Neural network model as Internet of Things congestion control using PID controller and immune-hill-climbing algorithm

Quwaider, Muhannad; Shatnawi, Yousef

doi:10.1016/j.simpat.2019.102022

Cited by 19 publications

(7 citation statements)

References 15 publications

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“…So, we tend to increase the lifetime of the network and present the proposed algorithm with the same parameter used for the LEACH protocol. Parameters values are picked depending on previous researches who analyzes the WSN parameter to pick the optimal resulted values for simulation and experiments and as explained in [20], [21], [22], [23], [24]. Table 1 below presents the proposed method simulation parameters.…”

Section: Experiments Methods and Resultsmentioning

confidence: 99%

An Enhancement Process for Reducing Energy Consumption in Wireless Sensor Network

Mohammed¹

2020

IJETER

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Wireless Sensor Network (WSN) is considered one of the utmost common techniques in various applications such as monitoring, military, health, and fire track. WSN composed of a huge of number sensor nodes which have restriction on memory resources, computation, and energy. The lifetime of WSN is affected by confined energy resources. In this proposed paper, we suggest a new method to obtain better enhancement of WSN in terms of energy-efficient and network lifetime. Then the simulated result of the suggested method was compared with the basic Low Energy Adaptive Clustering Hierarchy (LEACH) protocol with fixing parameters. The suggested method achieved a 200% improvement compared with the basic LEACH in the concept of energy-efficient.

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Section: Experiments Methods and Resultsmentioning

confidence: 99%

An Enhancement Process for Reducing Energy Consumption in Wireless Sensor Network

Mohammed¹

2020

IJETER

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“…Several works have been presented in Wireless Ad hoc and Sensor Networks for CC in constrained application protocol; recently, CC mechanisms for CoAP are in other studies, [27][28][29][30][31][32][33] and notable contributions on CC for reliable exchanges of CoAP communications include other studies. [20][21][22]24,[34][35][36] The Internet Engineering Task Force (IETF) RFC 7641 18 provides an extension of the CoAP base specification for CC in unreliable CoAP communications as CoAP with Observe, which uses a publish-subscribe mechanism, with the introduction of an upper limit for the permitted rate of outgoing messages.…”

Section: Related Survey Workmentioning

confidence: 99%

An optimization‐based congestion control for constrained application protocol

2021

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The Constrained Application Protocol (CoAP) is a lightweight web transfer protocol designed based on the REST architecture standardized by the Internet Engineering Task Force (IETF) to meet and accommodate the requirements of the constrained Internet of Things (IoT) environments. Managing congestion control in a resource-constrained lossy network with a high bit error rate is a significantly challenging task that needs to be addressed. The primary congestion control mechanism defined by CoAP specification leverages on basic binary exponential backoff and often fails to utilize the network dynamics to the best of its traffic conditions. As a result, CoCoA has been introduced for better IoT resource utilization. In addition, CoCoA retransmission timeout (RTO) for network dynamics is based on constant coefficient values. The resource-constrained nature of IoT networks poses new design challenges for congestion control mechanisms. In this paper, we propose a new particle swarm optimization (PSO)-based congestion control approach called psoCoCoA as a variation of CoCoA. The psoCoCoA applies random and optimal parameter-driven simulation to optimize default CoAP parameters and update the fitness and velocity positions to adapt to the traffic conditions. This process is performed for different traffic scenarios by varying the retransmission and max-age values by using the optimization-based algorithm. We carried out extensive simulations to validate the congestion control performance for CoAP with Observe, CoCoA, and psoCoCoA with different network topologies. The results indicate that psoCoCA outperforms or very similar to CoCoA and achieves better performance compared to CoAP with Observe under different network scenarios. | INTRODUCTIONThe ever-growing Internet of Things (IoT) is paving the way for the global network infrastructure, and the number of devices interconnected to the IoT is proliferating, which in turn leads to several diverse smart applications and services. 1 In view of the tremendous potential of the IoT, it is predicted that more than 50 billion Internet-capable smart things will be connected over the Internet shortly, and this will tend to revolutionize the global world. 2 The IoT connects numerous heterogeneous devices by applying a wide range of technologies that include communication, networking, and information processing and ensures inter-operability among global Internet services by integrating smart objects into the existing network and information systems. [3][4][5]

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“…The proposed ANN-2DOF-PID [22][23][24][25][26][27][28] controller block diagram for Francis turbine active power/speed control has been shown in Figure 5. It has been depicted from Figure 5 speed, power output, and reference speed, and power along with per unit conversion parameter generates the composite error.…”

Section: Controller Designmentioning

confidence: 99%

“…Two degree-offreedom proportional integral derivative (2DOF-PID) controller design approach recently has been applied to various complex dynamics system. 22,23 Researchers reported controller design with artificial neural network (ANN) and PID [24][25][26][27] for nonlinear dynamic system. Some researchers are reported nonlinear adaptive fuzzy and ANN based controller with restricted error 28,29 for complex dynamics system.…”

Section: Introductionmentioning

confidence: 99%

Francis turbine electrohydraulic inlet guide vane control by artificial neural network 2 degree-of-freedom PID controller with actuator fault

Vinod

Sarkar

2020

Proceedings of the Institution of Mechanical Engineers, Part I:

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Hydropower system has great attention due to the cheapest and simplest renewable power generation, available potential and environmental concern. Francis turbine has wide operating range compare to other hydro turbine runner. Electrohydraulic Francis turbine inlet guide vane system has advantages like, high power density, self-lubrication property, very good controllability and rugged. Francis turbine inlet guide vane system consists of inlet guide vane actuation system, ring inlet guide blade arrangement, and controller. The main challenges with the electrohydraulically actuated inlet guide vane system are nonlinear characteristic, parametric uncertainty, and external disturbances. Further with respect to the real-life application of the electrohydraulic actuator piston seal damage may occur due to fitting problem of the seal, impurities in the hydraulic oil, high temperature of the hydraulic oil and so on. Recently some researchers are reported on modelling accuracy, accurate controller design and stability analysis. In the present study mathematical model of the Francis turbine has been developed based on velocity diagram to capture effect of water flow dynamics on turbine power generation with the consideration of Euler’s equation of turbo machinery. Detailed mathematical model of the inlet guide vane actuating system has been integrated with Francis turbine model. Artificial neural network 2 degree-of-freedom proportional integral derivative controller has been developed for Francis turbine power/speed control. The controller performance has been studied with the consideration of actuator piston seal damage. The controller performance has been studied with 30%, 60%, and 90% step increase of power demand. The controller performance also has been studied with 0.005 and 0.05 Hz frequency sinusoidal power demand. The proposed controller performance has been compared with conventional proportional integral derivative controller through various performance indexes. The proposed controller performances also have been compared with recently reported work due to step increase of torque and step decreases of power demand.

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Neural network model as Internet of Things congestion control using PID controller and immune-hill-climbing algorithm

Cited by 19 publications

References 15 publications

An Enhancement Process for Reducing Energy Consumption in Wireless Sensor Network

An Enhancement Process for Reducing Energy Consumption in Wireless Sensor Network

An optimization‐based congestion control for constrained application protocol

Francis turbine electrohydraulic inlet guide vane control by artificial neural network 2 degree-of-freedom PID controller with actuator fault

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