Recently, the adoption of the industrial Internet of things (IIoT) has optimized many industrial sectors and promoted industry “smartization.” Smart factories and smart industries connect the real and virtual worlds through cyber-physical systems (CPS). However, these linkages will increase the cyber security danger surface to new levels, putting millions of dollars' worth of assets at risk if communications in big network systems like IIoT settings are left unsecured. To solve these problems, the fundamental method is security, such as authentication and confidentiality, and it should require the encryption key. However, it is challenging the security performance with the limited performance of the sensor. Blockchain-based identity management is emerging for lightweight, integrity and persistence. However, the key generation and management issues of blockchain face the same security performance issues. First, through blockchain smart contracts and hierarchical deterministic (HD) wallets, hierarchical key derivation efficiently distributes and manages keys by line and group in the IIoT environment. Second, the pairing verification value based on an elliptic curve single point called Root Signature performs efficient public key certificate registration and verification and improves the key storage space. Third, the identity log recorded through the blockchain is the global transparency of the key lifecycle, providing system reliability from various security attacks. Keyless Signature Infrastructure (KSI) is adopted to perform efficiently via hash-based scheme (hash calendar, hash tree etc.). We analyze our framework compared to hash-based state commitment methods. Accordingly, our method achieves a calculation efficiency of O(nlog N) and a storage space saving of 60% compared to the existing schemes.

The integration of social networking concepts into the Internet of Things has led to the Social Internet of Things (SIoT) paradigm, and trust evaluation is essential to secure interaction in SIoT. In SIoT, when resource-constrained nodes respond to unexpected malicious services and malicious recommendations, the trust assessment is prone to be inaccurate, and the existing architecture has the risk of privacy leakage. An edge-cloud collaborative trust evaluation architecture in SIoT is proposed in this paper. Utilize the resource advantages of the cloud and the edge to complete the trust assessment task collaboratively. An evaluation algorithm of relationship closeness between nodes is designed to evaluate neighbor nodes' reliability in SIoT. A trust computing algorithm with enhanced sensitivity is proposed, considering the fluctuation of trust value and the conflict between trust indicators to enhance the sensitivity of identifying malicious behaviors. Simulation results show that compared with traditional methods, the proposed trust evaluation method can effectively improve the success rate of interaction and reduce the false detection rate when dealing with malicious services and malicious recommendations.

This paper presents a fully integrated yet compact receiver front-end for Sub-GHz applications such as Internet-of-Things (IoT). The low noise amplifier (LNA) matching network leverages an inductance boosting technique. A relatively small on-chip inductor with a compact area achieves impedance matching in such a low frequency. Moreover, a passive-mixer-first mode bypasses the LNA to extend the receiver dynamic-range. The passive mixer provides matching to the 50Ω antenna interface to eliminate the need for additional passive components. Therefore, the receiver can be fully-integrated without any off-chip matching components. The flipped-voltage-follower (FVF) cell is adopted in the low pass filter (LPF) and the variable gain amplifier (VGA) for its high linearity and low power consumption. Fabricated in 65nm LP CMOS process, the proposed receiver front-end occupies 0.37mm2 core area, with a tolerable input power ranging from -91.5dBm to -1dBm for 500kbps GMSK signal at 924MHz frequency. The power consumption is 1mW power under a 1.2V supply.

In Internet of Things (IoT) applications, system-on-chip (SoCs) with embedded processors are widely used. As an embedded processor, RISC-V, which is license-free and has an extensible instruction set, is receiving attention. However, designing such embedded processors requires an enormous effort to achieve a highly efficient microarchitecture in terms of performance, power consumption, and circuit area, as well as the design verification of running complex software, including modern operating systems such as Linux. In this paper, we propose a method for directly describing the RTL structure of a pipelined RISC-V processor with cache memories, a memory management unit (MMU), and an AXI bus interface using the C++ language. This pipelined processor C++ model serves as a functional simulator of the complete RISC-V core, whereas our C2RTL framework translates the processor C++ model into a cycle-accurate RTL description in the Verilog-HDL and RTL-equivalent C model. Our processor design methodology using the C2RTL framework is unique compared to other existing methodologies because both the simulation and RTL models are derived from the same C++ source, which greatly simplifies the design verification and optimization processes. The effectiveness of our design methodology is demonstrated on a RISC-V processor that runs Linux OS on an FPGA board, achieving a significantly short simulation time of the original C++ processor model and RTL-equivalent C model in comparison to a commercial RTL simulator.

Intrusion is one of major security issues of internet with the rapid growth in smart and Internet of Thing (IoT) devices, and it becomes important to detect attacks and set out alarm in IoT systems. In this paper, the support vector machine (SVM) and principal component analysis (PCA) based method is used to detect attacks in smart IoT systems. SVM with nonlinear scheme is used for intrusion classification and PCA is adopted for feature selection on the training and testing datasets. Experiments on the NSL-KDD dataset show that the test accuracy of the proposed method can reach 82.2% with 16 features selected from PCA for binary-classification which is almost the same as the result obtained with all the 41 features; and the test accuracy can achieve 78.3% with 29 features selected from PCA for multi-classification while 79.6% without feature selection. The Denial of Service (DoS) attack detection accuracy of the proposed method can achieve 8.8% improvement compared with existing artificial neural network based method.

Real-time and scalable multicast services are of paramount importance to Industrial Internet of Things (IIoT) applications. To realize these services, the multicast algorithm should, on the one hand, ensure the maximum delay of a multicast session not exceeding its upper delay bound. On the other hand, the algorithm should minimize session costs. As an emerging networking paradigm, Software-defined Networking (SDN) can provide a global view of the network to multicast algorithms, thereby bringing new opportunities for realizing the desired multicast services in IIoT environments. Unfortunately, existing SDN-based multicast (SDM) algorithms cannot meet the real-time and scalable requirements simultaneously. Therefore, in this paper, we focus on SDM algorithm design for IIoT environments. To be specific, the paper first converts the multicast tree construction problem for SDM in IIoT environments into a delay-bounded least-cost shared tree problem and proves that it is an NP-complete problem. Then, the paper puts forward a shared tree (ST) algorithm called SDM4IIoT to compute suboptimal solutions to the problem. The algorithm consists of five steps: 1) construct a delay-optimal shared tree; 2) divide the tree into a set of subpaths and a subtree; 3) optimize the cost of each subpath by relaxing the delay constraint; 4) optimize the subtree cost in the same manner; 5) recombine them into a shared tree. Simulation results show that the algorithm can provide real-time support that other ST algorithms cannot. In addition, it can achieve good scalability. Its cost is only 20.56% higher than the cost-optimal ST algorithm. Furthermore, its computation time is also acceptable. The algorithm can help to realize real-time and scalable multicast services for IIoT applications.

The energy efficiency of intelligent reflecting surface (IRS) enabled internet of things (IoT) networks is studied in this letter. The energy efficiency is mathematically expressed, respectively, as the number of reflecting elements and the spectral efficiency of the network and is shown to scale in the logarithm of the reflecting elements number in the high regime of transmit power from source node. Furthermore, it is revealed that the energy efficiency scales linearly over the spectral efficiency in the high regime of transmit power, in contrast to conventional studies on energy and spectral efficiency trade-offs in the non-IRS wireless IoT networks. Numerical simulations are carried out to verify the derived results for the IRS enabled IoT networks.

High reliability is required, even in Internet of things (IoT) communications, which are sometimes used for crucial control such as automatic driving devices. Hence, both the uplink (UL) and downlink (DL) communication quality must be improved in the physical layer. In this study, we focus on the communication quality of broadcast DL, which is configured using orthogonal frequency-division multiplexing (OFDM) as a multiplexing scheme and turbo code as forward error correction (FEC). To reduce the frame-error rate (FER) in the DL, we consider two transmit-diversity (TD) techniques that use space-time block code (STBC) or cyclic-delay diversity (CDD). The purpose of this paper is to evaluate the TD performance and to enhance FER performance of CDD up to that of STBC. To achieve this goal, a channel estimation method is proposed to improve FER for CDD. For this purpose, we first evaluate the FER performance of STBC and CDD by performing computer simulations and conducting hardware tests using a fading emulator. Then, we conduct field experiments in the 2.5GHz band. From the results of these evaluations, we confirm that STBC and CDD improved FER compared with single antenna transmission. CDD with the proposed channel estimation method achieved almost the same performance as STBC by accurately estimating the channel frequency response (CFR) and appropriately adjusting the amount of cyclic shift (ACS). When moving a received device around Yokosuka Research Park, STBC and CDD, using spatial diversity with omni antennas for TD, improved the FER from 3.84×10-2 to 1.42×10-2 and 1.19×10-2, respectively.

In this paper, we propose a novel Hybrid-Hierarchical spatial-aerial-Terrestrial Edge-Centric (H2TEC) for the space-air integrated Internet of Things (IoT) networks. (H2TEC) comprises unmanned aerial vehicles (UAVs) that act as mobile fog nodes to provide the required services for terminal nodes (TNs) in cooperation with the satellites. TNs in (H2TEC) offload their generated tasks to the UAVs for further processing. Due to the limited energy budget of TNs, a novel task allocation protocol, named TOP, is proposed to minimize the energy consumption of TNs while guaranteeing the outage probability and network reliability for which the transmission rate of TNs is optimized. TOP also takes advantage of the energy harvesting by which the low earth orbit satellites transfer energy to the UAVs when the remaining energy of the UAVs is below a predefined threshold. To this end, the harvested power of the UAVs is optimized alongside the corresponding harvesting time so that the UAVs can improve the network throughput via processing more bits. Numerical results reveal that TOP outperforms the baseline method in critical situations that more power is required to process the task. It is also found that even in such situations, the energy harvesting mechanism provided in the TOP yields a more efficient network throughput.

IEEE P802.3cg established a new pair of Ethernet physical layer devices (PHY), one of which, the short-reach 10BASE-T1S, uses 4B/5B mapping over Differential Manchester Encoding to maintain a data rate of 10 Mb/s at MAC/PLS interface, while providing in-band signaling between transmitter and receivers. However, 10BASE-T1S does not have any error correcting capability built into it. As a response to emerging building, industrial, and transportation requirements, this paper outlines research that leads to the possibility of establishing low-complexity, backward-compatible Forward Error Correction with per-frame configurable guaranteed burst error and erasure correcting capabilities over any 10BASE-T1S Ethernet network segment. The proposed technique combines a specialized, systematic Reed-Solomon code and a novel, three-tier, technique to avoid the appearance of certain inadmissible codeword symbols at the output of the encoder. In this way, the proposed technique enables error and erasure correction, while maintaining backwards compatibility with the current version of the standard.

Elliptic curve cryptography (ECC), one of the asymmetric cryptography, is widely used in practical security applications, especially in the Internet of Things (IoT) applications. This paper presents a low-power reconfigurable architecture for ECC, which is capable of resisting simple power analysis attacks (SPA) and can be configured to support all of point operations and modular operations on 160/192/224/256-bit field orders over GF(p). Point multiplication (PM) is the most complex and time-consuming operation of ECC, while modular multiplication (MM) and modular division (MD) have high computational complexity among modular operations. For decreasing power dissipation and increasing reconfigurable capability, a Reconfigurable Modular Multiplication Algorithm and Reconfigurable Modular Division Algorithm are proposed, and MM and MD are implemented by two adder units. Combining with the optimization of operation scheduling of PM, on 55 nm CMOS ASIC platform, the proposed architecture takes 0.96, 1.37, 1.87, 2.44 ms and consumes 8.29, 11.86, 16.20, 21.13 uJ to perform one PM on 160-bit, 192-bit, 224-bit, 256-bit field orders. It occupies 56.03 k gate area and has a power of 8.66 mW. The implementation results demonstrate that the proposed architecture outperforms the other contemporary designs reported in the literature in terms of area and configurability.

Any Internet-connected device is vulnerable to being hacked and misused. Hackers can find vulnerable IoT devices, infect malicious codes, build massive IoT botnets, and remotely control IoT devices through C&C servers. Many studies have been attempted to apply various security features on IoT devices to prevent IoT devices from being exploited by attackers. However, unlike high-performance PCs, IoT devices are lightweight, low-power, and low-cost devices and have limitations on performance of processing and memory, making it difficult to install heavy security functions. Instead of access to applying security functions on IoT devices, Internet-wide scanning (e.g., Shodan) studies have been attempted to quickly discover and take security measures massive IoT devices with weak security. Over the Internet, scanning studies remotely also exist realistic limitations such as low accuracy in analyzing security vulnerabilities due to a lack of device information or filtered by network security devices. In this paper, we propose a system for remotely collecting information from Internet-connected devices and using scanning techniques to identify and manage vulnerability information from IoT devices. The proposed system improves the open-source Zmap engine to solve a realistic problem when attempting to scan through real Internet. As a result, performance measurements show equal or superior results compared to previous Shodan, Zmap-based scanning.

Lightweight cryptographic systems for services delivered by the recently developed Internet of Things (IoT) are being continuously researched. However, existing Public Key Infrastructure (PKI)-based cryptographic algorithms are difficult to apply to IoT services delivered using lightweight devices. Therefore, encryption, authentication, and signature systems based on Certificateless Public Key Cryptography (CL-PKC), which are lightweight because they do not use the certificates of existing PKI-based cryptographic algorithms, are being studied. Of the various public key cryptosystems, signcryption is efficient, and ensures integrity and confidentiality. Recently, CL-based signcryption (CL-SC) schemes have been intensively studied, and a multi-receiver signcryption (MRSC) protocol for environments with multiple receivers, i.e., not involving end-to-end communication, has been proposed. However, when using signcryption, confidentiality and integrity may be violated by public key replacement attacks. In this paper, we develop an efficient CL-based MRSC (CL-MRSC) scheme using CL-PKC for IoT environments. Existing signcryption schemes do not offer public verifiability, which is required if digital signatures are used, because only the receiver can verify the validity of the message; sender authenticity is not guaranteed by a third party. Therefore, we propose a CL-MRSC scheme in which communication participants (such as the gateways through which messages are transmitted) can efficiently and publicly verify the validity of encrypted messages.

With the widespread use of Internet of Things (IoT) devices in recent years, we utilize a variety of hardware devices in our daily life. On the other hand, hardware security issues are emerging. Power analysis is one of the methods to detect anomalous behaviors, but it is hard to apply it to IoT devices where an operating system and various software programs are running. In this paper, we propose an anomalous behavior detection method for an IoT device by extracting application-specific power behaviors. First, we measure power consumption of an IoT device, and obtain the power waveform. Next, we extract an application-specific power waveform by eliminating a steady factor from the obtained power waveform. Finally, we extract feature values from the application-specific power waveform and detect an anomalous behavior by utilizing the local outlier factor (LOF) method. We conduct two experiments to show how our proposed method works: one runs three application programs and an anomalous application program randomly and the other runs three application programs in series and an anomalous application program very rarely. Application programs on both experiments are implemented on a single board computer. The experimental results demonstrate that the proposed method successfully detects anomalous behaviors by extracting application-specific power behaviors, while the existing approaches cannot.

We propose a spectrum regeneration and demodulation method for multiple direct RF undersampled real signals by using a new algorithm. Many methods have been proposed to regenerate the RF spectrum by using undersampling because of its simple circuit architecture. However, it is difficult to regenerate the spectrum from a real signal that has a band wider than a half of the sampling frequency, because it is difficult to include complex conjugate relation of the folded spectrum into the linear algebraic equation in this case. We propose a new spectrum regeneration method from direct undersampled real signals that uses multiple clocks and an extended algorithm considering the complex conjugate relation. Simulations are used to verify the potential of this method. The validity of the proposed method is verified by using the simulation data and the measured data. We also apply this algorithm to the demodulation system.

This paper presents the physical-layer cell identity (PCID) detection probability using the narrowband primary synchronization signal (NPSS) and narrowband secondary synchronization signal (NSSS) based on the narrowband Internet-of-Things (NB-IoT) radio interface considering frequency offset and the maximum Doppler frequency in the 28-GHz band. Simulation results show that the autocorrelation based NPSS detection method is more effective than the cross-correlation based NPSS detection using frequency offset estimation and compensation before the NPSS received timing detection from the viewpoints of PCID detection probability and computational complexity. We also show that when using autocorrelation based NPSS detection, the loss in the PCID detection probability at the carrier frequency of fc =28GHz compared to that for fc =3.5GHz is only approximately 5% at the average received signal-to-noise ratio (SNR) of 0dB when the frequency stability of a local oscillator of a user equipment (UE) set is 20ppm. Therefore, we conclude that the multiplexing schemes and sequences of NPSS and NSSS based on the NB-IoT radio interface associated with autocorrelation based NPSS detection will support the 28-GHz frequency spectra.

The automation of the home through Internet of Things (IoT) devices presents security challenges for protecting the safety and privacy of its inhabitants. In spite of standard wireless communication security protocols, an attacker inside the wireless communication range of the smart home can extract identifier and statistical information, such as the MAC address and packet lengths, from the encrypted wireless traffic of IoT devices to make inferences about the private activities of the user. In this paper, to prevent this breach on privacy in the wireless LAN, we accomplish the following three items. First, we demonstrate that performing traffic shaping simultaneously on the upload and download node is necessary; second, we demonstrate that traffic shaping by random packet generation is impracticable due to the excessive bandwidth requirement; third, we propose traffic shaping by variable padding durations to reduce the bandwidth requirement for injecting dummy traffic during periods of user activity and inactivity to decrease the confidence of the local attacker from identifying genuine user activity traffic. From our performance evaluation, we decreased the data generated on several WiFi and ZigBee-enabled IoT devices by over 15% by our proposal of variable padding durations compared to the conventional method of fixed padding durations at low attacker confidence.

This paper presents a high jitter performance injection-locked clock multiplier (ILCM) using an ultra-low power (ULP) voltage-controlled oscillator (VCO) for IoT application in 65-nm CMOS. The proposed transformer-based VCO achieves low flicker noise corner and sub-100µW power consumption. Double cross-coupled NMOS transistors sharing the same current provide high transconductance. The network using high-Q factor transformer (TF) provides a large tank impedance to minimize the current requirement. Thanks to the low current bias with a small conduction angle in the ULP VCO design, the proposed TF-based VCO's flicker noise can be suppressed, and a good PN can be achieved in flicker region (1/f3) with sub-100µW power consumption. Thus, a high figure-of-merit (FoM) can be obtained at both 100kHz and 1MHz without additional inductor. The proposed VCO achieves phase noise of -94.5/-115.3dBc/Hz at 100kHz/1MHz frequency offset with a 97µW power consumption, which corresponds to a -193/-194dBc/Hz VCO FoM at 2.62GHz oscillation frequency. The measurement results show that the 1/f3 corner is below 60kHz over the tuning range from 2.57GHz to 3.40GHz. Thanks to the proposed low power VCO, the total ILCM achieves 78 fs RMS jitter while using a high reference clock. A 960 fs RMS jitter can be achieved with a 40MHz common reference and 107µW corresponding power.

The Internet of Things (IoT) implicates an infrastructure that creates new value by connecting everything with communication networks, and its construction is rapidly progressing in anticipation of its great potential. Enhancing the security of IoT is an essential requirement for supporting IoT. For ensuring IoT security, it is desirable to create a situation that even a terminal component device with many restrictions in computing power and energy capacity can easily verify other devices and data and communicate securely by the use of public key cryptography. To concretely achieve the big goal of penetrating public key cryptographic technology to most IoT end devices, we elaborated the secure cryptographic unit (SCU) built in a low-end microcontroller chip. The SCU comprises a hardware cryptographic engine and a built-in access controlling functionality consisting of a software gate and hardware gate. This paper describes the outline of our SCU construction technology's research and development and prospects.

The spread of the Internet of Things (IoT) has led to the generation of large amounts of data, requiring massive communication, computing, and storage resources. Cloud computing plays an important role in realizing most IoT applications classified as massive machine type communication and cyber-physical control applications in vertical domains. To handle the increasing amount of IoT data, it is important to reduce the traffic concentrated in the cloud by distributing the computing and storage resources to the network edge side and to suppress the latency of the IoT applications. In this paper, we first present a recent literature review on fog/edge computing and data aggregation as representative traffic reduction technologies for efficiently utilizing communication, computing, and storage resources in IoT systems, and then focus on data aggregation control minimizing the latency in an IoT gateway. We then present a unified modeling for statistical and nonstatistical data aggregation and analyze its latency. We analytically derive the Laplace-Stieltjes transform and average of the stationary distribution of the latency and approximate the average latency; we subsequently apply it to an adaptive aggregation number control for the time-variant data arrival. The transient traffic characteristics, that is, the absorption of traffic fluctuations realizing a stable optimal latency, were clarified through a simulation with a time-variant Poisson input and non-Poisson inputs, such as a Beta input, which is a typical IoT traffic model.