Anomaly Detection Based Latency-Aware Energy Consumption Optimization For IoT Data-Flow Services

Luo, Yuansheng; Li, Wenjia; Qiu, Shi

doi:10.3390/s20010122

Cited by 12 publications

(7 citation statements)

References 37 publications

(62 reference statements)

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“…Content may change prior to final publication. Referenced Papers [119], [120], [78], [121], [122], [95], [13], [111], [123], [80], [56], [11], [124] [89], [125], [126], [127], [128], [129], [31], [79], [76], [130], [131], [132], [133], [134], [20], [135], [18], [88], [106], [127], [136], [137], [41], [138], [139], [71] [10], [82], [130], [81], [140], [60], [141], [30], [142], [29], [129], [19], …”

Section: Application Context Of Schmentioning

confidence: 99%

Trends, Technologies, and Key Challenges in Smart and Connected Healthcare

et al. 2021

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Cardio Vascular Diseases (CVD) is the leading cause of death globally and is increasing at an alarming rate, according to the American Heart Association's Heart Attack and Stroke Statistics-2021. This increase has been further exacerbated because of the current coronavirus (COVID-19) pandemic, thereby increasing the pressure on existing healthcare resources. Smart and Connected Health (SCH) is a viable solution for the prevalent healthcare challenges. It can reshape the course of healthcare to be more strategic, preventive, and custom-designed, making it more effective with value-added services. This research endeavors to classify state-of-the-art SCH technologies via a thorough literature review and analysis to comprehensively define SCH features and identify the enabling technology-related challenges in SCH adoption. We also propose an architectural model that captures the technological aspect of the SCH solution, its environment, and its primary involved stakeholders. It serves as a reference model for SCH acceptance and implementation. We reflected the COVID-19 case study illustrating how some countries have tackled the pandemic differently in terms of leveraging the power of different SCH technologies, such as big data, cloud computing, Internet of Things, artificial intelligence, robotics, blockchain, and mobile applications. In combating the pandemic, SCH has been used efficiently at different stages such as disease diagnosis, virus detection, individual monitoring, tracking, controlling, and resource allocation. Furthermore, this review highlights the challenges to SCH acceptance, as well as the potential research directions for better patient-centric healthcare.

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Section: Application Context Of Schmentioning

confidence: 99%

Trends, Technologies, and Key Challenges in Smart and Connected Healthcare

et al. 2021

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“…Fog computing is similar to multi-access edge computing (MEC), which is treated as one of the key technologies towards 5G by European Telecommunications Standards Institute (ETSI) [10,11,12]. Fog computing is expected to play an important role in the industrial IoT and cyber-physical systems [13], such as smart industry [14] and smart agricultural technology [15] Without loss of generality, we use "fog nodes" to represent the nodes with communication and computing resources near IoT devices [16]. Because the nodes of edge computing and fog computing are limited by their geographical location, UAV assisted edge computing is proposed to expand the ability of edge computing [17][18][19][20][21].…”

Section: B Related Workmentioning

confidence: 99%

“…As presented in [23], the BCD is widely used to solve the complicated non-convex multivariable joint optimization problems [3,16,21,23]. Although the BCD algorithm cannot guarantee the global optimal solution of the MINLP problem, it can obtain the suboptimal solution via division and iteration and greatly reduce the difficulty of solving the non-convex multivariable joint optimization problem.…”

Section: Problem Formulationmentioning

confidence: 99%

“…Although the BCD algorithm cannot guarantee the global optimal solution of the MINLP problem, it can obtain the suboptimal solution via division and iteration and greatly reduce the difficulty of solving the non-convex multivariable joint optimization problem. The performance of the BCD algorithm has been verified in many research works [3,16,21,23]. In the block coordinate descent method, the vector of variables is partitioned into different blocks.…”

Section: Problem Formulationmentioning

confidence: 99%

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Revenue Optimization of a UAV-Fog Collaborative Framework for Remote Data Collection Services

Luo

Wang

et al. 2020

IEEE Access

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Unmanned aerial vehicles (UAVs) can provide remote data collection services with quality of service guarantees. The typical application fields include geographic information systems, such as topological survey and natural disasters and hazards monitoring. In the bad geographic environment, wireless communication performance of UAVs cannot be guaranteed. Therefore, the efficiency of remote data collection cannot be guaranteed. This paper proposes a collaborative framework of UAVs and fog computing for remote data collection. Our goal is to maximize the revenue of UAVs with the support of fog computing, so we need to find the optimal computation resources allocation and task assignment scheme. This is a mixed integer nonlinear programming problem. The block coordinate descent method is used to solve this problem, which allows the original problem to be divided into the optimal task assignment subproblem and the optimal computation resource allocation sub-problem. The greedy algorithm, heuristic algorithm and brute force algorithm are proposed to solve the optimal task assignment sub-problem. The convex optimization analysis method is used to solve the optimal resource allocation sub-problem. The genetic algorithm is used as a benchmark to compare with the heuristic-based block coordinate descent algorithm. The numerical simulation and network simulator based-simulation results show that the proposed UAV-Fog collaborative data collection problem can be efficiently solved by the block coordinate descent algorithm based on the heuristic strategy.

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“…These detection methodologies could be applied to many other examples in which data from sensors are obtained in a periodic pattern [ 7 ]. If our research were taken further, the theories and principles discussed here could be extended to healthcare with IoT sensors in smartphone devices [ 8 , 9 , 10 ] and to IoT related sensing systems in the field of power estimation in buildings and land [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

Detecting Anomalous Transactions via an IoT Based Application: A Machine Learning Approach for Horse Racing Betting

Min

Lee

Park³

et al. 2021

Sensors

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During the past decade, the technological advancement have allowed the gambling industry worldwide to deploy various platforms such as the web and mobile applications. Government agencies and local authorities have place strict regulations regarding the location and amount allowed for gambling. These efforts are made to prevent gambling addictions and monitor fraudulent activities. The revenue earned from gambling provides a considerable amount of tax revenue. The inception of internet gambling have allowed professional gamblers to par take in unlawful acts. However, the lack of studies on the technical inspections and systems to prohibit unlawful internet gambling has caused incidents such as the Walkerhill Hotel incident in 2016, where fraudsters placed bets abnormally by modifying an Internet of Things (IoT)-based application called “MyCard”. This paper investigates the logic used by smartphone IoT applications to validate the location of users and then confirm continuous threats. Hence, our research analyzed transactions made on applications that operated using location authentication through IoT devices. Drawing on gambling transaction data from the Korea Racing Authority, this research used time series machine learning algorithms to identify anomalous activities and transactions. In our research, we propose a method to detect and prevent these anomalies by conducting a comparative analysis of the results of existing anomaly detection techniques and novel techniques.

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Anomaly Detection Based Latency-Aware Energy Consumption Optimization For IoT Data-Flow Services

Cited by 12 publications

References 37 publications

Trends, Technologies, and Key Challenges in Smart and Connected Healthcare

Trends, Technologies, and Key Challenges in Smart and Connected Healthcare

Revenue Optimization of a UAV-Fog Collaborative Framework for Remote Data Collection Services

Detecting Anomalous Transactions via an IoT Based Application: A Machine Learning Approach for Horse Racing Betting

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