Computing Resource Allocation for Blockchain-Based Mobile Edge Computing

Users and edge servers are not fullymutually trusted inmobile edge computing(MEC),and hence blockchain can be introduced to provide trustableMEC.In blockchain-basedMEC,each edge server functions as a node in bothMEC and blockchain,processing users’tasks and then uploading the task related information to the blockchain.That is,each edge server runs both users’offloaded tasks and blockchain tasks simultaneously.Note that there is a trade-off between the resource allocation for MEC and blockchain tasks.Therefore,the allocation of the resources of edge servers to the blockchain and theMEC is crucial for the processing delay of blockchain-based MEC.Most of the existing research tackles the problem of resource allocation in either blockchain or MEC,which leads to unfavorable performance of the blockchain-based MEC system.In this paper,we study how to allocate the computing resources of edge servers to the MEC and blockchain tasks with the aimtominimize the total systemprocessing delay.For the problem,we propose a computing resource Allocation algorithmfor Blockchain-based MEC(ABM)which utilizes the Slater’s condition,Karush-Kuhn-Tucker(KKT)conditions,partial derivatives of the Lagrangian function and subgradient projection method to obtain the solution.Simulation results show that ABM converges and effectively reduces the processing delay of blockchain-based MEC.

QoS-Constrained,Reliable and Energy-Efficient Task Deployment in Cloud Computing

Reliability,QoS and energy consumption are three important concerns of cloud service providers.Most of the current research on reliable task deployment in cloud computing focuses on only one or two of the three concerns.However,these three factors have intrinsic trade-off relationships.The existing studies show that load concentration can reduce the number of servers and hence save energy.In this paper,we deal with the problem of reliable task deployment in data centers,with the goal of minimizing the number of servers used in cloud data centers under the constraint that the job execution deadline can be met upon single server failure.We propose a QoS-Constrained,Reliable and Energy-efficient task replica deployment(QSRE)algorithm for the problem by combining task replication and re-execution.For each task in a job that cannot finish executing by re-execution within deadline,we initiate two replicas for the task:main task and task replica.Each main task runs on an individual server.The associated task replica is deployed on a backup server and completes part of the whole task load before the main task failure.Different from the main tasks,multiple task replicas can be allocated to the same backup server to reduce the energy consumption of cloud data centers by minimizing the number of servers required for running the task replicas.Specifically,QSRE assigns the task replicas with the longest and the shortest execution time to the backup servers in turn,such that the task replicas can meet the QoS-specified job execution deadline under the main task failure.We conduct experiments through simulations.The experimental results show that QSRE can effectively reduce the number of servers used,while ensuring the reliability and QoS of job execution.

Research and Application of Ecological Outdoor Ground Structure with Composite Water Storage and Drainage Function

In order to solve the problems of surface runoff increase,water accumulation in rainy days and urban heat island effect,an ecological outdoor ground structure with composite water storage and drainage functions was studied and applied in this paper:Through the comprehensive design of road ground,road inspection well,garden inspection well and drainage pipe network,it can quickly store and drain ground water,alleviate the urban heat island effect,realize plant infiltration irrigation,and achieve the purpose of saving water and energy.

A Flight Trajectory Prediction Method Based on Internal Relationships between Attributes

The rapid development of the aviation industry urgently requires airspace traffic management,and flight trajectory prediction is a core component of airspace traffic management.Flight trajectory is a multidimensional time series with rich spatio-temporal characteristics,and existing flight trajectory prediction methods only target the trajectory point temporal relationships,but not the implicit interrelationships among the trajectory point attributes.In this paper,a graph convolutional network(AR-GCN)based on the intra-attribute relationships is proposed for solving the flight track prediction problem.First,the network extracts the temporal features of each attribute and fuses them with the original features of the attribute to obtain the enhanced attribute features,then extracts the implicit relationships between attributes as inter-attribute relationship features.Secondly,the enhanced attribute features are used as nodes and the inter-attribute relationship features are used as edges to construct the inter-attribute relationship graph.Finally,the graph convolutional network is used to aggregate the attribute features.Based on the full fusion of the above features,we achieved high accuracy prediction of the trajectory.In this paper,experiments are conducted on ADS-B historical track data.We compare our method with the classical method and the proposed method.Experimental results show that our method achieves significant improvement in prediction accuracy.

Throughput-oriented associated transaction assignment in sharding blockchains for IoT social data storage

Blockchain is a viable solution to provide data integrity for the enormous volume of 5G IoT social data, while we need to break through the throughput bottleneck of blockchain. Sharding is a promising technology to solve the problem of low throughput in blockchains. However, cross-shard communication hinders the effective improvement of blockchain throughput. Therefore, it is critical to reasonably allocate transactions to different shards to improve blockchain throughput. Existing research on blockchain sharding mainly focuses on shards formation, configuration, and consensus, while ignoring the negative impact of cross-shard communication on blockchain throughput. Aiming to maximize the throughput of transaction processing, we study how to allocate blockchain transactions to shards in this paper. We propose an Associated Transaction assignment algorithm based on Closest Fit (ATCF). ATCF classifies associated transactions into transaction groups which are then assigned to different shards in the non-ascending order of transaction group sizes periodically. Within each epoch, ATCF tries to select a shard that can handle all the transactions for each transaction group. If there are multiple such shards, ATCF selects the shard with the remaining processing capacity closest to the number of transactions in the transaction group. When no such shard exists, ATCF chooses the shard with the largest remaining processing capacity for the transaction group. The transaction groups that cannot be completely processed within the current epoch will be allocated in the subsequent epochs. We prove that ATCF is a 2-approximation algorithm for the associated transaction assignment problem. Simulation results show that ATCF can effectively improve the blockchain throughput and reduce the number of cross-shard transactions.

Detecting Domain Generation Algorithms with Bi-LSTM

Botnets often use domain generation algorithms(DGA)to connect to a command and control(C2)server,which enables the compromised hosts connect to the C2 server for accessing many domains.The detection of DGA domains is critical for blocking the C2 server,and for identifying the compromised hosts as well.However,the detection is difficult,because some DGA domain names look normal.Much of the previous work based on statistical analysis of machine learning relies on manual features and contextual information,which causes long response time and cannot be used for real-time detection.In addition,when a new family of DGA appears,the classifier has to be re-trained from the very beginning.This paper presents a deep learning approach based on bidirectional long short-term memory(Bi-LSTM)model for DGA domain detection.The classifier can extract features without the need for manual feature extraction,and the trainable model can effectively deal with new unknown DGA family members.In addition,the proposed model only needs the domain name without any additional context information.All domain names are preprocessed by bigram and the length of each processed domain name is set as a value longer than the most samples.Bidirectional LSTM model receives the encoded data and returns labels to check whether domain names are normal or not.Experiments show that our model outperforms state-of-the-art approaches and is able to detect new DGA families reliably.

Path for Building Characteristic Towns in Industrial Towns: A Case Study of Chengkou Town in Wudi County

At present,China’s new urbanization construction is making rapid progress,and the construction of characteristic towns has become a hot spot. The promotion of characteristic towns in various places has brought many opportunities for new urbanization. In addition,it has greatly promoted the transformation and development of the characteristic resources. However,many new problems have been exposed during the transformation process. Taking construction of characteristic town Chengkou Town in Wudi County as an example,this paper discussed the development paths for building characteristic towns in light industrial towns,in the hope of finding a new direction and solution.

Low Trap Density Para-F Substituted 2D PEA_(2)PbI_(4)(X=Cl,Br,I)Single Crystals with Tunable Optoelectrical Properties and High Sensitive X-Ray Detector Performance

Exploring halogen engineering is of great significance for reducing the density of defect states in crystals of organic-inorganic hybrid perovskites and hence improving the crystal quality.Herein,high-quality single crystals of PEA_(2)PbI_(4)(X=CI,Br,I)and their para-F(p-F)substitution analogs are prepared using the facile solution method to study the effects of both p-F substitution and halogen anion engineering.After p-F substitution,the triclinic PEA_(2)PbX_(4)(X=Cl,Br)and cubic PEA_(2)PbX_(4)(X=I)crystals unifies to monoclinic crystal structure for p-F-PEA_(2)PbX_(4)(X=Cl,Br,I)crystals.The p-F substitution and halogen engineering,together with crystal structure variation,enable the tunability of optoelectrical properties.Experimentally,after the p-F substitution,the energy levels are lowered with increased Fermi levels,and the bandgaps of p-F-PEA_(2)PbX_(4)(X=Cl,Br,I)are slightly reduced.Beneftting from the enhancement of the charge transfer and the reduced trap density by p-F substitution and halogen anion engineering,the average carrier lifetime of the p-F-PEA_(2)PbI_(4)is obviously reduced.Compared with PEA_(2)PbI_(4)the X-ray detector based on p-F-PEA_(2)PbI_(4)single crystals exhibits higher radiation stability under high-dose X-ray irradiation,implying long-term operando stability.

Development of the peanut precision fertilization control system based on threshold velocity algorithm

In view of the fact that the current ground wheel velocimetry of the peanut precision fertilizer control system cannot solve the phenomenon of ground wheel slippage,and signal interference and delay loss cannot be excluded by BeiDou positioning velocimetry,a set of peanut precision fertilizer control system was designed based on the threshold speed algorithm.The system used STM32F103ZET6 microcontroller as the main controller,and touch screen for setting the operating parameters such as operating width,fertilizer type,and fertilizer application amount.The threshold speed algorithm combined with BeiDou and ground wheel velocimetry was adopted to obtain the forward speed of the tractor and adjust the speed of the DC drive motor of the fertilizer applicator in real time to achieve precise fertilizer application.First,through the threshold speed algorithm test,the optimal value of the length N of the ground wheel speed measurement queue was determined as 3,and the threshold of the speed variation coefficient was set to 4.6%.Then,the response performance of the threshold speed algorithm was verified by comparative test with different fertilization amounts(40 kg/hm^(2),50 kg/hm^(2),60 kg/hm^(2),70 kg/hm^(2))under two speed acquisition methods of ground wheel speed measurement and threshold speed algorithm(combination of Beidou single-point speed and ground wheel speed measurement)in different operation speeds(3 km/h,4 km/h,5 km/h).The response performance test results showed that the average value of the velocimetry delay distance of the BeiDou single-point positioning velocimetry method was 0.58 m,while the average value of that with the threshold velocity algorithm was 0.27 m,which decreased by 0.31 m and indicated more accurate with the threshold velocity algorithm.The field comparison test for fertilizer application performance turned out an over 96.08%accuracy rate of fertilizer discharge by applied with the threshold speed algorithm,which effectively avoided the inaccurate fertilizer application caused by wheel slippage and raised the accuracy of fertilizer discharge by at least 1.2%compared with that of using the ground wheel velocimetry alone.The results showed that the threshold speed algorithm can meet the requirements of precise fertilizer application.