The Internet of Things (IoT) is a rapidly evolving technology that is changing almost every business, and aquaculture is no exception. In this work we present an integrated IoT platform for the acquisition of environmental data and the monitoring of aquaculture environments, supported by a real-time communication and processing network. The complete monitoring platform consists of environmental sensors equipped in a swarm of mobile Unmanned Surface Vehicles (USVs) and Buoys, capable of collecting aquatic and outside information, and sending it to a central station where it will be stored and processed. The sensing platform, formed by the USVs and Buoys, are equipped with multi-communication technology: IEEE 802.11n (Wi-Fi) and Bluetooth for short range communication, for mission delegation and the transmission of data collection, and LoRa for periodic report. On the back-end side, supported by FIWARE technology, an interactive web-based platform can be used to define sensing missions and for data visualization. Results on the sensing platform lifetime, mission control and delay processing time are presented to assess the performance of the aquatic monitoring system.

Having connectivity as a pivotal element, Internet of Things (IoT) systems rely on wireless technologies to provide communication to end devices. A growing set of technologies, designated as Low-Power Wide Area Networks (LPWANs), are becoming prominent technologies for smart cities applications due to their long range, effective cost and energy efficiency. The purposed multi-technology opportunistic platform for environmental data gathering addresses the heterogeneity of IoT devices, through the employment of multi-technology communications, LoRa and WiFi, and serves as infrastructure for a network with opportunistic communication capabilities. Furthermore, to increase the operability of the proposed platform, we present and evaluate a novel Medium Access Control (MAC) scheme capable of supporting a reliable communication through LoRa technology over a multi-sink environment without direct contact. The results show that, by increasing the number of LoRa sinks, with the purpose of covering a wider area, the proposed MAC protocol is able to manage efficiently the medium access of each data gathering unit, therefore increasing the channel occupancy and reducing the delay of each transmission.

The Smart City concept is starting to extend into maritime environments alongside with the increase of Unmanned Surface Vehicles (USV) models on the market. Consequently, by joining both Smart City and USV technologies, a set of platforms and applications for aquatic environments are emerging. This work proposes a low-cost aquatic mobile sensing platform for data gathering with a swarm of USVs communicating through a Delay-Tolerant Network (DTN). A set of DTN link quality-based routing strategies select the best quality path in a dynamic approach so the sensed information is able to reach the mobile gateway in a reliable way. A Link Quality Estimation (LQE) approach is proposed and its accuracy is evaluated through real experimentation. An aquatic simulation environment, considering both navigation and communication layers, was also proposed and used to evaluate the performance of the proposed routing strategies, and complement real environment performance studies.

Spontaneous networks may need to establish connections with already-in-place devices and their resources, irrespectively of their capabilities, for example, in emergency scenarios. Interoperability and integration are a key issue when combining multiple wireless networks and heterogeneous devices. To address this issue, this article extends the SDN concept, and presents a new hybrid network architecture that combines legacy devices in a wireless data plane, therefore increasing the range, and making the network more efficient, dynamic and programmable. In this article, we propose an optimized adaptive routing approach in a network architecture that joins SDN-capable devices with legacy devices for emergency scenarios. This solution takes into consideration the impact that a flow can have in the network, therefore minimizing its channel interference, and to a certain degree, balancing the load in the network’s devices. In this work, the importance of the network and service parameters is optimized through machine learning for each flow’s data rate, and then feed into the routing approach to determine the best hybrid legacy and SDN-based path. Results show that the new architecture can successfully integrate the legacy devices in the wireless software defined data plane, and utilize them to increase the performance. The combination of delivery ratio and delay are always relevant to every data rate. The link stability is more relevant for lower data rates, while the break probability is more relevant for higher data rates. Moreover, the number of hops and possible interference combined have a high importance in every data rates, and the nodes’ speed knowledge can have a good impact on the network performance.