Fog computing paradigm is emerging after a decade's dominance of cloud-based system design and architecture. Now, instead of centralising the computation and coordination to remote services, these are deployed and distributed to all over physical surroundings and network nodes, including cloud services, smart gateways, and network edge devices. At the moment, the majority of the Internet of things (IoT) systems and software has built on top of open Web-based technologies. The authors assume that with the ever-growing number and heterogeneity of connected devices, it becomes ever-more crucial to have open standards that support interoperability and enable interactions. They review the current technological space for architecting Web technology-based IoT software in the coming era of fog computing. They focus on fundamental research challenges and discuss the emerging issues.

The entire world is moving towards the era of smart technology. Internet of Things (IoT) is the key factor behind this revolutionary change. IoT converts real-world objects into interactive smart systems which can be controlled from remote locations. These physical real-world objects tagged as ‘Things’ in IoT should have a unique identity. These ‘things’ can be remotely controlled and monitored from anywhere in the world through Internet. Due to this feature it has been applied in various domains like home automation, patient health monitoring, agricultural sector, environment surveillance and so on. Due to the increased use of IoT-based system, there arises a need for a well-defined software architecture. Though various IoT developers follow their own design strategies for IoT architecture, there is a need for domain specific architecture for IoT applications. The authors propose a software architecture pattern selection model which can be followed in designing IoT systems. Non-functional requirements such as scalability, availability, reliability, security and heterogeneity determine the decision making of the proposed model. Analytical results prove that this model can be used as a reference model, for IoT developers in choosing a suitable software architecture pattern for their IoT system.

The Semantic Web ontologies can contain vague axioms, which means the knowledge about them is imprecise and then query answering will not possible due to the open world assumption if the necessary information is incomplete (there is an ignorance about information). An axiom description can be very exact (crisp axiom) or exact (fuzzy axiom) if its knowledge is complete, otherwise it is inexact (vague axiom) if its knowledge is incomplete. Here, the author proposes vagueness description with meta-level logic programming to describe vague ontologies. These vagueness descriptions are inputs to vagueness reasoning procedure implemented at meta-level, which is based on extended tableau algorithm. The extended tableau algorithm is intended to answer queries even with the presence of imprecise information.

As Internet-of-Things (IoT) devices become more powerful, they can also become full participants of Internet architectures. For example, they can consume and provide RESTful services. However, the typical network infrastructures do not support the architecture and middleware solutions used in the cloud-based Internet. The authors show how systems designed with RESTful architecture can be implemented by using an IoT-specific technology called message queuing telemetry transport (MQTT). Their example case is an application development and deployment system that can be used for remote management of IoT devices. To evaluate the proposed solution, they performed resource consumption experiments to compare HTTP and request-response usage of MQTT. The results suggest that MQTT uses less central processing unit time and memory.

This study presents a P300-based brain-computer interface (BCI) for mechatronic device driving, i.e. without the need for any physical control. The technique is based on a machine learning (ML) algorithm, which exploits a spatio-temporal characterization of the P300, analyses all the discrimination scenarios through a multiclass classification problem. The BCI is composed of the acquisition unit, the processing unit and the navigation unit. The acquisition unit is a wireless 32-channel electroencephalography headset collecting data from six electrodes. The processing unit is a dedicated µPC performing stimuli delivery, ML and classification, leading to the user intention interpretation. The ML stage is based on a custom algorithm (tuned residue iteration decomposition) which trains the classifier on the user-tuned P300 features. The extracted features undergo a dimensionality reduction and are used to define decision boundaries for the real-time classification. The real-time classification performs a functional approach for the features extraction, reducing the amount of data to be analyzed. The Raspberry-based navigation unit actuates the received commands, supporting the wheelchair motion. The experimental results, based on a dataset of seven subjects, demonstrate that the classification chain is performed in 8.16 ms with an accuracy of 84.28 ± 0.87%, allowing the real-time control of the wheelchair.

Recent technologies are offering today many possibilities to end users, which ask for continuous support in a variety of situations. Internet of things (IoTs) and the proliferation of smart devices are offering many opportunities that raise the need to standardise protocols for their interoperability and interaction languages for their management. This study proposes EUDroid, a system composed of a mobile application and an IoT device used as a pill reminder to allow the patients to correctly take their prescribed drugs. A web server stores and manages the therapies that can be defined by the end users. The web server also manages the communication between the app and the device. In order to validate the management of the therapies, a formal language has been proposed. It describes the behaviour of different components of the IoT device, such as LEDs or buzzers, and defines when, with which delay, and for how long time a given event will last, to manage technical concepts related to smart devices for supporting them in following therapies more accurately.

Internet of things (IoT) systems are becoming ubiquitous and assuring their quality is fundamental. Unfortunately, a few proposals for testing these complex, and often safety-critical, systems are present in the literature. The authors propose an approach for acceptance testing of IoT systems adopting graphical user interfaces as a principal way of interaction. Acceptance testing is a type of black box testing based on test scenarios, i.e. sequences of steps/actions performed by the user or the system. In their approach, test scenarios are derived from a state machine that expresses the behaviour of the system under test, and test cases are derived from them by specifying the actual data and assertions and made executable by implementing the corresponding test scripts. As a case study, they selected a mobile health IoT system for diabetes management composed of local sensors/actuators, smartphones, and a remote cloud-based system. The effectiveness of the approach has been evaluated by measuring the capability of two test suites implemented using different localisation strategies (visual and structure-based) in detecting mutants of the original m-health system. Results show the effectiveness of the test suites implemented by following the proposed approach since 93% of the generated mutants have been detected.