Development of Wireless Sensor Node for Landslide Detection

Landslides have frequently occurred on natural slopes during periods of intense rainfall. With a rapidly increasing population on or near steep terrain in Korea, landslides have become one of the most significant natural hazards. Thus, it is necessary to protect people from landslides and to minimize the damage of houses, roads and other facilities. To accomplish this goal, many landslide prediction methods have been developed around the world. In this study, a prototype of landslide detection is introduced. This system is based on the wireless sensor network (WSN) that is composed of sensor nodes, gateway, and server system. Sensor nodes comprising sensing and communication part are implemented to detect ground movement. A sensing part is designed to measure inclination angle and acceleration accurately, and a communication part is deployed with Bluetooth (IEEE 802.15.1) module to transmit the data to the gateway. To verify the feasibility of this landslide prediction system, a series of experimental studies was performed at a small-scale earth slope equipped with an artificial rainfall dropping device. It is found that sensing nodes planted at slope can detect the ground motion when the slope starts to move. It is expected that the prototype of landslide detection can provide early warnings when landslides occurs

Location-Aware Dynamic Session-Key Management for Grid-Based Wireless Sensor Networks

Security is a critical issue for sensor networks used in hostile environments. When wireless sensor nodes in a wireless sensor network are distributed in an insecure hostile environment, the sensor nodes must be protected: a secret key must be used to protect the nodes transmitting messages. If the nodes are not protected and become compromised, many types of attacks against the network may result. Such is the case with existing schemes, which are vulnerable to attacks because they mostly provide a hop-by-hop paradigm, which is insufficient to defend against known attacks. We propose a location-aware dynamic session-key management protocol for grid-based wireless sensor networks. The proposed protocol improves the security of a secret key. The proposed scheme also includes a key that is dynamically updated. This dynamic update can lower the probability of the key being guessed correctly. Thus currently known attacks can be defended. By utilizing the local information, the proposed scheme can also limit the flooding region in order to reduce the energy that is consumed in discovering routing paths

Energy-aware approaches for energy harvesting powered wireless sensor nodes

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this record.Intensive research on energy harvesting powered wireless sensor nodes (WSNs) has been driven by the needs of reducing the power consumption by the WSNs and the increasing the power generated by energy harvesters. The mismatch between the energy generated by the harvesters and the energy demanded by the WSNs is always a bottleneck as the ambient environmental energy is limited and time-varying. This paper introduces a combined energy-aware interface (EAI) with an energy-aware program to deal with the mismatch through managing the energy flow from the energy storage capacitor to the WSNs. These two energy-aware approaches were implemented in a custom developed vibration energy harvesting powered WSN. The experimental results show that, with the 3.2 mW power generated by a piezoelectric energy harvester (PEH) under an emulated aircraft wing strain loading of 600 με at 10 Hz, the combined energy-aware approaches enable the WSN to have a significantly reduced sleep current from 28.3 µA of a commercial WSN to 0.95 µA and enable the WSN operations for a long active time of about 1.15 s in every 7.79 s to sample and transmit a large number of data (388 bytes), rather than a few ten milliseconds and a few bytes, as demanded by vibration measurement. When the approach was not used, the same amount of energy harvested was not able to power the WSN to start, not mentioning to enabling the WSN operation, which highlighted the importance and the value of the energy-aware approaches in enabling energy harvesting powered WSN operation successfully

Error Resilient Video Streaming with BCH Code Protection in Wireless Sensor Networks

Video streaming in Wireless Sensor Networks (WSNs) is a promising and challenging application for enabling high-value services. In such a context, the reduced amount ofavailable bandwidth, as well as the low-computational power available for acquiring and processing video frames, imposes the transmission of low resolution images at a low frame rate. Considering the aforementioned limitations, the amount of information carried by each video frame must be considered of utmost importance and preserved, as much as possible, against network losses that could introduce possible artifacts in the reconstructed dynamics of the scene.In this paper we first evaluate the impact of the bit error rate on the quality of the received video stream in a real scenario, then we propose a forward error correction technique based on the use of BCH codes with the aim of preserving the video quality. The proposed technique, against already proposed techniques in the WSN research field, has been specially designed to maintain a full back-compatibility with the IEEE802.15.4 standard in order to create a suitable solution aiming at accomplishing the Internet of Things (IoT) vision. Performance results evaluated in terms of Peak Signal-to-Noise Ratio (PSNR) show that the proposed solution reaches a PSNR improvement of 4.16 dB with respect to an unprotected transmission, while requiring an additional overhead equal to 22.51% in number of transmitted bits, and minimal impact on frame rate reduction and energy consumption. When higher protection levels have been imposed, bigger PSNR values have been experienced at the cost of an increased additional overhead, lower frame rates, and bigger energy consumption values

The design and evaluation of Wireless Sensor Networks for applications in industrial locations

In manufacturing industries, there exist many applications where Wireless Sensor Networks (WSN\u27s) are integrated to provide wireless solution for the automated manufacturing processes. It is well known that industrial environments characterized by extreme conditions such as high temperature, pressure, and electromagnetic (EM) interference that can affect the performance of the WSN\u27s. The key solution to overcome this performance issue is by monitoring the received Signal Strength Index (RSSI) at the received sensor of the WSN device and track frame error rate of wireless packets. ZigBee is a wireless sensor network (WSN) standard designed for specific needs of the remote monitoring sensor system. Zigbee networks can be established by three different topologies: start, hybrid, and mesh. In this research project, the interest in analyzing the characteristics of the Zigbee performance was completed using a star topology network. Three performance parameters were obtained: the RSSI signal to monitor the received wireless packets from the sending node, path-lost exponent to determine the effect of industrial environment on wireless signals, and the frame error rate to know the discontinue time. The study was in three phases and took place in two settings: The first was at the manufacturing laboratory at the University of Northern Iowa, the second and the third were at the facility of a Midwestern manufacturing company. The study aimed to provide an analytical tool to evaluate the performances of Zigbee networks in industrial environments and compare the results to show that harsh environments do affect its performance. The study also involved testing the performance of WSN. This was done by simulating input/output Line passing with digital and analog data. Packets were sent from one node and counted at the receiving side to measure the packet error rate of WSN in industrial environment. In conclusion, investigating the WSN\u27s systems performance in industrial environment provides is crucial to identify the effects of the harsh conditions. It is necessary to run similar investigation to prevent the malfunction of the manufacturing applications. Testing a simple WSN in industrial environment can be capable of predicting the performance of the network. It is also recommended to have an embedded approach to WSN applications that can self-monitor its performance

Application of advanced non-destructive testing methods on bridge health assessment and analysis

Bridge structures have an important role in economic, social and environmental aspects of society life. Bridges are also subject to a natural process of deterioration of construction materials, as well as natural and environmental events such as flooding, freezing, thawing etc. Health monitoring and assessment of the structural integrity of bridges have been the focus of engineers and researchers for decades. Currently, the various aspects of bridge health are monitored separately. However, measuring these aspects independently does not give the overall health of the bridge and crucial indicators of structural damage can be neglected. Generally, bridge health assessments take the form of individual NDT (non-destructive techniques) detecting individual defects. However value can be added to these results by combining and comparing the findings of several different NDT surveys. By completing this, a more accurate assessment of bridge health is obtained. This increases confidence in the decision as to whether remedial action is necessary. In this thesis an integrated bridge health monitoring approach is proposed which applies several NDT specifically chosen for bridge health assessments, thus achieving this added value. This method can be used as a part of a comprehensive bridge monitoring strategy as an assessment tool to evaluate the bridges structural health. This approach enables the user of this approach to obtain a detailed structural report on the bridge with all the necessary information pertaining to its’ health, allowing for a fully educated decision to be made regarding whether remedial action is necessary. This research presents the results of the applications of such methods on case studies utilising Ground Penetrating Radar (GPR), IBIS-S technology / system (deflection and vibration detection sensor system with interferometric capability) and Accelerometer sensors. It also evaluates the effectiveness of the adopted methods and technologies by comparing and validating the yielded results with conventional methods (modelling and visual inspection). The research presents and discusses processed data obtained by the above mentioned methods in detail and reports on challenges encountered in setting up and materialising the assessment process. This work also reports on Finite Element Modelling (FEM) of the main case study (Pentagon Road Bridge) using specialist software (SAP2000 and ANSYS) in order to simulate the perceived movement of the bridge under dynamic and static conditions. The analytical results output were compared with results obtained by the applications of the above non-destructive methods. Thus by using these techniques the main aim of this thesis is to develop an integrated model/approach for the assessment and monitoring of the structural integrity and overall functionality of bridges. All the above methods were validated using preliminary case studies (GPR), additional equipment (accelerometers for IBIS-S validation) and additional techniques and information (SAP 2000 and ANSYS were compared to one another and IBIS-S results). All of these techniques were applied on the Pentagon Road Bridge. This bridge was chosen as no information was available regarding its structural composition. Visual inspection showed the external defects of the structure: cracking, moisture ingress and concrete delamination was present in one of the spans of the bridge. The GPR surveys gave the position of the rebars and also signs of moisture ingress at depths of 20cm (confirmed using velocity analysis). IBIS-S gave results for the deflection of the structure. FEM was used to model the behaviour of the bridge assuming no defects. To achieve additional model accuracy the results of the rebar position were input in to the model and it was calibrated using IBIS-S data. The deflection results from the model were then compared to the actual deflection data to identify areas of deterioration. It was found that excessive deflection occurred on one of the spans. It was thus found that all NDT indicated that a particular span was an area of significant deterioration and remedial action should be completed on this section in the near future. Future prediction was also completed by running simulations in ANSYS for increasing crack lengths and dynamic loading. It was found that if there is no remedial action excessive beam bending moments will occur and eventual collapse. The results of this research demonstrated that GPR provided information on the extent of the internal structural defects of the bridge under study (moisture ingress and delamination) whilst IBIS-S technology and Accelerometer sensors permitted measurement of the magnitude of the vibration of the bridge under dynamic and static loading conditions. The results depicted similarities between the FEM results and the adopted non-destructive methods results in location and pattern. This work can potentially contribute towards a better understanding of the mechanical and physical behaviours of bridge structures and ultimately assess their life expectancy and functionality

Intelligent Sensor Networks

In the last decade, wireless or wired sensor networks have attracted much attention. However, most designs target general sensor network issues including protocol stack (routing, MAC, etc.) and security issues. This book focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on their world-class research, the authors present the fundamentals of intelligent sensor networks. They cover sensing and sampling, distributed signal processing, and intelligent signal learning. In addition, they present cutting-edge research results from leading experts