Opportunistic Fog for IoT: Challenges and Opportunities

Fernando, Niroshinie; Loke, Seng W.; Avazpour, Iman; Chen, Feifei; Abkenar, Amin B.; Ibrahim, Amani S.

doi:10.1109/jiot.2019.2924182

Cited by 37 publications

(37 citation statements)

References 78 publications

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“…There have been lots of studies on distribution of load utilizing OFNs with different objectives [ 17 , 18 , 19 , 20 , 21 , 22 , 23 ]. Minimizing service latency has been one of the major issues [ 17 , 22 ].…”

Section: Related Workmentioning

confidence: 99%

“…In addition, various issues on OFN have been covered as follows. Fernando et al [ 18 ] reviewed motivations and identified the requirements to enable OFN applications. Then, they provided a model of OFNs to support IoT applications, especially for hazardous and volatile events.…”

Section: Related Workmentioning

confidence: 99%

“…Recently, task distribution considering not only static FNs but also mobile FNs has gained increasing attention [ 17 , 18 , 19 , 20 , 21 , 22 , 23 ]. Owing to the intermittent availability of mobile FNs, they are considered as the concept of opportunistic FNs (OFNs) [ 18 ]. Research coverage on OFNs has been extended to include smart phones, vehicles, and unmanned aerial vehicles (UAVs) [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

“…In addition, communication and computing latency can be additionally considered. This means that the service requirements of delay-sensitive services cannot be guaranteed through offloading with the OFNs [ 18 ]. In order to handle this problem, this paper introduces the prioritized task distribution scheme, which utilizes OFNs when they are available only for delay-tolerant tasks and static FNs for delay-sensitive tasks.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Prioritized Task Distribution Considering Opportunistic Fog Computing Nodes

Kyung

2021

Sensors

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As service latency and core network load relates to performance issues in the conventional cloud-based computing environment, the fog computing system has gained a lot of interest. However, since the load can be concentrated on specific fog computing nodes because of spatial and temporal service characteristics, performance degradation can occur, resulting in quality of service (QoS) degradation, especially for delay-sensitive services. Therefore, this paper proposes a prioritized task distribution scheme, which considers static as well as opportunistic fog computing nodes according to their mobility feature. Based on the requirements of offloaded tasks, the proposed scheme supports delay sensitive task processing at the static fog node and delay in-sensitive tasks by means of opportunistic fog nodes for task distribution. To assess the performance of the proposed scheme, we develop an analytic model for the service response delay. Extensive simulation results are given to validate the analytic model and to show the performance of the proposed scheme, compared to the conventional schemes in terms of service response delay and outage probability.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Prioritized Task Distribution Considering Opportunistic Fog Computing Nodes

Kyung

2021

Sensors

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“…For any of them, reliable delivery of sensed data (e.g., to cloud or edge servers) is extremely important. To address such an issue, the concept of opportunistic IoT applies the paradigm of delay-tolerant networking to urban scenarios [2]. Accordingly, sensory data sampled by IoT devices are collected by mobile gateways, generally people carrying smartphones, as in mobile crowd sourcing (MCS) [3].…”

Section: Introductionmentioning

confidence: 99%

Incentivizing Opportunistic Data Collection for Time-Sensitive IoT Applications

Kortoçi

Mehrabi

Joe‐Wong

et al. 2021

2021 18th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)

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Urban environments are the most prevalent application scenario for the Internet of Things (IoT). In this context, effective data collection and forwarding to a cloud (or edge) server are particularly important. This work leverages opportunistic data collection based on the mobile crowd sourcing (MCS) paradigm for time-sensitive IoT applications. Specifically, it introduces an incentive mechanism for the crowd to collect data that are valuable to data consumers in terms of regions of interest and time constraints. The proposed approach successfully incorporates the willingness of the crowd to participate in the data collection as part of the related incentives. It also ensures collection of valuable data via selective user incentivization. Accordingly, a weighted social welfare maximization problem is defined for users to decide which sensors to visit subject to deadline constraints. Following the NP-hardness of the problem, an online heuristic algorithm is proposed for sensors to dynamically incentivize mobile users with a low message and time complexity. The proposed solution is shown to be effective for time-sensitive quality data collection through extensive simulations on realistic mobility traces. It significantly increases the overall social welfare as well as the amount of collected data compared to other approaches.

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Osmotic management of distributed complex systems: A declarative decentralised approach

Forti

Lera

Guerrero

et al. 2021

J Software Evolu Process

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Osmotic computing encompasses emerging Cloud‐Internet of Things (IoT) computing paradigms, by featuring the possibility for application services to adapt into different functionally equivalent flavours, depending on the contextually available resources and on specific requirements of running applications. This article proposes a fully decentralised declarative framework that enables both application and infrastructure operators to declare management policies for the service instances and the nodes they manage, respectively. Policies are composed of a simple and well‐defined set of management operations, declared in Prolog, which trigger based on locally available contextual information on application requests and infrastructure resources. A prototype implementation of the framework is showcased and assessed via simulation over a lifelike Smart Campus use case with multiple applications, at increasing infrastructure sizes and number of mobile users. Experimental results show that the proposed management framework scales to large infrastructure sizes and suits the needs of multiflavoured Osmotic applications in dynamic deployment conditions, by improving the trade‐off between their response times and suitable service usage.

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Opportunistic Fog for IoT: Challenges and Opportunities

Cited by 37 publications

References 78 publications

Prioritized Task Distribution Considering Opportunistic Fog Computing Nodes

Prioritized Task Distribution Considering Opportunistic Fog Computing Nodes

Incentivizing Opportunistic Data Collection for Time-Sensitive IoT Applications

Osmotic management of distributed complex systems: A declarative decentralised approach

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