A study of sensor movement and selection strategies for strong barrier coverage

Intruder detection and border surveillance are some of the many applications of sensor networks. In these applications, sensors are deployed along the perimeter of a protected area such that no intruder can cross the perimeter without being detected. The arrangement of sensors for this purpose is referred to as the barrier coverage problem in sensor networks. A primary question centering such a problem is: How to achieve barrier coverage? On the other hand, sensor nodes are usually battery-powered and have limited energy. It is critical to design energy-efficient barrier construction schemes while satisfying the coverage requirement. First, we studied how to achieve strong barrier coverage with mobile sensors. We leverage the mobility of sensors and relocate them to designated destinations to form a strong horizontal barrier after the random deployment. Algorithms were proposed to calculate the optimal relocating destinations such that the maximum moving distance of sensors is minimized. Depending on the number of sensors on the final barrier, two problems were investigated: (1) constructing a barrier with the minimum number of sensors on the final barrier, and (2) constructing a barrier with any number of sensors on the final barrier. For both problems, we optimized the barrier location instead of fixing it a priori as other works. We proposed algorithms which first identify a set of discrete candidates for the barrier location, then check the candidates iteratively. Both problems could be solved in polynomial time. Second, we investigated how to achieve strong barrier coverage by selectively activating randomly deployed static sensors. We aimed to select the minimum number of sensors to be active to achieve barrier coverage under a practical probabilistic model. The system false alarm probability and detection probability were jointly considered, and a (P_D^{min}, P_F^{max})-barrier coverage was defined where P_D^{min} is the minimum system detection probability and P_F^{max} is the maximum system false alarm probability. Our analysis showed that with the constraint on the system false alarm probability, the number of active sensors affects the detection capability of sensors, which would bring new challenges to the min-num sensor selection problem. We proposed an iterative framework to solve the sensor selection problem under the probabilistic model. Depending on whether the decision fusion was applied, different detection capability evaluation methods were used in the iterative framework. Finally, we studied how to achieve strong barrier coverage in a hybrid network with a mix of mobile and static sensors. A two-step deployment strategy was adopted where static sensors are first randomly deployed, and then mobile sensors are deployed to merge the coverage gap left by the static sensors. We aimed to find the proper coverage gaps to deploy mobile sensors such that (P_D^{min}, P_F^{max})-barrier coverage is achieved, and the total cost of the barrier is minimized. Under the probabilistic model, we solved the problem by iteratively trying multiple assumptions of the number of active sensors, and obtained the min-cost deployment strategy with the help of graph algorithms

On the Management of Scientific Research Work in Colleges and Universities of Western Areas

According to the existing condition analysis of academic research management in colleges and universities of the western areas, pointing out there being some existing problems at present, and putting forward to the countermeasures solving these problems. Thinking, as for the colleges and universities of the western areas, on the management of academic research work, are supposed to grasp the complication of it, highlight the local unique features, strengthen the ability of serving local economic, and take an important part in the local economic development

Analysing the Effectiveness of University Scientific Research Projects on the Knowledge Management

This paper briefly analyzes the connotation and characteristic of knowledge management. Elaborating the problems which exist in the present scientific research project management, analyzing the relationship between university scientific research project management and knowledge management; putting forward the knowledgeable, informatization, modernization and humanization strategy into the process of scientific research project management under the view of knowledge management, and exploring theory and method to make full use of knowledge management, establishing the comprehensive and whole process management mechanism in local university scientific research project management, so as to improve the efficiency of scientific research project management, promoting the development of local colleges and universities’ scientific research

Characteristics of University’s Scientific Research Project Management i n the New Times

Along with the comprehensive deepening of reform and open, China\u27s economic has developed rapidly. National and provincial’s longitudinal and transverse scientific research projects are increasing. In particular, our country has many universities and they have close relationship with economics’ development. Also, scientific research projects that involved are multitude of names. Understand our country’s characteristics of scientific research project i n the new period correctly. Adjust local university’s scientific research project management method, means and key in time, and make out corresponding management system to promote local universities’ further development

Linkage analysis and association analysis in the presence of linkage using age at onset of COGA alcoholism data

Complex disease mapping usually involves a combination of linkage and association techniques. Linkage analysis can scan the entire genome in a few hundred tests. Association tests may involve an even greater number of tests. However, association tests can localize the susceptibility genes more accurately. Using a recently developed combined linkage and association strategy, we analyzed a subset of the Collaborative Study on the Genetics of Alcoholism (COGA) data for the Genetic Analysis Workshop 14 (GAW14). In this analysis, we first employed linkage analysis based on frailty models that take into account age of onset information to establish which regions along the chromosome are likely to harbor disease susceptibility genes for alcohol dependence. Second, we used an association analysis by exploiting linkage disequilibrium to narrow down the peak regions. We also compare the methods with mean identity-by-descent tests and transmission/disequilibrium tests that do not use age of onset information

Predicting Patent Value: A Data Mining Approach

Patents have long been recognized as a rich source of data for studying innovation, technical changes, and value creation. Patent data includes citations to previous patents, and patent citations allow one to create an indicator of patent value. Identifying valuable patents in a timely manner is essential for effectively harnessing the business value of inventions in the increasingly competitive global market. However, the existing methods of evaluating patent value suffer the issues of timeliness and accuracy. In this paper, we propose a data mining approach that utilizes the structural properties of patent citations networks to predict the value of patents while aiming to improve timeliness and accuracy