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The IoT for smart sustainable cities of the future: An analytical framework for sensor-based big data applications for environmental sustainability
اینترنت اشیا برای شهرهای پایدار هوشمند از آینده: یک چارچوب تحلیلی برای کاربردهای داده های بزرگ مبتنی بر حسگر برای سازگاری با محیط زیست-2018 The Internet of Things (IoT) is one of the key components of the ICT infrastructure of smart sustainable cities as
an emerging urban development approach due to its great potential to advance environmental sustainability. As
one of the prevalent ICT visions or computing paradigms, the IoT is associated with big data analytics, which is
clearly on a penetrative path across many urban domains for optimizing energy efficiency and mitigating en
vironmental effects. This pertains mainly to the effective utilization of natural resources, the intelligent man
agement of infrastructures and facilities, and the enhanced delivery of services in support of the environment. As
such, the IoT and related big data applications can play a key role in catalyzing and improving the process of
environmentally sustainable development. However, topical studies tend to deal largely with the IoT and related
big data applications in connection with economic growth and the quality of life in the realm of smart cities, and
largely ignore their role in improving environmental sustainability in the context of smart sustainable cities of
the future. In addition, several advanced technologies are being used in smart cities without making any con
tribution to environmental sustainability, and the strategies through which sustainable cities can be achieved fall
short in considering advanced technologies. Therefore, the aim of this paper is to review and synthesize the
relevant literature with the objective of identifying and discussing the state-of-the-art sensor-based big data
applications enabled by the IoT for environmental sustainability and related data processing platforms and
computing models in the context of smart sustainable cities of the future. Also, this paper identifies the key
challenges pertaining to the IoT and big data analytics, as well as discusses some of the associated open issues.
Furthermore, it explores the opportunity of augmenting the informational landscape of smart sustainable cities
with big data applications to achieve the required level of environmental sustainability. In doing so, it proposes a
framework which brings together a large number of previous studies on smart cities and sustainable cities,
including research directed at a more conceptual, analytical, and overarching level, as well as research on
specific technologies and their novel applications. The goal of this study suits a mix of two research approaches:
topical literature review and thematic analysis. In terms of originality, no study has been conducted on the IoT
and related big data applications in the context of smart sustainable cities, and this paper provides a basis for
urban researchers to draw on this analytical framework in future research. The proposed framework, which can
be replicated, tested, and evaluated in empirical research, will add additional depth to studies in the field of
smart sustainable cities. This paper serves to inform urban planners, scholars, ICT experts, and other city sta
keholders about the environmental benefits that can be gained from implementing smart sustainable city in
itiatives and projects on the basis of the IoT and related big data applications.
Keywords: Smart sustainable cities , The IoT , Big data analytics , Sensor technology , Data processing platforms , Environmental sustainability , Big data applications , Cloud computing , Fog/edge computing |
مقاله انگلیسی |
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Data Mining and Fusion Techniques for WSNs as a Source of the Big Data
داده کاوی و تکنیک ادغامی برای WSN ها به عنوان یک منبع داده های بزرگ-2015 The wide adoption of the Wireless Senor Networks (WSNs) applications around the world has increased the amount of the sensor data which contribute to the complexity of Big Data. This has emerged the need to the use of in-network data processing techniques which are very crucial for the success of the big data framework. This article gives overview and discussion about the state-of-the- art of the data mining and data fusion techniques designed for the WSNs. It discusses how these techniques can prepare the sensor data inside the network (in-network) before any further processing as big data. This is very important for both of the WSNs and the big data framework. For the WSNs, the in-network pre-processing techniques could lead to saving in their limited resources. For the big data side, receiving a clean, non-redundant and relevant data would reduce the excessive data volume, thus an overload reduction will be obtained at the big data processing platforms and the discovery of values from these data will be accelerated.§c 2014 The Authors. Published by Elsevier B.V.Peer-review under responsibility of Universal Society for Applied Research.© 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).Peer-review under responsibility of Universal Society for Applied Research
Wireless Sensor Networks | Big Data | Data Mining | Data Fusion | Machine learning |
مقاله انگلیسی |
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داده کاوی و ادغام تکنولوژی در WSN به عنوان منبع داده های بزرگ
سال انتشار: 2015 - تعداد صفحات فایل pdf انگلیسی: 9 - تعداد صفحات فایل doc فارسی: 15 در گسترشِ برنامهء شبکه های سنسور بی سیم (WSN) در سراسر جهان مقدار داده های حسگر که به پیچیدگی داده های بزرگ کمک می کند افزایش یافته است. و این کار نیاز به تکنیک های پردازش داده در چارچوب داده های بزرگ دارد که مهم تلقی می شوند. این مقاله مروری بر بحث درباره دولت هاست و اطلاعات داده کاوی و ادغام تکنولوژی های طراحی شده در شبکه های بی سیم را می دهد. این بحث که چگونه این تکنیک می تواند داده های حسگر داخل شبکه را قبل از هرگونه پردازش به عنوان داده های بزرگ اماده کند مطرح می شود. این برای هر دو شبکه گیرنده بی سیم و چارچوب داده های بزرگ بسیار مهم است. شبکه های گیرنده بی سیم در شبکه تکنیک های پیش پردازش می توانند منجر به صرفه جویی در منابع محدود شوند. در سمت داده های بزرگ دریافت داده ها درست و بدون حشو بوده و مربوط به کاهش بیش از حد داده می باشد در نتیجه کاهش بیش از حد در سیستم عامل پردازش داده های بزرگ به کشف مقادیر زیادی از داده ها با شتاب بالا منجر می شود.
کلمات کلیدی: شبکه های حسگر بی سیم | داده های بزرگ | داده کاوی | ترکیب داده ها | یادگیری ماشینی |
مقاله ترجمه شده |
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Asymptotic scheduling for many task computing in Big Data platforms
زمانبندی مجانبی برای تعداد زیادی از محاسبات کار در چارچوب های داده های بزرگ-2015 Due to the advancement of technology the datasets that are being processed nowadays in
modern computer clusters extend beyond the petabyte scale – the 4 detectors of the Large
Hadron Collider at CERN produced several petabytes of data in 2011. Large scale computing
solutions are increasingly used for genome sequencing tasks in the Human Genome
Project. In the context of Big Data platforms, efficient scheduling algorithms play an essen-30 tial role. This paper deals with the problem of scheduling a set of jobs across a set of machi-31 nes and specifically analyzes the behavior of the system at very high loads, which is specific
to Big Data processing. We show that under certain conditions we can easily discover the
best scheduling algorithm, prove its optimality and compute its asymptotic throughput.
We present a simulation infrastructure designed especially for building/analyzing different
types of scenarios. This allows to extract scheduling metrics for three different algorithms
(the asymptotically optimal one, FCFS and a traditional GA-based algorithm) in order to
compare their performance. We focus on the transition period from low incoming job rates
load to the very high load and back. Interestingly, all three algorithms experience a poor
performance over the transition periods. Since the Asymptotically Optimal algorithm
makes the assumption of an infinite number of jobs it can be used after the transition,
when the job buffers are saturated. As the other scheduling algorithms do a better job
under reduced load, we will combine them into a single hybrid algorithm and empirically
determine what is the best switch point, offering in this way an asymptotic scheduling
mechanism for many task computing used in Big Data processing platforms.
Keywords:
Asymptotic scheduling
Many-task computing
Cloud computing
Big Data platforms
Simulation |
مقاله انگلیسی |