ICT advances in emerging domains such as Internet of Things (IoT), Augmented Reality/ Virtual Reality (AR/VR), big data analytics, cyber-physical systems and cloud computing have revolutionized and boosted the creation of cyberworlds as information spaces that allow us to augment the way we interact with each other and with the physical world. Naturally, other than businesses cyberworlds can benefit modern hyper connected societies at their entirety (transport, mobility, health, smart living, etc.) and further to that the physical world around us can also be part of such process. In the present paper the focus is given on citizen-powered cyberworlds for Environmental Monitoring which are created from crowdsourced observations engaging, through gamification, citizens and communities. The means of engagement include serious gaming, collection of geo-tagged IoT, such as images, video and sensor measurements as well as management and storage of diverse IoT as OGC compliant observations all conveyed into a dedicated information space.

Emergency situations, such as fire incidents or terrorist attacks, have imposed strongest safety requirements for crowds gathering. The enhancement of situation awareness, decision support and emergency notification systems that facilitate the safety and effectiveness of evacuation processes in a major incident is therefore of utmost importance. ICT, IoT and smart systems have gained much attention in many industries and application domains in the latest years as they can play a significant role by introducing novel systems and applications for intelligent, safe and fast evacuation of people. This work focuses on the development of a communication gateway that can support and improve emergency notification systems by providing appropriate, updated and accessible information to operators and people in affected zones in a reliable way. The presented approach uses an adaptable data model that can be applied to evacuation systems consisting of heterogeneous components, such as legacy systems, novel devices, sensors and actuators. The proposed communication gateway follows the publish/subscribe communication standard for real-time systems and is built on Data Distribution Service (DDS) of the Object Management Group (OMG). The evacuation systems are hereby transformed into interoperable, distributed, scalable, robust, and real-time IoT infrastructures.

An analytically solvable mathematical model is presented for an IEEE 802.11 network, where two terminals, both visible to an access point but not to each other, contend for the channel. This situation, known as the hidden node topology, has been identified as a key reason for the degradation of the protocol's performance. It is shown that the renewal theory, the analytical tool used traditionally, is not suitable for this topology and a discrete time Markov chain is introduced for the modelling of the channel contention. The model permits the accurate computation of several key performance metrics and is in agreement with simulation results over a variety of scenarios.

Recently research interest in the performance analysis of wireless networks was revived. An issue of an utmost importance in this class of networks, classified as one of the severest reasons for the degradation of their performance, is the hidden terminal problem. In this paper we argue that an accurate analysis of the effect of hidden nodes in the performance of a random access protocol is still an open issue. Firstly, we thoroughly explain the reasons behind the limitations of previous modelling methods, and show that their performance is reliable only for certain configurations. Secondly, and most importantly, we propose a novel method of modelling time that uses a fixed-length channel slot as the unit of time and does not rely on renewal theory. With these features the model is able to successfully take into account the desynchronisation of nodes in a hidden terminal environment. Our analytical model is shown to have a very close match to simulation results for an IEEE 802.11 MAC protocol and for all the system parameters considered, unlike conventional methods.

A promising new class of wireless networks has recently emerged, the so-called Wireless Mesh Networks (WMNs). WMNs inherit many advantages from both wired infrastructure and also ad-hoc networks. However, in spite of their distinct characteristics, they also face many technical problems that were present in older classes of wireless networks, such as wireless LANs or pure ad-hoc networks. The much discussed hidden terminal problem is one of them. We argue that an accurate analysis of the effect of hidden nodes in the performance of a random access protocol is still an open issue. In this paper we show the problems and the limitations stemming from common assumptions used in the literature and propose a novel method of modeling time that uses a fixed size channel slot as the unit of time in order to take into account the desynchronisation of nodes. Finally we apply our general method to the classic hidden terminal problem incorporating an iterative step. The proposed analytical model is shown to have a very good match to simulation results for an IEEE 802.11 MAC protocol.

The last couple of years were characterised by the revival of interest in the performance evaluation of wireless multihop networks. An issue of great significance in carrier-sense based multihop networks is the hidden node problem. In our recent work we showed that previous techniques are suitable for limited node configurations and we proposed a novel time model that relaxes most assumptions in the literature and exhibits high accuracy of performance evaluation. The present paper extends our recent work, which was based on a constant contention window assumption, to the binary exponential backoff (BEB) mechanism of IEEE 802.11, with an equally high degree of agreement to simulation results. Key points of our analysis are the consideration of different collision probabilities, dependent on the backoff stage, and the use of a discrete time Markov chain embedded in the channel state evolution that allows us to compute the 'backoff-dwelling' distribution of each transmitter