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This paper presents a traffic-adaptive contention-free period (CFP) extension (TaCFPext) protocol for IEEE 802.15.4 deterministic and synchronous multichannel extension (DSME) medium access control (MAC), which aims to satisfy the traffic adaptability requirement of industrial wireless sensor networks (IWSNs). The legacy DSME standard has limitations in accommodating highly varying traffic load in IWSNs due to its fixed multi-superframe structure. TaCFPext enables a node to adaptively use a contention access period (CAP) as an extended CFP (extCFP) on the demands of traffic loads in a distributed manner. The node starts TaCFPext when it determines that the CFP of the current multi-superframe is insufficient to accommodate the traffic load. Then, the node selects a CAP to be used as an extCFP, on which it is allocated an extended guaranteed time slot (extGTS). When the traffic load decreases, the extGTS is deallocated before the GTS in CFP, and the extCFP returns to CAP again. An experimental simulation was performed to verify the superiority of TaCFPext. The results demonstrated that TaCFPext outperforms the legacy DSME for aggregate throughput and average delay under various traffic conditions.

This paper presents a time-slotted channel hopping (TSCH) multiple slotframe scheduling (TMSS) protocol to ensure the timeliness of energy harvesting and data transmission for sensor devices with different transmission periods in Internet of Things (IoT) networks enabled with time-switching simultaneous wireless information and power transfer (TS-SWIPT). The TMSS uses a modified three-step 6P transaction to allocate power and data cells within the slotframe. The sensor device sets the slotframe length equal to the transmission period and estimates the number of power and data cells for allocation in the configured slotframe and requests cell allocation to the hybrid access point (HAP). Upon request from a sensor device, the HAP executes a cell-overlapping prevention (COP) algorithm to resolve the cell-overlapping problem and responds to the sensor device with a candidate cell list. Upon receiving the response from HAP, the sensor device determines its power and data cells by referring to the cell list. We conducted experimental simulations and compared the TMSS performance to that of the legacy TSCH medium access control (MAC) with a single slotframe and the harvest-then-transmit-based modified enhanced distributed coordination function (EDCF) MAC protocol (HE-MAC). The results showed that TMSS outperforms legacy TSCH MAC and HE-MAC in terms of delay, effective throughput and energy utilization.

This paper presents a failure prediction model using iterative feature selection, which aims to accurately predict the failure occurrences in industrial Internet of Things (IIoT) environments. In general, vast amounts of data are collected from various sensors in an IIoT environment, and they are analyzed to prevent failures by predicting their occurrence. However, the collected data may include data irrelevant to failures and thereby decrease the prediction accuracy. To address this problem, we propose a failure prediction model using iterative feature selection. To build the model, the relevancy between each feature (i.e., each sensor) and the failure was analyzed using the random forest algorithm, to obtain the importance of the features. Then, feature selection and model building were conducted iteratively. In each iteration, a new feature was selected considering the importance and added to the selected feature set. The failure prediction model was built for each iteration via the support vector machine (SVM). Finally, the failure prediction model having the highest prediction accuracy was selected. The experimental implementation was conducted using open-source R. The results showed that the proposed failure prediction model achieved high prediction accuracy.

This paper presents a group-based concurrent transmission (GCT) scheme for visible light communications (VLCs), which aims to improve the spatial efficiency of IEEE 802.15.7-based VLC personal area networks (VPANs). To this end, the GCT combines the VLC devices that interfere with each other into a group taking into account the directionality of the light sources, and thus allows the concurrent transmissions of device groups. Then, it differentiates the BE value of each device group according to the number of VLC devices in the group, to prevent unnecessary backoff delay and collision. The GCT has two main operations: (1) VLC device grouping and (2) concurrent transmission scheduling. In the former, each device conducts neighbor discovery to create VLC device table (VDT) entries, through which the central controller builds groups considering the interference between neighbors. In the latter, the BE decision procedure is performed, in which the central controller allocates the different BE value to each group in accordance with the number of devices belonging to the group. To evaluate the performance of GCT, we developed the analytical model using the discrete-time Markov chain. The numerical results show that the GCT achieves a better network performance than the IEEE 802.15.7 standard.

This paper presents a variable-categorized clustering algorithm (VCCA) using fuzzy logic for Internet of Things (IoT) local networks. The VCCA selects the cluster head (CH) that has the highest network capacity through a classification process of cluster variables in accordance with the characteristics in order to configure a clustered network, which differs for different IoT applications. To achieve this, the VCCA employs a fuzzy inference system (FIS) that calculates an outcome through rule-based variable mapping for low complexity in the CH election and high scalability of cluster variables. In addition, experimental simulations using MATLAB are conducted to evaluate the performance of the VCCA. The simulation results show that the VCCA exhibits better network performance compared to the existing algorithms in terms of throughput, end-to-end latency, network lifetime, and energy consumption.

This paper presents a categorization-based video streaming approach (CVS) to mitigate the Internet traffic in content delivery services. The CVS uses the statistical information related to the users’ view patterns (i.e., the average view duration in accordance with the playback time of the content) to adjust the request period of content chunks. Therefore, it can reduce the amount of Internet traffic by reducing unnecessary chunk requests and content chunks. The operation of the CVS is based on the average view duration provided by the content provider (CP). However, even if the CP does not provide the average view duration of the content, the CVS can properly predict the average view duration by using the content categorization and adjust the request period of content chunks. The simulation results show that the CVS achieves better performance in terms of the average waste ratio of network resources, the amount of network traffic, and the number of chunk requests.

This paper empirically presents the impact of the correlation-based feature selection on the accuracy of the photovoltaic (PV) power prediction, and then selects the weather variables that maximize prediction accuracy. To this end, the experiments are conducted using the weather dataset consisting of eighteen weather variables (i.e., features). For experiments, we first calculate a correlation coefficient of each weather variable by analyzing the correlation between PV power and each weather variable. Then, we create the subsets of weather variables considering the absolute value of correlation coefficient and generate the multiple prediction models using the created subsets. Finally, the accuracy of the generated prediction models is compared with each other to find the most accurate prediction model. The experiment results provide a reference guideline for selecting the weather variables that maximize the accuracy of PV power prediction.

IEEE 802.15.4 Time Slotted Channel Hopping (TSCH) is designed to provide high reliability and predictable time delay for industrial applications that require strict preconditions. IEEE 802.15.4 TSCH operates on the slotframe structure consisting of multiple slotOffsets and channelOffsets. However, the IEEE 802.15.4-2015 standard does not specify how to determine the slotframe length. Thus, the inappropriate slotframe length can cause a long end-to-end delay in industrial applications. Therefore, in this paper, we analyze the impact of the slotframe length on the end-to-end delay performance in IEEE 802.15.4 TSCH network. The results of the analysis can be used as the guideline that determines the appropriate slotframe length for various industrial applications.

This paper presents a dynamic frame length adaptation (DFLA) for anti-collision management in Wi-Fi backscatter system, which reduces the collisions and access delay of the Wi-Fi backscatter tags. The Q-algorithm of EPCglobal Gen2 specification adjusts the frame length (i.e., total number of slots in a frame) at the beginning of each frame, so that Wi-Fi backscatter tags have different access delays depending on the number of collisions. Thus, it is generally used for anti-collision management in Wi-Fi backscatter system. However, the existing Q-algorithm may cause frequent collisions and long access delays since it uses the initial frame of fixed length and adjusts the frame length without considering the number of Wi-Fi backscatter tags. To address this problem, the DFLA dynamically determines the frame length at the beginning of each frame considering the number of Wi-Fi backscatter tags. Specifically, the DFLA estimates throughput for all the slot-count parameters (Qs) in the range of [0, 15] taking into account the number of backscatter tags. Then, it determines the frame length by deciding the Q that maximizes the throughput of Wi-Fi backscatter system. The simulation results show that the DFLA obtains a better performance than the existing Q-algorithm management.

This paper presents a flexible and scalable network simulation framework (FS-IIoTSim) for performance evaluation of the Industrial Internet of Things (IIoT) system. FS-IIoTSim provides flexibility and scalability for performance analysis of the IIoT system by supporting not only existing legacy industrial sensor network communication models, but also user protocol modification, in order to accommodate the complexity of systems such as communication standards, smart machine traffic, and applications over the complex IIoT environment. The FS-IIoTSim consists of three parts: (1) scenario modeling, (2) performance evaluation, and (3) user interface. Each of these parts interacts and cooperates with each other to simulate a target IIoT system. Scenario modeling utilizes user simulation scenarios to create an industrial sensor network (ISN) system model and perform simulations to generate a trace file recorded with log information about the device’s operations. Performance evaluation is an analytical process of a trace file, generated in the scenario modeling part, which is used to derive a user’s preferred performance metric. The user interface part plays a role in interfacing with the detailed operations of the other parts. Within the user interface part, the user can set and control the FS-IIoTSim framework. In order to prove the effectiveness of this FS-IIoTSim, experiments were conducted in various industrial application scenarios, and simulation performance was compared and analyzed with a dedicated simulator. The experimental results showed that FS-IIoTSim provided more effective approaches to modeling and performance analysis of real ISN environments, compared to a dedicated simulator.

In this paper, we propose a neighbor stability-based VANET clustering (NSVC) that can efficiently deliver data in urban vehicular environments. The salient features of urban vehicles are their high mobility and unpredictable direction of movement, so vehicle-to-vehicle and vehicle-to-infrastructure (V2X) communication should take into consideration the frequent changes in the topology of vehicular ad hoc networks (VANETs). These technical challenges are addressed with NSVC by including a neighbor stability-based VANET clustering scheme and the corresponding supplementary transmission scheduling method. Thereby, NSVC supports fast cluster formation, minimizes the number of cluster head elections, and moreover guarantees the reliable delivery of data for emergency messages. The results of the simulation indicate that NSVC achieves better network performance when compared to existing approaches.

We investigated the influence of low-concentration indium (In) doping on the chemical and structural properties of solution-processed zinc oxide (ZnO) films and the electrical characteristics of bottom-gate/top-contact In-doped ZnO thin-film transistors (TFTs). The thermogravimetry and differential scanning calorimetry analysis results showed that thermal annealing at 400 °C for 40 min produces In-doped ZnO films. As the In content of ZnO films was increased from 1% to 9%, the metal-oxygen bonding increased from 5.56% to 71.33%, while the metal-hydroxyl bonding decreased from 72.03% to 9.63%. The X-ray diffraction peaks and field-emission scanning microscope images of the ZnO films with different In concentrations revealed a better crystalline quality and reduced grain size of the solution-processed ZnO thin films. The thickness of the In-doped ZnO films also increased when the In content was increased up to 5%; however, the thickness decreased on further increasing the In content. The field-effect mobility and on/off current ratio of In-doped ZnO TFTs were notably affected by any change in the In concentration. Considering the overall TFT performance, the optimal In doping concentration in the solution-processed ZnO semiconductor was determined to be 5% in this study. These results suggest that low-concentration In incorporation is crucial for modulating the morphological characteristics of solution-processed ZnO thin films and the TFT performance.

This paper focuses on a wireless solution for vehicular infrastructure systems. In order to achieve both low cost and high efficiency, infrastructures can be connected to each other in vehicular networks by a wireless link similar to a mesh router in wireless mesh networks (WMNs). However, the existing WMN solutions cannot appropriately support various vehicular applications that require high rate and low latency communications. Therefore, in this paper, we present the design and performance evaluation of an adaptive multi-channel allocation for vehicular infrastructure mesh systems (abbreviated AMCA). In order to meet both high rate and low latency communications, AMCA is designed to provide optimal channel assignment duration for each flow to efficiently utilize multiple non-overlapping channels. The performance evaluation of AMCA is conducted by the QualNet 5.0 simulator under various network scenarios to consider diverse network conditions. Simulation results show that AMCA can achieve higher network throughput and lower average packet delay than other well known wireless solutions.