Fine-grained Conflict Detection of IoT Services

Chaki, Dipankar; Bouguettaya, Athman

doi:10.1109/scc49832.2020.00049

Cited by 18 publications

(11 citation statements)

References 21 publications

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“…The authors in 29 have used the concept of entropy and information gain from information theory based on the user usage habits of the devices and services, and developed an algorithm based on temporal proximity to detect the multi-user conflict. The research work in 30 developed Kratos: a multi-user and multi-device-aware access control mechanism.…”

Section: Related Workmentioning

confidence: 99%

Resolving User Conflicts in Multi-user Context-aware Home Environment

Aljawarneh

Dhomeja

Malkani

et al. 2021

Preprint

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Context-awareness is an enabling technology of pervasive computing that allows applications to adapt themselves in responseto contexts (e.g. activity, location, temperature level, etc.). However, an issue of user conflicts in context-aware applicationsmay arise when multiple users want to access the same application. Our research focuses on this issue and proposes aconflict resolution approach that resolves the conflicts in context-aware home applications. The proposed approach takes intoconsideration the users’ special case contexts (e.g. illness of user) along with their priorities and preferences. The proposedapproach is also useful in cases where multiple users with multiple special cases try to access one application or service.To show the usefulness of the proposed approach, we have integrated the proposed conflict manager with the UbiREAL, asimulated context-aware home environment. The conflict manager utilizes different strategies and different approaches toresolve user conflicts according to the involved situation to suit the need of the family. The prototype evaluation shows thatthe users are satisfied with the proposed system and suggests that the use of users’ special case contexts in detecting andresolving the user conflicts is essential and necessary in the context-aware smart home environments.

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Section: Related Workmentioning

confidence: 99%

Resolving User Conflicts in Multi-user Context-aware Home Environment

Aljawarneh

Dhomeja

Malkani

et al. 2021

Preprint

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“…Service selection and composition also play an important role in emerging fields such as cloud computing, IoT-based smart systems [6] [24]. In IoT, services are mainly composed according to their spatio-temporal features [13].…”

Section: Related Workmentioning

confidence: 99%

Fairness-aware Crowdsourcing of IoT Energy Services

Lakhdari¹,

Bouguettaya²

2021

Preprint

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We propose a Novel Fairness-Aware framework for Crowdsourcing Energy Services (FACES) to efficiently provision crowdsourced IoT energy services. Typically, efficient resource provisioning might incur an unfair resource sharing for some requests. FACES, however, maximizes the utilization of the available energy services by maximizing fairness across all requests. We conduct a set of preliminary experiments to assess the effectiveness of the proposed framework against traditional fairness-aware resource allocation algorithms. Results demonstrate that the IoT energy utilization of FACES is better than FCFS and similar to Max-min fair scheduling. Experiments also show that better fairness is achieved among the provisioned requests using FACES compared to FCFS and Max-min fair scheduling.

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“…Applying Equation 9 to the running example 1 , we get current resident item matrix as: CRIM = ((1.00, 0.00, 0.00). Ṽ ( [3,2,4], 1 : 2) + (1.00, 0.00, 0.00). Ṽ ( [2,3,4] Ideal Resident Item Matrix.…”

Section: Preference Aggregationmentioning

confidence: 99%

“…Ṽ ( [3,2,4], 1 : 2) + (1.00, 0.00, 0.00). Ṽ ( [2,3,4] Ideal Resident Item Matrix. Given CRIM, if we want to resolve conflict (i.e., provide group-oriented optimal services), we have to figure out the most similar items to CRIM.…”

Section: Preference Aggregationmentioning

confidence: 99%

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Dynamic Conflict Resolution of IoT Services in Smart Homes

Chaki¹,

Bouguettaya²

2021

Preprint

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We propose a novel conflict resolution framework for IoT services in multi-resident smart homes. The proposed framework employs a preference extraction model based on a temporal proximity strategy. We design a preference aggregation model using a matrix factorization-based approach (i.e., singular value decomposition). The concepts of current resident item matrix and ideal resident item matrix are introduced as key criteria to cater to the conflict resolution framework. Finally, a set of experiments on real-world datasets are conducted to show the effectiveness of the proposed approach.

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Fine-grained Conflict Detection of IoT Services

Cited by 18 publications

References 21 publications

Resolving User Conflicts in Multi-user Context-aware Home Environment

Resolving User Conflicts in Multi-user Context-aware Home Environment

Fairness-aware Crowdsourcing of IoT Energy Services

Dynamic Conflict Resolution of IoT Services in Smart Homes

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