• We present 26 design principles, which are applicable to Internet of Things (IoT)-enabled mobile augmented reality (MAR) applications, summarized from 23 design principles and 195 usability heuristics identified from systematic literature review. • We demonstrate how design principles can be applied to user interface (UI) and user experience design of IoT-enabled MAR applications by presenting UI mockups. • We propose five new design principles derived from the analysis of IoT-enabled MAR applications designed for healthcare and energy management scenarios. Technological developments, such as mobile augmented reality (MAR) and Internet of Things (IoT) devices, have expanded available data and interaction modalities for mobile applications. This development enables intuitive data presentation and provides real-time insights into the user’s context. Due to the proliferation of available IoT data sources, user interfaces (UIs) have become complex and diversified, while mobile devices have limited screen spaces. This state increases the necessity of design principles that help to secure sufficient user experience (UX). We found that studies of design principles for IoT-enabled MAR applications are limited. Therefore, we conducted a systematic literature review to identify existing design principles applicable to IoT-enabled MAR applications. From the state-of-the-art research, we compiled and categorized 26 existing design principles into seven categories. We analyzed the UIs of three IoT-enabled MAR applications with the identified design principles and user feedback gathered from each application’s evaluation to understand what design principles can be considered in designing these applications. Among the 26 principles, we find eight principles that are commonly identified as possible improvements for the applications based on their purposes. We demonstrate the practical use of the identified principles by redesigning the UIs, and we propose five new design principles derived from the application analysis. As a result, we summarized a total of 31 design principles, including the five new ones. We expect that our findings will give insight into the UX/UI design of IoT-enabled MAR applications for researchers, educators, and practitioners interested in UX/UI development.

The next generation of immersive applications, such as eXtended reality (XR), will likely be cloud-based and streamed over mobile networks using myriad technologies such as WiFi and 6th-generation mobile networks. Mobile networks promise ubiquitous connectivity but are prone to stochastic network conditions that may be detrimental to end users' quality of experience (QoE). The impact of network conditions on QoE has been studied extensively by industry and academia regarding various multimedia services such as audio, video, and gaming. However, the impact of network conditions on users' QoE for XR-based social applications has yet to be thoroughly investigated. This paper presents novel results assessing the impact of network conditions$(\boldsymbol{N}=\mathbf{20})$involving factors such as round trip time (RTT), jitter (RJ), and packet losses (PL) on users' QoE via realistic subjective tests$(\boldsymbol{N}=\mathbf{28})$regarding social XR application. Our results show that social XR applications require stringent QoS conditions. In particular, our results show that increasing RTT values do not significantly affect users' QoE up to 77ms. Combined PL and RTT cases cause significant QoE degradation from 77ms onward with greater than 2% PL. Most importantly, results show that a very small jitter value with one standard deviation beyond 52 milliseconds can lead to significant QoE degradation. Further, jitter values beyond three standard deviations for 27ms RTT and beyond should be avoided.

Cloud-based social eXtended Reality (XR) services are the cornerstone for realizing the promises of the Metaverse. These services hosted either on datacenters or edge, will demand stringent mobile network quality of service (QoS) to operate effectively and provide an acceptable user quality. It becomes fundamental to study how mobile networks QoS factors round-trip time (RTT), packet loss (PL), and jitter affect these services by measuring their effect on users' perceived quality of experience (QoE). Subjective QoE assessment involves carefully controlled laboratory environments to generate the desired conditions between a large set of users. The requirements for a cloud-based social XR service lab-setup are complex: Identify a reliable streaming service, a customizable VR application, emulate network conditions, define activities or tasks for users to perform, collect their data, label it; all while mitigating possible human mistakes. To address these requirements, we present an effective technical setup that can consistently repeat the same conditions between users and that can be easily replicated to other labs conducting cloud-based social XR research.

In the future, extended reality (XR) applications will be hosted on cloud and edge infrastructures and streamed over heterogeneous access networks such as Wi-Fi and 6G. These infrastructures promise ubiquity but are prone to stochastic conditions, such as network congestion and wireless signal fading and attenuation, that can be detrimental to the quality of experience (QoE) regarding XR applications. This paper presents extensive and novel results assessing the impact of network conditions (N = 20) on users’ QoE via realistic subjective tests (N = 28) involving factors such as round-trip time (RTT), jitter (RJ), and packet loss (PL) in a social XR application context. Our results indicate that social XR applications require stringent quality of service to support users’ QoE. We demonstrate that RTT values up to 77 ms do not significantly impact users’ QoE. Furthermore, combined (PL and RTT) values lead to significant QoE degradation when RTT values exceed 77 ms and PL exceeds 2%. We also demonstrate that even minimal jitter values, with 1 standard deviation beyond 52 ms RTT values, can lead to significant QoE degradations. Furthermore, jitter values exceeding 3 standard deviations for 27ms RTT value and beyond should be avoided. Finally, based on network traffic data between Sweden and various AWS data centers in Europe, we show that social XR applications can be hosted at a few data center locations with minimal QoE impact for wired network connections. However, due to high jitter values, both 4G and 5G networks are not conducive to users’ QoE.

Subjective Quality of Experience (QoE) studies often require setting up complex lab environments to study users' perceptions of the application or service under controlled test conditions. These lab environments must control applications and devices to generate the required test conditions accurately, reliably, repeatedly, and error-free under study. Further, the data collection should be performed on many devices, such as clients and servers, often in real-time, and correctly labelled according to each test condition. To circumvent the complex task of configuring the lab environment and the laborious and error-prone work of data collection, we demonstrate ALTRUIST, a multi-platform tool to conduct subjective tests efficiently. In particular, we present the use of ALTRUIST in two lab setups involving immersive applications such as mobile cloud gaming and virtual reality gaming.

Mobile cloud gaming (MCG) lets users play cloud games (CG) on mobile devices anywhere via mobile networks. However, the stochastic nature of network quality of service (QoS) can result in varying user quality of experience (QoE). Understanding, modeling, and predicting the impact of mobile networks' QoS on users' QoE is crucial. This helps stakeholders optimize networks, and game developers efficiently create cloud-hosted games provisioned over mobile networks. This paper investigates the impact of QoS on users' QoE and proposes, develops and validates novel models for predicting QoE for MCG in mobile networks using realistic subjective tests. In particular, we propose and develop three QoE models using multiple, polynomial, and non-linear regression. Our results validate that multiple regression (with R2=0.79, RMSE=0.45) can model complex relationships between QoS factors that impact QoE. Multiple polynomial regression achieved the overall fit with (R2=0.94, RMSE=0.24). Lastly, the non-linear model achieved a good RMSE of 0.24. To select the best model out of the three, we applied the F-test and determined that polynomial regression had the best statistical fit.

Smart homes can facilitate older adults to live independently longer in their homes. One of the most important activities affecting the health and well-being of the elderly is sleep behaviour. In sleep study approaches, data collection (e.g. based on interviews and survey forms) of sleep activity relies on people's interpretations of their sleep routines, which might be subjective and may not reflect day-to-day sleep behaviour sufficiently. This paper proposes a method to discover seasonal effects on sleep behaviour using non-intrusive motion sensors for six single-resident with a median age of 89 in northern Sweden smart homes. There is a need for a data-driven method to validate that actual sleep patterns conform to those observed through interviews. We applied K-means clustering to discover sleep patterns between 0.4-0.5 Average Silhouettes (AS) score and further used DTs to classify sleep behaviour with an average accuracy of between 87% and 95%. We analyzed the results to discover the seasonal effects on sleep. Our key findings show overall minor seasonal effects on sleep patterns for most participants. We observed changes through four aspects: sleep time, going to sleep and waking up, sleep duration, restlessness or activeness during sleep, and the daytime activity in the bedroom, where different effects of seasonal variation are observed for each older adult.

Virtual reality cloud-based gaming (VRCG) services are becoming widely available on virtual reality (VR) devices delivered over computer networks. VRCG brings users worldwide an extensive catalog of games to play anywhere and anytime. Delivering these gaming services in existing broadband mobile networks is challenging due to their stochastic nature and the user’s perceived Quality of Experience (QoE)’ sensitivity towards them. More research is needed regarding developing effective methods to measure the impact of network QoS factors on users’ QoE in the VRCG context. Therefore, this paper proposes, develops, and validates three novel regression models trained on a real dataset collected via subjective tests (N=30); the dataset contains subjective users’ QoE ratings regarding VR shooter games affected by network conditions (N=28), such as round-trip time (RTT), random jitter (RJ), and packet loss (PL). Our findings reveal that due to the nonlinear relationship of (RTT and RJ) tested together, nonlinear (mean absolute error (MAE)=0.14) and polynomial (MAE=0.15) regression models have the best performance; yet, simple linear regression model (MAE=0.19) is also suitable to predict QoE for VRCG. Further, we found that feature importance depends on the model’s choice (either RTT or RJ). Finally, our models’ prediction of QoE for real-world traffic measurements suggests that mobile network traffic (4G, 5G non-standalone, 5G standalone) provides a 2.5 ≤MOSQoE≤ 3.0 experience for VRCG, while 4.2 ≤MOSQoE≤ 4.4 for wired connections, suggesting the need for improvements in the current commercial 5G network deployments to deliver VRCG.

Quality of experience (QoE) is an essential metric for stakeholders to understand how customers perceive the quality of their products or services. Gaming-as-a-Service (GaaS) is a challenging model to deliver efficiently to customers worldwide since it involves the joint force of cloud service providers, network operators, and game developers. The recent move of the cloud gaming (CG) industry to virtual reality (VR) platforms brings the benefits of the cloud to the most immersive quality of service (QoS) and QoE-sensitive VR content. Virtual reality cloud-based gaming (VRCG) necessitates understanding of stochastic broad-band network connections on users' QoE so that stakeholders can deliver quality content by optimizing their services to underlying QoS conditions. Very few studies exist in the literature that study the impact of network QoS on users' QoE for VRCG. This paper presents subjective tests (N=30) and investigates the effect of network-emulated QoS metrics (N=28) on the commercial Nvidia CloudXR service and their impact on the users' perceived QoE while playing Serious Sam VR shooter game. Our findings reveal that QoE was most affected by round trip time (RTT)$\geq 75$ms or packet loss (PL)$> 6\%$. Random jitter (RJ) caused QoE degradation for values more significant than one standard deviation, while the combined RTT and PL degraded QoE the most for RTT$\geq 25$ms and PL$\geq 4\%$, Finally, based on actual network traffic data between Sweden and various data centers in Europe, we suggest VRCG can be hosted anywhere in these data centers with minimal impact on QoE for wired connections. However, for 4G and 5G networks, high jitter values could pose a challenge to VRCG services.

Sleep is an essential activity that affects an individual’s health and ability to perform Activities of Daily Living (ADL). Inadequate sleep reduces cognitive capacity and leads to health-related issues such as cardiovascular diseases. Sleep disorders are more prevalent in older adults. Therefore, it is essential to recognize sleep patterns and support older adults and their caregivers. In our study, we collect data in real-world unconstrained and non-intrusive environments. This paper presents a novel sleep activity recognition method using motion sensors for recognizing nighttime and daytime sleep, which can further enable the development of insightful healthcare applications. The research objectives are to evaluate the application of using Multi-Armed Bandit methods to (i) learn normal sleep patterns, (ii) evaluate sleep quality, and (iii) detect anomalies in sleep activity for 11 elderly participants living in single-resident smart homes. We evaluate the performance of Thompson Sampling, Random Selection, and Upper Confidence Bound MAB methods. Thompson Sampling outperformed the other two methods. Our findings show most elderly participants slept between 6 and 8 hours with 85% sleep efficiency and up to 3 awakenings per night.

IoT-enabled innovative elderly healthcare facilitated by machine learning (ML) can address the challenges pertaining to the global aging population. For instance, it can enable the early detection of debilitating conditions such as Alzheimer's and dementia. This paper addresses this challenge by developing IoT and ML-based methods to recognize changes in long-term activities of daily living (ADLs) that may lead to the conditions mentioned above. In particular, we gather real-world long-term (approx. three years) data from 6 real-life single-resident elderly smart homes in Sweden, equipped with motion sensors in each room; and use unsupervised ML methods incorporating K-means clustering and local outlier factor to recognize changes in long-term behaviour efficiently. Our results have shown that K-means show similar performance in identifying outliers over all datasets while local outlier factorization fluctuates more but is more sensitive to identify small changes in living conditions. We foresee that our methods to detect long-term behaviour changes can support caregivers in carrying out their assessment for discovering the early onset of health conditions, thereby preventing further progression and providing timely treatment.

Quality of Experience (QoE) subjective assessment often demands setting up expensive lab experiments that involve controlling several software programs and services. In addition, these experiments may pose significant challenges regarding man-agement of testbed software components as they may have to be synchronized for efficient data collection, leading to human errors or loss of time. Further, maintaining error-free repeatability between subsequent subjective tests and comprehensive data collection is essential. Therefore, this paper proposes, develops and validates ALTRUIST, a multi-platform tool that assists the experimenter in conducting subjective tests by controlling external applications, facilitates data collection and automates test execution for conducting repeatable subjective tests in broad application areas.

The emergence of novel cellular network technologies, within 5G, are envisioned as key enablers of a new set of use-cases, including industrial automation, intelligent transportation, and tactile internet. The critical nature of the traffic requirements ranges from ultra-reliable communications, massive connectivity, and enhanced mobile broadband. Thus, the growing research on cellular network monitoring and prediction aims for ensuring a satisfied user-base and fulfillment of service level agreements. The scope of this study is to develop an approach for predicting the cellular link throughput of end-users, with a goal to benchmark the performance of network slices. First, we report and analyze a measurement study involving real-life cases, such as driving in urban, sub-urban, and rural areas, as well as tests in large crowded areas. Second, we develop machine learning models using lower-layer metrics, describing the radio environment, to predict the available throughput. The models are initially validated on the LTE network and then applied to a non-standalone 5G network. Finally, we suggest scaling the proposed model into the future standalone 5G network. We have achieved 93% and 84% R^2 accuracy, with 0.06 and 0.17 mean squared error, in predicting the end-user's throughput in LTE and non-standalone 5G network, respectively.

The rise of mobile cloud gaming (MCG) has necessitated understanding its impact on mobile network design and deployment for end users' QoE maximization. MCG is a dynamic service that requires stringent quality from network operators. Therefore, this paper investigates the subjective QoE of MCG over mobile networks played on smartphones. We conducted subjective tests (N=31); our results indicate that MCG is affected differently by QoS attributes such as packet loss (PL), round trip time (RTT) and jitter compared to cloud games and online mobile games. We identify that RTT values above 100 milliseconds significantly impact users' QoE, measured via the mean opinion score (MOS). Further, lower RTT values with high PL; and higher RTT values with low PL cause a strong negative effect on MOS. Lastly, bursty jitter seems to affect the MOS, while random jitter does not significantly impact MOS.

With the rapid growth of the Internet of Things (IoT) applications in smart regions/cities, for example, smart healthcare, smart homes/offices, there is an increase in security threats and risks. The IoT devices solve real-world problems by providing real-time connections, data and information. Besides this, the attackers can tamper with sensors, add or remove them physically or remotely. In this study, we address the IoT security sensor tampering issue in an office environment. We collect data from real-life settings and apply machine learning to detect sensor tampering using two methods. First, a real-time view of the traffic patterns is considered to train our isolation forest-based unsupervised machine learning method for anomaly detection. Second, based on traffic patterns, labels are created, and the decision tree supervised method is used, within our novel Anomaly Detection using Machine Learning (AD-ML) system. The accuracy of the two proposed models is presented. We found 84% with silhouette metric accuracy of isolation forest. Moreover, the result based on 10 cross-validations for decision trees on the supervised machine learning model returned the highest classification accuracy of 91.62% with the lowest false positive rate.

Connected and automated vehicles (CAVs) are envisioned to revolutionize the transportation industry, enabling autonomous processes and real-time exchange of information among vehicles and infrastructure. To safely navigate the roadways, CAVs rely on sensor readings and data from the surrounding vehicles. Hence, a fault or anomaly arising from the hardware, software, or the network can lead into devastating consequences regarding safety. This study investigates potential performance degradation caused by anomalies, by analyzing real-life vehicles’ sensory and network-related data. The aim is to utilize unsupervised learning for anomaly detection, with a goal to describe the cause and effect of the detected anomalies from a performance perspective. The results show around 93% F1-score when detecting anomalies imposed by the cellular network and the vehicle’s sensors. Moreover, with approximately 90% F1-score we can detect anomalous predictions from a deployed network-related ML model predicting cellular throughput and describe the root-causes behind the detected anomalies.

Today's research on Quality of Experience (QoE) mainly addresses multimedia services. With the introduction of the Internet of Things (IoT), there is a need for new ways of evaluating the QoE. Emerging IoT services, such as autonomous vehicles (AVs), are more complex and involve additional quality requirements, such as those related to machine-to-machine communication that enables self-driving. In fully autonomous cases, it is the intelligent machines operating the vehicles. Thus, it is not clear how intelligent machines will impact end-user QoE, but also how end users can alter and affect a self-driving vehicle. This article argues for a paradigm shift in the QoE area to cover the relationship between humans and intelligent machines. We introduce the term Quality of IoT-experience (QoIoT) within the context of AV, where the quality evaluation, besides end users, considers quantifying the perspectives of intelligent machines with objective metrics. Hence, we propose a novel architecture that considers Quality of Data (QoD), Quality of Network (QoN), and Quality of Context (QoC) to determine the overall QoIoT in the context of AVs. Finally, we present a case study to illustrate the use of QoIoT.

The use of video streaming services are increasing in the cellular networks, inferring a need to monitor video quality to meet users’ Quality of Experience (QoE). The so-called no-reference (NR) models for estimating video quality metrics mainly rely on packet-header and bitstream information. However, there are situations where the availability of such information is limited due to tighten security and encryption, which necessitates exploration of alternative parameters for conducting video QoE assessment. In this study we collect real-live in-smartphone measurements describing the radio link of the LTE connection while streaming reference videos in uplink. The radio measurements include metrics such as RSSI, RSRP, RSRQ, and CINR. We then use these radio metrics to train a Random Forrest machine learning model against calculated video quality metrics from the reference videos. The aim is to estimate the Mean Opinion Score (MOS), PSNR, Frame delay, Frame skips, and Blurriness. Our result show 94% classification accuracy, and 85% model accuracy (R2value) when predicting the MOS using regression. Correspondingly, we achieve 89%, 84%, 85%, and 82% classification accuracy when predicting PSNR, Frame delay, Frame Skips, and Blurriness respectively. Further, we achieve 81%, 77%, 79%, and 75% model accuracy (R2value) regarding the same parameters using regression.

With the increasing number of devices performing Machine-Type Communications (MTC), mobile networks are expected to encounter a high load of burst transmissions. One bottleneck in such cases is the Random Access Channel (RACH) procedure, which is responsible for the attachment of devices, among other things. In this paper, we performed a rich-parameter based simulation on RACH to identify the procedure bottlenecks. A finding from the studied scenarios is that the Physical Downlink Control Channel (PDCCH) capacity for the grant allocation is the main limitation for the RACH capacity rather than the number of Physical Random Access Channel (PRACH) preambles. Guided by our simulation results, we proposed improvements to the RACH procedure and to PDCCH.

The stochastic nature of the cloud systems makes cloud quality of service (QoS) performance diagnosis and prediction a challenging task. A plethora of factors including virtual machine types, data centre regions, CPU types, time-of-the-day, and day-of-the-week contribute to the variability of the cloud QoS. The state-of-the-art methods for cloud performance diagnosis do not capture and model complex and uncertain inter-dependencies between these factors for efficient cloud QoS diagnosis and prediction. This paper presents ALPINE, a proof-of-concept system based on Bayesian networks. Using a real-life dataset, we demonstrate that ALPINE can be utilised for efficient cloud QoS diagnosis and prediction under stochastic cloud conditions.

This research paper presents CoMA, a Container Monitoring Agent, that oversees resource consumption of operating system level virtualization platforms, primarily targeting container-based platforms ...

The ageing population worldwide is constantly rising, both in urban and regional areas. There is a need for IoT-based remote health monitoring systems that take care of the health of elderly people without compromising their convenience and preference of staying at home. However, such systems may generate large amounts of data. The key research challenge addressed in this paper is to efficiently transmit healthcare data within the limit of the existing network infrastructure, especially in remote areas. In this paper, we identified the key network requirements of a typical remote health monitoring system in terms of real-time event update, bandwidth requirements and data generation. Furthermore, we studied the network communication protocols such as CoAP, MQTT and HTTP to understand the needs of such a system, in particular the bandwidth requirements and the volume of generated data. Subsequently, we have proposed IReHMo - an IoT-based remote health monitoring architecture that efficiently delivers healthcare data to the servers. The CoAP-based IReHMo implementation helps to reduce up to 90% volume of generated data for a single sensor event and up to 56% required bandwidth for a healthcare scenario. Finally, we conducted a scalability analysis to determine the feasibility of deploying IReHMo in large numbers in regions of north Sweden.

Mobile devices have become an integral part of our daily lives. Applications running on these devices may avail storage and compute resources from the cloud(s). Further, a mobile device may also connect to heterogeneous access networks (HANs) such as WiFi and LTE to provide ubiquitous network connectivity to mobile applications. These devices have limited resources (compute, storage and battery) that may lead to service disruptions. In this context, mobile cloud computing enables offloading of computing and storage to the cloud. However, applications running on mobile devices using clouds and HANs are prone to unpredictable cloud workloads, network congestion and handoffs. To run these applications efficiently the mobile device requires the best possible cloud and network resources while roaming in HANs. This paper proposes, develops and validates a novel system called M2C2which supports mechanisms for: i.) multihoming, ii.) cloud and network probing, and iii.) cloud and network selection. We built a prototype system and performed extensive experimentation to validate our proposed M2C2. Our results analysis shows that the proposed system supports mobility efficiently in mobile cloud computing.

Quality of Experience (QoE) as an aggregate of Quality of Service (QoS) and human user-related metrics will be the key success factor for current and future mobile computing systems. QoE measurement and prediction are complex tasks as they may involve a large parameter space such as location, delay, jitter, packet loss, and user satisfaction just to name a few. These tasks necessitate the development of practical context-aware QoE models that efficiently determine relationships between user context and QoE parameters. In this paper, we propose, develop, and validate a novel decision-theoretic approach called CaQoEM for QoE modelling, measurement, and prediction. We address the challenge of QoE measurement and prediction where each QoE parameter can be measured on a different scale and may involve different units of measurement. CaQoEM is context-aware and uses Bayesian networks and utility theory to measure and predict users' QoE under uncertainty. We validate CaQoEM using extensive experimentation, user studies and simulations. The results soundly demonstrate that CaQoEM correctly measures range-defined QoE using a bipolar scale. For QoE prediction, an overall accuracy of 98.93% was achieved using 10-fold cross validation in multiple diverse network conditions such as vertical handoffs, wireless signal fading and wireless network congestion.

Network Service Providers are struggling to reduce cost and still improve customer satisfaction. We have looked at three underlying challenges to achieve these goals; an overwhelming flow of low-quality alarms, understanding the structure and quality of the delivered services, and automation of service configuration. This thesis proposes solutions in these areas based on domain-specific languages, data-mining and self-learning. Most of the solutions have been validated based on data from a large service provider. We look at how domain-models can be used to capture explicit knowledge for alarms and services. In addition, we apply data-mining and self-learning techniques to capture tacit knowledge. The validation shows that models improve the quality of alarm and service models, and enables automatic rendering of functions like root cause correlation, service and SLA status, as well as service configuration. The data-mining and self-learning solutions show that we can learn from available decisions made by experts and automatically assign alarm priorities.

Measuring and predicting users quality of experience (QoE) in dynamic network conditions is a challenging task. This paper presents results related to a decision-theoretic methodology incorporating Bayesian networks (BNs) and utility theory for quality of experience (QoE) measurement and prediction in mobile computing scenarios. In particular, we show how both generative and discriminative BNs can be used to measure and predict users QoE accurately for voice applications under several wireless network conditions such as wireless signal fading, vertical handoffs, wireless network congestion and normal hotspot traffic. Through extensive simulation studies and results analysis, we show that our proposed methodology can achieve an average accuracy of 98.70% using three different types of Bayesian network.

This paper presents a pioneering context-aware approach for quality of experience (QoE) measurement and prediction. The proposed approach incorporates an intuitive context-aware framework and decision theory. It is capable of incorporating several QoE related classes and context information to correctly measure and predict the overall QoE on a single scale. Our approach can be used in measuring and predicting QoE in both lab and living-lab settings based on user, device and network related context parameters. The predicted QoE can be beneficial for network operators to minimize network churn and can help application developers to build smart user-centric applications. We perform extensive experimentation and the results validate our approach.

Mobility exists in many shapes; when migrating a running application (code, states and data) from one device to another, one has achieved application mobility. In this article we combine this mobility type with context awareness, defining context awareness supported application mobility (CASAM) as when using context in the act of moving an application between hosts during its execution, to provide relevant information and/or services, where relevancy depends on the user's task. We identify and present five CASAM challenges, that are used to create requirements for a CASAM architecture. A proposal for an architecture, building on peer-to-peer (P2P) technology in combination with mobile IP, is then presented, addressing the identified challenges, providing a decentralized framework for global application mobility. As such, our architecture differs from earlier centralized and/or locally bound solutions for application mobility.

In this paper, we present a scalability study of AAA support in mobile heterogeneous access networks with respect to server and network load related to AAA processes using the RADIUS protocol. Technologies such as IEEE 802.11, CDMA 2000 and UMTS which all support the RADIUS protocol for AAA handling are discussed and analyzed. Typical performance data are gathered and complemented with a theoretical study in order to achieve an overview of what parameters will affect the performance and scalability of the network. Also, guidelines are developed for network design in order to achieve the desired performance for a given number of users. Results of this study include the conclusion that the main bottleneck of the AAA procedure is not necessarily the AAA server CPU power. Aside the cases with a high proportion of computationally intensive WiFi sign-ons with strong encryption, performance issues may be caused by AAA server network connection bandwidth, and RAM memory. In cases where a high number of users reside in the same user database, database performance becomes a significant issue. In order to achieve better performance, CPU load balancing over several servers may be performed.

An important feature of the upcoming fourth generation wireless networks is support for heterogeneous radio access technologies in combination with an all-IP type of overall architecture. Operators and users will benefit from a smooth technology transition leveraging existing investments and use a variety of access technologies simultaneously. This paper describes and evaluates an innovative mobility management scheme in such an environment. It does not require any changes to the IP stack in the mobile node and does not introduce any additional overhead to the payload traffic over air interfaces. Furthermore, it does not add any signaling overhead and outperforms existing mobility management schemes for heterogeneous environments in terms of bandwidth consumption. The architecture uses a make-before-break principle for vertical handovers and bidirectional tunneling using various tunneling mechanisms connecting mobile nodes through access networks to a home network. Also, it proposes a packet inspection routine for timely handover execution in the home network.

We present a scheme for flow management with heterogeneous access technologies available indoors and in a campus network such as GPRS, 3G and Wi-Fi. Statistical learning is used as a key for optimizing a target variable namely video quality of experience (QoE). First we analyze the data using passive measurements to determine relationships between parameters and their impact on the main performance indicator, video Quality of Experience (QoE). The derived weights are used for performing prediction in every discrete time interval of our designed autonomic control loop to know approximately the QoE in the next time interval and perform a switch to another access technology if it yields a better QoE level. This user-perspective performance optimization is in line with operator and service provider goals. QoE performance models for slow vehicular and pedestrian speeds for Wi-Fi and 3G are derived and compared.

Starting with an efficient mobility management scheme for heterogeneous wireless networks, this paper proposes a solution for AAA handling using a common database for storing user information. Regardless of the access technology selected, user@realm identities are used for authentication, authorization, and accounting. In particular, a new function is introduced in which port-based network access control is used in combination with dynamic host configuration protocol mechanisms for IP address allocation. This way, PPP-based and Ethernet-based access technologies are handled uniformly. Advantages with the proposed solution include: using only standardized mechanisms in the mobile node, as well as in the access networks. Only an additional plug-in in the AAA server (located in the access networks) needs to be deployed. The proposed AAA architecture has been implemented and evaluated in a live experimental environment. Results show authentication and authorization to perform efficiently and seamlessly.

This paper presents a blueprint for proactive context-aware mobility support architecture for heterogeneous access networks called PRONET. In particular, we leverage upon the principles of cognitive networking to support proactive context-awareness for user-centric application adaptation via quality-of-experience (QoE) provisioning. Our proposed architecture is built upon port-based multi-homed mobile IPv6 (PM-MIPv6) solution to support several applications via path diversity. In this paper our contributions are two-fold. Firstly, we identify and present gaps in our research domain related to mobility, QoE, cognitive networks and cross-layer design. We then present our architecture for providing seamless mobility in heterogeneous access networks. Currently, we are in the process of collecting results via our test bed and prototype implementation for 802.11g and HSDPA wireless networks.

With the recent developments in wireless networks, different radio access technologies are used in different places depending on capacity in terms of throughput, cell size, scalability etc. In this context, mobile users, and in particular highly mobile users and vehicular networks, will see an increasing number and variety of wireless access points enabling Internet connectivity. Such a heterogeneous networking environment needs, however, an efficient mobility management scheme offering the best connection continuously. In this paper, a mobility management architecture focusing on efficient network selection and timely handling of vertical and horizontal hand-overs is proposed. The solution is based on Mobile IP where hand-over decisions are taken based upon calculations of a metric combining delay and delay jitter. For efficiency reasons, the frequency of binding updates is dynamically controlled, depending on speed and variations in the metric. The dynamic frequency of binding updates helps the timely discovery of congested access points and cell edges so as to allow efficient hand-overs that minimize packet drops and hand-over delays. Results show that the overall signaling cost is decreased and changes in networking conditions are detected earlier compared to standard Mobile IP.

This paper proposes a new interworking scheme for Mobile IP and the Session Initiation Protocol being the most popular solutions for mobility management at the network and application layers respectively. The goal is to deliver seamless mobility for both TCP-based and UDP-based applications taking the best features from each mobility management scheme. In short, this paper proposes that TCP connections are handled through Mobile IP while UDP-based connection-less applications may use the Session Initiation Protocol for handling mobility. The two mobility management solutions are integrated into one common solution.

With today's heterogeneous access to the Internet, users will move between wired and wireless environments and between infrastructure mode and ad hoc mode of wireless communication. When a mobile node moves from an infrastructure connection and connects multihop to an Internet gateway, the performance will degrade and it may not be able to send all of its traffic via a single gateway. This highlights the need of load-balancing between Internet gateways, especially since the behavior of users today involves a multitude of parallel activities generating multiple flows. This paper proposes a solution that enables distribution of individual traffic flows via different Internet gateways instead of using one single gateway. The proposal includes extensions to mobile IP in order to handle flow mobility bindings. The performance of the solution is verified by simulation studies.

In this paper, a proof-of-concept and a software architecture, M4 (MultiMedia Mobility Manager), is presented. In short, M4 is offering seamless mobility management to multimedia applications using a variety of wireless access networks. First, M4 is built on multihomed Mobile IP building on the principle of soft handovers. Second, network selection in M4 is based on a network layer metric combining round-trip times and jitter in round-trip times. Third, the end-user can enter its own preferences on network selection through a policy-based extension to the proposed network selection algorithm.

Abstract—This paper presents a mobility management solution to support both applications who are mobility-aware and those who are not. Mobility management in heterogeneous network environments needs to address the double meaning of the IP address as an endpoint identifier and a location identifier. Application-layer mobility use a non-IP endpoint identifier (e.g.

Future wireless networks are expected to be based on coexistence of multiple different access network technologies. Mobile devices will then be equipped with multiple wireless interfaces enabling connectivity via multiple architectures. Different wireless technologies differ widely considering their capabilities and coverage. This requires a mobile node to maintain multiple active connections depending on the applications used and available access networks. To enable this, a mobile node needs to be multihomed and able to direct traffic flows via different interfaces. This paper describes a proposal extending mobile IPv6 with multihoming functionality. Multihoming is managed using IP address, protocol and port number

The need for maintaining gateway connectivity in an ad hoc access network is vital considering the 80/20 ratio of Internet traffic. There are several proposals of how to integrate gateway forwarding strategies but they all rely on the route discovery procedure of reactive routing protocols. We propose a proactive approach to avoid the delay of the route discovery process. Mobile IP is often suggested to handle macro mobility and we use the advertisements periodically sent by the gateway to update routing tables in the ad hoc network. Since advertisements may arrive to a mobile host through multiple paths, it is important to keep track of the best path to each gateway. We demonstrate the use of a proposed dynamic metric and how to handle location of correspondent hosts. A simulation study demonstrates the usefulness and efficiency of our approach