The Variational Auto-Encoder (VAE) is a popular generative model as the variance inference in the latent layer, the prior is an important element to improve inference efficient. This research explored the prior in the VAE by comparing the Normal family distributions and other location-scale family distributions in three aspects (performance, robustness, and complexity) in order to find a suitable prior for cybersecurity anomaly detection. Suitable distributions can improve the detection performance, which was verified at UNSW-NB15 and CIC-IDS-2017.

The surge in autonomous robot deployments across diverse domains is undeniable. The Robot Operating System (ROS) stands out as the prevailing standard for robotics systems, with ROS 2 emerging as its revitalized version. ROS 2 uses Data Distribution Service (DDS) as its communication middleware, aligning itself with the blossom of decentralized and distributed smart systems. However, the security of ROS 2 is dependent on the implementation of the DDS security plugins, which provides domain-level access protection under the assumption of trust in local machines. The problem arises when a malicious ROS 2 node, compromised by malware, can disseminate false information or pilfer sensitive data from other legitimate nodes within the system. To address this vulnerability, this paper harnesses the Trusted Execution Environment (TEE) to build a trustworthy ROS 2 platform with remote attestation. The proposed solution not only verifies the identity but also ensures the integrity of ROS 2 nodes before they provide/consume data and/or collaborate with each other. Our design establishes trust between communication parties and improves ROS 2 security by incorporating the hardware level protection.

Limited research has explored the impact of spatial audio on viewer experience in 360° videos. This research aims to address this gap by investigating the effects of spatial versus non-spatial audio configurations on the Quality of Experience (QoE) and visual attention. Spatial audio simulates real-world soundscapes, while non-spatial audio lacks contextual depth. A robust methodology combining subjective assessments with head pose and eye tracking and physiological response analysis provides insights into the role of audio spatiality. Preliminary results suggest that spatial audio significantly enhances immersion and directs visual attention. These findings offer actionable guidelines for content creators and contribute to the understanding of audio-visual integration in immersive content. Additionally, the research informs potential advancements in content delivery optimization. Spatial audio understanding can guide adaptive streaming techniques to prioritize relevant audio streams, potentially reducing bandwidth requirements. This can improve viewer experience in bandwidth-constrained environments.

Uncertainty quantification of cybersecurity anomaly detection results provides critical guidance for decision makers on whether or not to accept the results. Improving the trustworthiness of anomaly predictions can reduce the amount of alert false positives that security teams have to process. In this work we investigate the use of Bayesian Autoencoder (BAE) models for uncertainty quantification in anomaly detection. A novel heteroscedastic aleatoric uncertainty modeling method is explored that jointly considers aleatoric and epistemic uncertainty. Heteroscedastic aleatoric uncertainty is modelled on the latent layer of the BAE and further explored through considering the variational lower bound. An uncertainty quantification framework for cybersecurity is designed and verified on UNSW-NB15 and CIC-IDS-2017 data sets. This research enhances the modelling of uncertainty in the BAE model and expands its application in cybersecurity.

This article presents the results of an empirical study that aimed to investigate the influence of various types of audio (spatial and non-spatial) on the user quality of experience (QoE) of and visual attention in 360° videos. The study compared the head pose, eye gaze, pupil dilations, heart rate, and subjective responses of 73 users who watched ten 360° videos with different sound configurations. The configurations evaluated were no sound; non-spatial (stereo) audio; and two spatial sound conditions (first- and third-order ambisonics). The videos covered various categories and presented both indoor and outdoor scenarios. The subjective responses were analyzed using an ANOVA (Analysis of Variance) to assess mean differences between sound conditions. Data visualization was also employed to enhance the interpretability of the results. The findings reveal diverse viewing patterns, physiological responses, and subjective experiences among users watching 360° videos with different sound conditions. Spatial audio, in particular third-order ambisonics, garnered heightened attention. This is evident in increased pupil dilation and heart rate. Furthermore, the presence of spatial audio led to more diverse head poses when sound sources were distributed across the scene. These findings have important implications for the development of effective techniques for optimizing processing, encoding, distributing, and rendering content in virtual reality (VR) and 360° videos with spatialized audio. These insights are also relevant in the creative realms of content design and enhancement. They provide valuable guidance on how spatial audio influences user attention, physiological responses, and overall subjective experiences. Understanding these dynamics can assist content creators and designers in crafting immersive experiences that leverage spatialized audio to captivate users, enhance engagement, and optimize the overall quality of VR and 360° video content. The dataset, scripts used for data collection, ffmpeg commands used for processing the videos, and the subjective questionnaire and its statistical analysis are publicly available.

Abstract In video streaming, bandwidth constraints significantly affect client-side video quality. Addressing this, deep neural networks offer a promising avenue for implementing video super-resolution (VSR) at the user end, leveraging advancements in modern hardware, including mobile devices. The principal challenge in VSR is the computational intensity involved in processing temporal/spatial video data. Conventional methods, uniformly processing entire scenes, often result in inefficient resource allocation. This is evident in the over-processing of simpler regions and insufficient attention to complex regions, leading to edge artifacts in merged regions. Our innovative approach employs semantic segmentation and spatial frequency-based categorization to divide each video frame into regions of varying complexity: simple, medium, and complex. These are then processed through an efficient incremental model, optimizing computational resources. A key innovation is the sparse temporal/spatial feature transformation layer, which mitigates edge artifacts and ensures seamless integration of regional features, enhancing the naturalness of the super-resolution outcome. Experimental results demonstrate that our method significantly boosts VSR efficiency while maintaining effectiveness. This marks a notable advancement in streaming video technology, optimizing video quality with reduced computational demands. This approach, featuring semantic segmentation, spatial frequency analysis, and an incremental network structure, represents a substantial improvement over traditional VSR methodologies, addressing the core challenges of efficiency and quality in high-resolution video streaming.

Industry 4.0 warehousing is characterised by autonomous multi-robot collaboration systems (MRSs) along with other technologies such as digital communication capabilities and the Internet of Things. These MRSs need to behave coherently for the efficient completion of the assigned cooperative tasks. Multi-agent reinforcement learning (MARL) frameworks are currently considered state-of-the-art to control the behaviour of autonomous MRSs. These MARL frameworks can be with learnable or predefined communication. Current works lack any worthwhile evaluation of communicative MARL frameworks on multi-robot cooperative tasks. This work empirically evaluates current state-of-the-art seminal learnable communicative MARL frameworks by comparing their performance against non-communicative MARL frameworks on multi-robot coop-erative tasks in the context of Industry 4.0 warehousing with the assumptions of partial observability and reward sparsity. The results demonstrate that communicative MARL frameworks outperform their counterparts by a fair margin in training (average returns between 11 and 6 against 8 and 4 for highest and lowest values respectively) and execution performances (average returns between 1.24 and 0.29 against 0.49 and 0.19 for highest and lowest values respectively). This leads to the conclusion that communicative MARL is better suited to multi-robot cooperative tasks under the above-mentioned assumptions.

The surge in surveillance camera deployment in the era of Big Data and the Internet of Things (IoT) has emphasized the paramount importance of safeguarding the privacy of individuals, objects, and locations they record. Therefore, this paper proposes VidSearch – a secure system designed for storing, searching, and retrieving videos captured by CCTV cameras. VidSearch system enhances visual data protection through encryption, query-by-text video searching within encrypted data, and anonymized video retrieval using pixelization. During storage, encrypted videos and their metadata are stored separately to facilitate text-based search and video retrieval from encrypted videos. Fernet encryption is applied to protect videos, and two anonymization algorithms i.e., a Mixture of Gaussians 2 (MOG2) and K-Nearest Neighbors (KNN) are used for detecting the foreground (moving objects) and background of the videos at the retrieval stage. Video retrieval results demonstrate that KNN excels in accuracy for visual content detection, while MOG2 is more efficient in terms of processing time. VidSearch system is extensively tested on a general-purpose Intel system and an IoT NVIDIA Jetson. Results confirm the system's ability to operate in a Big Data and IoT ecosystem across multiple devices and platforms.

This paper presents an implementation of a feedback mechanism for a workflow management framework. A chatbot that uses natural language processing (NLP) is central to the proposed feedback mechanism. NLP is used to transform text-based plain language input, both human-written and machine-generated, into a form that the framework can use to generate a workflow for execution in an environment of interest. The example environment described here is containerized network management, in which the workflow management framework, using feedback, can detect anomalies and mitigate potential incidents.

Pipeline parallelism is the newly proposed model parallelism paradigm for efficiently training giant size Deep Neural Network (DNN) models across multiple accelerators. Gpipe, as a popular pipeline parallelism scheme, has been integrated into the PyTorch framework. Training a model with Gpipe involves choosing a large number of parameters, e.g. determining a DNN model partitioning scheme for the given number of accelerators, selecting the number of GPUs for the training. Therefore, it is crucial to investigate the effects of different Gpipe configurations on the model training performance and assess the scenarios that are suitable for Gpipe. This paper presents a systematic evaluation of Gpipe performance under various settings, including different DNN models, GPU types, GPU numbers, datasets, and model partition strategies. The experiments show several counterintuitive results, i.e. training a DNN model without using Gpipe performs better than using Gpipe, and utilising more GPUs does not guarantee a better performance and sometimes using less GPUs is better while training with Gpipe. Moreover, the test results also show that the GPU type, model size, dataset size, and the DNN model partition scheme clearly influence the training speed. Based on the observation of the evaluation results, the paper proposes a theoretical model to estimate the performance gain ratio while using Gpipe under different setups.

Exactly-once data processing/delivery can be guaranteed in traditional big data processing systems, e.g. Apache Flink. Checkpoint is commonly used as the solution. Each operator in these systems can restart from the last successfully saved state whenever a failure happens. It is not necessary to restore the logical job graph onto the same device(s) in traditional datacentre scenarios with powerful servers close to each other. However, the datacentre oriented solutions are not suitable for IoT collaborative edge computing scenarios. The logical job graph is tightly coupled to the physical topology in IoT networks. Data processing task(s) cannot be placed at a random edge device to recover from a network failure as it needs to evaluate the benefits of transmitting data versus processing/aggregating the data. To address the above challenges, this paper proposes an Information Centric Networking based solution and correspondent protocols to provide Exactly-once-computation for the Collaborative Edge in IoT (ECE). It contains a job execution scheme to deliver IoT jobs with exactly once data computation guarantee and a recovery procedure to dynamically change the IoT job execution graph while experiencing link failures. The protocol also provides a checking procedure on data state (received/un-received and computed/un-computed) to prevent any data loss or duplicated data processing due to the updated job graph. A data identification approach based on the job graph is devised to support the ECE functionality. A testbed has been developed on ndnSIM and the simulation results have verified the feasibility and scalability of ECE design. It also evaluates the overhead incurred by the ECE protocol to guarantee exactly once data computation.

Immersive technologies are rapidly gaining traction across a variety of application domains. 360° video is one such technology, which can be captured with an omnidirectional multi-camera arrangement. With a Virtual Reality (VR) Head Mounted Display (HMD), users have the freedom to look in any direction they wish within the scene. While there is a plethora of work focused on modeling visual attention (VA) in VR, little research has considered the influence of the audio modality on VA in VR. It is well known that audio has an important role in VR experiences. Listeners can experience sound in all directions with high quality spatial audio. One such technique, Ambisonics or 3D audio, provides a full 360° sound soundscape.

Industry 4.0 is aimed at the full manufacturing domain automatization and digitalization. Humans and robots working together are being discussed widely both in the academic and industrial sectors. As being discussed, there is a need for a more novel type of interaction method between humans and robots. This demonstrational paper is presenting the technical advancement and prototype of remote control and re-programming of the Industrial robot. This development is safe and efficient and allows the operator to focus rather on the final task than the programming process.

Workflow implementation in network management is a challenge, especially when it depends on human interaction. Traditional workflow generation, which was primarily focused on business management, employed a graphical representation of managed elements, supported using a human-understandable explanation. The difficulty with this approach is workflow adaptation to avoid execution problems when unexpected errors occur. In such circumstances, human intervention is necessary to resolve the problem. Current workflow generation solutions are inflexible and inefficient with regards to self-checking and self-healing, and they cannot be easily scaled or adapted. This paper presents a novel workflow management framework that controls an environment using dynamic workflow generation. The framework detects inconsistencies in workflow execution and reacts automatically to prevent a workflow termination or to replace a corrupted workflow if execution is not possible. A language-based text-oriented ticket input is used to describe the problem to be solved. This text is translated using natural language processing to a syntax understandable by the framework in order to generate the required workflow. The functionality of the workflow management framework is demonstrated in a software-defined networking environment.

This paper presents a workflow management framework in which natural language processing is used to support workflow creation. This work demonstrates an application of the framework for network management. Advantages of the framework include ease of implementation, low power consumption, flexibility, and scalability. Central to the framework is a distributed management structure. As input to the framework, a text-based ticket, written using plain language, is translated by NLP to a form that the framework can understand and act on. The message in the ticket input is used to create a dynamic workflow that can be executed in a controlled environment. Executed workflows are monitored for incident detection and mitigation.

Currently, microservices are trending as the most popular software application design architecture. Software organisations are also being targeted by more cyber-attacks every day and newer security measures are in high demand. One available measure is the application of anomaly detection, which is defined as the discovery of irregular or unusual activity that occurs to a greater or lesser degree than normal occurrences in a data series. In this paper, we continue existing work where various real-world cyber-attacks are executed against a running microservices application, and the application traffic is logged and returned in the form of distributed traces. A Diffusion Convolutional Recurrent Neural Network is used to model the set of distributed traces and learn the spatial and temporal dependencies of the application traffic. Subsequently, the model is used to make predictions for ongoing microservice activity and threshold-based anomaly detection is applied to detect irregular microservice activity indicating the presence of seeded cyber security attacks, or anomalies. The cyber-attacks used to evaluate this approach include a brute force attack, a batch registration of bot accounts and a distributed denial of service attack.

Content replication and name-based routing lead to a natural multi-source and multipath transmission paradigm in NDN. Due to the unique connectionless characteristic of NDN, current end-to-end multipath congestion control schemes (e.g. MPTCP) cannot be used directly on NDN. This paper proposes a Network Utility Maximization (NUM) model to formulate multi-source and multipath transmission in NDN with in-network caches. From this model, a family of receiver-driven transmission solutions can be derived, named as path-specified congestion control. The path-specified congestion control enables content consumers to separate the traffic control on each path, which consequently facilitates fair and efficient bandwidth sharing amongst all consumers. As a specific instance, a Delay-based Path-specified Congestion Control Protocol (DPCCP) is presented, which utilizes queuing delays as signals to measure and control congestion levels of different bottlenecks. In addition, a set of high-performance congestion control laws are designed to accelerate bandwidth and fairness convergence towards the optimum defined by the NUM model. Finally, DPCCP is compared with state-of-the-art solutions. The experimental evaluations show that DPCCP outperforms existing solutions in terms of bandwidth utilization, convergence time and packet loss.

Content replication and name-based routing in Named Data Network (NDN) naturally lead to connectionless multi-source and multipath transmissions. Traditional congestion control designed for end-to-end connections cannot well fit this architecture. Explicit congestion notification (ECN) can better support NDN because congestion is detected where it occurs and ECN can timely notify the traffic initiator of congestion. NDN can be deployed as an overlay protocol (sharing the underlying devices with other protocols), which means the congestion may also occur at an underlying device (e.g. a switch). In this case, the NDN nodes cannot access the queue or other link status at a remote underlying device for congestion detection. A promising ECN scheme must be able to detect congestion happening anywhere (at an NDN node or an underlying device) without using underlying link information. This paper proposes Hop-byHop Congestion Measurement (HbHCM) and Practical Active Queue Management (PAQM) to enable detecting congestion and generating ECN at NDN nodes via monitoring the change of transmission delays. HbHCM measures the transmission delay at the hop level and PAQM converts the delay to ECN signals to notify consumers. We compared HbHCM + PAQM with two milestone solutions (router-label and ECN-based). The simulation results show that HbHCM + PAQM can accurately detect congestion, improve bandwidth utilisation and better support multipath transmission, no need to rely on route or link information.

Real world virtual world communication and interaction will be a cornerstone of future intelligent manufacturing ecosystems. Human robotic interaction is considered to be the basic element of factories of the future. Despite the advancement of different technologies such as wearables and Augmented Reality (AR), human-robot interaction (HRI) is still extremely challenging. Whilst progress has been made in the development of different mechanisms to support HRI, there are issues with cost, naturalistic and intuitive interaction, and communication across heterogeneous systems. To mitigate these limitations, RoSTAR is proposed. RoSTAR is a novel open-source HRI system based on the Robot Operating System (ROS) and Augmented Reality. An AR Head Mounted Display (HMD) is deployed. It enables the user to interact and communicate through a ROS powered robotic arm. A model of the robot arm is imported directly into the Unity Game engine, and any interactions with this virtual robotic arm are communicated to the ROS robotic arm. This system has the potential to be used for different process tasks, such as robotic gluing, dispensing and arc welding as part of an interoperable, low cost, portable and naturalistically interactive experience.

360° video, also known as immersive video, is the recording of video content which simultaneously captures scene information in every direction, using an omnidirectional camera. Due to their immersive nature, the popularity of 360° videos has grown significantly. Understanding user Visual Attention when watching 360° videos is very important. This knowledge can help develop effective techniques for processing, encoding, distributing, and rendering 360° content. Whilst major efforts have concentrated on the visual element of immersive experiences, recently there has been growing interest in different forms of audio and in particular high-quality spatial audio. Spatial audio allows listeners to experience sound in all directions. Ambisonics or 3D audio is one such technique which offers a complete 360° soundscape. Although several models of visual and audio-visual attention have been proposed, very few have investigated the role of spatial audio in guiding attention in 360° videos. This demo shows our dataset and our methodological approach to understanding the user's audio-visual attention and QoE when experiencing 360° videos enhanced with spatial sound (first and third order ambisonic). Our research focus is to understand how audio affects Visual Attention in 360° videos and to evaluate its impact on the user's Quality of Experience (QoE).

Human-Robot Interaction (HRI) will be a crucial component of smart factories of the future (FoF). This demo presents a reliable and cost-effective HRI system based on Augmented Reality (AR). In this demonstration, we offer concepts and methods currently being developed to enable high-level human-robot collaboration and interaction. The user interaction and instructions are captured via AR and communicated to a Robot Operating System (ROS) powered robotic arm.

Edge computing is proposed to remedy the Cloud-only processing architecture for Internet of Things (IoT) because of the massive amounts of IoT data. The challenge is how to deploy and execute data processing tasks on heterogeneous IoT edge network. As MapReduce is a well-known model in Cloud computing for distributed processing of big data, this paper aims to devise a MapReduce-based protocol to achieve IoT edge computing. Our design is built upon the novel Information Centric Networking (ICN), which supports function naming and forwarding so as to facilitate task distribution among edge devices. To guarantee the correctness of task execution, a tree topology is formed in our approach to establish the logical connection between different types of edge devices, namely processing-capable nodes and forward-only ones. Moreover, the proposed protocol includes a task maintenance scheme that enables the coexistence of multiple IoT computation jobs. A testbed is implemented on ndnSIM to verify the feasibility of our design. The results show our approach could significantly decrease the network traffic compared with centralized data processing.

The number of devices connected by the Internet of Things (IoT) has exceeded billions today. As IoT grows, so do the volumes of data it produces. Distributed data process (such as: MapReduce) performs better than a single central server by considering the big data set. Moreover, many IoT network spans widely in geographical areas, such as smart cities and supply chain management. Thus, the collected data can be processed in distributed way before transmission. This paper studies how to develop a MapReduce framework to process massive IoT data. Because in many cases IoT consumers desire more to get the meaningful knowledge than to build connections with multiple devices. The proposed framework should also fits well with the information-centric nature of IoT applications. As a result, our design (MR-IoT) is built upon a novel Information Centric Networking (ICN) architecture - NDN. It defines two schemes to execute MapReduce tasks on IoT: computational tree construction and computational task dissemination. A testbed is built on ndnSIM to verify the design and the result shows the defined schemes work correctly and the network traffic is significantly decreased.

This paper considers the security problem of outsourcing storage from user devices to the cloud. A secure searchable encryption scheme is presented to enable searching of encrypted user data in the cloud. The scheme simultaneously supports fuzzy keyword searching and matched results ranking, which are two important factors in facilitating practical searchable encryption. A chaotic fuzzy transformation method is proposed to support secure fuzzy keyword indexing, storage and query. A secure posting list is also created to rank the matched results while maintaining the privacy and confidentiality of the user data, and saving the resources of the user mobile devices. Comprehensive tests have been performed and the experimental results show that the proposed scheme is efficient and suitable for a secure searchable cloud storage system.

Named Data Networking (NDN) is a promising communication paradigm to support content distribution for the Future Internet. The objective of this paper is to maximize the consumer downloading rate by retrieving content via multiple paths concurrently. This is supported by adaptive forwarding in NDN. The majority of solutions for selecting the forwarding interfaces do so based on latency. However, this can overload the low-latency paths quickly. This can occur as users reduce requesting rates based on congestion signals, from the low-latency paths. Hence, the high-latency paths are not fully utilized. This paper solves this problem by introducing Backpressure Interest Control Protocol (B-ICP). In B-ICP, routers estimate the forwarding capabilities of interfaces based on congestion signals and limit the forwarding rates to interfaces accordingly. Thus, B-ICP avoids congesting certain paths earlier than others, with the aim being evenly distributed path utilization. Simulation-based evaluations show that B-ICP improves throughput, converges to equilibriums quickly and supports the producers that join the network dynamically in comparison with existing solutions.

Named Data Networking (NDN) is a promising Future Internet architecture to support efficient content distribution. Specifically, P2P may gain benefits from NDN, as NDN inherently provides a flexible forwarding plane for multi-source and multi-path communications. Existing studies in this area have proposed solutions, but these are adversely affected by link latency. This leads to illogical resource allocation and low link utilization for P2P. In this paper, we propose a new Heterogeneous-Latency Adaptive Forwarding (HLAF) strategy for peer-assisted video streaming in NDN. In peer-assisted video streaming, users (peers) proactively share the available content to others. By measuring the performance of forwarding interfaces, using both the level of congestion and the round-trip time, HLAF enables efficient P2P communication, which minimizes the latency and enhances the throughput. The experimental results show that the proposed strategy can enhance the peers' Quality of Experience (QoE).

Recently, the concept of adding multisensory media components to complement and extend user Quality of Experience (QoE) of traditional media has gained attention from both academia and industry. Research works stimulating additional senses like olfaction (sense of smell), haptic (sense of touch) and gustation (sense of taste) have emerged. In particular in theme parks, multisensory experiences that also offer ambient lighting effects, vibrating seats, wind generators, mist effects, heaters/coolers, etc. are appearing. Considering this growing awareness and popularity, a key research challenge is to experimentally evaluate if and how these different effects affect user QoE. In this context, there is a lack of common test content and raw data results to support reproducible research and cross research team verification. This paper fills this gap. We share: the data from the empirical study; the video content; the olfactory components employed to enrich the video; the methodologies employed and various other aspects found through experience to be important. Uniquely, this work is complemented by two datasets, obtained in two separate but related empirical studies, one conducted in the UK, and the other in Ireland.

In the quest to increase user perceived quality of experience (QoE), the classic audio-visual content paradigm can be extended to include media components that stimulate other human senses. Among these, olfaction-enhanced multimedia has attracted significant attention, as it is both attractive from user point of view and challenging from research perspective. This paper presents the results of two subjective studies which analyzed user QoE of olfaction-enhanced multimedia. Diverse scent types and video content were considered. In particular, QoE levels were studied when one and two olfaction stimuli enhanced audiovisual media. The results presented show that scent type influences user QoE. Statistically significant differences between pleasant and unpleasant scent types existed. Also, in certain cases, users were prepared to forgive the presence of unpleasant scent types with respect to QoE. Finally, users reported a clear preference for olfaction presented after the video sequence with which the olfaction effect should be synchronized, as opposed to before the video sequence.

Hadoop is one of the most important Big Data processing and storage systems. In recent years, a lot of efforts have been put to enhance Hadoop's performance from networking perspectives. However, there are limited tools that can help researchers to verify their networking algorithm design in terms of Hadoop's performance. This paper proposes Doopnet which is a framework and toolset for creating Hadoop clusters in a virtualized environment and for monitoring/analysing of Hadoop's networking characteristics under different network configurations. Doopnet enables users to automatically set up a Hadoop cluster over Docker containers running inside Mininet. The Hadoop traffic is collected inside the containers and virtual switches through network flow monitors. The users can easily modify network topologies or configurations through Mininet, observe the networking behaviour through network flow monitors, and analyse the effects of different network settings on Hadoop's performance. Examples are presented to demonstrate how to setup the Doopnet testbed and analyse Hadoop traffic.

The Internet of Things (IoT) places significant demands on network infrastructure in order to process data captured by ubiquitous sensor devices. One existing technique to support this sensor data processing involves transporting captured data to cloud servers. This approach suffers from numerous issues such as increased transmission costs i.e. bandwidth consumption and delays. To help resolve these issues, this paper proposes Programmable IoT (PIoT), a novel IoT data processing architecture. It is an application layer design which operates over Named Data Networking (NDN) to enable the execution of reconfigurable processing-logic in the network. In addition, a novel naming scheme and computation service for IoT is presented to describe the processing requirements using Lambda Expressions. To verify the feasibility of our design, a real-world implementation was created and evaluated. It compares efficiency of the in-network versus out-network approaches.

Recently, we have seen an emergence of affordable Head Mounted Displays (HMD) such as the Oculus Rift, HTC Vive, and the PS4 Project Morpheus which allow users to experience 3D virtual reality (VR). These types of hardware aim to facilitate new and novel experiences for users above and beyond what is possible with traditional audiovisual displays. However, a very limited number of studies exist in the literature to determine the influence of these technologies on user Quality of Experience (QoE). In order to evaluate QoE as users consume VR content, this paper proposes the use of affordable consumer electronics to capture objective physiological metrics: Heart Rate (HR) and ElectroDermal Activity (EDA). Our findings indicate different HR and EDA dependent on VR and non-VR environments. Additionally, we examine the relationship between these objective metrics and user QoE captured via a post-test questionnaire. To the best of the authors knowledge, this is the first work which demonstrates a tangible relationship between the EDA/HR combination and user QoE of immersive VR environments.

With the aim to enrich users' perceived multimedia experience, the authors present the results of an empirical study which looked at user perception of olfaction based mulsemedia. The goal is to evaluate the influence of users' age and gender on user quality of experience (QoE) considering various scent types and categories (pleasant or not). The results present a complex relationship between these variables and how they influence user QoE. They indicate that different user groups report different perception of content level factors for olfaction based mulsemedia.

Distributed compressive video sensing (DCVS) is an emerging low-complexity video coding framework which integrates the merits of distributed video coding (DVC) and compressive sensing (CS). Because the human visual system (HVS) is the ultimate receiver of visual signals, we aim to improve the perceptual rate-distortion performance of DCVS by designing a novel scalable saliency-aware DCVS codec. Firstly, we perform saliency estimation in the the side information (SI) frame generated at the decoder side and adaptively control the size of region-of-interest (ROI) according to the measurements budget by applying a saliency guided foveation model. Subsequently, based on online estimation of the correlation noise between a non-key frame and its SI, we develop a saliency-aware block compressive sensing scheme to more accurately reconstruct the ROI of each non-key frame. The obtained experimental results reveal that our DCVS codec outperforms the legacy DCVS codecs in terms of the perceptual rate-distortion performance.

By combining the advantages of distributed video coding (DVC) and compressive sensing (CS), distributed compressive video sensing (DCVS) poses itself as a very promising low-complexity video coding framework for distributed applications. In order to improve the rate-distortion performance of DCVS, much research efforts have been focused on exploring the best ways to utilize the spatial/temporal redundancy of video data to achieve efficient sparse representation and reconstruction at the decoder. Unlike the existing DCVS schemes, we aim to improve the perceptual rate-distortion performance of DCVS by designing a novel perceptually-aware DCVS codec. Based on online estimation of the correlation noise between a non-key frame and its side information (SI) considering the effect of human visual system (HVS), we design an efficient perceptually-aware block compressive sensing scheme for a non-key frame in our DCVS codec, in order to more accurately reconstruct the salient regions in the video frames. The obtained experimental results reveal that our DCVS codec outperforms the legacy DCVS codecs in terms of the perceptual rate-distortion performance.

This study looked at users' perception of interstream synchronization between audiovisual media and two olfactory streams. The ability to detect skews and the perception and impact of skews on user Quality of Experience (QoE) is analyzed. The olfactory streams are presented with the same skews (i.e., delay) and with variable skews (i.e., jitter and mix of scents). This article reports the limits beyond which desynchronization reduces user-perceived quality levels. Also, a minimum gap between the presentations of consecutive scents is identified, necessary to ensuring enhanced user-perceived quality. There is no evidence (not considering scent type) that overlapping or mixing of scents increases user QoE levels for olfaction-enhanced multimedia.

As a step towards enhancing users' perceived multimedia quality levels, this article presents the results of a study which looked at user's perception of inter-stream synchronization between scent and video. The ability to detect and the perception of and impact of skew on user's quality of experience is analyzed considering user's age, sex, and culture (user profile). The results indicate that skews beyond a certain level between olfaction and video have a negative impact on user-perceived experience. Olfaction before video is more noticeable to users than olfaction after video, and assessors are more tolerable of olfactory data presented after video.

Lately, significant efforts have being put into proposing various solutions for increasing multimedia viewers' perceived quality levels. One innovative avenue is to enhance users' quality of experience (QoE) by extending the classic audio-visual multimedia content to stimulate also other human senses such as olfaction, tactile, etc. In this context, this paper focuses on olfaction-enhanced multimedia content and presents the results of an experimental study which looked at user perception of inter-stream synchronization between olfactory data and video, whereby the audio used provides no contextual information. The study investigates how age and gender influence users' perception of the temporal boundaries within which they perceive olfactory data and video to be synchronized. The impact on user QoE levels (considering sense of enjoyment, relevance and reality) during synchronous and asynchronous presentations of olfactory and video media is also analyzed and discussed. The results show that there are significant differences in terms of how users of various gender and age groups perceive the skew between olfaction and video content and in their QoE levels.

As a step towards enhancing users' perceived multimedia quality levels beyond the level offered by the classic audiovisual systems, the authors present the results of an experimental study which looked at user's perception of inter-stream synchronization between olfactory data (scent) and video (without relevant audio). The impact on user's quality of experience (by considering enjoyment, relevance and reality) comparing synchronous with asynchronous presentation of olfactory and video media is analyzed and discussed. The aim is to empirically define the temporal boundaries within which users perceive olfactory data and video to be synchronized. The key analysis compares the user detection and perception of synchronization error. State of the art works have investigated temporal boundaries for olfactory data with audiovisual media, but no works document the integration of olfactory data and video (with no related audio). The results of this work show that the temporal boundaries for olfactory and video only are significantly different from olfactory, video and audio. The authors conclude that the absence of contextual audio reduces considerably the acceptable temporal boundary between the scent and video. The results also indicate that olfaction before video is more noticeable to users than olfaction after video and that users are more tolerable of olfactory data after video rather than olfactory data before video. In addition the results show the presence of two main synchronization regions. This work is a step towards the definition of synchronization specifications for multimedia applications based on olfactory and video media.

The emerging Media Independent Handover (MIH) standard proposes to support session continuity during handover between heterogeneous networks. One of the critical features provided by MIH is an Event Service which includes predictive network degradation events, such as Link_Going_Down (LGD), which are triggered based on link layer metrics. Our results highlight the reactivness of media stream quality to network degradation. The point of degradation however, is specific to the characteristics of the class of media streaming service. Many existing event algorithms utilize static performance thresholds which are unresponsive to the requirements of individual application service classes. In this paper we propose a Service Oriented Link Triggering Algorithm (SOLTA) which triggers the LGD and Link_Down (LD) events based on link layer metrics but subject to the performance characteristics of the supported class of service. SOLTA illustrates that for 802.11, it is necessary to have aggressive service class specific, link event triggering. SOLTA also illustrates how a soft path handover approach such as Stream Control Transmission Protocols Concurrent Multi-path Transfer (SCTP-CMT) variant is necessary to support seamless session migration.

As wireless communication infrastructures such as 3G, WIFI and WIMAX networks are widely deployed, mobile IP communications are expected to grow rapidly. Although there are many mobile IP communication solutions such as GPRS Tunneling Protocol, Mobile IP and so on, they are still unable to provide large-scale mobile communication services in IPv4 networks because of the exhaustion of IPv4 addresses. In this paper, we present an Identifier/Locator split technique called the Mobile Port Mapping (MPM) which supports steady connections at the Transport layer when handover occurs. A MPM based SIP architecture (MPM-SIP) is proposed, in which part ports of a global IPv4 address is allocated to a particular mobile terminal instead of the total IPv4 address. MPM-SIP not only avoids the exhaustion of global IPv4 Addresses, but also provides backward compatibility to correspondence nodes. The architecture is verified and the performance is tested, which demonstrates that it is a promising technique to provide transparent mobility in IPv4 networks.

Recent research on concurrent multipath transfer (CMT) and CMT with a potentially-failed destination state (CMT-PF) uses the transport layer multi-homing protocol stream control transmission protocol (SCTP) to increase application throughput by distributing transmitted data across multiple end-to-end paths. This paper investigates and evaluates the performance of CMT with partial reliability (CMT-PR) and CMT-PF with partial reliability (CMT-PF-PR), novel extensions of SCTP for real-time video distribution. The Evalvid-CMT platform was implemented in the University of Delaware's SCTP/CMT ns-2 module to perform emulation experiments in order to compare CMT and CMT-PR, CMT-PF and CMT-PF- PR, respectively. The results presented in the paper show how the CMT-PR and CMT-PF-PR outperform CMT and CMT-PF respectively. Consequently the former are suggested as strategies for real-time video concurrent multipath transmissions.

The increased number and diversity of underlying networks have made transparent network migration a necessity. Through its support for multi-homing the Stream Control Transmission Protocol (SCTP) enables seamless network mobility by abstracting multiple underlying physical paths into a single end-to-end association. One of these paths is selected as the primary. When a number of retransmission failures occur on the primary path, switchover is initiated to a secondary path. The number of retransmission attempts before switchover is initiated can be configured; however, the delay between each retransmission is managed internally in SCTP using a Retransmission TimeOut (RTO) value. This paper shows that the current SCTP mechanism for calculating RTO values is inappropriate in WLAN environments, since increased Round Trip Times (RTT) significantly distort RTO calculations. Experimental and simulated results indicate that SCTP behaves in a counterintuitive manner which allows more time for switchover as network conditions degrade: delays of up to 187 seconds can be experienced before switchover occurs. We show that additional SCTP parameters need to be carefully configured in order to reduce this switchover delay to a more acceptable level.

The performance of multi-homed transport protocols tolerant of network failure is studied. It evaluates the performance of different retransmission policies combined with path failure detection thresholds, infinite or finite receive buffers for various path bandwidths, delays and loss rate conditions through stream control transmission protocol simulation. The results show that retransmission policies perform differently with different path failure detection threshold configurations. It identifies that retransmission of all data on an alternate path with the path failure detection threshold set to zero performs the best in symmetric path conditions but its performance degrades acutely in asymmetric path conditions even when the alternate path delay is shorter than the primary path delay. It illustrates that retransmission of all data on the same path with the path failure detection threshold set to one or zero gives the most stable performance in all path configurations.

The rapid evolution of 3G technology demands an adaptive, robust network management system. In the 3G UMTS (Universal Mobile Telecommunications System) system, a new radio access network (RAN) called UTRAN (UMTS terrestrial radio access network) is introduced based on W-CDMA (instead of TDMA/FDMA) air interface transmission. The normal network management functionality needs to be supported that is, fault configuration, accounting, performance and security management (FCAPS). The network resource model for UMTS is defined by 3GPP [1] and is object oriented Output data for bulk configuration has a standard XML format defined by the 3GPP. By applying standard software engineering techniques for exporting XML from a relational database we demonstrate that using a relational database for storing management data can achieve very good performance for bulk configuration management export.