In this paper we present a novel platform for underwater sensor networks to be used for long-term monitoring of coral reefs and fisheries. The sensor network consists of static and mobile underwater sensor nodes. The nodes communicate point-to-point using a novel high-speed optical communication system integrated into the TinyOS stack, and they broadcast using an acoustic protocol integrated in the TinyOS stack. The nodes have a variety of sensing capabilities, including cameras, water temperature, and pressure. The mobile nodes can locate and hover above the static nodes for data muling, and they can perform network maintenance functions such as deployment, relocation, and recovery. In this paper we describe the hardware and software architecture of this underwater sensor network. We then describe the optical and acoustic networking protocols and present experimental networking and data collected in a pool, in rivers, and in the ocean. Finally, we describe our experiments with mobility for data muling in this network.

Abstract-We describe the design, implementation, and programming of a set of robots that, starting from an amorphous arrangement, can be assembled into arbitrary shapes and then commanded to self-disassemble in an organized manner, to obtain a goal shape. We present custom hardware, distributed algorithms, and experimental results from hundreds of trails which show the system successfully forming complex threedimensional shapes. Each of the 28 modules in the system is implemented as a 1.8-inch autonomous cube-shaped robot able to connect to and communicate with its immediate neighbors. Embedded microprocessors control each module's magnetic connection mechanisms and infrared communication interfaces. When assembled into a structure, the modules form a system that can be virtually sculpted using a computer interface and a distributed process. The group of modules collectively decide who is on the final shape and who is not using algorithms that minimize information transmission and storage. Finally, the modules not in the structure disengage their magnetic couplings and fall away under the influence of an external force, in this case, gravity.

Abstract-We present algorithms, systems, and experimental results for underwater data muling. In data muling a mobile agent interacts with static agents to upload, download, or transport data to a different physical location. We consider a system comprising an Autonomous Underwater Vehicle (AUV) and many static Underwater Sensor Nodes (USN) networked together optically and acoustically. The AUV can locate the static nodes using vision and hover above the static nodes for data upload. We describe the hardware and software architecture of this underwater system, as well as experimental data.

Abstract-This paper describes a novel experiment in which two very different methods of underwater robot localization are compared. The first method is based on a geometric approach in which a mobile node moves within a field of static nodes, and all nodes are capable of estimating the range to their neighbours acoustically. The second method uses visual odometry, from stereo cameras, by integrating scaled optical flow. The fundamental algorithmic principles of each localization technique is described. We also present experimental results comparing acoustic localization with GPS for surface operation, and a comparison of acoustic and visual methods for underwater operation.

In this paper we describe the design and control algorithms of AMOUR, a low-cost autonomous underwater vehicle (AUV) capable of missions of marine survey and monitoring. AMOUR is a highly maneuverable robot capable of hovering and carrying dynamic payloads during a single mission. The robot can carry a variety of payloads. It uses internal buoyancy and balance control mechanisms to achieve power efficient motions regardless of the payload size. AMOUR is designed to operate in synergy with a wireless underwater sensor network (WUSN) of static nodes. The robot's payload was designed in order to deploy, relocate and recover the static sensor nodes. It communicates with the network acoustically for signaling and localization and optically for data muling. We present control algorithms, navigation algorithms, and experimental data from pool and ocean trials with AMOUR that demonstrate its basic navigation capabilities, power efficiency, and ability to carry dynamic payloads.