Knowledge of positions of sensor nodes in Wireless Sensor Networks (WSNs) will make possible many applications such as asset monitoring, object tracking and routing. In WSNs, the errors may happen in the measurement of distances and angles between pairs of nodes in WSN and these errors will be propagated to different nodes, the estimation of positions of sensor nodes can be difficult and have huge errors. In this paper, we will propose localization algorithm based on both distance and angle to landmark. So, we introduce a method of incident angle to landmark and the algorithm to exchange physical data such as distances and incident angles and update the position of a node by utilizing multiple landmarks and multiple paths to landmarks.

Recently, with the rapid change to an aging society and the increased interest in healthcare, disease prediction and management through various healthcare devices and services is attracting much attention. In particular, stroke, represented by cerebrovascular disease, is a very dangerous disease, in which death or mental and physical aftereffects are very large in adults and the elderly. The sequelae of such stroke diseases are very dangerous, because they make social and economic activities difficult. In this paper, we propose a new system to prediction and in-depth analysis stroke severity of elderly over 65 years based on the National Institutes of Health Stroke Scale (NIHSS). In addition, we use the algorithm of decision tree of C4.5, which is a methodology of prediction and analysis of machine learning techniques. The C4.5 decision trees are machine learning algorithms that provide additional in-depth rules of the execution mechanism and semantic interpretation analysis. Finally, in this paper, it is verified that the C4.5 decision tree algorithm can be used to classify and predict stroke severity, and to obtain additional NIHSS features reduction effects. Therefore, during the operation of an actual system, the proposed model uses only 13 features out of the 18 stroke scale features, including age, so that it can provide faster and more accurate service support. Experimental results show that the system enables this by reducing the patient NIH stroke scale measurement time and making the operation more efficient, with an overall accuracy, using the C4.5 decision tree algorithm, of 91.11%.

Telemonitoring is not a new term, in information technology (IT), which has been employed to remotely monitor the health of patients that are located not in common places, such hospitals or medical centers. For that, wearable medical sensors, such as electrocardiography sensors, blood pressure sensors, and glucometer, have commonly been used to make possible to acquire the real-time information from the remotely located patients; therefore, the medical information is further carried, via the Internet, to perform medical diagnosis and the corresponding treatments. Like in other IT sectors, there has been tremendous progress accounted in medical sectors (and in telemonitoring systems) that changes the human life protection against several chronic diseases, and the patient’s medical information can be accessed wirelessly via Wi-Fi and cellular systems. Further, with the advents of cloud computing technology, medical systems are now more efficient and scalable in processing, such as storage and access, the medical information with minimal development costs. This study is also a piece of enhancement made to track and monitor the real-time medical information, bounded in authorized area, through the modeling of private cloud computing. The private cloud-based environment is designed, for patient health monitoring called bounded telemonitoring system, to acquire the real-time medical information of patients that resided in the boundary, inside medical wards and outside medical wards, of the medical center. A new wireless sensor network scenario is designed and modeled to keep or monitor the patients’ health information whole day, 24 hours. This research is a new secured sight towards medical information access and gives directions for future developments in the medical systems.

Today, the Internet of Things (IoT) is used for convenience in everyday life in many areas. Because data collected from IoT are generated in large quantities, cloud computing is inevitably used to store and analyze the data. However, cloud storage is not owned by the user, so it is unreliable. Verifying the integrity of data collected in an IoT environment and stored in a cloud has two problems: a large amount of data needs to be verified, and the data verification should be done directly on the IoT device. Many methods for data integrity verification use trusted third parties and devices that provide sufficient resources. However, it is difficult to directly apply existing research to IoT devices that have limited resources. This paper proposes a secure cloud storage service for an IoT environment that is based on a provable data possession model and uses Bloom filters. The experimental results showed that the proposed method saves time and has no significant differences in the verification rate with existing methods, even though the Bloom filter causes false positives. Therefore, the proposed service can effectively process a large amount of data generated in an IoT environment.