Deep-Green A Dispersed Energy-Efficiency Computing Paradigm for Green Industrial IoT
ABSTARCT :
The rapid development of the Industrial Internet of Things (IIoT) has led to the explosive growth of industrial control data. Cloud computing-based industrial control models cause vast energy consumption. Most existing solutions try to reduce the overall energy consumption by optimizing task scheduling and disregard how to reduce the load of computing and data transmission. On the other hand, due to the rigid architecture and limited capability of the edge computing platform, solutions based on edge computing urgently need to be deeply optimized in terms of data processing and energy efficiency. This paper proposes Deep-Green, which is a dispersed energy-efficient computing paradigm for the Industrial Internet of Things. The core idea of Deep-Green is to realize the joint optimization of computing and network resources by merging data transmission and data processing. Deep-Green provides a novel method of constructing an IIoT edge layer based on a dispersed computing platform. By using an energy-efficiency task scheduling algorithm, container service technology, and programmable protocol stack, the data processing service is dispatched from the cloud side to the on-site controller. Therefore, the data from manufacturing equipment can be processed while they are forwarded by the on-site industrial controller. The results of experiments show that Deep-Green can not only effectively reduce the computing load and communication overhead of the cloud-side server, but also simplify the network topology and the number of devices at the edge layer of the IIoT.
EXISTING SYSTEM :
? As there are variety of sensor and actuator devices, communication technologies, and data computing approached, in this section, we explain the existing technologies which enable IoT.
? The recent developments in digital technologies have provided a driving force to apply smart, IoT based solutions for the existing problems in a smart city context .
? This paper reviews the existing literature on the application of IoT in in energy systems, in general, and in the context of smart grids particularly.
? The main contribution of this paper is to extend the existing body of literature by providing energy policy-makers, economists, energy experts, and managers with a general overview of the opportunities and challenges of applying IoT in different parts of the entire energy sector.
DISADVANTAGE :
? IoT is going to have a huge impact on how we deal with certain problems in our daily life and it is certainly going to make our lives easier and better but with ease come the challenges.
? We have to deal with the large scale consumption of energy resources by IoT and the earlier we tackle this problem, the more efficient will be the IoT.
? This is called the overhearing problem which is responsible for a huge wastage of energy resources.
? In a smart phones charger can be the cause of the environmental damage because its main component is print wiring boards, the main problem was the electronic components.
PROPOSED SYSTEM :
• The work proposes a platform by integrating multiple systems, such as air-conditioning, lighting, and energy monitoring to perform building energy optimization.
• Other solutions propose designing co-simulation models for energy systems to integrate the system and minimize synchronization delay error between the subsystems.
• The research in proposes a wireless sensor and actuator network to provide an IoT-based automatic intelligent system.
• Whereas, by optimizing the operation of devices and machines in the IoT, the proposed system achieves reduction in their overall energy consumption at a given time.
ADVANTAGE :
? Many methods were introduced in for the improvement of the power consumption and performance of the smart phones.
? We evaluate if an approach is realistic or not on the basis of its performance in real life scenarios or whether that model can be implemented or not in a large scale IoT network and all the proposed models are found to be realistic.
? However, using another medium for energy storage instead of batteries could lead to more energy efficiency.
? Data Centers can be pivotal to an energy efficient IoT network but energy efficiency needs to be introduced in data centers to make them viable for IoT.
? A CS framework to achieve energy efficiency using priori data sparsity information proposed in can minimize the redundant data collection.
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