiconductor fabrication, which is not compatible flexible plastic substrates. Therefore, the new approaches must be demonstrated to overcome the thermal limits of previous methodology and achieve the flexible inorganic electronics on various flexible plastic substrates. In this review paper, we introduce the recent progress of technologies to realize flexible and high-performance inorganic electronics on plastic substrates over the thermal limits, i.e., laser-assisted procedure, chemical or mechanical exfoliation approaches. They are compatible not only to flexible plastic substrates but also to conventional device processes. We also explain the novel application devices such as flexible optoelectronics, flexible large-scale integration (LSI) devices, flexible energy harvesters, and flexible sensors using the recent-developed technologies beyond the previous thermal limit. This paper highlights the proper direction to complete future flexible inorganic electronics for high-performance self-powered systems.

Path Loss Model for Outdoor Parking Environments at 28 GHz and 38 GHz for 5G Wireless Networks

It has been widely speculated that the performance of the next generation Internet of Things (IoT) based wireless network should meet a transmission speed on the order of 1000 times more than current wireless networks; energy consumption on the order of 10 times less and access delay of less than 1 ns that will be provided by future 5G systems. To increase the current mobile broadband capacity in future 5G systems, the millimeter wave (mmWave) band will be used with huge amounts of bandwidth available in this band. Hence, to support this wider bandwith at the mmWave band, new radio access technology (RAT) should be provided for 5G systems. The new RAT with symmetry design for downlink and uplink should support different scenarios such as device to device (D2D) and multi-hop communications. This paper presents the path loss models in parking lot environment which represents the multi-end users for future 5G applications. To completely assess the typical performance of 5G wireless network systems across these different frequency bands, it is necessary to develop path loss (PL) models across these wide frequency ranges. The short wavelength of the highest frequency bands provides many scatterings from different objects. Cars and other objects are some examples of scatterings, which represent a critical issue at millimeter-wave bands. This paper presents the large-scale propagation characteristics for millimeter-wave in a parking lot environment. A new physical-based path loss model for parking lots is proposed. The path loss was investigated based on different models. The measurement was conducted at 28 GHz and 38 GHz frequencies for different scenarios. Results showed that the path loss exponent values were approximately identical at 28 GHz and 38 GHz for different scenarios of parking lots. It was found that the proposed compensation factor varied between 10.6 dB and 23.1 dB and between 13.1 and 19.1 in 28 GHz and 38 GHz, respectively. The proposed path loss models showed that more compensation factors are required for more scattering objects, especially at 28 GHz.

Vehicle-Based Relay Assistance for Opportunistic Crowdsensing Over Narrowband IoT (NB-IoT)

The Internet of Things (IoT) undergoes a fundamental transformation by augmenting its conventional sensor network deployments with more advanced and mobile devices, such as connected and self-driving cars. This fusion of embedded and automotive domains promises to deliver unprecedented mutual benefits, where vehicles will receive timely updates from their proximate sensors while assisting them in delivering their sensory data to the remote network infrastructure. In this paper, we put forward the vision of opportunistic crowdsensing applications, in which the ubiquitous deployments of low-cost and battery-constrained IoT sensors take advantage of more capable and energy-abundant vehicle-mounted mobile relays. In particular, we consider the use of the emerging narrowband IoT radio technology recently ratified by 3GPP and offering efficient means for underlying wireless connectivity. Our rigorous mathematical analysis supported with comprehensive system-level evaluations reveals the effects of vehicle-based relays on the important metrics of interest, such as connection reliability, transmission latency, and communication energy efficiency. These systematic findings advocate for an extensive utilization of vehicular relays as part of the next-generation IoT ecosystem.

Securing the Internet of Things in the Age of Machine Learning and Software-Defined Networking

The Internet of Things (IoT) realizes a vision where billions of interconnected devices are deployed just about everywhere, from inside our bodies to the most remote areas of the globe. As the IoT will soon pervade every aspect of our lives and will be accessible from anywhere, addressing critical IoT security threats is now more important than ever. Traditional approaches where security is applied as an afterthought and as a "patch" against known attacks are insufficient. Indeed, next-generation IoT challenges will require a new secure-by-design vision, where threats are addressed proactively and IoT devices learn to dynamically adapt to different threats. To this end, machine learning and software-defined networking will be key to provide both reconfigurability and intelligence to the IoT devices. In this paper, we first provide a taxonomy and survey the state of the art in IoT security research, and offer a roadmap of concrete research challenges related to the application of machine learning and software-defined networking to address existing and next-generation IoT security threats.

Towards a proper service placement in combined Fog-to-Cloud (F2C) architectures

The Internet of Things (IoT) has empowered the development of a plethora of new services, fueled by the deployment of devices located at the edge, providing multiple capabilities in terms of connectivity as well as in data collection and processing. With the inception of the Fog Computing paradigm, aimed at diminishing the distance between edge-devices and the IT premises running IoT services, the perceived service latency and even the security risks can be reduced, while simultaneously optimizing the network usage. When put together, Fog and Cloud computing (recently coined as fog-to-cloud, F2C) can be used to maximize the advantages of future computer systems, with the whole greater than the sum of individual parts. However, the specifics associated with cloud and fog resource models require new strategies to manage the mapping of novel IoT services into the suitable resources. Despite few proposals for service offloading between fog and cloud systems are slowly gaining momentum in the research community, many issues in service placement, both when the service is ready to be executed admitted as well as when the service is offloaded from Cloud to Fog, and vice-versa, are new and largely unsolved. In this paper, we provide some insights into the relevant features about service placement in F2C scenarios, highlighting main challenges in current systems towards the deployment of the next-generation IoT services.

Microprocessor Optimizations for the Internet of Things: A Survey

The Internet of Things (IoT) refers to a pervasive presence of interconnected and uniquely identifiable physical devices. These devices' goal is to gather data and drive actions in order to improve productivity, and ultimately reduce or eliminate reliance on human intervention for data acquisition, interpretation, and use. The proliferation of these connected low-power devices will result in a data explosion that will significantly increase data transmission costs with respect to energy consumption and latency. Edge computing reduces these costs by performing computations at the edge nodes, prior to data transmission, to interpret and/or utilize the data. While much research has focused on the IoT's connected nature and communication challenges, the challenges of IoT embedded computing with respect to device microprocessors has received much less attention. This paper explores IoT applications' execution characteristics from a microarchitectural perspective and the microarchitectural characteristics that will enable efficient and effective edge computing. To tractably represent a wide variety of next-generation IoT applications, we present a broad IoT application classification methodology based on application functions, to enable quicker workload characterizations for IoT microprocessors. We then survey and discuss potential microarchitectural optimizations and computing paradigms that will enable the design of right-provisioned microprocessors that are efficient, configurable, extensible, and scalable. This paper provides a foundation for the analysis and design of a diverse set of microprocessor architectures for next-generation IoT devices.

Energy Harvesting Using a Low-Cost Rectenna for Internet of Things (IoT) Applications

Traditionally employed human-to-human and human-to-machine communication has recently been replaced by a new trend known as the Internet of things (IoT). IoT enables device-to-device communication without any human intervention, hence, offers many challenges. In this paradigm, machine’s self-sustainability due to limited energy capabilities presents a great challenge. Therefore, this paper proposed a low-cost energy harvesting device using rectenna to mitigate the problem in the areas where battery constraint issues arise. So, an energy harvester is designed, optimized, fabricated, and characterized for energy harvesting and IoT applications which simply recycles radio-frequency (RF) energy at 2.4 GHz, from nearby Wi-Fi/WLAN devices and converts them to useful dc power. The physical model comprises of antenna, filters, rectifier, and so on. A rectangular patch antenna is designed and optimized to resonate at 2.4 GHz using the well-known transmission-line model while the band-pass and low-pass filters are designed using lumped components. Schottky diode (HSMS-2820) is used for rectification. The circuit is designed and fabricated using the low-cost FR4 substrate ( ${h}$ = 16 mm and $\varepsilon _{r} = 4.6$ ) having the fabricated dimensions of 285 mm $\times \,\,90$ mm. Universal software radio peripheral and GNU Radio are employed to measure the received RF power, while similar measurements are carried out using R&S spectrum analyzer for validation. The received measured power is −64.4 dBm at the output port of the rectenna circuit. Hence, our design enables a pervasive deployment of self-operable next-generation IoT devices.

Big-Sensing-Data Curation for the Cloud is Coming: A Promise of Scalable Cloud-Data-Center Mitigation for Next-Generation IoT and Wireless Sensor Networks

Modern sensing devices play a pivotal role in achieving data acquisition, communication, and dissemination for the Internet of Things (IoT). Naturally, IoT applications and intelligent sensing systems supported by sensing devices, such as wireless sensor networks (WSNs), are closely coupled. Modern intelligent sensing systems generate huge volumes of sensing data, well beyond the processing capabilities of common techniques and tools. As a result, collecting, managing, and processing IoT big sensing data within an acceptable time duration is a new challenge for both research and industrial applications. The massive size, extreme complexity, and high speed of big sensing data bring new technical requirements including data collection, data storage, data organization, data analysis, and data publishing in real time when deploying real-world IoT applications. To better facilitate these IoT applications, the convergent research of WSNs, big data, the IoT, and cloud computing is a natural scientific development trend. In this article, we concentrate on big-sensing-data curation and preparation issues with cloud computing under the theme of the IoT. There are three especially critical issues that need to be addressed: scalable big-sensing-data cleaning, scalable big-sensing-data compression, and cloud-based data curation response for IoT device optimization. Viewed from the IoT side, all IoT sensing devices are integrated together in an adaptive solution and upload their data onto the cloud. The automatic responses from both the cloud and intelligent sensors will change the status or behavior of sensing devices and, therefore, the status of the IoT itself.

Toward next-generation Internet of Things: guest editorial

The Internet of Things (IoT) is envisioned as a network of billions or trillions of machines communicating with one another and rapidly gains global attention from academia, industry, and government. IoT is recognized as one of the most important areas of the future Internet; enabling ubiquitous computing among global networkedmachines and physical objects. Equipped things with auto identification devices such RFID tags, sensors, and machine-to-machine (M2M) devices, IoT are applicable in a wide variety of areas such as smart grid, e-health, intelligent transportation, and logistics. This special issue in telecommunication systems aims to present the latest advances of the fundamental technologies and market trends that will impact the design and standardization of IoT by bringing together academic and industrial researchers to identify and discuss technical challenges related to IoT. In response to the call for papers, we were pleased to see many submissions from many countries and areas all over the world, which was far more than we expected. In this section, we have selected the following outstanding articles that reflect the recent research advance. In the article “A Survey on Position-based Routing for Vehicular Ad hoc Networks”, J. Liu et al. outlines the background and the latest development in VANETs and surveyed the state-of-the-art routing protocols previously used in VANETs. Then, the authors present the pros and cons for each routing protocol, and make a detailed comparison. The open issues, challenges and future research directions are