scholarly journals MODM-Based Evaluation Model of Service Quality in the Internet of Things

2011 ◽  
Vol 11 ◽  
pp. 63-69 ◽  
Author(s):  
Fan Shaoshuai ◽  
Shi Wenxiao ◽  
Wang Nan ◽  
Liu Yan
Keyword(s):  
Internet Of Things ◽  
Service Quality ◽  
Evaluation Model ◽  
The Internet ◽  
The Internet Of Things

scholarly journals Green Energy Strategic Management for Service of Quality Composition in the Internet of Things Environment

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Jianhao Gao
Keyword(s):  
Energy Consumption ◽  
Internet Of Things ◽  
Service Composition ◽  
Evaluation Model ◽  
Green Energy ◽  
The Internet ◽  
Energy Evaluation ◽  
Internet Of Things Environment ◽  
Qos Evaluation ◽  
The Internet Of Things

With the rapid development of Internet of Things (IoT) technology, the energy consumption of service composition in the IoT environment is a key problem to be studied. At present, the problems of service composition in the IoT environment mostly focus on the evaluation research based on quality of service (QoS), ignoring the overall energy consumption in the process of dynamic configuration of service composition. Therefore, we construct the service composition structure for the IoT and propose the QoS evaluation model and energy evaluation model for the service composition in the IoT environment. Considering that the service composition in the Internet of things environment is NP hard, moth algorithm (MFO) is successfully applied to the QoS evaluation model and energy evaluation model. The simulation results reveal that MFO has good optimization effect in the abovementioned models, and the optimization effect of MFO is improved by 8% and 6% compared with the genetic algorithm and particle swarm optimization, so as to realize the green energy strategic management of QoS composition in the environment of IoT.

scholarly journals Increasing fault tolerance of data plane on the internet of things using the software-defined networks

2021 ◽  
Vol 7 ◽  
pp. e543
Author(s):  
Katayoun Bakhshi Kiadehi ◽  
Amir Masoud Rahmani ◽  
Amir Sabbagh Molahosseini
Keyword(s):  
Fault Tolerance ◽  
Internet Of Things ◽  
Service Quality ◽  
Packet Loss ◽  
Recovery Time ◽  
The Internet ◽  
Software Defined Networks ◽  
Data Plane ◽  
Shared Risk ◽  
The Internet Of Things

Considering the Internet of Things (IoT) impact in today’s world, uninterrupted service is essential, and recovery has received more attention than ever before. Fault-tolerance (FT) is an essential aspect of network resilience. Fault-tolerance mechanisms are required to ensure high availability and high reliability in systems. The advent of software-defined networking (SDN) in the IoT plays a significant role in providing a reliable communication platform. This paper proposes a data plane fault-tolerant architecture using the concepts of software-defined networks for IoT environments. In this work, a mathematical model called Shared Risk Link Group (SRLG) calculates redundant paths as the primary and backup non-overlapping paths between network equipment. In addition to the fault tolerance, service quality was considered in the proposed schemes. Putting the percentage of link bandwidth usage and the rate of link delay in calculating link costs makes it possible to calculate two completely non-overlapping paths with the best condition. We compare our two proposed dynamic schemes with the hybrid disjoint paths (Hybrid_DP) method and our previous work. IoT developments, wireless and wired equipment are now used in many industrial and commercial applications, so the proposed hybrid dynamic method supports both wired and wireless devices; furthermore multiple link failures will be supported in the two proposed dynamic schemes. Simulation results indicate that, while reducing the error recovery time, the two proposed dynamic designs lead to improved service quality parameters such as packet loss and delay compared to the Hybrid_DP method. The results show that in case of a link failure in the network, the proposed hybrid dynamic scheme’s recovery time is approximately 12 ms. Furthermore, in the proposed hybrid dynamic scheme, on average, the recovery time, the packet loss, and the delay improved by 22.39%, 8.2%, 5.66%, compared to the Hybrid_DP method, respectively.

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