The problems of RFID indoor positioning algorithm LANDMARC are optimized. In view of the abnormal correlation degree of local reference tags in this algorithm, this paper repairs the correlation degree of bad tags. Aiming at the problem of label misselection in the process of selecting adjacent labels, an improvement of minimum target area block was proposed to reduce the influence of bad labels, and then the location of target labels was estimated. Simulation results show that the proposed optimization scheme is significantly improved compared with the original algorithm, the positioning accuracy is increased by 22%, and the maximum error is reduced by 27%.
Edge computing is to migrate terminal tasks to the edge server for execution,and reduce task execution delay and terminal energy consumption through reasonable migration decision.In view of the fact that a large number of literatures only consider the unloading decision of multi-task single server and ignore the balance of computing tasks between servers,a multi-task multi-server joint optimization strategy based on particle swarm optimization(PSO)is proposed.Taking the delay and energy consumption as the optimization objectives,considering the queuing delay and the balance of the amount of computation of the edge computing server(MEC),the global computing resources were fully utilized,which further reduced energy consumption and time delay.Simulation results show that,compared with other algorithms,the average task cost of the proposed algorithm is the smallest,which proves the feasibility and high efficiency of the method.
Identity authentication is a very important aspect of information security theory. Usually, the problem that identity authentication is prone to is that the user name and password are transmitted in the network in plaintext. The security is not enough, and it is very vulnerable to replay attacks and dictionary attacks. Dynamic passwords Although authentication is more secure, if there is a problem with the time synchronization between the server and the client, the user will not be able to login for a long time. Moreover, the password of the dynamic password is the same within a period of time, and it is also vulnerable to replay attacks. Therefore, a decentralized dynamic identity authentication system based on blockchain isn’t proposed. By using a public key + nonce (integer number that increases from 0) to sign in, It only solves the problem of over-centralized identity authentication, but also solves the replay attack. Improved the security of the identity authentication process.
Aimed at the problem of gradient disappearance and network degradation caused by the use of recurrent neural networks in traditional question answering models,an automatic question answering model based on IMGRU-Seq2seq(Identity Mapping Gated Recurrent Unit-Sequence to Sequence)is proposed.The text was represented by weighted word vectors through the TF-IDF method.Based on the gated recurrent unit,the batch normalization technology and the rectified linear unit activation function were combined and the identity mapping was added to construct the IMGRU model.As the semantic extraction unit of the question answering model,the bidirectional IMGRU introduced the attention mechanism and the beam search algorithm to realize automatic question and answer.The experimental results show that the proposed method is 18.87％and 4.35％higher than the existing methods BLEU and ROUGE-L respec-tively.
Autoencoders have been successfully used for graph embedding, and many variants have been proven to effectively express graph data and conduct graph analysis in low-dimensional space. However, previous methods ignore the structure and properties of the reconstructed graph, or they do not consider the potential data distribution in the graph, which typically leads to unsatisfactory graph embedding performance. In this paper, we propose the adversarial attention variational graph autoencoder (AAVGA), which is a novel framework that incorporates attention networks into the encoder part and uses an adversarial mechanism in embedded training. The encoder involves node neighbors in the representation of nodes by stacking attention layers, which can further improve the graph embedding performance of the encoder. At the same time, due to the adversarial mechanism, the distribution of the potential features that are generated by the encoder are closer to the actual distribution of the original graph data; thus, the decoder generates a graph that is closer to the original graph. Experimental results prove that AAVGA performs competitively with state-of-the-art popular graph encoders on three citation datasets.
