Mobile cloud computing for indoor emergency response: the IPSOS assistant case study

Facchinetti, Dario; Psaila, Giuseppe; Scandurra, Patrizia

doi:10.1007/s40860-019-00088-9

Cited by 12 publications

(22 citation statements)

References 25 publications

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“…This paradigm combines CC and MC. The main purpose is to move the most demanded computational process from the mobile/wearable devices to the Cloud in order to optimize the use of local resources [ 54 ]. It avoids processing large amounts of data in the user’s device.…”

Section: Resultsmentioning

confidence: 99%

Cloud Platforms for Context-Adaptive Positioning and Localisation in GNSS-Denied Scenarios—A Systematic Review

Quezada-Gaibor

Torres-Sospedra

Nurmi

et al. 2021

Sensors

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Cloud Computing and Cloud Platforms have become an essential resource for businesses, due to their advanced capabilities, performance, and functionalities. Data redundancy, scalability, and security, are among the key features offered by cloud platforms. Location-Based Services (LBS) often exploit cloud platforms to host positioning and localisation systems. This paper introduces a systematic review of current positioning platforms for GNSS-denied scenarios. We have undertaken a comprehensive analysis of each component of the positioning and localisation systems, including techniques, protocols, standards, and cloud services used in the state-of-the-art deployments. Furthermore, this paper identifies the limitations of existing solutions, outlining shortcomings in areas that are rarely subjected to scrutiny in existing reviews of indoor positioning, such as computing paradigms, privacy, and fault tolerance. We then examine contributions in the areas of efficient computation, interoperability, positioning, and localisation. Finally, we provide a brief discussion concerning the challenges for cloud platforms based on GNSS-denied scenarios.

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Section: Resultsmentioning

confidence: 99%

Cloud Platforms for Context-Adaptive Positioning and Localisation in GNSS-Denied Scenarios—A Systematic Review

Quezada-Gaibor

Torres-Sospedra

Nurmi

et al. 2021

Sensors

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“…While this concept of cloud computing applied to AAL technologies holds potential, but these existing systems also have several limitations. For instance-the system proposed by Navarro et al [53] is environment specific and was designed, adapted, and built specifically for Fundació Ave Maria [56], which is a non-profit organization in Spain, so, the same design cannot not be seamlessly applied to any other environment consisting of varying environment parameters that would be associated with diverse range of human behavior and user interactions; the system proposed by Nikoloudakis et al [52] uses an outdoor positioning mechanism that can only detect whether the user leaves the premises of their location; the work proposed by Facchinetti et al [55] is a mobile app and that brings into context these challenges-(i) elderly people are less likely to download a mobile app as compared to the other age groups [57], (ii) elderly people are naturally resistant to using different kinds of technology-based apps on their phones, tablets, and other interactive devices [58], and (iii) older adults face multiple usability issues with such apps [59]; even though another system proposed by Nikoloudakis et al [54] presents approaches for both indoor and outdoor positioning, it cannot model and analyze the fine grain levels of human activity such as atomic activity, context attributes, core atomic activity, and core context attributes along with their associated weights, in the context of dynamic user interactions during ADLs. To add to the above, for all these systems [52][53][54][55], the RMSE for detection of the indoor location of the user is also not less than 1 m. Thus, to summarize, the main research challenges in this field are as follows:…”

Section: Literature Reviewmentioning

confidence: 99%

Multimodal Approaches for Indoor Localization for Ambient Assisted Living in Smart Homes

Thakur

Han

2021

Information

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This work makes multiple scientific contributions to the field of Indoor Localization for Ambient Assisted Living in Smart Homes. First, it presents a Big-Data driven methodology that studies the multimodal components of user interactions and analyzes the data from Bluetooth Low Energy (BLE) beacons and BLE scanners to detect a user’s indoor location in a specific ‘activity-based zone’ during Activities of Daily Living. Second, it introduces a context independent approach that can interpret the accelerometer and gyroscope data from diverse behavioral patterns to detect the ‘zone-based’ indoor location of a user in any Internet of Things (IoT)-based environment. These two approaches achieved performance accuracies of 81.36% and 81.13%, respectively, when tested on a dataset. Third, it presents a methodology to detect the spatial coordinates of a user’s indoor position that outperforms all similar works in this field, as per the associated root mean squared error—one of the performance evaluation metrics in ISO/IEC18305:2016—an international standard for testing Localization and Tracking Systems. Finally, it presents a comprehensive comparative study that includes Random Forest, Artificial Neural Network, Decision Tree, Support Vector Machine, k-NN, Gradient Boosted Trees, Deep Learning, and Linear Regression, to address the challenge of identifying the optimal machine learning approach for Indoor Localization.

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“…Users can dynamically purchase resources from cloud service providers according to their own needs. When users do not need redundant computing resources, they can also release them in time [11]. Cloud service provider Yi can provide more virtualized resources to meet the needs of different users to obtain more users…”

Section: Features Of Cloudmentioning

confidence: 99%

“…First, determine whether the idle time 7 Wireless Communications and Mobile Computing period can execute the task to be migrated, and then determine after the task is migrated whether it will affect the completion time of the subsequent tasks; if the idle time period ½Ds, Df can execute the task to be migrated and does not affect the completion time of the subsequent tasks, then the task will be migrated. That is, for a given cloud workflow task y i ∈ Y, if Sucðy i Þ ≠ 0, the migration condition (10), condition (11), and condition (12) are met, and the task can be migrated; if Succðy iÞ = 0, only condition (10) and condition (11) can migrate, where b k is its newly allocated virtual machine, b p is its previously allocated virtual machine, and cost sum ′ is the cost of all tasks after task y i migrates from virtual machine b k to virtual machine b p .…”

Section: Schedulingmentioning

confidence: 99%

Workflow Scheduling Based on Mobile Cloud Computing Machine Learning

Gong

2021

Wireless Communications and Mobile Computing

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In recent years, cloud workflow task scheduling has always been an important research topic in the business world. Cloud workflow task scheduling means that the workflow tasks submitted by users are allocated to appropriate computing resources for execution, and the corresponding fees are paid in real time according to the usage of resources. For most ordinary users, they are mainly concerned with the two service quality indicators of workflow task completion time and execution cost. Therefore, how cloud service providers design a scheduling algorithm to optimize task completion time and cost is a very important issue. This paper proposes research on workflow scheduling based on mobile cloud computing machine learning, and this paper conducts research by using literature research methods, experimental analysis methods, and other methods. This article has deeply studied mobile cloud computing, machine learning, task scheduling, and other related theories, and a workflow task scheduling system model was established based on mobile cloud computing machine learning from different algorithms used in processing task completion time, task service costs, task scheduling, and resource usage The situation and the influence of different tasks on the experimental results are analyzed in many aspects. The algorithm in this paper speeds up the scheduling time by about 7% under a different number of tasks and reduces the scheduling cost by about 2% compared with other algorithms. The algorithm in this paper has been obviously optimized in time scheduling and task scheduling.

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Mobile cloud computing for indoor emergency response: the IPSOS assistant case study

Cited by 12 publications

References 25 publications

Cloud Platforms for Context-Adaptive Positioning and Localisation in GNSS-Denied Scenarios—A Systematic Review

Cloud Platforms for Context-Adaptive Positioning and Localisation in GNSS-Denied Scenarios—A Systematic Review

Multimodal Approaches for Indoor Localization for Ambient Assisted Living in Smart Homes

Workflow Scheduling Based on Mobile Cloud Computing Machine Learning

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