Internet Of Things Communication Research Articles

RIS-Assisted Ambient Backscatter Communication for SAGIN IoT

The space, air and ground integrated network (SAGIN) will greatly promote the development of the Internet of Things (IoT). Green IoT will be an important part of SAGIN. Ambient backscatter communication (AmBC) is a potential solution for green SAGIN IoT. To improve the achievable sum rate (ASR) of the AmBC system, we propose a reconfigurable intelligent surface (RIS) assisted AmBC system. In the single-BD AmBC scenario, we firstly give the phase shifts that maximize the gain of the reflection link of the AmBC system, and then give the optimal reflection coefficient. The proposed scheme does not need to solve the convex semidefinite program (SDP) problem, and has the characteristics of low computational complexity. In the multi-BD AmBC scenario, we first propose a multi-BD phase shifts initialization strategy to ensure the stability of the proposed scheme. Then, we give the optimal reflection coefficient and phase shifts based on iterative method. Simulations show that the RIS-assisted AmBC scheme is superior to the non-RIS-assisted AmBC scheme.

Mobile Communication Among COTS IoT Devices via a Resonant Gyroscope With Ultrasound

Incompatible protocols and electromagnetic interference obstruct the realization of an everything-connected Internet of Things (IoT) communication network. Our system, Deaf-Aid, utilizes a stealthy speaker-to-gyroscope channel to build robust communication. Compared with existing solutions adopting physical covert channels, Deaf-Aid is free from the limitations of manual receiver distinction, additional hardware, conditional placement, or physical contact. It exploits ultrasounds to force gyroscopes embedded in receivers to resonate, so as to convey information. We investigate the relationship among axes in a gyroscope to deal with frequency offset and support multi-channel communication. Meanwhile, receivers are identified automatically via device fingerprints consisting of diversity of gyroscopes’ resonant frequency ranges. Furthermore, we enable Deaf-Aid the capability of mobile communication, which is an essential demand for IoT devices. We address the challenge of recovering accurate signals from motion interference. Extensive evaluations, including that on the commercial off-the-shelf devices, demonstrate that Deaf-Aid yields 47 bps with BER below <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1\%$</tex-math> </inline-formula> . To our best knowledge, Deaf-Aid is the first work to enable stealthy mobile IoT communication based on inertial sensors.

College English teaching reform and innovative methods under the new media platform based on the IoT

Hybrid teaching has emerged along with the growth of web technology. Teaching activities are combined with online and offline teaching. Some are online teaching and some are offline teaching. In addition to completing classroom teaching, teachers also need to help students answer questions and learn interactively online. The implementation of these links needs to be reflected in the teacher’s teaching evaluation. The article adopted the analytic hierarchy process, which enabled teachers (managers) to more objectively select and determine the evaluation factors, and decompose them at different levels to make quantitative evaluation, so as to provide appropriate information for teachers (managers) and reduce decision-making errors. In this paper, the effectiveness of the new media English teaching platform in colleges and universities in mainland China was evaluated by the method of final test, and all students signed the informed consent. According to different organizational structures, the new media English teaching platform in universities can be divided into user layer, terminal layer, application layer, platform layer and network layer. Each layer is connected with each other, which organizes all components of the new media English teaching platform of the whole university. The network layer is an essential part of college new media English teaching, including the Internet, Internet of Things (IoT), campus network, education network, etc. Through the Internet, educational network and campus network, various educational institutions and institutions were connected, and information exchange was carried out through the university’s new media English platform. The emergence of the IoT and mobile communication networks has greatly improved the network coverage and the utilization rate of the network. The average students' test score using the proposed platform (80.6) is higher than the average test score without the platform (69.1).The new media teaching platform proposed in the article has enabled teachers to effectively complete the teaching content and effectively enhance the teaching quality.

Low Earth Orbit Satellite Communications for Internet-of-Things Applications

Internet of Things (IoT) is a new direction for future internet, where anything embedded with sensors, software, and transceiver can access the Internet. The applications of IoT technology include home automation, smart city, smart grid, smart industry, E-health, environmental monitoring, and geologic disaster forecasting, to name a few. As the demand for high data rate increases due to the huge number of IoT devices, the so-called Low Power Wide Area Networks (LPWAN) have emerged. These LPWAN are designed to cover wide terrestrial areas. Nevertheless, in open areas such deserts, forests and coastal waters of seas, rivers and oceans, there are cost and practical limitation in constructing LPWAN. Therefore, to enable LPWAN in such remote areas, Low Earth Orbit (LEO) satellite communications for IoT have recently attracted considerable interest from researchers. Among the open research issues are interference mitigation, Doppler spread estimation, LEO satellite constellation structure, efficient spectrum allocation, and access and routing protocols. In particular, as the satellite IoT system shares the same frequency band with the existing terrestrial IoT networks and other wireless systems, this results in multiple access interference issue. In addition, the high speed motion of LEO satellites induce a Doppler shift, resulting in inter-carrier interference problem.

Low Earth Orbit Satellite Communications for Internet-of-Things Applications

Internet of Things (IoT) is a new direction for future internet, where anything embedded with sensors, software, and transceiver can access the Internet. The applications of IoT technology include home automation, smart city, smart grid, smart industry, E-health, environmental monitoring, and geologic disaster forecasting, to name a few. As the demand for high data rate increases due to the huge number of IoT devices, the so-called Low Power Wide Area Networks (LPWAN) have emerged. These LPWAN are designed to cover wide terrestrial areas. Nevertheless, in open areas such deserts, forests and coastal waters of seas, rivers and oceans, there are cost and practical limitation in constructing LPWAN. Therefore, to enable LPWAN in such remote areas, Low Earth Orbit (LEO) satellite communications for IoT have recently attracted considerable interest from researchers. Among the open research issues are interference mitigation, Doppler spread estimation, LEO satellite constellation structure, efficient spectrum allocation, and access and routing protocols. In particular, as the satellite IoT system shares the same frequency band with the existing terrestrial IoT networks and other wireless systems, this results in multiple access interference issue. In addition, the high speed motion of LEO satellites induce a Doppler shift, resulting in inter-carrier interference problem.

Low Earth Orbit Satellite Communications for Internet-of-Things Applications

Internet of Things (IoT) is a new direction for future internet, where anything embedded with sensors, software, and transceiver can access the Internet. The applications of IoT technology include home automation, smart city, smart grid, smart industry, E-health, environmental monitoring, and geologic disaster forecasting, to name a few. As the demand for high data rate increases due to the huge number of IoT devices, the so-called Low Power Wide Area Networks (LPWAN) have emerged. These LPWAN are designed to cover wide terrestrial areas. Nevertheless, in open areas such deserts, forests and coastal waters of seas, rivers and oceans, there are cost and practical limitation in constructing LPWAN. Therefore, to enable LPWAN in such remote areas, Low Earth Orbit (LEO) satellite communications for IoT have recently attracted considerable interest from researchers. Among the open research issues are interference mitigation, Doppler spread estimation, LEO satellite constellation structure, efficient spectrum allocation, and access and routing protocols. In particular, as the satellite IoT system shares the same frequency band with the existing terrestrial IoT networks and other wireless systems, this results in multiple access interference issue. In addition, the high speed motion of LEO satellites induce a Doppler shift, resulting in inter-carrier interference problem.

Joint Active and Passive Beamforming Design for RIS-Aided IBFD IoT Communications: QoS and Power Efficiency Considerations

In a reconfigurable intelligent surface (RIS) aided communication network, a RIS equipped with multiple passive reflecting elements assists in extending the coverage of wireless networks by overcoming the effects of non-line-of-sight propagation. Accordingly, to present the case for utilizing RIS panels in future consumer internet of things (IoT) networks, in this paper, we focus on a RIS-aided IoT network operating in in-band full-duplex (IBFD) mode. Since all the IoT node pairs communicate simultaneously through individual IBFD links at the same time-frequency resources, the nodes suffer from strong self-interference (SI) and inter-node interference. Consequently, in this paper, we investigate a joint active and passive beamforming design at each IoT node and the RIS, respectively, by minimizing the total transmission power of the network, subject to a minimum rate constraint for each IoT node and a constraint that guarantees a unit modulus phase shift at the RIS. As the original joint active and passive beamforming design problem is non-convex and intractable, we decompose the problem into two sub-problems. For a given passive beamformer design, we solve the original problem to obtain the optimal beamforming vectors at each IoT node. Next, for the obtained active beamformer, we propose two algorithms based on i) gradient descent (GD) search and ii) Riemannian conjugate gradient (RCG) to obtain the optimal passive beamformer at the RIS. Through extensive numerical analysis, we verify the effectiveness of the proposed algorithms and also show the effects of using RIS and transmitting in IBFD mode on the average rate and power of the considered consumer IoT network. We investigate the impact of several system parameters such as RIS elements, node pairs, level of hardware impairments at the transmitter and receiver, etc. We also highlight the trade-off between performance improvement and complexity due to elements used at RIS.

Quantum Genetic Algorithm for the Design of Miniaturized and Reconfigurable IoT Antennas

This paper leverages an automated quantum genetic algorithm (QGA) to design miniaturized antennas for Internet of Things (IoT) devices. The QGA implemented in this work automates the design process and enables the antenna designer to generate static or reconfigurable miniaturized microstrip folded antenna topologies. The QGA is also implemented to generate a pixelation pattern within the antenna radiating surface to optimally tune the antenna operation for IoT communication needs as well as for Wi-Fi operation. In this paper, we also present a QGA-optimized folded miniaturized antenna in both static and reconfigurable topologies. The reconfiguration is achieved through a single RF MEMS whose location is optimized through the automated process. The automated design process achieves 87% antenna size reduction at 868 MHz compared to a typical patch antenna operating at the same frequency. As a result, the static and reconfigurable antenna prototypes achieve good radiation efficiencies in comparison to their miniature size with acceptable gains thus satisfying the IoT physical and operational constraints. Both antennas are fabricated and tested, where excellent agreement is noticed between simulated and measured results.

Emergency Communication System Based on Wireless LPWAN and SD-WAN Technologies: A Hybrid Approach

Emergency Communication Systems (ECS) are network-based systems that may enable people to exchange information during crises and physical disasters when basic communication options have collapsed. They may be used to restore communication in off-grid areas or even when normal telecommunication networks have failed. These systems may use technologies such as Low-Power Wide-Area(LPWAN) and Software-Defined Wide Area Networks (SD-WAN), which can be specialized as software applications and Internet of Things (IoT) platforms. In this article, we present a comprehensive discussion of the existing ECS use cases and current research directions regarding the use of unconventional and hybrid methods for establishing communication between a specific site and the outside world. The ECS system proposed and simulated in this article consists of an autonomous wireless 4G/LTE base station and a LoRa network utilizing a hybrid IoT communication platform combining LPWAN and SD-WAN technologies. The LoRa-based wireless network was simulated using Network Simulator 3 (NS3), referring basically to firm and sufficient data transfer between an appropriate gateway and LP-WAN sensor nodes to provide trustworthy communications. The proposed scheme provided efficient data transfer posing low data losses by optimizing the installation of the gateway within the premises, while the SD-WAN scheme that was simulated using the MATLAB simulator and LTE Toolbox in conjunction with an ADALM PLUTO SDR device proved to be an outstanding alternative communication solution as well. Its performance was measured after recombining all received data blocks, leading to a beneficial proposal to researchers and practitioners regarding the benefits of using an on-premises IoT communication platform.

Hybrid Centralized Peer to Peer Architecture for Resource Discovery and Secure Communication in Internet of Things

The Internet of Things (IoT) made communication between people and objects easy. It helps to build smart cities, homes, manufacturing systems, health monitoring systems, etc., for mankind. The increased adoption of IoT applications enabled many smart devices on the Internet platform. These devices deployed across the globe may have varying computational and communication capabilities. It is a great challenge to manage IoT resources efficiently. Some of well-known protocols are defined to identify and access IoT resources locally in a real-world environment. Many authors have adopted the Distributed Hash Table (D.H.T.) based Peer to Peer (P2P) model for global and massive resource management. However, D.H.T. based solutions have many shortcomings and are not perfectly suitable for the IoT domain. In this paper, it has been proposed a novel Hybrid Centralized Peer to Peer (HCP2P) architecture for efficient resource discovery and access mechanism. The proposed solution builds a secure communication channel among trusted peer devices with the aid of an HCP2P server. The trusted devices can discover and access the required resource efficiently and securely with reduced load on the central server. The proposed HCP2P solutions are evaluated on both hardware prototypes and simulations. The proposed model gives almost constant resource registration, discovery, and access time. This evaluation showed that HCP2P architecture performance is superior to traditional DHT-based P2P architecture. Finally, the performance parameters of the proposed scheme are evaluated in terms of resource registration time, discovery time, and hop-count.

IoT-Enabled Gait Assessment: The Next Step for Habitual Monitoring.

Walking/gait quality is a useful clinical tool to assess general health and is now broadly described as the sixth vital sign. This has been mediated by advances in sensing technology, including instrumented walkways and three-dimensional motion capture. However, it is wearable technology innovation that has spawned the highest growth in instrumented gait assessment due to the capabilities for monitoring within and beyond the laboratory. Specifically, instrumented gait assessment with wearable inertial measurement units (IMUs) has provided more readily deployable devices for use in any environment. Contemporary IMU-based gait assessment research has shown evidence of the robust quantifying of important clinical gait outcomes in, e.g., neurological disorders to gather more insightful habitual data in the home and community, given the relatively low cost and portability of IMUs. The aim of this narrative review is to describe the ongoing research regarding the need to move gait assessment out of bespoke settings into habitual environments and to consider the shortcomings and inefficiencies that are common within the field. Accordingly, we broadly explore how the Internet of Things (IoT) could better enable routine gait assessment beyond bespoke settings. As IMU-based wearables and algorithms mature in their corroboration with alternate technologies, such as computer vision, edge computing, and pose estimation, the role of IoT communication will enable new opportunities for remote gait assessment.

DIdM-EIoTD: Distributed Identity Management for Edge Internet of Things (IoT) Devices.

The Internet of Things (IoT) paradigm aims to enhance human society and living standards with the vast deployment of smart and autonomous devices, which requires seamless collaboration. The number of connected devices increases daily, introducing identity management requirements for edge IoT devices. Due to IoT devices' heterogeneity and resource-constrained configuration, traditional identity management systems are not feasible. As a result, identity management for IoT devices is still an open issue. Distributed Ledger Technology (DLT) and blockchain-based security solutions are becoming popular in different application domains. This paper presents a novel DLT-based distributed identity management architecture for edge IoT devices. The model can be adapted with any IoT solution for secure and trustworthy communication between devices. We have comprehensively reviewed popular consensus mechanisms used in DLT implementations and their connection to IoT research, specifically identity management for Edge IoT devices. Our proposed location-based identity management model is generic, distributed, and decentralized. The proposed model is verified using the Scyther formal verification tool for security performance measurement. SPIN model checker is employed for different state verification of our proposed model. The open-source simulation tool FobSim is used for fog and edge/user layer DTL deployment performance analysis. The results and discussion section represents how our proposed decentralized identity management solution should enhance user data privacy and secure and trustworthy communication in IoT.

Internet of Things Communication, Networking, and Security: A Survey

Internet of Things Communication, Networking, and Security: A Survey

Energy-Efficient Computation Offloading With DVFS Using Deep Reinforcement Learning for Time-Critical IoT Applications in Edge Computing

Internet of Things (IoT) is a technology that allows ordinary physical devices to collect, process, and share data with other physical devices and systems over the internet. It provides pervasively connected infrastructures to support innovative applications and services that can automate otherwise intensely laborious manual effort. Edge computing (EC) complements the powerful centralized cloud servers by providing powerful computation capability close to the data source, minimizing communication latency, and securing data privacy. The energy consumption problem has continued to receive much attention from the IoT community in applying various techniques to reduce energy consumption while still meeting the computational demand. In this paper, we propose an application-deadline-aware data offloading scheme using deep reinforcement learning and Dynamic Voltage and Frequency Scaling (DVFS) in an edge computing environment to reduce the energy consumption of IoT devices. The proposed scheme learns the optimal data distribution policies and local computation DVFS frequency scaling by interacting with the system environment and learning the behavior of the device, network, and edge servers. The proposed scheme was tested on multiple edge computing environments with different IoT devices. Experimental results show that this scheme can reduce energy consumption while achieving the IoT application and services timing and computational goals. The proposed scheme has substantial energy savings when compared with the native Linux governors.

Internet of Things: A Comprehensive Overview on Protocols, Architectures, Technologies, Simulation Tools, and Future Directions

The Internet of Things (IoT) is a global network of interconnected computing, sensing, and networking devices that can exchange data and information via various network protocols. It can connect numerous smart devices thanks to recent advances in wired, wireless, and hybrid technologies. Lightweight IoT protocols can compensate for IoT devices with restricted hardware characteristics in terms of storage, Central Processing Unit (CPU), energy, etc. Hence, it is critical to identify the optimal communication protocol for system architects. This necessitates an evaluation of next-generation networks with improved characteristics for connectivity. This paper highlights significant wireless and wired IoT technologies and their applications, offering a new categorization for conventional IoT network protocols. It provides an in-depth analysis of IoT communication protocols with detailed technical information about their stacks, limitations, and applications. The study further compares industrial IoT-compliant devices and software simulation tools. Finally, the study provides a summary of the current challenges, along with a broad overview of the future directions to tackle the challenges, in the next IoT generation. This study aims to provide a comprehensive primer on IoT concepts, protocols, and future insights that academics and professionals can use in various contexts.

Resource Allocation for Cognitive LEO Satellite Systems: Facilitating IoT Communications.

Due to the characteristics of global coverage, on-demand access, and large capacity, the low earth orbit (LEO) satellite communication (SatCom) has become one promising technology to support the Internet-of-Things (IoT). However, due to the scarcity of satellite spectrum and the high cost of designing satellites, it is difficult to launch a dedicated satellite for IoT communications. To facilitate IoT communications over LEO SatCom, in this paper, we propose the cognitive LEO satellite system, where the IoT users act as the secondary user to access the legacy LEO satellites and cognitively use the spectrum of the legacy LEO users. Due to the flexibility of code division multiple access (CDMA) in multiple access and the wide use of CDMA in LEO SatCom, we apply CDMA to support cognitive satellite IoT communications. For the cognitive LEO satellite system, we are interested in the achievable rate analysis and resource allocation. Specifically, considering the randomness of spreading codes, we use the random matrix theory to analyze the asymptotic signal-to-interference-plus-noise ratios (SINRs) and accordingly obtain the achievable rates for both legacy and IoT systems. The power of the legacy and IoT transmissions at the receiver are jointly allocated to maximize the sum rate of the IoT transmission subject to the legacy satellite system performance requirement and the maximum received power constraints. We prove that the sum rate of the IoT users is quasi-concave over the satellite terminal receive power, based on which the optimal receive powers for these two systems are derived. Finally, the resource allocation scheme proposed in this paper has been verified by extensive simulations.

Securing the Internet of Things-enabled smart city infrastructure using a hybrid framework

Since the Internet of Things (IoT) employs a diverse range of new technologies, it is impossible to build a single recommended design adopted as a master plan for all possible requests. Some possible IoT application areas have not been looked into yet or do not have enough information on how to approach them. This shows that more research needs to be done in this difficult area to find new and potentially big benefits for society. Although smart cities offer residents and providers of capital several advantages, there are numerous ways that breaches could compromise the safety and security of individuals. As a result, several different recommendation designs can coexist in the IoT. This research examines the effects of ethics and technology on the security of IoT-enabled systems in smart city infrastructure. Hence provides a secure IoT network architecture for smart cities combining blockchain and deep learning to safeguard privacy and credibility. This research presents a Secure Smart City Infrastructure using Blockchain and Deep Learning (SSCI-BDL) framework to ensure privacy protection and trustworthiness among IoT communication in smart cities. This framework involves the blockchain network for security management in the smart city infrastructure. This framework integrates the deep learning model with an optimization algorithm that maintains efficient resource utilization in the smart city infrastructure. The simulation results show that the system has high security of 99.5% and the lowest latency rate of 4.1% compared to existing models. Overall, the proposed framework’s efficiency gives the highest rate of 99.8%.

A Wideband Dual-Circularly Polarized, Simultaneous Transmit and Receive (STAR) Antenna Array for Integrated Sensing and Communication in IoT

This article presents a novel 5.8-GHz ISM-band dual-circularly polarized (CP) simultaneous transmit and receive (STAR) common-aperture antenna array (CAAA) for integrated sensing and communication (ISAC) in Internet of Things (IoT). A new near-field cancellation technique based on differential feeding and geometrical symmetry is proposed for dual-CP to realize high TX/RX isolation. In addition, a high-impedance surface is introduced to further improve the TX/RX isolation. Furthermore, the parasitic patches are added to enhance the gain and isolation bandwidth (BW). The simulation results demonstrate that the proposed CAAA possesses the merits of high isolation (up to 67 dB), wide isolation BW (better than 40 dB for 240 MHz BW), high gains (up to 10/10.5 dB for right-hand CP (RHCP)/left-hand CP (LHCP)), wide 1-dB gain BWs (better than 230/350 MHz BW for RHCP/LHCP), and low-axial ratios (lower than 1.4 dB). The designed CAAAs are fully characterized and the experimental results meet the simulation very well. For the first time, systematic validations for both wireless communication and radar sensing have been thoroughly carried out by integrating the proposed CAAA in the custom-designed full-duplex radio system. The experimental results not only prove the validity of the design methodology but also the feasibility of employing the proposed CAAA for ISAC, which indicates potential applications in IoT era.

Ring slot CP antenna for the hybrid electromagnetic solar energy harvesting and IoT application

The research proposed a compact antenna integrated with dual-function solar cells to optimize internet of things (IoT) communication devices and energy harvesting. The use of integrated solar cells with electromagnetic elements as antennas and energy-gathering in a design is a solution for wireless communication sensors and simultaneously electromagnetic and solar energy harvesting. Radiation performance testing of solar cell antennas and optical experiments were carried out to test the ability of solar energy collection. A double feed ring slot circular polarization (CP) antenna coplanar antenna with a configuration has highly sensitive to vertical and horizontal waves. It is to accommodate the impact of circular polarization wave and solar cell distortion on antenna performance. This technology promises green communications, renewable energy, and environmental protection to combine wireless communication and green battery components. The study presents the performance and design results of a double line-feed microstrip antenna circuit connected with CP antenna on the S-band for IoT communication by accommodating electromagnetic energy transmission. The importance of adopting an integrated radio frequency (RF) solar energy harvesting strategy rather than a single source method makes this research very significant in its novelty by optimizing the analysis of multiple signal access in IoT communications.

Detecting while accessing: A semi-supervised learning-based approach for malicious traffic detection in Internet of Things

In the upcoming large-scale Internet of Things (IoT), it is increasingly challenging to defend against malicious traffic, due to the heterogeneity of IoT devices and the diversity of IoT communication protocols. In this paper, we propose a semi-supervised learning-based approach to detect malicious traffic at the access side. It overcomes the resource-bottleneck problem of traditional malicious traffic defenders which are deployed at the victim side, and also is free of labeled traffic data in model training. Specifically, we design a coarse-grained behavior model of IoT devices by self-supervised learning with unlabeled traffic data. Then, we fine-tune this model to improve its accuracy in malicious traffic detection by adopting a transfer learning method using a small amount of labeled data. Experimental results show that our method can achieve the accuracy of 99.52% and the F1-score of 99.52% with only 1% of the labeled training data based on the CICDDoS2019 dataset. Moreover, our method outperforms the state-of-the-art supervised learning-based methods in terms of accuracy, precision, recall and F1-score with 1% of the training data.