Internet‐of‐Things‐Infrastructure‐as‐a‐Service: The democratization of access to public Internet‐of‐Things Infrastructure

Ilyas, Muhammad; Ahmad, Muneeb; Saleem, Sajid

doi:10.1002/dac.4562

Cited by 12 publications

(9 citation statements)

References 27 publications

(34 reference statements)

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“…The size of the set C is calculated according to Equation (3); it represents the number of possible configurations. The probability p of moving from the C i configuration to the C j configuration is given by Equation (7) where C i represents the current configuration and C j is a configuration in the neighborhood of C i . C j is considered a configuration in the neighborhood of C i if in C j there is only one RSU whose location is different than the one in C i .…”

Section: Sa-based Approachesmentioning

confidence: 99%

“…where Cov Rate ðC i Þ and Cov Rate ðC j Þ represent respectively the coverage rates of the C i and C j configurations, k is a coefficient that has to be adapted to the application, (e.g., k ¼ 1) and θ is a formal parameter that represents the temperature. The transition from a configuration C i to a neighboring configuration C j is carried out according to the probability p presented in Equation (7) where:…”

Section: Sa-based Approachesmentioning

confidence: 99%

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Efficient deployment of roadside units in vehicular networks using optimization methods

Lehsaini

Gaouar

Nebbou

2022

Int J Communication

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Summary Nowadays, vehicles have become more and more intelligent and equipped with highly sophisticated systems. This allows them to communicate with each other and with the roadside units (RSUs). Furthermore, to ensure efficient data dissemination in vehicular ad hoc networks (VANETs), it is recommended that a Vehicle‐to‐Infrastructure (V2I) architecture be chosen where RSUs will be installed at intersections. Nevertheless, it is not advisable to place an RSU at each intersection because of their high cost. It is therefore appropriate to reduce the number of RSUs by choosing locations at intersections that maximize the surface covered of the urban area and minimize the area of overlapping zones. Moreover, deploying an optimal number of RSUs in an urban area meeting the above requirements is an NP‐hard problem since the number of combinations is very high when the number of intersections is very large. For this purpose, we used metaheuristic approaches. The first approach is represented by the standard version of genetic algorithms (GA‐Basic) and its improved version (GA‐Improved) while the second approach is based on the standard version of simulated annealing (SA‐Basic) and its improved version (SA‐Improved). The proposed approaches are evaluated over OMNET++ simulator. The results obtained showed that the GA‐Improved approach deploys a reduced number of RSUs (37.5%) while guaranteeing acceptable routing performance compared to the GA‐Basic, SA‐Basic, and SA‐Improved approaches which deploy 46.25%, 65%, and 52.50%, respectively, for the same routing performance.

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Section: Sa-based Approachesmentioning

confidence: 99%

Section: Sa-based Approachesmentioning

confidence: 99%

Efficient deployment of roadside units in vehicular networks using optimization methods

Lehsaini

Gaouar

Nebbou

2022

Int J Communication

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“…Regarding infrastructure-based technologies, investigations such as the one from Usman et al [ 2 ] propose a pure infrastructure-based approach where they use a Data Center based on OpenStack. In this manner, thanks to the deployment of a specific OpenStack component in the IoT device, as well as with using what they call a Sensor Data Transfer Service, the information coming from an IoT device can be introduced into the OpenStack Controller Node by using an IoT Data Acquisition Server, which stores the data coming from the devices into an centralized InfluxDB.…”

Section: Related Workmentioning

confidence: 99%

“…This paper proposes a Cloud of Things platform, named VirIoT (from “Virtual IoT”), that brings the infrastructure as a service model to the world of the Internet of Things, precisely matching an equivalent goal. The platform offers sensor/actuator virtualisation, as well as interoperability and support to current IoT standards, to create the concept of virtual IoT infrastructures in the cloud that improves the current state of the art [ 1 , 2 ]. Indeed, although cloud computing providers (e.g., Amazon AWS [ 3 ] or Microsoft Azure [ 4 ]) offer IoT services, they only help with the collection, distribution and processing of IoT data, but the IoT infrastructure that produces and consumes that data is currently outside the cloud and must usually be deployed, connected and managed by application developers themselves [ 5 , 6 ].…”

Section: Introductionmentioning

confidence: 99%

VirIoT: A Cloud of Things That Offers IoT Infrastructures as a Service

Detti

Nakazato

Navarro³

et al. 2021

Sensors

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Many cloud providers offer IoT services that simplify the collection and processing of IoT information. However, the IoT infrastructure composed of sensors and actuators that produces this information remains outside the cloud; therefore, application developers must install, connect and manage the cloud. This requirement can be a market barrier, especially for small/medium software companies that cannot afford the infrastructural costs associated with it and would only prefer to focus on IoT application developments. Motivated by the wish to eliminate this barrier, this paper proposes a Cloud of Things platform, called VirIoT, which fully brings the Infrastructure as a service model typical of cloud computing to the world of Internet of Things. VirIoT provides users with virtual IoT infrastructures (Virtual Silos) composed of virtual things, with which users can interact through dedicated and standardized broker servers in which the technology can be chosen among those offered by the platform, such as oneM2M, NGSI and NGSI-LD. VirIoT allows developers to focus their efforts exclusively on IoT applications without worrying about infrastructure management and allows cloud providers to expand their IoT services portfolio. VirIoT uses external things and cloud/edge computing resources to deliver the IoT virtualization services. Its open-source architecture is microservice-based and runs on top of a distributed Kubernetes platform with nodes in central and edge data centers. The architecture is scalable, efficient and able to support the continuous integration of heterogeneous things and IoT standards, taking care of interoperability issues. Using a VirIoT deployment spanning data centers in Europe and Japan, we conducted a performance evaluation with a two-fold objective: showing the efficiency and scalability of the architecture; and leveraging VirIoT’s ability to integrate different IoT standards in order to make a fair comparison of some open-source IoT Broker implementations, namely Mobius for oneM2M, Orion for NGSIv2, Orion-LD and Scorpio for NGSI-LD.

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“…The advancements in the Internet of things (IoT) and wireless communication technologies make vehicles more intelligent 1–3 and promote the development of Intelligent Transportation Systems (ITS). One of ITS' critical functions is to predict the future condition of traffic 4 and support various vehicle‐to‐everything (V2X) applications that improve road safety.…”

Section: Introductionmentioning

confidence: 99%

Stackelberg game‐based task offloading in vehicular edge computing networks

Liu

Tian

Deng

et al. 2021

Int J Communication

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Summary With the emergence of intelligent vehicles, how to satisfy the demands of the vehicles with computing‐intensive and delay‐sensitive tasks has become a challenging issue. Vehicular edge computing (VEC) is proposed as an advanced paradigm to improve the service of vehicles through offloading the task to the VEC servers. Nevertheless, VEC servers always have limited computation resources and do not satisfy the offloading requirements of vehicles. To this end, in this paper, we propose a more flexible offloading scheme by jointly considering the offloading strategies and the price strategy. In the proposed scheme, where the task can be dynamically divided into two parts parallel executed at the vehicles and VEC servers. A multi‐leader and multi‐follower Stackelberg game ‐based distributed algorithm is proposed to maximize the utilities of the vehicles and the VEC servers under the delay constraint. Finally, the game equilibrium is analyzed and achieved. Extensive experiments demonstrate that the proposed offloading scheme converges fast and always outperforms the existing schemes in terms of the vehicular utility under different network conditions. For instance, the proposed scheme achieves the utility improvement over 56.62% compared to the fixed selection strategy and achieves the utility improvement up to 161.0% compared to the complete offloading with fixed price strategy when the number of vehicles is 10. Additionally, the effects of key parameters such as the offloading strategies and, the price strategy, and the computation resource on the average utility of vehicles are also discussed and analyzed based on the simulation results.

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Internet‐of‐Things‐Infrastructure‐as‐a‐Service: The democratization of access to public Internet‐of‐Things Infrastructure

Cited by 12 publications

References 27 publications

Efficient deployment of roadside units in vehicular networks using optimization methods

Efficient deployment of roadside units in vehicular networks using optimization methods

VirIoT: A Cloud of Things That Offers IoT Infrastructures as a Service

Stackelberg game‐based task offloading in vehicular edge computing networks

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