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Human decomposition studies aim to understand the various factors influencing human decay to assess the deceased and develop postmortem interval (PMI) estimation methods. These types of studies are typically conducted through physical experiments examining the deceased; however, big data systems have the potential to transform how large‐scale forensic anthropology research questions can be addressed with curated images of donors with known demographic, climatic, and postmortem historical data. This study introduces ICPUTRD (Image Cloud Platform for Use in Tagging and Research on Decomposition), a web‐based software system, which enables forensic scientists to easily access, enhance (or curate), and analyze very large photographic collections documenting the longitudinal process of human decomposition. ICPUTRD, a JavaScript‐based application, was designed and built through a combination of the Waterfall and Agile software development life‐cycle methods and provides an image search and tagging features with a predefined nomenclature of forensic‐related keywords. To evaluate the system, a user study was conducted, involving 27 participants who completed pre‐ and post‐study surveys and three research tasks. Analysis of the study results confirmed the feasibility and practicality of ICPUTRD to facilitate aspects of forensic research and casework involving large collections of digital photographs of human decomposition. It was observed that the nomenclature lacked certain law enforcement keywords, so future work will focus on expanding it to ensure ICPUTRD is suited for all its intended users. [ABSTRACT FROM AUTHOR]

As the number of cars increases annually, driving behavior analysis has become a direction worthy of in-depth research in the field of public transportation. Traditional vehicle brakes generate torque during non-braking high-speed rotation, which has a negative impact on transmission efficiency. In this paper, the sparrow search algorithm was combined with the backpropagation neural network algorithm in the context of collecting and analysing multi-source data during vehicle driving, and the backpropagation neural network algorithm was improved by using the sparrow search algorithm’s global search ability. A driving behavior recognition model was built in order to accurately distinguish various driving behaviors and evaluate their impact on fuel consumption and driving safety. The experimental outcomes showed that the vehicle driving behavior recognition method based on cloud computing and the backpropagation neural network algorithm combined with the sparrow search algorithm featured a high driving behavior recognition efficiency, and the research method featured a small error in the identification of vehicle driving behavior. The prior accuracy of the driving behavior-based fuel consumption evaluation model was as high as 89.216%, and the regression fit for the employed model after training reached around 98%. To sum up, the obtained results not only allow the implementation of new ideas and methods for the intelligent vehicle driving and traffic safety management, but they also provide an important theoretical support and practical guidance for the design and optimization of intelligent transportation systems. [ABSTRACT FROM AUTHOR]

Copyright of Automobile Technology is the property of Automobile Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper presents a novel, sensor-enabled centralised monitoring system for fault detection in streetlights. Unlike traditional methods that depend on citizen reports or manual inspections, this system utilizes Internet of Things (IoT) technology to identify faults automatically and efficiently. The system comprises monitoring devices equipped with sensors and transceivers attached directly to streetlights. These devices collect data on light source functionality and potential wiring faults. This data is then wirelessly transmitted to a central transmitter, which relays it to a cloud platform. A machine learning model, trained with an accuracy of 98.8%, analyzes the data and predicts the streetlight's operational status. The predicted status and detailed fault information are displayed on a dedicated webpage, allowing stakeholders to monitor remotely. This eliminates manual searches, expedites repairs due to precise fault knowledge, and empowers linemen for faster resolution. Overall, this cost-effective system significantly improves streetlight maintenance efficiency, enhancing public safety and contributing to a well-lit urban environment. [ABSTRACT FROM AUTHOR]

The mining and metallurgical industry is a fundamental sector of China's industry, consisting of various subdivisions such as mining, mineral processing, smelting, etc. Mining and metallurgical equipment are characterized by harsh operating conditions, complex operation and maintenance, high energy consumption, significant pollution, substantial loss, and a high accident rate, resulting in an urgent need to leverage new infrastructure like 5G and the Industrial Internet to provide cloud-based digital and intelligent services for the "deaf, dumb, and dull" equipment in this industry. Therefore, constructing an industrial internet platform for cloud-based mining and metallurgical equipment is one of the crucial means to achieve resource aggregation and optimal allocation, enhance equipment efficiency and safety levels, and is also an inevitable choice for realizing the industry's digital transformation and upgrading. Based on the background and current situation of the construction of the cloud industrial Internet platform on equipment and the needs of the mining and metallurgy industry, the cloud industry Internet platform on mining and metallurgy industry equipment is studied. The construction contents of the device layer, edge layer, IaaS layer, PaaS layer, application layer, and security protection system are provided, respectively. Through constructing the three major functional systems: network, platform, and security, key industrial equipment assets, production and manufacturing systems, management systems, and resource applications have been fully integrated. The specific construction steps and effects of the platform are analyzed through a practical case of a mining and metallurgical enterprise. The proposed construction scheme can meet the needs of constructing a cloud industrial Internet platform for equipment in the mining and metallurgy industry and solve the common problems of cloud industrial equipment in the industry, so as to provide a theoretical and practical reference for mining and metallurgy enterprises to carry out similar platform construction, support fine equipment management, reduce equipment operation and maintenance costs and accident rates, promote the public service ecological construction of cloud industrial equipment solutions, and help the collaborative development of upstream and downstream of the industrial chain. [ABSTRACT FROM AUTHOR]

Objective With the rapid development and expansion of urban rail transit, the problem of noise pollution is increasingly prominent due to the continuous extension of train operation lifespan. This not only causes severe harm to passenger riding experience and health, but also directly affects the brand image and service quality of rail transit enterprises. Therefore, it is necessary to collect and monitor noise levels during train operation to implement targeted noise control measures, maximizing the effectiveness of noise mitigation efforts. Method With a combination of hardware and software resources such as noise sensors, STM32 microcontrollers, 4G transmission modules, Keil μVision5, Python, and OneNET cloud platform, an online noise monitoring system based on IoT (Internet of Things) is designed and developed specifically for urban rail transit trains. Result & Conclusion The system is successfully tested and validated on Kunming Metro Line 4, achieving real-time monitoring of train interior noise with advantages such as short sampling cycles, high data accuracy, and low data loss and latency rates. The system can issue timely alerts when noise levels exceed national standards, effectively meeting the design objectives. Additionally, the system can be applied to other environmental noise monitoring scenarios, providing reference for environmental noise treatment, enhancing the quality of life for residents, and promoting sustainable urban development.

Objective To advance the modernization of rail transit control systems and enhance their safety, reliability and operational efficiency, a cloud-based control system architecture for rail transit is designed. The aim is to effectively integrate and deploy both the safety-critical and non-safety-critical functions of rail transit system onto a cloud platform. Method A cross-regional distributed control is realized by connecting target controllers to cloud platform servers via optical fiber. Characteristics of the rail transit cloud control system are analyzed, safety requirements of the system are outlined across six aspects: time management, space management, shared resource management, operation mode management, degradation mode management and safety monitoring. Result & Conclusion The virtualization layers of the cloud platform must be designed to ensure spatial isolation, temporal isolation, fault isolation, as well as management, predictability, security, and confidentiality. The platform scheduling mechanism includes both time-triggered and event-driven approaches, each impacting the platform temporal constraints differently. The hierarchical scheduling of the cloud platform involves four resource-sharing protocols, which must be selected based on different tasks.

Background and objectives: Clinical data are essential for developing cloud platforms for intelligent diagnosis and treatment decision of diseases. However, cloud platforms for data sharing and exchange with clinicians are poorly suited. We aim to establish Eyecare-cloud, a platform which provide a novel method for clinical data and medical image sharing, to provide a convenient tool for clinicians. Methods: In this study, we displayed the main functions of Eyecare-cloud that we established. Based on clinical data from the cloud platform, we analyzed the incidence trend of the most common infantile retinal diseases, such as retinopathy of prematurity (ROP), over the past 20 years, as well as the associated risk factors for ROP occurrence. Statistical analyses were performed using GraphPad Prism (V.8.0) and SPSS software (V.26.0). Results: The Eyecare-cloud offers numerous advantages, including systematic archiving of patient information, one-click export data, simplifying data collection and management, eliminating the need for manual input of clinical information, reducing clinical data migration time, and lowering data management costs significantly. A total of 22,913 premature infants from Eyecare-cloud were included in the data analysis. Based on 20 years of premature infant screening data analysis, we found that the ROP incidence began to slowly decline starting in 2003 but showed a gradual increase trend again in 2016. The incidence of severe ROP remained relatively stable at a low level since 2010. The number of premature infants increased steadily before 2016 but decreased since then. ROP occurrence was significantly associated with male sex, lower gestational age, and lower birth weight (P < 0.001). Conclusion: Eyecare-cloud provides clinicians and researchers with convenient tools for big data analysis, which helps alleviate clinical workloads and integrate research data. This cloud platform supports the principles of predictive, preventive, and personalized medicine (PPPM/3PM), empowering clinicians and researchers to deliver more precise, proactive, and patient-centered eye care. [ABSTRACT FROM AUTHOR]

This study designs an automatic monitoring and alarm system for integrated meteorological observation based on the cloud platform to improve the automatic monitoring ability for integrated meteorological observation and the operation and maintenance management level of meteorological departments. A meteorological data collector was used to collect comprehensive meteorological observation data, transmit the collected comprehensive meteorological observation data to the main control processing module, preprocess the comprehensive meteorological observation data, and finally transmit the pre-processed comprehensive meteorological observation data to the cloud service module through the data transmission module. In the cloud service module, cloud computing and an improved K-means algorithm were used to analyze the comprehensive meteorological observation data, mine abnormal comprehensive meteorological observation data, and send alarm information to the application module. Users can view the comprehensive meteorological observation data in the cloud server on the client in real-time, thus realizing automatic monitoring and alarm for comprehensive meteorological observation. Results show that the average transmission rate of various meteorological comprehensive observation data transmitted by the system is as high as 99.48%, thereby achieving clustering of abnormal meteorological comprehensive observation data. Upon detecting abnormal meteorological data, the system sends alarm information to the mobile phone client to complete the monitoring of abnormal meteorological information. [ABSTRACT FROM AUTHOR]

A large-scale street lights send data to the cloud platform, causing load imbalance among nodes of the server in high concurrency, resulting in communication failure. In view of this problem, based on microservices, this study proposes a dynamic weight load balancing algorithm based on the smart street lamp cloud platform. The algorithm calculates the respective weight coefficients and the initial weights of each node according to the hardware performance of each server during initialization, and dynamically adjusts the server weights according to the idle rate of CPU(Central Processing Unit) and network bandwidth during the request process to realize load optimization. By setting the minimum threshold and comparing it with the calculated remaining load rate, the weight of the server that reaches the upper limit of the load is set to 0 to prevent the server from being overloaded. The test results show that the proposed algorithm has a better load balancing effect than the minimum number of connections algorithm and the smooth weighted round robin algorithm in the experimental environment. Compared with the dynamic weight algorithm, the average response time and the actual number of concurrent connections of the proposed method are also improved. [ABSTRACT FROM AUTHOR]

The industrial internet of things (IIoT) encompasses smart devices, manufacturing systems, humans, and networks for automated productive outcomes. The placement of devices and networks is vulnerable to distributed denial of service (DDOS) attacks that degrade the productivity and efficiency of IIoT. In this article, we propose a checkpoint‐intrigued adversary mitigation scheme (CIAMS) for improving the security features and recommendations of the detection systems. Features that use the recommendation to provide relevant information maintain their security level. A DDoS attack is dealt with at the outset, resulting in increased productivity. The IIoT's smart devices are less productive and efficient because of this DDoS attack. This CIAMS method is designed to address vulnerability and the ability to survive the features checkpoints. The proposed scheme substantiates the security breach and lag in the checkpoint systems against DDOS attacks. The checkpoints' vulnerability level and surviving features are assessed using a classified learning approach. In this assessment, the degrading features are reimbursed by improving the security functions, control, and access methods. Periodic checkpoint replacement and mutual security measures are used for mitigating the prolonging DDOS impact in the network. The proposed scheme's performance is verified using false positives, service distribution, lag, an efficiency score. Improvements have been made to the industrial environment's service delivery and efficiency. By reducing false positives by 10.35%, the proposed scheme improves service distribution ratio and efficiency score by 11.68% and 12.55% for different devices. [ABSTRACT FROM AUTHOR]

In order to improve the effect and timeliness of ground fault monitoring and improve the effect of fault early warning, a DC ground fault monitoring method for electrical equipment in 110 kV smart substations based on improved rough sets is proposed. The characteristics of the DC ground fault current are analyzed, and the equivalent model of the DC system is constructed according to the analysis results. The low-frequency signal is enhanced according to the fuzzy rough set, and the low-frequency sampled transient zero-sequence current signal is enhanced preprocessing and wavelet packet decomposition according to the signal enhancement proportional coefficient, and the fault line is selected according to the principle of maximum energy. The wavelet packet transform method is used to decompose and analyze the waveform of the leakage current generated when a single-point grounding fault occurs in a branch of the DC system, and the grounding fault location of the DC system of the substation is realized by relative entropy operation. With the help of the cloud platform, a DC ground fault monitoring platform is built, and the ground fault monitoring is completed by using this platform. The experimental results show that the method can obtain the characteristic harmonics of the ground fault, accurately monitor whether the mutation occurs, and can realize the early warning of the ground fault of the electrical equipment in a faster time, indicating that it can realize the accurate and timely monitoring of the ground fault. [ABSTRACT FROM AUTHOR]

High-speed railway train control system has high safety level, various equipment components, complex functional structure and interface relationship, and great difficulty in dynamic inspection. By using the simulation test technology, it will completely rely on the on-site integrated commissioning and test, and be completed through the implementation of simulation test. It can further improve the test coverage and reduce the impact on transportation. On the basis of researches on the necessity of project construction, construction objectives and technical solutions, specific researches are carried out on ground equipment, on-board equipment, simulation system plan, etc. Through comparison and selection, the ground equipment of simulation testing platform for train control system engineering of the high-speed railway adopts the semi-physical and semi-simulation structure, and uses the cloud platform technology, which has better scalability and economy. The on-board equipment adopts the completely real equipment, which improves the credibility and authenticity of the test. Through the construction of simulation testing platform and the verification based on the simulation environment of Huzhou -- Hangzhou High-speed Railway, the research results show that compared with the field test, the simulation test has completed more scenarios, cases and route tests, and their numbers have increased by 275%, 336% and 442% respectively, which can further improve the quality, depth and breadth of the train control system test. [ABSTRACT FROM AUTHOR]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Railway Signalling & Communication Engineering is the property of Railway Signalling & Communication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

[Objective] With the theme of promoting urban rail transit information, developing intelligent system, and building 'smart urban rail' for a highly integrated urban rail transit cloud platform is the direction of technological development in the new era. The integration of signaling system and cloud platform is an important development trend of signaling systems. In-depth research is needed on how to build an efficient, reliable, and cost-effective signaling system based on cloud architecture. [Method] Combining the needs of Changzhou Metro new round of urban rail transit construction scheme, based on the technological characteristics of existing cloud platform and current situation of signaling system, the feasibility and construction scheme of signaling system cloudification are analyzed and verified. The research of contents such as line network unified ATS (automatic train supervision) construction scheme, signaling system cloudification scope, cloud platform architecture selection, and signaling system ATS interoperability scheme after cloudification are emphasized. [Result & Conclusion] The definition of signaling system cloudification is clarified, discussing the feasibility of signaling system ATS cloudification. According to the estimation whether the signaling system can pass SIL2 (safety, integrity level 2) after cloudification, a list of specific equipment for ATS cloudification is determined. The new-generation signaling system construction scheme based on cloud architecture is proposed, and the urban rail transit cloud platform architecture carrying ATS is suggested to transit from mode 1 to mode 3 gradually, possessing the ability to update and iterate. Signaling system equipment can level up the processing capacity of the signaling system and reduce hardware device amount after integration into cloud platform, contributing to realizing line network resource/information exchange and promoting modulating the development of intelligent scheduling.

AbstractPreviously, we described a Team-Based Ubiquitous Learning Model on Cloud Platform Model (PITTA Model). In this paper, we aimed to demonstrate the value of this model by compare learning achievement and creative problem-solving ability using the PITTA model of learning, compare between experimental group and control group by The Randomized Posttest-Only Control Group Design. The sample group comprised students from Bunditpatanasilpa Institute of Fine Arts who were enrolled in the Innovation Information Technology and Communication in Education course during the second semester of Feb-Mar 2023. A total of 50 students from two classrooms were selected using cluster random sampling. The students were divided into two groups: one consisting of 25 students who participated in PITTA model learning activities, and another group of 25 students who engaged in conventional learning activities. The research employed two main instruments: the learning achievement test and the creative problem-solving ability assessment test. Statistical analyses included the use of measures such as mean (x¯) and standard deviation (SD), as well as One-way MANOVA. The research findings revealed that the group of students utilizing the PITTA Model in their learning activities exhibited higher levels of learning achievement compared to the group engaged in regular learning activities, with statistical significance observed at the .05 level. Furthermore, the students who participated in learning activities based on the PITTA Model demonstrated superior creative problem-solving abilities compared to those involved in conventional learning activities, again reaching a statistical significance level of 0.05.

With the emergence of smart home appliances, traditional power sockets are becoming less compatible with modern living styles. Furthermore, modern commercialized sockets are expensive and unaffordable. This project presents the development of a low-cost Wi-Fi smart home socket using internet of things (IoT) technology that is user-friendly for smartphone users to control home appliances. Smart home socket devices can turn on and off power outlets automatically from any location if they are linked to the internet and providing the user with more convenience and energy savings. This project uses a node microcontroller unit (NodeMCU) Wi-Fi module (ESP8266) as the main microcontroller unit to connect to a cloud platform. It also uses a mobile phone application to send instructions to the microcontroller for turning on and off household appliances remotely through a smart socket. The switching mechanism is monitored and controlled through the Blynk platform. A 4-channel relay module is used to transition DC current loads to AC current loads in order to activate switching processes. According to the study's findings, the Wi-Fi smart home socket system is able to save on excessive usage of electrical appliances while also increasing electrical appliance safety. [ABSTRACT FROM AUTHOR]

Cloud computing has emerged as a vital tool for facilitating information-sharing and collaboration in supply chain operations. However, how the information is shared in the cloud-based supply chain has not been thoroughly investigated. Thus, adopting a system dynamics approach, this research deepens the understanding of information transmission within the supply chain by proposing a general stock and flow model and endorsing the model in an equipment manufacturing setting in China. Building upon the general model, this paper establishes a conceptual framework for cloud-based information-sharing mechanisms both within and across supply chains. The impact of cloud-based information-sharing on supply chain performance is evaluated by inventory level and customer satisfaction. The findings indicate a positive influence of information-sharing on supply chain performance. Notably, sharing information across supply chains enhances customer satisfaction to a greater extent than within the supply chain. However, it does not lead to a significant increase in inventory levels. This research expounds on the impact of information-sharing on the supply chain in quantitative forms, which is of theoretical and practical significance to the field of supply chain management. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Hybrid encryption techniques are gaining popularity in the cloud platform due to their ability to address security concerns and data storage challenges. In order to ensure confidentiality at every stage of processing, this article presents an innovative strategy that merges fully homomorphic encryption (FHE) and secure hash algorithm 3 (SHA-3), offering robust protection against cyber threats. FHE and SHA-3 offer a solution to this problem. We conducted experiments using different datasets to test the performance and security strength of this hybrid approach. Our method takes into account factors such as computational overheads, complexity requirements, key management challenges, and limited support from vendors/providers offering specialized hardware/software capable of supporting complex mathematical functions required by each technique separately before combined into a hybrid form suitable enough scaled-out horizontally across different nodes within clouds themselves today globally among end-users relying upon them heavily day-to-day tasks involving mission-critical workloads running inside virtualized machines hosted on public/private clouds ecosystems worldwide. The results show that implementing FHE together with SHA-3 can significantly improve an organization's overall cyber security posture when operating critical infrastructure components residing within cloud environments. According to the results, it performs better than other methods that are currently in used complexity of encryption and decoding, including homomorphic encryption and RSA, AES & DSA, and AES & RSA. The hybrid algorithm we are proposed using fully homomorphic & SHA3 with 2048 bits 0.789 seconds of encryption time and decryption time with 0.001 seconds and execution time with 4.25 seconds. Therefore, we conclude that our proposed hybrid encryption technique is the best approach to address cyber security risks associated with storing sensitive data in cloud computing environments. [ABSTRACT FROM AUTHOR]

Ticketing systems are key to ensuring the normal operation of medium and low speed maglev lines. The ticketing systems of traditional urban rail transit usually adopt five-layer structure, which has the characteristics of complex structure and high construction cost. In view of the small scale of the medium and low speed maglev line networks, this paper proposes a medium and low speed maglev ticketing system architecture based on the cloud platform, which reduces the ticketing system architecture to three-layer structure. Then, based on the idea of micro-service, the architecture of the ticketing system is designed, and the functions of each module are designed in detail, which improves the scalability and maintainability of the system. Finally, taking the realization of user access management function as an example, the functions of the main modules in the micro-service architecture are described in detail. [ABSTRACT FROM AUTHOR]

Copyright of Urban Mass Transit is the property of Urban Mass Transit Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Unmanned aerial vehicle (UAV) swarms have gained significant attention for their potential applications in various fields. The effective coordination and control of UAV swarms require the development of robust mathematical models that can capture their complex dynamics. The paper introduces mathematical models and relevant paradigms based on the design and analysis of self-organizing swarms of UAVs. The logical and technological construction of the model relies on the theorems developed by authors for obtaining full information exchange during the swarm quasi-random walk. The suggested rotor-router model interprets the discrete-time walk accompanied by the deterministic evolution of configurations of rotors randomly placed on the vertices of the swarm graph. The recommended optimal and fault-tolerant gossip/broadcast schemes support the resilience of swarm to internal failures and external attacks, and cryptographic protocols approve the security. The proposed cloud network topology serves as the implementation framework for the model, encompassing various connectivity options to ensure the expected behavior of the UAV swarms.

[Objective] In the rapidly evolving landscape of digital technology, cloud computing is widely utilized in various industries including metro systems. To ensure passenger travel safety and achieve real-time acquisition and efficient analysis of large-scale passenger flow data, it is necessary to propose an elastic cluster cloud platform tailored for passenger flow analysis scenarios. [Method] The core functional requirements of the above platform are analyzed, and the architecture of it is proposed, highlighting the platform unique capabilities. Two deployment plans for the elastic cluster cloud platform are put forward for metro systems at different stages of development, and the pros and cons of each deployment plan are analyzed. Taking Shanghai Metro as example, the investment costs of the deployment plans are compared based on capability requirements. The alignment between the architecture of the elastic cluster cloud platform and the characteristics of metro passenger flow analysis business is also examined. [Result & Conclusion] Metro passenger flow analysis scenarios exhibit characteristics such as multi-system concurrency, high real-time requirements, and large data volume. The elastic cluster cloud platform can meet various demands of passenger flow analysis business.

The adoption of Internet of Things (IoT) sensing devices is growing rapidly due to their ability to provide real-time services. However, it is constrained by limited data storage and processing power. It offloads its massive data stream to edge devices and the cloud for adequate storage and processing. This further leads to the challenges of data outliers, data redundancies, and cloud resource load balancing that would affect the execution and outcome of data streams. This paper presents a review of existing analytics algorithms deployed on IoT-enabled edge cloud infrastructure that resolved the challenges of data outliers, data redundancies, and cloud resource load balancing. The review highlights the problems solved, the results, the weaknesses of the existing algorithms, and the physical and virtual cloud storage servers for resource load balancing. In addition, it discusses the adoption of network protocols that govern the interaction between the three-layer architecture of IoT sensing devices enabled edge cloud and its prevailing challenges. A total of 72 algorithms covering the categories of classification, regression, clustering, deep learning, and optimization have been reviewed. The classification approach has been widely adopted to solve the problem of redundant data, while clustering and optimization approaches are more used for outlier detection and cloud resource allocation.