Internet-of-Things-Based Smart Cities: Recent Advances and Challenges

Mehmood, Yasir; Ahmad, Farhan; Yaqoob, Ibrar; Adnane, Asma; Imran, Muhammad; Guizani, Sghaier

doi:10.1109/mcom.2017.1600514

Cited by 549 publications

(284 citation statements)

References 12 publications

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“…Despite the potential for decision provenance, work is needed to make it a reality. 21 Though, as mentioned, provenance has been considered for some specific compliance aspects (see for e.g. [22], [23], [27]- [29]), the broader accountability considerations have yet to be explored in depth.…”

Section: Moving Forward: Implementation Considerations and Researmentioning

confidence: 99%

Decision Provenance: Harnessing Data Flow for Accountable Systems

2019

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Demand is growing for more accountability regarding the technological systems that increasingly occupy our world. However, the complexity of many of these systems -often systems-of-systems -poses accountability challenges. A key reason for this is because the details and nature of the information flows that interconnect and drive systems, which often occur across technical and organisational boundaries, tend to be invisible or opaque. This paper argues that data provenance methods show much promise as a technical means for increasing the transparency of these interconnected systems. Specifically, given the concerns regarding ever-increasing levels of automated and algorithmic decision-making, and so-called 'algorithmic systems' in general, we propose decision provenance as a concept showing much promise. Decision provenance entails using provenance methods to provide information exposing decision pipelines: chains of inputs to, the nature of, and the flow-on effects from the decisions and actions taken (at design and run-time) throughout systems. This paper introduces the concept of decision provenance, and takes an interdisciplinary (tech-legal) exploration into its potential for assisting accountability in algorithmic systems. We argue that decision provenance can help facilitate oversight, audit, compliance, risk mitigation, and user empowerment, and we also indicate the implementation considerations and areas for research necessary for realising its vision. More generally, we make the case that considerations of data flow, and systems more broadly, are important to discussions of accountability, and complement the considerable attention already given to algorithmic specifics.

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Section: Moving Forward: Implementation Considerations and Researmentioning

confidence: 99%

Decision Provenance: Harnessing Data Flow for Accountable Systems

2019

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“…This type of data is mainly expressed interests of users, e.g., "Likes" and ratings of photos/posts shared by others. [36], e-health [37], smart grids [38], Maritime Industry [39], smart city [40], etc.…”

Section: Data Sources and Necessities Of Bdamentioning

confidence: 99%

Big Data Analytics for Large-scale Wireless Networks

et al. 2019

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The wide proliferation of various wireless communication systems and wireless devices has led to the arrival of big data era in large scale wireless networks. Big data of large scale wireless networks has the key features of wide variety, high volume, real-time velocity and huge value leading to the unique research challenges that are different from existing computing systems. In this paper, we present a survey of the state-of-art big data analytics (BDA) approaches for large scale wireless networks. In particular, we categorize the life cycle of BDA into four consecutive stages: Data Acquisition, Data Preprocessing, Data Storage and Data Analytics. We then present a detailed survey of the technical solutions to the challenges in BDA for large scale wireless networks according to each stage in the life cycle of BDA. Moreover, we discuss the open research issues and outline the future directions in this promising area.

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“…An example of this workflow is smart road traffic monitoring as a service of smart city services that utilizes processing power of connected vehicles in addition of roadside infrastructure (e.g. traffic lights, cameras) to create real-time view of road traffic conditions [3]. This type of workflow is also called stream workflow.…”

Section: Introductionmentioning

confidence: 99%

Scheduling Algorithms for Efficient Execution of Stream Workflow Applications in Multicloud Environments

Barika

Garg

Chan

et al. 2022

IEEE Trans. Serv. Comput.

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Big data processing applications are becoming more and more complex. They are no more monolithic in nature but instead they are composed of decoupled analytical processes in the form of a workflow. One type of such workflow applications is stream workflow application, which integrates multiple streaming big data applications to support decision making. Each analytical component of these applications runs continuously and processes data streams whose velocity will depend on several factors such as network bandwidth and processing rate of parent analytical component. As a consequence, the execution of these applications on cloud environments requires advanced scheduling techniques that adhere to end user's requirements in terms of data processing and deadline for decision making. In this paper, we propose two Multicloud scheduling and resource allocation techniques for efficient execution of stream workflow applications on Multicloud environments while adhering to workflow application and user performance requirements and reducing execution cost. Results showed that the proposed genetic algorithm is an adequate and effective for all experiments.

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Internet-of-Things-Based Smart Cities: Recent Advances and Challenges

Cited by 549 publications

References 12 publications

Decision Provenance: Harnessing Data Flow for Accountable Systems

Decision Provenance: Harnessing Data Flow for Accountable Systems

Big Data Analytics for Large-scale Wireless Networks

Scheduling Algorithms for Efficient Execution of Stream Workflow Applications in Multicloud Environments

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