An Intelligent Algorithm for Resource Sharing and Self-Management of Wireless-IoT-Gateway

Ramirez, Pedro Luis Gonzalez; Taha, Miran; Lloret, Jaime; Tomás, Jaime Prohens

doi:10.1109/access.2019.2960508

Cited by 22 publications

(12 citation statements)

References 19 publications

(25 reference statements)

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“…The architecture of this proposal is an architecture with centralized management based on AI [34,35]. This architecture is divided into five logical layers to organize and maintain divided the functions of each object connected to the network.…”

Section: Network Architecturementioning

confidence: 99%

Artificial intelligent system for multimedia services in smart home environments

et al. 2021

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Internet of Things (IoT) has introduced new applications and environments. Smart Home provides new ways of communication and service consumption. In addition, Artificial Intelligence (AI) and deep learning have improved different services and tasks by automatizing them. In this field, reinforcement learning (RL) provides an unsupervised way to learn from the environment. In this paper, a new intelligent system based on RL and deep learning is proposed for Smart Home environments to guarantee good levels of QoE, focused on multimedia services. This system is aimed to reduce the impact on user experience when the classifying system achieves a low accuracy. The experiments performed show that the deep learning model proposed achieves better accuracy than the KNN algorithm and that the RL system increases the QoE of the user up to 3.8 on a scale of 10.

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Section: Network Architecturementioning

confidence: 99%

Artificial intelligent system for multimedia services in smart home environments

et al. 2021

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“…This proposal is an application of a network model based on workgroup theory [10]. In this case, each Sn is a directed graph that maintains a reference to its parent node M(n), according to the graph theory [11] and data structure [12], merging both architecture layers into one.…”

Section: 𝑅 𝑀 𝑖 𝑀 𝑗mentioning

confidence: 99%

“…The algorithms developed for the Broker App or IoT Gateway (Broker) [10], are based on equations 1, 2, and 3. The algorithm comparing the previously stored typical These parame ostat [16] and the parameters strated the ver ogram in the E ignal in voltage ts produced du the mobile ap t vs. voltage cal e ADS1115 mo + 1-bit of prec lect more par res (I) on the p s DAC applies H), and the Ul 3 shows the eters of each mo is shown in F g tool [17].…”

Section: Mobile Application (App)mentioning

confidence: 99%

IoT-WLAN Proximity Network for Potentiostats

González

Lloret

Tomás

et al. 2020

2020 Fifth International Conference on Fog and Mobile Edge Computing (FMEC)

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The implementation of potentiostats as portable and communicated devices has reached significant progress to benefit research, industry, and education. The Internet of Things (IoT) is a good opportunity to interconnect devices such as the potentiostats together with electronics, communication technologies, and chemistry into a single system. This work proposes a network for potentiostats using machine-tomachine (M2M) protocols, modifying its functioning mechanism in the broker to check the payload of the message that passes through it and synchronize the sensors depending on its content. Although one sensor can be synchronized directly to another, the broker decides which sensor to pair. This modification was made in the M2M protocol algorithm, both in the Broker and in the Client (sensor). In addition to this, the network uses an interconnection architecture of IoT smart networks of proximity with centralized management. The results of the tests carried out showed that the use of a modified M2M such as the one proposed in the architecture allows synchronization and comparison of the measurements of several sensors in real-time.

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“…Moreover, the new generation of IoT has evolved for attaining maximized quality of service (QoS). In specific, bandwidth, delay, energy consumptions, and throughput are considered to be major important factors that need to be managed in the IoT environment for the purpose of attaining a higher QoS degree 5 . Furthermore, the requirements for facilitating multimedia services have shown a great momentum as the diversified services offered in IoT environment necessitate different QoS strategies and features 6 .…”

Section: Introductionmentioning

confidence: 99%

Galactic swarm‐improved whale optimization algorithm‐based resource management in Internet of things

Karthick

Gomathi

2021

Int J Communication

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Summary Internet of things (IoT) is the reliable alternative among the networking technologies for achieving high performance with improved potentialities of flexible adoptions, data exchanges, resource allocations, and system controls. The existing IoT suffers from the limitations of resource allocation ranging between complicated service provisioning environments and networking service quality mismatching. IoT environment needs to handle the resource allocation issue for attaining satisfactory degree of quality of experience (QoE) that maps multiple resources to gateways. This problem of mapping multiple resources to gateways belongs to the class of NP‐complete problem and can be ideally solved through intelligent metaheuristic algorithms. In this paper, galactic swarm‐improved whale optimization algorithm‐based resource management (GSIWOA‐RM) scheme is proposed for efficient mapping of multiple resources to gateways in IoT. It specifically utilizes galactic swarm optimization algorithm (GSOA) for establishing global control with inherited multiple adaptive cycles of exploitation and exploration during resource allocation. It further prevents the limitations of early convergence in the exploitation phase by utilizing the evolution‐based whale optimization algorithm (WOA) that aids in better balance between exploitation and exploration. The simulation results of the proposed GSIWOA‐RM scheme confirm a better throughput of 28.32% with minimized delay and energy consumptions of 19.24% and 21.82%, when compared to the baseline resource management schemes.

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An Intelligent Algorithm for Resource Sharing and Self-Management of Wireless-IoT-Gateway

Cited by 22 publications

References 19 publications

Artificial intelligent system for multimedia services in smart home environments

Artificial intelligent system for multimedia services in smart home environments

IoT-WLAN Proximity Network for Potentiostats

Galactic swarm‐improved whale optimization algorithm‐based resource management in Internet of things

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