Modeling the Internet of Things, Self-Organizing and Other Complex Adaptive Communication Networks: A Cognitive Agent-Based Computing Approach

Laghari, Samreen; Niazi, Muaz A.

doi:10.1371/journal.pone.0146760

Cited by 52 publications

(30 citation statements)

References 29 publications

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“…Their work presents a chemical reaction-inspired computational model using the concepts of graphs and reflection, which attempts to address the complexities associated with the visualization, modeling, interaction, analysis and abstraction of information in the IoT. Laghari et al [26] model a self-adaptive architecture for managing the Carbon footprint in a corporate environment using a heterogeneous multiagent system. They use Cognitive Agent-based Computing (CABC) to create models.…”

Section: Related Workmentioning

confidence: 99%

A conceptual model to support security analysis in the internet of things

Mavropoulos

Mouratidis

Fish

et al. 2017

ComSIS

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This paper proposes a conceptual model to support decision makers during security analysis of Internet of Things (IoT) systems. The world is entering an era of ubiquitous computing with IoT being the main driver. Taking into account the scale of IoT, the number of security issues that are arising are unprecedented. Both academia and industry require methodologies that will enable reasoning about security in IoT system in a concise and holistic manner. The proposed conceptual model addresses a number of challenges in modeling IoT to support security analysis. The model is based on an architecture-oriented approach that incorporates sociotechnical concepts into the security analysis of an IoT system. To demonstrate the usage of the proposed conceptual model, we perform a security analysis on a small scale smart home example.

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Section: Related Workmentioning

confidence: 99%

A conceptual model to support security analysis in the internet of things

Mavropoulos

Mouratidis

Fish

et al. 2017

ComSIS

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“…Over the ensuing two decades, the expansion of the World-Wide Web (the Web) has resulted in an extremely complex global environment of hyper-connections and mutual dependencies (Phister 2010). We have seen the rapid evolution of the static Web 1.0 into the interactive Web 2.0 (O'Reilly 2007) and then to the Internet of Things and the explosion of social media platforms (Yoo 2010;Laghari and Niazi 2016). The combination of diverse digital technologies and the Internet has created ecologies of sociotechnical networks which have significant impact on human agency and have enabled a revolution in the range of activities in organisations (Huang and Sun 2016) and in society (Castells 2015;Rosenberg 2013).…”

Section: Introductionmentioning

confidence: 99%

Making IS Relevant in a Connected World

Linger

Hasan

2020

AJIS

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The exponential growth of the Internet since the mid-1990s has greatly expanded the capacity of people everywhere to interconnect and engage through digital technologies. As a complex adaptive system of systems, the Internet has extended the range and complexity of phenomena of interest to Information Systems (IS) scholars. This is both an exciting opportunity and a challenge which we explore in this paper by revisiting the Intellectual Structures Framework (Hirshheim et al. 1996) which attempted to make sense of the fragmented adhocracy of IS, before the expansion and penetration of the Internet. We suggest that the IS adhocracy, with its multi-disciplinary and systems-oriented nature, gives IS researchers the requisite variety to contend with the increasingly diverse digital ecologies of IS-enabled human activities that have emerged in the ensuing two decades. Based on relevant research over these two decades we present a revised framework that (1) reflects the complexities of contemporary IS phenomena and (2) can act as an instrument for analysing such phenomena across a spectrum of human activities. We justify the form and content of the Revised Intellectual Structures Framework, providing examples of its application in IS research using appropriate research methods and techniques. We argue that our revisions to the original framework provides individuals, organisations, and societies with a conceptual lens that is necessary to better address the challenges and opportunities posed by the complexities of contemporary digital ecologies.

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“…Reference [9] provides a survey of recent works on use of ABM for modelling IoT systems. The authors of [10] show how a cognitive agentbased computing modeling approach, such as ABM, can be used to model complex problems in the domain of IoT. Following an approach similar to [10], our work examines the use of ABM to model an IoT network that requires distributed decision making.…”

Section: Introductionmentioning

confidence: 99%

Agent-Based Modelling for Distributed Decision Support in an IoT Network

Butt¹,

Dey²,

Dzaferagic³

et al. 2020

Preprint

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<div>An increasing number of emerging applications, e.g.,</div><div>Internet of Things (IoT), vehicular communications, augmented reality, and the growing complexity due to the interoperability requirements of these systems, lead to the need to change the tools used for the modeling and analysis of those networks. Agent-Based Modeling (ABM) as a bottom-up modeling approach considers a network of autonomous agents interacting with each other, and therefore represents an ideal framework to comprehend the interactions of heterogeneous nodes in a complex environment. Here, we investigate the suitability of ABM to</div><div>model the communication aspects of a road traffic management system as an example of an IoT network. We model, analyze and compare various Medium Access Control (MAC) layer protocols for two different scenarios, namely uncoordinated and coordinated. Besides, we model the scheduling mechanisms for the coordinated scenario as a high level MAC protocol by using three different approaches: Centralized Decision Maker, DESYNC and decentralized learning MAC (L-MAC). The results clearly</div><div>show the importance of coordination between multiple decision makers in order to improve the information reporting error and spectrum utilization of the system.</div>

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Modeling the Internet of Things, Self-Organizing and Other Complex Adaptive Communication Networks: A Cognitive Agent-Based Computing Approach

Cited by 52 publications

References 29 publications

A conceptual model to support security analysis in the internet of things

A conceptual model to support security analysis in the internet of things

Making IS Relevant in a Connected World

Agent-Based Modelling for Distributed Decision Support in an IoT Network

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