Sensors of Smart Devices in the Internet of Everything (IoE) Era: Big Opportunities and Massive Doubts

Masoud, Mohammad; Jaradat, Yousef; Manasrah, Ahmad; Jannoud, Ismael

doi:10.1155/2019/6514520

Cited by 98 publications

(43 citation statements)

References 138 publications

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“…where P < The difference of (7) from (3) is that (7) uses forecasted message incoming rate. The equation from [9] is as follows:…”

Section: Producing Rate-based (Pr-b) As Scale-out Equation and Forecamentioning

confidence: 99%

“…The scalability of processing and storage capacity is useful for a limited fraction of IoT scenarios. One of the most prominent examples is a smartwatch equipped with sensors measuring pulse, body temperature, altitude and various other characteristics of their owner [7]. These devices can be turned on and off frequently by their owners, on top of that these devices can change the location.…”

Section: Introductionmentioning

confidence: 99%

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Self-Adaptive Data Processing to Improve SLOs for Dynamic IoT Workloads

2020

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Internet of Things (IoT) covers scenarios of cyber-physical interaction of smart devices with humans and the environment and, such as applications in smart city, smart manufacturing, predictive maintenance, and smart home. Traditional scenarios are quite static in the sense that the amount of supported end nodes, as well as the frequency and volume of observations transmitted, does not change much over time. The paper addresses the challenge of adapting the capacity of the data processing part of IoT pipeline in response to dynamic workloads for centralized IoT scenarios where the quality of user experience matters, e.g., interactivity and media streaming as well as the predictive maintenance for multiple moving vehicles, centralized analytics for wearable devices and smartphones. The self-adaptation mechanism for data processing IoT infrastructure deployed in the cloud is horizontal autoscaling. In this paper we propose augmentations to the computation schemes of data processing component's desired replicas count from the previous work; these augmentations aim to repurpose original sets of metrics to tackle the task of SLO violations minimization for dynamic workloads instead of minimizing the cost of deployment in terms of instance seconds. The cornerstone proposed augmentation that underpins all the other ones is the adaptation of the desired replicas computation scheme to each scaling direction (scale-in and scale-out) separately. All the proposed augmentations were implemented in the standalone self-adaptive agent acting alongside Kubernetes' HPA such that limitations of timely acquisition of the monitoring data for scaling are mitigated. Evaluation and comparison with the previous work show improvement in service level achieved, e.g., latency SLO violations were reduced from 2.87% to 1.70% in case of the forecasted message queue length-based replicas count computation used both for scale-in and scale-out, but at the same time higher cost of the scaled data processor deployment is observed.

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“…where P < The difference of (7) from (3) is that (7) uses forecasted message incoming rate. The equation from [9] is as follows:…”

Section: Producing Rate-based (Pr-b) As Scale-out Equation and Forecamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Self-Adaptive Data Processing to Improve SLOs for Dynamic IoT Workloads

2020

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“…Zhao et al suggested an interference synchronization mechanism to control spectrum sharing for Cognitive Radio (CR) networks, which is considered to be a powerful inference control strategy [8]. Yao Wang et al proposes a Cost and Connection Degree (CCB) algorithm that creates a new useful method to build a prototype to represent the overall utility of the process [14].…”

Section: Background Workmentioning

confidence: 99%

Spectrum Allocation in Cognitive Radio Simplified Swarm Optimization Based Method

Rajalakshmi¹,

Sumathy²

2020

IJEAT

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Communication through wireless mode is accelerated its expansion in broad manner that make a way to communicate with different type of computing devices to interact each other. As the number of users continues to increase, there is a constant demand for the usability of radio spectrum, which is a limited resource.Therefore a maximum utilization of spectrum is necessary at any moment. Moreover it is desired to share the capacity of the bandwidth between the user's application on the basis of different channel utilization without compromising efficiency and fairness. Because cognitive system accommodate a dynamic spectrum allocation environment and it becomes an essential to compare performance in terms of Throughput, Latency, End-To-End Delay, Average Power Consumption, Average Adaptation Time and Average Total Utility were provided to illustrate the improved behaviour of the proposed system. A quality conscious spectrum assessment work is proposed, where spectrum bands are examined based on the requirements of application as well as the complex existence of the spectrum bands. The author used Simplified Swam Optimization (SSO) in this paper to communicate spectrum allocation and the performance of the proposed method is compared with the existing methods; Genetic Algorithm (GA) and Particle Swam Optimization (PSO) . It has been found that SSO gives an optimal solution than GA and PSO.

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“…Regardless of the device it must always can sense elements of the environment they are calibrated to measure and then communication the data to then direct the implementation of the automated action, this communication also allows for machine learning. [143] Masoud, Jaradat, Manasrah and Jannoud [92] provide additional insight in their article, Sensors of Smart Devices in the Internet of Everything (IoE) Era: Big Opportunities and Massive Doubts, "a fridge with an embedded processor is not smart until it has the ability to communicate with people, other fridges, and supermarkets to order missing items. Moreover, it should select from different supermarkets to buy the items with price offers.…”

Section: Sensing In Smart Homesmentioning

confidence: 99%

The Future of Emerging IoT Paradigms: Architectures and Technologies

Salam¹,

Hoang²,

Meghna³

et al. 2019

Preprint

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With Internet of Things (IoT) gaining presence throughout different industries a lot of new technologies have been introduced to support this undertaking. Implications on one such technology, wireless systems allowed for the use of different communication methods to achieve the goal of transferring data reliably, with more cost efficiency and over longer distances. Anywhere from a single house with only a few IoT devices such as a smart light bulb or a smart thermostat connected to the network, all the way to a complex system that can control power grids throughout countries, IoT has been becoming a necessity in everyday lives. This paper presents an overview of the devices, systems and wireless technologies used in different IoT architectures (Healthcare, Vehicular Networks, Mining, Learning, Energy, Smart Cities, Behaviors and Decision Making), their upbringings and challenges to this date and some foreseen in the future.

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Sensors of Smart Devices in the Internet of Everything (IoE) Era: Big Opportunities and Massive Doubts

Cited by 98 publications

References 138 publications

Self-Adaptive Data Processing to Improve SLOs for Dynamic IoT Workloads

Self-Adaptive Data Processing to Improve SLOs for Dynamic IoT Workloads

Spectrum Allocation in Cognitive Radio Simplified Swarm Optimization Based Method

The Future of Emerging IoT Paradigms: Architectures and Technologies

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