Self-organized power consumption approximation in the Internet of Things

Batool, Komal; Niazi, Muaz A.

doi:10.1109/icce.2015.7066427

Cited by 7 publications

(5 citation statements)

References 10 publications

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“…The main concept of SOPCA algorithm is presented in Fig. 2, earlier proposed in (Batool and Niazi 2015). The idea is to exploit wireless connectivity between peer devices in conjunction with a server.…”

Section: Sopca Algorithmmentioning

confidence: 99%

“…We propose a methodology for modeling complex scenarios in the IoT domain using a combination of complex networks and agent-based modeling used in conjunction with each other. We would like to also note our previous introductory work in the domain published in conference proceedings such as (Batool and Niazi 2015;. The current work, however, is both considerably more extensive as well as demonstrates the modeling of IoT dynamics in a number of realistic network topologies-namely small-world and scale-free networks in addition to lattice, and scale-free networks.…”

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confidence: 91%

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Modeling the internet of things: a hybrid modeling approach using complex networks and agent-based models

Batool¹,

Niazi

2017

Complex Adapt Syst Model

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We live in a time where electronic gadgets and integrated sensors are all around usfrom versatile Smartphones and tablets to portable PCs, and from indoor temperature regulators to microwave ovens. We live in a new world-a world of smart*-where intelligence and connectivity is added to every conceivable object. The vision of the internet of things (IoT) by Ashton (2009) appears to have manifested itself-albeit in unexpected ways. This emergence of the IoT in our everyday lives obviously has numerous implications resulting in a very different environment and society. Considering that the IoT concept is itself quite new, it is understandable that it is difficult to model. Researchers from the communication systems area often focus primarily Abstract Sensors, coupled with transceivers, have quickly evolved from technologies purely confined to laboratory test beds to workable solutions used across the globe. These mobile and connected devices form the nuts and bolts required to fulfill the vision of the so-called internet of things (IoT). This idea has evolved as a result of proliferation of electronic gadgets fitted with sensors and often being uniquely identifiable (possible with technological solutions such as the use of Radio Frequency Identifiers). While there is a growing need for comprehensive modeling paradigms as well as example case studies for the IoT, currently there is no standard methodology available for modeling such real-world complex IoT-based scenarios. Here, using a combination of complex networks-based and agent-based modeling approaches, we present a novel approach to modeling the IoT. Specifically, the proposed approach uses the Cognitive Agent-Based Computing (CABC) framework to simulate complex IoT networks. We demonstrate modeling of several standard complex network topologies such as lattice, random, small-world, and scale-free networks. To further demonstrate the effectiveness of the proposed approach, we also present a case study and a novel algorithm for autonomous monitoring of power consumption in networked IoT devices. We also discuss and compare the presented approach with previous approaches to modeling. Extensive simulation experiments using several network configurations demonstrate the effectiveness and viability of the proposed approach.

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Section: Sopca Algorithmmentioning

confidence: 99%

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confidence: 91%

Modeling the internet of things: a hybrid modeling approach using complex networks and agent-based models

Batool¹,

Niazi

2017

Complex Adapt Syst Model

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“…Modeling and Simulation play a very important role in developing modern complex infra-structures involving Complex Adaptive Systems and Complex Physical Systems [4] such as the Internet of Things [5] and Cloud computing [6]. A key problem in new domains however is the lack of precedence in terms of how exactly to model these complex problems using different paradigms.…”

Section: Introductionmentioning

confidence: 99%

Modeling for a Greener Internet of Things: Carbon Footprint Planning and Allocation: A Tutorial Approach Using Monte Carlo Simulation

Laghari

Niazi²

2015

2015 13th International Conference on Frontiers of Information Technology (FIT)

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Internet of Things involves the interconnectivity of various computing devices of all shapes, sizes and configurations forming complex networks. A key problem with the large-scale proliferation of such devices is the inability to manage their global environmental impact. While, some techniques exist for lowering energy wastage in the form of power-saving techniques, the modeling problem associated with the Carbon Footprint allocation in the Internet of Things has not been solved. In this paper, we propose first steps towards modeling and simulation of Carbon Footprint allocation in complex network scenarios as part of the Internet of Things. We do a comparative study using two baseline schemes of Carbon footprint allocation across a network of computing devices. Extensive simulation experiments demonstrate the effectiveness of the proposed approach in modeling and evaluation of the Carbon Footprint in the Internet of Things.

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“…CAS exists in different forms. The management of such systems is carried out with efficient algorithms and is only controlled by different computational methods (Batool and Niazit 2015). Big data is one of the examples of such complex systems.…”

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confidence: 99%

Energy efficiency in big data complex systems: a comprehensive survey of modern energy saving techniques

Majeed

Shah

2015

Complex Adapt Syst Model

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IntroductionDue to the advancement in computer technology, the computer systems have become widespread and complex. This complex arrangement of the system results into a complex adaptive system (CAS). A huge increase in the scale and complexity of the systems have been observed (Niazi and Laghari 2012) since from last few years. CAS exists in different forms. The management of such systems is carried out with efficient algorithms and is only controlled by different computational methods (Batool and Niazit 2015). Big data is one of the examples of such complex systems. CAS is comprised of large entities and components which require interaction and adoption in performing certain operations. The nature of the big data is more or less the same as complex systems. In other words, big data is one of the forms of the complex systems. Most of the CASs are tied to AbstractThe growing need of computation and processing has led to the generation of data centers. These data centers are usually comprised of hundreds of thousands of servers and other components. This complicated arrangement of the systems lead to the adoption of complex systems. Complex systems prevail in our society as combination of lots of entities, e.g., immune system, human brain and ecosystems. The adoption and interaction of the entities is possible through nonlinear interactions. The interaction between the components of the complex system is carried out in distributed fashion. Big data which is comprised of thousands of machines is also considered to be a form of complex adaptive systems which makes use of large entities, components and nonlinear interactions with each other. The development of such a complex systems raises certain challenges. Apart from management, energy is the most concerned one which is the core discussion of this research. This paper, surveys the state of the art on modern tools, techniques, architectures and algorithms which has been proposed and deployed to achieve energy efficiency in big data over the period of 2007-2015. We group existing approaches aimed at achieving energy efficiency in the complex paradigm of big data. In this categorization, we aim to provide an easy and concise view of the underlined model adapted by each approach in the context of big data. which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Majeed and Shah Complex Adapt Syst Model (2015) 3:6 the concept of our society, i.e., immune system, human brain (neuron structure), ecosystem and human societies. REVIEWBig data is providing numerous services and infrastructures to the companies and has opened new research directions in the field of computer science. Most of the current applications of the cloud computing uses distributed computing with varying degree of connectivity and interaction. Big data is providing computation and efficient processing to millions of user...

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Self-organized power consumption approximation in the Internet of Things

Cited by 7 publications

References 10 publications

Modeling the internet of things: a hybrid modeling approach using complex networks and agent-based models

Modeling the internet of things: a hybrid modeling approach using complex networks and agent-based models

Modeling for a Greener Internet of Things: Carbon Footprint Planning and Allocation: A Tutorial Approach Using Monte Carlo Simulation

Energy efficiency in big data complex systems: a comprehensive survey of modern energy saving techniques

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