Zhang Zhengyu

Bio: Zhang Zhengyu is an academic researcher from Jilin University. The author has contributed to research in topics: Wireless sensor network & Energy consumption. The author has an hindex of 2, co-authored 2 publications receiving 18 citations.

Wireless temperature sensor optimizing arrangement method in environment monitoring system

Abstract: The invention relates to a wireless temperature sensor optimizing arrangement method in an environment monitoring system. By forecasting the temperature values of sensors according to historical data of nodes of the temperature sensors, sensor network arrangement is optimized, energy consumption of the nodes is saved, and the situation of invalidation of the nodes can be dealt with. The method has the advantages that when the sensors are well arranged in an environment and high-frequency sampling is carried out in a period with forecasting needed, the collected historical data can be fully utilized, and a user can forecast the monitoring value of a certain sensor easily when data at a certain moment are collected; when energy consumption of a sensor network needs to be reduced, the user can reduce the number of the nodes of the wireless sensors properly to save energy for the sensor network; when node invalidation occurs when the sensor network operates, the user can forecast the temperature value of a corresponding sensor easily to well deal with the situation.

The Future Internet of Things: Secure, Efficient, and Model-Based

Abstract: The Internet of Things’ (IoT’s) rapid growth is constrained by resource use and fears about privacy and security. A solution jointly addressing security, efficiency, privacy, and scalability is needed to support continued expansion. We propose a solution modeled on human use of context and cognition, leveraging cloud resources to facilitate IoT on constrained devices. We present an architecture applying process knowledge to provide security through abstraction and privacy through remote data fusion. We outline five architectural elements and consider the key concepts of the “data proxy” and the “cognitive layer.” The data proxy uses system models to digitally mirror objects with minimal input data, while the cognitive layer applies these models to monitor the system’s evolution and to simulate the impact of commands prior to execution. The data proxy allows a system’s sensors to be sampled to meet a specified quality of data target with minimal resource use. The efficiency improvement of this architecture is shown with an example vehicle tracking application. Finally, we consider future opportunities for this architecture to reduce technical, economic, and sentiment barriers to the adoption of the IoT.

Resource Optimisation in 5G and Internet-of-Things Networking

Abstract: Fifth generation (5G), the currently evolving communication standard, promises better performance in terms of capability, capacity, speed, latency, etc. than recent technologies such as WiMax, LTE and LTE-Advanced. Similarly, the internet-of-things (IoT), the newly developing internet computing paradigm, has the potential for providing seamless, efficient human-device and device-device communication and connectivity. Both 5G and IoT technologies are definite key players in achieving a smart, interconnected world. However, one great limitation is that the resources needed to drive 5G and IoT technologies are extremely limited. To address this challenge, efficient solution models that optimise the use of the scarce resources are required. In this paper, an investigation into the various optimisation approaches that are being explored for addressing resource problems in 5G and IoT is carried out. The solution approaches are categorised and strengths and weaknesses are revealed, while new and exciting research directions are discussed. One of the research areas identified, namely, the aspect of spectrum availability, is addressed. In addressing the spectrum scarcity problem of 5G and IoT, a solution model is developed whereby an allotted spectrum is employed by two networks simultaneously. The results obtained from the analysis show that with such arrangement, a marked improvement in resource usage and overall productivity of the 5G and IoT network is achievable.

Data proxies, the cognitive layer, and application locality : enablers of cloud-connected vehicles and next-generation internet of things

Abstract: Intelligent and Connected Vehicles reduce cost, improve safety, and enhance comfort relative to isolated vehicles. This ability for cars to sense, infer, and act facilitates data-driven improvements in occupant experience and vehicle design. This thesis explores informed individual vehicle improvements and proposes a secure and efficient architecture supporting connected vehicle applications. Applying On-Board Diagnostic and smartphone data, I built a suite of prognostic applications. Engine coolant temperature data supports inference of oil viscosity and remaining life. A linear SVM using Fourier, Wavelet, and Mel Cepstrum audio features provides 99% accurate engine misfire detection. PCA-transformed Fourier acceleration features and GPS data inform decision trees attaining 91% wheel imbalance and 80% tire pressure and tread depth classification accuracy. These applications demonstrate the ability for local vehicle and peripheral device data to proactively improve individual vehicle reliability and performance. Connectivity facilitates crowdsourced data to further improve current vehicles and future designs. Exploring vehicular connectivity, I consider data timeliness, availability and bandwidth cost in the context of an efficiency-improving idle time predictor. This predictor uses contextual information to eliminate short idle shutoffs in Automatic Engine Start/Stop systems, minimizing driver annoyance and improving compliance. These applications reveal an opportunity to address excess resource consumption and system insecurity in Connected Vehicles and other constrained devices. I introduce a secure and efficient model-based Internet of Things (IoT) architecture consisting of a “Data Proxy” utilizing a Cloud-run estimator to mirror an object with limited sensor input. The use of digital duplicates abstracts physical from digital objects, allowing the use of a mediating “Cognitive Layer” consisting of firewall and supervisory elements. These “Cognitive” elements apply the system model to monitor system evolution and simulate the impact of commands against known and learned limits.

Weighted Centroid Range Free Localization Algorithm based on IOT

Abstract: Today, IOT (Internet of Things or Internet of Objects) is popular upcoming topic in Wireless network and in Wireless Network. Localization has become a crucial factor for various monitoring applications such as search, rescue, disaster relief, target tracking etc. Various WSN localization technologies are designed for calculating location of the unknown node. IOT is a latest approach where it combines many technologies, such as network technology, communication technology, database technology (Internet, Bluetooth, infrared, Wi-Fi, GPRS, 3G, SQL, etc) to provide location based service which enables different ways to obtain the location information of various objects. In this paper, we focus on to achieve higher localization accuracy based on services in which we deploy sensing devices to calculate the information of sensor nodes and its surrounding nodes where it requires less hardware and less implementation, To improve the localization accuracy, we propose a Weighted Centroid based on IOT mechanism capable to give accurate calculation between Anchor node and Unknown node.