Internet Demand Research Articles

Obstacle-Aware Intelligent Fault Detection Scheme for Industrial Wireless Sensor Networks

Nowadays, the demand for the Industrial Internet of Things (IIoT) technology has increased immensely in various fields, such as the agriculture industry, smart mines, smart factories, healthcare industry, etc. Industrial wireless sensor networks (IWSNs) act as a backbone of any IIoT system by forming a network of heterogeneous sensors. In IWSNs, the fault occurrence probability is more due to continuous exposure to harsh environments. Furthermore, the presence of obstacles creates an extra burden in fault detection. In this article, the proposed scheme presents an optimal fault diagnostic point selection mechanism that significantly reduces fault detection latency and energy consumption. Multiple intelligent mobile fault detectors effectively avoid obstacles during the fault detection process that significantly improves fault detection accuracy (FDA). Extensive simulations and a testbed experiment demonstrate the effectiveness of the proposed scheme in terms of FDA, false alarm rate, false positive rate, F1-score, energy consumption, and network lifetime.

Collaborative task offloading and resource allocation with hybrid energy supply for UAV-assisted multi-clouds

Cloud computing has emerged as a promising paradigm for meeting the growing resource demands of Internet of Things (IoT) devices. Meanwhile, with the popularity of mobile aerial base stations, Unmanned Aerial Vehicle (UAV) assisted cloud computing is essential for providing diversified service at areas without available infrastructure. However, it is difficult to meet the requirements of a number of IoT devices which distribute a large area through one single UAV cloud server, and thus multi-clouds have been applied in large-scale IoT environments. Due to the limited battery capacity of UAV, hybrid energy supply has been considered as an effective approach. How to allocate the computing resources and offload the tasks to the UAV-assisted clouds is a challenging task. In this paper, we study the trade-off between the energy consumption and system performance in a UAV-assisted multi-clouds system. Considering the transmission and execution cost, a dynamic optimization problem with the objective of minimizing the power consumption of UAVs with the constraint of queue stability is formulated, which is further decomposed into three sub-problems using stochastic optimization techniques. A collaborative task offloading and resources allocation algorithm (CTORAA) based on artificial intelligent (AI) technique is proposed to jointly determine task offloading and energy harvesting. We provide corresponding mathematical analysis showing that CTORAA can reach the arbitrary profit-stability trade-off. Finally, we conduct simulation experiments to validate the efficacy of our algorithm.

A Fuzzy-Based Method for Objects Selection in Blockchain-Enabled Edge-IoT Platforms Using a Hybrid Multi-Criteria Decision-Making Model

The broad availability of connected and intelligent devices has increased the demand for Internet of Things (IoT) applications that require more intense data storage and processing. However, cloud-based IoT systems are typically located far from end-users and face several issues, including high cloud server load, slow response times, and a lack of global mobility. Some of these flaws can be addressed with edge computing. In addition, node selection helps avoid common difficulties related to IoT, including network lifespan, allocation of resources, and trust in the acquired data by selecting the correct nodes at a suitable period. On the other hand, the IoT’s interconnection of edge and blockchain technologies gives a fresh perspective on access control framework design. This article provides a novel node selection approach for blockchain-enabled edge IoT that provides a quick and dependable node selection. Moreover, fuzzy logic to approximation logic was used to manage numerical and linguistic data simultaneously. In addition, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), a powerful tool for examining Multi-Criteria Decision-Making (MCDM) problems, is used. The suggested fuzzy-based technique employs three input criteria to select the correct IoT node for a given mission in IoT-edge situations. The outcomes of the experiments indicate that the proposed framework enhances the parameters under consideration.

Cooperative Scheduler to Enhance Massive Connectivity in 5G and Beyond by Minimizing Interference in OMA and NOMA

The fifth-generation (5G) and beyond 5G (B5G) wireless networks introduced massive machine-type communications (mMTC) to cope with the growing demand of massive Internet of Things (IoT) applications. However, the heterogeneous characteristics of massive IoT and diverse quality of service (QoS) requirements may lead to severe interference that could degrade the expected QoS of the cellular ecosystem. Therefore, this article studies the impact of interference caused by mMTC connections. We theoretically model the intercell interference (ICI) minimization problem for the existing orthogonal multiple access (OMA) technique and propose its corresponding solution. Furthermore, we jointly solve the ICI and the cochannel interference minimization problem for the IoT users when the nonorthogonal multiple access (NOMA) technique is used. For the proposed OMA and NOMA schemes, we design a cooperative scheduler to reduce the impact of such interference. The results show that our proposed schemes provide up to 58%, 75%, and 100% more improvements in terms of user’s data rates, energy consumption, and connection density, respectively.

Event-Driven Computation Offloading in IoT With Edge Computing

Edge computing, which provides computation services at the edge of networks, has become a promising method to meet the massive computation demands of Internet of Things (IoT). To make full use of resources, a computation offloading scheme is needed in edge computing system. In this work, we propose an event-driven computation offloading scheme for the first time. Compared with the existing time-driven schemes, the proposed scheme has a smaller implementation complexity in some scenarios with computation-intensive task computing. In the proposed scheme, the priority of different tasks is jointly considered. To decide the optimal offloading action of the scheme, we formulate the offloading problem as a semi-Markov decision process (SMDP). Then, a model-based method is proposed to derive the optimal offloading policy under fully explored system by addressing the challengs of modeling. On the other hand, considering partially explored system, we propose an online double deep Q-network algorithm, which can deal with the poor scalability of the standard Q-learning algorithm, to derive the optimal offloading policy. In addition, we also introduce some tricks to accelerate the learning procedure. The simulation results show the superior performance of our proposed scheme.

Analysis of GPON capacity by hybrid splitting-ratio base on customer segmentation for Indonesian market during the Covid-19 pandemic

During the Covid-19 pandemic, the impact on internet demand is very high throughout the world. In Indonesia, through a survey conducted, broadband users recorded 175 million people or equivalent to 64 % of the population. Based on field observations, the Passive Optical Network (PON) port utility in the Optical Line Terminal (OLT) is presently below 75 %, as opposed to the initial value, which was more than 95 % [1]. Furthermore, it was also found that more than 80 % of IndiHome customers use a maximum bandwidth of 20 Mbps with majority using 10 Mbps, which is very ineffective, especially related to network capacity. Generally, passive Optical Distribution Network (ODN) devices are not optimally used, therefore this research aims to determine the optimization level of the FTTH network by maximizing the available infrastructure capacity. This process was carried out by increasing the associate ratio of 1:32 to 1:64 while taking into account the Optical Line Terminal (OLT) capacity factor and the standard link budget with schemas A, B and C used to obtain adequate efficiency. Scheme A consists of a 1:2 passive splitter in the Optical Distribution Cabinet (ODC) & 1:32 at the Optical Distribution Point (ODP). Scheme B consists of a 1:4 (ODC) & 1:16 (ODP) passive splitter, while scheme C consists of a 1:8 (ODC) & 1:8 (ODP) passive splitter intended for the use of different customer area types. The result showed an increase in network capacity from 32 to 64 per PON port while taking into account the technical quality of the Link Budget. In conclusion, to ensure this solution functions properly, both simulation and direct measurement tests are carried out using the existing network

Nurses' Willingness and Demand for Internet +home Care Services and Its Influencing Factors in Different Levels of Hospitals in China - A Nationwide Survey.

PurposeThe present study aimed to investigate nurses’ willingness and demand for Internet +home care services in different levels of hospitals in China and analyze the influencing factors.Participants and MethodsFrom October 1 to December 31, 2021, 5714 nurses from 15 hospitals in five regions of China were recruited in this cross-sectional study using a two-stage random sampling method. A self-designed questionnaire with good reliability and validity was used to measure nurses’ willingness and demand for Internet +home care services. χ2 test, Welch t-test, and multiple linear regression analyses were used to analyze the data.ResultsNurses were highly willing to provide Internet +home care services. Statistical differences were found in the willingness to provide Internet +home care services and the preference for service distance, service platform, and single service fee between nurses in different levels of hospitals (both P<0.05). The willingness to provide “catheter maintenance service” and “rehabilitation nursing service” of nurses in different levels of hospitals were statistically significant (both P<0.05). Nurses’ demand for Internet +home care services increased with the level of their hospital. Multiple linear regression showed that professional title, educational level, monthly family income, and mortgage or car loan influence nurses’ demand for Internet +home care services.ConclusionNurses’ willingness and demand for Internet +home care services vary with the level of their hospitals. It is recommended that government and hospitals regulate the service items, the service distance, single service fee, and other contents according to nurses’ willingness and demand and establish relevant laws and regulations to ensure the steady and orderly development of the Internet +home care services.

Privacy-Aware Task Assignment for IoT Audit Applications on Collaborative Edge Devices

To meet the rapidly increasing demand for Internet of Things (IoT) applications, edge computing, as a novel computing paradigm, can combine devices at the edge of the network to collaboratively provide computing resources for IoT applications. However, the dynamic, heterogeneous, distributed, and resource-constrained nature of the edge computing paradigm also brings some problems, such as more serious privacy leakages and performance bottlenecks. Therefore, how to ensure that the resource requirements of the application are satisfied, while enhancing the protection of user privacy as much as possible, is a challenge for the task assignment of IoT applications. Aiming to address this challenge, we propose a privacy-aware IoT task assignment approach at the edge of the network. Firstly, we model the resource and privacy requirements for IoT applications and evaluate the resource satisfaction and privacy compatibility between edge devices and tasks. Secondly, we formulate the problem of privacy-aware IoT task assignment on edge devices (PITAE) and develop two solutions to the PITAE problem based on the greedy search algorithm and the Kuhn–Munkres (KM) algorithm. Finally, we conduct a series of simulation experiments to evaluate the proposed approach. The experimental results show that the PITAE problem can be solved effectively and efficiently.

Frontispiece: Perovskite Photovoltaics for Artificial Light Harvesting

With increasing demand for internet of things, the commensurate necessity for reliable low-power sources has also arisen. Perovskite materials have been recently identified as palpable candidates for harnessing artificial indoor light in these emerging applications. In this review, the milestones that have been achieved regarding compositional tuning, surface treatment, interlayer and device engineering were summarized. For more information, see the Review by J. W. Shim, J. W. Jo and co-workers (DOI: 10.1002/chem.202200266)

Study on site selection combination evaluation of pumped-storage power station based on cycle elimination — Based on the empirical analysis of North China

Energy structure reform is the common choice of all countries to deal with climate change and environmental problems. Pumped-storage power station (PPS) will play an important role in the green and low-carbon energy era of “source-grid-load-storage” synergy and multi-energy complementary optimization. In this context, this paper puts forward a PPS selection evaluation index system and combination evaluation model for energy internet. First of all, on the basis of the construction conditions of traditional PPS, considering the multi-energy complementary demand of energy internet, a three-dimensional evaluation index system of “hydrology & geological conditions”, “construction conditions” and “energy structure demand” has been established. Secondly, concepts such as degree of center cohesion, similarity threshold and core method set are introduced to construct a cycle elimination mechanism and eliminate the edge single evaluation method (SEM) to improve the scientificity of combination evaluation. Site selection combination evaluation of PPS based on cycle elimination is constructed, and effectiveness measure test of site selection combination evaluation method is defined. Finally, PPS in North China is taken as an example to verify the effectiveness of the model and the sensitivity analysis is carried out. The research shows that the effectiveness of the combination evaluation method is not only determined by the mechanism of the method itself, but also affected by the consistency of the SEM set, and the cycle elimination mechanism can improve the effectiveness of the combination evaluation method. The evaluation index system, site selection model based on cycle elimination and effectiveness measure are constructed in this paper has high feasibility. The classification characteristics of PPS sites are proposed for the first time, and PPS sites are divided into seven types according to three index dimensions: it provides the basis for differentiated decision making of PPS site selection.

IoT forensics: What kind of personal data can be found on discarded, recycled, or re-sold IoT devices

With the advancement in science and technology, the demand for electronic Internet of Things (IoT) devices has increased to store and process digital data. With the advancement in IoT technology, we are more reliant on IoT devices to perform our regular tasks. IoT Forensics deals with the investigation of IoT devices such as smartphones, tablets, smart watches, or any device which is connected to the Internet. The data on these devices can still be retrieved via IoT forensics even if they are destroyed, resold, or misplaced. Before discarding any IoT device, we must consider how to secure the data stored on them. We are then going to discuss the several types of personal data that could be retrieved from discarded IoT devices. Finally, we are going to discuss the protection measures for the safe disposal of IoT devices.

A survey on IoT-based smart agriculture to reduce vegetable and fruit waste

Agriculture automation is a top focus and developing area for a number of countries right now. We are seeing a surge in demand for Internet of Things (IoT) in various industries these days. One of the most essential applications of IoT is agriculture. Today, we notice that the world’s population is quickly rising, and an agro product plays a critical part in this population’s existence. We are conscious of the fact that resources are limited. If we continue to farm in the traditional manner, it will be extremely difficult for the rising population to survive. Due to improper fertilizers, quantity of water, chemicals and huge amount of pesticides decreases the fertility of land. There is a need for smart agriculture to monitor all these factors which affect the fertility of soil. When the fertility is decreasing it will impact on the growth of fruits and vegetables. In this paper we are going to study how we can manage our resources through Internet of Things, multispectral camera, hyper spectral camera, and thermal camera and RGB camera. Plant diseases, pesticide control, weed control, proper irrigation, and water management are all problems in agriculture that can be readily solved with the various automated and control approaches stated above.

Sustainable and Optimized Data Collection via Mobile Edge Computing for Disjoint Wireless Sensor Networks

With the ever-increasing demand for Internet of Things (IoT) applications, wireless sensor networks (WSNs) have become the central means to disseminate data for analysis in the era of mobile edge computing. Mobile sinks (MSs) as edge nodes have emerged as an efficient solution to the performance enhancement of WSNs. One important task of the MSs is to collect data in a sustainable and optimized manner by visiting certain rendezvous points (RPs) inside the WSN. However, most existing works focus only on connected WSNs, while disjoint networks are the reality in many IoT applications. Moreover, none of them have considered a realistic propagation model. They have also ignored optimizing both the number of RPs and MSs. This paper proposes a novel data collection scheme while paying attention to all these issues. The scheme is specially designed for delay-harsh applications. First, we propose a convex hull-based algorithm to determine RPs for constructing an optimal tour of a MS. Then using the resulting set of RPs, we present another algorithm based on the Jaya metaheuristic to determine an optimal number of MSs and their balanced tours. Rigorous simulations show that our scheme outperforms existing algorithms in terms of various performance metrics.

The role of IOT in transforming community pharmacy

During Covid-19 Pandemic the entire world experiences the role and importance of Internet of Things (IoT) in healthcare system and especially in Community Pharmacy. Internet of Things can be described as a network architecture incorporating an abundance of sensors software hardware, computing devices, technologies, machines and many more utilities, assisting us in our relevant domains as per the needful. Over the yesteryears it has been observed that the footprints of Internet of Things over the healthcare sector have increased eloquently. This observation leads us to the fact that with the escalating population and the healthcare plight Internet of Things can really prove itself to be serious breakthrough in the community pharmacy. By revising the current healthcare system in the community pharmacies in a more socio-economic fashion. This paper reviews the technological advancements in Internet of Things, for the medical management of public health so that it can be vitally made use of in the domain of community pharmacy. There is a demand of efficient Internet of Things framework that would seriously be able to challenge the abysmal state of community pharmacy mostly in the remote areas and the distant regions. Keywords: Internet of Things (Iot), Community pharmacy, Medical management, Sensor technology.

Electrochromic Displays Manufactured by a Combination of Vapor Phase Polymerization and Screen Printing

AbstractSmart label technology such as indicators is a growing field due to society's demand for Internet of Things devices. New materials and technologies are continuously being discovered and developed in order to provide better resolution, better performance, or more environmentally friendly devices. Within this report, screen printing technology is combined with vapor phase polymerization to synthesize three conductive polymers; poly(3,4‐ethylenedioxythiophene) (PEDOT), polypyrrole (PPy), and polythiophene (PTh). The conductive polymers are created in micrometer resolution and investigated for their electrochromic properties. PEDOT and PPy samples are combined into printed, laminated, transmissive electrochromic displays. The technology is further advanced to establish separate PEDOT, PPy, and PTh all‐printed electrochromic displays using several screen printed layers. The PEDOT displays show improved color retention as compared to displays created with commercially available PEDOT:poly(styrene sulfonate) (PSS) with comparable contrast and switching behavior. All‐printed PPy and PTh electrochromic displays with impressive electrochromic behavior are demonstrated. More complex patterns of 7‐segment displays are created, thereby highlighting flexibility and individually switched sections of the conductive polymers. This research extends the screen printing and vapor phase polymerization combination to other conductive polymers and the potential commercialization of multicolor electrochromic displays that has been otherwise dominated by monochromatic PEDOT:PSS.

An ensemble method for feature selection and an integrated approach for mitigation of distributed denial of service attacks

AbstractDistributed denial of service attacks (DDoS) penetrate numerous computer system and implant malicious codes thereby making them ready for launching a collaborative attack. These attacks paralyze the target system mainly the web server by exhausting their network resources of the target server. The threats posed by DDoS attacks on the Internet demands for effective detection and mitigation methods of these attacks. In the paper, we proposed an integrated method for detection and mitigation of DDoS attack using machine learning and a line of defenses respectively. The detection phase consists of feature selection through ensemble feature selection algorithm and classification using machine learning algorithm. Feature selection algorithms are important as they reduce the dimension of the dataset. The selection of an efficient classification model will improve the detection rate of the proposed system. In the mitigation phase, we introduce two lines of defense to minimize the exhaustion of the victim server's resources. Using the existing dataset, we show experimentally that it is possible to detect the presence of attacks and mitigate them to a minimum level. The proposed integrated method yields an accuracy of 97.8% in detecting the attacks and able to reduce the utilization of processors upto an average of 25.95%.

ENGLISH STUDENTS’ SELF-RESILIENCE AND CHALLENGES IN DISTANCE LEARNING

Due to the Covid-19 epidemic, learning has shifted from offline to online, affecting pupils' learning resilience and the complexity of learning obstacles. As a result, the focus of this study is on English students' resilience and the obstacles they experience during distance learning. This study used qualitative methodologies and included 20 English students who were purposefully chosen as participants. They were questioned in accordance with the study's objectives. An interactive model was used to analyse the interview data. The data suggest that in remote learning, students' self-control is lacking. They are under psychological duress. Their physical state was deteriorating, and several of them were experiencing headaches. They are mentally bored and unable to concentrate, and they cannot enjoy learning due to a lack of social interaction. Furthermore, students encounter issues connected to internet signals, budgeting to meet internet demands, and difficulty in locating learning resources during remote learning, all of which are classified as examples of poor technical competence.

Threat-modeling-guided Trust-based Task Offloading for Resource-constrained Internet of Things

There is an increasing demand for Internet of Things (IoT) networks consisting of resource-constrained devices executing increasingly complex applications. Due to these resource constraints, IoT devices will not be able to execute expensive tasks. One solution is to offload expensive tasks to resource-rich edge nodes, which requires a framework that facilitates the selection of suitable edge nodes to perform task offloading. Therefore, in this article, we present a novel trust-model-driven system architecture , based on behavioral evidence , that is suitable for resource-constrained IoT devices and supports computation offloading. We demonstrate the viability of the proposed architecture with an example deployment of the Beta Reputation System trust model on real hardware to capture node behaviors. The open environment of edge-based IoT networks means that threats against edge nodes can lead to deviation from expected behavior. Hence, we perform a threat modeling to identify such threats. The proposed system architecture includes threat handling mechanisms that provide security properties such as confidentiality, authentication, and non-repudiation of messages in required scenarios and operate within the resource constraints. We evaluate the efficacy of the threat handling mechanisms and identify future work for the standards used.

Analysis and Implementation of OVSF Address Decoders

Demand for Internet of Things (IoT) devices has generated interest in fault-tolerant address decoders. Address decoders affect the energy required to establish a data link and monitor a wireless channel through metrics such as detection probability and false alarm probability. This brief establishes a framework for calculating detection and false alarm probabilities and shows that limiting the number of correctable errors eliminates simultaneous false alarms, reduces the probability of false alarm, and reduces the maximum normalized power consumption of a sleep node due to false alarms by 16.9 dB. The probability expressions are formulated so that the detection probability, the false alarm probability, the energy to transmit, and the power to monitor a channel can be adjusted digitally, yielding a counter-based implementation that reduces the energy consumption of the address decoder to 1.3 pJ/bit for a data rate of 200 bps in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.18~\mu \text{m}$ </tex-math></inline-formula> CMOS.

A Knowledge Transfer-based Semi-Supervised Federated Learning for IoT Malware Detection

As the demand for Internet of Things (IoT) technologies continues to grow, IoT devices have been viable targets for malware infections. Although deep learning-based malware detection has achieved great success, the detection models are usually trained based on the collected user records, thereby leading to significant privacy risks. One promising solution is to leverage federated learning (FL) to enable distributed on-device training without centralizing the private user records. However, it is non-trivial for IoT users to label these records, where the quality and the trustworthiness of data labeling are hard to guarantee. To address the above issues, this paper develops a semi-supervised federated IoT malware detection framework based on knowledge transfer technologies, named by FedMalDE. Specifically, FedMalDE explores the underlying correlation between labeled and unlabeled records to infer labels towards unlabeled samples by the knowledge transfer mechanism. Moreover, a specially designed subgraph aggregated capsule network (SACN) is used to efficiently capture varied malicious behaviors. The extensive experiments conducted on real-world data demonstrate the effectiveness of FedMalDE in detecting IoT malware and its sufficient privacy and robustness guarantee.