Major challenges are presented when managing a large number of heterogeneous vehicles that have to communicate underwater in order to complete a global mission in a cooperative manner. In this kind of application domain, sending data through the environment presents issues that surpass the ones found in other overwater, distributed, cyber-physical systems (i.e., low bandwidth, unreliable transport medium, data representation and hardware high heterogeneity). This manuscript presents a Publish/Subscribe-based semantic middleware solution for unreliable scenarios and vehicle interoperability across cooperative and heterogeneous autonomous vehicles. The middleware relies on different iterations of the Data Distribution Service (DDS) software standard and their combined work between autonomous maritime vehicles and a control entity. It also uses several components with different functionalities deemed as mandatory for a semantic middleware architecture oriented to maritime operations (device and service registration, context awareness, access to the application layer) where other technologies are also interweaved with middleware (wireless communications, acoustic networks). Implementation details and test results, both in a laboratory and a deployment scenario, have been provided as a way to assess the quality of the system and its satisfactory performance.

Nowadays, autonomous vehicles are incorporated into farms to facilitate manual labour. Being connected vehicles, as IoT systems, they are susceptible to cyber security attacks that try to cause damage to hardware, software or even living beings. Therefore, it is important to provide sufficient security mechanisms to protect both the communications and the data, mitigating any possible risk or harm to farmers, livestock or crops. Technology providers are aware of the importance of ensuring security, and more and more secure solutions can be found on the market today. However, generally, these particular solutions are not sufficient when they are part of complex hybrid systems, since there is no single global solution proposal. In addition, as the number of technologies and protocols used increases, the number of security threats also increases. This article presents a cyber-security architecture proposal for swarms of heterogeneous vehicles in smart farming, which covers all of the aspects recommended by the ISO 7798-2 specification in terms of security. As a result of this analysis, a detailed summary of the possible solutions and available technologies for each of the communication channels of the target system as well as some recommendations are presented.

The utilization of autonomous maritime vehicles is becoming widespread in operations that are deemed too hazardous for humans to be directly involved in them. One of the ways to increase the productivity of the tools used during missions is the deployment of several vehicles with the same objective regarding data collection and transfer, both for the benefit of human staff and policy makers. However, the interchange of data in such an environment poses major challenges, such as a low bandwidth and the unreliability of the environment where transmissions take place. Furthermore, the relevant information that must be sent, as well as the exact size that will allow understanding it, is usually not clearly established, as standardization works are scarce in this domain. Under these conditions, establishing a way to interchange information at the data level among autonomous maritime vehicles becomes of critical importance since the needed information, along with the size of the transferred data, will have to be defined. This manuscript puts forward the Maritime Data Transfer Protocol, (MDTP) a way to interchange standardized pieces of information at the data level for maritime autonomous maritime vehicles, as well as the procedures that are required for information interchange.

The Internet of Things (IoT) envisions billions of devices seamlessly connected to information systems, thus providing a sensing platform for applications. The availability of such a huge number of smart things will entail a multiplicity of devices collecting overlapping data and/or providing similar functionalities. In this scenario, efficient discovery and appropriate selection of things through proper context acquisition and management will represent a critical requirement and a challenge for future IoT platforms. In this work we present a practical approach to model and manage context, and how this information can be exploited to implement QoS-aware thing service selection. In particular, it is shown how context can be used to infer knowledge on the equivalence of thing services through semantic reasoning, and how such information can be exploited to allocate thing services to applications while meeting QoS requirements even in case of failures. The proposed approach is demonstrated through a simple yet illustrative experiment in a smart home scenario.