Internet Data Collection Research Articles

Energy efficient and sustainable mobile data collection in internet of things: a variable dimension SSO-based approach

Energy efficient and sustainable mobile data collection in internet of things: a variable dimension SSO-based approach

Research on Bionic Design of Packaging Forms for Snacks in Fishing Villages

This article aims to explore the bionic design of the packaging form of snacks in fishing village. Taking Quanzhou xunpu village as an example, through the in-depth understanding of the culture and natural environment of the fishing village, as well as the application of the principle of bionics, a snack package with bionic morphology is designed in line with the local characteristics of XunPu Village and the marine culture. The research combines the analysis of the morphological characteristics of marine organisms such as oysters, shells and fishes, and combines these characteristics with the traditional cultural background of the characteristic snacks of the fishing village to design a new type of packaging scheme that meets the requirements of aesthetics, functionality and sustainability. Through fieldwork research and internet data collection, and research and analysis were carried out. The final design solution is not only artistic and culturally expressive, but also considers the impact of packaging on the environment and enhances the market competitiveness of the product and consumer experience. Future research can further explore the application of bionic design in packaging and its potential in promoting sustainable development and cultural heritage.

Analisis Rasio Likuiditas, Solvabilitas dan Profitabilitas Terhadap Kinerja Keuangan

This research was conducted to finding out how big a percentage of PT's financial performance is. Mayora Indah Tbk. using Liquidity, Solvency and Profitability ratios for the 2020-2022 period. The nature of the research in this research is qualitative research. In this research, the data used in the annual financial reports of PT. Mayora Indah Tbk. 2020-2022 period. The population in this research is all assets, equity and income of PT. Mayora Indah Tbk. The sampling technique used in this research used purposive sampling. The data collection method used by the author is using internet research data collection methods, namely collecting indirect observation data by collecting data from PT's annual financial reports. Mayora Indah Tbk. 2020-2022 period. The results of this research show that the liquidity ratio as measured using the current ratio and quick ratio shows good results for company performance, the solvency ratio as measured using the Debt to Asset Ratio is 43% for 2020 and in 2021, 2022 is 42%, and Debt to equity ratio was 73.5% in the 2020 and 2022 periods, while in 2021 it was 75.35.

SMS2DC: Synchronous mobile sinks scheduling for data collection in internet of things‐enabled wireless sensor networks

SummaryEnergy‐efficient data collection in wireless sensor networks (WSNs) is crucial due to the limited battery capacity of sensor nodes (SNs). Using a mobile sink (MS) for data collection can lower the energy consumption of SNs to avoid relaying in WSNs. However, a single MS is not a feasible solution for large‐scale WSNs, so it was necessary to use multiple MSs to collect data. A synchronous MS scheduling strategy for data collection (SMS2DC) is proposed in this paper, which uses two types of MS, a local MS to collect data from SN and a global MS to collect data from local MS. In this process, we begin by partitioning the network based on chemical reaction optimization. For each partition, a MS is assigned and scheduled using a path construction strategy according to a geometric path construction approach. In addition, a global MS is scheduled based on a local MS trajectory by identifying the most appropriate collision point to collect data. As a result, the algorithm increases data collection accuracy while minimizing network data loss. The asymptotic time complexity of the proposed SMS2DC algorithm needed . The comparison results show the superiority of the proposed SMS2DC strategy under multiple scenarios under various deployment conditions.

Prevalence of acne vulgaris, its contributing factors, and treatment satisfaction among the Saudi population in the Western Region of Saudi Arabia: a cross-sectional study

Objective: The current study aimed to assess the prevalence of acne vulgaris, its contributing factors, and treatment satisfaction among the Saudi population in the Western region of Saudi Arabia. Methods: The research employed a cross-sectional design with 390 participants, utilizing a convenience sampling technique. An online questionnaire was distributed via the Internet for data collection. Results: The results revealed that the majority of the participants had either oily (33.8%) or mixed (35.9%) skin, while dry (14.6%) and normal (15.6%) skin types were less common. In terms of acne severity, the largest proportion of participants reported experiencing mild acne (49.2%), followed by those with no acne (36.4%), moderate acne (11.8%), and severe acne (2.6%). Regarding dermatological factors, acne scarring was observed in 45.9% of the participants, and post-inflammatory hyperpigmentation was also prevalent, with 45.1% of the participants experiencing it. In terms of psychological factors, shyness was reported by 37.2% of the individuals, and depression was noted in 16.2% of the participants. The individuals aged 15-24 years demonstrated a higher odds ratio (3.153) as compared to other groups indicating a potential association between younger age and the condition. The females exhibited a higher odds ratio (1.838) as compared to males, albeit not statistically significant. Conclusion: This study revealed a considerable impact of acne on individuals. Additionally, the presence of acne scarring, along with psychological challenges such as shyness, depression, and social isolation, underscores the necessity for comprehensive skincare approaches addressing both physical and mental health aspects.

Determinants of electronic word of mouth in the restaurant industry: An evidence from Vietnam

The food and beverage industry in Vietnam has developed dramatically, resulting in a highly competitive market. To win the competition, the restaurant managers not only strive to increase customer satisfaction but also create customers’ positive electronic Word-Of-Mouth (eWOM). This study aims to investigate the relationship between customer satisfaction, customer trust, customer loyalty, and eWOM. The quantitative method was implemented to examine the hypotheses in the research model. Data were collected from Ho Chi Minh City respondents using a convenient sampling method. Self-administrated questionnaires were delivered via the Internet for data collection. After data screening, 299 responses were qualified for analysis. Partial Least Squares Structural Equation Modeling (PLS-SEM) analysis was employed by SmartPLS software version 3.0. The measurement model test showed that one item of customer trust has a factor loading of less than 0.7 as a result, this item was removed from the research model. The structural model evaluation revealed the highly predictive accuracy of the model as all dependent variables have R2 values above 0.5. A bootstrapping test with 5,000 subsamples also showed that customer loyalty significantly influences eWOM, but customer trust does not. Customer satisfaction directly and significantly impacts customer trust and loyalty but has little influence on eWOM. However, the relationship between customer satisfaction and eWOM is significant without the presence of customer loyalty and trust. The indirect effect test also determines the full mediation role of customer loyalty in the relationship between customer satisfaction and eWOM.

Average AoI minimization for data collection in UAV-enabled IoT backscatter communication systems with the finite blocklength regime

Thanks to the autonomy of unmanned aerial vehicle (UAV), UAV-enabled data collection in Internet of Things (IoT) networks has become a key application for the next generation communication network. In this paper, we consider a scenario where an UAV is responsible for collecting data from sensor equipments (SEs) one by one and finally carrying the collected data to the computation center for processing. Different from the commonly used assumption that SEs always generate data at the beginning of each time slot, it is assumed that SEs can generate data at any instant during one time slot, which is more practical. Since SEs are energy limited, they upload data to the UAV by adopting backscatter communication technology to reduce energy consumption. Meanwhile, the updated information usually contains a small number of information bits but requires low latency and high reliability, thus the finite blocklength regime in ultra-reliable and low-latency communication is adopted for SEs’ data transmission to the UAV. To keep the freshness of the updated information, a joint resource allocation problem including data collection time allocation, transmission power and trajectory design of the UAV is formulated as an optimization problem to minimize the average age of information (AoI) of all SEs. The formulated problem mixes discrete and continuous variables, which makes it difficult to solve. Thereby, we decompose the optimization problem into data collection time minimization subproblem and UAV trajectory design subproblem, which are solved by the successive convex approximation method, and the backtracking algorithm and the genetic algorithm, respectively. Numerical results show that the backtracking-based algorithm that can obtain the optimal trajectory of the UAV gains the minimal average AoI, and the genetic-based algorithm achieves sub-optimal average AoI with much lower computational complexity. The results also demonstrate that the average AoI of the backtracking-based algorithm and the genetic-based algorithm is reduced by up to 54% and 46% compared with the greedy-based benchmark algorithm, respectively.

Age–energy‐aware trajectory planning for UAV‐assisted data collection in Internet of Things

AbstractUnmanned aerial vehicles (UAVs) are employed as mobile relay nodes to enable timely remote monitoring by collecting information from monitoring devices and transferring the collected information to base station. The freshness of delivered information is critical to system performance, which can be quantified by the concept of age of information (AoI). Nevertheless, the fresher information comes at the cost of higher energy consumption at UAVs. Considering the limited onboard energy, it is essential to strike a balance between the age of delivered information and the required energy budget. here, both straight trajectory and circular trajectory of UAV are considered, and study a problem with the goal of supporting timely data collection while minimizing the energy consumption at UAV. The problem is formulated as a multi‐criteria optimization problem by jointly considering the aging of collected information, the trajectory planning of UAV and energy consumption at UAV. To solve the formulated problem, a solution procedure to find a sequence of Pareto‐optimal points is proposed. Simulation results demonstrate the Pareto‐optimal curve, which yields the energy‐efficient UAV trajectory for timely data collection.

P2AE: Preserving Privacy, Accuracy, and Efficiency in Location-Dependent Mobile Crowdsensing

With the widespread prevalence of smart devices, mobile crowdsensing (MCS) becomes a new trend to encourage mobile nodes to participate in cooperative data collection in various Internet of Things (IoT) applications. In location-dependent MCS, location information of mobile nodes are collected and analyzed by service provider to assist in task allocation. If the service provider is not fully trusted, mobile node's privacy is leaked and accessed by unauthorized parties. How to preserve privacy while maintaining task allocation accuracy and efficiency becomes challenging. To this end, we propose a learning-based mechanism that involves two parts: 1) privacy-preserving task release and task allocation; 2) accurate and efficient task allocation. In the first part, we design a location-based symmetric key generator, which enables two parties to self-generate a symmetric key without depending on fully trusted authorities. By utilizing this key generator and Proxy Re-encryption, we propose a privacy preserving protocol to protect location information in task release and task allocation. In the second part, we design a reinforcement learning based task allocation algorithm to optimize the winners selection, which obtains high accuracy and efficiency. The performance analysis reveals that our proposed mechanism achieves accurate and efficient task allocation while preserving privacy in location-dependent MCS.

DDC‐OMDC: Deadline‐based data collection using optimal mobile data collectors in Internet of Things

SummaryMobile data collectors (MDCs) are very efficient for data collection in internet of things (IoT) sensor networks. These data collectors collect data at rendezvous points to reduce data collection latency. It is paramount to determine these points in an IoT network to collect data in real time. It is important to consider IoT network characteristics to collect data on a specific deadline. First, the disconnected IoT sensor network is a real challenge in IoT applications. Second, it is essential to determine optimal data collection points (DCPs) and MDCs simultaneously to collect data in real time. In this study, Deadline‐based Data Collection using Optimal Mobile Data Collectors (DDC‐OMDC) scheme is proposed that aims to collect data in a disconnected network with the optimal number of mobile data collectors in a specific deadline for delay‐intolerant applications. DDC‐OMDC works in two phases. In the first phase, the optimal number of MDCs is determined to collect data at the optimal data collection points to guarantee one‐hop data collection from each cluster. The optimal mobile data collectors are determined using optimal DCPs, data collection stopping time, and a specific deadline. In the second phase, the optimal data collection trajectory is determined for each MDC using the nearest neighbor heuristic algorithm to collect data in real time. The simulation results show that the proposed scheme outperforms in collecting data in real time and determines optimal mobile data collectors and optimal data collection trajectory to collect data in a specific deadline for delay‐intolerant applications.

Industrial Internet Data Collection and Processing Technology Research

With the rapid development of the Industrial Internet of Things, data acquisition and processing technology has become a research hotspot. This paper focuses on the industrial Internet data acquisition and processing technology. In view of the current situation and existing problems of industrial Internet data collection technology, such as slow transmission speed, low data quality, poor security, etc., from the three aspects of data transmission, data processing and data security. Specifically, the transmission mode based on the Internet of Things, distributed data processing technology and encryption algorithm and other technical means are adopted to improve the speed and quality of data transmission and ensure the data security. The research results of this paper provide reference and reference for the further development of industrial Internet data acquisition and processing technology, which has certain theoretical and practical significance.

Unequal sized cells based on cross shapes for data collection in green Internet of Things (IoT) networks

Unequal sized cells based on cross shapes for data collection in green Internet of Things (IoT) networks

Energy-Aware Design of UAV-Mounted RIS Networks for IoT Data Collection

Data collection in massive Internet of Things networks requires novel and flexible methods. Unmanned aerial vehicles (UAVs) are foreseen as a means to collect data rapidly even in remote areas without static telecommunication infrastructure. In this direction, UAV-mounted reconfigurable intelligent surfaces (RISs) aid in reducing the hardware requirements and signal processing complexity at the UAV side, while increasing the network’s energy efficiency and coverage. Hence, in this paper, we propose the utilization of a UAV-mounted RIS for data collection and study the coverage probability in such networks. Additionally, we propose a novel medium access control protocol based on slotted ALOHA and Code Combining to handle the communication of multiple sensors. To account for the crucial energy issue in UAVs, we devise an energy model that considers both the UAV and the RIS weight, as well as the environmental conditions and the UAV’s velocity. Finally, we characterize the performance of the proposed data collection scheme by analyzing the average throughput and the average collected data per flight, while providing useful insights for the design of such networks.

Resilient Path Planning for UAVs in Data Collection Under Adversarial Attacks

In this paper, we investigate jamming-resilient UAV path planning strategies for data collection in Internet of Things (IoT) networks, in which the typical UAV can learn the optimal trajectory to elude such jamming attacks. Specifically, the typical UAV is required to collect data from multiple distributed IoT nodes under collision avoidance, mission completion deadline, and kinematic constraints in the presence of jamming attacks. We first design a fixed ground jammer with continuous jamming attack and periodical jamming attack strategies to jam the link between the typical UAV and IoT nodes. Defensive strategies involving a reinforcement learning (RL) based virtual jammer and the adoption of higher SINR thresholds are proposed to counteract against such attacks. Secondly, we design an intelligent UAV jammer, which utilizes the RL algorithm to choose actions based on its observation. Then, an intelligent UAV anti-jamming strategy is constructed to deal with such attacks, and the optimal trajectory of the typical UAV is obtained via dueling double deep Q-network (D3QN). Simulation results show that both non-intelligent and intelligent jamming attacks have significant influence on the UAV’s performance, and the proposed defense strategies can recover the performance close to that in no-jammer scenarios.

Benefits and Challenges of Making Data More Agile: A Review of Recent Key Approaches in Agriculture

Having reliable and timely or ongoing field data from development projects or supply chains is a perennial challenge for decision makers. This is especially true for those operating in rural areas where traditional data gathering and analysis approaches are costly and difficult to operate while typically requiring so much time that their findings are useful mostly as learning after the fact. A series of innovations that we refer to as Agile Data are opening new frontiers of timeliness, cost, and accuracy. They are leveraging a range of technological advances to do so. This paper explores the differences between traditional and agile approaches and offers insights into costs and benefits by drawing on recent field research in agriculture conducted by diverse institutions such as the World Bank (WB), World Food Program (WFP), United States Agency for International Development (USAID), and the Committee on Sustainability Assessment (COSA). The evidence collected in this paper about agile approaches—including those relying on internet and mobile-based data collection—contributes to define a contemporary dimension of data and analytics that can contribute to more optimal decision-making. Providing a theoretical, applied, and empirical foundation for the collection and use of Agile Data can offer a means to improve the management of development initiatives and deliver new value, as participants or beneficiaries are better informed and can better respond to a fast-changing world.

화투의 과거, 현재, 미래

Hwatu was a negative image in that the cards had Japanese-style picture and strong gambling character. On the other hand, Hwatu has maintained its status as a national game to promote friendship in leisure and daily life. Recently, art therapy using Hwatu has been actively performed to prevent dementia in the elderly. Hwatu, which has shown such a multifaceted aspect, is trying to show us a different side in the 21st century. While attempts are being made to localize it by combining with the nature and cultural properties of each region in Korea, it is now approaching people all over the world impressively through works such as the movie Minari. After successful localization, Hwatu is now in the stage of dreaming of globalization. In this paper, the past and present of Korean Hwatu were examined, and the future possibilities were explored. Due to the nature of the Hwatu, various research data and methods such as literature review, one-on-one interview with research participants, and Internet data collection were mixed to understand the overall picture.

Dynamic monitoring frequency for energy-efficient data collection in Internet of Things

With growth of Internet of Things, number of connected sensors increases as well, along with data being collected by those sensors. Most sensors are battery powered and commonly collect data in short and equally spaced time periods resulting with large amount of redundant and often irrelevant data. In this paper, we propose a dynamic monitoring frequency (DMF) algorithm that aims at collecting data only when sensor readings change by more than a predefined value between consecutive readings. Thus, a sensor is turned on only when a change in monitored phenomenon value exceeds a predefined threshold. Two algorithms are analyzed, namely statistical and machine learning. DMF shows notable performance, resulting either with up to ∼70% less missed readings, or it collects up to ∼40% less data compared to the baseline algorithm that collects data with static monitoring frequency.

Authenticating Data Transfer Using RSA-Generated QR Codes

Lack of security measures for cybersecurity threat is somewhat vulnerable and can even put one’s digital life at high risk of phishing attack which is so alarming nowadays. Perpetrators may even use fictitious personal information to infiltrate various institutions for their malicious acts. It is the aim of this paper to present a security measure for a safe data transfer by means of vaccination card. Vaccination information is one of the most commonly acquired pieces of data today. However, because most internet data collection processes lack encryption, personal data becomes very vulnerable to threats. As a result, the researchers presented a Centralized Covid-19 Record System, which illustrates secure data transfer via RSA-generated QR codes. A descriptive research design was employed in which a survey questionnaire together with secondary data sources and online tools i.e., RSA Express Encryption/Decryption Calculator and QR code generator were utilized as data instruments. Through a culmination of knowledge on asymmetric cryptography, RSA algorithm, QR codes, and web system development, answers to the founded research questions were unveiled. The web system’s architecture comprises several components and sub-components building its digital makeup. For the system development process, the most essential structural components are the web browser, web server, and database server. Through a message encryption/decryption feature that makes use of the RSA algorithm in generating a key pair, cryptography was implemented in the system. The essential mathematical parameters comprising such features are RSA encryption algorithm, RSA decryption algorithm, and Euler phi function. As for the system development environment, several hardware and software requirements that build and support the system’s end-to-end process were also specified. Upon the employment of those specifications, the system was able to offer several security features including the 15-digit account user IDs and QR code scanning for log-in, secured acquisition of public and private keys, and an admin verification process. Lastly, it was found that asymmetric cryptosystem provides a secured channel for data transfer due to the computational difficulty of factoring the large integers that constitute modulo . Upon the strategic culmination of the study’s framework, well-established system architecture, required system specifications, and security measures, the researchers were able to successfully develop VacciFied.net, a Centralized Covid-19 Record System involving authenticated data transfer process.

Joint Cluster Head Selection and Trajectory Planning in UAV-Aided IoT Networks by Reinforcement Learning With Sequential Model

Employing unmanned aerial vehicles (UAVs) has attracted growing interests and emerged as the state-of-the-art technology for data collection in Internet of Things (IoT) networks. In this article, with the objective of minimizing the total energy consumption of the UAV-IoT system, we formulate the problem of jointly designing the UAV’s trajectory and selecting cluster heads in the IoT network as a constrained combinatorial optimization problem, which is classified as NP-hard, and challenging to solve. We propose a novel deep reinforcement learning (DRL) with a sequential model strategy that can effectively learn the policy represented by a sequence-to-sequence neural network for the UAV’s trajectory design in an unsupervised manner. Through extensive simulations, the obtained results show that the proposed DRL method can find the UAV’s trajectory that requires much less energy consumption when compared to other baseline algorithms and achieves close-to-optimal performance. In addition, simulation results show that the trained model by our proposed DRL algorithm has an excellent generalization ability to larger problem sizes without the need to retrain the model.

A Cloud-Assisted Reliable Trust Computing Scheme for Data Collection in Internet of Things

Large number of Internet of Things (IoT) applications with intelligent sensing devices (ISDs) are combined with cloud computing to collect and process data more efficiently. However, ISDs are vulnerable to various attacks. Compromised devices may maliciously provide unreliable data to the cloud, causing damage to IoT applications. Therefore, it is a critical issue to design an effective mechanism to ensure the security of data collection in cloud computing. In this article, a cloud-assisted reliable trust computing (CRTC) scheme is proposed to identify the trust of ISDs at low cost, providing high-quality data for IoT applications. The CRTC scheme mainly includes the following parts: First, a reliable approach of obtaining the real data of ISDs at a low cost is proposed to identify the trust of ISDs. In the proposed method, ISDs fits the forwarding packets information to form inspection information (II) with a small amount of data and then sends II to inspection nodes, effectively reducing the cost of routing II. Second, an effective method of trust computing is given to evaluate the trustworthiness of ISDs based on the data and II collected by unmanned aerial vehicles (UAV). Then, the aggregators are selected from high-trust ISDs to ensure secure data collection. Third, to obtain more reliable trust at lower cost, a trajectory optimization algorithm for UAV is proposed to collect as much II as possible and reduce the moving distance. Theoretical analysis and experimental results show that the proposed CRTC scheme is superior to previous strategies in terms of the success rate of data collection, the speed of identifying the trust of ISDs, and the UAV's trajectory distance.