Yingchi Mao

An energy-aware coverage control protocol for wireless sensor networks

Sensing coverage and energy consumption are two primary issues in wireless sensor networks. Sensing coverage is closely related to network energy consumption. The performance of a sensor network depends to a large extent on the sensing coverage, and its lifetime is determined by its energy consumption. In this paper, an energy-aware location-independent coverage control protocol for wireless sensor networks (EACCP) is proposed. EACCP can achieve a good performance in terms of sensing coverage, lifetime by minimizing energy consumption for control overhead, and balancing the energy load among all nodes. Adopting the hierarchical clustering idea, EACCP elects the working nodes based on the average residual energy of neighbouring nodes and its own residual energy parameters. Our simulation study and analysis demonstrate that EACCP not only provide the high quality of sensing coverage, but also has the good performance in the energy efficiency. In addition, EACCP can better adapt the applications with the great heterogeneous energy capacities in the sensor networks, as well as effectively reduce the control overhead.

A Segment-Based Trajectory Similarity Measure in the Urban Transportation Systems

With the rapid spread of built-in GPS handheld smart devices, the trajectory data from GPS sensors has grown explosively. Trajectory data has spatio-temporal characteristics and rich information. Using trajectory data processing techniques can mine the patterns of human activities and the moving patterns of vehicles in the intelligent transportation systems. A trajectory similarity measure is one of the most important issues in trajectory data mining (clustering, classification, frequent pattern mining, etc.). Unfortunately, the main similarity measure algorithms with the trajectory data have been found to be inaccurate, highly sensitive of sampling methods, and have low robustness for the noise data. To solve the above problems, three distances and their corresponding computation methods are proposed in this paper. The point-segment distance can decrease the sensitivity of the point sampling methods. The prediction distance optimizes the temporal distance with the features of trajectory data. The segment-segment distance introduces the trajectory shape factor into the similarity measurement to improve the accuracy. The three kinds of distance are integrated with the traditional dynamic time warping algorithm (DTW) algorithm to propose a new segment–based dynamic time warping algorithm (SDTW). The experimental results show that the SDTW algorithm can exhibit about 57%, 86%, and 31% better accuracy than the longest common subsequence algorithm (LCSS), and edit distance on real sequence algorithm (EDR) , and DTW, respectively, and that the sensitivity to the noise data is lower than that those algorithms.

An Adaptive Trajectory Clustering Method Based on Grid and Density in Mobile Pattern Analysis

Clustering analysis is one of the most important issues in trajectory data mining. Trajectory clustering can be widely applied in the detection of hotspots, mobile pattern analysis, urban transportation control, and hurricane prediction, etc. To obtain good clustering performance, the existing trajectory clustering approaches need to input one or more parameters to calibrate the optimal values, which results in a heavy workload and computational complexity. To realize adaptive parameter calibration and reduce the workload of trajectory clustering, an adaptive trajectory clustering approach based on the grid and density (ATCGD) is proposed in this paper. The proposed ATCGD approach includes three parts: partition, mapping, and clustering. In the partition phase, ATCGD applies the average angular difference-based MDL (AD-MDL) partition method to ensure the partition accuracy on the premise that it decreases the number of the segments after the partition. During the mapping procedure, the partitioned segments are mapped into the corresponding cells, and the mapping relationship between the segments and the cells are stored. In the clustering phase, adopting the DBSCAN-based method, the segments in the cells are clustered on the basis of the calibrated values of parameters from the mapping procedure. The extensive experiments indicate that although the results of the adaptive parameter calibration are not optimal, in most cases, the difference between the adaptive calibration and the optimal is less than 5%, while the run time of clustering can reduce about 95%, compared with the TRACLUS algorithm.

Designing permutation–substitution image encryption networks with Henon map

Abstract In traditional permutation–substitution architecture type image cipher, the permutation and substitution generally are two independent parts, and the diffusion performed by substitution is more like the cipher block chaining mode of operation. However, such operation approaches clearly downgrade the encryption efficiency because the pixel values need to be modified one by one for 2–4 overall rounds and images are scanned twice for permutation and substitution in each round. To improve the encryption efficiency, a new two-point diffusion strategy realized by discrete Henon map is proposed in this paper, which can significantly accelerate the diffusion process if there is more than one processing unit. Besides, the permutation and substitution are no longer two independent parts and they intermingle with each other so that the image required to be scanned just one time. To achieve the better ability of resisting chosen-plaintext or known-plaintext attack, the substitution keystream generated in our method is dependent on the plain image. Consequently, different plain images produce the distinct keystream for substitution. The results of various security analyses prove that our proposed image cryptosystem owns superior security, meanwhile, time complexity analysis shows that it can achieve faster encryption speed than most typical image encryption schemes.

A Fine-Grained and Dynamic MapReduce Task Scheduling Scheme for the Heterogeneous Cloud Environment

MapReduce framework is becoming more and more popular in various applications. However, Hadoop is a seriously limited by its MapReduce scheduler which does not work well in the heterogeneous environment. LATE MapReduce scheduling algorithm takes heterogeneous environment into consideration. However, it falls short of solving the poor performance due to the static manner during computing the tasks progress. In order to improve the cluster performance in a heterogeneous cloud environment, FiGMR -- a Fine-Grained and dynamic MapReeduce scheduling algorithm, is proposed. FiGMR can significantly reduce the tasks execution time and improve the resources utilization. FiGMR includes historical and real-time online information obtained from each node to select the appropriate parameters to find the real slow task dynamically. Meanwhile, in order to further improve the cluster performance, FiGMR classifies map nodes into high-performance map node and low-performance map node. FiGMR classifies slow tasks into slow map tasks and slow reduce tasks. Map/Reduce slow nodes means nodes which execute map/reduce tasks using a longer time than most other nodes. In this way, FiGMR launches backup map tasks on nodes which are high-performance map nodes.

Image scrambling based on life-like cellular automata

This paper presents an image scrambling method based on the life-like cellular automata (CA). In the scrambling process, a life-like CA with an initial random configuration is set to run for several generations to obtain scrambling matrices. The restoration process is the reverse process of scrambling. In order to achieve a good diffusion property, we analyze how the scrambling effect is influenced by different CA initial configurations, and give the results of which life-like rules generate the best scrambling effect.

Design of image cipher using Life-like cellular automata and chaotic map

Abstract Recently, a number of image ciphers using cellular automata (CA) or combined with chaotic maps have been proposed. However, most of them suffer from some intrinsic drawbacks such as small rule space, low diffusion and no explicit classification of CA rules. To overcome these drawbacks, this paper presents a novel image cipher based on Life-like cellular automata and chaos. The proposed image cipher consists of two sub-processes: permutation and substitution. In the permutation, a two-dimensional Logistic-adjusted-Sine map (2D-LASM) with excellent properties is adopted to shuffle the pixel positions. In the substitution, a second-order Life-like CA with a balanced rule is employed. The balanced rules make the distribution of 0 and 1 in Life-like CA gradually be in equilibrium during the process of iteration. Second-order CA can preserve the result of CA after each iteration to obtain the reversibility. Furthermore, to resist chosen-plaintext and known-plaintext attacks, the algorithm controls the initial conditions of 2D-LASM by the key and the weighted histogram of the plain-image. Theoretical analysis and experimental results both show that the proposed scheme has prominent cryptographic performances and can resist the common attacks effectively, which is very suitable for image encryption.

Event Detection with Multivariate Water Parameters in the Water Monitoring Applications

The real-time time series data of multiple water quality parameters are obtained from the water sensor networks deployed in the water supply network. The accurate and efficient detection and warning of contamination events to prevent pollution from spreading is one of the most important issues when the pollution occurs. In order to comprehensively reduce the event detection deviation, a Temporal Abnormal Event Detection Algorithm for Multivariate time series data (M-TAEDA) was proposed. In M-TAEDA, first, Back Propagation neural network models are adopted to analyze the time series data of multiple water quality parameters and calculate the possible outliers. Then, M-TAEDA algorithm determines the potential contamination events through Bayesian sequential analysis to estimate the probability of a contamination event. Finally, it can make decision based on the multiple event probabilities fusion in the water supply system. The experimental results indicate that the proposed M-TAEDA algorithm can obtain the 90% accuracy with BP neural network model and improve the rate of detection about 40% and reduce the false alarm rate about 45%, compared with the temporal event detection of Single Variate Temporal Abnormal Event Detection Algorithm (S-TAEDA).

Contamination Event Detection with Multivariate Time-Series Data in Agricultural Water Monitoring

Time series data of multiple water quality parameters are obtained from the water sensor networks deployed in the agricultural water supply network. The accurate and efficient detection and warning of contamination events to prevent pollution from spreading is one of the most important issues when pollution occurs. In order to comprehensively reduce the event detection deviation, a spatial–temporal-based event detection approach with multivariate time-series data for water quality monitoring (M-STED) was proposed. The M-STED approach includes three parts. The first part is that M-STED adopts a Rule K algorithm to select backbone nodes as the nodes in the CDS, and forward the sensed data of multiple water parameters. The second part is to determine the state of each backbone node with back propagation neural network models and the sequential Bayesian analysis in the current timestamp. The third part is to establish a spatial model with Bayesian networks to estimate the state of the backbones in the next timestamp and trace the “outlier” node to its neighborhoods to detect a contamination event. The experimental results indicate that the average detection rate is more than 80% with M-STED and the false detection rate is lower than 9%, respectively. The M-STED approach can improve the rate of detection by about 40% and reduce the false alarm rate by about 45%, compared with the event detection with a single water parameter algorithm, S-STED. Moreover, the proposed M-STED can exhibit better performance in terms of detection delay and scalability.

Two-phase PT-Topk Query Processing Algorithm for Uncertain IOT Data in Dam Safety Monitoring

AbstractUncertain data has become ubiquitous due to the development of Internet of Things (IOT) for collecting data in an imprecise way, such as in the dam safety monitoring applications. Efficient Top-k processing of uncertain data is an important requirement in the field of dam safety monitoring. In order to reduce energy consumption and query response time in the applications of IOTs, an uncertain data PT-Topk query processing scheme was studied in a hierarchical structural sensor network. Based on the x-tuple Rule of uncertain data, adopting intra-cluster and inter-cluster two phases query processing, a distributed Two-Phase PT-Topk Query Processing approximation algorithm (TPQP) was proposed. In the intra-cluster phase and inter-cluster phase, the local and global pruning upper bounds can be computed respectively. The data ranked lower than the two bounds cannot be forwarded to the sink node. Therefore, the proposed TPQP algorithm can reduce the transmission cost and shorten the query response time. ...