IoT_ProSe: Exploiting 3GPP services for task allocation in the Internet of Things

Pilloni, Virginia; Abd-Elrahman, Emad; Hadji, Makhlouf; Atzori, Luigi; Afifi, Hossam

doi:10.1016/j.adhoc.2017.08.006

Cited by 11 publications

(17 citation statements)

References 26 publications

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“…The studies in [21]- [23] deals with the allocation of tasks in such a way that the resources are utilized in an optimized manner and also the load are balanced on all edge nodes. IoT _ProSe [24] framework extends this further to consider the challenge of task allocation in mobile nodes. Task mapping, on the other hand, is somewhat unfamiliar in the IoT domain but a variety of efforts have been made in WSN and distributed systems.…”

Section: Related Workmentioning

confidence: 99%

Towards the Task-Level Optimal Orchestration Mechanism in Multi-Device Multi-Task Architecture for Mission-Critical IoT Applications

2019

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Internet of Things is advancing, and the augmented role of architecture in automating processes is at its vanguard. Mission-critical applications are becoming a vital category of future IoT applications, and due to the advancements in the 5G, the design of mission-critical application overcome a big hurdle. Missioncritical applications must be reliable, and the output should be known in advance. Therefore, to model such application, architecture is considered the cornerstone. One of the major requirements is the flexibility of the operation and the adaptability to new devices. In this paper, an optimal orchestration mechanism is proposed to automate the processes in a conventional multi-device and multi-task mission-critical architecture for flexible and scalable operations. The central goal of this paper is to threefold; first, to model tasks in such a way to maximize the flexibility in the operation plane. Secondly, to design a strongly correlated pair which has maximum relation and thus the chance to hit the task on the devices will be potentially maximized and also the idle time among operations is minimal. Lastly, to register devices in a network which is optimal for the group of this device in terms of correlation. We propose a multi-layer particle swarm optimization for each of the optimization objectives. Results show that the operation plan is flexible and with scaling up the problem size, the orchestration is still graceful and within the requirements of mission-critical applications. The performance of multi-level particle swarm optimization is compared with conventional single-level particle swarm optimization and it has been learned that the later is not only slower but also less accurate.INDEX TERMS Internet of Things, task modeling, mission-critical IoT systems, task mapping, task deployment, service orchestration, task orchestration.

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Section: Related Workmentioning

confidence: 99%

Towards the Task-Level Optimal Orchestration Mechanism in Multi-Device Multi-Task Architecture for Mission-Critical IoT Applications

2019

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“…Nevertheless, there are only handful of works in literature that address the problem of finding the optimal resource allocation independently of the application assigned to the network. For instance, Pilloni et al in [6] propose an IoT Prose framework for IoT applications in D2D scenarios. A Game theoretical solution is derived around the utility function that allows individual objects to maximise their utility.…”

Section: Related Workmentioning

confidence: 99%

“…A Game theoretical solution is derived around the utility function that allows individual objects to maximise their utility. The authors in [6] considered energy cost for repeated tasks where devices perform tasks repeatedly while waiting for the next task assignment from CH.…”

Section: Related Workmentioning

confidence: 99%

Task Allocation in Clusters of Cognitive Nodes: A Remuneration-Aided Approach

Rahman

Pilloni

Atzori

2019

ICC 2019 - 2019 IEEE International Conference on Communications (ICC)

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In this work, we propose a remuneration-aided Game theoretical solution for task allocation in cognitive radio (CR) enabled Internet of things (IoT) scenarios, where cognitive nodes (CNs) in close proximity and with similar sensing capabilities are clustered around a cluster head (CH). We consider a framework in which task allocation in the system is driven by CNs with spectrum sensing capabilities. In the proposed approach, the CH assigns a remuneration to CNs for their contribution in spectrum sensing prior to initiating the task allocation procedure. Such remunerations can be used by CNs in proposing the bids to win the task in the Game. Hence a non-cooperative Game approach modelled as an auction process is proposed. We show that the proposed framework is able to exploit cognitive behaviour efficiently in conditions suitable for cognitive radios (low spectrum occupancy), and under the same conditions the overall system utility increases by 29% w.r.t the case when licensed users (LUs) occupy the band 70% of the time. Additionally, the framework allows the system to reap benefits of energy efficiency while experimenting cognitivity.

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“…This is the case for instance of the local storage and transport (by moving devices) of data or the processing of a flow of data to detect events of interest happening locally. This could happen with the involvement of just the local wireless sensor network [7] or exploiting other additional services accessible through the Internet [8].…”

Section: Introductionmentioning

confidence: 99%

Assignment of Sensing Tasks to IoT Devices: Exploitation of a Social Network of Objects

Atzori

Girau

Pilloni

et al. 2019

IEEE Internet Things J.

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The Social Internet of Things (SIoT) is a novel communication paradigm according to which the objects connected to the Internet create a dynamic social network that is mostly used to implement the following processes: route information and service requests, disseminate data, and evaluate the trust level of each member of the network. In this paper, the SIoT paradigm is applied to a scenario where geolocated sensing tasks are assigned to fixed and mobile devices, providing the following major contributions. The SIoT model is adopted to find the objects that can contribute to the application by crawling the social network through the nodes profile and trust level. A new algorithm to address the resource management issue is proposed so that sensing tasks are fairly assigned to the objects in the SIoT. To this, an energy consumption profile is created per device and task, and shared among nodes of the same category through the SIoT. The resulting solution is also implemented in the SIoTbased Lysis platform. Emulations have been performed, which showed an extension of the time needed to completely deplete the battery of the first device of more than 40% with respect to alternative approaches. Index Terms-Social Internet of Things; Mobile CrowdSensing; resource allocation LIST OF MAIN SYMBOLS AND ACRONYMS D G Geofence size E i,k Energy consumption for task k in node i E res i Residual energy for node i f i,k Frequency at which node i performs task k F M SF k Minimum Sampling Frequency for task k G Reference geofence P drain i

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IoT_ProSe: Exploiting 3GPP services for task allocation in the Internet of Things

Cited by 11 publications

References 26 publications

Towards the Task-Level Optimal Orchestration Mechanism in Multi-Device Multi-Task Architecture for Mission-Critical IoT Applications

Towards the Task-Level Optimal Orchestration Mechanism in Multi-Device Multi-Task Architecture for Mission-Critical IoT Applications

Task Allocation in Clusters of Cognitive Nodes: A Remuneration-Aided Approach

Assignment of Sensing Tasks to IoT Devices: Exploitation of a Social Network of Objects

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