関屋 大雄

This article presents a load-independent zero-current switching (ZCS) parallel-resonant inverter with a constant output current. The proposed inverter features constant-current output inherently without the need for any control method. Moreover, ZCS is achieved despite load variations, ensuring high power efficiency even at MHz-order switching frequencies. We conduct a comprehensive circuit analysis of the proposed inverter and provide a step-by-step parameter design method for achieving load-independent conditions. Additionally, a 25 W, 1 MHz prototype of the proposed inverter was implemented. In the circuit experiment, constant current output and ZCS were achieved across the entire range of load variations, which demonstrated the effectiveness of the proposed load-independent inverter.

Synchronisability of limit cycle oscillators has been measured by the width of the synchronous frequency band, known as the Arnold tongue, concerning external forcing. We clarify a fundamental limit on maximizing this synchronisability within a specified extra low power budget, which underlies an important and ubiquitous problem in nonlinear science related to an efficient synchronisation of weakly forced nonlinear oscillators. In this letter, injection-locked Class-E oscillators are considered as a practical case study, and we systematically analyse their power consumption; our observations demonstrate the independence of power consumption in the oscillator from power consumption in the injection circuit and verify the dependency of power consumption in the oscillator solely on its oscillation frequency. These systematic observations, followed by the mathematical optimisation establish the existence of a fundamental limit on synchronisability, validated through systematic circuit simulations. The results offer insights into the energetics of synchronisation for a specific class of injection-locked oscillators.

Sparse Mobile CrowdSensing (SMCS) effectively lowers sensing costs while maintaining data quality, offering an alternative approach to data collection. Unfortunately, the fact that data contain sensitive information raises serious privacy concerns. Local Differential Privacy (LDP) has emerged as the de facto standard for ensuring data privacy. However, the LDP based on the perturbation concept causes a substantial reduction in the data utility of the SMCS system. To address this problem, we propose a novel scheme named enhancing Sparse mobile crowdsensing With manifold Optimization and differential Privacy (SWOP). Specifically, we first revisit the Gaussian mechanism based on the fact that data utility intervals are ubiquitous in sensing tasks, and introduce a novel perturbation mechanism, namely Truncated Gaussian Mechanism (TGM). Subsequently, we perturb user-collected data by locally injecting noise sampled from TGM and deduce a sufficient condition for the scale parameter to ensure ϵ -LDP. Furthermore, we model the data inference with privacy-preserving properties as an unconstrained optimization problem on a Riemannian manifold and solve it using the nonlinear conjugate gradient method. Extensive experiments on large-scale real-world and synthetic datasets are conducted to evaluate the proposed scheme. The results demonstrate that SWOP can greatly enhance the utility of data inference while ensuring workers' data privacy compared to baseline models.

This paper proposes a high-frequency multiple-receiver wireless power transfer (WPT) system with a load-independent class-E/F inverter. Each receiver has a post-regulator, which changes the equivalent resistance seen from the inverter to obtain the necessary power for output voltage regulation. Because the load-independent class-E/F inverter generates constant AC current ((Formula presented.)), the transmitter supplies the minimum required power to the receivers by the change of the equivalent resistances. Besides, the load-independent inverter consistently achieves zero-voltage switching (ZVS) without any control. As a result, no information feedback by wireless communication is necessary for output regulation and ZVS achievement, simplifying the system configuration and improving the transient response of the control. This paper presents analytical expressions of the proposed system. Besides, the experiment was carried out with a two-receiver WPT system. The implemented system worked well by individual and independent output regulation of the post regulator at each receiver. The implemented WPT system achieved the power-delivery efficiency of 83.4% at the 6.78 MHz transmission frequency and the total output power of 40 W.

This paper proposes the load-independent (LI) class-E frequency multiplier along with a unified circuit analysis method with the LI class-E amplifier. A circuit-parameter determination strategy is presented to achieve LI operation and maximum power output capability at the rated condition. We designed the class-E amplifier and frequency doubler using the unified analytical expressions. Both the implemented circuits achieved the LI operation, namely constant output voltage amplitude and zero-voltage switching against load variations without any control. The experimental results showed quantitative agreements with the analysis results, namely waveforms and power conversion efficiency, which indicates the validity of the derived analytical expressions and design procedure.

This paper presents an analysis and design of a load-independent (LI) series resonant (SR) power amplifier with constant current (CC) output, along with its application for an MHz wireless power transfer (WPT) system. A novel inverse Class E power amplifier is introduced, which essentially produces a sinusoidal output current even with a low-<inline-formula><tex-math notation="LaTeX">$\bm Q$</tex-math></inline-formula> SR filter. Besides, the proposed amplifier achieves zero-current switching (ZCS) and CC output simultaneously, regardless of the load resistance. The LI operation is obtained for a specific set of component values, whose design conditions are clarified analytically in this paper. The experiment was carried out with a WPT system incorporating the proposed amplifier as a transmitter and the Class D rectifier as a receiver. Although the input reactance of the Class D rectifier changed against DC-load variations due to the parasitic capacitances, the proposed amplifier showed consistent CC operation by using the low-<inline-formula><tex-math notation="LaTeX">$\bm Q$</tex-math></inline-formula> SR filter. Also, the proposed WPT system maintained a low total harmonic distortion (THD) of the transmission current over the wide load range, even with the low-<inline-formula><tex-math notation="LaTeX">$\bm Q$</tex-math></inline-formula> output filter. The prototype WPT system with the proposed amplifier achieved 88 &#x0025; power-delivery efficiency with 60 W output power at 3.39 MHz transmission frequency. The experimental results showed the effectiveness of the proposed amplifier.

This paper presents an analysis and design of the load-independent (LI) high-frequency magnetic resonant wireless power transfer (MR-WPT) system with robustness against load variations and coil misalignments. It is clarified from the analysis that robustness against load variations and coil misalignment can be obtained when LI inverter, series-resonant to series-resonant (S-S) coupling topology, input-reactance invariant rectifier against load variations, and post regulator are adopted. The output reactance of the transmitter does not vary against load variations and coil misalignment. Therefore, the inverter works with the LI mode, achieving soft switching without control. As a result, the system ensures soft switching and output regulation against both load variations and coil misalignment without wireless communication feedback. The design example of the system with LI class-E/F inverter, class-D rectifier, and buck converter is given. The quantitative agreements between the analytical prediction and experiment show the effectiveness and validity of the system and its analysis.

Cognitive radio networks' evolution hinges significantly on the use of automatic modulation classification (AMC). However, existing research reveals limitations in attaining high AMC accuracy due to ineffective feature extraction from signals. To counter this, we propose a vision-centric approach employing diverse kernel sizes to augment signal extraction. In addition, we refine the transformer architecture by incorporating a dual-branch multi-layer perceptron network, enabling diverse pattern learning and enhancing the model's running speed. Specifically, our architecture allows the system to focus on relevant portions of the input sequence, thus, it improves classification accuracy for both high and low signal-to-noise regimes. By utilizing the widely recognized DeepSig dataset, our pioneering deep model, termed as VT-MCNet, outshines prior leading-edge deep networks in terms of classification accuracy and computational costs. Notably, VT-MCNet reaches an exceptional cumulative classification rate of up to 99.24%, while the state-of-the-art method, even with higher computational complexity, can only achieve 99.06%.

This paper presents an analysis-based design method for designing the class-Φ22 wireless power transfer (WPT) system, taking its subsystems as a whole into account. By using the proposed design method, it is possible to derive accurate design values which can make sure the class-E Zero-Voltage-Switching/Zero-Derivative-Switching (ZVS/ZDS) to obtain without applying any tuning processes. Additionally, it is possible to take the effects of the switch on resistance, diode forward voltage drop, and equivalent series resistances (ESRs) of all passive elements on the system operations into account. Furthermore, design curves for a wide range of parameters are developed and organized as basic data for various applications. The validities of the proposed design procedure and derived design curves are confirmed by LTspice simulation and circuit experiment. In the experimental measurements, the class-Φ22 WPT system achieves 78.8% power-transmission efficiency at 6.78 MHz operating frequency and 7.96 W output power. Additionally, the results obtained from the LTspice simulation and laboratory experiment show quantitative agreements with the analytical predictions, which indicates the accuracy and validity of the proposed analytical method and design curves given in this paper.

An increasing number of devices are connecting to the Internet via Wi-Fi networks, ranging from mobile phones to Internet of Things (IoT) devices. Moreover, Wi-Fi technology has undergone gradual development, with various standards and implementations. In a Wi-Fi network, a Wi-Fi client typically uses the Transmission Control Protocol (TCP) for its applications. Hence, it is essential to understand and quantify the TCP performance in such an environment. This work presents an emulator-based approach for investigating the TCP performance in Wi-Fi networks in a time- and cost-efficient manner. We introduce a new platform, which leverages the Mininet-WiFi emulator to construct various Wi-Fi networks for investigation while considering actual TCP implementations. The platform uniquely includes tools and scripts to assess TCP performance in the Wi-Fi networks quickly. First, to confirm the accuracy of our platform, we compare the emulated results to the results in a real Wi-Fi network, where the bufferbloat problem may occur. The two results are not only similar but also usable for finding the bufferbloat condition under different methods of TCP congestion control. Second, we conduct a similar evaluation in scenarios with the Wi-Fi link as a bottleneck and those with varying signal strengths. Third, we use the platform to compare the fairness performance of TCP congestion control algorithms in a Wi-Fi network with multiple clients. The results show the efficiency and convenience of our platform in recognizing TCP behaviors.

It is expected that understanding and estimating the human mental state will be helpful for mental health measures, improving learning and labor work efficiency, and preventing human error. Our research group has been developing a methodology and model for estimating the mental state of humans in their environment from indoor environmental data regarding cognitive function obtained by wireless sensor network technology. This study constructed a model to estimate mental states from multidimensional time-series indoor environmental data such as temperature, humidity, and illumination. The experiment results show that the proposed system shows higher estimation accuracy. In particular, the CNN-LSTM model with multidimensional time-series indoor environmental data shows about 90% estimation accuracy. The results obtained in this study, showing that mental states can be determined with high accuracy from environmental data, are helpful for future research approaches. It may also contribute to the creation of a less stressful environment.

In recent years, there have been an increasing number of IoT applications, such as smart cities, which benefit human lives. The IoT applications face security and privacy protection challenges since they usually adopt a centralized structure (i.e., client-server architecture). The blockchain technology, with decentralized and trustworthy guarantee characteristics, has the potential to solve such IoT challenges. Hence, there are efforts to integrate IoT and blockchain into IoT-Blockchain systems, where the IoT devices are normally battery-powered and low-resource. Therefore, it is essential to understand the performance of IoT devices in cooperating with blockchain, which generally requires significant resources (e.g., computing for consensus). In this paper, we first build a private IoT. Blockchain system using the Ethereum framework and Raspberry Pi4 (RPi4) as IoT devices. We then evaluate the system with two consensus algorithms (i.e., Proof of Work (PoW) and Proof of Authority (PoA)), considering CPU, memory, disk, and power consumption of the IoT devices. The evaluation results show that the power consumption of PoW is double PoA's. Using the mining time data from running PoW on the RPi4, we calculated the expected mining time through curve fitting.

In this paper, the analysis and design of a class-E/F_n inverter using analytically expressions is provided for any grading coefficient <italic>m</italic> of the MOSFET body junction diode at 50% duty ratio. Generally, the class-E zero-voltage switching (ZVS) and zero-derivative voltage switching (ZDVS) conditions prepared the switch-mode operation inverter to obtain high power conversion efficiency performance. On the other hand, harmonics tuning and waveforms shaping in the load-network lead to the class-F inverter configuration. The combination of the switch-mode operating with waveforms shaping provides the class E/F_n inverter. The MOSFET nonlinear drain-source parasitic capacitance is highlighted as determinative element in this operation mode. The nonlinearity characteristic of the MOSFET drain-source parasitic capacitance is required to include as the design specification for the satisfaction of the peak switch voltage and output power simultaneously. Furthermore, the grading coefficient <italic>m</italic> has considerably and directly made effects on both the output power capability and maximum operating frequency. The design and implementation of the class-E/F₃ inverter using the grading coefficient <italic>m</italic> as an adjustment parameter is performed. The close agreement between the analytical and PSpice simulations proved the effectiveness of the provided theoretical expressions. Therefore, the usefulness of analytical expressions is confirmed by high accuracy obtaining results from the laboratory measurements for the prototype fabricated circuit.

The evolving Internet of Things (IoT) promisingly improves the quality of life and transforms many industries. However, the IoT application challenges the wireless networks since the resource-constrained IoT devices typically need to send data to the cloud or edge server. Therefore, it is necessary to introduce an intermediate device between IoT devices and the servers, for example, to reduce the cost of direct communication between them. In another case, the device may move and collect the data from IoT devices before transmitting it to the server. The intermediate device should be designed to have resilient Internet connections and sufficient bandwidth in such a context. This work implements and evaluates a Multipath TCP (MPTCP) IoT router, which uses multiple radios to connect a server to address the demanding design. The router leverages MPTCP, an extension of TCP for simultaneous transmission over several paths on top of Wi-Fi interfaces. MPTCP has also supported several working modes for throughput and (or) resilience enhancements. First, we implement the MPTCP kernels, which can run on the popular IoT devices Raspberry Pi 3B+ and 4. Second, we extensively evaluate the performance of IoT routers in a static and mobility scenario. The static scenario’s evaluation results show that the MPTCP-based router can achieve seamless handover and bandwidth aggregation. In the mobility scenario, the MPTCP router with one backup path performs better than the single-path TCP. Besides, the MPTCP routers are more energy-efficient than TCP on the same hardware.

Many IoT-blockchain systems in which blockchain connections run on an infrastructure-based network, such as Wi-Fi or LTE, face a severe problem: the single point of failure (SPoF) (i.e., depending on the availability, an access point of an LTE base station). Using infrastructure-less networks (i.e., ad hoc networks) is an efficient approach to prevent such highly disruptive events. An ad hoc network can automatically restore blockchain communication using an ad hoc routing protocol, even if a node fails. Moreover, an ad hoc routing protocol is more efficient when considering the IoT nodes’ mobility. In this paper, we first construct IoT-blockchain systems on emulated and real ad hoc networks with Ethereum and three ad hoc routing protocols (i.e., OLSR, BATMAN, and BABEL). We then evaluate the blockchain recovery time in static and mobile scenarios. The results show that BATMAN achieves the best blockchain recovery performance in all investigated scenarios because BATMAN only determines whether to switch a route by comparing the number of OGM packets received from a different next-hop. More specifically, in the small-scale real IoT-blockchain, BATMAN recovers at least 73.9% and 59.8% better than OLSR and BABEL, respectively. In the medium-scale emulated IoT-blockchain, the recovery time of BATMAN is at least 69% and 60% shorter than OLSR or BABEL, respectively.

This paper proposes a load-independent inverse class-E zero-voltage switching (ZVS) inverter. The proposed inverter achieves the constant output current and the ZVS at any load resistance without any control. The waveforms and design equations of the proposed inverter are shown. Besides, a wireless-power-transfer system was implemented using the proposed inverter. The designed WPT system kept the constant output voltage and the ZVS against load variations, which denoted the effectiveness of the proposed inverter and validities of the analytical equations.

This paper presents a design procedure for wireless power transfer (WPT) systems with the load-independent class-E inverter family. The design procedure guarantees that the WPT system will achieve constant output and soft switching in response to load variations without particular control, which can apply not only single-hop single-output but multi-hop multi-output WPT system designs. It is analytically explained that a WPT-system output type is determined by properly selecting inverter, rectifier, and coupling resonant structure. As a concrete design example, we designed the three-hop three-output WPT system for installation into a robot arm. Experimental results exemplified the validity and usefulness of the established procedure.

Now is the age of neuromorphic computing that creates brain circuits. The analog and digital circuit theory changes because the values of the basic conductance elements can be made variable by learning. The computer structure changes to in-memory computing technology in cooperation with a von Neumann architecture. In this paper, we propose a gyrator neuron (GN) that enables analog computer operations by nodal equation. The GN is constructed based on memristor elements. The GN executes learning by back propagation processing and association by forward propagation processing.

Multipath communication is a well-developed technology that enhances communication effectiveness and resilience. Moreover, it can flexibly utilize network resources through load balancing among available paths. However, traditionally, deploying such load balancing functions on network devices is costly due to the required configuration changes and complicated signaling mechanisms on the devices' control planes. Programming Protocol-independent Packet Processors (P4) has recently emerged as a programming language that enables programmability on the data plane, with the potential to relieve such issues in multipath communication. This work introduces and implements three P4-based multipath schedulers that can split traffic over several paths in wireless networks. The first is P4-based Random Splitting, which distributes traffic randomly. The second is P4-based Weighted Round Robin, with path scheduling based on weights in accordance with path capability. The last is P4-based Dynamic Weighted Round Robin (DWRR), which can improve bandwidth utilization by shifting the weights following dynamic changes in the available bandwidth (i.e., when congestion occurs). We have extensively evaluated the implementation of these three P4-based schedulers in a Mininet-WiFi/P4 environment with User Datagram Protocol (UDP) traffic. The results show that these schedulers can achieve multipath communication with the designed scheduling mechanisms.

Blockchain technologies have been emerging with the potential to disrupt many fields (e.g., cryptocurrencies replacing the traditional ones, enabling trustworthy voting, etc.). The Internet of Things (IoT) has been predictably strengthened when integrating to the private blockchain, such as Ethereum. In an IoT deployment with private Ethereum, a thorough understanding of the latency is a critical issue that has not been adequately understood in the literature. Motivated by that, this work aims to comprehend the latency performance in the IoT Ethereum with two popular consensus algorithms: Proof-of-Work (PoW) and Proof-of-Authority (PoA). Initially, we clarify different latency segments from transaction submission to execution, namely the transaction lifecycle in a private blockchain. We then consider the three related latency metrics: transactionoriented latency, mining time, and block-oriented latency in the PoW case. With PoA, the mining time&#x2019;s consideration is omitted since the mining process is not necessary. After that, we construct a realistic private Ethereum IoT network (i.e., using a laptop and seven Raspberry Pi 3b+ nodes) and a large-scale emulated one with 30 nodes. We write and deploy a smart contract to read and write data to the blockchain and measure the latencies in various scenarios. The measurement results reveal the values of transaction-oriented and block-oriented latency with PoW, PoA in both the actual and emulated networks. Moreover, we derive the expected value for the PoW&#x2019;s mining time by fitting the probabilities to an exponential curve.

This paper presents a design method of a two-hop wireless power transfer (WPT) system for installing on a robot arm. The class-E inverter and the class-D rectifier are used on the transmission and receiving sides, respectively, in the proposed WPT system. Analytical equations for the proposed WPT system are derived as functions of the geometrical and physical parameters of the coils, such as the outer diameter and height of the coils, winding-wire diameter, and number of turns. Using the analytical equations, we can optimize the WPT system to obtain the design values with the theoretically highest power-delivery efficiency under the size limitation of the robot arm. The circuit experiments are in quantitative agreement with the theoretical predictions obtained from the analysis, indicating the validity of the analysis and design method. The experimental prototype achieved 83.6% power-delivery efficiency at 6.78 MHz operating frequency and 39.3 W output power.

This article proposes a load-independent zero-current switching (ZCS) parallel-resonant inverter along with its analytical expressions and a design method. The proposed inverter achieves constant output voltage amplitude and ZCS against load variations, which are called the load-independent operation in this article. The analytical expressions provide design equations for achieving the load-independent operation. The analytical expressions and the design equations were verified from the quantitative agreements between the experimental measurements and analytical predictions.

The fifth generation of mobile wireless networks (5G) will provide an infrastructure with abundant and reliable connectivity for innovative and complicated applications. In 5G, 5G mobile devices, which will have improved computing resources for such applications, play an essential role. However, the network stack of 5G devices may continue to be borrowed from 4G legacy operating systems, thereby degrading user experience. In this paper, we posit that 5G users should have flexibility in utilizing networks and gain more awareness of network selection. To this end, we exploit network softwarization technologies to empower 5G devices. We then devise 5GSoft, a novel softwarized networking stack on each 5G mobile device. 5GSoft includes wireless virtualization to relax the dependence on hardware, thereby enabling sharing and multiple access. The 5GSoft device can concurrently exploit surrounding wireless networks using software-defined networking. Finally, the 5GSoft device is aware of network selection for each application process by applying network namespace. Qualitative evaluation of 5GSoft, in comparison to other approaches, highlights its effectiveness in terms of awareness and flexibility provision. Moreover, real experiments show that the virtualization in 5GSoft has negligible overhead.

Blockchain includes distributed records that are immutable and transparent through replicating among public or private networks. The open-source Ethereum is one of the emerging blockchain platforms featuring smart contracts. The private Ethereum has been obtaining interest due to its applicability in various applications, including the Internet of Things (IoT). Hence, understanding and quantifying blockchain performance is crucial to facilitate the blockchain application. In this paper, assuming IoT scenarios, we conduct an experimental study to investigate various performance parameters of private Ethereum networks. Initially, we clarify the latency processes according to the transaction lifecycle (i.e., transaction-oriented and block-oriented latency) and measure them in different deployments. Then, we track and report the performance of blockchain nodes during the processes of utilizing transaction. Our deployment networks include an indoor IoT blockchain network (i.e., with a laptop and several Raspberry Pi 3b+ (RPI 3b+)) and a private blockchain over the cloud. In both cases, we write and deploy a smart contract to read and write data to the blockchain and measure the performance in various scenarios. The experiment results reveal not only the blockchain node’s performance but also the latencies-hop correlation, as well as the latencies’ relation in different workloads. Notably, the latency values in the cloud deployment latency strongly depend on Round Trip Time (RTT) between the blockchain nodes.

This paper presents a single-switch zero-voltage switching (ZVS) power-factor correction converter based on the class-E (Formula presented.) converter at 1 MHz switching frequency. A design method for ensuring the ZVS for the entire line-voltage period is proposed. By visualising the ZVS region in the parameter space, circuit parameters can be easily obtained to achieve the ZVS for the entire line-voltage period. Additionally, a closed-loop controller is applied for achieving a high power factor, low total harmonic distortion of the input current and output voltage regulation. The experimental circuit achieved the ZVS in the entire line-voltage period against load variations. As a result, the implemented converter achieved the same level of power-conversion efficiency as the 100-kHz power-factor correction converters and a high power factor with low total harmonic distortion, which denoted the effectiveness of the proposed design method.

In this paper, an extended continuous class-F power amplifier (PA) is investigated, designed, and fabricated. The new auxiliary parameter (β+ αcos 2θ) , is proposed to increase the efficiency in comparison with the old auxiliary parameter (1 + δcos θ). A novel methodology based on the smith chart design space and the proposed auxiliary parameter is introduced and analyzed. The design methodology, by controlling harmonic interferences, expands the amplifier bandwidth to below 1 GHz (up to 200 MHz). The laterally diffused metal–oxide–semiconductor (LDMOS) is selected, and an optimal bias point for its best performance is considered. Also, microstrip feedback based on the low-impedance coupled line is designed to accomplish transistor unconditional stability. Then, a ladder network based on the radial lines is designed as a harmonic control circuit, which controls harmonics up to 5th into the proposed design space. To verify this design approach, an extended continuous class-F power amplifier is designed and fabricated to operate at 0.2–1.7 GHz frequency range. Measurement results of the implemented PA shown that the output power of 38–40.2 dBm and the power gain of 13–15.2 dB were obtained. In addition, the final PA achieved a remarkable 53–79% drain efficiency over the whole operation bandwidth.

In the IoT, a small amount of payment (i.e., micropayment) enables the trading of sensor data collected by IoT devices. The IOTA cryptocurrency, which achieves high-speed transactions without transaction fees, shows the potential to realize such micropayment. In this work, we introduce and implement an IOTA-based micropayment system for IoT devices (i.e., Raspberry Pi), assuming an air quality monitoring application. We then evaluate the latency performance and power consumption.

With data transparency and immutability, the blockchain can provide trustless and decentralized services for Internet of Things (IoT) applications. However, most blockchain-IoT networks, especially those with a private blockchain, are built on top of an infrastructure-based wireless network (i.e., using Wi-Fi access points or cellular base stations). Hence, they are still under the risk of Single-Point-of-Failure (SPoF) on the network layer, hindering the decentralization merit, for example, when the access points or base stations get failures. This paper presents an Optimized Link State Routing (OLSR) protocol-based solution for that issue in a private blockchain-IoT application. By decentralizing the underlying network with OLSR, the private blockchain network can avoid SPoF and automatically recover after a failure. Single blockchain connections can be extended to multiple ad hoc hops. Services over blockchain become flexible to fit various IoT scenarios. We show the effectiveness of our solution by constructing a private Ethereum blockchain network running on IoT devices (i.e., Raspberry Pi model 4) with environmental data sensing (i.e., Particular Matter (PM)). The IoT devices use OLSR to form an ad hoc network. The environment data are collected and propagated in transactions to a pre-loaded smart contract periodically. We then evaluate the IoT blockchain network’s recovery time when facing a link error. The evaluation results show that OLSR can automatically recover after the failure. We also evaluate the transaction-oriented latency and block-oriented latency, which indicates the blocks have a high transmission quality, while transactions are transferred individually.

With the development of sensor-clouds, the traditional WSN is expanded and the computing capacity is greatly improved. However, there are still challenges to be solved in sensor-clouds, such as how to disseminate codes to all nodes in a fast and energy-saving way. In this paper, an early wake-up ahead (EWA) code dissemination scheme is proposed to disseminate codes more efficiently. The main innovations of EWA code dissemination scheme are as follows: (a) An early wake-up message (EWM) routing protocol is proposed to propagate the early wake-up message ahead to awake the nodes in the routing path before data packets arrive. The forecast message can be forwarded for multi-hops during a time slot, while the packet can only be forwarded for one hop. Therefore, the nodes in the routing path can be awakened before the sending node starts to transmit the packets, the sleep latency is thus reduced as the sending node has not to wait for the receiver to be awakened for transmission. (b) The proposed EWA scheme aims at accelerating the code dissemination by increasing the duty cycle of the nodes which are far from the sink based on the EWM routing protocol. Meanwhile, the EWA scheme can improve the energy utilization rate without affecting the network lifetime. Theoretical analysis has verified the proposed protocol and scheme do accelerate code dissemination and improve energy utilization ratio. The experimental results show that the proposed EWA scheme reduces the network delay by 16.53%-37.13% compared to the conventional schemes.

This chapter studies the congestion control of Multipath TCP (MPTCP) in a heterogeneous wireless network with LTE and Wi-Fi (i.e., the canonical use case ofMPTCP). In such a network, an MPTCP-capable mobile device uses two wireless links for data transferring to an application server. We conduct an experimental study of the four congestion control algorithms, namely balia, olia, lia, wvegas. We aim to reveal three critical issues that may be appeared when deploying MPTCP in the networks. First, we evaluate how the algorithms are backward compatible with the environment with only a single path. The evaluation results indicate that except wvegas, the three others are compatible. Second, we observe how the network selection for the initial subflow of an MPTCP connection affects the overall performance. While all the algorithms have a similar initialization time, their throughput values are substantially different. In the case of olia, the throughput with Wi-Fi selection is more than 200% the one with LTE selection. Finally, we investigate the impact of buffer size at a receiver on the MPTCP performance. Our evaluation shows that selecting the suitable buffer size can gain theMPTCP throughput up to 2.4 times.

Advances in communication and sensing technologies have enabled low-cost air quality monitoring devices that are easy to deploy. Moreover, the diverse deployment of the devices, which share a huge amount of sensing data, may help monitor and predict air quality at a fine grain granularity. In such context, valuing the data and introducing micropayment may encourage more people to install monitoring devices and share their data. More specifically, the micropayment, a small amount of electronic currency, will be paid for each portion of shared sensing data. To this end, IOTA cryptocurrency shows potential due to its high-speed transactions without transaction fees. This paper introduces a novel IOTA-based micropayment system for air quality monitoring applications. Our system allows IoT devices (i.e., Raspberry Pi) running IOTA clients to exchange the data on the public IOTA network (i.e., the Tangle). We also implement IOTA nodes, which can join the public IOTA or form a private IOTA. Our system has been proven to work well with real air quality monitoring devices. We have also evaluated various system performance parameters, including latency, jitter, and throughput.

Minimizing energy consumption is a critical challenge for real-time workflows, particularly in heterogeneous cloud computing systems. State-of-the-art algorithms aim to minimize the energy consumed for processing such applications by choosing virtual machines (VMs) to shut down from all opened VMs (i.e., VM merging). However, such VM merging through an “on-to-close” approach usually incurs high computational complexity. This paper proposes an energy-efficient VM opening (EEVO) algorithm that is capable of choosing VMs to turn on from all closed VMs while satisfying the real-time constraint of applications. Considering that there are slacks that can be eliminated or reduced between adjacently scheduled tasks after using the EEVO algorithm, a dynamic scaling down EEVO algorithm (DEEVO) is further proposed. DEEVO is implemented by scaling down the frequency of VMs executing each task based on the dynamic voltage and frequency scaling (DVFS) technique. Experimental results demonstrate that, with the above-mentioned improvements, DEEVO achieves lower energy consumption for real-time workflows than state-of-the-art algorithms do. In addition, DEEVO outperforms state-of-the-art algorithms in the computational efficiency of accomplishing task scheduling.

This paper proposes comprehensive and simplified numerical design procedures for the class-E switching circuits with the Particle Swarm Optimization (PSO) algorithm. The PSO algorithm does not require approximate solutions and partial differentiation derivations. Therefore, the proposed design method has higher convergence stability than Newton&#x2019;s method-based design. It is also a feature that the PSO algorithm allows the mismatch between the design condition and the design parameter numbers. As a design example, we successfully draw the multiple design curves of the class-E amplifier and frequency multipliers by one-time optimization execution with the modified PSO algorithm. Besides, we show a pretty simple design procedure of the class-E amplifier, which uses only one evaluation function for determining three design parameters. The design accuracy of the proposed design method does not deteriorate compared with the previous design method. We carried out circuit experiments, which achieved power-conversion efficiency of 94.0%. The circuit experiment showed agreement with numerical predictions, which confirmed the validity and usefulness of the proposed design method.

The Internet of Things (IoT) device typically needs to send data to the cloud, which is a server on the Internet, for IoT applications. In the scenario with a large number of IoT devices, keeping direct communication between the device and server is costly. One of the suitable solutions is to provide an IoT router to reduce the cost. The IoT router should have resilient Internet connections as well as sufficient bandwidth. In this paper, we implement and evaluate an IoT router, which uses multiple radios to connect a server, aiming to address the problems. The router leverages Multipath TCP (MPTCP), which is an extension of TCP for simultaneous transmission over several radio interfaces. We then enable the operation of MPTCP over multiple Wi-Fi links. The evaluation results show that the router could achieve seamless handover and bandwidth aggregation.

In this study, the design of the class-E power amplifier is presented. In this design, the effects of on-state resistance and non-linear parasitic capacitances of the transistors are investigated. Two metal oxide semiconductor field-effect transistors (MOSFETs) of IRFZ24N and IRF510 with different drain-source resistances are used in the presented circuits. In the given design, the values of the operational frequency and duty ratio are 3.5 MHz and 0.5, respectively. This study shows the importance of considering non-linear parasitic elements of MOSFET, especially drain-source resistance in the designing of the class-E power amplifiers. It is shown that the class-E power amplifier with high MOSFET drain-source resistance needs high DC input voltage for both the primary and auxiliary circuits. In the previous works, non-linear on-state resistance and non-linear drain-source and gate-drain capacitances have not been included at the same time in the analyses. Two class-E amplifiers contain IRF510 and IRFZ24N are designed, simulated, and measured. The efficiency equal to 96.6% with 11.851 W output power at 3.5 MHz and the efficiency equal to 88.4% with 12.361 W output power are achieved for presented class-E amplifiers contain IRFZ24N and IRF510, respectively. M M M M M

This brief proposes a design approach for a high-efficiency continuous class-F power amplifier (PA) using quasi-elliptic low-pass filtering matching network (QELMN). In comparison with Chebyshev and modified elliptic matching networks, QELMN is composed of the quasi-elliptic low-pass filter (QEL) and optimized transmission lines (TL). QEL is a harmonic controller and matching circuit with low insertion loss, and its passband can be adjusted for each desired amplifier according to the operating band. Sharp roll-off and wide stopband of QEL controls harmonics, and low insertion loss of QEL improves delivered power from transistor to the load. The input impedance of QELMN is optimized with the goals of the stability and high power-Added efficiency. The proposed approach is adopted to implement a continuous class-F PA using an LDMOS device under a class-AB bias condition. Experimental results verify a wide bandwidth from 0.3 to 1 GHz, with measured drain efficiency of 62-81%, the output power of 37-40.3 dBm and power gain of 12-15.3 dB.

In an IEEE 802.11 Distributed Coordination Function–based wireless network with multiple hops, a node operates on its own with several predefined data rates (i.e. following modulation and coding schemes). Moreover, the IEEE 802.11 Distributed Coordination Function node’s communication is characterized by transmission and carrier-sensing distances. The transmission one is, in general, reverse proportional to the data rate. Meanwhile, the carrier distance keeps constant regardless of the modulation and coding scheme. Therefore, when a node has a high transmission rate, within its carrier-sensing range, the number of nodes may increase. The previous works have not yet extensively investigated the impact of data rates on such a scenario. This article addresses that issue aiming to quantify the network performance of the multi-hop IEEE 802.11 networks. As a solution, we propose the mathematical expressions, which consider data rates, for end-to-end throughputs, as well as delays in the network with string topology. We confirm the expressions’ correctness by presenting the quantitative agreements between the analytical and simulation results.

In a dense wireless network, concurrent transmissions normally increase interference and reduce network performance. In such an environment, however, there is a possibility that a frame can be decoded correctly if its receive power is higher than that of another frame by some predefined value (i.e., the so-called capture effect). As a result, the unfairness of throughputs among network nodes likely occurs in that context. This research aims to quantify the throughput performance of only one access point Wireless Local Area Networks (WLANs) with dense network nodes in the presence of the capture effect. We first propose a new analytical model, which can express not only WLANs' throughputs but also WLANs' unfairness transmission. The validity of the proposed model is confirmed by simulation results. Second, relying on the model, we present a novel Medium Access Control (MAC) protocol-based solution, which realizes throughput fairness between network nodes induced by the capture effect.

We propose a method for designing optimal external forcings of injection-locked oscillators. This method enables to maximize the locking range of general injection-locked oscillators under certain practical constraints that encompass the power, magnitude, or area of forcing waveforms. Here, as a case study, we focus on a class-E oscillator in the injection-locked mode, for which we obtain the following systematic results. First, by using the proposed method, the optimal waveform of external forcings is obtained analytically under three different constraints, i.e., the power-(i.e., root mean square-) reduced, magnitude-reduced, and area-reduced constraints. Second, through systematic circuit simulations, those predicted optimal forcings are verified to outperform any other forcings. Third, circuits experiments confirm that the theoretical predictions and the circuit simulation results are correct and consistent, regarding lock range maximization. These findings result in a first demonstration of locking range maximization for practical electrical oscillators.

There is an inherent reliance on collaboration among the participants of mobile ad hoc networks in order to achieve the fixed functionalities. However, they are susceptible to the destruction of the malicious attacks or denial of cooperation. Therefore, it becomes obvious that the security issue is urgently needed to be addressed. Over the last few years, many trust-considered countermeasures have been proposed. The design of trust quantification methods is the key of these countermeasures. In this study, we abstract a novel light-weight subjective trust inference framework, which is divided into trust assessment and trust prediction. The process of node trust assessment is based on node's historical behaviours. Then utilizing the obtained trust data sequence, we introduce the SCGM(1,1)-weighted Markov stochastic chain measure to predict node's trust for future decision making. Experimental results have been conducted to evaluate the effectiveness of the proposed trust model. As an important security application, based on the standard On-Demand Multicast Routing Protocol (ODMRP), we make four major improvements which take the issue of trust into consideration, and propose a novel trust-based routing protocol called the On-Demand Trust-Based Multicast Routing protocol (ODTMRP). And finally, convincing experimental results are presented using three routing evaluation metrics.

© 2017 Elsevier B.V. Cyber–Physical Cloud System is emerging as a promising system that enables a wide range of applications. In many applications, smart grids, sensing operations generate large amount of data. In order to effectively and efficiently collect large amount of data, a Global view of Context-aware Sensing and Collection (GCSC) scheme is proposed for exploiting both local and global of spatial–temporal correlations to perform data collection in cyber–physical cloud system (CPC system). In a GCSC scheme, the size of the representative region varies according to the residual energy of its smart sensor nodes. For areas far from the sink decrease the size of the representative region to keep high accuracy in the collected data, while areas near the Sink increase the size of the representative region to lower energy consumption to ensure efficient energy management. Thus, the accuracy of sensing data and lifetime can be enhanced at same time. Both theoretical analysis and experimental results indicate that the performance of GCSC scheme is better than the performance in previous studies. Compared with previous schemes, GCSC scheme can improve the data accuracy by 7.56%∼23.16% and increase the network lifetime by more than 11%, also increase energy efficiency as much as 12.39%.

Recently, we often see the environment where many one-to-one Wireless Local Area Networks (WLANs) exist in a small area. In this environment, the network throughput of certain WLAN reduces significantly because of the interference from other networks (i.e., inter-network interference). The inter-network interference is the effect of carrier-sensing activities when there are ongoing transmissions in neighbor networks. This paper presents analytical expressions using airtime concept, which newly take into account the inter-network interference, for network throughputs of WLANs. There are existing works that similarly address the WLAN's carrier-sensing duration. However, they either consider a simple interference model or assume the simultaneous transmission time is negligible. Different from them, we consider the significant impact of simultaneous transmission. As a result, our analytical model can precisely express each network carrier-sensing duration by subtracting the simultaneous transmission time. More specifically, we have successfully obtained each network throughput by expressing frame-existence probabilities concerning each network's End Device (ED). We also confirm the validity of the analysis by comparison with simulation. The analytical results and the simulation results agree well.

This paper proposes a Medium Access Control (MAC) protocol using directional antennas in wireless ad-hoc networks, which achieves frame-collision reduction, freezing-state duration reduction, and deafness-problem mitigation simultaneously. The idea of the proposed protocol is that Pulse/Tone exchange is applied to the Opportunistic Directional MAC protocol (OPDMAC). By applying the Pulse/Tone exchange prior to Request to Send/Clear to Send (RTS/CTS) handshake, RTS-to-RTS frame collisions are reduced dramatically. Additionally, RTS-to-DATA frame collisions in the OPDMAC are changed to Pulse signal-to-DATA frame overlaps in the proposed protocol. This change makes the DATA-frame transmissions in success because the Pulse signal-to-DATA frame overlaps are regarded as a deafness problem. On that basis, the deafness-problem mitigation can be obtained in the proposed protocol by adaptive transmission-direction switching, which follows the OPDMAC technique. The freezing-state durations can be also reduced by the transmission-direction switching. As a result, the proposed protocol provides high network throughput compared with conventional protocols. Simulation results show the validity and effectiveness of the proposed protocol.