Typical Internet Of Things Application Research Articles

An improved clock synchronization model for typical IoT applications

In today’s scenario, the Internet of Things (IoT) has transformed people’s lives by enabling data exchange among pervasive devices in various applications. However, clock synchronization is essential to ensure seamless transmission and synchronization among IoT entities involved in processing and communication. This paper proposes a clock synchronization algorithm based on linear-quadratic regression (LQR) to address synchronization-errors in IoT applications. The algorithm uses a linear model of skew and offsets to estimate clock parameters, and performance is evaluated in terms of R-square error (RSE) and Root Mean Square Error (RMSE). Our proposed algorithm outperformed traditional algorithms with an R-Square Error of 0.71% and RMSE of 0.379%. This paper also evaluated the stability of the proposed model using the correlation coefficient, which indicated a high correlation among the variables at 86%. The results below demonstrate the proposed algorithm’s effectiveness and goodness of fit in addressing clock synchronization errors for IoT applications.

Cell-Free IoT With Non-Orthogonal Random Pilots: Key Research and Future Directions

With the proliferation of Internet of things (IoT) applications, the massive machine-type connections (MTCs) bring great challenges to traditional cellular networks, wherein cellboundary devices with severe path-loss and inter-cell interference normally suffer from poor performance. By contrast, cell-free massive MIMO is a promising technique to support massive MTCs, since its user-centric architecture can shorten the distance between devices and access points, and boost the network coverage performance significantly. In this article, we first introduce the benefits and the main challenges of cell-free IoT with non-orthogonal random pilots. Then, we present the key research topics and several promising solutions for two typical IoT application scenarios: grant-based cell-free IoT and grantfree cell-free IoT. Some future research directions of cell-free IoT are also highlighted.

When Internet of Things Meets Metaverse: Convergence of Physical and Cyber Worlds

In recent years, the Internet of Things (IoT) has been studied in the context of the Metaverse to provide users with immersive cyber-virtual experiences in mixed-reality environments. This survey introduces six typical IoT applications in the Metaverse, including collaborative healthcare, education, smart city, entertainment, real estate, and socialization. In the IoT-inspired Metaverse, we also comprehensively survey four pillar technologies that enable augmented reality (AR) and virtual reality (VR), namely, responsible artificial intelligence (AI), high-speed data communications, cost-effective mobile edge computing (MEC), and digital twins. According to the physical-world demands, we outline the current industrial efforts and seven key requirements for building the IoT-inspired Metaverse: immersion, variety, economy, civility, interactivity, authenticity, and independence. In addition, this survey describes the open issues in the IoT-inspired Metaverse, which need to be addressed to eventually achieve the convergence of physical and cyber worlds.

Online Multiple-Pedestrian Tracking With Detection-Pair-Based Graph Convolutional Networks

The typical Internet of Things application, unattended driving systems, will need the ability to recognize relevant traffic participants and detect dangerous situations ahead of time. An important component of these systems is one that is able to distinguish pedestrians and track their motion to make intelligent driving decisions. This article develops a high-accuracy multiple pedestrian tracking algorithm which is vital for intelligent transportation. Here, we use the off-the-shelf detectors and explore the benefits of modeling pedestrian interactions, such as the interaction of two pedestrians simultaneously matched to two pedestrians in another frame, for robust detection association. Explicitly studying interactions is nontrivial. Previous works often manually selected interacting detections (or “tracklets”) to simplify the association process. In this article, we propose a novel association method based on deep graph convolutional affinity networks (DGCANs) and extend detection-level interactions to the association-level, which treats a potential association of a detection pair as a node in the graph, and explicitly modeling the interactions among potential associations. Specifically, with the novel node, two corresponding edges are readily designed to model the compatible and colliding interactions between related associations. Our proposed method, by redefining nodes and edges, enables us to blend sufficient interaction cues from appearance and motion and learns a robust affinity measure in an end-to-end fashion. Using the Hungarian algorithm as an online tracker, our method archives state-of-the-art performance on benchmark data sets 2-D MOT15, MOT16, and MOT17.

Multiscale Network Traffic Prediction Method Based on Deep Echo-State Network for Internet of Things

As a typical Internet of Things application, network traffic prediction (NTP) plays a decisive role in congestion control, resource allocation, and anomaly detection. The trend of network traffic is different at different scales, so multiscale is an important characteristic of network traffic. In addition, the network traffic is nonlinear on each scale and dependent between scales. The existing NTP methods cannot comprehensively consider these characteristics, which limits their performance. In view of the characteristics of network traffic, such as multiscale, nonlinearity, and scale dependence, this article proposes a new multiscale NTP method based on a deep echo-state network (ESN). First, a multiscale parallel layered structure based on deep ESN is designed to fully consider the influence of each scale on the prediction result and then reduce the prediction error. Second, a feature extraction algorithm is proposed to improve the nonlinear approximation ability by extracting more abundant dynamic features with multiple reservoirs. Third, an NTP model based on scale dependence is proposed to reduce the influence from partial scale missing and then improve the prediction accuracy. Finally, simulation results demonstrate that compared with the state-of-the-art NTP methods, the proposed method significantly improves the prediction performance of network traffic with a slight increase in running time.

A Survey on Attribute-Based Encryption Schemes Suitable for the Internet of Things

The Internet of Things (IoT) is an information service paradigm based on the integration of smart objects, mobile devices, and computers via the Internet. IoT technologies are key enablers for a multitude of applications in diverse fields, such as digital health, smart city, industrial automation, and supply chain. This raises new security and privacy challenges that can be addressed by advanced cryptographic methods. One of the most prominent is attribute-based encryption (ABE), which allows one to encrypt data while enforcing fine-grained access control on it. ABE is advantageous in many IoT applications since it allows data to be safely stored on untrusted storage, such as third-party cloud servers, hackable publish-subscribe brokers, physically accessible sensors, etc. This article surveys the ABE literature proposing schemes and solutions that are best suited for IoT applications. To do so, it first identifies three performance indicators that are key in IoT, namely, the data producer CPU efficiency, the data producer bandwidth efficiency, and the key authority bandwidth efficiency. Then, it analyzes only those schemes that are promising from the point of view of one or more indicators and, therefore, more applicable in typical IoT applications. As a further contribution, this article selects a subset of representative schemes and assesses their efficiency by thorough simulations. Such simulations show that no scheme excels in all three performance indicators at once, but some simultaneously perform well in two indicators.

RISC32-LP: Low-Power FPGA-Based IoT Sensor Nodes With Energy Reduction Program Analyzer

Field-programmable gate array (FPGA)-based sensor nodes are popular for their flexible design approach and field reconfigurability. RISC32 is one of the recent Internet of Things (IoT) processors proposed for the development of FPGA-based sensor nodes, and it includes the ability to reconfigure the microarchitecture on the fly in order to reduce dynamic energy consumption. However, such a method does not minimize static energy consumption, which is important in FPGA-based systems. In this work, clock gating (CG) and dynamic voltage and frequency scaling (DVFS) are applied to further reduce the energy consumption in RISC32. In the research presented here, we implemented a new software called the energy reduction program analyzer to estimate the parameters that configure a sensor node to achieve minimum energy consumption, targeting the typical IoT application scenario. Experimental results show that the low-power techniques applied in this work (RISC32-LP) can reduce energy consumption by 47%, compared to the standard RISC32 processor.

A Survey on the Bottleneck Between Applications Exploding and User Requirements in IoT

The rapid growth of the Internet of Things (IoT) and the increasing number of connected devices have propelled the proliferation of offered applications, causing “applications exploding.” In the context of IoT, filtering and selecting the most relevant applications in a given situation is a challenging task. Thus, developing techniques that can alleviate applications exploding and meet users’ requirements is highly demanded for IoT development. This survey focuses on applications exploding in the IoT and reviews some of the existing techniques, such as intelligent sensing, content distribution network, microservices, and 5G, which help mitigate the effects of applications exploding. Furthermore, the survey discusses how to describe user requirements and offer application services to better match the two. In addition, this survey presents the smart home as an instance of typical IoT applications and explores how adaptive users’ requirements for food ordering can be better met when various food provider applications are available for choice. Finally, partially resolved and unresolved bottlenecks brought by applications exploding are put forward to be further researched by the technical and scientific community.

EdgeLSTM: Towards Deep and Sequential Edge Computing for IoT Applications

The time series data generated by massive sensors in Internet of Things (IoT) is extremely dynamic, heterogeneous, large scale and time-dependent. It poses great challenges (e.g. accuracy, reliability, stability) on the real-time analysis and decision making for different IoT applications. In this paper, we design, implement and evaluate EdgeLSTM, a unified data-driven system to enhance IoT computing at the network edge. The EdgeLSTM leverages the grid long short-term memory (Grid LSTM) to provide an agile solution for both deep and sequential computation, therefore can address important features such as large-scale, variety, time dependency and real time in IoT data. Our system exploits the advantages of Grid LSTM network and extends it with a multiclass support vector machine by rigorous regularization and optimization approaches, which not only has strong prediction capability of time series data, but also achieves fine-grained multiple classification through the predictive error. We deploy the EdgeLSTM into four IoT applications, including data prediction, anomaly detection, network maintenance and mobility management by extensive experiments. Our evaluation results of real-world time series data with different short-term and long-term time dependency from these typical IoT applications show that our EdgeLSTM system can guarantee robust performance in IoT computing.

IoT Botnet Detection Using Salp Swarm and Ant Lion Hybrid Optimization Model

In the last decade, the devices and appliances utilizing the Internet of Things (IoT) have expanded tremendously, which has led to revolutionary developments in the network industry. Smart homes and cities, wearable devices, traffic monitoring, health systems, and energy savings are typical IoT applications. The diversity in IoT standards, protocols, and computational resources makes them vulnerable to security attackers. Botnets are challenging security threats in IoT devices that cause severe Distributed Denial of Service (DDoS) attacks. Intrusion detection systems (IDS) are necessary for safeguarding Internet-connected frameworks and enhancing insufficient traditional security countermeasures, including authentication and encryption techniques. This paper proposes a wrapper feature selection model (SSA–ALO) by hybridizing the salp swarm algorithm (SSA) and ant lion optimization (ALO). The new model can be integrated with IDS components to handle the high-dimensional space problem and detect IoT attacks with superior efficiency. The experiments were performed using the N-BaIoT benchmark dataset, which was downloaded from the UCI repository. This dataset consists of nine datasets that represent real IoT traffic. The experimental results reveal the outperformance of SSA–ALO compared to existing related approaches using the following evaluation measures: TPR (true positive rate), FPR (false positive rate), G-mean, processing time, and convergence curves. Therefore, the proposed SSA–ALO model can serve IoT applications by detecting intrusions with high true positive rates that reach 99.9% and with a minimal delay even in imbalanced intrusion families.

Application-Oriented Traffic Modeling of WiFi-Based Internet of Things Gateways

Many Internet of Things (IoT) devices generate relatively small-sized data and have limited energy supply. These two factors limit their ability to connect directly to cloud servers through a wireless backbone network without imposing a burden on this network in providing efficient data transfer. In this article, we consider an IoT network architecture where a number of different IoT devices send their data wirelessly to an IoT gateway (or a fog node) via a WiFi network. We focus on characterizing incoming traffic patterns to the gateway for three typical IoT applications with real-time and nonreal-time data transfer requirements, such as video surveillance, smart city, and e-healthcare. Our study is based on generating real IoT traffic traces in a lab environment from various sensors and devices for the aforementioned applications and employing these traces to emulate a network of IoT nodes connected to a gateway via WiFi. In the conducted experiments, different homogenous and nonhomogeneous traffic patterns of the selected applications are examined for synchronized and unsynchronized data sources. Based on our empirical data, the experimental results reveal that the packet interarrival time distribution at the gateway is close to generalized Pareto distribution for homogeneous eHealth and smart city traffic, whereas the Weibull distribution is the nearest to model the empirical packet interarrival time for the rest of the examined traffic patterns. Moreover, we show that employing the experimental findings to analyze the delay performance of connecting the gateway to the cloud, given certain backbone network resources, leads to accurate results.

IoT Workload Emulation for Data Centers.

The Internet of Things (IoT) domain has been one of the fastest growing areas in the computer industry for the last few years. Consequently, IoT applications are becoming the dominant work load for many data centers. This has implications for the designers of data centers, as they need to meet their customers' requirements. Since it is not easy to use real applications for the design and test of data center setups, a tool is required to emulate real applications but is easy to configure, scale and deploy in a data center. This paper will introduce a simple but generic way to model the work load of typical IoT applications, in order to have a realistic and reproducible way to emulate IT loads for data centers. IoT application designers are in the process of harmonizing their approaches on how architectures should look, which building blocks are needed, and how they should interwork. While all IoT subdomains are diverse when it comes to the details, the architectural blueprints are becoming more and more aligned. These blueprints are called reference architectures and incorporate similar patterns for the underlying application primitives. This paper will introduce an approach to decompose IoT applications into such application primitives, and use them to emulate a workload as it would be created by the modeled application. The paper concludes with an example application of the IoT Workload Emulation in the BodenTypeDC experiment, where new cooling approaches for data centers have been tested under realistic work load conditions.

DR-TSP: A Data Repairing Framework for Time Synchronization Problems in ERI Data

Timestamps are often problematic in Internet-of-Things (IoT) systems due to time synchronization problems of distributed radio-frequency identification (RFID) readers or sensors. This issue may seriously affect the data quality in some fields, such as transportation. A typical IoT application in transportation is electronic registration identification of the motor vehicle (ERI), an emerging traffic data acquisition technology based on RFID. ERI data play a vital role in intelligent transportation. However, the data quality is often affected seriously by the inaccurate timestamps, which arise from the time-unsynchronized distributed ERI readers. To solve this issue, we propose a novel framework, data repairing of time synchronization problems (DR-TSP), which can detect the time-unsynchronized ERI readers and correct timestamp-deviated ERI data. Precisely, DR-TSP consists of three components. Problem reader discovery component employs a statistics-based method to detect the time-unsynchronized ERI readers and discovers the clock leaps of the problematic ERI reader through a smoothing-based method. Travel-time estimation component constructs a spatial correlative travel-time estimation model based on the neural network to infer timestamp deviation. The influence of clock deviation is considered in the model training. Data correction component utilizes the above results to correct the timestamp-deviated data. Experiments over large-scale ERI data collected from a big China city, Chongqing, show that our method can significantly improve data quality.

Group-based delivery of critical traffic in cellular IoT networks

Fifth generation (5G) networks are expected to connect a huge number of Internet of Things (IoT) devices in many usage scenarios. The challenges of typical massive IoT applications with sporadic and short packet uplink transmissions are well studied, while not enough attention is given to the delivery of content of common interest, such as software/firmware updates and remote control, towards IoT devices in emerging point-to-multipoint scenarios. Moreover, the delivery of delay-sensitive IoT traffic is not sufficiently addressed in the literature. In this work we (i) identify the drawbacks of the current Single-Cell Point-to-Multipoint (SC-PTM) solution for unplanned critical traffic delivery in cellular IoT (cIoT) networks, and (ii) propose paging and multicast schemes for a fast distribution of critical updates after, e.g., bug fixes or system failures. We benchmark the performance of the proposed paging scheme against similar solutions available in the literature. Our extended SC-PTM framework is energy efficient and guarantees low service latency, as demonstrated both analytically and by simulations.

Lattice-based double-authentication-preventing ring signature for security and privacy in vehicular Ad-Hoc networks

Amidst the rapid development of the Internet of Things (IoT), Vehicular Ad-Hoc NETwork (VANET), a typical IoT application, are bringing an ever-larger number of intelligent and convenient services to the daily lives of individuals. However, there remain challenges for VANETs in preserving privacy and security. In this paper, we propose the first lattice-based Double-Authentication-Preventing Ring Signature (DAPRS) and adopt it to propose a novel privacy-preserving authentication scheme for VANETs, offering the potential for security against quantum computers. The new construction is proven secure against chosen message attacks. Our scheme is more efficient than other ring signature in terms of the time cost of the message signing phase and verification phase, and also in terms of signature length. Analyses of security and efficiency demonstrate that our proposed scheme is provably secure and efficient in the application.

Octopus: Context-Aware CNN Inference for IoT Applications

Modern convolutional neural networks (CNNs) in computer vision are trained on a large number of images from numerous categories to form rich discriminative feature extractors. Inference using such models on resource-constrained Internet-of-Things (IoT) platforms poses a challenge and an opportunity. Having limited computation, storage, and energy budgets, most IoT platforms are not capable of hosting such compute intensive models. However, typical IoT applications demand detection of a relatively small number of categories, albeit the specific categories of interest may change at runtime as the context evolves dynamically. In this letter, we take advantage of the opportunity to address the challenge. Specifically, we develop a novel transformation to the architecture of a given CNN, so that the majority of the inference workload is allocated to class-specific disjoint branches, which can be dynamically executed or skipped, based on the context, to fulfill the application requirements. Experiments demonstrate that our approach preserves the classification accuracy for the classes of interest, while proportionally decreasing the model complexity and inference workload.

Joint Task Assignment, Transmission, and Computing Resource Allocation in Multilayer Mobile Edge Computing Systems

In this paper, we propose a multilayer data flow processing system, i.e., EdgeFlow, to integrally utilize the computing capacity throughout the whole network, i.e., the cloud center (CC) on the top layer, the mobile edge computing (MEC) servers on the middle layer, and the edge devices (EDs) on the bottom layer. To realize the efficient data processing in EdgeFlow, we optimally assign the tasks to multiple layers, and allocate the wireless transmission resources between the MEC servers and EDs as well as the wired transmission resources between the CC and MEC servers. We prove that the system is naturally classified into two states, the nonblocking state and the blocking state, according to various data generation speed at the EDs. The system latency is minimized for the nonblocking state even though the problem is nonconvex. As for the blocking state, the recovery time is minimized through solving a min-max problem. Based on the analytical results, the EdgeFlow system is implemented on the universal software radio peripheral and the Intel next units of computing. A typical Internet of Things application, photo recording and face recognition, is used for the simulation and the experiment, and indicates that the EdgeFlow can achieve a low latency and recovery time than the previous distributed frameworks, e.g., the Cloudlet and the Markov decision process.

Is DNS Ready for Ubiquitous Internet of Things?

The vision of the Internet of Things (IoT) covers not only the well-regulated processes of specific applications in different areas but also includes ubiquitous connectivity of more generic objects (or things and devices) in the physical world and the related information in the virtual world. For example, a typical IoT application, such as a smart city, includes smarter urban transport networks, upgraded water supply, and waste-disposal facilities, along with more efficient ways to light and heat buildings. For smart city applications and others, we require unique naming of every object and a secure, scalable, and efficient name resolution which can provide access to any object's inherent attributes with its name. Based on different motivations, many naming principles and name resolution schemes have been proposed. Some of them are based on the well-known domain name system (DNS), which is the most important infrastructure in the current Internet, while others are based on novel designing principles to evolve the Internet. Although the DNS is evolving in its functionality and performance, it was not originally designed for the IoT applications. Then, a fundamental question that arises is: can current DNS adequately provide the name service support for IoT in the future? To address this question, we analyze the strengths and challenges of DNS when it is used to support ubiquitous IoT. First, we analyze the requirements of the IoT name service by using five characteristics, namely security, mobility, infrastructure independence, localization, and efficiency, which we collectively refer to as SMILE. Then, we discuss the pros and cons of the DNS in satisfying SMILE in the context of the future evolution of the IoT environment.

BLE Neighbor Discovery Parameter Configuration for IoT Applications

Since the Internet of Things (IoT) applications today employ many different sensors to provide information, a large number of the Bluetooth low energy (BLE) devices will be developed as part of the IoT systems. The low-power and low-cost requirements of all BLE nodes are among the most challenging issues when supporting the neighbor discovery process (NDP) for such a large number of devices. Since the parameter settings are essential to achieve the required performance for the NDP, a parameter configuration method for neighbor discovery in BLE can be beneficial for determining some critical parameter metrics, such as the AdvInterval, ScanInterval, and ScanWindow. In this paper, we propose a parameter configuration scheme to balance the tradeoff between discovery latency and energy consumption. In the proposed scheme, the neighbor discovery latency and average energy consumption are expressed based on the Chinese Remainder Theory (CRT). With the expected primary performance, the parameters are configured accordingly using the parameter configuration algorithm. The experimental results show that the performance of the neighbor discovery varies with the parameter settings. Furthermore, two typical IoT applications are assessed in this paper. Compared with the simulation results, the proposed parameter configuration scheme can achieve high accuracy.

Automata-Based Generic Model for Interoperating Context-Aware Ad-Hoc Devices in Internet of Things

Internet of Things (IoT) applications can be seen as network-controlled systems with a distributed architecture connecting many smart objects through the Internet. A typical IoT application aims to connect a set of nodes, capable of processing data and making decisions regarding the goal of the IoT application, with other nodes containing smart objects with robotic components to collect data from their environment. In this paper, a generic model for novel context-aware and adaptive IoT architecture is presented. A group of nodes contains a controller interface (CI) that is used to control other nodes containing a controller unit (CU) which communicates randomly. CU nodes have complex control systems comprising both actuators and sensors, however CI nodes typically do not have detailed knowledge of such individual functionalities. Instead, at run time, control program logic (CPL) is downloaded by the CI to facilitate control and communication. The system also allows for different CPLs for any particular CU. In such an open and adaptive IoT environment, an architecture is required that can provide sufficient flexibility in designing nodes while maintaining uniformity in the IoT system to allow for interoperability and reliability. Using the notion of automata, a generalized mathematical description is presented that allows modeling of the CI and CU nodes and their operation. This model can then be used to formulate and solve problems regarding the interaction between CIs and CUs. The application of this model is demonstrated through a case study involving remote access laboratories where the CI–CU model is used.