Underwater mobile acoustic sensor networks are promising tools for the exploration of the oceans. These networks require new robust solutions for fundamental issues such as: localization service for data tagging and networking protocols for communication. All these tasks are closely related with connectivity, coverage and deployment of the network. A realistic mobility model that can capture the physical movement of the sensor nodes with ocean currents gives better understanding on the above problems. In this paper, we propose a novel physically-inspired mobility model which is representative of underwater environments. We study how the model affects a range-based localization protocol, and its impact on the coverage and connectivity of the network under different deployment scenarios.

In this paper we consider the problem of constructing a coordinate system in a sensor network where location information is not available. To this purpose we introduce the Virtual Coordinate assignment protocol (VCap) which defines a virtual coordinate system based on hop distances. As compared to other approaches, VCap is simple and have very little requirements in terms of communication and memory overheads. We compare by simulations the performances of greedy routing using our virtual coordinate system with the one using the physical coordinates. Results show that the virtual coordinate sys- tem can be used to efficently support geographic routing.

Unmanned Aerial Vehicles (UAVs) are autonomous devices employed as data collectors in precision agriculture to support a large number of applications. These UAVs gather data from on-the-ground wireless sensor networks, especially in scenarios that lack any kind of fixed communication infrastructure or where the available infrastructure does not fit the application requirements. Sensors on the ground can store sensing data, and in scenarios that do not require a real-time observation and analysis of the data, like in smart farming, a drone can be used maybe once or two times each day to collect and report the data to a command-and-control center that use them directly, without any other infrastructure (cloud or edges). In this paper, we study analytically how close the drone, that uses a LoRa radio, needs to fly over the sensors to collect data with a given quality of data collection. This can be used to properly spacing the sensor on the field at deployment time, to select among different type of drones, and to properly solve some tradeoff related to field size vs autonomy of the drones and the path used by the latter when collecting the data.

Underwater sensor networks (USN) are used for harsh oceanographic missions where human operation is dangerous or impossible. Localization is essential for USNs. It is required for data tagging, node tracking and position-based routing algorithms. Localization is challenging because Global Positioning System(GPS) is not available in underwater; at the same time, existing GPS-less schemes based on fixed landmarks have high communication cost. Such cost is critical in mobile underwater sensor networks (MUSN), since sensor nodes drift with the ocean currents, thus requiring continuous refresh. In this paper, we propose a multi-stage localization scheme using mobile beacons. The beacons periodically ascent and descent in the water column. When they resurface, they receive new GPS coordinates. Then, they dive to the level of the underwater sensors to advertise these coordinates. In turn, localized sensors become proxy beacons and propagate their own coordinates, etc. This iterative, multi-stage localization is the major innovation of this paper. The goal is to localize the nodes with the smallest number of beacons using proxies instead, yet achieving an adequate accuracy. The major benefit is the reduction in operating costs. Mobility is a critical factor in determining performance. In this paper, performance (i.e., the percentage of localized nodes during a cycle, accuracy, delay and communication cost) is tested in a simulation scenario based on a realistic mobility model. The "meandering current mobility with surface effect" (MCM-SE) model - a composite model combining surface and subsurface currents.

Outdoor IoT applications usually exploit energy harvesting systems to guarantee virtually uninterrupted operations. However, the use of energy harvesting poses issues concerning the optimization of the utility of the application while guaranteeing energy neutrality of the devices. In this context, we propose a new dynamic programming algorithm for the optimization of the scheduling of the tasks in IoT devices that harvest energy by means of a solar panel. We show that the problem is NP-Hard and that the algorithm finds the optimum solution in a pseudo-polynomial time. Furthermore, we show that the algorithm can be executed with a small overhead on three popular IoT platforms (namely TMote, Raspberry PI and Arduino) and, by simulation, we show the behavior of the algorithm with different settings and at different conditions of energy production.