Fluid Composition of Intermittent IoT Energy Services

2021

Preprint

Self Cite

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.

Section: Related Workmentioning

confidence: 99%

Fairness-aware Crowdsourcing of IoT Energy Services

Lakhdari¹,

2021

Preprint

Self Cite

“…Conflict is common in multi-occupant homes, however, a conflict may happen in single-occupant homes. For instance, a conflict may occur based on contradictory intentions like saving energy and comfort at the same time [13]. Some preference aggregation strategies such as average (AVG), least-misery (LM), and most-pleasure (MP) are used for conflict resolution in existing research [2,8,9].…”

Section: Related Workmentioning

confidence: 99%

Dynamic Conflict Resolution of IoT Services in Smart Homes

Chaki¹,

2021

Preprint

Self Cite

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.

“…It has been proven that human mobility is highly predictable according to an extensive set of experiments on capturing the movement of millions of humans in metropolitan areas [13]. The regularity in human mobility could be reflected on the IoT devices associated with their owners to define the mobility patterns of energy services users in a crowdsourced IoT environment [9]. Additionally, the energy consumption behavior of IoT devices reflects the usage behavior of the devices by their owners.…”

Section: Patterns Of Energy Providers and Consumersmentioning

confidence: 99%

“…Existing energy service composition frameworks mainly consist of the real-time discovery and selection of nearby energy services to accommodate an energy request. These techniques assume that a request can be fulfilled by the available energy services within its vicinity [9]. The focus of these composition techniques was only on the spatio-temporal composability [5] and addressing the challenges related to the energy fluctuation, the mobility, and the conflicts of the available services [10] [11] [12].…”

Section: Introductionmentioning

confidence: 99%

Proactive Composition of Mobile IoT Energy Services

Lakhdari¹,

2021

Preprint

Self Cite

We propose a novel proactive composition framework of wireless energy services in a crowdsourced IoT environment. We define a new model for energy services and requests that includes providers' and consumers' mobility patterns and energy usage behavior. The proposed composition approach leverages the mobility and energy usage behavior to generate energy services and requests proactively. Preliminary experimental results demonstrate the effectiveness of generating proactive energy requests and composing proactive services.