A quantitative approach to static sensor network design. Issue 10 (23rd September 2014)

• Record Type: Journal Article
• Authors: Pedersen, Martin W.; Burgess, Greg; Weng, Kevin C.; Spencer, Matthew
• Is Part Of: Methods in ecology and evolution. Volume 5:Issue 10(2014:Oct.)
• Page Start: 1043
• Page End: 1051
• Publication Date: 2014-09-23
• Subjects: Ecology -- Periodicals; Evolution -- Periodicals; 577
• DOI: 10.1111/2041-210X.12255
• Languages: English
• ISSNs: 2041-210X

Abstract

<abstract abstract-type="main" id="mee312255-abs-0001"> <title>Summary</title> <p> <list id="mee312255-list-0001" list-type="order"> <list-item> <p>Static sensor networks to observe animals are widely used in ecological, management and conservation research, but quantitative methods for designing these networks are underdeveloped.</p> </list-item> <list-item> <p>In the context of aquatic systems, we present a method for quasi‐optimal network design, which accounts for blocking of detections by obstacles, horizontal and vertical movement behaviour of the target animals, and type of research question (is the network intended for estimation of detailed movement or home range?). Optimal design is defined as the sensor configuration that maximizes the expected number of unique animal detections. As finding the global optimum is generally time consuming, we use a greedy algorithm instead, which places sensors optimally relative to already placed sensors. The design method requires access to topographic data of the study site and knowledge of the sensor detection range.</p> </list-item> <list-item> <p>We illustrate the method with real topographic data from a rugose coral reef where network performance is highly influenced by detection shadowing. Network performance is visualized by a coverage map indicating the probability of detection at any location in the study area. The reported unique recovery rate summarizes the expected ability of the network to collect data given the design constraints. Because sensors are placed sequentially, the information gain per sensor can be evaluated and used as a proxy for sensor value.</p> </list-item> <list-item> <p>The presented method formalizes important considerations, when designing sensor networks, that were previously often based on heuristics and intuition. The method provides a guide to maximizing the information potential of future monitoring studies as well as a means to improve existing networks. The method is available as an <sc>R</sc> package and can be tested via an online web tool.</p> </list-item> </list> </p> </abstract>