Task requirement aware pre-processing and Scheduling for IoT sensory environments

Bharti, Sourabh; Pattanaik, K. K.

doi:10.1016/j.adhoc.2016.07.005

Cited by 26 publications

(15 citation statements)

References 13 publications

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“…A widely studied research subject is task scheduling in Wireless Sensor Networks (WSNs). Task mapping and scheduling should take into consideration energy constraints to secure an efficient execution [48], [6]. A task pre-processor and a scheduler can be responsible for the final allocation.…”

Section: Related Work and Contributionmentioning

confidence: 99%

Multi-criteria optimal task allocation at the edge

Kolomvatsos

Anagnostopoulos

2019

Future Generation Computer Systems

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In Internet of Things (IoT), numerous nodes produce huge volumes of data that are subject of various processing tasks. Tasks execution on top of the collected data can be realized either at the edge of the network or at the Fog/Cloud. Their management at the network edge may limit the required time for concluding responses and return the final outcome/analytics to endusers or applications. IoT nodes, due to their limited computational and resource capabilities, can execute a limited number of tasks over the collected contextual data. A challenging decision is related to which tasks IoT nodes should execute locally. Each node should carefully select such tasks to maximize the performance based on the current contextual information, e.g., tasks' characteristics, nodes' load and energy capacity. In this paper, we propose an intelligent decision making scheme for selecting the tasks that will be locally executed. The remaining tasks will be transferred to peer nodes in the network or the Fog/Cloud. Our focus is to limit the time required for initiating the execution of each task by introducing a two-step decision process. The first step is to decide whether a task can be executed locally; if not, the second step involves the sophisticated selection of the most appropriate peer to allocate it. When, in the entire network, no node is capable of executing the task, it is, then, sent to the Fog/Cloud facing the maximum latency. We comprehensively evaluate the proposed scheme demonstrating its applicability and optimality at the network edge.

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Section: Related Work and Contributionmentioning

confidence: 99%

Multi-criteria optimal task allocation at the edge

Kolomvatsos

Anagnostopoulos

2019

Future Generation Computer Systems

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“…This approach achieves a dramatic reduction in latency and ensures the security of locality information [ 24 ]. Regarding to this issue, intensive research is carried out to adapt the job-based scheduling methods to embedded devices and types of applications involved in the IoT environments to fully utilize the nodes and to achieve performance improvements [ 15 , 25 , 26 , 27 , 28 ].…”

Section: Background Of Iot Distributed Computingmentioning

confidence: 99%

Collaborative Working Architecture for IoT-Based Applications

Mora

Pont

Gil

et al. 2018

Sensors

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The new sensing applications need enhanced computing capabilities to handle the requirements of complex and huge data processing. The Internet of Things (IoT) concept brings processing and communication features to devices. In addition, the Cloud Computing paradigm provides resources and infrastructures for performing the computations and outsourcing the work from the IoT devices. This scenario opens new opportunities for designing advanced IoT-based applications, however, there is still much research to be done to properly gear all the systems for working together. This work proposes a collaborative model and an architecture to take advantage of the available computing resources. The resulting architecture involves a novel network design with different levels which combines sensing and processing capabilities based on the Mobile Cloud Computing (MCC) paradigm. An experiment is included to demonstrate that this approach can be used in diverse real applications. The results show the flexibility of the architecture to perform complex computational tasks of advanced applications.

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“…The focus of the algorithm is on the elimination of the transmission collisions. In [3], the authors propose a model for allocating the incoming tasks in multiple sensors according the energy requirements. The focus is on a task pre-processor and a scheduler that allocates the tasks in the available nodes.…”

Section: Related Workmentioning

confidence: 99%

Scheduling the execution of tasks at the edge

Kolomvatsos

Loukopoulos

2018

2018 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS)

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The Internet of Things provides a huge infrastructure where numerous devices produce, collect and process data. These data are the basis for offering analytics to support novel applications. The processing of huge volumes of data is a demanding process, thus, the power of Cloud is already utilized. However, latency, privacy and the drawbacks of this centralized approach became the motivation for the emerge of edge computing. In edge computing, data could be processed at the edge of the network; at the IoT nodes to deliver immediate results. Due to the limited resources of IoT nodes, it is not possible to have a high number of demanding tasks locally executed to support applications. In this paper, we propose a scheme for selecting the most significant tasks to be executed at the edge while the remaining are transferred into the Cloud. Our distributed scheme focuses on mobile IoT nodes and provides a decision making mechanism and an optimization module for delivering the tasks that will be executed locally. We take into consideration multiple characteristics of tasks and optimize the final decision. With our mechanism, IoT nodes can be adapted to, possibly, unknown environments evolving their decision making. We evaluate the proposed scheme through a high number of simulations and give numerical results.

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Task requirement aware pre-processing and Scheduling for IoT sensory environments

Cited by 26 publications

References 13 publications

Multi-criteria optimal task allocation at the edge

Multi-criteria optimal task allocation at the edge

Collaborative Working Architecture for IoT-Based Applications

Scheduling the execution of tasks at the edge

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