Recently both academic and industry worlds have started to define the successor of long term evolution, so-called 5G networks, which will most likely appear by the end of the decade. It is widely accepted that those 5G networks will have to deal with significantly more challenging requirements in terms of provided bandwidth, latency and supported services. This will lead to not only modifications in the access segment and parts of core networks, but will trigger changes throughout the whole network, including the Back-haul segment. In this work the authors present their vision of a 5G Back-haul network and identify the associated challenges. They then describe their wireless Back-haul architecture, which implements software defined network concepts and further extends them into the wireless domain. Finally the authors present a brief overview of their evaluation results.

Networked visual applications such video streaming have grown exponentially in recent years, yet are known to be sensitive to network impairments. However, available measurement techniques that adopt a full reference model are impractical in real-time streaming because they require the original video sequence available at the receivers side. The primary aim of this study is to present a hybrid no-reference prediction model for the perceptual quality of 4kUHD H.265-coded video in the wireless domain. The contributions of this paper are two-fold: first, an investigation of the impact of quality of service (QoS) parameters on 4kUHD H.265-coded video transmission in an experimental environment; second, objective model based on fuzzy logic inference system is developed to predict the visual quality by mapping QoS parameters to the measured quality of experience. The model is evaluated in contrast to random neural networks. The results show that good prediction accuracy was obtained from the proposed hybrid prediction model. This study will help in the development of a reference-free video quality prediction model and QoS control methods for 4kUHD video streaming.

The ability to transmit video and carry real-time multimedia applications such as video-streaming, video conferencing and mobile TV are considered important to support the viability and survivability of next generation mobile wireless networks. High peak transmission rate, spectral efficiency and multiple channel bandwidth requirements influence the design of next generation networks. For efficient usage of resources a number of block assignment strategies are followed based on congestion of traffic and priority of the service. Efficient block assignment schemes such as dynamic channel assignment (DCA) and dynamic packet Assignment (DPA) have been analysed in the literature. The authors propose a block assignment strategy for video traffic which sends video bursts over multiple blocks of orthogonal frequency division multiplexed access subcarriers simultaneously considering the interference characteristics of the channel. They present an analytical model using stochastic petrinets and compute the performance of the system in terms of mean delay and normalised system throughput. The results are validated using simulations. They compare the performance of the proposed sub-sequence DPA scheme in terms of mean delay and normalised throughput with DPA and DCA. It is observed that with the proposed strategy there is an increase in normalised throughput by 66 and 57% and reduction in mean delay by 74 and 61% for DCA and DPA, respectively.

The major challenge in sensor networks is how to have a perfect coverage of the region to be monitored. This study reports a novel algorithm inspired by centralised cum sub-centralised scheme that works efficiently for handling multiple events that occur simultaneously, while reducing the number of unnecessarily activated cameras so as to cover the occurring event region effectively. When any event takes place, the sensing of event occurs up to certain extent outside the exact event boundary. Hence, the cameras present beyond the boundary of event are activated unnecessarily. Since the proposed methodology avoids undesirable activation of cameras, the additional energy and power expenditure due to those cameras as well as the redundant data transmission prevailing due to them are eliminated. Results obtained from experimental evaluation reveal that the proposed methodology significantly reduces the number of unnecessarily activated cameras while providing the adequate coverage of event region for multi-event occurrence. Further  more, the reduced camera actuation with increased coverage ratio, minimised redundancy ratio, improved field of view utilisation, less energy as well as power expenditure required for camera activation obtained from the investigation justifies the effectiveness of the proposed algorithm as compared with two other approaches recently proposed in the literature.

Exploitation of smart sensor data has the potential to significantly improve the way in which content is wirelessly delivered to end user devices within visitor attractions. Content delivery over wireless infrastructure can use knowledge and predictions of performance for intelligently prefetching and adaptively exploiting opportunities in order to enhance the overall user experience. The advantage of this approach over prior static multicast and on-demand approaches is that it is possible to exploit location awareness (such as using global positioning system, iBeacons) combined with accelerometer and compass and other sensors, to plan and adapt content delivery in a more efficient and flexible manner. This study assesses different options for prioritisation and prefetching of content over dedicated wireless fidelity off-load infrastructure to compare the ability to satisfy the user demands. The overall assessment, based on trials performed at two visitor attractions, shows that intelligent predictive prefetching of content can greatly reduce the need for expensive infrastructure compared with alternative static reliable multicast or on-demand delivery strategies.

Smart microgrid initiatives for recent urbanisation at power distribution level need an integrated and interoperable environment that can work collectively with local energy systems and utility grid. This is achieved through the design of an architecture by using contemporary information and communication technology. System architecture provides a common work process that span across all the critical subsystems of a building for better informed decisions with reduced human interventions. Two key aspects of architecture development are, the way of its presentation and network reliability. The architecture should be clear in its presentation for the understanding of various stakeholders and possess fault tolerant communication mechanisms to have high network reliability. Considering aforementioned aspects, this study presents review of state-of-the-art architectures developed by IEEE-1547, ISA-95, National Institute of Standards and Technology, IEC-61850 for smart microgrids formation and suggests retrofitted architectures to improve architecture clarity by presenting in a more lucid way and network reliability by the concept of redundant communication paths.

Monitoring and analysis of energy use and indoor environmental conditions is an urgent need in large buildings to respond to changing conditions in an efficient manner. Correct estimation of occupancy will further improve energy performance. In this work, a smart controller for maintaining a comfortable environment using multiple random neural networks (RNNs) has been developed. The implementation of RNN-based controller is demonstrated to be more efficient on hardware and requires less memory compared to both artificial neural networks and model predictive controllers. This controller estimates the number of room occupants by using the information from wireless sensor nodes placed in the Heating, Ventilation and Air Conditioning (HVAC) duct and the room. For an occupied room, the controller can switch between thermal comfort mode (based on predicted mean vote set points) and user defined mode (i.e. occupant defined set points for heating/cooling/ventilation). Furthermore, the hybrid particle swarm optimisation with sequential quadratic programming training algorithms are used (for the first time to the best of the authors' knowledge) for training the RNN and results show that this algorithm outperforms the widely used gradient descent algorithm for RNN. The results show that occupancy estimation by smart controller is 83.08% accurate.