Fuzzy based job classification and resource allocation in IoT

Hatti, Daneshwari I.; Sutagundar, Ashok V.

doi:10.1109/icisc.2017.8068676

Cited by 5 publications

(7 citation statements)

References 9 publications

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“…The evaluation of the proposed method which is called IoTR4HealthCare system was benchmarked against two existing systems using cost and latency criteria [46]. Hatti et al [47] proposed a RA algorithm based on fuzzy classification of jobs. In their method they improved a fuzzy interference system (FIS) for classification of the jobs into emergent and non-emergent.…”

Section: Methods Based On Heuristic Algorithmmentioning

confidence: 99%

IoT Resource Allocation and Optimization Based on Heuristic Algorithm

Sangaiah

Hosseinabadi

Shareh

et al. 2020

Sensors

141

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The Internet of Things (IoT) is a distributed system that connects everything via internet. IoT infrastructure contains multiple resources and gateways. In such a system, the problem of optimizing IoT resource allocation and scheduling (IRAS) is vital, because resource allocation (RA) and scheduling deals with the mapping between recourses and gateways and is also responsible for optimally allocating resources to available gateways. In the IoT environment, a gateway may face hundreds of resources to connect. Therefore, manual resource allocation and scheduling is not possible. In this paper, the whale optimization algorithm (WOA) is used to solve the RA problem in IoT with the aim of optimal RA and reducing the total communication cost between resources and gateways. The proposed algorithm has been compared to the other existing algorithms. Results indicate the proper performance of the proposed algorithm. Based on various benchmarks, the proposed method, in terms of “total communication cost”, is better than other ones.

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Section: Methods Based On Heuristic Algorithmmentioning

confidence: 99%

IoT Resource Allocation and Optimization Based on Heuristic Algorithm

Sangaiah

Hosseinabadi

Shareh

et al. 2020

Sensors

141

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“…It is performed at two levels that is pre-processing of sensed data to avoid communication traffic and resource adaptation for various heterogeneous requests. Dynamic changing of environment is monitored by Reinforcement learning (RL) and decision is forced by Fuzzy Logic Controller (FLC) [16]. Agent approach [17] [18] helps in interaction among several devices, as agents can communicate with heterogeneous devices.…”

Section: Modelmentioning

confidence: 99%

“…If adequate resources are not available at user, then processing is forwarded to nearest fog device. 16. If resources are not sufficient at user and fog device then request is forwarded to cloud by FRMA.…”

Section: A Device Agencymentioning

confidence: 99%

Multi-Agent System for Resource Adaptation in IoT to Monitor Leakage in Irrigation Tanks

Hatti

Sutagundar²

2021

Preprint

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The sporadic changes in the requirements by the end users has led to the problems in managing the resources of IoT devices. The problem of managing the heterogeneous requests with the available resources in view of ensuring Quality of service (QoS) to end users is challenging issue. The proposed model inculcates the adaptation policy for adapting the resources to fulfill the requirements of the user. The dynamic changes in the environment are handled by Reinforcement learning model with Fuzzy Interference system to apply the policy. Periodically monitoring of irrigation tank to alert the flow of water above Full Tank Level (FTL) by Reinforcement learning agent, prioritization of tasks (requests) by Fuzzy logic is performed. It is done by interacting through agency, providing video conferencing or video calling facility to the user based on availability of the user resources. It also adapts according to communication and computational resources. The proposed model is simulated to monitor and control the leakage in tank. It helps to remotely control the leakages in irrigation tanks/ bridges through Multi-Agent Fuzzy Q learning model. It focuses on adapting the resources of the devices and the action considering the user resources. The algorithm is simulated in Ifogsim and python and performance are evaluated in terms of resource cost, latency, execution time, energy consumption and network usage.

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“…The number of smart entities that need to be connected to a network and the scalability requirement of network coverage has comparatively increased with the rapid advent of emerging new technologies 2 . In this context, Internet of things (IoT) has emerged as new pattern by connecting smart objects and people with dramatic scale‐up in the amount of information generated on the Internet 3 . This IoT environment poses challenges that are most commonly involved in the establishment of connection between human beings and smart things independent of the location and time of existence 4 .…”

Section: Introductionmentioning

confidence: 99%

“…2 In this context, Internet of things (IoT) has emerged as new pattern by connecting smart objects and people with dramatic scale-up in the amount of information generated on the Internet. 3 This IoT environment poses challenges that are most commonly involved in the establishment of connection between human beings and smart things independent of the location and time of existence. 4 Moreover, the new generation of IoT has evolved for attaining maximized quality of service (QoS).…”

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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Fuzzy based job classification and resource allocation in IoT

Cited by 5 publications

References 9 publications

IoT Resource Allocation and Optimization Based on Heuristic Algorithm

IoT Resource Allocation and Optimization Based on Heuristic Algorithm

Multi-Agent System for Resource Adaptation in IoT to Monitor Leakage in Irrigation Tanks

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

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