The higher education sector faces significant challenges, including the need to enhance student employability, engagement, and financial accessibility. Universities must equip graduates with both academic knowledge and practical skills while addressing financial barriers that hinder many students' progress. The MDX Internship Scheme, launched at Middlesex University in January 2024, responds to these challenges by offering paid, discipline-specific internships that integrate practical work experience with academic learning. The scheme enhances students' career readiness by enabling them to apply theoretical knowledge in real-world settings, develop essential skills such as teamwork and problem-solving, and build confidence through project achievements. Moreover, the internships alleviate financial burdens and foster a sense of belonging. This paper showcases the experiences of engineering undergraduates, postgraduates, and doctoral candidates, who participated in the MDX Internship scheme and undertook diverse projects spanning academic research, industry placements, and collaborations with local authorities, deepening their understanding of engineering and transferable skills. Local businesses also benefited from the scheme, gaining access to innovative student talent that positively influenced workplace dynamics and project outcomes, while building long-term partnerships with the university. The MDX Internship Scheme serves as a model for enhancing employability, engagement, and financial support in higher education.

The increasing diversity of vertical applications in Beyond 5G (B5G) networks has driven widespread adoption of wireless network emulators for developing and evaluating innovative solutions. However, the effectiveness of these solutions heavily depends on the precise parameters configuration, accurate network model design, and the fidelity of the emulation process. Furthermore, achieving real-time resource allocation for heterogeneous traffic remains a significant challenge. To address these complexities, the Open Radio Access Network (O-RAN) concept has gained prominence, leveraging enhanced programmability of RAN functionalities to enable data-driven, intelligent control loops in cellular networks. In this context, we propose ORBIT-DT, an Open RAN-Based Intelligent Twin - Digital Twin, to address the challenges of accurate modeling and resource allocation in B5G networks. Leveraging the open-source Colosseum platform, we design and emulate the ORBIT-DT of POWDER, a publicly available over-the-air sub-6GHz indoor/outdoor testbed. Through extensive experiments, we validate the performance of the proposed ORBIT-DT across various wireless scenarios including eMBB, mMTC, and URLLC slices, comparing it with its Physical Twin (PT). Results demonstrate the ORBIT-DT's ability to support large-scale data collection and achieve a high degree of fidelity, with an average similarity of up to 98.8% in downlink throughput, underscoring its effectiveness as a realistic and reliable representation of the PT.

The recent digital transformation of higher education underscores the crucial role of STEM disciplines in addressing real-world challenges, emphasizing the urgent need for problem-solving skills, creativity, and diversity within the STEM student community. Despite growing awareness of the gender disparity in STEM professions, women remain underrepresented in these fields. This paper conducts a comprehensive study delving into the determinants influencing the decision to pursue a STEM degree and the challenges faced during STEM education. Through survey data analysis, the research explores actionable mechanisms to enhance the presence of women in STEM subjects and support their career progression. The study not only identifies obstacles but also formulates a set of recommendations to establish a robust support system for women in STEM. These recommendations aim to foster their academic retention, facilitate continuous professional development, and contribute to narrowing the gender gap in STEM occupations. By addressing the root causes and proposing concrete solutions, this research seeks to contribute to a more inclusive and equitable STEM landscape, fostering an environment where women can thrive and make significant contributions to the ever-evolving field of science and technology. Furthermore, this research serves as a call to action for institutions, policymakers, and stakeholders to collectively champion initiatives that empower and propel women to excel in STEM fields.

With the latest developments in both the automotive and communications industries, especially concerning the emerging 5G networks, IoV, and the adoption of Vehicle-to-Everything (V2X) connectivity, there has been a shift towards the establishment of Heterogeneous Vehicular Networks (HetVNets) environments. The rapid growth of data traffic and the drastic expansion of heterogeneous network infrastructure have resulted in a significant increase in energy consumption within wireless communication systems. Balancing energy efficiency and spectral efficiency has become a major challenge in Heterogeneous Vehicular networks, particularly concerning energy optimization, making the design of network systems considerably more challenging. Therefore, this paper attempts to optimize the energy utilized for each packet transmission, considering its stochastic nature and the optimized control parameters of two meta-heuristic algorithms-Particle Swarm Optimization and Artificial Bee Colony Optimization. The optimization process is executed using the Particle Bee Colony Swarm algorithm. Subsequently, a comparison is made with other proposed algorithms, namely LDOD, FO, RO, and MATO, in terms of energy efficiency and spectral efficiency. The performance analysis reveals that the numerical results outperform existing algorithms, demonstrating a 30.32% increase in spectral efficiency and 73.25% increase in energy efficiency.

Omnidirectional videos have become a leading multimedia format for Virtual Reality applications. While live 360$^\circ$videos offer a unique immersive experience, streaming of omnidirectional content at high resolutions is not always feasible in bandwidth-limited networks. While in the case of flat videos, scaling to lower resolutions works well, 360$^\circ$video quality is seriously degraded because of the viewing distances involved in head-mounted displays. Hence, in this article, we investigate first how quality degradation impacts the sense of presence in immersive Virtual Reality applications. Then, we are pushing the boundaries of 360$^\circ$technology through the enhancement with multisensory stimuli. 48 participants experimented both 360$^\circ$scenarios (with and without multisensory content), while they were divided randomly between four conditions characterised by different encoding qualities (HD, FullHD, 2.5K, 4K). The results showed that presence is not mediated by streaming at a higher bitrate. The trend we identified revealed however that presence is positively and significantly impacted by the enhancement with multisensory content. This shows that multisensory technology is crucial in creating more immersive experiences.

This article presents a general system framework that lays the foundation for reconfigurable intelligent surface (RIS)-enhanced broadcast communications in Industrial Internet of Things (IIoTs). In our system model, we consider multiple sensor clusters co-existing in a smart factory where the direct links between these clusters and a central base station (BS) are blocked completely. In this context, a RIS is utilized to reflect signals broadcast from BS toward cluster heads (CHs) which act as a representative of clusters, where BS only has access to the statistical distribution of the channel state information (CSI). An analytical upper bound of the total ergodic spectral efficiency (SE) and an approximation of outage probability are derived. Based on these analytical results, two algorithms are introduced to control the phase shifts at RIS, which are the Riemannian conjugate gradient (RCG) method and the deep neural network (DNN) method. While the RCG algorithm operates based on the conventional iterative method, and the DNN technique relies on unsupervised deep learning (DL). Our numerical results show that both algorithms achieve satisfactory performance based on only statistical CSI. In addition, compared to the RCG scheme, using DL reduces the computational latency by more than ten times with an almost identical total ergodic SE achieved. These numerical results reveal that while using the conventional RCG method may provide unsatisfactory latency, and the DNN technique shows much promise for enabling RIS in ultrareliable and low-latency communications (URLLC) in the context of IIoTs.

This article proposes a novel Industrial Internet of Things framework to monitor the machine health conditions (MHCs) in a smart factory. The framework utilizes the reconfigurable intelligent surface (RIS) to address propagation blockages while employing a novel power mapping scheme and an autoencoder to facilitate the transmission and classification of the MHCs. Analytical and numerical analyses are then performed to study the ergodic capacity (primary information) and the MHC accuracy (secondary information) in terms of the RIS size ($K$) and the transmit power ($P$). We observe that the accuracy of detecting MHCs does not change significantly with$K$and$P$, implying that the MHC alerts can be efficiently conveyed in parallel with the primary information. In contrast, a careful choice of different power mapping levels is necessary in order to achieve the two main goals: 1) reasonably high data rate for primary transmission and 2) high accuracy for secondary MHC information.

Contact tracing apps used in tracing and mitigating the spread of COVID-19 have sparked discussions and controversies worldwide. The major concerns in relation to these apps are around privacy. Ireland was in general praised for the design of its COVID tracker app, and the transparency through which privacy issues were addressed. However, the ”voice” of the Irish public was not really heard or analysed. This study aimed to analyse the Irish public sentiment towards privacy and COVID tracker app. For this purpose we have conducted sentiment analysis on Twitter data collected from public Twitter accounts from Republic of Ireland. We collected COVID-19 related tweets generated in Ireland over a period of time from January 1, 2020 up to December 31, 2020 in order to perform sentiment analysis on this data set. Moreover, the study performed sentiment analysis on the feedback received from a national survey on privacy conducted in Republic of Ireland. The findings of the study reveal a significant criticism towards the app that relate to privacy concerns, but other aspects of the app as well. The findings also reveal some positive attitude towards the fight against COVID-19, but these are not necessarily related to the technological solutions employed for this purpose. The findings of the study contributed to the formulation of useful recommendations communicated to the relevant Irish actors.

Contact tracing apps used in tracing and mitigating the spread of COVID-19 have sparked discussions and controversies worldwide with major concerns around privacy. COVID Tracker app used in the Republic of Ireland was praised in general for the way it addressed privacy and was used as baseline for other contact tracing apps worldwide. The success of the app is dependent on the general public uptake, hence their voice and attitude is the one that really matters. This paper focuses on developing a survey and the methods aiming to examine the attitudes toward privacy during COVID-19 of the general public in the Republic of Ireland and their impact on the uptake of the COVID tracker app. Various privacy models are used and health belief model as well in this purpose. A pilot study with 286 participants show a change in attitude towards privacy during COVID-19 pandemic, with more people willing to share their data in the interest of saving lives. However, privacy attitudes are shown to have impacted the adoption of the app in Ireland.

Most of the existing performance evaluation studies of various routing algorithms are done under limited experimental setups leading to an incomplete picture of the routing algorithm performance under dynamic network conditions. This paper presents a study that compares state-of-the-art routing algorithms over realistic multimedia-based Software Defined Networks (SDNs) with dynamic network conditions and various topology. Routing algorithms remain a key element of the networking landscape as they determine the path the data packets follow. The next-generation networking paradigm offers wide advantages over traditional networks through simplifying the management layer, especially with the adoption of SDN. However, Quality of Service (QoS) provisioning still remains a challenge that needs to be investigated especially for multimedia-based SDNs. This study investigates the impact of state-of-the-art centralized routing algorithms (e.g. MHA, WSP, SWP, MIRA) on multimedia QoS traffic under a realistic environment in terms of PSNR, Throughput, Packet Loss, Delay and QoS rejection.

The massive growth in mobile data traffic and the heterogeneity and stringency of Quality of Service (QoS) requirements of various applications have put significant pressure on the underlying network infrastructure and represent an important challenge even for the very anticipated 5G networks. In this context, the solution is to employ smart Radio Resource Management (RRM) in general and innovative packet scheduling in particular in order to offer high flexibility and cope with both current and upcoming QoS challenges. Given the increasing demand for bandwidth-hungry applications, conventional scheduling strategies face significant problems in meeting the heterogeneous QoS requirements of various application classes under dynamic network conditions. This paper proposes 5MART, a 5G smart scheduling framework that manages the QoS provisioning for heterogeneous traffic. Reinforcement learning and neural networks are jointly used to find the most suitable scheduling decisions based on current networking conditions. Simulation results show that the proposed 5MART framework can achieve up to 50% improvement in terms of time fraction (in sub-frames) when the heterogeneous QoS constraints are met with respect to other state-of-the-art scheduling solutions.

Previous research has shown that adding multisensory media-mulsemedia-to traditional audiovisual content has a positive effect on user Quality of Experience (QoE). However, the QoE impact of employing mulsemedia in 360° videos has remained unexplored. Accordingly, in this paper, a QoE study for watching a 360° video-with and without multisensory effects-in a full free-viewpoint VR setting is presented. The parametric space we considered to influence the QoE consists of the encoding quality and the motion level of the transmitted media. To achieve our research aim, we propose a wearable VR system that provides multisensory enhancement of 360° videos. Then, we utilise its capabilities to systematically evaluate the effects of multisensory stimulation on perceived quality degradation for videos with different motion levels and encoding qualities. Our results make a strong case for the inclusion of multisensory effects in 360° videos, as they reveal that both user-perceived quality, as well as enjoyment, are significantly higher when mulsemedia (as opposed to traditional multimedia) is employed in this context. Moreover, these observations hold true independent of the underlying 360° video encoding quality-thus QoE can be significantly enhanced with a minimal impact on networking resources.

With the emerging trends of virtualization of cloud computing and big data applications, network management has become a challenging problem for optimizing the network state while satisfying the applications' Quality of Service (QoS) requirements. This paper proposes a policy-based management framework over Software-Defined Networks (SDN) for QoS provisioning. The proposed approach monitors the QoS parameters of the active flows and dynamically enforces new decisions on the underlying SDN switches to adapt the network state to the current demanded high-level policies. Moreover, the proposed solution makes use of Neural Networks to identify the violating flows causing the network congestion. Upon detection of a policy violation two route management techniques are implemented, such as: rerouting and rate limiting. The proposed framework was implemented and evaluated within an experimental testbed setup. The results indicate that the proposed PBNM-based SDN framework enables QoS provisioning and outperforms the default SDN in terms of throughput, packet loss rate and latency.

The next generation of wireless networks targets aspiring key performance indicators, like very low latency, higher data rates and more capacity, paving the way for new generations of video streaming technologies, such as 360° or omnidirectional videos. One possible application that could revolutionize the streaming technology is the 360° MULtiple SEnsorial MEDIA (MULSEMEDIA) which enriches the 360° video content with other media objects like olfactory, haptic or even thermoceptic ones. However, the adoption of the 360° Mulsemedia applications might be hindered by the strict Quality of Service (QoS) requirements, like very large bandwidth and low latency for fast responsiveness to the user's inputs that could impact their Quality of Experience (QoE). To this extent, this paper introduces the new concept of 360° Mulsemedia as well as it proposes the use of Reinforcement Learning to enable QoS provisioning over the next generation wireless networks that influences the QoE of the end-users.

Dominated by delay-sensitive and massive data applications, radio resource management in 5G access networks is expected to satisfy very stringent delay and packet loss requirements. In this context, the packet scheduler plays a central role by allocating user data packets in the frequency domain at each predefined time interval. Standard scheduling rules are known limited in satisfying higher quality of service (QoS) demands when facing unpredictable network conditions and dynamic traffic circumstances. This paper proposes an innovative scheduling framework able to select different scheduling rules according to instantaneous scheduler states in order to minimize the packet delays and packet drop rates for strict QoS requirements applications. To deal with real-time scheduling, the reinforcement learning (RL) principles are used to map the scheduling rules to each state and to learn when to apply each. Additionally, neural networks are used as function approximation to cope with the RL complexity and very large representations of the scheduler state space. Simulation results demonstrate that the proposed framework outperforms the conventional scheduling strategies in terms of delay and packet drop rate requirements.

The latest tremendous growth in the Internet traffic has determined the entry into a new era of mega-data centers meant to deal with this explosion of data traffic. However, this big data with its dynamically changing traffic patterns and flows might result in degradations of the application performance eventually affecting the network operators' revenue. In this context, there is a need for an intelligent and efficient network management system that makes the best use of the available bisection bandwidth abundance to achieve high utilization and performance. This paper proposes OFLoad, an OpenFlow-based dynamic load balancing strategy for data center networks that enables the efficient use of the network resources capacity. A real experimental prototype is built and the proposed solution is compared against other solutions from the literature in terms of load-balancing. The aim of OFLoad is to enable the instant configuration of the network by making the best use of the available resources at the lowest cost and complexity.

As Software-Defined Networks (SDN) emerged, the control and forwarding planes were abstracted using the standardized OpenFlow protocol which led to the increasing demand for optimal usage of the control link between the two planes especially for network monitoring. This paper proposes a data collection scheme based on a compression technique for SDN-based networks. It employs sparsity approximation algorithms for compressing the aggregated data in the SDN switch, while the recovery of the sparse data is taking place at the controller. The approach aims at further decreasing the link usage for Quality of Service (QoS) applications while increasing the network observability. The proposed solution extends the functionality of the SDN switch by integrating dictionary learning algorithms like K-SVD and Orthogonal Matching Pursuit (OMP) methods for the purpose of sparsity approximation. Experimental setup and the QoS link utilization metric for link monitoring were used for performance evaluation. The proposed solution was analysed over a range of sparsity levels, showing the data recovery accuracy of the controller under different compression ratios and using real internet traces. The results show that the proposed method reduces the control link overhead cost with up to 98% when compared to the case of periodic acquisition network monitoring of the SDN network.

This paper investigates double-threshold-based energy detector for cooperative spectrum sensing mechanisms in cognitive wireless radio networks. We first propose a hybrid double-threshold-based energy detector (HDTED) to improve the sensing performance at secondary users (SUs) by exploiting both the local binary/energy decisions and global binary decisions feedback from the fusion center (FC). Significantly, we derive closed-form expressions and bounds for the probabilities of missed detection and false alarm considering a practical scenario where all channel links suffer from Rayleigh fading and background noise. The derived expressions not only show the improved performance achieved with the HDTED scheme but also enable us to analyze the impacts of the number of the SUs and the fading channels on the cooperative spectrum sensing performance. Furthermore, based on the derived bounds, we propose an optimal SU selection algorithm for forwarding the local decisions to the FC, which helps reduce the number of forwarding bits for a lower-complexity signaling. Finally, numerical results are provided to demonstrate the validity of the analytical findings.

The rapid advances in technology lead to a mass-market adoption of the multi-radio high-end smartphone devices and an exponential increase in the mobile broadband data traffic. In the search for a solution to cope with this explosion of data traffic, Device-to-Device (D2D) communications become an attractive approach for enhancing the performance of cellular networks. The emerging of WiFi Direct framework introduced new opportunities for mobile-to-mobile opportunistic offloading. In this context, this paper proposes ECO-M, an Energy-efficient Cluster-Oriented solution for Multimedia delivery in a LTE D2D environment using WiFi Direct. Experimental test-bed measurements and numerical results show the efficiency of such a solution in terms of energy efficiency, battery discharge and battery lifetime when compared with a standard LTE environment. Subjective tests were carried out to study not only the impact of user perceived quality on the various multimedia quality levels choices but also the impact of user preferences in terms of energy conservation versus multimedia quality.

This paper investigates downlink performance of a heterogeneous wireless cellular network (HWCN) consisting of multi-tier cells operating within various indoor and outdoor propagation environment models. The coverage probability of the practical HWCN is derived using stochastic geometry modeling. The derived expressions not only characterise the effects of building infrastructures and transmission environment on the coverage region in practice but also enable us to propose an optimal power allocation at cells in different tiers. Specifically, a heuristic algorithm is developed for the power allocation at the tiered cells within various propagation models to minimise the total power consumption. The proposed scheme is shown to achieve a lower total power consumption saving up to 45% for a given target coverage probability of a specific region when compared to the equal power allocation technique.

Due to the ever growing mobile broadband data traffic over the cellular networks, the small cell deployment is seen as a promising solution for the network operators to increase their network capacity at low cost. This in turn would lead to an increase number of handovers (HOs) for the mobile users, which could affect the device power consumption. In this context, this paper investigates the impact of the HO process on the device energy consumption while performing VoD over a real LTE Small Cell experimental environment. Subjective tests are carried out to study the impact of the video quality on the user perceived QoE. The results show that by changing the quality level of the multimedia stream the energy can be greatly conserved while the user perceived QoE is still acceptable. Furthermore, by adapting to a lower quality level during the HO process, up to 56% energy savings could be achieved.

Over the past decade there has been an exponential increase in the Internet traffic especially with the proliferation of cloud computing and other distributed data services. This explosion of data traffic with its dynamically changing traffic patterns and flows might result in degradation of the network performance. In this context, there is a need for an intelligent and efficient network management system that delivers guaranteed services. To this extent, this paper proposes BaProbSDN, a probabilistic Quality of Service (QoS) routing mechanism for Software Defined Networks (SDN). The QoS routing algorithm employs the bandwidth availability metric as a QoS routing constraint for unicast data delivery. BaProbSDN makes use of Bayes' theorem and Bayesian network model to determine the link probability in order to select the route that satisfies the given bandwidth constraint. The performance of the proposed probabilistic QoS routing algorithm was tested in a simulation-based environment and compared against the widest-shortest path routing (WSR) algorithm. The results demonstrate that BaProbSDN can achieve up to 8.02% decrease in the bandwidth blocking rate when compared to WSR in the presence of link update inaccuracies of threshold and time delay.

One of the main challenges towards reliable multicast transmissions over wireless networks is the dynamics of the wireless links (e.g. wireless errors, fading, interference, collisions, etc.) that can cause retransmissions overhead over the limited available bandwidth. To this end this paper considers the scenario of wireless multicast networks where network coding is applied to improve network throughput. We first propose a novel cross-layer optimisation framework for network topology design in order to optimise the wireless multicast rate, data flow of the wireless links, energy supply and node lifetime. The performance of the proposed solution is evaluated and compared against other solutions from the literature in terms of system throughput, total energy, and network lifetime. The results show that the proposed cross-layer design outperforms the other schemes involved, reaching up to 50% increase in the system throughput.

This paper is concerned with cooperative spectrum sensing (CSS) in cognitive wireless radio networks (CWRNs). A practical scenario is investigated where all channels suffer from Nakagami-m fading. Specifically, we analyse the probabilities of missed detection and false alarm for two CSS schemes where the collaboration is carried out either at fusion centre (FC) only or at both the FC and secondary user (SU). By deriving closed-form expressions and bounds of these probabilities, we not only show that there are significant impacts of the m-parameter of Nakagami fading realisation for different channel links on the sensing performance but also evaluate and compare the effectiveness of the two CSS schemes with respect to various fading parameters and the number of SUs. Finally, numerical results are provided to validate the theoretical analysis and findings.

Mobile devices became an essential part of every person daily routine enabling them to browse the Internet, watch videos, work and play online anytime and anywhere. However this led to a tremendous growth in user generated data traffic putting significant pressure on the underling network technology. Thus, in order to cope with this explosion of data traffic, Wi-Fi offload became a popular solution for network operators. The solution enables the network operators to accommodate more mobile users and keep up with their traffic demands. Moreover, with the energy conservation becoming a critical issue around the world, it provides motivation for this paper to propose an Energy-aware Device-Oriented Adaptive multimedia Scheme (eDOAS) for Wi-Fi Data Offload. eDOAS adapts the interactive multimedia application to the underlying Wi-Fi network conditions, device characteristics and device energy consumption, in order to prolong the battery lifetime of the mobile device and maintain an acceptable user perceived quality level. Real test-bed energy consumption measurements were conducted on five different devices and the performance of eDOAS was analyzed against other schemes from the literature, in terms of energy consumption, service outage, average throughput, packet loss and PSNR.

The reduction of energy consumption is a major concern in the current telecommunications environment - especially with the growth in usage of energy-hungry multimedia-centric applications on high-end mobile devices. In this context, this paper proposes eSMART, an Energy-efficient Scalable Multimedia Broadcast Transmission mechanism, that considers the energy-quality trade-off to reduce battery power consumption (increase energy saving) of heterogeneous mobile devices while maintaining acceptable perceived quality levels of received video. A real experimental test-bed has been built to analyze the impact of different multimedia scalability factors on the energy consumption of various mobile devices receiving broadcast content. Overall mobile device energy-saving is modeled using the accumulative effect of adaptive scalable video playback energy saving and time-sliced broadcast reception based radio-receiver's energy saving. eSMART's optimization framework performs user-centric adaptive encoding of scalable video that is broadcast to heterogeneous user equipments. eSMART serves more users at improved quality of experience levels and achieves up to 69% increase in mobile device energy savings as compared to a non-adaptive time-slicing scheme from the literature.

This paper presents a novel cross-layer optimization framework to improve the quality of user experience (QoE) and energy efficiency of the heterogeneous wireless multimedia broadcast receivers. This joint optimization is achieved by grouping the users based on their device capabilities and estimated channel conditions experienced by them and broadcasting adaptive content to these groups. The adaptive multimedia content is obtained by using scalable video coding (SVC) with optimal source encoding parameters resulted from an innovative cooperative game. Energy saving at user terminals results from using a layer-aware time slicing approach in the transmission stage. A trade-off between energy saving and QoE is observed, and is incorporated in the definition of a utility function of the players in the formulated heterogeneous user composition and physical channel aware game. An adaptive modulation and coding scheme is also optimally incorporated in order to maximize the reception quality of the broadcast receivers, while maximizing the network broadcast capacity. Compared to the conventional broadcast schemes, the proposed framework shows an appreciable improvement in QoE levels for all users, while achieving higher energy-savings for the energy constrained users.

Being able to react fast to campaign events such as missing persons or disaster preventions, is of paramount importance. In these situations narrowing down the search area to a targeted and accurate location is imperative. Nowadays, modern mobile devices have the location awareness capabilities that can be used to determine the users Global Positioning System (GPS) coordinates. However in order to determine if a user is located within a specific area, complex floating point calculations are required. Moreover if the area is determined by a polygon, this calculation is further complicated. In this paper we propose a novel algorithm which makes use of spatial indices to determine if a mobile is located within a predefined polygon shape area. The algorithm determines the optimal length of the spatial index such as to ensure accuracy-processing time-memory trade-off. We build a prototype system, using free and open source software, to deliver alerts to mobile devices within a predetermined geographical area. The system is assessed in terms of accuracy, processing time and memory usage.

In the current telecommunication environment, network operators are trying to cope with a significant increase in data traffic by adopting different solutions to expand their network capacity. One of these solutions is the convergence of next generation wireless networks (e.g., HSDPA, LTE and WiMAX) which involve closely interworking of existing 2G/2.5G/3G networks with the new next generation networks in terms of handover and network selection. However, the diversification in mobile devices and the heterogeneity of the wireless environment make the seamless always best connectivity of mobile users a challenge for the service providers. We propose RLoad, a novel Reputation-based Load-balancing Network Selection Strategy for heterogeneous wireless environments, built on top of the IEEE 802.21 Media Independent Handover (MIH) standard. The proposed solution makes use of a reputation-based mechanism to select the most appropriate set of networks for the mobile user and a load balancing mechanism to distribute the traffic load among the networks by making use of the Multipath TCP (MPTCP) protocol. Preliminary simulation results show significant benefits when using the proposed RLoad solution.

The growing popularity of the high-end mobile computing devices - smartphones, tablets, notebooks and more - equipped with high-speed network access, enables the mobile user to watch multimedia content from any source on any screen, at any time, while on the move or stationary. In this context, the network operators must ensure smooth video streaming with the lowest service delay, jitter, and packet loss. This paper proposes a resource efficient Device-Oriented Adaptive Multimedia Scheme (DOAS) built on top of the downlink scheduler in LTE-Advanced systems. DOAS bases its adaptation decision on the end-user device display resolution information and Quality of Service (QoS). DOAS is implemented on top of the Proportional Fair (PF) and the well-known Modified Largest Weighted Delay First (M-LWDF) scheduling algorithms within the 3GPP LTE/LTE-Advanced system. The performance of the proposed adaptive multimedia scheme was analyzed and compared against a non-adaptive solution in terms of throughput, packet loss and PSNR.

In the context of wireless user's increasing demands for better device power and battery management, this paper investigates some factors that can impact the power consumption on the energy consumption of mobile devices. The focus is on two factors when performing multimedia streaming: the impact of the traffic location within a WLAN; and the impact of the radio access network technology (WLAN, HSDPA, UMTS). The energy measurement results show that by changing the quality level of the multimedia stream the energy can be greatly conserved while the user perceived quality level is still acceptable. Moreover, by using the cellular interface much more energy is consumed (up to 61%) than by using the WLAN interface.

Energy conservation has become a critical issue around the world. In smart phones, battery power capabilities are not keeping up with the advances in other technologies (e.g., processing and memory) and are rapidly becoming a concern, especially in view of the growth in usage of energy-hungry mobile multimedia streaming. The deficiency in battery power and the need for reduced energy consumption provides motivation for researchers to develop energy efficient techniques in order to manage the power consumption in next-generation wireless networks. As there is little analysis in the literature on the relationship between the wireless environment and the mobile device energy consumption, this paper investigates the impact of network-related factors (e.g., network load and signal quality level) on the power consumption of the mobile device in the context of video delivery. This paper analyzes the energy consumption of an Android device and the efficiency of the system in several scenarios while performing video delivery (over UDP or TCP) on an IEEE 802.11g network. The results show that the network load and the signal quality level have a combined significant impact on the energy consumption. This analysis can be further used when proposing energy efficient adaptive multimedia and handover mechanisms.

The demand for multimedia services is increasing and users expect rich services at high quality levels, even while on the move and connected via different wireless networks. This paper proposes a novel signal strength-based adaptive multimedia delivery mechanism (SAMMy) that makes use of the IEEE 802.11k standard when dynamically adjusting multimedia delivery based on estimated signal strength and actual loss rates, in order to increase user perceived quality for video streaming applications in WLAN. Location and time dimensions are used together with the receive signal strength estimations in order to predict the QoS characteristics along the user's path. The proposed mechanism is evaluated by simulation and compared with a non-adaptive multimedia delivery mechanism and with two other adaptive schemes, in terms of loss, throughput and Peak Signal to Noise Ratio (PSNR). The results show that the proposed signal strength-based adaptive multimedia delivery scheme outperforms the other schemes involved making more efficient use of the wireless network resources and increasing the user perceived quality.