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.

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.

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.

The current definition of quality of experience (QoE) designates delight and annoyance as diametrically opposing indicators of the degree of fulfilment of an application, service or system user's pragmatic and hedonic needs and expectations. However, these inherited emotion terms are rarely used to describe emotions of equal amounts of arousal or opposing amounts of valence in the literature. This work assesses the significance of this asymmetry to the definition of QoE by determining the utility of emotion terms to communicate the emotion component of QoE. This was done in the context of a QoE evaluation of augmented reality training instruction formats. Correlates were sought between various measures of emotional state. This included physiological ratings, facial expressions and eye gaze. Emotional state was subjectively reported using three distinct methods: self-assessment manikin questionnaire; 2D emotion space terms; and open-ended terms. Regression analysis showed multiple significant correlations between implicit and explicit metrics, but not to the emotion terms used by the participants. This calls into question the utility of such vaguely understood terms. The use of more symmetrically opposing emotions in the definition of QoE may benefit consensual interdisciplinary communication.

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.

The integration of drones, the Internet of Things (IoT), and Artificial Intelligence (AI) domains can produce exceptional solutions to today complex problems in smart cities. A drone, which essentially is a data-gathering robot, can access geographical areas that are difficult, unsafe, or even impossible for humans to reach. Besides, communicating amongst themselves, such drones need to be in constant contact with other ground-based agents such as IoT sensors, robots, and humans. In this paper, an intelligent technique is proposed to predict the signal strength from a drone to IoT devices in smart cities in order to maintain the network connectivity, provide the desired quality of service (QoS), and identify the drone coverage area. An artificial neural network (ANN) based efficient and accurate solution is proposed to predict the signal strength from a drone based on several pertinent factors such as drone altitude, path loss, distance, transmitter height, receiver height, transmitted power, and signal frequency. Furthermore, the signal strength estimates are then used to predict the drone flying path. The findings show that the proposed ANN technique has achieved a good agreement with the validation data generated via simulations, yielding determination coefficient$R^2$to be 0.96 and 0.98, for variation in drone altitude and distance from a drone, respectively. Therefore, the proposed ANN technique is reliable, useful, and fast to estimate the signal strength, determine the optimal drone flying path, and predict the next location based on received signal strength.

Augmented reality (AR) has significant potential as a training platform. The pedagogical purpose of training is learning or transfer. Learning is the acquisition of an ability to perform a procedure as taught while transfer involves generalising that knowledge to similar procedures in the same domain. Quality of experience (QoE) concerns the fulfilment of the application, system or service user's pragmatic and hedonic needs and expectations. Learning or transfer fulfil the AR trainee's pragmatic needs. Training instructions can be presented in procedural, and example formats. Procedural instructions tell the trainee what to do while examples show the trainee how to do it. These two different instruction formats can influence learning, transfer, and hardware resource availability differently. The AR trainee's hedonic needs and expectations may be influenced by the impact of instruction format resource consumption on system performance. Efficient training efficacy is a design concern for mobile AR training applications. This work aims to inform AR training application design by evaluating the influence of procedural and example instruction formats on AR trainee QoE.

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.

Recently, Unmanned Aerial Vehicles (UAVs) present a promising advanced technology that can enhance people life quality and smartness of cities dramatically and increase overall economic efficiency. UAVs have attained a significant interest in supporting many applications such as surveillance, agriculture, communication, transportation, pollution monitoring, disaster management, public safety, healthcare, and environmental preservation. Industry 4.0 applications are conceived of intelligent things that can automatically and collaboratively improve beyond 5G (B5G). Therefore, the Internet of Things (IoT) is required to ensure collaboration between the vast multitude of things efficiently anywhere in real-world applications that are monitored in real-time. However, many IoT devices consume a significant amount of energy when transmitting the collected data from surrounding environments. Due to a drone's capability to fly closer to IoT, UAV technology plays a vital role in greening IoT by transmitting collected data to achieve a sustainable, reliable, eco-friendly Industry 4.0. This survey presents an overview of the techniques and strategies proposed recently to achieve green IoT using UAVs infrastructure for a reliable and sustainable smart world. This survey is different from other attempts in terms of concept, focus, and discussion. Finally, various use cases, challenges, and opportunities regarding green IoT using UAVs are presented.

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.

Augmented reality (AR) is a key technology to enhance worker effectiveness during increasing automation of repetitive jobs in the workplace. AR will achieve this by assisting the user to successfully perform complex and frequently changing procedures. The design of AR applications for these roles is critical to their acceptability and utility. User quality of experience (QoE) will inform these design decisions. A user arrives at a quality judgment upon post-experience reflection of their degree of delight or annoyance relating to the degree of fulfilment of pragmatic and hedonic needs and expectations of the medium under consideration. QoE researchers have largely depended upon post-experience subjective reports to determine the user's QoE. Subjective reports have been shown to be biased by primacy, recency and maxima of experience stimuli. Recent research involves the identification of implicit metrics that can be used to determine user QoE continuously during a multimedia experience. This work evaluates head rotation frequency as an objective metric of user QoE. The literature shows that emotion is expressed in the frequency of a person's head rotation around three axes of movement (pitch, yaw and roll). Low frequency head rotation has been shown to include expression of happy emotion while high frequency exclusively expresses anger emotion. These emotions are analogous to those reflected on by a user during the quality formation process. This demo paper analyses the amount of high frequency head rotation exhibited by the user upon task completion using an AR procedure assistance application or a paper-based control. An optimal Rubik's Cube solving AR application was used as a proof of concept for AR-based procedure assistance. Preliminary results showed that the AR environment yielded higher task success rates and significantly shorter task completion durations. The AR users exhibited significantly lower amplitudes of anger frequencies in their head rotations than the control group.

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.

Augmented reality (AR) can support a user in performing an expert task by overlaying real world objects with the domain specific information required to complete the task. Understanding how users can process and use such information is very important for informing the design of AR technologies and applications. In this paper, the results of a quality of experience (QoE) evaluation of an AR application for the task of solving a Rubik's Cube are presented. The Rubik's Cube was selected based on its familiarity and the expertise needed to solve it unaided. An empirical approach was taken to identify the QoE features that affect the usability and utility of an AR head-mounted display (HMD) compared with paper-based instruction. The QoE evaluation methodology involved the capture and analysis of implicit and explicit QoE metrics. The utility (in terms of performance) of each mode of instruction was objectively measured using: (a) cube completion success rates; and (b) time-to-completion. The implicit metrics of electrodermal activity (EDA), skin temperature, heart rate and the novel use of facial action units (AUs) were recorded to infer emotional state during the task completion. Finally, with respect to explicit metrics, the test subjects completed a Likert scale questionnaire post the experience to subjectively report QoE as well as a self-assessment manikin (SAM) questionnaire to self-report emotional state upon task completion. The results show that AR yielded higher success rates and significantly lower time-to-completion rates. The AR group explicitly reported higher levels of positive valance (affective state) than the paper-based group. The physiological data showed that the AR group were less stressed (via EDA) than the paper-based group. Finally, analysis of the AU data reflected a greater than chance (total: 21.85%) accuracy when predicting affective state based on SAM questionnaires as ground-truth.

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 presents a new combined neural network and chaos based pseudo-random sequence generator and a DNA-rules based chaotic encryption algorithm for secure transmission and storage of images. The proposed scheme uses a new heterogeneous chaotic neural network generator controlling the operations of the encryption algorithm: pixel position permutation, DNA-based bit substitution and a new proposed DNA-based bit permutation method. The randomness of the generated chaotic sequence is improved by dynamically updating the control parameters as well as the number of iterations of the chaotic functions in the neural network. Several tests including auto correlation, 0/1 balance and NIST tests are performed to show high degree of randomness of the proposed chaotic generator. Experimental results such as pixel correlation coefficients, entropy, NPCR and UACI etc. as well as security analyses are given to demonstrate the security and efficiency of the proposed chaos based genetic encryption method.

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.

As the computing landscape evolves towards distributed architectures such as Internet of Things (IoT),enterprises are moving away from traditional perimeter based security models toward so called zero trust networking (ZTN) models that treat both the intranet and Internet as equally untrustworthy. Such security models incorporate risk arising from dynamic and situational factors, such as device location and security risk level risk, into the access control decision. Researchers have developed a number of risk models such as RAdAC (Risk Adaptable Access Control) to handle dynamic contexts and these have been applied to medical and other scenarios. In this position paper we describe our ongoing work to apply RAdAC to ZTN. We develop a policy management framework, FURZE, to facilitate fuzzy risk evaluation that also defines how to adapt to dynamically changing contexts. We also consider how enterprise security situational awareness (SSA) - which describes the potential impact to an organisations mission based on the current threats and the relative importance of the information asset under threat - can be incorporated into a RAdAC scheme

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.

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.

In this paper, we consider a use case for national border control and management involving the assurance of privacy and protection of personally identifiable information (PII) in a shared multi-tenant environment, i.e. the cloud. A fuzzy searchable encryption scheme is applied on a watch list of names which are used as indexes for the identification files that are in their turn encrypted and stored on the cloud. Two propositions are described and tested in this paper. The first entails the application of a chaotic fuzzy searchable encryption scheme directly on the use case and its subsequent verification on a number of phonetics synonyms for each name. In the second version, a metaphone based chaotic fuzzy transformation method is used to perform a secure search and query. In this latter case, the fuzzy transformation is performed in two stages: the first stage is the application of the metaphone algorithm which maps all the words pronounced in the same way to a single code and the second stage is the application of the chaotic Local Sensitive Hashing (LSH) to the code words. In both the first and second propositions, amplification of the LSH is also performed which permits controlled fuzziness and ranking of the results. Extensive tests are performed and experimental results show that the proposed scheme can be used for secure searchable identification files and a privacy preserving scheme on the cloud.

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.

IP networks are increasingly being used for the provision of real time media services. This raises new challenges for QoS assurance through real time monitoring due to the complexity of distribution and the need for information correlation across a number of protocols. New tools and approaches are being developed. In this paper we report early results on one such tool, DRAI. In particular we show how a distributed shared memory approach can enable seamless application distribution and integration between the component parts with minimal impact on the application. The initial application of the framework is for VoIP but the framework can be extended to other IP based content services. Preliminary results from commercial ISP deployment are reported.