Internet of Things (IoT) devices promote the rapid development of cloud storage outsourcing data service, which is favored by more and more terminal users. Therefore, how to ensure the integrity verification of user data in cloud server has become a hot issue that needs to be solved urgently. For resource-constrained users, current cloud data audit scheme has the problems such as complex computation, high cost and low efficiency. To solve these problems, an efficient dynamic data audit scheme for resource-constrained users was proposed. First, a new data structure was proposed based on Novel Counting Bloom Filter (NCBF) and Multi-Merkle Hash Tree (M-MHT) to support dynamic audit, namely NCBF-M-MHT. In this data structure, the NCBF structure was able to realize the dynamic updating request of data within O(1) time, thereby ensuring the efficiency of audit. And the root node of M-MHT structure performed signing by user authentication to ensure the security of data. Then, different allocation methods were adopted for different audit entities, and the data evidence and label evidence were used to verify the correctness and integrity of data. Experimental results show that compared with the audit scheme based on Dynamic Hash Table (DHT), the audit scheme based on Merkle Hash Tree (MHT) and the audit scheme based on Location Array-Doubly Linked Info Table (LA-DLIT), the time cost of the proposed scheme in the audit verification phase is reduced by 45.40%, 23.71% and 13.85%, and the time cost in the dynamic update phase is reduced by 43.33%, 27.50% and 17.58% respectively.

In order to solve the problems of high identification limitation and low identification rate in nonlinear system identification with fixed structure and parameters, a Subsystem-based Structural Adaptive Filtering (SSAF) method for nonlinear system identification was proposed with introducing structural adaptation into the optimization of identification. Multiple subsystems with linear-nonlinear hybrid structure were cascaded to form the model for this method. The linear part is a 1-order or 2-order Infinite Impulse Response (IIR) digital filter with uncertain parameters, and the nonlinear part is a static nonlinear function. In the initial stage, the parameters of the subsystems were randomly generated, and the generated subsystems were connected randomly according to the set connection rules, and the effectiveness of the nonlinear system was guaranteed by the connection mechanism with no feedback branches. An Adaptive Multiple-Elites-guided Composite Differential Evolution with a shift mechanism(AMECoDEs) algorithm was used for loop optimization of the adaptive model until the optimal structure and parameters were found, that is, the global optimal. The simulation results show that AMECoDEs performs well on nonlinear test functions and real data sets with high identification rate and good convergence rate. Compared with the Focused Time Lagged Recurrent Neural Network (FTLRNN), the number of parameters used in SSAF is reduced to 1/10, and the accuracy of fitness is improved by 7%, which proves the effectiveness of the proposed method.

In order to improve the diversity of population and the convergence performance of algorithm, a Scheduled competition learning based Multi-Objective Particle Swarm Optimization (SMOPSO) algorithm was proposed. The multi-objective particle swarm optimization algorithm and the competition learning mechanism were combined and the competition learning mechanism was used in every certain iterations to maintain the diversity of the population. Meanwhile, to improve the convergence of algorithm without using the global best external archive, the elite particles were selected from the current swarm, and then a global best particle was randomly selected from these elite particles. The performance of the proposed algorithm was verified on 21 benchmarks and compared with 8 algorithms, such as Multi-objective Particle Swarm Optimization algorithm based on Decomposition (MPSOD), Competitive Mechanism based multi-Objective Particle Swarm Optimizer (CMOPSO) and Reference Vector guided Evolutionary Algorithm (RVEA). The experimental results prove that the proposed algorithm can get a more uniform Pareto front and a smaller Inverted Generational Distance (IGD).

Focused on the issue that the traditional multiplierless Infinite Impulse Response (ⅡR) digital filters have fixed structure and poor performance, a random structure based design method for multiplierless ⅡR digital filters was proposed. Stable 2-order subsystems with shifters were directly used to design the multiplierless filter structure. Firstly, a set of encoding structures of the multiplierless digital filter were created randomly. Then, Differential Evolution with a Successful-Parent-Selecting Framework (SPS-DE) was used to optimize the multiplierless filter structure. The proposed method realized diversified structure design, and SPS-DE effectively balanced exploration and exploitation due to adopting a Successful-Parent-Selecting framework, which achieved good results in the optimization of the multiplierless filter structure. Compared with state-of-the-art design methods, the passband ripple of the multiplierless ⅡR filter designed in this paper is reduced by 43% and the stopband maximum attenuation is decreased by 40.4%. Simulation results show that the multiplierless ⅡR filter designed by the proposed method meets structural requirements and has good performance.

Due to the conflict of interest between the component services of the cloud composite service from different service providers, it is necessary to control the information flow of the cloud services to avoid the flow of sensitive information between the conflicting component services. For the conflict of interest in cloud composite service, it was formally described that the information flow of complex composite structure with the weighted directed graph model of cloud composite service, and defined that the concept of the dependency relation of data and the alliance relation of cloud services. Moreover, the conflict of interest relation was extended to the composite service-conflict of interest relation in Chinese Wall policy. Based on this, the Chinese Wall-based Cloud Composite Service Information Flow Control (CW-CCSIFC) model was proposed and the formal description as well as the proof of the relevant theorems was given. The analysis shows that the CW-CCSIFC model can block the illegal information flow between cloud services with conflicting interests and protect the information flow security of cloud composite service.

Facing to jammers and eavesdroppers, conventional security data transmission is generally based on the cryptographic method. Enormous issues arise when the cryptographic method applied in dynamic wireless scenarios, such as key distribution for symmetric cryptosystems, and high computing complexity of asymmetric cryptosystems. With the rapid growing of wireless traffic and massive device accessing of Internet-of-Things (IoT), the computing complexity and energy consumption aggravate, which leads to security degrade of wireless networks. To address this issue, a secure communication scheme based on physical layer security was proposed for Full-Duplex (FD) wireless-powered IoT networks, which limited the amount of information received at the unauthorized receiver by exploiting the randomness of noise and wireless channel. In this method, secrecy capacity was analyzed based on information theory, and then the Secrecy Outage Probability (SOP) was derived with the analysis model of secrecy capacity. In addition, considering the influence of noise, jammer interference, spatial mutual interference, and residual self-interference on safety capacity, a secure beamforming method was proposed to increase the mutual information between the transmitting and receiving ends and improve the secrecy capacity of the full-duplex wireless-powered IoT network by decreasing the joint interference. Derivation results are verified through Monte Carlo simulation. Simulation results show that the FD wireless-powered IoT with secure beamforming is superior to the conventional wireless-powered IoT in terms of secrecy capacity and SOP.

In real-time operation state analysis of power grid, in order to meet the requirements of real-time analysis and processing of large-scale real-time data, such as real-time electricity consumption data, and provide fast and accurate data analysis support for power grid operation decision, the system architecture for large-scale data analysis and processing based on stream computing and memory computing was proposed. The Discrete Fourier Transform (DFT) was used to construct abnormal electricity behavior evaluation index based on the real-time electricity consumption data of the users by time window. The K-Means clustering algorithm was used to classify the users' electricity behavior based on the characteristics of user electricity behavior constructed by sampling statistical analysis. The accuracy of the proposed evaluation indicators of abnormal behavior and user electricity behavior was verified by the experimental data extracted from the actual business system. At the same time, compared with the traditional data processing strategy, the system architecture combined with stream computing and memory computing has good performance in large-scale data analysis and processing.

Code-based cryptography has natural advantage to resist the attack from quantum computers. Considering the long ciphertext length and the large key size of the traditional Goppa-codes-based cryptography, Low-Density Generator-Matrix (LDGM) code and hash function were used to construct a provably secure signcryption scheme. The generator matrix of LDGM code is sparse, so it can effectively reduce the amount of data, and the hash function is of high computation efficiency. It satisfies IND-CCA2 (INDistinguishability under Adaptive Chosen Ciphertext Attacks) and EUF-CMA (Existential UnForgeability under Chosen Message Attacks) security under random oracle model. As it guarantees data confidentiality and integrality, the ciphertext is reduced by 25% compared with the traditional case of "sign then encrypt"; compared with the "two birds one stone" and the SCS signcryptions, its computational efficiency gets significant improvement.

Focusing on the problems in bus priority systems, like hysteresis, being not able to fully exploit road capacity, an Adaptive Bus Priority (ABP) model based on the Internet of Vehicles (IOV) was proposed. First, with powerful communication capability of IOV, using time division multiplexing idea, road multiplexed control rules for ordinary bus was designed, and the "space priority" was achieved by setting a virtual bus lane. Secondly, real-time data acquisition of arrival vehicle was used to replace the historical data to solve the problem of hysteresis. Finally, the bus priority signal control model was designed, and bus priority was realized by inserting short phases to make public transit priority. VISSIM software was used to design simulation experiment to compare the ABP model and the traditional model. Simulation results indicate that the ABP model can improve the operation efficiency of bus and intersection pass capacity without causing great impact to social vehicles.

In order to further improve the performance of Infinite Impulse Response (IIR) digital filter, a design method of the IIR digital filter based on structure evolution and parameter evolution was proposed. Firstly, initial filter structure was got by using Genetic Algorithm (GA). Then, Differential Evolution (DE) was used to optimize the parameters of the filter. Finally, an improved optimization strategy was used to further optimize the parameters of the filter by using adjustment search-step and bidirectional heuristic search. Furthermore, the proposed method was applied to the design of low-pass filter and high-pass filter. Compared with the design method based on GA, the pass-band performance of low-pass filter based on the proposed method is not much different from that of the previous algorithm, however, the transition zone width of it is reduced by 65%, the minimum stop-band attenuation of it was reduced by 36.48 dB; the pass-band ripple of high-pass filter based on the proposed method is reduced by 75%, the transition zone width of it is reduced by 44%, and the minimum stop-band attenuation of it is reduced by 12.13 dB. Simulation results show that the proposed method can get effective filters with better performance, therefore it is suitable for the IIR digital filter design.

Focused on the issue that the transfer function of Infinite Impulse Response (IIR) digital filters is not optimal in the entire design process adopting traditional filter design methods, a structure evolution based design method for IIR digital filters using Genetic Algorithm (GA) was proposed. The method evolved the filter structure directly without the preparation of the transfer function. Firstly, the Structure Generation Instruction Sequences (SGIS) were generated randomly. Those SGIS not only controlled the process of structure generation but also represented those structures. Then, the SGIS were coded and seemed as chromosomes. Finally, GA was used to optimize those chromosomes to obtain a best filer. In the comparison experiments with the traditional coefficient evolution based design method for IIR digital filters using GA, the pass-band ripple of the proposed algorithm decreased by 40.58%, the transition zone width of it decreased by 87.62%, and the minimum stop-band attenuation of it declined by 9.22%.

In view of the problem that how to reduce the storage space of the trajectory data and improve the speed of data analysis and transmission in the Global Positioning System (GPS), a hybrid trajectory compression algorithm based on the multiple spatiotemporal characteristics was proposed in this paper. On the one hand, in the algorithm, a new online trajectory compression strategy based on the multiple spatiotemporal characteristics was adopted in order to choose the characteristic points more accurately by using the position, direction and speed information of GPS point. On the other hand, the hybrid trajectory compression strategy which combined online compression with batched compression was used, and the Douglas batched compression algorithm was adopted to do the second compression process of the hybrid trajectory compression. The experimental results show that the compression error of the new online trajectory compression strategy based on multiple spatiotemporal characteristics reduces significantly, although the compression ratio fells slightly compared with the existing spatiotemporal compression algorithm. By choosing appropriate cycle time of batching, the compression ratio and compression error of this algorithm are improved compared with the existing spatiotemporal compression algorithm.

Concerning the problems and challenges in Cyber Physical System (CPS), a new modeling and verification method of CPS was proposed based on service composition ideas. Firstly, a composition structure of CPS was proposed, including the physical world, sensor systems, information processing systems, control systems and time constraints. Based on this proposed structure, the service classification and composition framework of CPS resources were proposed. The physical environment modeling, atomic service modeling and service composition of CPS were also given based on the timed automata theory. Finally, through case design and model checking tool Uppaal, the experimental results were given to illustrate the correctness of the CPS service-oriented modeling approach, including system security, accessibility, liveness and time constraints. The results verify the above properties and the correctness of the proposed method.

Inspired by the cryptography method of confidential communication and the idea of secret shcaring, the authors proposed a secure communication scheme suitable to the wiretap network that employed network coding for data transmission. The proposed scheme used the original random bits to generate a new random bit string for enrypting the source messages, and then mixed the original random bit string with a secret string which was generated by the obtianed ciphertexts. Lastly, efficient use of network capacity with randong linear network coding was achieved. What's more, our scheme neither resorted to a secret channel which transmited the secret key to sinks, nor constucted linear network coding to meet special conditions which depended on network topology.

As an improved algorithm of Fuzzy C-Means (FCM), generalized fuzzy c-means algorithm with improved fuzzy partitions (GIFP-FCM) can reduce the influence of image noises on image segmentation to some extent. However, since the neighbor information is not taken into consideration, GIFP-FCM cannot work well on image with much noises. In order to solve this problem, a new objective function was established with neighbor information and local membership. Every pixel with local membership and neighbor information was recomputed to overcome the influences of noises. The experimental results on synthesized images and brain images show that the proposed algorithm can get the maximum partition coefficient and the minimum partition entropy, which shows the effectiveness of the proposed algorithm.

In order to solve the shortage of the method based on fragile watermarking, a new chaotic system and fragile watermarking based image tamper detection algorithm was proposed. Firstly, the method used Arnold cat map k times to scramble the original image, and then chose the Least Significant Bit (LSB) plane of the scrambled image as the embedding position. The actual watermark information was formed by using exclusive-or operation between a random binary sequence produced by Logistic map and the original watermark, and the LSB replacement method was utilized to embed watermark. Finally, the watermarked image was obtained by utilizing Arnold cat map T-k times. The experimental results show that, the introduction of chaotic system to a great extend improves the security of the proposed scheme. In addition, the proposed method achieves superior tamper detection and localization accuracy under different common attacks.

The deformation of 3D models is one of the hot issues of computer graphics and other cross-discipline research. In view of the artificially using the linear strain on the model deformation, the authors proposed an effective model deformation approach. First, the original model was simplified through geometric delete method and the simplified models stayed as the form of the original model. Then, using the tetrahedron subdivision, the tetrahedral model was deformed by adopting the Green strain. Finally, the experimental results between linear strain and non-linear strain were presented in detail. The result also demonstrates that first simplifying the models then generating the tetrahedron, the simulation rate could be increased.

Energy efficiency is an important metric in wireless Ad Hoc networks. Until now, there is no universally accepted definition of energy efficiency, and the related results are asymptotic or qualitative, which has limited its applicability. By regarding a bit as a physical particle with one unit of mass, the authors assumed that a bit in transmission possessed a certain amount of kinetic information energy. As a result, the energy efficiency of wireless transmission was defined as the ratio of information energy to physical energy. A quantitative analysis on energy efficiency in wireless transmission was carried out and meaningful results were obtained. It is concluded that the energy efficiency changes non-monotonously with the transmission power, and there is an optimal transmission power, with which the maximum energy efficiency will be acquired. The optimal transmission power was given to help the protocol design. Based on the theoretical results, a practical solution for transmission power control was proposed, and an extensive experimental study of it was given on the CC2420 radio. The results show the effectiveness of the proposed transmission power control scheme.

In order to solve the energy limitation problem on wireless sensor network nodes, this paper proposed a new adaptive update asynchronous MAC protocol — AU-MAC protocol. This protocol combined the sleep-work state switching mode, asynchronous mode with adaptive update to effectively extend the network life. AU-MAC protocol improved channel usage efficiency by making use of sender monitoring and receiver activating data transfer. And, it established a neighbor node information table and introduced adaptive updating mechanism, to reduce the free monitor. The functions of AU-MAC protocol had been estimated on NS2 network simulation platform. It shows that, AU-MAC protocol improves the energy efficiency at the basis of maintaining the same throughput and end-end transit delay.

The application of data classification is usually confronted with a problem named imbalanced dataset in the machine learning. To improve the performance of imbalanced dataset classification, the over-sampling classification algorithm based on quotient space theory (QMSVM) was proposed. The algorithm partitioned majority data on clustering structure, and combined the results and minority data for linear Support Vector Machine (SVM) learning. Support vectors and sample of fault of majority data were obtained from those granules. On the other hand, support vectors and sample of fault of minority data were obtained and the Synthetic Minority Over-sampling Technique (SMOTE) was adopted. Thus, two new kinds of samples were merged for SVM learning, so as to rebalance the training set and get a more reasonable classification of hyperplanes. The experimental results show that, in comparison with several other algorithms, the accuracy of the proposed algorithm decreases, but it significantly improves the g_means value and classification accuracy of positives and the effect is better on the imbalance rate of larger datasets.

Since the real node number in cash transport network changes dynamically, a route planning strategy for dynamic cash transport routing was proposed. The strategy did partitioning and optimizing in sequence. Firstly, Dijkstra algorithm was adopted to compute the shortest route between two nodes, and then vehicle number and node on each route were gotten by nearest neighbor algorithm and workload balancing factor. Secondly, the pre-cross genetic algorithm was adopted to optimize each route and get node sequence on the route, which could get the route with shortest distance and minimum time consumption. The experimental results show that the proposed strategy can meet the requirements of dynamic vehicle number and route, and achieve the purpose of saving resources.

A simple and fast 2-D DCT algorithm was presented.Firstly the transform coefficient of 2-D DCT was multiplied by the value of pixels,which formed a lookup table.When realizing the direct 2-D DCT,it put the table into memory,and got the product value by addressing without multipliers.This algorithm can work with high speed, so it is specially suitable for the design of embedding system.The structure was optimized and the memory space was reduced.Experiment results show that the algorithm is effective.