An IoT-DaaS Approach for the Interoperability of Heterogeneous Sensor Data Sources

Barros, Vinícius A.; Estrella, Júlio Cézar; Prates, Leonardo B.; Bruschi, Sarita Mazzini

doi:10.1145/3242102.3242141

Cited by 9 publications

(5 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the availability of openly available datasets about the distribution of requests in real IoT scenarios is scarce [78]. Therefore, considering literature [22], [79], this paper explores the effect of two distributions that consist of a random (denoted as Rand in the experimental results) [80] and a Beta distribution [81] as Beta (2.0, 5.0) on the performance results. (iii) Host proximity (H): This parameter investigates the impact of the distance between sources (i.e., service requesters) and corresponding destinations (i.e., hosts) on the evaluated metrics.…”

Section: Discussionmentioning

confidence: 99%

Decentralized Edge-to-Cloud Load Balancing: Service Placement for the Internet of Things

et al. 2021

View full text Add to dashboard Cite

The Internet of Things (IoT) requires a new processing paradigm that inherits the scalability of the cloud while minimizing network latency using resources closer to the network edge. On the one hand, building up such flexibility within the edge-to-cloud continuum consisting of a distributed networked ecosystem of heterogeneous computing resources is challenging. On the other hand, IoT traffic dynamics and the rising demand for low-latency services foster the need for minimizing the response time and a balanced service placement. Load-balancing for fog computing becomes a cornerstone for cost-effective system management and operations. This paper studies two optimization objectives and formulates a decentralized load-balancing problem for IoT service placement: (global) IoT workload balance and (local) quality of service (QoS), in terms of minimizing the cost of deadline violation, service deployment, and unhosted services. The proposed solution, EPOS Fog, introduces a decentralized multi-agent system for collective learning that utilizes edge-to-cloud nodes to jointly balance the input workload across the network and minimize the costs involved in service execution. The agents locally generate possible assignments of requests to resources and then cooperatively select an assignment such that their combination maximizes edge utilization while minimizes service execution cost. Extensive experimental evaluation with realistic Google cluster workloads on various networks demonstrates the superior performance of EPOS Fog in terms of workload balance and QoS, compared to approaches such as First Fit and exclusively Cloud-based. The results confirm that EPOS Fog reduces service execution delay up to 25% and the load-balance of network nodes up to 90%. The findings also demonstrate how distributed computational resources on the edge can be utilized more cost-effectively by harvesting collective intelligence.INDEX TERMS Agent, cloud computing, collective learning, distributed optimization, edge computing, fog computing, internet of things (IoT), load-balancing, service placement.

show abstract

Section: Discussionmentioning

confidence: 99%

Decentralized Edge-to-Cloud Load Balancing: Service Placement for the Internet of Things

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Though, the availability of openly available datasets about the distribution of requests in real IoT scenarios in scarce [73]. Therefore, considering literature [27], [74], this paper explores the effect of two different distributions that consist of a random distribution (denoted as Rand in the experimental results) [75] and a Beta distribution [76] as Beta (2.0, 5.0) on the performance results. • Host proximity (H): This parameter investigates the impact of the distance between source nodes (i.e., service requesters) and corresponding destinations (i.e., hosts) on the evaluated metrics.…”

Section: Discussionmentioning

confidence: 99%

Decentralized Edge-to-Cloud Load-balancing: Service Placement for the Internet of Things

Nezami,

Zamanifar,

Djemame

et al. 2020

Preprint

View full text Add to dashboard Cite

The Internet of Things (IoT) has revolutionized everyday life and expanded the scope of smart services to a broad range of domains. In ubiquitous environments, fog computing has emerged leveraging the resources in the edge-to-cloud continuum to improve the quality of service, while reducing the traffic on cloud infrastructure and networks. In such a distributed ecosystem with heterogeneous resources of various sizes and inherent dynamics such as varying service demand over time, managing resources and services is a major challenge. This paper studies two optimization objectives and formulates a decentralized load-balancing problem for IoT service placement: (global) IoT workload balance and (local) quality of service, in terms of minimizing the cost of deadline violation, service deployment, and unhosted services. The proposed solution, EPOS Fog, introduces a decentralized multi-agent system for collective learning that utilizes edge-to-cloud nodes to jointly balance the input workload across the network and minimize the costs involved in service execution. The agents locally generate possible assignments of requests to resources and then cooperatively select an assignment such that their combination maximizes edge utilization while minimizes service execution cost. Extensive experimental evaluation with realistic Google cluster workloads on various networks demonstrates the superior performance of EPOS Fog in terms of workload balance and quality of service, compared to approaches such as First Fit and exclusively Cloud-based. The findings demonstrate how distributed computational resources on the edge can be utilized more cost-effectively by harvesting collective intelligence.

show abstract

“…ese services can be offered through sensor management middleware, such as GSN, which can provide services at both the edge of the network and the cloud [22]. ENLACE was based on the Internet of ings Data as a Service Module (IoTDSM) approach [5].…”

Section: Emotions As a Servicementioning

confidence: 99%

“…ere is a wide scope for applications in this domain, and for this reason, HSH environments are structured in three separate levels: (i) hardware (sensing and wireless networks), (ii) middleware (capture, security, and data integration), and (iii) services (biological signal processing and other services) [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

ENLACE: A Combination of Layer-Based Architecture and Wireless Communication for Emotion Monitoring in Healthcare

Mano

Barros

Nunes

et al. 2019

Mobile Information Systems

Self Cite

View full text Add to dashboard Cite

Owing to the increase in the number of people with disabilities, as a result of either accidents or old age, there has been an increase in research studies in the area of ubiquitous computing and the Internet of Things. They are aimed at monitoring health, in an efficient and easily accessible way, as a means of managing and improving the quality of life of this section of the public. It also involves adopting a Health Homes policy based on the Internet of Things and applied in smart home environments. This is aimed at providing connectivity between the patients and their surroundings and includes mechanisms for helping the diagnosis and prevention of accidents and/or diseases. Monitoring gives rise to an opportunity to exploit the way computational systems can help to determine the real-time emotional state of patients. This is necessary because there are some limitations to traditional methods of health monitoring, for example, establishing the behavior of the user’s routine and issuing alerts and warnings to family members and/or medical staff about any abnormal event or signs of the onset of depression. This article discusses how a layer-based architecture can be used to detect emotional factors to assist in healthcare and the prevention of accidents within the context of Smart Home Health. The results show that this process-based architecture allows a load distribution with a better service that takes into account the complexity of each algorithm and the processing power of each layer of the architecture to provide a prompt response when there is a need for some intervention in the emotional state of the user.

show abstract

An IoT-DaaS Approach for the Interoperability of Heterogeneous Sensor Data Sources

Cited by 9 publications

References 12 publications

Decentralized Edge-to-Cloud Load Balancing: Service Placement for the Internet of Things

Decentralized Edge-to-Cloud Load Balancing: Service Placement for the Internet of Things

Decentralized Edge-to-Cloud Load-balancing: Service Placement for the Internet of Things

ENLACE: A Combination of Layer-Based Architecture and Wireless Communication for Emotion Monitoring in Healthcare

Contact Info

Product

Resources

About