{"id":{"repo_id":"mississippi","oai_identifier":"oai:egrove.olemiss.edu:etd-1447"},"canonical_url":"https://search.dev.ndltd.org/etd/mississippi/oai:egrove.olemiss.edu:etd-1447","repository":{"repo_id":"mississippi","name":"University of Mississippi","base_url":"https://egrove.olemiss.edu/do/oai/"},"display":{"title":"Obstacle-Aware Wireless Video Sensor Network Deployment For 3D Indoor Space Monitoring","abstract":"In recent years wireless video sensors networks (WVSNs) have emerged as a leading technology for monitoring 3D indoor space in campus, industrial and medical areas as well as other types of environments. In contrast to traditional sensors such as heat or light sensors often considered with omnidirectional sensing range, the sensing range of a video sensor is directional and can be deemed as a pyramid-shape in 3D. Moreover, in an indoor environment, there are often obstacles such as lamp stands or furniture, which introduce additional challenges and further render the deployment solutions for traditional sensors and 2D sensing field inapplicable or incapable of solving the WVSN deployment problem for 3D indoor space monitoring. In this thesis, we take the first attempt to address this by modeling the general problem in a continuous space and strive to minimize the number of required video sensors to cover the given 3D regions. We then convert it into a discrete version by incorporating 3D grids for our discrete model, which can achieve arbitrary approximation precision by adjusting the grid granularity. We also create two strategies for dealing with stationary obstacles existed in the 3D indoor space, namely, Divide and Conquer Detection strategy and Accurate Detection strategy. We propose a greedy heuristic and an enhanced Depth First Search (DFS) algorithm to solve the discrete version problem where the latter, if given enough time can return the optimal solution. We evaluate our solutions with a customized simulator that can emulate the actual WVSN deployment and 3D indoor space coverage. The evaluation results demonstrate that our greedy heuristic can reduce the required video sensors by up to 47% over a baseline algorithm, and our enhanced DFS can achieve an additional reduction of video sensors by up to 25%.","abstract_html":"In recent years wireless video sensors networks (WVSNs) have emerged as a leading technology for monitoring 3D indoor space in campus, industrial and medical areas as well as other types of environments. In contrast to traditional sensors such as heat or light sensors often considered with omnidirectional sensing range, the sensing range of a video sensor is directional and can be deemed as a pyramid-shape in 3D. Moreover, in an indoor environment, there are often obstacles such as lamp stands or furniture, which introduce additional challenges and further render the deployment solutions for traditional sensors and 2D sensing field inapplicable or incapable of solving the WVSN deployment problem for 3D indoor space monitoring. In this thesis, we take the first attempt to address this by modeling the general problem in a continuous space and strive to minimize the number of required video sensors to cover the given 3D regions. We then convert it into a discrete version by incorporating 3D grids for our discrete model, which can achieve arbitrary approximation precision by adjusting the grid granularity. We also create two strategies for dealing with stationary obstacles existed in the 3D indoor space, namely, Divide and Conquer Detection strategy and Accurate Detection strategy. We propose a greedy heuristic and an enhanced Depth First Search (DFS) algorithm to solve the discrete version problem where the latter, if given enough time can return the optimal solution. We evaluate our solutions with a customized simulator that can emulate the actual WVSN deployment and 3D indoor space coverage. The evaluation results demonstrate that our greedy heuristic can reduce the required video sensors by up to 47% over a baseline algorithm, and our enhanced DFS can achieve an additional reduction of video sensors by up to 25%.","abstract_has_math":false,"creators":["Wang, Zhonghui"],"institution":null,"degree_name":"M.S. in Engineering Science","degree_level":"Thesis","degree_discipline":"Computer and Information Science","degree_department":null,"school":null,"contributors":["Feng Wang","Byunghyun Jang","Yixin Chen"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2016,"date_issued":"2016-01-01T08:00:00Z","date_published":"2016-01-01T08:00:00Z","updated_at":"2026-07-24T03:05:36Z","subjects":["Computer Sciences"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://egrove.olemiss.edu/etd/448","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Feng Wang","Byunghyun Jang","Yixin Chen"]},{"key":"dc:creator","label":"Author","values":["Wang, Zhonghui"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2019-06-20T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer and Information Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S. in Engineering Science"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Computer Sciences"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://egrove.olemiss.edu/etd/448"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["In recent years wireless video sensors networks (WVSNs) have emerged as a leading technology for monitoring 3D indoor space in campus, industrial and medical areas as well as other types of environments. In contrast to traditional sensors such as heat or light sensors often considered with omnidirectional sensing range, the sensing range of a video sensor is directional and can be deemed as a pyramid-shape in 3D. Moreover, in an indoor environment, there are often obstacles such as lamp stands or furniture, which introduce additional challenges and further render the deployment solutions for traditional sensors and 2D sensing field inapplicable or incapable of solving the WVSN deployment problem for 3D indoor space monitoring. In this thesis, we take the first attempt to address this by modeling the general problem in a continuous space and strive to minimize the number of required video sensors to cover the given 3D regions. We then convert it into a discrete version by incorporating 3D grids for our discrete model, which can achieve arbitrary approximation precision by adjusting the grid granularity. We also create two strategies for dealing with stationary obstacles existed in the 3D indoor space, namely, Divide and Conquer Detection strategy and Accurate Detection strategy. We propose a greedy heuristic and an enhanced Depth First Search (DFS) algorithm to solve the discrete version problem where the latter, if given enough time can return the optimal solution. We evaluate our solutions with a customized simulator that can emulate the actual WVSN deployment and 3D indoor space coverage. The evaluation results demonstrate that our greedy heuristic can reduce the required video sensors by up to 47% over a baseline algorithm, and our enhanced DFS can achieve an additional reduction of video sensors by up to 25%."]},{"key":"dc:title","label":"Title","values":["Obstacle-Aware Wireless Video Sensor Network Deployment For 3D Indoor Space Monitoring"]}]}],"canonical_facts":{"dc:contributor":["Feng Wang","Byunghyun Jang","Yixin Chen"],"dc:creator":["Wang, Zhonghui"],"dc:date.available":["2019-06-20T07:00:00Z"],"dc:description.abstract":["In recent years wireless video sensors networks (WVSNs) have emerged as a leading technology for monitoring 3D indoor space in campus, industrial and medical areas as well as other types of environments. In contrast to traditional sensors such as heat or light sensors often considered with omnidirectional sensing range, the sensing range of a video sensor is directional and can be deemed as a pyramid-shape in 3D. Moreover, in an indoor environment, there are often obstacles such as lamp stands or furniture, which introduce additional challenges and further render the deployment solutions for traditional sensors and 2D sensing field inapplicable or incapable of solving the WVSN deployment problem for 3D indoor space monitoring. In this thesis, we take the first attempt to address this by modeling the general problem in a continuous space and strive to minimize the number of required video sensors to cover the given 3D regions. We then convert it into a discrete version by incorporating 3D grids for our discrete model, which can achieve arbitrary approximation precision by adjusting the grid granularity. We also create two strategies for dealing with stationary obstacles existed in the 3D indoor space, namely, Divide and Conquer Detection strategy and Accurate Detection strategy. We propose a greedy heuristic and an enhanced Depth First Search (DFS) algorithm to solve the discrete version problem where the latter, if given enough time can return the optimal solution. We evaluate our solutions with a customized simulator that can emulate the actual WVSN deployment and 3D indoor space coverage. The evaluation results demonstrate that our greedy heuristic can reduce the required video sensors by up to 47% over a baseline algorithm, and our enhanced DFS can achieve an additional reduction of video sensors by up to 25%."],"dc:identifier":["https://egrove.olemiss.edu/etd/448"],"dc:subject":["Computer Sciences"],"dc:title":["Obstacle-Aware Wireless Video Sensor Network Deployment For 3D Indoor Space Monitoring"],"thesis:degree_discipline":["Computer and Information Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S. in Engineering Science"]},"updated_at":"2026-07-24T03:05:36Z"}