Due to the popularity of smart mobile terminals, sensitive information such as personal location privacy, check-in data privacy and trajectory privacy in the collected spatio-temporal data are easy to be leaked. In the current researches, protection technologies are proposed for the above privacy leakages respectively, and there is not a personalized spatio-temporal data privacy protection method to prevent the above privacy leakages for users. Therefore, a personalized privacy protection model for spatio-temporal data named ( p, q, ε)-anonymity and a Personalized Privacy Protection for Spatio-Temporal Data (PPP _ST) algorithm based on this model were proposed to protect the users' privacy data with personalized settings (location privacy, check-in data privacy and trajectory privacy). The heuristic rules were designed to generalize the spatio-temporal data to ensure the availability of the published data and realize the high availability of spatio-temporal data. In the comparison experiments, the data availability rate of PPP _ST algorithm is about 4.66% and 15.45% higher than those of Information Data Used through K-anonymity (IDU-K) and Personalized Clique Cloak (PCC) algorithms on average respectively. At the same time, the generalized location search technology was designed to improve the execution efficiency of the algorithm. Experiments and analysis were conducted based on real spatio-temporal data. Experimental results show that PPP _ST algorithm can effectively protect the privacy of personalized spatio-temporal data.

Due to the large number, wide distribution and complex environments of Internet of Things (IoT) devices, IoT is more vulnerable to DDoS (Distributed Denial of Service) attacks than traditional networks. Concerning this problem, a Distributed Denial of Service (DDoS) attack detection method based on Equal Length of Value Range K-means (ELVR- Kmeans) algorithm in Software Defined IoT (SD-IoT) architecture was proposed. Firstly, the centralized control characteristic of the SD-IoT controller was used to extract the flow tables of the OpenFlow switch to analyze the DDoS attack traffic characteristics in SD-IoT environment and extract the seven-tuple features related to the DDoS attack traffic. Secondly, the obtained flow tables were classified by the ELVR- Kmeans algorithm to detect whether a DDoS attack had occurred. Finally, the simulation experiment environment was built to test the detection rate, accuracy and error rate of the method. The simulation results show that the proposed method can effectively detect DDoS attacks in SD-IoT environment with detection rate and accuracy of 96.43% and 98.71% respectively, and error rate of 1.29%.

Simultaneous Localization and Mapping (SLAM) is a key technology for robots to realize autonomous navigation in unknown environments. Aiming at the poor real-time performance and low accuracy of the commonly used RGB-Depth (RGB-D) SLAM system, a new RGB-D SLAM system was proposed to further improve the real-time performance and accuracy. Firstly, the Oriented FAST and Rotated BRIEF (ORB) algorithm was used to detect the image feature points, and the extracted feature points were processed by using the quadtree-based homogenization strategy, and the Bag of Words (BoW) was used to perform feature matching. Then, in the stage of system camera pose initial value estimation, an initial value which was closer to the optimal value was provided for back-end optimization by combining the Perspective n Point (P nP) and nonlinear optimization methods. In the back-end optimization, the Bundle Adjustment (BA) was used to optimize the initial value of the camera pose iteratively for obtaining the optimal value of the camera pose. Finally, according to the correspondence between the camera pose and the point cloud map of each frame, all the point cloud data were registered in a coordinate system to obtain the dense point cloud map of the scene, and the octree was used to compress the point cloud map recursively, so as to obtain a 3D map for robot navigation. On the TUM RGB-D dataset, the proposed RGB-D SLAM system, RGB-D SLAMv2 system and ORB-SLAM2 system were compared. Experimental results show that the proposed RGB-D SLAM system has better comprehensive performance on real-time and accuracy.

To solve problems such as easily getting trapped in local optimum and slow convergence rate in BareBones Particle Swarm Optimization (BBPSO) algorithm, a dynamic Multi-Subgroup collaboration Barebones Particle Swarm Optimization based on Kernel Fuzzy Clustering (KFC-MSBPSO) was proposed. Based on the standard BBPSO algorithm, firstly, kernel fuzzy clustering method was used to divide the main group into several subgroups, and the subgroups optimized collaboratively to improve the searching efficiency. Then, nonlinear dynamic mutation factor was introduced to control subgroup mutation probabilities according to the number of particles and convergence conditions, the main group was reconstructed by means of particle mutation and the exploration ability was improved. The main group particle absorption strategy and subgroup merge strategy were proposed to strengthen the information exchange between main group and subgroups and enhanced the stability of the algorithm. Finally, the subgroup reconstruction strategy was used to adjust the iterations of subgroup reconstruction by combining the optimal solutions. The results of experiments on six benchmark functions, such as Sphere, show that the accuracy of KFC-MSBPSO algorithm has improved by at least 11.1% compared with classical BBPSO algorithm, Opposition-Based Barebones Particle Swarm Optimization (OBBPSO) algorithm and other improved algorithms. The best mean value in high dimensional space accounts for 83.33% and has a faster convergence rate. This indicates that KFC-MSBPSO algorithm has good search performance and robustness, which can be applied to the optimization of high dimensional complex functions.

Aiming at the current status of Niederreiter public key cryptosystem which is vulnerable to distinguishing attack and ISD (Information Set Decoding), an improved Niederreiter public key cryptosystem was proposed. Firstly, the permutation matrix in the Niederreiter cryptography scheme was improved, and the original permutation matrix was replaced by a random matrix. Secondly, the error vector in the Niederreiter cryptography scheme was randomly divided to conceal the Hamming weight. Finally, the encryption and decryption processes of the Niederreiter cryptography scheme were improved to improve the security. The analysis shows that the improved scheme can resist the distinguishing attack and ISD. The public key size of the improved scheme is smaller than that of the scheme proposed by Baldi, et al. (BALDI M, BIANCHI M, CHIARALUCE F, et al. Enhanced public key security for the McEliece cryptosystem. Journal of Cryptology, 2016, 29(1):1-27). At the 80-bit security level, the public key of the improved scheme is reduced from 28408 bits to 4800 bits. At the 128-bit security level, the public key size of the improved scheme is reduced from 57368 bits to 12240 bits. As one of the anti-quantum cryptography schemes, the viability and competitiveness of the improved scheme are enhanced.

Coding-based encryption scheme, with the advantages of anti-quantum feature and fast encryption and decryption speed, is one of the candidate schemes for anti-quantum cryptography. The existing coding-based hybrid encryption schemes have the INDistinguishability under Chosen Ciphertext Attack (IND-CCA) security, which have the disadvantage that the public key size used to encrypt the shared secret key of the sender and receiver is large. The problem of large size of public key in hybrid encryption scheme based on Niederreiter coding was solved by the following three steps. Firstly, the private key of Niederreiter coding scheme was randomly split. Then, the plaintext of Niederreiter coding scheme was split randomly. Finally, the encryption and decryption processes of Niederreiter coding scheme were improved. It is concluded through analysis that, the public key size of the improved scheme is less than that of Maurich scheme. Compared with Maurich scheme, the public key of the improved scheme is reduced from 4801 bits of the original scheme to 240 bits under the security level of 80 bits, and the public key of the improved scheme is reduced from 9857 bits to 384 bits under the security level of 128 bits. Although the improved scheme is more complicated than the original scheme, its storage cost and calculation cost are smaller, and the practicability of the improved scheme is enhanced.

Due to a large amount of data and high dimension in currently network intrusion detection, a Moth-Flame Optimization (MFO) algorithm was applied to the feature selection of network intrusion detection. Since MFO algorithm converges fast and easy falls into local optimum, a Binary Moth-Flame Optimization integrated with Particle Swarm Optimization (BPMFO) algorithm was proposed. On one side, the spiral flight formula of the MFO algorithm was introduced to obtain strong local search ability. On the other side, the speed updating formula of the Particle Swarm Optimization (PSO) algorithm was combined to make the individual to move in the direction of global optimal solution and historical optimal solution, in order to increase the global convergence and avoid to fall into local optimum. By adopting KDD CUP 99 data set as the experimental basis, using three classifiers of Support Vector Machine (SVM), K-Nearest Neighbor ( KNN) and Naive Bayesian Classifier (NBC), Binary Moth-Flame Optimization (BMFO), Binary Particle Swarm Optimization (BPSO), Binary Genetic Algorithm (BGA), Binary Grey Wolf Optimization (BGWO) and Binary Cuckoo Search (BCS) were compared in the experiment. The experimental results show that, BPMFO algorithm has obvious advantages in the comprehensive performance including algorithm accuracy, operation efficiency, stability, convergence speed and jumping out of local optima when it is applied to the feature selection of network intrusion detection.

The existed TD-FTT (Time Dependent Fast Travel Time) algorithm, for answering K Nearest Neighbors ( KNN) query in time dependent road network, requires that the issued time and the arrival time of a query must be in the same time interval, which costs a long time in the preprocessing phase. To solve these problems, an Improved TD-FTT (ITD-FTT) algorithm based on dynamically selecting heuristic values was proposed. Firstly, in the preprocessing phase, the road network G _min with the minimum cost for each time interval was created by using time functions of edges. Secondly, a parallel method of utilizing Network Voronoi Diagram (NVD) in road network G _min was used to compute the nearest neighbors of nodes to reduce the time cost. Finally, in the query phase, the heuristic value was dynamically selected to get rid of the time interval limitation by calculating the time interval of the current arrival time of nodes. The experimental results show that in the preprocessing phase, the time cost of ITD-FTT is reduced by 70.12% compared with TD-FTT. In the query phase, the number of traversal nodes of ITD-FTT is 46.52% and 16.63% lower than TD-INE (Time Dependent Incremental Network Expansion) and TD-A (Time Dependent A star) algorithm respectively, and the response time of ITD-FTT is 47.76% and 18.24% lower than TD-INE and TD-A. The experimental results indicate that the ITD-FTT algorithm reduces the number of nodes by query expansion, decreases the time of searching the KNN results and improves the query efficiency.

To improve the workshop environment of textile mill and enhance the automatic control level on the environment, an intelligent environment measuring and controlling system of textile workshop based on Internet of Things (IoT) was proposed. The overall design scheme of the system was given. In order to reduce traffic loads of sink nodes and improve the data transmission rate of network, the wireless network topology structure of single-hop multi-sink nodes was designed. The concrete implementation scheme of hardware design and software work process of sensing nodes, controlling nodes and other nodes were represented detailedly. The improved Newton interpolation algorithm was used as the fitting function to process the detection data, which improved the precision of detection and control of system. The application results show that the system is simple, stable and reliable, low in cost, easy to maintain and upgrade, and obtains good application effect.

Concerning the problem that the weak target might be covered by the range side-lobes of the strong one and the range side-lobes could only be suppressed to a certain value, an improved Kalman-Minimum Mean-Square Error (K-MMSE) algorithm was proposed in this paper. This algorithm combined the Kalman filter with the Minimum Mean-Square Error (MMSE), and it was an effective method for suppressing range side-lobes of adaptive pulse compression. In the simulation, the proposed algorithm was compared with the traditional matched filter and other improved matched filters such as MMSE in a single target or multiple targets environments, and then found that the side-lobe levels, the Peak-SideLobe Ratio (PSLR) and Integrated SideLobe Ratio (ISLR) of the Point Spread Function (PSF) were all decreased obviously in comparison with the previous two methods. The simulation results show that the method can suppress range side-lobes well and detect the weak targets well either under both the condition of a single target and the condition of multiple targets.

High energy efficiency is one of the most important targets for Ad hoc network design,and topology control is an effective measure at network level to achieve this goal.Two basic topology control mechanisms for energy efficiency were introduced firstly.And then,more attention is paid on the analyzing and summarizing of present research on energy efficient topology control that are based on transmission power adjustment.At last,some promising directions are suggested.Then present research on energy efficient topology control based on transmission power adjustment was analyzed,and some promising were suggested.

WRR and DWRR in the architecture of DiffSever was discussed. And based on WRR, an adaptive weighted round-robin (AWRR) and how to carry out this algorithm was presented. By this algorithm, different packets have different weight, and schedule packets according to this. So that AWRR not only can obtain the requirement of QoS, but also can according to the node’s condition dynamically assigning the bandwidth and the bandwidth resource sharing.

Vulnerability in IEEE 802.11 authentication protocol and Denial of Service (DoS) attacks against wireless network were anatomized. IEEE 802.11 MAC frames were captured and analysed. DoS attacks against authorized legitimate clients were detected by sequence number analysis method. DoS attacks against access points were detected by statistic analysis method.

The design and simulation of resource mapping algorithms in grid environment is a difficult undertaking, mainly due to resource heterogeneity, geographic distribution, autonomy and the requirements of the QoS of tasks. This paper introduced auction into resource mapping and presented a resource mapping algorithm based on multi-item auction in grid. The algorithm could satisfy the requirements of the grid environment and solve the problem of the resource mapping for a set of independent tasks. And some simulation toolkits of resource mapping were analyzed. A simulation environment is established based on the Gridsim toolkit and the simulation experiments indicate the good performance of the algorithm.