LAUSR.org creates dashboard-style pages of related content for over 1.5 million academic articles. Sign Up to like articles & get recommendations!

Optimizing Node Deployment in Rechargeable Camera Sensor Networks for Full-View Coverage

Photo from wikipedia

Full-view coverage realized by camera sensor networks (CSNs) is highly demanded for monitoring and recognizing objects appearing at target points. However, it aggravates the energy shortage in CSNs as caused… Click to show full abstract

Full-view coverage realized by camera sensor networks (CSNs) is highly demanded for monitoring and recognizing objects appearing at target points. However, it aggravates the energy shortage in CSNs as caused by the need to generate and process much sensed data. Undoubtedly, enabling CSN nodes to be rechargeable and harvest energy from their surroundings is an effective method to overcome the energy limitation of a CSN and ensuring its perpetual operation. Moreover, using rechargeable nodes can avoid the replacement of batteries, and thus can reduce network maintenance cost. In this article, we investigate how to design and deploy a rechargeable CSN with the fewest nodes to achieve full-view coverage of all target points while guaranteeing its connectivity and perpetual operation. We first formulate the problem as an integer linear program and prove its NP-hardness, and then propose a greedy heuristic and a differential evolution algorithm to solve it. Extensive simulation results reveal that the latter is able to achieve a larger success rate and higher solution quality but spends more time than the former.

Keywords: full view; view coverage; camera sensor; sensor networks

Journal Title: IEEE Internet of Things Journal
Year Published: 2022

Link to full text (if available)


Share on Social Media:                               Sign Up to like & get
recommendations!

Related content

More Information              News              Social Media              Video              Recommended



                Click one of the above tabs to view related content.