In the mobile edge computing(MEC),the local device can offload tasks to the edge node near the network for computation processing,thereby reducing the delay,power consumption and overload of the client,also the computing loading core network.For the complex MEC environment of multi-type edge nodes,a three-stage computing offloading decision is modeled based on linear programming relaxation,that is CART-CRITIC-LR(CCLR) algorithm.First,the classification and regression decision tree algorithm(CART) is used to screen out the locally executed calculation tasks.Secondly,the multi-attribute decision-making algorithm(CRITIC) is used to determine the weight of the three performance indicators respectively.Then the calculation offloa-ding problem is modeled as a linear programming relaxation(LR ) to optimize the equilibrium solutions among the total delay,total energy consumption and total cost.Each offloading strategy is analyzed by comprehensively comparing the energy consumption,cost,delay.experimental results show that the CCLR algorithm achieves the shortest total delay while ensuring the multi-objective global optimization,which illustrates the effectiveness and applicability of the algorithm.

Relay-assisted mobile edge computing(MEC) has recently emerged as a promising paradigm to enhance resource utilization and data processing capability of low-power networks,such as 5G networks and Internet of things (IoT).Nevertheless,the design of relay selection and computation offloading policies to improve the energy efficiency for queue stability system remains challenging.In order to solve the energy consumption optimization problem in relay-assisted MEC system,a mixed integer nonli-near stochastic optimization model is established,with the objective of minimizing the long-term average energy consumption,subject to a task buffer stability constraint.The problem is solved by decomposing into two stages:relay selection and relay offloa-ding decision.In relay selection stage,the relay node is determined by setting a weighted parameter V₁ to minimize the weighted sum of transmission energy consumption and buffer queue length.In offloading decision stage,the stochastic optimization is converted to a deterministic optimization problem based on Lyapunov optimization method.Specifically,at each time slot,the theore-tical expressions of optimal relay calculation frequency,relay transmission power and remote calculation frequency are obtained under the constraint of task buffer queue stability.Simulation results show that the energy optimization strategy can effectively reduce the long-term average energy consumption under the constraint of buffer queue stability,and converge to the optimal solution obtained by exhaustive searching.Besides,the weight of energy consumption and waiting time can be changed by adjusting the values of parameters V₁ and V₂ in algorithm.

Aiming at the problem that the cluster head selection of clustering routing protocol in wireless sensor networks is unreasonable,resulting in uneven network load and shortened network life cycle,a cluster head selection algorithm CIBOA based on improved butterfly optimization algorithm IBOA is proposed.Firstly,based on the butterfly optimization algorithm BOA,the Circle chaotic map and nonlinear dynamic convergence factor are introduced to control the parameter,which improves the search speed and convergence accuracy of butterfly optimization algorithm,and makes the search ability stronger..In the process of cluster head selection,a new fitness function is built on the basis of the residual energy,distance among the nodes and BS and average distance between neighbor nodes.The IBOA is used for improving the random problem of cluster head selection and comprehensively select better cluster heads.Simulation results show that the cluster head selection algorithm CIBOA based on the improved butterfly optimization algorithm can comprehensively consider the factors such as node energy and distance and prolong the network lifetime.

The most basic function of radio frequency identification(RFID) system is tag identification.However,the current authentication system cannot detect forged or cloned tags,which leads to potential security and privacy issues.At present,there are encryption based authentication protocols and feature extraction based solutions,among which encryption based authentication protocol is incompatible with existing protocols and feature extraction based authentication protocol has limitations such as difficulty in feature extraction or short recognition distance.This paper proposes a tag identification method for UHF RFID systems to overcome the two shortenings.The core idea is to first extract signals irrelevant to the logical information of tags from the backscattered RFID signals,and then send them to the convolutional neural network for similarity matching.According to the score of similarity matching and a given threshold,the authenticity of the tag is finally recognized.In this paper,we establish an experimental system which contains an USRP N210 used as the reader of the RFID system,and contains 150 UHF commercial tags to backscatter signals from the reader.We then collects the RFID signals based on this experiment.Experimental results show that the tag recognition accuracy based on deep learning can reach more than 94%,and its equal error ratio(EER) is 0.034 when the recognition distance is up to 2m.

At present,the traditional cloud computing mode can not meet the needs of users in low latency scenarios,so mobile edge computing comes into being.In order to make the edge servers placed in the same area have lower total energy consumption and balanced workload,and an ant colony optimization energy consumption load balancing placement algorithm ACO-ELP(ant colony optimization energy-consumption load-balancing placement) for energy consumption optimization and load balancing is proposed.Firstly,by constructing the power consumption model and load balancing model,the problem is defined,and the actual parameters are matched with the algorithm variables.In the iterative process,the ant colony algorithm is optimized.By dynamically controlling the volatilization and retention rate of pheromone,the iterative speed of the algorithm is accelerated,and the maximum and minimum value of pheromone is controlled to ensure that the algorithm can search the global optimal solution as much as possible and will not fall into the local optimal solution.Finally,the algorithm is simulated and evaluated with the data of Telecom base stations in Shanghai.The results show that compared with the basic placement algorithm,the algorithm not only reduces the number of servers and energy consumption,but also significantly reduces the load deviation.

With the rapid development of cloud computing technology,more and more users choose to use cloud services,and the problem of mismatch between load requests and resource supply becomes increasingly prominent.As a result,user requests cannot be timely responded,which greatly affects the cloud service quality.Real-time prediction of load requests will help the timely supply of resources.To solve the problem of low performance of load prediction methods in the cloud computing environment,a cloud computing load prediction method based on hybrid model of complete ensemble empirical mode decomposition with adaptive noise and convolutional long short-term memory(CEEMDAN-ConvLSTM) is proposed.To begin with,the data sequence is decomposed into several sub-sequences which are easy to analyze and model.Then the convolutional long short-term memory(ConvLSTM) prediction model is used to predict the series of sub-sequences.The research idea based on multi-process parallel computation is adopted to realize multi-sequence parallel prediction and Bayesian optimization parameter tuning.Finally,the prediction values are integrated and superimposed to obtain the prediction output of the whole model,to achieve the goal of high-precision prediction of the original complex sequence data.The CEEMDAN-ConvLSTM hybrid model is verified by using the Google cluster workload data set.Experiment results show that the CEEMDAN-ConvLSTM hybrid model had a good prediction effect.Compared with the autoregressive differential moving average model(ARIMA),long short-term memory network(LSTM) and the convolutional long short-term memory(ConvLSTM),the Root Mean Square Error(RMSE) increases by 30.9%,30.1% and 22.5%,respectively.

In simultaneous wireless information and power transfer networks,multiple antennas are usually equipped at the transmitter,which is able to serve all sensors in one-time transmission over the same frequency band.However,collecting channel state information from all sensors may cause a colossal waste of time and frequency resources.Therefore,the energy-saving beamfor-ming design with only channel distribution information at the transmitter is studied in multi-user multi-input single-output network.Under the constraints of information interruption probability,total available power and available power of authorized users,the network energy efficiency is maximized by the improved teaching-learning-based optimization algorithm.In addition,for the proposed power consumption scheme,the nonlinear energy receiving mechanism is considered,and the power-splitting energy harvesting receiver architecture is proposed to prevent the receiver from entering the saturation region,so as to improve the power receiving efficiency.The improved teaching-learning-based optimization algorithm has the advantages of whale algorithm,solves the constructed nonconvex optimization problem,and improves the convergence speed.Simulation experiments analyze the effects of outage probability,dynamic power consumption coefficient and available power at the transmitter on the system energy efficiency in the dynamic energy allocation scenario,and verify the effectiveness of the proposed algorithm.

For the problem of missing localization characteristics caused by the activation failure of passive UHF RFID tags,given the significant challenges in precisely modeling the channel,this paper proposes a missing localization characteristic estimation algorithm based on the linear model of signal strength Euclidean distance-space Euclidean distance to improve the localization accuracy of the scene analysis algorithms by increasing the number of characteristic dimensions.To increase the completeness of the scene matching data for nonactivated reference tags,the missing localization characteristics could be calculated directly by using the linear model.For the nonactivated target tags,the linear model is used to estimate the distance between the target tag and multiple benchmark reference tags,the least squares algorithm is used to estimate the preliminary location information of the target tag,and again the missing localization features are estimated using the linear model in reverse to complete the localization characteristics of the target tags.Experiments show that the proposed algorithm can not only effectively improve the localization accuracy of all missing target tags,but also the target tags around the missing reference tags.In addition,there is no additional hardware equipment included for this algorithm,which meets the application requirements of low-cost and high-precision.

The normal operation of modern trains is inseparable from the cooperation of mechanical and electronic systems,especially the train control and management system(TCMS) plays a key role in it.TCMS-related applications and services run on the train communication network(TCN),which is wired and often redundant,resulting in a large number of wired links interconnection,making network deployment and maintenance difficulties,and poor flexibility.This paper proposes a Wi-Fi 6-based train wireless communication networking scheme,which applies Wi-Fi 6 technology to the vehicle-level ECN network.The work includes the design of network architecture,the selection of communication data and the experimental verification in simulation environment.Experimental results show that the proposed Wi-Fi 6-based train communication QoS in terms of delay,jitter and packet loss rate meets the IEC 61375-3-4 standard,and have advantages over long term evolution(LTE).

The quantum teleportation is one of the hot topics in the quantum communication.The short-distance teleportation,different from the traditional teleportation,can further save costly quantum entanglement resource based on the restriction in the distance.However,this also increases the probability in terms of cheating for the participants.Therefore,this paper proposes another short-distance quantum teleportation for arbitrary two-particles state scheme with a controller in order to enhance the safety.At the same time,it is impossible for quantum teleportation in an ideal environment,due to a fact that the particle will be ine-vitably affected by the noise channel during the distributing period.This paper also analyzes the influence of Pauli noise,which is a widely used noise channel model,on the fidelity of a two-particles state.As a result,the different concurrence in the two-particles state can generate different fidelity in some typical Pauli noise channel.This research can provide some theoretical value in the aspect of quantum communication network and the experiment research.