A Middleware for Discovering Proximity-Based Service-Oriented Industrial Internet of Things

Chang, Chii; Srirama, Satish Narayana; Mass, Jakob

doi:10.1109/scc.2015.27

Cited by 22 publications

(23 citation statements)

References 23 publications

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“…With millions of things generating data, transferring all of that to the cloud is neither scalable nor suitable for real‐time decision making. The dynamic nature of IoT environments and its associated real‐time requirements and increasing processing capacity of edge devices (entry point into provider core networks, eg, gateways) has lead to the evolution of the Fog computing paradigm. Fog computing extends cloud services to the edge of networks, which results in latency reduction through geographical distribution of IoT application components, and increased scalability for handling large‐scale deployments.…”

Section: Introductionmentioning

confidence: 99%

iFogSim: A toolkit for modeling and simulation of resource management techniques in the Internet of Things, Edge and Fog computing environments

et al. 2017

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Summary Internet of Things (IoT) aims to bring every object (eg, smart cameras, wearable, environmental sensors, home appliances, and vehicles) online, hence generating massive volume of data that can overwhelm storage systems and data analytics applications. Cloud computing offers services at the infrastructure level that can scale to IoT storage and processing requirements. However, there are applications such as health monitoring and emergency response that require low latency, and delay that is caused by transferring data to the cloud and then back to the application can seriously impact their performances. To overcome this limitation, Fog computing paradigm has been proposed, where cloud services are extended to the edge of the network to decrease the latency and network congestion. To realize the full potential of Fog and IoT paradigms for real‐time analytics, several challenges need to be addressed. The first and most critical problem is designing resource management techniques that determine which modules of analytics applications are pushed to each edge device to minimize the latency and maximize the throughput. To this end, we need an evaluation platform that enables the quantification of performance of resource management policies on an IoT or Fog computing infrastructure in a repeatable manner. In this paper we propose a simulator, called iFogSim, to model IoT and Fog environments and measure the impact of resource management techniques in latency, network congestion, energy consumption, and cost. We describe two case studies to demonstrate modeling of an IoT environment and comparison of resource management policies. Moreover, scalability of the simulation toolkit of RAM consumption and execution time is verified under different circumstances.

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Section: Introductionmentioning

confidence: 99%

iFogSim: A toolkit for modeling and simulation of resource management techniques in the Internet of Things, Edge and Fog computing environments

et al. 2017

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“…The framework utilizes dynamic elastic Cloud computing and workflow automation to optimize the task allocation among localhost services and Cloud services based on resource availability and cost-performance index model. This paper is an extension of our work presented at (Chang et al, 2015). The proposed cost-performance index scheme assists the decision-making of the workflow configuration based on fuzzy set and weight of context schemes.…”

Section: Imagine People With Disability In An Outdoor Area Can Use Thmentioning

confidence: 94%

Adaptive Mobile Cloud Workflow Management System for Service Discovery in Proximity-Based Internet of Things

Chang¹,

Srirama²,

Mass³

2015

STSC

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In the near future, Industrial Internet of Things can help people in realizing the smart cities and urban sensing scenarios by providing various useful spatial information. Based on real-time resource discovery and data retrieval technologies, mobile devices can continuously interact with surrounding things and provide real-time content mashup services. However, continuous resource discovery and content mashup processes can be resource intensive for mobile devices. In order to reduce the resource usage, certain tasks of the mobile application can be delegated to the utility Cloud. Nevertheless, task offloading isn't always cost-efficient and the decision to offload a process needs to be context-aware. This paper proposes a service-oriented workflow-based mobile Cloud middleware framework for balancing the task allocation between the mobile terminal and Cloud. The proposed cost-performance index scheme assists the decision-making of the workflow configuration by utilizing the fuzzy set and weight of context schemes. The evaluation consists of a data set generated by experts' preferences considering fuzzy logic and inference, on which linear regression was performed, for extracting the weights of the context. The prototype has been implemented in real devices and the evaluation has shown that the workflow system can automatically configure the task allocation based on resource availability.

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“…The design that hides the detail topology and communication at the edge network may cause the process designer unable to properly define the behaviours and processes of the devices in terms of adding participants, modifying or customising the processes, react to or prevent failures and so on. Further, recent IoT management systems often involve edge nodes as a part of the BPMS in the scenarios such as logistics [22], crowdsourcing processes [26], HVAC system [19], health care services [20], Ambient Assisted Living [27], real-time sensing [28] where mobile and wireless network objects are involved.…”

Section: Problem Statementmentioning

confidence: 99%

Mobile Cloud Business Process Management System for the Internet of Things

2016

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The Internet of Things (IoT) represents a comprehensive environment that consists of a large number of smart devices interconnecting heterogeneous physical objects to the Internet. Many domains such as logistics, manufacturing, agriculture, urban computing, home automation, ambient assisted living and various ubiquitous computing applications have utilised IoT technologies. Meanwhile, Business Process Management Systems (BPMS) have become a successful and efficient solution for coordinated management and optimised utilisation of resources/entities. However, past BPMS have not considered many issues they will face in managing large scale connected heterogeneous IoT entities. Without fully understanding the behaviour, capability and state of the IoT entities, the BPMS can fail to manage the IoT integrated information systems. In this paper, we analyse existing BPMS for IoT and identify the limitations and their drawbacks based on Mobile Cloud Computing perspective. Later, we discuss a number of open challenges in BPMS for IoT.

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A Middleware for Discovering Proximity-Based Service-Oriented Industrial Internet of Things

Cited by 22 publications

References 23 publications

iFogSim: A toolkit for modeling and simulation of resource management techniques in the Internet of Things, Edge and Fog computing environments

iFogSim: A toolkit for modeling and simulation of resource management techniques in the Internet of Things, Edge and Fog computing environments

Adaptive Mobile Cloud Workflow Management System for Service Discovery in Proximity-Based Internet of Things

Mobile Cloud Business Process Management System for the Internet of Things

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