Cross-layer Optimized Wireless Video Surveillance

A wireless video surveillance system contains three major components, the video capture and preprocessing, the video compression and transmission over wireless sensor networks (WSNs), and the video analysis at the receiving end. The coordination of different components is important for improving the end-to-end video quality, especially under the communication resource constraint. Cross-layer control proves to be an efficient measure for optimal system configuration. In this dissertation, we address the problem of implementing cross-layer optimization in the wireless video surveillance system. The thesis work is based on three research projects. In the first project, a single PTU (pan-tilt-unit) camera is used for video object tracking. The problem studied is how to improve the quality of the received video by jointly considering the coding and transmission process. The cross-layer controller determines the optimal coding and transmission parameters, according to the dynamic channel condition and the transmission delay. Multiple error concealment strategies are developed utilizing the special property of the PTU camera motion. In the second project, the binocular PTU camera is adopted for video object tracking. The presented work studied the fast disparity estimation algorithm and the 3D video transcoding over the WSN for real-time applications. The disparity/depth information is estimated in a coarse-to-fine manner using both local and global methods. The transcoding is coordinated by the cross-layer controller based on the channel condition and the data rate constraint, in order to achieve the best view synthesis quality. The third project is applied for multi-camera motion capture in remote healthcare monitoring. The challenge is the resource allocation for multiple video sequences. The presented cross-layer design incorporates the delay sensitive, content-aware video coding and transmission, and the adaptive video coding and transmission to ensure the optimal and balanced quality for the multi-view videos. In these projects, interdisciplinary study is conducted to synergize the surveillance system under the cross-layer optimization framework. Experimental results demonstrate the efficiency of the proposed schemes. The challenges of cross-layer design in existing wireless video surveillance systems are also analyzed to enlighten the future work. Adviser: Song C

Cross-layer Optimized Wireless Video Surveillance

A wireless video surveillance system contains three major components, the video capture and preprocessing, the video compression and transmission over wireless sensor networks (WSNs), and the video analysis at the receiving end. The coordination of different components is important for improving the end-to-end video quality, especially under the communication resource constraint. Cross-layer control proves to be an efficient measure for optimal system configuration. In this dissertation, we address the problem of implementing cross-layer optimization in the wireless video surveillance system. The thesis work is based on three research projects. In the first project, a single PTU (pan-tilt-unit) camera is used for video object tracking. The problem studied is how to improve the quality of the received video by jointly considering the coding and transmission process. The cross-layer controller determines the optimal coding and transmission parameters, according to the dynamic channel condition and the transmission delay. Multiple error concealment strategies are developed utilizing the special property of the PTU camera motion. In the second project, the binocular PTU camera is adopted for video object tracking. The presented work studied the fast disparity estimation algorithm and the 3D video transcoding over the WSN for real-time applications. The disparity/depth information is estimated in a coarse-to-fine manner using both local and global methods. The transcoding is coordinated by the cross-layer controller based on the channel condition and the data rate constraint, in order to achieve the best view synthesis quality. The third project is applied for multi-camera motion capture in remote healthcare monitoring. The challenge is the resource allocation for multiple video sequences. The presented cross-layer design incorporates the delay sensitive, content-aware video coding and transmission, and the adaptive video coding and transmission to ensure the optimal and balanced quality for the multi-view videos. In these projects, interdisciplinary study is conducted to synergize the surveillance system under the cross-layer optimization framework. Experimental results demonstrate the efficiency of the proposed schemes. The challenges of cross-layer design in existing wireless video surveillance systems are also analyzed to enlighten the future work. Adviser: Song C

Implementing Efficient and Multi-Hop Image Acquisition In Remote Monitoring IoT systems using LoRa Technology

Remote sensing or monitoring through the deployment of wireless sensor networks (WSNs) is considered an economical and convenient manner in which to collect information without cumbersome human intervention. Unfortunately, due to challenging deployment conditions, such as large geographic area, and lack of electricity and network infrastructure, designing such wireless sensor networks for large-scale farms or forests is difficult and expensive. Many WSN-appropriate wireless technologies, such as Wi-Fi, Bluetooth, Zigbee and 6LoWPAN, have been widely adopted in remote sensing. The performance of these technologies, however, is not sufficient for use across large areas. Generally, as the geographical scope expands, more devices need to be employed to expand network coverage, so the number and cost of devices in wireless sensor networks will increase dramatically. Besides, this type of deployment usually not only has a high probability of failure and high transmission costs, but also imposes additional overhead on system management and maintenance. LoRa is an emerging physical layer standard for long range wireless communication. By utilizing chirp spread spectrum modulation, LoRa features a long communication range and broad signal coverage. At the same time, LoRa also has low power consumption. Thus, LoRa outperforms similar technologies in terms of hardware cost, power consumption and radio coverage. It is also considered to be one of the promising solutions for the future of the Internet of Things (IoT). As the research and development of LoRa are still in its early stages, it lacks sufficient support for multi-packet transport and complex deployment topologies. Therefore, LoRa is not able to further expand its network coverage and efficiently support big data transfers like other conventional technologies. Besides, due to the smaller payload and data rate in LoRa physical design, it is more challenging to implement these features in LoRa. These shortcomings limit the potential for LoRa to be used in more productive application scenarios. This thesis addresses the problem of multi-packet and multi-hop transmission using LoRa by proposing two novel protocols, namely Multi-Packet LoRa (MPLR) and Multi-Hop LoRa (MHLR). LoRa's ability to transmit large messages is first evaluated in this thesis, and then the protocols are well designed and implemented to enrich LoRa's possibilities in image transmission applications and multi-hop topologies. MPLR introduces a reliable transport mechanism for multi-packet sensory data, making its network not limited to the transmission of small sensor data only. In collaboration with a data channel reservation technique, MPLR is able to greatly mitigate data collisions caused by the increased transmission time in laboratory experiments. MHLR realizes efficient routing in LoRa multi-hop transmission by utilizing the power of machine learning. The results of both indoor and outdoor experiments show that the machine learning based routing is effective in wireless sensor networks