Qingzhang Chen

3D Self-Deployment Algorithm in Mobile Wireless Sensor Networks

The sensor deployment problem of wireless sensor networks (WSNs) is a key issue in the researches and the applications of WSNs. Fewer works focus on the 3D autonomous deployment. Aimed at the problem of sensor deployment in three dimensional spaces, the 3D Self-Deployment Algorithm (3DSD) in mobile sensor networks is proposed. A 3D virtual force model is utilized in the 3DSD method. A negotiation tactic is introduced to ensure network connectivity, and a density control strategy is used to balance the node distribution. The proposed algorithm can fulfill the nodes autonomous deployment in 3D space with obstacles. Simulation results indicate that the deployment process of 3DSD is relatively rapid, and the nodes are well distributed. Furthermore, the coverage ratio of 3DSD approximates the theoretical maximum value.

3D self-deployment algorithm in mobile wireless sensor networks

The sensor deployment problem of wireless sensor networks (WSNs) is a key issue in the researches and the applications of WSNs. Fewer works focus on the 3D autonomous deployment. Aimed at the problem of sensor deployment in three-dimensional spaces, the 3D self-deployment (3DSD) algorithm in mobile sensor networks is proposed. A 3D virtual force model is utilized in the 3DSD method. A negotiation tactic is introduced to ensure network connectivity, and a density control strategy is used to balance the node distribution. The proposed algorithm can fulfill the nodes autonomous deployment in 3D space with obstacles. Simulation results indicate that the deployment process of 3DSD is relatively rapid and the nodes are well distributed. Furthermore, the coverage ratio of 3DSD approximates the theoretical maximum value.

Study on Tree-Based Clustering MDS Algorithm for Nodes Localization in WSNs

Localization of sensors nodes is a key and fundamental issue in WSNs due to random deployment. In this paper, we propose a tree based clustering (TBC) multidimensional scaling algorithm for wireless sensor networks with the purpose of overcoming the shortage of classical MDS algorithms in its localization accuracy and computing complexity. Clustering is adopted to degrade the problem scale in our approach and a moderate number of common nodes between clusters are kept during clustering. Inner cluster local coordinates are calculated and then mapped into global coordinates according the tree structure formed by clustering. The simulations on MATLAB are conducted and the results show that the proposed algorithm has better localization coverage and higher accuracy than the traditional MDS based algorithms.

Data Storage Scheme Supporting for Multidimensional Query

The query and storage of data is very important in wireless sensor networks (WSN). It is mainly used to solve how to effectively manage the distributed data in the monitored area with extremely limited resources. Recent advances in technology have made the number of the sensing modules in one sensor develop from single to multiple. The existing storage scheme for one-dimensional data is not suitable for the multidimensional data or costs too much energy. We proposed a kind of data storage scheme supporting multidimensional query inspired by K-D tree. The scheme can effectively store the high-dimensional similar data to the same piece of two-dimensional area. It can quickly fix the storage area of the event by analyzing the query condition and then fetch back the query result. Meanwhile the scheme has a certain degree of robustness to packet loss and node failure. Finally the experiment on the platform of Matlab showed that our scheme has some advantages compared with the existing methods.

Data-Centric Routing Mechanism Using Hash-Value in Wireless Sensor Network

Traditional routing protocols as TCP/IP can not be directly used in WSN, so special data-centric routing protocols must be established. The raised data-centric routing protocols can not identify the sensor nodes, because many nodes work under a monitoring task, and the source of data is not so important some times. The sensor node in the network can not judge weather data is come from the some sink node. What’s more, the traditional method use IP to identify sensors in Internet is not suitable for WSN. In this paper, we propose a new naming scheme to identify sensor nodes, which based on a description of sensor node, the description of a sensor node is hashed to a hash value to identify this sensor. The different description generates different identifier. Different from IP schema, this identifier is something about the information of the sensor node. In the above naming scheme, we propose a new data-centric routing mechanism. Finally, the simulation of the routing mechanism is carried out on MATLAB. The result shows our routing mechanism’s predominate increase when network size increase.

Beacon moving location algorithm in WSN

Location is one of the key technologies in Wireless Sensor Networks WSN. A lot of location algorithms assume that beacon nodes are fixed, but in most practical applications, the locations of some beacons will be changed after deployment, which leads to other unknown nodes rely on these beacons cannot be accurately located. In this paper we proposed an Area Division-based Beacon Movement Detection Algorithm AD-BMD to solve the location problems with beacons movement, through two judgement processes to determine which beacon have moved, and set a coordinate confidence value for each beacon. We also proposed a Beacon Movement Detection based Beacon Optimal Selection Location Algorithm BMD-BOS to use moving and unmoving beacons more reasonably, by selecting beacons reasonably to calculate the position of unknown nodes. Experimental results show that AD-BMD has high-correct rate and low-error rate. Compared with LB and SSV, AD-BMD has better performance. The location accuracy of BMD-BOS is much higher than N-BMDs and D-BMDs.

Clustering problem using adaptive genetic algorithm

Clustering is very important to data analysis and data minig. The K-Means algorithm, one of the partitional clustering approaches, is an iterative clustering technique that has been applied to many practical clustering problems successfully. However, the K-Means algorithm suffers from several drawbacks. In this paper, an adaptive genetic algorithm be present , it solve disadvantages of K-Means by combine parallel genetic algorithm, evolving flow and adaptive. Experimental results show that the adaptive genetic algorithm have advantages over traditional Clustering algorithm.

The research on fuzzy data mining applied on browser records

With the technological advances, the Internet has been an important part of everyday life. Governmental institutions and enterprises tend to advertise and market through the internet. With the travelling records of browsers, one can analyze the preference of web pages, further understand the demands of consumers, and promote the advertising and marketing. In this study, we use Maximum Forward Reference (MFR) algorithm to find the travel pattern of browsers from web logs. Simultaneously, experts are asked to evaluate the fuzzy importance weightings for different webs. Finally, we employ fuzzy data mining technique that combines apriori algorithm with fuzzy weights to determine the association rules. From the yielded association rules, one can be accurately aware of the information consumers need and which webs they prefer. This is important to governmental institutions and enterprises. Enterprises can find the commercial opportunities and improve the design of webs by means of this study. Governmental institutions can realize the needs of people from the obtained association rules, make the promotion of policy more efficiently, and provide better services.

Sensor Location Verification Scheme Based on Range-Free Localizations in WSNs

Localization is a pivotal technology in wireless sensor networks and location information of sensor nodes is essential to location-based applications. In the beacon-based localization, the reliability of beacons’ location information is critical to the quality of network service. In this paper, we study the influences of drifting beacons the network localization. So according to this scenario mentioned above, we propose a distributed and lightweight beacons locations verification algorithm based on neighborhood-similarity (BLVNS), which utilizes similarity of the beacons’ neighborhood in different time slot to recognize drifting beacons. The whole algorithms can be applied to the static and dynamic WSNs to improve the accuracy of range-free localization. Experiment results show that our algorithms can recognize unreliable beacons with detection rate higher than 90%.

A Node Localization Verification Model for WSN

Localization is one of the most important technologies in wireless sensor networks. Range-based localization methods are widely used in many applications. However, traditional RSS-based methods cannot work well in scenarios with unreliable anchors. A reputation scheme based distributed location verification model called UNDA is proposed to solve the unreliable node recognizing issue in such scenarios. UNDA combines direct reputation and third-party reputation to recognize unreliable anchor. Furthermore, a credibility-updating scheme is presented to make the third-party reputation more accuracy. Extensive simulation experiments indicate that the location verification algorithm has relatively high accuracy as well as low communication overhead, and the convergence of UNDA is rapid. The UNDA can be a used as an underlayer for traditional RSS-based localization algorithms to realize reliable localization.