Research On Dynamic Resource Allocation Strategy For Energy Saving In IoT Network

With continuous improvement of social productivity and people’s requirements for work efficiency and living standards,as well as strong support from the national policies,more and more industries or fields are actively transforming and upgrading their traditional systems with Internet of Things(IoT)technologies.Not only in industrial and agricultural production,but also in oil and gas field exploration and home automation,IoT network has already been everywhere and effectively improved people’s production and life.The human society is accelerating into the era of true interconnection of everything.With the widespread deployment of IoT devices,all the IoT devices are always consuming energy.In particular,a large number of wireless devices are powered by batteries.If the energy consumption issues of the IoT network cannot be well addressed,it will lead to a large amount of resource waste and even potential environmental pollution issues.Therefore,the issue of energy consumption in IoT network has always been an unavoidable issue in the development process of IoT network,and deserves extensive and in-depth research.This dissertation starts research from directions of saving resources and utilizing new resources.Saving resources originally means saving expenses.In this dissertation,it refers to reducing the energy consumption of IoT systems through hardware optimization or software optimization based on the existing IoT system.Looking for new resources originally means increasing revenue,and in this dissertation it refers to utilize new energy sources to power IoT systems.The traditional IoT network generally only supports wireless information transfer(WIT),relying mainly on wireless communication technologies known as Wi Fi,Bluetooth,Lo Ra,3G,4G,and so on.People can use natural energy sources such as solar or wind energy to power IoT devices.However,due to the high randomness and poor controllability of the energy arrival,people begin to explore new energy resources.Due to the fact that electromagnetic waves carry both information and energy,wireless power transfer(WPT)in the radio domain is receiving increasing attention due to its flexible and controllable characteristics.Scientific researchers combine WIT and WPT in the radio domain to form the Wireless Information and Power Transfer(SWIPT)technology.The new IoT network using this SWIPT technology is generally called Data and Energy Integrated Network(DEIN).This dissertation has conducted in-depth research on energy consumption issues in the IoT network from multiple perspectives: 1)Research on energy conservation strategies for integrating the edge server and end devices in the IoT network; 2)Design of energy saving algorithms in the DEIN network with fixed base station; 3)Design of energy saving algorithms in the DEIN network with mobile base station; 4)Design of energy saving algorithms in the DEIN network assisted by MEC; 5)Verification with experiment system for DEIN network.The first part of this dissertation mainly considers adding an edge server to the architecture of the traditional IoT network.Based on that,this dissertation proposes an energysaving optimization strategy that combines edge servers with IoT devices.A dynamic sampling algorithm based on time series prediction is proposed on the edge server side,which processes the data sampled by devices in the IoT system into time series,and uses the Automatic Integrated Moving Average model(ARIMA)to predict future sampling data.Then the algorithm dynamically adjusts the sampling frequency of the IoT devices based on the error between actual data and predicted data,thereby extending the sleep time of the device while maintaining system sampling performance to achieve the goal of reducing energy consumption.A more lightweight working mode adaptive switching algorithm is running on the IoT device side,which allows IoT devices to adaptively switch between different working modes based on their remaining energy and data transmission needs,thereby reducing their own energy consumption and extending battery life.The second part of this dissertation is based on a new type of IoT system that introduces SWIPT technology.In this part of research work,the fixed DEIN base station transmits mixed signals of data and energy.The DEIN devices utilize a power splitter to split the received DEIN signal,demodulating the data information sent to them,and extracting energy from it through matching and rectifying circuits for storage and use.This dissertation designs a data transmission protocol between the DEIN base station and the DEIN equipment based on Time Division Multiple Access(TDMA),and establishes a communication and energy consumption model for the system.By jointly optimizing the RF transmission power of the fixed base station,the uplink and downlink transmission time slots between the base station and the user equipment,and the power division factor of the DEIN user equipment,the goal of minimizing the energy consumption of the fixed base station is achieved.In the third part of this dissertation,on the basis of the fixed base station,we further consider the scenario of mobile base stations,and choose Unmanned Aerial Vehicle(UAV)as the specific form of mobile base stations.As a mobile base station,the UAV can be deployed and used more flexibly,which is an advantage that fixed base stations do not have.However,due to the limited energy carried by batteries,the UAV’s power consumption is also an important challenge.This dissertation considers a DEIN network based on UAV,establishes the communication and energy consumption models of UAV and ground DEIN user equipment respectively,and achieves an optimization goal of minimizing the energy consumption of the UAV by jointly optimizing the trajectory of the UAV,the communication scheduling and charging ratio of ground DEIN equipment.In the fourth part of this dissertation,the research work is carried out on the basis of the scenario of the mobile DEIN base station based on UAV,and the design of energysaving algorithm in DEIN network assisted by Mobile Edge Computing(MEC)is deeply studied.Due to the limited computing resources and energy carried by general IoT devices,it is difficult to meet the increasingly complex application needs.Therefore,this part of the research work considers a scenario where a UAV supporting edge computing services is used as a mobile DEIN base station and the ground IoT devices are used as users.By jointly optimizing the computing task offloading decision of ground devices,the flight trajectory of the UAV and computing resource allocation strategy of UAV,the optimization goal of minimizing the total energy consumption of the UAV supporting MEC is achieved.The fifth part of the research work of this dissertation,based on the previous theoretical research,further studies the principle of RF energy transmission.On this basis,this dissertation implements a complete physical prototype system of the DEIN network.Based on the prototype system,this dissertation designed a variety of experiments to verify the feasibility of the DEIN technology mentioned in previous theoretical research,and analyzed the performance of the RF energy transmission.Finally,the wireless charging strategy based on closed-loop feedback proposed in this dissertation is also verified by experiments.