Jia Wu

Data Decision and Transmission Based on Mobile Data Health Records on Sensor Devices in Wireless Networks

The contradiction between a large population and limited and unevenly distributed medical resources is a serious problem in many developing countries. This problem not only affects human health but also leads to the occurrence of serious infection if treatment is delayed. With the development of wireless communication network technology, patients can acquire real-time medical information through wireless network equipment. Patients can have the opportunity to obtain timely medical treatment, which may alleviate the shortage of medical resources in developing countries. This study establishes a new method that can decide and transmit effective data based on sensor device mobile health in wireless networks. History data, collection data, and doctor-analyzed data could be computed and transmitted to patients using sensor devices. According to probability analysis, patients and doctors may confirm the possibility of certain diseases.

Sensor communication area and node extend routing algorithm in opportunistic networks

In opportunistic networks, sensor communication areas are established by node movements. Information can be transmitted among communication areas. Relevance nodes in communication areas are important because they carry information and build bridges between areas and data packets so that information can be delivered to the destination node. This study proposes a routing algorithm called sensor communication area node extend (SCANE), which aims to select relevance nodes and to recombine communication areas. This algorithm also enables information to be transmitted from the start node to the destination node easily. The simulation indicates that compared with Epidemic, PRoPHET, and Spray and wait algorithms in opportunistic networks, the proposed algorithm improves delivery ratio, overhead, and delay time.

Human Activity Optimal Cooperation Objects Selection Routing Scheme in Opportunistic Networks Communication

There are some important features of opportunistic networks such as mobility of nodes, randomness of information transmission and no links in transmission. These features are similar with the information transmission among human beings in social activities in which people choose the objects they need to transmit information according to the situation of information sending and receiving. Traditional methods in opportunistic networks can not receive good result in the application of social network since the neglecting of societal and subjectivity of human beings. Thus, this paper suggests a routing scheme: Optimal Cooperation Objects Selection Routing in opportunistic networks based on the features of the opportunistic network. In this scheme, dependability ratio, usability ratio and weight factor are counted as weight in the human activity topology to get the optimal cooperation objects. This scheme is proved as a better method by the simulation compare with the traditional methods in opportunistic networks.

Reducing Energy Consumption and Overhead Based on Mobile Health in Big Data Opportunistic Networks

A great number of people and non-equalizing medical resources, in developing countries, have become a serious contradiction. Not only does it affects the person’s life, but also causes serious epidemic contagious, because patients can not get help with hospital on time. With the development of wireless communication network, patient may get medical information by wireless network device. It can alleviate contradictions between patients and medical resources. But in developing countries, population quantity is a big data. How to solve data packets in wireless communication network is a big problem when researchers face huge population. In order to solve some problems in big data communication, this paper founds availability data transmission routing algorithm. This algorithm can reduce energy consumption and overhead, then improve deliver ratio in big data communication. Compare with Spray and Wait algorithm, Binary spray and wait algorithm in opportunistic networks, this algorithm acquires good results by reduce energy consumption, overhead and deliver ratio.

Optimal Objects of Cooperation Selection for Human Activity in Opportunistic Networks

Randomness and disconnections during transmission in opportunistic networking have some characteristics similar to human activity. Many traditional methods in opportunistic networks, however, have never achieved effective results when applied to human activity. Consequently, this paper establishes and analyzes a structure tree and then, based on count dependability and usability in personal relationships, an optimized cooperation path is selected. Furthermore, the paper presents a new algorithm?the Optimal Cooperation Path Algorithm ?for opportunistic networks. This algorithm solves the problem of how to choose an appropriate path for human activity in opportunistic networks. When tested during simulations, this algorithm achieved good results.

Small Data: Effective Data Based on Big Communication Research in Social Networks

Big data research is difficult because of its complex structure, vast data storage, and unpredictable change. Social network communication involves a significant amount of incomputable data created by wireless devices across the world. Such data can be used to analyze human activities, seek certain patterns using communication data, and predict emergencies. However, most data are effect of human to research our activities. So, recording effective node distribution and investigating the topological structure in communication are particularly important in big data communication. This study establishes a big data communication simulation environment by searching small data and calculating the influence of small data nodes. The experiment shows that 1% of small data can connect 75% of communication nodes and 20% of small data can transmit 80% of data packets.

Information Transmission Probability and Cache Management Method in Opportunistic Networks

In real network environment, nodes may acquire the communication destination during data transmission and find a suitable neighbor node to perform effective data classification transmission. This is similar to finding certain transmission targets during data transmission with mobile devices. However, the node cache space in networks is limited, and waiting for the destination node can also cause end-to-end delay. To improve the transmission environment, this study established Data Transmission Probability and Cache Management method. According to selection of high meeting probability node, cache space is reconstructed by node. It is good for nodes to improve delivery ratio and reduce delay. Through experiments and the comparison of opportunistic network algorithms, this method improves the cache utilization rate of nodes, reduces data transmission delay, and improves the overall network efficiency.

Data Decision and Drug Therapy Based on Non-Small Cell Lung Cancer in a Big Data Medical System in Developing Countries

In many developing or underdeveloped countries, limited medical resources and large populations may affect the survival of mankind. The research for the medical information system and recommendation of effective treatment methods may improve diagnosis and drug therapy for patients in developing or underdeveloped countries. In this study, we built a system model for the drug therapy, relevance parameter analysis, and data decision making in non-small cell lung cancer. Based on the probability analysis and status decision, the optimized therapeutic schedule can be calculated and selected, and then effective drug therapy methods can be determined to improve relevance parameters. Statistical analysis of clinical data proves that the model of the probability analysis and decision making can provide fast and accurate clinical data.

Decision based on big data research for non-small cell lung cancer in medical artificial system in developing country

Non-small cell lung cancer (NSCLC) is a high risk cancer and is usually scanned by PET-CT for testing, predicting and then give the treatment methods. However, in the actual hospital system, at least 640 images must be generated for each patient through PET-CT scanning. Especially in developing countries, a huge number of patients in NSCLC are attended by doctors. Artificial system can predict and make decision rapidly. According to explore and research artificial medical system, the selection of artificial observations also can result in low work efficiency for doctors. In this study, data information of 2,789,675 patients in three hospitals in China are collected, compiled, and used as the research basis; these data are obtained through image acquisition and diagnostic parameter machine decision-making method on the basis of the machine diagnosis and medical system design model of adjuvant therapy. By combining image and diagnostic parameters, the machine decision diagnosis auxiliary algorithm is established. Experimental result shows that the accuracy has reached 77% in NSCLC.

OSTP: Optimization Selection of Path Transmission Routing Algorithm in Opportunistic Networks

Opportunistic network is a random network and do not communicate with each other among the respective communication areas. This situation leads to the message transfer process become very difficult. In this paper a reducing energy consumption optimization selection of path transmission routing algorithm (OSPT) in opportunistic networks is proposed. Algorithm is applied to the design of dynamic random network topology, created a dynamic link, optimized and selected the path. It solves the problem of undeliverable messages for a long time in opportunistic networks. According to the simulation experiment and compare with epidemic algorithm and spray and wait algorithm. Experimental results show that OSPT algorithm improves deliver ratio, reduce energy consumption, cache time and transmission delay.