# Behavior Composition for Marine Pollution Source Localization Using a Mobile Sensor Network
## 1 Introduction  
As the worldwide industries develop rapidly, marine pollution is becoming increasingly serious [1]. Marine pollution can cause heavy damage to marine ecosystems, reduce the production of culture fisheries, and harm human health. One of the most common sources of marine pollution is the chemical sewage discharged by factories and ships into seas and oceans. Locating the sources of chemical contaminants is critical to reducing chemical pollution in marine environments, in which the transport of chemical materials is dominated by turbulences with a high Reynolds number [2]. The continuously released chemical materials in marine environments form meandering plumes with intermittent or patchy chemical distributions [3, 4], which poses difficulties for static sensor nodes to detect the chemical pollution in a \"wait-and-measure\" manner. Furthermore, a network of static chemical sensing platforms/nodes, which is typically used for locating a chemical source in environments with low Reynolds numbers [5, 6] or a fixed transportation network [7], cannot sense chemicals transported outside its limited coverage area. In contrast, mobile chemical sensing nodes [3, 8, 9, 10, 11], which are equipped with flow-velocity and chemical sensors, can theoretically cover an indefinitely large area if the power supply is guaranteed. This makes mobile sensing nodes more flexible and particularly suitable for locating chemical plume sources in large-scale marine environments. Moreover, multiple mobile sensor nodes (SNs) have higher robustness, stronger spatial sampling ability, and lower cost thana single mobile sensing robot/node, owing to the faculty of cooperation among multiple nodes [12]. Thus, the problem of locating a continuous chemical pollution source in marine environments though cooperation among a network of mobile sensor nodes is the focus in this paper. To realize cooperation, a mobile sensor network (MSN) is defined as a wireless communication network that provides the infrastructure for sharing information among the SNs, with a designed cooperation scheme to control them. Existing methods for solving the chemical plume source localization (CPSL) problem using multiple SNs can be tentatively classified into deliberative and behavior-based methods. Deliberative methods, which select the best action by inferring, based on an implicit or explicit internal world representation [13], dominate existing multi-SN-based CPSL methods. Some recent typical deliberative and quasi-deliberative multi-SN-based CPSL methods are as follows. [PERSON] et al. [2] proposed an information theoretic search strategy in which a particle filter representation of the posterior source location belief distribution was employed to determine the robot's actions. They also proposed a multi-agent collaborative \"infotaxis\" strategy [14], in which the rate of detecting the presence of a material plume is modeled as Poisson's distribution. [PERSON] et al. [15] proposed a modified convergence-guaranteed particle swarm optimizer method, in which the memorized particle optima, global optimum, and optimal group can be considered as a customized representation of the plume environment. [PERSON] [16] proposed a modified artificial potential field method that controls the robots to track the chemical plume based on the constructed source distribution probability model. Compared with pure reactive methods, which tightly couple sensory inputs and effector outputs, deliberative methods are less sensitive to environmental dynamics [13]. However, pure reactive methods can stagnate in the absence of sensory inputs, which means they lack intelligence based on world representation. Behavior-based systems are best suited for dynamically changing environments, where a fast response and adaptivity, as well as the reasoning ability, are required [13]. In behavior-based methods, different SNs can simultaneously perform different and frequently switched elementary actions, enabling behavior composition [17] in the MSN.