Internet Of Things Communication System Research Articles

Optimal Capacitance Design for IRS Aided Wireless Power Transfer for Sustainable IoT Communication

ABSTRACTIntelligent reflecting surface (IRS) is a cutting‐edge technique that can significantly improve wireless propagation. It can efficiently utilize wireless power transfer to enable sustainable Internet‐of‐Things (IoT) transmission by reconfiguring the incident signal from the active transmitter. However, the flexibility of capacitance tuning in the IRS system, which controls underlying reflections, is often overlooked. The effective capacitance design in the IRS system can provide a new degree of freedom in the IoT communication system, which can enable additional performance gain in the received power. To achieve this, a novel IRS circuital optimization model is proposed in this work. It incorporates various electrical parameters of the meta‐surface unit cell for improved IoT‐enabled communication. The proposed optimization model provides an optimal capacitance as a function of phase shift (PS), which is controlled by IRS, incident frequency, and other IRS electrical parameters. This optimal capacitance is then used to define the received power. The convexity of the optimization problem is proved, and the global optimal capacitance is obtained for received power maximization. Our simulations show that the proposed optimization model outperforms the existing constructive interference‐based optimal PS method, for which the capacitance is first calculated. Finally, the analytical results are numerically validated with several optimal design insights.

Designing a Secure and Scalable Service Agent for IoT Transmission through Blockchain and MQTT Fusion

With the rapid development of the Internet of Things (IoT) in recent years, many IoT devices use communication systems to transmit data. Data packets are inevitably at risk of tampering during data transmission, which can lead to information errors and damage during communication. Since IoT terminals are often operated under human supervision, it is essential to improve and ensure the transmission of information, avoid data tampering, and ensure the accuracy of packet transmission. This research successfully improves the message transmission method of IoT communication and the communication model by combining it with blockchain architecture. Combining communication protocols with blockchain serial connections eliminates the need to control operating and managing processes. By replacing message transmission with coverage, the reliability of data transmission in the IoT communication system is improved, and the flexibility of the data transmission process is enhanced. Through practical verification through experimentation, the study successfully improved the incapability of effectively constructing a cross-coded message transmission mode in the Message Queuing Telemetry Transport (MQTT) communication protocol, removed a single layer of encryption rule, and created an encryption mode capable of providing complex arrangement organization.

An Intelligent Fault Alert Mechanism for Dynamic IoT Communication Microarchitecture

The usage Internet of Things (IoT) was maximized throughout the entire world. Hence, the different core processors incorporated microarchitecture makes this IoT communication system. However, the rise of faults due to the malicious event and the data overload might maximize energy and power utilization. So, the current study has proposed a novel Chimp-based Domain adaptation Alert System (CbDAAS) for the dynamic IoT communication microarchitecture. Before initiating the communication sharing process, the present fault in the designed IoT dynamic core microarchitecture was predicted, and those cores were removed for the current data broadcasting process. Henceforth, the designed fault alert microarchitecture is tested in the MATLAB platform. The reliability was valued using different metrics like power usage, energy consumption and detection exactness value. Finally, the validated metrics were compared with the associated studies and scored the finest outcome in fault detection score as 98% and less energy usage at 0.025mj.

Strengthening IoT Intrusion Detection through the HOPNET Model

The rapid growth of Internet of Things (IoT) applications has raised concerns about the security of IoT communication systems, particularly due to a surge in malicious attacks leading to network disruptions and system failures. This study introduces a novel solution, the Hyper-Parameter Optimized Progressive Neural Network (HOPNET) model, designed to effectively detect intrusions in IoT communication networks. Validation using the Nsl-Kdd dataset involves meticulous data preprocessing for error rectification and feature extraction across diverse attack categories. Implemented on the Java platform, the HOPNET model undergoes comprehensive evaluation through comparative analysis with established intrusion detection methods. Results demonstrate the superiority of the HOPNET model, with improved attack prediction scores and significantly reduced processing times, highlighting the importance of advanced intrusion detection methods for enhancing IoT communication security. The HOPNET model contributes by establishing robust defense against evolving cyber threats, ensuring a safer IoT ecosystem, and paving the way for proactive security measures as the IoT landscape continues to evolve.

A Comparative Study of Post-Quantum Cryptographic Algorithm Implementations for Secure and Efficient Energy Systems Monitoring

The Internet of Things (IoT) has assumed a pivotal role in the advancement of communication technology and in our daily lives. However, an IoT system such as a smart grid with poorly designed topology and weak security protocols might be vulnerable to cybercrimes. Exploits may arise from sensor data interception en route to the intended consumer within an IoT system. The increasing integration of electronic devices interconnected via the internet has galvanized the acceptance of this technology. Nonetheless, as the number of users of this technology surges, there must be an aligned concern to ensure that security measures are diligently enforced within IoT communication systems, such as in smart homes, smart cities, smart factories, smart hospitals, and smart grids. This research addresses security lacunae in the topology and configuration of IoT energy monitoring systems using post-quantum cryptographic techniques. We propose tailored implementations of the Rivest–Shamir–Adleman (RSA), N-th degree Truncated Polynomial Ring Units (NTRU), and a suite of cryptographic primitives based on Module Learning With Rounding (Saber) as post-quantum cryptographic candidate algorithms for IoT devices. These aim to secure publisher–subscriber end-to-end communication in energy system monitoring. Additionally, we offer a comparative analysis of these tailored implementations on low-resource devices, such as the Raspberry Pi, during data transmission using the Message Queuing Telemetry Transport (MQTT) protocol. Results indicate that the customized implementation of NTRU outperforms both SABER and RSA in terms of CPU and memory usage, while Light SABER emerges as the front-runner when considering encryption and decryption delays.

Blockwise Joint Detection of Physical Cell Identity and Carrier Frequency Offset for Narrowband IoT Applications

This paper presents a novel formulation for detecting the secondary synchronization signal in a narrowband Internet of Things communication system. The proposed approach is supported by a noncoherent algorithm that eliminates the need for channel information. A robust joint synchronization scheme is developed by decoupling the estimations of the physical cell identity and the carrier frequency offset. We derive the detection probability of the proposed physical cell identity detector and the mean squared error of the carrier frequency offset estimator, demonstrating their accuracy through simulation results. The performance of the proposed detection scheme is compared with that of existing detection schemes in terms of both estimation accuracy and computational complexity. Experimental results confirm that the proposed synchronization method exhibits superior performance while maintaining relatively lower complexity compared with benchmark methods.

Optimized RNN-based performance prediction of IoT and WSN-oriented smart city application using improved honey badger algorithm

Internet of Things (IoT) has been widely utilized as the major significant element of Information and Communications Technology (ICT) for feasible smart cities due to the capability of IoT for supporting sustainability in numerous domains. There is a need for fault avoidance via the continuous and dynamic utilization of network behavior for achieving the necessary quality of the IoT communication system that allows us to get sustainable enhancement in smart cities regarding IoT communication systems. While considering the IoT-assisted network, the basic part of the IoT model is considered a Wireless Sensor Network (WSN), especially for data management that consists of various sensor nodes in the smart city area. Moreover, every node in a WSN is considerably utilized for a specific reason and thus, every node is handled by a battery that increases the consumption of energy in the entire smart city scheme for processing the data regarding communication. On the other hand, there exist various limitations like scalability, communication latency, centralization, privacy, security, etc. Thus, this paper plans to develop the IoT and WSN-based smart city application using novel intelligent techniques, aiming for the optimal performance classification of the network. The infrastructure of IoT will consist of WSN, Vehicular Ad-hoc NETwork (VANET), Mobile Ad-hoc NETworks (MANET), Radio Frequency Identification (RFID), and Wireless Body Area Networks (WBAN). Here, the efficiency of the whole IoT system is acquired from the efficiency of the whole IoT module. Hence, the IoT network efficiency rates of the individual must be predicted in an earlier stage to predict the effectiveness of the complete IoT system. The efficiency of each network is forecasted with the help of the constraints such as energy consumption, gathered data size, transmitted data size, mobility, false positive, throughput, packet loss, and delay. The output from each network is considered as input to the Optimized Recurrent Neural Network (ORNN) for predicting the ending superiority of the whole IoT network. Here, the parameter tuning in the RNN is done by the Self Adaptive Honey Badger Algorithm (SA-HBA). The empirical outcomes confirmed that the designed method has forecasted and enhanced the superiority of a whole simulated IoT system in an accurate manner. Throughout the result analysis, the given designed method attains minimum energy consumption rate and also better prediction accuracy.

IEEE 802.15.4g/4x-Based Orthogonal Frequency-Division Multiplexing Transmission Scheme for Wide-Area and Mobile IoT Communication Systems

Radio communication systems using the frequency-shift keying (FSK) scheme standardized by the IEEE 802.15.4g standard can realize low-power consumption and highly reliable communications and are mainly adopted for smart metering as part of the Internet of Things (IoT). However, smart metering has recently broadened its application expectations, with broadband and long-distance communications now required. To fulfill these requirements, orthogonal frequency-division multiplexing (OFDM) has been incorporated into the IEEE 802.15.4g and 802.15.4x standards. However, comprehensive transmission performance analysis of OFDM under static and multipath fading environments has not been conducted. In this article, we first evaluated the packet error rate (PER) of OFDM assuming fixed communications using computer simulations. We then evaluated the transmission distance of OFDM and showed that OFDM can communicate at transmission distances up to 3.0 times longer than those specified in IEEE 802.15.4g FSK. Next, the PER in a mobile communication environment was evaluated using computer simulations. When only the long training field (LTF) was used to estimate multipath radio channels, the required PER of 10% was not achieved, even in a mobile communication environment at a speed of several km/h. To solve this problem, we proposed a receiving scheme which included a new channel estimation scheme. This scheme successively utilized not only the LTF but also pilot signals inserted into the transmitter to estimate radio propagation characteristics. Computer simulation results showed that the required PER was achieved even in a mobile communication environment at 30–40 km/h by using the proposed scheme.

Threshold-free multi-attributes physical layer authentication based on expectation–conditional maximization channel estimation in Internet of Things

With the number of Internet of Things devices continually increasing, the endogenous security of Internet of Things communication systems is growingly critical. Physical layer authentication is a powerful means of resisting active attacks by exploiting the unique characteristics inherent in wireless signals and physical devices. Many existing physical layer authentication schemes usually assume physical layer attributes obey certain statistical distributions that are unknown to receivers. To overcome the uncertainty, machine learning–based authentication approaches have been employed to implement threshold-free authentication. In this article, we utilize an expectation–conditional maximization algorithm to provide the physical layer attribute estimates required for the authentication phase and a logistic regression model to achieve threshold-free physical layer authentication. Moreover, a Frank–Wolfe algorithm is considered to achieve fast convergence of the logistic regression parameters and multi-attributes are adopted to increase the differentiation of transmitters. Simulation results demonstrate that the obtained attribute estimates are sufficient to provide a reliable source of data for authentication and the proposed threshold-free multi-attributes physical layer authentication scheme can effectively improve authentication accuracy, with the false alarm rate P f reduced to 0.0263% and the miss detection rate P m reduced to 0.3466%.

Finite Block Length Analysis of RIS-Assisted UAV-Based Multiuser IoT Communication System With Non-Linear EH

Reconfigurable intelligent surface (RIS) has emerged as an important transmission technology for numerous applications in Internet of Things (IoT) systems. Thus, in this paper, we investigate the application of RIS in energy harvesting (EH) based unmanned aerial vehicle (UAV) communication network with finite block length (BL) codes, where a rotary wing type flying UAV communicates with the multiple single antenna IoT users with the aid of multiple RISs mounted on several skyscraper buildings. To transmit the signal to a particular IoT user, the UAV selects an RIS on the basis of either UAV-RIS (i.e., partial) or UAV-RIS-IoT (i.e., full) channel state information (CSI) and then transmits the signal through the selected RIS along with the direct link transmission. In particular, we derive (i) the expression for probability of RIS selection, (ii) the statistical distribution of instantaneously received information signal-to-noise ratio (SNR) at the IoT user. Based on the derived statistics, we analyze the performance of the considered system under finite BL codes in terms of the average outage probability, average block error rate (ABLER) and goodput averaged over entire flying duration. Moreover, the BLER performance with finite BL codes is also compared with the infinite BL codes scenario. Additionally, we also investigate the impact of various channel and system parameters like imperfect CSI, number of RISs and the number of reflecting elements at each RIS, location of IoT users, variable altitude of the UAV, and the severity of channel fading of UAV-RIS link on the system performance. Furthermore, we have obtained the optimum UAV location in each time slot which minimizes the ABLER per time slot over all the users in the network. The analytical results are corroborated with Monte Carlo simulations.

Robust Tensor-Based Algorithm for UAV-Assisted IoT Communication Systems via Nested PARAFAC Analysis

In this paper, we propose a robust tensor-based strategy to jointly estimate channel state information (CSI) and detect information symbols for unmanned aerial vehicle (UAV)-assisted Internet of Things (IoT) communication systems. First, a superimposed signal transmission scheme is designed for each IoT device, and the superimposed signals are transmitted to UAVs simultaneously. Then, UAVs amplify and forward the received signals to the base station (BS), where a combined nested parallel factor (PARAFAC) tensor model is constructed. Finally, a robust tensor-based algorithm is derived to estimate full knowledge of CSI and detect information symbols. Simulation results show that the proposed algorithm offers superior performance compared with the competitive methods.

Internet of Things Communication Systems based on MQTT and BLE Protocols

Internet of Things Communication Systems based on MQTT and BLE Protocols

Bluetooth Low Energy Error Correction Based on Convolutional Coding

Contemporarily, the Internet of Things (IoT) is recently a newly emerging technology for connecting small devices into a platform; the IoT has been an increasingly demanded front-edge technology in terms of connecting different devices using information transmission and storage technology. To adapt to the small capacities of device batteries, Bluetooth Low Energy is adopted as the protocol of communication. However, the existing standards do not have a suitable and specific error correction method. As there is no ideal information transmission channel, there must be errors that occurred during message transmission. The performance and capacity of error correction become decisive factors in evaluating how efficient the IoT communication system performs. This article uses convolutional coding—a better-performing coding scheme than block coding—to correct errors in information transmission and reception on Internet of Things devices. It is better competent to control and correct bit errors in information transmission. To achieve this goal, convolutional coding algorithms devised by Dr Justin Coon at the University of Oxford have been referred to. By simulation using MATLAB, it has been found that the error rate is enhanced significantly for high Signal-to-Noise Ratio (SNR) in convolutional codes compared to uncoded messages.

Research and Implementation of Internet of Things Communication System Based on MQTT Protocol

With the advent of the information age, many communication systems and chat systems have appeared on the market. At present, every system has the function of subscribing to news,and MQTT is the main protocol to realize this function.It mainly uses the IBM bluemix IoT service to build mobile applications and realize device interconnection communication.This paper mainly studies the design and implementation of the Internet of Things communication system,which is based on the MQTT protocol.

Secure IoT Communications Using HARQ-Based Beamforming for MISOSE Channels

In this article, we consider physical-layer security (PLS) of the Internet-of-Things (IoT) communications based on hybrid automatic repeat request (HARQ) protocols in multiple-input single-output single-eavesdropper (MISOSE) channels. Owing to the reciprocity of wireless channels, a negative acknowledge frame can be used to obtain the channel state information (CSI) from a source with multiple antennas to an IoT destination device, and this CSI of a legitimate link is used for beamforming for the next transmission round (TR). Therefore, a higher secrecy rate can be expected from retransmissions with beamforming based on the CSI as compared to the first TR without CSI. To evaluate the secrecy throughput under time-invariant and time-varying channels, connection and secrecy outage probabilities (OPs) are derived. By exploiting the characteristics of the OPs, we suggest an approximated secrecy throughput. We then consider the optimization of the approximated secrecy throughput considering the rate parameters for legitimate and eavesdropping links. With an alternating optimization approach, an algorithm to find convergent system parameters, i.e., the rates for legitimate and eavesdropping links, is proposed. Moreover, an asymptotic analysis with the large number of transmit antennas indicates an interesting behavior of the secrecy rate with regard to the number of TRs. The analytical results are verified by rigorous numerical simulations and they can serve as a useful guideline to those involved in the design of secure IoT communication systems with HARQ in MISOSE channels.

Multi-cell, Multi-user, and Multi-carrier Secure Communication Using Non-Orthogonal Signals’ Superposition with Dual-Transmission for IoT in 6G and Beyond

Considering the advancements of the internet of things (IoT) in 6G and beyond communications, data transmission security in IoT devices has received extensive interest because of their significant features, such as low computational complexity, led by low power requirements. In such devices, the conventional cryptographic techniques may fail to provide secure communication. To fight this drawback, physical layer security (PLS) has remarkable potential to provide security solutions suitable for such applications. In this work, a highly effective PLS technique is proposed for providing secure communication against external and internal eavesdroppers in a downlink multi-cell, multi-user, and multi-carrier IoT communication system. In our proposed system, we considered two base stations, where each base station uses a single radio frequency (RF) chain to link two antennas that are used for the transmission of data. Further, we transmit the data in two rounds, and each round of transmission occurs through a single active antenna of each base station. A different antenna is used for each round of transmission to communicate with two single antenna IoT devices/users in the presence of a passive eavesdropper. In the proposed algorithm, frequency selective channel-based pre-coder matrices and the dual transmission approach are jointly employed. The dual-transmission is performed simultaneously from two base stations to provide security against internal and external eavesdroppers. The proposed system is suitable for IoT-based applications. Also, the potential capabilities of our proposed algorithm are proved by extensive mathematical and simulation analysis.

Accurate performance prediction of IoT communication systems for smart cities: An efficient deep learning based solution

The Internet of Things (IoT), owing to its ability to support sustainability in various fields, has recently been considered one of the most important components of the information and communications technology (ICT) for sustainable smart cities. To achieve the required quality of the IoT communication system, performance prediction is necessary; it is beneficial for fault avoidance through the dynamic and continuous adaptation of network behavior, which helps achieve sustainable improvement of IoT communication systems in smart cities. Herein, a deep-learning (DL) model to evaluate and predict the performance of an IoT communication system is proposed. In the proposed model, the number of dynamic neural networks is equal to the number of networks in the IoT system. Each neural network predicts the performance of a given IoT network. Subsequently, the performance of the entire IoT communication system can be predicted from the outputs of individual networks, which will be considered the inputs for another dynamic neural network. The proposed IoT DL model was tested using network simulator (ns-3) to construct a simulated IoT environment. The simulation results verify that the proposed DL model predicted and improved the performance of an entire simulated IoT system highly accurately.

A critical review of intrusion detection systems in the internet of things: techniques, deployment strategy, validation strategy, attacks, public datasets and challenges

The Internet of Things (IoT) has been rapidly evolving towards making a greater impact on everyday life to large industrial systems. Unfortunately, this has attracted the attention of cybercriminals who made IoT a target of malicious activities, opening the door to a possible attack on the end nodes. To this end, Numerous IoT intrusion detection Systems (IDS) have been proposed in the literature to tackle attacks on the IoT ecosystem, which can be broadly classified based on detection technique, validation strategy, and deployment strategy. This survey paper presents a comprehensive review of contemporary IoT IDS and an overview of techniques, deployment Strategy, validation strategy and datasets that are commonly applied for building IDS. We also review how existing IoT IDS detect intrusive attacks and secure communications on the IoT. It also presents the classification of IoT attacks and discusses future research challenges to counter such IoT attacks to make IoT more secure. These purposes help IoT security researchers by uniting, contrasting, and compiling scattered research efforts. Consequently, we provide a unique IoT IDS taxonomy, which sheds light on IoT IDS techniques, their advantages and disadvantages, IoT attacks that exploit IoT communication systems, corresponding advanced IDS and detection capabilities to detect IoT attacks.

Energy-Efficient Hybrid Routing Protocol for IoT Communication Systems in 5G and Beyond.

One of the major concerns in wireless sensor networks (WSNs) is most of the sensor nodes are powered through limited lifetime of energy-constrained batteries, which majorly affects the performance, quality, and lifetime of the network. Therefore, diverse clustering methods are proposed to improve energy efficiency of the WSNs. In the meantime, fifth-generation (5G) communications require that several Internet of Things (IoT) applications need to adopt the use of multiple-input multiple-output (MIMO) antenna systems to provide an improved capacity over multi-path channel environment. In this paper, we study a clustering technique for MIMO-based IoT communication systems to achieve energy efficiency. In particular, a novel MIMO-based energy-efficient unequal hybrid clustering (MIMO-HC) protocol is proposed for applications on the IoT in the 5G environment and beyond. Experimental analysis is conducted to assess the effectiveness of the suggested MIMO-HC protocol and compared with existing state-of-the-art research. The proposed MIMO-HC scheme achieves less energy consumption and better network lifetime compared to existing techniques. Specifically, the proposed MIMO-HC improves the network lifetime by approximately as long as the first node and the final node dies as compared with the existing protocol. Moreover, the energy that cluster heads consume on the proposed MIMO-HC is 40% less than that expended in the existing protocol.

Enhanced Lightweight Algorithm to Secure Data Transmission in IoT Systems

The Internet of Things (IoT) is the most recent Internet evolution that integrates many smart devices, such as constrained devices, smart tablets, smart vehicles, smart sensors and services used by organizations or individuals to support a variety of areas to improve life quality including precision agriculture, smart health, and environment monitoring. IoT applications are vulnerable to malicious threats, hence the need for a robust and lightweight algorithm for a complete IoT communication system. The proposed solution treats the majority of security issues and guarantees trust registration and anonymous mutual authentication. Also, our solution deals with the intractability problem between IoT nodes using anonymity of link direction. In this context, Elliptic Curve Cryptography, Identity Based Encryption and Pseudonym Based Cryptography techniques were used to ensure a lightweight secure IoT system. The results show the performance of the algorithm using Raspberry Pi 3 model B cards.