The internet of things (IoT) and cloud computing are two technologies which have recently changed both the academia and industry and impacted our daily lives in different ways. However, despite their impact, both technologies have their shortcomings. Though being cheap and convenient, cloud services consume a huge amount of network bandwidth. Furthermore, the physical distance between data source(s) and the data centre makes delays a frequent problem in cloud computing infrastructures. Fog computing has been proposed as a distributed service computing model that provides a solution to these limitations. It is based on a para-virtualized architecture that fully utilizes the computing functions of terminal devices and the advantages of local proximity processing. This paper proposes a multi-layer IoT-based fog computing model called IoT-FCM, which uses a genetic algorithm for resource allocation between the terminal layer and fog layer and a multi-sink version of the least interference beaconing protocol (LIBP) called least interference multi-sink protocol (LIMP) to enhance the fault-tolerance/robustness and reduce energy consumption of a terminal layer. Simulation results show that compared to the popular max–min and fog-oriented max–min, IoT-FCM performs better by reducing the distance between terminals and fog nodes by at least 38% and reducing energy consumed by an average of 150 KWh while being at par with the other algorithms in terms of delay for high number of tasks.
Inefficient healthcare is a major concern among many African nations and can be mitigated by building world-class infrastructure connecting different medical facilities for collaboration and resource sharing. Such infrastructure should support the collection and exchange of medical data among healthcare practitioners for the purpose of accessing expertise not available locally. It should be equipped with the most recent technologies of the fourth Industrial Revolution (4IR), providing decision support to doctors thereby enabling African nations leapfrog from poorly equipped to medically prepared countries. Sadly, world-class healthcare infrastructure are a missing piece in the African public health ecosystem. Medical facilities are either non-existent or prohibitively expensive when they exist. Federated cloud computing can provide a solution to this challenge. Being a model that allows collaboration between multiple Cloud Service Providers (CSPs) by pooling computing resources together with the aim of meeting specific business or technological need; it allows for the execution of tasks on computing resources in a flexible and cost efficient manner. This paper aims to connect unconnected medical facilities in Africa by proposing a cloud federation for healthcare using co-operative and competitive collaboration models. Simulations were carried out to test the efficacy of these models using five different workload allocation schemes: First-Fit-Descending (FFD), Best-Fit-Descending (BFD) and Binary-Search-Best-Fit (BSBF); Genetic Algorithm meta-heuristic and the Stable Roommate Allocation (SRA) economic model for both light and heavy workloads. Results of simulations revealed that the co-operative cloud federation model resulted in lower allocation delays but higher resource utilisation; while the competitive model provided faster service delivery and better Quality of service (QoS) adherence. It also showed that BSBF and BFD gave the best resources utilisation and energy conservation, while FFD was the fastest overall. Finally, deployment considerations and potential business models for the federated cloud for healthcare in Africa were presented.
In this paper, a Topic-based probabilistic model named GIST is proposed to infer group activities, and make group recommendations. Compared with existing individual-based aggregation methods, it not only considers individual members' interest, but also consider some subgroups' interest. Intuition might seem that when a group of users want to take part in an activity, not every group member is decisive, instead, more likely the subgroups of members having close relationships lead to the final activity decision. That motivates our study on jointly considering individual members' choices and subgroups' choices for group recommendations. Based on this, our model uses two kinds of unshared topics to model individual members' interest and subgroups' interest separately, and then make final recommendations according to the choices from the two aspects with a weight-based scheme. Moreover, the link information in the graph topology of the groups can be used to optimize the weights of our model. The experimental results on real-life data show that the recommendation accuracy is significantly improved by GIST comparing with the state-of-the-art methods.
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