Context-aware Control Platform for Sensor Network Integration in IoT and Cloud

Merezeanu, Daniel; Vasilescu, Gheorghe; Dobrescu, Radu

doi:10.24846/v25i4y201610

Cited by 22 publications

(20 citation statements)

References 17 publications

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“…The resource allocation of MCC is a great challenging to face while estimating the relative size of the infrastructures [18,19]. Few investigations have discussed these issues earlier [20][21][22]. Consequently, it is a prominent issue for service providers to design resource management systems in real time applications [23].…”

Section: Introductionmentioning

confidence: 99%

An Optimal Energy Consumption Based Resource Management in Mobile Cloud Computing

2019

ijrte

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This paper focused on optimal energy-efficient resource allocation management in the mobile cloud services. A resource management technique depicts the various resources reservation or blocking. Energy wastage is diminished, and revenue is amplified for mobile cloud providers. The recommended method holds two stages: a) beginning stage, the task impairment, delay time, resource utilization for every task has been individually calculated, and the enthalpy was measured, and b) the second stage, the enthalpy-based Optimal Energy Allocation Supervision (OEAS) algorithm was used to optimize the resources to the powerful resource management. In this paper, the problem of the limited and relatively small battery energy power in today’s mobile devices has been restricted functionality which can include into these platforms. Diverse mobile cloud suppliers helpfully share the resources in a pool for improving resource allocation based on the users demand and distribute revenue in mobile cloud providers. The recent upgraded research in MCC from an existing work has been examined on the issues of managing resources and vital challenges in energy consumption. The new hazing technology of mobile users and robust business interests in mobile cloud environment which escort the innovative progress in mobile cloud computing. It operates intense energy methods with a low cost. This paper exhibits the research extent and classified various issues in energy saving in mobile clouds. Later, it analyzes the presented research results and mechanisms which establish its strengths and weaknesses. Energy consumption is a major problem being faced by mobile cloud computing. This paper recognizes and explains the open issues and idea of future research. The main objective is to reduce energy consumption, increase energy efficiency in computing devices and resource allocations management as well as in their executions. Energy conservation can be the optimal solution which is minimized by using less of an energy service

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Section: Introductionmentioning

confidence: 99%

An Optimal Energy Consumption Based Resource Management in Mobile Cloud Computing

2019

ijrte

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“…With the recent advancements in communication technology, more and more data are generated and collected, therefore, the big data will grow exponentially and this will increase the challenges of extracting and retrieving the complexity of the valuable hidden data. There are more than three billion users of smart objects including smart phones, smart homes, as well as business and entertainment applications [16]. These smart devices allow Machine to Machine (M2M) electronic communication with or without an intermediary-user.…”

Section: Introductionmentioning

confidence: 99%

“…Data mining is one of the greatest tasks needed for the management of big sensing data stream [2]. WSNs are successfully deployed in detection applications and diverse monitoring [16]. In these applications, WSNs generate Big data in the form of streams.…”

Section: Introductionmentioning

confidence: 99%

A Big Data Framework for Mining Sensor Data Using Hadoop

El-Shafeiy¹,

El-Desouky²

2017

STUD INFORM CONTROL

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Abstract:The data gathered from IOTs is considered of high business value. The IOTs devices sense the natural conditions using sensor network comprised of sensor nodes. Mining of big sensor data for useful knowledge extraction is a very challenging task. Frequent itemsets is one of the most effective mining techniques that find important itemsets from big sensor data. In this paper, a MapReduce Frequent Nodesets-based Boundary POC tree (MR-FNBP) framework is proposed for mining Frequent Nodesets for big sensor data. The MapReduce framework is used to implement MR-FNBP to enhance its performance in highly distributed environments. Additionally, the proposed Boundary (FNBP) creates a Boundary as an early stage to exclude the infrequent itemsets, and this may reduce the overall memory and time usage. Moreover, a number of experiments were performed to evaluate the performance of MR-FNBP framework. The results show high scalability rate and a less time consuming process for MR-FNBP framework over different recent systems.

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“…The management of the network and cloud resources in cloud computing comes with several challenges, and to address these challenges, it is necessary to develop a resource management approach which ensures the scalability, manageability, adaptability, efficiency, and dependability of cloud computing [25] [15]. The management of MCC resources is also a great challenge owing to the number of objectives that need to be met while considering the relative size (large) of the infrastructures [17] [20]. Few studies have previously addressed these issues [18] [19] [20] [26], and as a consequence, it is a salient issue for service providers to design powerful resource management schemes that will satisfy the requirements of the MCC applications in real time [21].…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach of Reducing Energy Consumption by Utilizing Enthalpy in Mobile Cloud Computing

Mohammed¹,

Țăpuș²

2017

STUD INFORM CONTROL

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Abstract:The Mobile Cloud Computing (MCC) technology is a growing technology that aids in improving the quality of mobile services. The resources in MCC are dynamically allocated to the users based on their needs. The users pay for the resources consumed by their programs, but the drawbacks of process failures and knapsack problems of resource allocation still exist in MCC. Furthermore, the scheduling of energy consumption and computational cost is very high. To solve these issues, an optimized energy efficiency resource management technique is set forth in this study. The recommended method holds two stages: a) the initial stage, when the task loss, transmission probability, delay, utilization and reputation for every single task is individually measured and the enthalpy was calculated, and b) the second stage, when the enthalpy-related Cuckoo Search Optimization (CSO) algorithm was used to optimize and prioritize the resources to the powerful resource management. The execution of the recommended method automatically reduced the knapsack issue, energy and cost. A formal analysis of the resource management framework has ensured that the provider executes resources based on the power consumption. The performance of the suggested algorithm was benchmarked against the performance of other conventional algorithms.

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Context-aware Control Platform for Sensor Network Integration in IoT and Cloud

Cited by 22 publications

References 17 publications

An Optimal Energy Consumption Based Resource Management in Mobile Cloud Computing

An Optimal Energy Consumption Based Resource Management in Mobile Cloud Computing

A Big Data Framework for Mining Sensor Data Using Hadoop

A Novel Approach of Reducing Energy Consumption by Utilizing Enthalpy in Mobile Cloud Computing

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