In order to further improve the security and efficiency of Number Theory Research Unit (NTRU)-type Multi-Key Fully Homomorphic Encryption (MKFHE) schemes, based on the prime power cyclotomic rings, the properties of the original decryption structure of NTRU-type multi-key fully homomorphic encryption were studied, and two optimization methods of multi-key homomorphic decryption structures were proposed. Firstly, by reducing the polynomial's coefficients, the "Regev-Style" multi-key decryption structure was designed. Secondly, the "Ciphertext-Expansion" multi-key decryption structure was designed by expanding the dimension of ciphertexts. Compared with the original decryption structure of NTRU-type multi-key homomorphic encryption scheme, the "Regev-Style" multi-key decryption structure reduced the magnitude of error, which was able to reduce the number of key-switching and modulo-switching when it was used in the design of NTRU-type multi-key homomorphic encryption scheme; the "Ciphertext-Expansion" multi-key decryption structure eliminated the key-switching operation, reduced the magnitude of error, and was able to process the ciphertext product of repeated users more effectively. The security of the optimized multi-key decryption structures was based on the Learning With Errors (LWE) problem and Decisional Small Polynomial Ratio (DSPR) assumption on the prime power cyclotomic rings, so these structures were able to resist subfield attacks well. Therefore, they can be used to design a more secure and efficient NTRU-type multi-key fully homomorphic encryption scheme by selecting appropriate parameters.

The single-policy attribute-based fully homomorphic encryption scheme cannot perform homomorphic operation and access control of ciphertexts under different attribute vectors corresponding to different policy functions, and new participant ciphertexts cannot dynamically join into the homomorphic operation. In order to solve the above problems, an efficient multi-hop multi-policy attribute-based fully homomorphic encryption scheme based on Learning with Error (LWE) problem was proposed. Firstly, the single-policy attribute-based fully homomorphic encryption scheme was appropriately modified. Secondly, the scheme was mapped to multi-user scenarios. Finally, a multi-hop multi-policy fully homomorphic transformation mechanism was used to realize the homomorphic operation after adding new participant ciphertexts. The proposed scheme is proved to be INDistinguishability under Chosen Plaintext Attack (IND-CPA) secure under the chosen attribute, and has advantages of attribute-based encryption and multi-hop multi-key fully homomorphic encryption. Compared with multi-policy attribute-based fully homomorphic encryption scheme constructed by using target policy function set, the ciphertext/plaintext ratio of the proposed scheme is significantly reduced without changing the size of the individual participant's secret key.

To improve the recognition rate and reduce the false-recognition rate in real-time detection of flame in video surveillance, a fast flame recognition algorithm based on multi-feature logarithm regression model was proposed. Firstly, the image was segmented according to the chromaticity of the flame, and the Candidate Fire Region (CFR) was obtained by subtracting the moving target image with reference image. Secondly the features of the CRF such as area change rate, circularity, number of sharp corners and centroid displacement were extracted to establish the logarithmic regression model. Then, a total of 300 images including flame and non-flame images, which were got from National Institute of Standards and Technology (NIST), Computer Vision laboratory of Inha University (ICV), Fire detection based on computer Vision (VisiFire) and the experimental library consisting of the candle and paper combustion were used to parametric learning. Finally, 8 video clips including 11071 images were used to validate the proposed algorithm. The experimental results show that the True Positive Rate (TPR) and True Negative Rate (TNR) of the proposed algorithm are 93% and 98% respectively. The average time of identification is 0.058 s/frame. Because of its fast identification and high recognition rate, the proposed algorithm can be applied in embedded real-time flame image recognition.

Focusing on the issues of high cost, high susceptibility to damage and low prediction accuracy of soil moisture monitoring and forecasting system, the soil moisture monitoring based on non-fixed wireless sensor network and improved grey wolf algorithm optimization neural network was designed and implemented. In the proposed soil moisture monitoring system, non-fixed and plug-in sensor bluetooth network was used to collect moisture data, and high-precision multi-source location access fusion method was used for wide-area outdoor high-precision positioning. In terms of algorithms, focusing on the issue that Grey Wolf Optimizer (GWO) algorithm easily falls into local optima in its later iterations, an improved GWO algorithm based on rearward explorer mechanism was proposed. Firstly, according to the fitness value of the population, the explorer type was added to the original individual types of the algorithm. Secondly, the search period of population was divided into three parts: active exploration period, cycle exploration period and population regression period. Finally, the unique location updating strategy was used for the explorer during the different period, which made the algorithm more random in the early stage and keep updating in the middle and late stages, thus strengthening the local optimal avoidance ability of the algorithm. The algorithm was tested on the standard functions and applied to optimize the neural network prediction model of soil moisture system. Based on the datasets obtained from the experimental plot No. 2 in a city, the experimental results show that the relative error decreases by about 4 percentage points compared with the direct neural network prediction model, and decreases by about 1 to 2 percentage points compared with the traditional GWO algorithm and Particle Swarm Optimization (PSO). The proposed algorithm has smaller error, better local optimal avoidance ability, and improves the prediction quality of soil moisture.

To solve the problem of receiver buffer blocking and load unbalance of Concurrent Multipath data Transfer using Stream Control Transmission Protocol (CMT-SCTP) in heterogeneous network environments, an improved round-robin data scheduling algorithm was proposed. The network condition of each path was estimated by the proposed algorithm according to the sender queue information and the congestion status of links. Then the proposed data scheduling algorithm distributed the transmission task to each path based on its network condition, curtailed the queuing time of data chunks in sender buffer and reduced the number of out-of-order data chunks in receiver buffer. Simulation results show that the improved round-robin data scheduling algorithm can effectively enhance the transmission efficiency of CMT-SCTP in a heterogeneous wireless network environment and mitigate the receiver buffer blocking problem. It can also adapt to different network conditions.

To overcome the weakness of over-concentration when the population of Particle Swarm Optimization (PSO) is initialized and the search precision of basic PSO is not high, an Improved PSO (IPSO) for constrained optimization problems was proposed. A technique of Good Point Set (GPS) was introduced to distribute the initialized particles evenly and the population with diversity would not fall into the local extremum. Co-evolutionary method was utilized to maintain communication between the two populations; thereby the search accuracy of PSO was increased. The simulation results indicate that, the proposed algorithm obtains the theoretical optimal solutions on the test of five benchmark functions used in the paper and the statistical variances of four of them are 0. The proposed algorithm improves the calculation accuracy and robustness and it can be widely used in the constrained optimization problems.

Concerning the lack of research on User Datagram Protocol (UDP) flow detection, and its low accuracy and efficiency, an adaptive UDP flow detection based on multi-granularity was proposed. Two different dynamic timeout strategies based on different granularity were designed by analyzing the characteristics of UDP flow. Compared with other timeout strategies, the accuracy of the proposed method was similar to the fixed timeout strategy, and the memory usage was only about 75% of the others. The experimental results show that the proposed method is suitable to the UDP flow detection.

For solving combinational optimization problems, a Binary Harmony Search Algorithm (BHSA) based on three discrete operators of Harmony Search Algorithm （HSA）was proposed. Then, BHSA was used to solve the famous k-SAT problem and 0-1 knapsack problem. The numeral results of BHSA, Binary Particle Swarm Optimization （BPSO） and Genetic Algorithm (GA) show that the BHSA is feasible and highly efficient.

Firstly,the background of using energy-efficient MAC protocols for wireless Ad hoc networks was introduced.Some related issues were discussed,including power-consumption features of wireless network interface devices,several factors that would influence the energy-efficiency,critical principles that must be kept when designing energy-efficient MAC protocols and usual approaches that can be resort to.Then,several typical energy-efficient MAC protocols and techniques were reviewed.Finally,some future research directions and challenges that must be overcome on this topic were investigated.

The key functionality of backoff mechanism in wireless Ad Hoc networks was introduced and the flaws of the binary exponential backoff algorithm used in IEEE 802.11 standard were discussed. Then, various typical backoff algorithms currently proposed were reviewed in categories, their features as well as some related issues were discussed. Finally, the possible research trends on backoff algorithms for Ad Hoc networks were investigated.