Yong Sun

Bio: Yong Sun is an academic researcher. The author has contributed to research in topics: Wireless sensor network & Synchronization. The author has an hindex of 3, co-authored 6 publications receiving 15 citations.

Design of Micro Wireless Network Measurement and Control Server Based on ARM and μC/OS-II

Abstract: A novel developer application and platform framework for embedded Web measurement and control server was proposed, the hardware bases on ARM7 microprocessor, which used CS8900 as the net interface chip and nRF905 as Wireless communication chip, the software based on embedding real-time operating system muC/OS-II and TCP/IP protocol were designed and implemented in details. Described the main process of porting the open-source embedded operating system muC/OS-II to ARM processor, and achieved an embedded Web server based Internet multi-task system platform. Ultimately, some example using the embedded Web server is simply described

Fault locating arithmetic for multi-source network cluster nodes based on comparison

Abstract: On the basis of layer-built topology structure and one-to-many communication mode, a novel fault locating arithmetic based on comparison for multi-source network cluster nodes was presented to avoid fault node invalidation problem of wireless sensor networks. Mathematical model and the fault model were set up. Aiming at the locating model, the optimization process for the concentrative control of the cluster-heads was analyzed, and theoretical derivations were carried out to validate the availability and feasibility. Three cases of the diagnosis model, not changing network topology, changing network topology and larger network size, are simulated based on the evaluation criteria of fault locating arithmetic. Simulation results indicate that comparison-based cluster nodes fault diagnosis is characteristic of good diagnosis and expansibility, and system overhead and absolute error are much smaller than comparison-based system-level fault diagnosis.

Dynamic non-average-slot MAC layer protocol for wireless sensor networks

Abstract: To solve the ineffective power consumption generated by the idle listening, conflict and control expenses in wireless sensor networks MAC layer, a novel MAC method based on the dynamic non-equal distribution slot is studied. According to the network topology and monitoring type, we analyze more about the coverage model of network nodes, Afterwards, the dynamic election mechanism that the node with lowest energy level is turned to sleep equalizes the energy consumption is designed, and a novel allocation algorithm based on the non-average-slot protocol is derived. In order to achieve the same time base among network nodes, a single direction broadcast time synchronization mechanism is raised based on the local time series and recursive least square. In the end, simulation experiments is applied to verify the effectiveness of the protocol, the simulation results indicates that the method is used to bring about a fundamental function for the better in the number of dynamic nodes and the standard of non-average-slot, and can effectively use the network energy to extend the network life-cycle.

Location Discovery and Error Analysis of Wireless Sensor Networks Based on Difference of Arrival Time of Beacon Signals

Abstract: Nodes of wireless sensor networks are characterized with energy constraint, difficult to implement time synchronization, random deployment etc. Range measurement and location discovery principle of several methods, such as RSSI, ToA, TDoA and AoA are briefly introduced. Afterwards, a novel location discovery algorithm which is based on difference of arrival time of beacon signals is proposed. There is no requirement of time synchronization among anchor nodes and target nodes. Also, no additional equipment or circuit is needed. Particularly, error of location discovery is analyzed when anchor nodes are placed in the form of equidistant right-angled triangle. According to the expression of error, accuracy of location discovery can be improved through enlarging the distance between anchor nodes and enhancing clock precision of target node.

Calibration model of the output characteristic for sensor nodes based on CMAC neural network

Abstract: Using the characteristic of nonlinear function approach of CMAC neural network in cluster node, a novel model of comprehensive calibration, which aiming to improve the static state output characteristic of sensor nodes, was presented. Correction model of sensor nodes and comprehensive calibration model of cluster node were set up. The arithmetic is demonstrated, according to the simulation results, to be effective and efficient, and this model has advantage no matter what in accuracy, robustness and absolute error. CMAC offered a better system function and a higher static state output characteristic, as well as an easier hardware realization. Additionally, the model is suitable to transplant and expand in different wireless intelligent monitoring in limited resource.

Diagnosability evaluation for a system-level diagnosis algorithm for Wireless Sensor Networks

Abstract: In a Wireless Sensor Network (WSN) where the sensors monitor the environment to the purpose of raising alarms about the occurrence of some events, we consider the problem of detecting false alarms (i.e. false positives). We adopt a system level diagnosys model, where all the sensors in the region where the alarms have been raised cooperate to execute mutual tests. The test outcomes are given as input to a diagnosis algorithm that identifies the faulty sensors and thus confirms or neglects the alarm. The diagnosis algorithm can succeed in this work if the tests among sensors are sufficient to achieve a given diagnosability of the system, that depends on topological properties of the WSN. This work considers the problem of determining a test strategy of the sensors in a WSN in order to ensure that the desired system diagnosability is met. In particular it is shown by simulation that appropriate testing strategies are successful to guarantee given levels of system diagnosabilities depending of the network topology or its density.

Energy-aware test connection assignment for the self-diagnosis of a wireless sensor network

Abstract: Sensor nodes in Wireless Sensor Networks (WSNs) are prone to failures due to the fragile hardware, malicious attacks, or hostile or harsh environment. In order to assure reliable, long-term monitoring of the phenomenon under investigation, a major challenge is to detect node malfunctions as soon as possible and with an energy efficient approach. We address this problem by using a system-level diagnosis strategy in which the sink issues to the WSN a self-diagnosis task that involves a number of mutual tests among sensors. Based on the test outcomes, the sink executes the diagnosis procedure. This work presents an algorithm for the assignment of tests among the sensors of a WSN that assures the desired system diagnosability and that is aware of energy consumption. We show by simulation experiments that the present approach, as compared to a previous one, enables consistent energy savings on the sensors.

An embedded EEG analyzing system based on μC/os-II

Abstract: An EEG analyzing system based on advanced RISC machines (ARM) and μC/os-II real time operating system is discussed in this paper. The detailed system design including the producing of event signals and the synchronization between event signals and EEG signals is described. The details of data acquisition, data preprocessing, data transmitting through USB and system configurations are also contained in the system design. In this paper the design of high capability amplifier and the software of embedded subsystem are discussed. Also the design of realizing multi-task system in μC/os-II, the definition of communicating protocols between PC and the equipment and the detail configurations of USB are given out. The final test shows that the filter behaviors of this equipment are feasible.

Energy-Aware Test Connection Assignment for the Diagnosis of a Wireless Sensor Network

Abstract: In a Wireless Sensor Network (WSN) where the sensors raise alarms in response to anomalous events, we consider the problem of detecting false alarms (i.e. false positives) by using a system-level diagnosis approach. In particular, we assume that a set of t sensors in a geographic neighborhood generate the alarm, and the sink issues to the WSN a self test task that involves a number of reciprocal tests among the sensors in the WSN. Based on the test outcomes, the sink executes the diagnosis in order to identify the faulty sensors. This work presents an algorithm for the assignment of the tests (i.e. a strategy for the assignment of reciprocal tests among sensors) that assures the desired system diagnosability and that is aware of the energy consumption of the sensors. Simulation experiments show that, as compared to the existing approaches, our approach enables consistent energy savings on the sensors.