An approach for provisioning virtual sensors in sensor clouds

Lemos, Marcus; Rabêlo, Ricardo A. L.; Mendes, Douglas; Carvalho, Carlos Giovanni Nunes de; Filho, Raimir Holanda

doi:10.1002/nem.2062

Cited by 7 publications

(9 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Magnetic values provide a direction (x, y, z). The correction sensor present in a gyroscopic motion sensor adds further spatial information for the accelerometer by tracking rotation and measuring the angular velocity [11]. Together, the correction sensor and accelerometer measure the rate of change.…”

Section: Handheld Device Sensorsmentioning

confidence: 99%

“…It can determine how many times a mobile phone tilts, differentiating it from the accelerometer [25]. The gyroscope can remove errors due to gravity from the external accelerometer, which otherwise would result in inaccurate rotation measurement [11]. The gyroscope creates data drift when tracking a path [26].…”

Section: Handheld Device Sensorsmentioning

confidence: 99%

“…The limitations of these physical sensors and beacons, in terms of power, storage, and communication, affect their sensing capabilities [9,10]. Previous studies have focused on allocating many sensors in desired regions [11], resulting in problems related to power consumption. Pedestrian tracking sensors can be used to collect information, but require the Global Navigation Satellite System (GNSS) service [12].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Handheld Device-Based Indoor Localization with Zero Infrastructure (HDIZI)

Al-Sahly

Hassan

Saleem

et al. 2022

Sensors

View full text Add to dashboard Cite

The correlations between smartphone sensors, algorithms, and relevant techniques are major components facilitating indoor localization and tracking in the absence of communication and localization standards. A major research gap can be noted in terms of explaining the connections between these components to clarify the impacts and issues of models meant for indoor localization and tracking. In this paper, we comprehensively study the smartphone sensors, algorithms, and techniques that can support indoor localization and tracking without the need for any additional hardware or specific infrastructure. Reviews and comparisons detail the strengths and limitations of each component, following which we propose a handheld-device-based indoor localization with zero infrastructure (HDIZI) approach to connect the abovementioned components in a balanced manner. The sensors are the input source, while the algorithms are used as engines in an optimal manner, in order to produce a robust localizing and tracking model without requiring any further infrastructure. The proposed framework makes indoor and outdoor navigation more user-friendly, and is cost-effective for researchers working with embedded sensors in handheld devices, enabling technologies for Industry 4.0 and beyond. We conducted experiments using data collected from two different sites with five smartphones as an initial work. The data were sampled at 10 Hz for a duration of five seconds at fixed locations; furthermore, data were also collected while moving, allowing for analysis based on user stepping behavior and speed across multiple paths. We leveraged the capabilities of smartphones, through efficient implementation and the optimal integration of algorithms, in order to overcome the inherent limitations. Hence, the proposed HDIZI is expected to outperform approaches proposed in previous studies, helping researchers to deal with sensors for the purposes of indoor navigation—in terms of either positioning or tracking—for use in various fields, such as healthcare, transportation, environmental monitoring, or disaster situations.

show abstract

Section: Handheld Device Sensorsmentioning

confidence: 99%

Section: Handheld Device Sensorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Handheld Device-Based Indoor Localization with Zero Infrastructure (HDIZI)

Al-Sahly

Hassan

Saleem

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…The authors in [26] mainly focused on the similarity of sensing data. When the user initiates a service request, all the physical sensors are activated, then the collected data are sent to the middleware layer via the sink nodes for further processing.…”

Section: Related Workmentioning

confidence: 99%

Using Machine Learning Methods to Provision Virtual Sensors in Sensor-Cloud

Zhang

Wang

Xiong

2020

Sensors

View full text Add to dashboard Cite

The advent of sensor-cloud technology alleviates the limitations of traditional wireless sensor networks (WSNs) in terms of energy, storage, and computing, which has tremendous potential in various agricultural internet of things (IoT) applications. In the sensor-cloud environment, virtual sensor provisioning is an essential task. It chooses physical sensors to create virtual sensors in response to the users’ requests. Considering the capricious meteorological environment of the outdoors, this paper presents an measurements similarity-based virtual-sensor provisioning scheme by taking advantage of machine learning in data analysis. First, to distinguish the changing trends, we classified all the physical sensors into several categories using historical data. Then, the k-means clustering algorithm was exploited for each class to cluster the physical sensors with high similarity. Finally, one representative physical sensor from each cluster was selected to create the corresponding virtual sensors. The experimental results show the reformation of our scheme with respect to energy efficiency, network lifetime, and data accuracy compared with the benchmark schemes.

show abstract

“…RoseHost is responsible for the management of various resources on its host nodes, container start and stop, resource usage collection, etc. [9][10].…”

Section: Task Distribution Systemmentioning

confidence: 99%

Task Allocation Mechanism Based on Ant Colony Algorithm in Distributed System

2020

DPS

View full text Add to dashboard Cite

With the popularization and rapid development of the Internet, software applications have higher and higher requirements on concurrency and service quality, which promotes the continuous evolution of the Internet architecture. The rapidly increasing user scale and increasingly complex service system lead to the explosive growth of concurrent access traffic in the network. This paper mainly studies the application of task allocation mechanism based on ACA(ACA) in distributed system. In this paper, a task allocation system is constructed based on Master/Slave architecture, and a task allocation mechanism based on ACA is designed and implemented. By optimizing the resource allocation of workflow, the completion time of workflow is minimized. The simulation results show that ACA can improve the efficiency of task allocation.

show abstract

An approach for provisioning virtual sensors in sensor clouds

Cited by 7 publications

References 45 publications

Handheld Device-Based Indoor Localization with Zero Infrastructure (HDIZI)

Handheld Device-Based Indoor Localization with Zero Infrastructure (HDIZI)

Using Machine Learning Methods to Provision Virtual Sensors in Sensor-Cloud

Task Allocation Mechanism Based on Ant Colony Algorithm in Distributed System

Contact Info

Product

Resources

About