Internet of Vehicles (IoV) communication serves as a critical enabler for intelligent transportation systems, where the reliability of data sharing fundamentally determines overall system performance. Data reliability not only dictates the quality of IoV services but also constitutes a core factor in ensuring traffic safety and improving road operational efficiency. At present, IoV data sharing confronts several key challenges: (i) degradation of message credibility due to malicious attackers; (ii) ineffectiveness of conventional reputation mechanisms under high vehicular mobility; and (iii) channel congestion and information loss caused by redundant data transmissions. To address these issues, this paper proposes a Hybrid Hash Chord Protocol, which integrates Geohash geocoding with the Chord distributed lookup algorithm to establish a location-aware peer-to-peer data forwarding mechanism. This approach enables efficient data transmission with reduced hop count. In addition, a recursive traffic data filtering method is introduced to effectively suppress duplicate data reporting. Moreover, a Bayesian Dynamic Fading Reputation Model is developed, incorporating time decay factors and historical behavior weighting to mitigate intelligent attacks and node inertia, thereby enhancing road safety and traffic efficiency. Experimental results indicate that, within an IoV data-sharing context, the proposed model improves traffic efficiency by approximately 4% and reduces overall communication overhead by 36% compared to existing schemes. When the reputation threshold is set to 0.35, the model achieves a 100% malicious vehicle detection rate, whereas benchmark methods remain below 80% under the same threshold. In summary, the proposed framework achieves a notable balance among enhancing data trustworthiness, optimizing traffic performance, and minimizing communication costs, offering a practicable solution for building efficient and reliable IoV data-sharing systems.

In recent years, the accelerated advancement of Internet of Vehicles (IoV) technology has significantly enhanced user experiences by providing intelligent services, such as multimedia entertainment and autonomous driving in vehicles. However, the enforcement of regulations concerning vehicle violations in IoV environments predominantly relies on manual methods, which are both expensive and challenging. Moreover, the inherent constraints in existing surveillance systems result in regulatory blind spots. Consequently, it is imperative to develop intelligent IoV-based surveillance mechanisms to improve the efficiency of detecting and rectifying violations. In this article, we propose a blockchain-based self-supervision model for vehicle violations that utilizes intervehicle reporting and voting mechanisms to enhance the detection rate of violations and reduce regulatory pressure. A forensic blockchain is introduced in the model to enable a review of the reporting results, which improves the security and reliability of the system. Additionally, more vehicles are incentivized to participate in the system through reputation-based rewards, punishments, and incentives. The system was deployed on the Hyperledger Fabric platform. Simulation experiments were conducted using Veins, SUMO, and OMNeT++. The experimental results verify the effectiveness of the model. The reporting and voting mechanism significantly inhibit violations, and the reward and reputation mechanism effectively promote the participation of vehicles.

A warm welcome to the 2024 IEEE World Congress on Services (SERVICES). With Professor Zhi Jin and Professor Michael Sheng serving as the Congress General Chairs, I trust everyone will have a rewarding experience participating in the IEEE Computer Society's flagship annual event in services computing, whether attending on-site or remotely.

A warm welcome to the 2024 IEEE World Congress on Services (SERVICES). With Professor Zhi Jin and Professor Michael Sheng serving as the Congress General Chairs, I trust everyone will have a rewarding experience participating in the IEEE Computer Society's flagship annual event in services computing, whether attending on-site or remotely.

With the advent of the services computing era, challenges in educating capable future software services engineers and researchers have become more pressing than ever. Software services engineers are professionals whose training cuts across computer science, software engineering, services computing, as well as relevant educational elements in management science and engineering, social science, serviceology, and service science and engineering [1]. We foresee an urgent need in this fast-emerging multidisciplinary field "Software Services Engineering" (SSE) [2] [2.1] for a comprehensive collection of education and training artifacts including well-defined body of knowledge (BOK), model curricula, open-source platforms, certification requirements, accreditation criteria, articulation directives, exemplary professional course modules, among other related components.

Traditional Deep Learning (DL) approaches for medical image classification rely on centralized, static datasets, which do not adequately reflect the dynamic, real-world medical practice where data is continually generated. In contrast, Federated Learning (FL) enables decentralized model training on localized data while preserving privacy. Yet, current FL methods struggle to handle Non-Identically Independently Distributed (Non-IID) data streams over time. This paper introduces Med-TimeSplit, a novel dataset partitioning strategy that integrates Online Continual Learning (OCL) with FL to simulate real-world medical data flows more realistically and effectively. MedTimeSplit partitions data into Non-IID, time-based increments, mimicking dynamic sourcing in medical environments. We evaluate its impact on FL model performance for medical image classification, focusing on skin lesions, and analyze the system’s resilience to backdoor attacks. Our experiments demonstrate that MedTimeSplit outperforms existing methods in both accuracy and robustness, offering a viable solution for real-world medical applications. Additionally, we propose new metrics to measure model behavior over time, including Average Bad Decisions (ABD) and Overall Changing Mistakes (OCM), which provide deeper insights into model performance specifically under OCL conditions. The results highlight the promise of combining OCL with FL in the medical domain, paving the way for more secure and adaptive healthcare solutions. The source code is available on GitHub1.

Millions of patients suffer from rare diseases around the world. However, the samples of rare diseases are much smaller than those of common diseases. Hospitals are usually reluctant to share patient information for data fusion due to the sensitivity of medical data. These challenges make it difficult for traditional AI models to extract rare disease features for disease prediction. In this paper, we propose a Dynamic Federated Meta-Learning (DFML) approach to improve rare disease prediction. We design an Inaccuracy-Focused Meta-Learning (IFML) approach that dynamically adjusts the attention to different tasks according to the accuracy of base learners. Additionally, a dynamic weight-based fusion strategy is proposed to further improve federated learning, which dynamically selects clients based on the accuracy of each local model. Experiments on two public datasets show that our approach outperforms the original federated meta-learning algorithm in accuracy and speed with as few as five shots. The average prediction accuracy of the proposed model is improved by 13.28% compared with each hospital's local model.

Combining Large Language Models (LLMs) with search engine services marks a significant shift in the field of services computing, opening up new possibilities to enhance how we search for and retrieve information, understand content, and interact with internet services. This paper conducts an in-depth examination of how integrating LLMs with search engines can mutually benefit both technologies. We focus on two main areas: using search engines to improve LLMs (Search4LLM) and enhancing search engine functions using LLMs (LLM4Search). For Search4LLM, we investigate how search engines can provide diverse high-quality datasets for pre-training of LLMs, how they can use the most relevant documents to help LLMs learn to answer queries more accurately, how training LLMs with Learning-To-Rank (LTR) tasks can enhance their ability to respond with greater precision, and how incorporating recent search results can make LLM-generated content more accurate and current. In terms of LLM4Search, we examine how LLMs can be used to summarize content for better indexing by search engines, improve query outcomes through optimization, enhance the ranking of search results by analyzing document relevance, and help in annotating data for learning-to-rank tasks in various learning contexts. However, this promising integration comes with its challenges, which include addressing potential biases and ethical issues in training models, managing the computational and other costs of incorporating LLMs into search services, and continuously updating LLM training with the ever-changing web content. We discuss these challenges and chart out required research directions to address them. We also discuss broader implications for service computing, such as scalability, privacy concerns, and the need to adapt search engine architectures for these advanced models.

Emerging technologies such as Artificial Intelligence and the Internet of Things (IoT) promise to enable high-impact applications of computing in unprecedented ways that are bound to change how we live, work, learn, entertain, and conduct business. Unlike personal computing and its easy-to-understand and use applications such as word processing, spreadsheet applications, and email clients, it takes specialized skills to use artificial intelligence where one must almost become a data scientist first to be able to do simple intuitive tasks such as solving clustering, classification, or regression problems. The IoT is also difficult to program to achieve a desired and harmonious effect of a combination of things. Despite numerous advances in IoT, it remains the case that one must be of the DIY type or a computer geek to combine and program a collection of loT devices into a smart space. While human-computer interactions have contributed numerous design techniques addressing usability, accessibility, and inclusion, among other goals, a democratization paradigm shift is needed to more fully address equity, inclusiveness, freedom of choice, and empowerment in utilizing such emerging technologies. Software service engineering is very much suited to address these new democratization challenges by engineering or reengineering key software services that can bring much-sought equity into such emerging technological advances. This keynote will present the “inequitable utility” problem of emerging technologies with examples from the AI and IoT domains. The bases for potential solutions will be discussed using simple examples. I hope to be able to convince the IEEE SERVICES community of the important role it can partake in embedding democratization-by-design within these emerging technologies through fundamental software services engineering research and development.

As important indicators of urban public safety, public safety and environmental security (PSES) is related to residents’ living security and greatly affects their quality of life and happiness index. Due to PSES characteristics, such as diverse forms, wide distribution, and unpredictable occurrence times, traditional solutions consume huge manpower, and time in the implementation process. Although some professional software and hardware systems have emerged to assist in solving the problems, there are still challenges, such as limited sensing coverage and monotonous sensing modes, lack of interaction and understanding between systems and tasks, and scarcity of effective system architecture and functional modules. To meet these challenges, we design a PSES multiterminal fusion system (SafeCity) based on the idea and technology of heterogeneous mobile crowd sensing. With collaboration among humans, machines, and things (H-M–T), the proposed system makes full use of the idle mobility, sensing, and computing resources in the city, and systematically provides a solution to the various PSES issues. Apart from the system architecture, functions, core mechanism, and algorithm libraries, the task execution flow is explained in depth through the description of several cases. We implement a prototype to verify the rationality and effectiveness of SafeCity. And, comprehensive comparison and evaluation show that SafeCity is far superior to other solutions in terms of function, performance, and stability.

Graphic-pattern-based implicit authentication has been successfully exploited to elevate the security of smartphones. On-screen pressure is one of the key features in such an approach since it can reveal users’ touch pattern. However, state-of-the-art approaches rely on a system API to obtain on-screen pressure, which is not adequately accurate and cannot meet the demands of robust implicit authentication. To bridge this gap, we propose PresSafe, a novel implicit authentication system that utilizes the smartphone’s built-in barometer sensor to measure pressure during the unlocking process, and to utilize the pressure data in authentication. A key technical challenge in utilizing barometer sensing, however, is to understand the user activity through measured pressure. To overcome this challenge, PresSafe leverages barometer data along with data from other conventional but heterogeneous ambient sensors to produce accurate and robust user activity descriptions. PresSafe utilizes a transfer-learning-based hybrid workflow to integrate user activity representation learning with a lightweight classical authentication algorithm to obtain a unified model. This approach offloads the computational cost from the terminal and addresses privacy concerns. To ensure applicability of our approach despite data heterogeneity and insufficient training data, we utilize a channel-adaptive data processing mechanism. Extensive experiments utilizing more than 70000 records from 23 volunteers in six different locations show that PresSafe achieves an FAR of 0.45%, an FRR of 0.49%, and an EER of 0.47%, which clearly demonstrate its superiority over several existing solutions.

Smart spaces such as smart homes deliver digital services to optimize space use and enhance user experience. They are composed of an Internet of Things (IoT), people, and physical content. They differ from traditional computer systems in that their cyber-physical nature ties intimately with the users and the built environment. The impact of ill-programmed applications in such spaces goes beyond loss of data or a computer crash, risking potentially physical harm to the space and its users. Ensuring smart space safety is therefore critically important to successfully deliver intimate and convenient services surrounding our daily lives. By modeling smart space as a highly dynamic database, we present IoT Transactions, an analogy to database transactions, as an abstraction for programming and executing the services as the handling of the devices in smart space. Unlike traditional database management systems that take a “clear room approach,” smart spaces take a “dirty room approach” where imperfection and unattainability of full control and guarantees are the new normal. We identify Atomicity, Isolation, Integrity and Durability (AI 2 D) as the set of properties necessary to define the safe runtime behavior for IoT transactions for maintaining “permissible device settings” of execution and to avoid or detect and resolve “impermissible settings.” Furthermore, we introduce a lock protocol, utilizing variations of lock concepts, that enforces AI 2 D safety properties during transaction processing. We show a brief proof of the protocol correctness and a detailed analytical model to evaluate its performance.

Participant selection is a fundamental research issue in Mobile Crowdsensing (MCS). Previous approaches commonly assume that adequately long periods of candidate participants’ historical mobility trajectories are available to model their patterns before the selection process, which is not realistic for some new MCS applications or platforms. The sparsity or even absence of mobility traces will incur inaccurate location prediction, thus undermining the deployment of new MCS applications. To this end, this paper investigates a novel problem called “From-Scratch MCS” (FS-MCS for short), in which we study how to intelligently select participants to minimize such a “cold-start” effect. Specifically, we propose a novel framework based on reinforcement learning, named RL-Recruiter+. With the gradual accumulation of mobility trajectories over time, RL-Recruiter+ is able to make a good sequence of participant selection decisions for each sensing slot. Compared to its previous version, RL-Recruiter, Re-Recruiter+ jointly considers both the previous coverage and current mobility predictability when training the participant selection decision model. We evaluate our approach experimentally based on two real-world mobility datasets. The results demonstrate that RL-Recruiter+ outperforms the baseline approaches, including RL-Recruiter under various settings.

As we have entered the Internet-of-Things (IoT) era, further blessed with rapid advances in several key technological areas including DevOps, AI/ML, 5G/6G/, neurocomputing, to name a few, it is imperative we think big and aim high. This new venture will require professionals in both software engineering and services computing to collaborate with an unprecedented intensity, and jointly develop the new interdisciplinary field hereby named Software Services Engineering (SSE). In SSE, the ever-deepening system dynamics emerging from both environments and humans in varying contexts are imposing steep challenges to both researchers and practitioners. Humans, both developers and the vast number of end users, are embedded ever closer to IoT environments, and are being afforded ample opportunities to continuously inject inputs during system development and after deployment. In fact, humans are increasingly playing the roles of both sensor and actuator. Traditional requirements engineering researchers are being lured more than ever into exploiting the IoT environments where human users are deeply embedded, to gather contextual information that inevitably introduces lots of ambiguity and uncertainty. Provisioning of highly adaptable and scalable microservices would be key to timely meeting ever-changing human desires and ever-evolving system requirements in the nimblest manner. As such, an ultra-agile and field-programmable development methodology and environment will be imperative to achieving such ultrafine grained microservices provisioning. Such ultra-agility and ultrafine granularity requirements imposed to the services industry obligate company executives to expect extreme manageability assurance to become the centroid of system operations and administration. The ultimate goal in pursuit of such a noble dream will be to provide genuinely individualized and trustworthy service, possibly enabled by AI, but it should be both explainable and ethical. Facing such grand challenges, this declaration samples a subset of burning issues in SSE through observations in seven themes, only meant to be starting points for the SSE community to further investigate. Through our declarations we also call for heightened attention to an assorted array of existing, barely emerging or non-existent services computing and software engineering methods for a concerted effort to research and explore.

Population health data are becoming more and more publicly available on the Internet than ever before. Such datasets offer a great potential for enabling a better understanding of the health of populations, and inform health professionals and policy makers for better resource planning, disease management and prevention across different regions. However, due to the laborious and high-cost nature of collecting such public health data, it is a common place to find many missing entries on these datasets, which challenges the utility of the data and hinders reliable analysis and understanding. To tackle this problem, this paper proposes a deep-learning-based approach, called Compressive Population Health (CPH), to infer and recover (to complete) the missing prevalence rate entries of multiple chronic diseases. The key insight of CPH relies on the combined exploitation of both intra-disease and inter-disease correlation opportunities. Specifically, we first propose a Convolutional Neural Network (CNN) based approach to extract and model both of these two types of correlations, and then adopt a Generative Adversarial Network (GAN) based prevalence inference model to jointly fuse them to facility the prevalence rates data recovery of missing entries. We extensively evaluate the inference model based on real-world public health datasets publicly available on the Web. Results show that our inference method outperforms other baseline methods in various settings and with a significantly improved accuracy (from 14.8% to 9.1%).

Because of its important role in health policy-shaping, population health monitoring (PHM) is considered a fundamental block for public health services. However, traditional public health data collection approaches, such as clinic-visit-based data integration or health surveys, could be very costly and time-consuming. To address this challenge, this article proposes a cost-effective approach called Compressive Population Health (CPH), where a subset of a given area is selected in terms of regions within the area for data collection in the traditional way, while leveraging inherent spatial correlations of neighboring regions to perform data inference for the rest of the area. By alternating selected regions longitudinally, this approach can validate and correct previously assessed spatial correlations. To verify whether the idea of CPH is feasible, we conduct an in-depth study based on spatiotemporal morbidity rates of chronic diseases in more than 500 regions around London for over 10 years. We introduce our CPH approach and present three extensive analytical studies. The first confirms that significant spatiotemporal correlations do exist. In the second study, by deploying multiple state-of-the-art data recovery algorithms, we verify that these spatiotemporal correlations can be leveraged to do data inference accurately using only a small number of samples. Finally, we compare different methods for region selection for traditional data collection and show how such methods can further reduce the overall cost while maintaining high PHM quality.

Digital health technology utilizing wearables, IoT and mobile devices has been successfully applied in the monitoring of numerous diseases and conditions. However, intervention, in response to monitored data, is yet to benefit from technological support and continues to follow a traditional point-of-care delivery model by providers and health professionals. Mental health is an example of a critical health area in dire need for technology solutions to enable timely, effective and scalable interventions. This is especially the case with an increasing prevalence of mental health conditions and a declining capacity of the healthcare professional workforce. Numerous studies reveal the potential for peer support groups as an effective, scalable, cost-effective, first-line of response in mental health interventions. Peer support helps participants, at low and moderate risk, better understand their diseases or conditions and empowers them to take control of their own health. Peer support interactions also seems to inform health professionals with insights and intricate knowledge, making it effectively a learning health system. This paper proposes a software architecture to better enable "peer-sourcing". We present related work and show how the proposed architecture might draw similarity to and differences from crowd-sourcing architectures. We also present a study in which we interacted with service users (mental health patients) and mental healthcare professionals to better understand and elicit the key requirements for the software architecture.

Participant selection is a major research challenge in Mobile Crowdsensing (MCS). Previous approaches commonly assume that adequately long and fixed periods of candidate participants’ historical mobility trajectories are available before the selection process. This enables the frameworks to accurately model mobility which enables the optimization of selection. However, this assumption may not be realistic for newly-released MCS applications or platforms because the candidates have just boarded without previous mobility profiles. The sparsity or even absence of mobility traces will incur inaccurate location prediction of the individual participant, thus imposing negative effects on the participant selection process and hindering the practical deployment of new MCS applications. To this end, this paper investigates a novel problem called "From-Scratch MCS" (FS-MCS for short), in which we study how to intelligently select participants to minimize such "cold-start" effect. Specifically, we propose a novel framework based on reinforcement learning, which we name RL-Recruiter. With the gradual accumulation of mobility trajectories over time, RL-Recruiter can make a good sequence of participant selection decisions for each sensing slot by incrementally extracting and utilizing the collective mobility patterns of all candidate participants, thus avoiding the prediction of individual participant’s location that is very inaccurate when the training data is sparse. We test our approach experimentally based on two real-world mobility datasets. Our experiment results demonstrate that RL-Recruiter outperforms the baseline approaches under various settings.

In this article, we call for a "Walk Before You Run" adjustment in the Internet-of-Things (IoT) research and development exercise. Without first settling the quest for what thing is or could be or do, we run the risk of presumptuous visions, or hypes, that can only fail the realities and limits of what is actually possible, leading to customers and consumers confusion as well as market hesitations. Specifically, without a carefully-designed Thing architecture in place, it will be very difficult to find the "magic" we are so addicted and accustomed to - programming! Programming the IoT, as we once programmed the mainframe, the workstation, the PC and the mobile devices, is the natural way to realize a fancy IoT scenario or an application. Without Thing architectures and their enablement of new programming models for IoT - we will continue to only envision fancy scenarios but unable to unleash the IoT full potential. This article raises these concerns and provides a view into the future by first looking back into our short history of pervasive computing. The article focuses on the domain of "Personal" IoT and will address key new requirements for such Thing architecture. Also, practicing what we preach, we present our ongoing efforts on the Atlas Thing Architecture showing how it supports a variety of thing notions, and how it enables novel models for programmability.

The six papers in this special section focuses on services and software computing. Services computing provides a foundation to build software systems and applications over the Internet as well as emerging hybrid networked platforms motivated by it. Due to the open, dynamic, and evolving nature of the Internet, new features were born with these Internet-scale and service-based software systems. Such systems should be situation- aware, adaptable, and able to evolve to effectively deal with rapid changes of user requirements and runtime contexts. These emerging software systems enable and require novel methods in conducting software requirement, design, deployment, operation, and maintenance beyond existing services computing technologies. New programming and lifecycle paradigms accommodating such Internet- scale and service-based software systems, referred to as Internetware, are inevitable. The goal of this special section is to present the innovative solutions and challenging technical issues, so as to explore various potential pathways towards Internet-scale and service-based software systems.

We demonstrate how lay users may program their own smart spaces to create IoT apps with a few clicks on their smartphone. We present our Atlas Thing Architecture and the Runtime Interactive Development Environment (RIDE) which allow users to program custom apps based on the logical and functional relationships that tie IoT things available in their smart spaces. We also demo a Health IoT scenario using personal medical devices and mobile apps as things and show how mobile users can develop, install and use IoT apps utilizing these things using RIDE.

We make the case for mobile apps as crucial and influential things in the Internet of Things (IoT) and then present our Atlas Thing Architecture that provides the explicit support necessary for their inclusion. We present the World Cup demo scenario which involves media appliances and mobile app things, and which shows how we implemented it as an IoT application based on our architecture.

Social data from online social networks is expanding rapidly as the number of users and articles posted increases, making public opinion analysis a greater challenge. Real-time topic detection is a key part of public opinion analysis. The complex data processing involved in traditional clustering and text categorization can lead to time delays in topic detection. In this paper we construct similar networks and detect topics from similar communities that reduces the processing overhead in obtaining real-time topics. The similar communities consist of users with high similarity between them. We collect public topics from the microposts of delegates selected from each similar community. Selecting delegates can reduce the processing time of large amounts of redundant data during topic detection. Obtaining public opinion keywords in real time allows organizations to respond to public opinion security incidents in real time. Experiments showed that our scheme can find public topics faster and more effectively than two traditional algorithms.

For participatory sensing, task allocation is a crucial research problem that embodies a tradeoff between sensing quality and cost. An organizer usually publishes and manages multiple tasks utilizing one shared budget. Allocating multiple tasks to participants, with the objective of maximizing the overall data quality under the shared budget constraint, is an emerging and important research problem. We propose a fine-grained multitask allocation framework (MTPS), which assigns a subset of tasks to each participant in each cycle. Specifically, considering the user burden of switching among varying sensing tasks, MTPS operates on an attention-compensated incentive model where, in addition to the incentive paid for each specific sensing task, an extra compensation is paid to each participant if s/he is assigned with more than one task type. Additionally, based on the prediction of the participants' mobility pattern, MTPS adopts an iterative greedy process to achieve a near-optimal allocation solution. Extensive evaluation based on real-world mobility data shows that our approach outperforms the baseline methods, and theoretical analysis proves that it has a good approximation bound.

Recent advances in the Internet of Things (IoT) and pervasive and ubiquitous computing provide a glimpse into the future of our planet and reveal exciting visions of many smart things: smart cities, smart homes, smart cars, and other smart spaces such as malls, workplaces, hotels, schools, and much more. Driven by a technological revolution offering “low-power many things and wireless almost everything,” we could, in only a decade, envision and prototype impressive smart space systems that improve quality of life, enhance awareness of resources and the environment, and enrich users' experience. However, prototyping is one thing and actual large-scale deployments are another. The massive scale of sensors and devices that will be deployed in smart cities of the future will be challenging. Without an ecosystem and a scalable architecture in place, it will be extremely difficult to manage or program such an expanding and massive IoT. In this paper, we introduce the Cloud-Edge-Beneath (CEB) architecture and present its salient scalability features. We also present a validation study based on an event-driven programming model demonstrating CEB's scaling behavior in face of IoT expansion and under dynamically increasing loads.

Opportunistic networks enable mobile devices to communicate with each other through routes that are built dynamically, while messages are en route between the sender and the destination(s). The social structure and interaction of users of such devices dictate the performance of routing protocols in those networks. Community structures, commonly exhibited by social networks, is also observed in the encounter patterns in opportunistic networks and has an astounding impact in designing forwarding algorithms for such types of networks. In this paper, we explore the structural vulnerability of social-based forwarding and routing methods in opportunistic networks. In particular, we introduce Community Vulnerability Assessment (CVA), a new problem on assessing the performance reliability of opportunistic routing strategies in Delay Tolerant Networks (DTN) from a community structure point of view. Given a positive number k, CVA aims to find out the k most vulnerable devices in the network whose non-participation (due to out-of-service or permanent out-of-range) transforms the current network community structure to a totally different one. As the first study in this direction, we analyze and provide key insights into the separation of network communities, evaluated via the Normalized Mutual Information (NMI). Based on these findings, we suggest an approximation algorithm for the special case when k 1/4 1, and a heuristic, genEdge, for the general case. To certify the effectiveness of our proposed approaches, we first test them on synthesized data with known community structures, and then we show the impact of node removal on community structures in real social networks. Finally we evaluate the performance via different forwarding and routing strategies in multiple real-world DTN traces. Our results indicate that, in many forwarding and routing methods, the nonparticipation of only some important devices is significant enough to degrade the entire network's performance.

Automated understanding and recognition of human activities and behaviors in a smart space (e.g., smart house) is of paramount importance to many critical human-centered applications. Recognized activities are the input to the pervasive computer (the smart space) which intelligently interacts with the users to maintain the application's goal be it assistance, safety, child-development, entertainment or other goals. Research in this area is fascinating but severely lacks adequate validation which often relies on datasets that contain sensory data representing the activities. Availing adequate datasets that can be used in a large variety of spaces, for different user groups, and aiming at different goals is very challenging. This is due to the prohibitive cost and the human capital needed to instrument physical spaces and to recruit human subjects to perform the activities and generate data. Simulation of human activities in smart spaces has therefore emerged as an alternative approach to bridge this deficit. Traditional event-driven approaches have been proposed. However, the complexity of human activity simulation was proved to be challenging to these initial simulation efforts. In this paper, we present Persim 3D-an alternative context-driven approach to simulating human activities capable of supporting complex activity scenarios. We present the context-activity-action nexus and show how our approach combines modeling and visualization of actions with context and activity simulation. We present the Persim 3D architecture and algorithms, and describe a detailed validation study of our approach to verify the accuracy and realism of the simulation output (datasets and visualizations) and the scalability of the human effort in using Persim 3D to simulate complex scenarios. We show positive and promising results that validate our approach.

Existing telehealth systems do not perform as effectively as would be expected due to their asymmetric focus on sensing and monitoring with little support or assurance to affect or alter behaviors. We developed Action-based Behavior Model (ABM) that supports persuasive telehealth. However, ABM requires an ongoing assessment of user behavior response and compliance to cyber influence. Method: To measure compliance, we developed Situation-based Assess Tree (SAT) as a methodology and an algorithm for domain-specific behavior assessment under ABM. We followed a proof-of-concept validation approach based on a trace-driven simulation. Results: Preliminary results demonstrate that SAT is sentient to the full spectrum of compliance clearly discerning between compliant and noncompliant user responses. Results also demonstrate SAT's ability to learn different user personas through the assessment process.

In spite of the importance of activity recognition (AR) for intelligent human-computer interaction in emerging smart space applications, state-of-the-art AR technology is not ready or adequate for real-world deployments due to its insufficient accuracy. The accuracy limitation is directly attributed to uncertainties stemming from multiple sources in the AR system. Hence, one of the major goals of AR research is to improve system accuracy by minimizing or managing the uncertainties encountered throughout the AR process. As we cannot manage uncertainties well without measuring them, we must first quantify their impact. Nevertheless, such a quantification process is very challenging given that uncertainties come from diverse and heterogeneous sources. In this article, we propose an approach, which can account for multiple uncertainty sources and assess their impact on AR systems. We introduce several metrics to quantify the various uncertainties and their impact. We then conduct a quantitative impact analysis of uncertainties utilizing data collected from actual smart spaces that we have instrumented. The analysis is intended to serve as groundwork for developing “diagnostic” accuracy measures of AR systems capable of pinpointing the sources of accuracy loss. This is to be contrasted with the currently used accuracy measures.

Imminent massive-scale IoT deployments require a Cloud-Sensor architecture to facilitate an ecosystem of friction-free integration and programmability. In addition to these two functional requirements, challenging performance and scalability requirements must be addressed by any such architecture. We have introduced the Cloud-Edge-Beneath (CEB) architecture which addresses scalability and performance through a built-in distributed optimization framework. In this paper, we focus on CEB's optimization framework which follows a bi-directional waterfall model in which not only sensor data can move upward to applications, but applications (fragments) can move downward to lower layers of CEB closer to data sources. The framework enables many optimization ideas and opportunities, including our own. We present the bi-directional waterfall framework along with a sketch of several of our optimization algorithms enabled by the framework. We also present an example of an experimental study to determine dominant resources in the cloud -- a variable which as will be seen greatly affects the logic of some of the optimization algorithms.

Driven by critical and pressing smart city applications, accessing massive numbers of sensors by cloud-hosted services is becoming an emerging and inevitable situation. Naïvely connecting massive numbers of sensors to the cloud raises major scalability and energy challenges. An architecture embodying distributed optimization is needed to manage the scale and to allow limited energy sensors to last longer in such a dynamic and high-velocity big data system. We developed a multi-tier architecture which we call Cloud, Edge and Beneath (CEB). Based on CEB, we propose an Application Fragment Caching Algorithm (AFCA) which selectively caches application fragments from the cloud to lower layers of CEB to improve cloud scalability. Through experiments, we show and measure the effect of AFCA on cloud scalability.

Phenomena clouds are characterized by nondeterministic, dynamic variations of shapes, sizes, direction, and speed of motion along multiple axes. The phenomena detection and tracking should not be limited to some traditional applications such as oil spills and gas clouds but also be utilized to more accurately observe other types of phenomena such as walking motion of people. This wider range of applications requires more reliable, in-situ techniques that can accurately adapt to the dynamics of phenomena. Unfortunately, existing works which only focus on simple and well-defined shapes of phenomena are no longer sufficient. In this article, we present a new class of applications together with several distributed algorithms to detect and track phenomena clouds, regardless of their shapes and movement direction. We first propose a distributed algorithm for in-situ detection and tracking of phenomena clouds in a sensor space. We next provide a mathematical model to optimize the energy consumption, on which we further propose a localized algorithm to minimize the resource utilization. Our proposed approaches not only ensure low processing and networking overhead at the centralized query processor but also minimize the number of sensors which are actively involved in the detection and tracking processes. We validate our approach using both real-life smart home applications and simulation experiments, which confirm the effectiveness of our proposed algorithms. We also show that our algorithms result in significant reduction in resource usage and power consumption as compared to contemporary stream-based approaches.

Spaceify is a novel edge architecture and an ecosystem for smart spaces --- a technology that extends the mobile user view of today's common space services (e.g., WiFi) to a richer portfolio of space-centric, localized services and space-interactive applications.

As demands for human activity recognition technology increase, simulation of human activities for providing datasets and testing purposes is becoming increasingly important. Traditional simulation, however, is based on an event-driven approach, which focuses on single sensor events and models within a single human activity. It requires detailed description and processing of every low-level event that enters into an activity scenario. For many realistic and complex human scenarios, the event-driven approach burdens the simulator users with complicated low-level specifications required to configure and run the simulation. It also increases computational complexity and impedes scalable simulation. Thus, we propose a novel, context-driven approach to simulating human activities in smart spaces. In the proposed approach, vectors of sensors rather than single sensor events drive the simulation quicker from one context to another. Abstracting the space state into contexts highly simplifies the tasks and efforts of the simulation user in setting up and configuring the simulation components for smart space and human activities. We present the context-driven simulation approach and show how it works. Then we present fundamental concepts and algorithms and provide a comparative performance study between the event- and context-driven simulation approaches.

Emerging smart space applications are increasingly relying on capabilities for recognizing human activities. Activity recognition research is however challenged and slowed by the lack of data necessary for testing and validation. Collecting data through live-in trials in real world deployments is often very expensive and complicated. Legitimate limitations on the use of human subjects also renders a much smaller dataset than desired to be collected. To address this challenge, we propose a scenario generation approach in which a small set of scenarios is used to generate new relevant and realistic scenarios, and hence increase the base of testing data needed for activity recognition validation. Unlike existing methods for generating scenarios, which usually focus on scenario structure and complexity, we propose a Bayesian-based approach that learns the stochastic characteristics of a small number of collected datasets to generate additional scenarios of similar characteristics. Our approach is prolific and can generate enormous datasets with high degree of realism at affordable cost. The proposed approach is validated using a Viterbi-based algorithm and a real dataset case study. The validation experiment confirms that the generated dataset has highly similar stochastic characteristics as that of the real dataset.

Semantic exceptions mean undesirable contexts with respect to application semantics in context aware systems. Description of such semantic exceptions is based on regular expressions, which entails high complexity to detect exceptions. In this paper we devise an intermediate language for semantic exceptions of context aware applications. By bridging the gap between high level programming languages and event stream processing engines, this language is expected to mitigate the complexity and to provide opportunities to optimize exception detection process.

The first cohort of “baby boomers” are now 65 years or older, presaging a massive wave of aging “boomers” that could degrade health care and elder care over the next quarter-century. Cost-effective, high-impact technologies for aging, disabilities and independent living are urgently needed. In this paper, we present our experience in building “assistive environments” for older adults-the Gator Tech Smart House (GTSH) project. Numerous R&D efforts similar to ours are either underway or have recently been conducted. In most of these projects, prototypes have been built to achieve independence, well being, and in general, good quality of life. But prototyping a technology is one thing; commercial proliferation and creating a vibrant industry around such technology is an altogether different proposition. From the lessons learned in the GTSH, we analyze the impediments hindering the emergence of products and services for assistive environments, and present the blueprints of an ecosystem based on requirements drawn from the lessons learned. We believe the proposed ecosystem is an important beginning to providing better conditions for an accelerated proliferation of next-generation smart homes and assistive environments.

Activity recognition research relies heavily on test data to verify the modeling technique and the performance of the activity recognition algorithm. But data from real deployments are expensive and time consuming to obtain. And even if cost is not an issue, regulatory limitations on the use of human subjects prohibit the collection of extensive datasets that can test all scenarios, under all circumstances. A powerful and verifiable simulation tool is needed to accelerate research on human activity recognition. We present Persim, an event driven simulator of human activities in pervasive spaces. Persim is capable of capturing elements of space, sensors, behaviors (activities), and their inter-relationships. We focus on presenting the five main use cases for Persim addressing dataset synthesis, reuse and extension of existing datasets, sharing of data and simulation projects, as well as data validation.

Network coverage is one of the most decisive factors for determining the efficiency of a wireless sensor network. However, in dangerous or hostile environments such as battle fields or active volcano areas, we can neither deterministically or purposely deploy sensors as desired, thus the emergence of coverage holes (the unmonitored areas) is unavoidable. In addition, the introduction of new coverage holes during network operation due to sensor failures due to energy depletion shall significantly reduce coverage efficacy. Therefore, we need to either remotely control or set up a protocol to heal them as soon as possible in an automated fashion. In this paper, we focus on how to schedule mobile sensors in order to cope with coverage hole issues in a hybrid sensor network containing both static and mobile sensors. To this end, we introduce a new metric, namely to maximize the minimum remaining energy of all moved sensor since the more energy remains, the longer the network can operate. Based on this metric, we propose an efficient coverage healing algorithm that always determines an optimal location for each mobile sensor in order to heal all coverage holes, after all mobile sensors locations and coverage holes are located. Simulation results confirm the efficiency and utilization of our proposed method.

Unlike traditional distributed systems, where the resources/needs of computation and communication dominate the performance equation, sensor-based systems (SBS) raise new metrics and requirements for sensors as well as for computing and communication. This includes sensing latency and energy consumption. In this paper, we present a performance model for SBS based on a three-tier architecture that uses edge devices to connect massive-scale networks of sensors to the cloud. In this architecture, which we call Cloud, Edge, and Beneath (CEB), initial processing of sensor data occurs in- and near-network, in order to achieve system sentience and energy efficiency. To optimize CEB performance, we propose the concept of optimal push/pull envelope (PPE). PPE dynamically and minimally adjusts the base push and pull rates for each sensor, according to the relative characteristics of sensor requests (demand side from the Cloud) and sensor data change (supply side from Beneath). We demonstrate the CEB architecture and its push/pull envelope optimization algorithm in an experimental evaluation that measures energy savings and sentience efficiency over a wide range of practical constraints. In addition, from the experiments we demonstrate that by combining PPE optimization algorithm with lazy sampling algorithm, we can achieve further energy saving.

Summary form only given. A complete record of the panel discussion was not made available for publication as part of the conference proceedings. The expert panelists will present their views and experiences covering questions including, 1. What are the new individual roles engaged in building and maintaining pervasive spaces, and how are they inter-related? What are the boundaries of responsibilities for each role? 2. Developing and deploying a pervasive space is a multi-disciplined process where knowledge from various domains intersects. How can individual roles efficiently contribute their expertise and smoothly integrate their work to create a pervasive space? 3. How does system design and architecture enable effective decoupling of these roles? 4. Who has the business incentives to invest in the standards, open source, and product implementations that support the new roles and system architectures needed to support the full life cycle of pervasive spaces?

The generation of useful sensory data from real-world deployments of Ambient Intelligent Environments (AIEs) is challenging because of the high cost, significant groundwork and lack of access to human subjects. This situation can be improved by providing efficient simulators that can produce realistic simulation of the data collection from AIEs. One of the main problems for developing AIE simulators lies in the ability to verify how close the simulated data are to the real world data. In this paper, we present a fuzzy based verification agent for Persim - an event driven simulator for human activities in AIEs. The employed fuzzy based verification agent builds a data model that mimics the operation of Persim which allows for the latter's objective and subjective verification. We have conducted the verification on real world data captured from an actual smart apartment deployment. The results show the effectiveness of the fuzzy based verification agent in analyzing and comparing the Persim simulated data with the real world collected data. We also demonstrate how the verification agent is able to pinpoint specific changes to the simulation model to increase the realism of the simulation.

Dynamic phenomena such as oil spills, mud flow, diffusion or leakage of gases in the environment are characterized by non-deterministic variations in shape, size and direction of motion. Due to the absence of any well defined model for tracking their dynamics, the detection and tracking of such phenomena through Wireless Sensor Networks (WSNs) is very challenging. Most of the existing works consider static phenomena with certain shapes, only few of them study dynamic phenomena. However, existing works studying dynamic phenomena mainly concentrate on reducing the communication overheads and neglect the energy consumed in sensing, which result in higher energy consumption and shorter network lifetime. In this paper, we propose a novel protocol for detection and tracking dynamic phenomena through WSNs, with an objective to minimize the resource usage and energy consumption. We also propose a robust localized clustering method, which helps in reducing the network traffic generated by the information packets destined to a Centralized Query Processor (CQP). The experimental results show that, in comparison to the existing work, our protocol consumes 90% less energy and generates 65% less network traffic.

Till date, sensor network research has assumed that the cost of transmitting a sensor reading over the network is much higher than the cost of sampling a sensor. However, this assumption is no longer always valid, due to availability of new generation sensor platform hardware, which utilizes industry standard mesh-networking protocols such as ZigBee on top of relatively high-speed, yet low-power wireless radios. In fact, we have experimentally verified that the energy consumed for acquiring a sample from a sensor can be significantly higher than the energy consumed for transmitting its reading over the network. Hence, new querying strategies need to be formulated, which optimize the order of sampling sensors across the network in such a manner that sensors with expensive acquisition costs are not sampled unless absolutely required. We propose distributed pull-push querying mechanisms, which optimize the query plan by adapting to variable costs of acquiring readings from different sensors across the network. The goal of these mechanisms is to minimize the energy consumption of nodes executing a query while ensuring that the latency of query response does not exceed user-specified bounds. To validate our approach, we also describe experimental results, which analyze the performance of various plan options in terms of energy consumption and latency under the effect of various parameters such as selectivity of data and number of sensors participating in the query.

Context-awareness is a critical aspect of safe navigation especially for the blind and visually impaired in unfamiliar environments. Existing mobile devices for context-aware navigation fall short in many cases due to their dependence on specific infrastructure requirements as well as having limited access to resources that could provide a wealth of contextual clues. In this paper, we propose a mobile-cloud collaborative approach for context-aware navigation by exploiting the computational power of resources made available by Cloud Computing providers as well as the wealth of location-specific resources available on the Internet. We propose an extensible system architecture that minimizes reliance on infrastructure, thus allowing for wide usability. We present a traffic light detector that we developed as an initial application component of the proposed system. We present preliminary results of experiments performed to test the appropriateness for the real-time nature of the application.

Phenomena clouds are characterized by non-deterministic, dynamic variations over time, of their shape, size and direction of motion along multiple axes. In the past, the utility of phenomena detection and tracking has been limited to applications such as tracking oil spills and gas clouds. However, through our collective experience over the years in a completely different deployment domain (Smart Spaces), we have discovered great utility and value in applying this concept to accurately and efficiently observe other types of phenomena. In this paper, we propose distributed sensor network algorithms which utilize localized in-network processing to simultaneously detect and track multiple phenomena clouds in a sensor space. Our algorithms not only ensure low processing and networking overhead but also minimize the number of sensors which are actively involved in the detection and tracking processes at any given time. We validate our approach using both real-life smart home applications as well as simulation experiments. We also show that our algorithms result in significant reduction in resource usage and power consumption as compared to contemporary stream-based approaches.

Persistent problems in obtaining test data while developing technologies for pervasive spaces include the lack of data generation and representation standards, limited amount of available test data, incompleteness of existing data sets, and high cost of implementing a given pervasive space. This not only hinders the development and design of pervasive spaces, but also decreases the ability of researchers to compare results with those of other areas in computer science. For example, in the interaction between sensors and actuators, testing the robustness of new algorithms and evaluating risk requires large datasets that portray different scenarios. However, associated costs can be prohibitive as the data burden increases. In response to this situation, we propose a Pervasive Space Simulation Technique (PSST) that uses Markov chains, non-homogenous Poisson processes, and distribution fitting for generation of synthetic test data. PSST provides a realistic simulation of events in the pervasive space.

The generation of actual sensory data in real-world deployments of pervasive spaces is very costly and requires significant preparation and access to human subjects. This situation can be mitigated if practical forms of sharing of existing datasets are enabled among the research community. In this paper we address two main problems. First, we propose a standard for the representation of smart space datasets, based on a careful examination of several existing data. The standard specification should allow researchers to effortlessly position their existing or future datasets for sharing. We briefly present the specifications. Second, to enable higher utility of shared datasets, we propose algorithms and tools that can extend a shared dataset into a similar set of a slightly customized pervasive space (e.g., an original space with additional sensors/actuators or behaviors). Specifically, we propose the use of machine learning algorithms to generate the additional patterns of events and to automatically integrate them into the original shared dataset.

Abstract The thin‐client computing model has the potential to significantly increase the performance of mobile computing environments. By delivering any application through a single, small‐footprint client (called a thin client) implemented on a mobile device, it is possible to optimize application performance without the need for building wireless application gateways. We thus present two significant contributions in the area of wireless thin‐client computing. Firstly, a mathematical performance model is derived for wireless thin‐client system. This model identifies factors that affect the performance of the system and supports derivation and analysis of adaptation strategies to maintain a user‐specified quality of service (QoS). Secondly, a proxy‐based adaptation framework is developed for wireless thin‐client systems, which dynamically optimizes performance of a wireless thin client via dynamically discovered context. This is implemented with rule‐based fuzzy logic that responds to variations in wireless link bandwidth and client processing power. Our fuzzy inference engine uses contextual data to dynamically optimize tradeoffs among different quality of service parameters offered to the end users. Additionally, our adaptation framework uses highly scalable wavelet‐based image coding to provide scalable QoS that can degrade gracefully. Our thin‐client adaptation framework shields the user from ill effects of highly variable wireless network quality and mobile device resources. This improves performance of active applications, in which the display changes frequently. Further, active application behaviour may produce high transmission latency for screen updates, which can adversely affect user perception of QoS, resulting in poor interactivity. We report measured adaptive performance under realistic mobile device and network conditions for several different clients and servers. Copyright © 2008 John Wiley & Sons, Ltd.

Pervasive computing systems provide personalized and intimate services to improve users' quality of life by integrating computation and communication into the environments. With the capability to interact with the physical world and the promise in assisting or managing aspects of users' daily lives, the requirement for safety is high and imminent. The difficulty in providing safety is the result of the dynamicity, complexity, heterogeneity and uncertainly typical in pervasive computing. After examining and analyzing worst-case scenarios of safety violation, we identify four fundamental elements whose individual safety assurances add greatly to the overall system safety. We propose safety enhancing mechanisms for each of the four elements and their interactions.

Abstract In this paper, the performance of MIP/WLAN at different moving speeds is evaluated on a testbed. The result shows that current MIP protocol is not suitable for rapid moving environments. Through analyzing the relationship of the performance and the moving speed and breaking down the handoff latency of MIP over WLAN, we propose a speed adaptive MIP protocol extension. In this protocol extension, a concept, handoff rate, is defined and is used to extend the MIP protocol. An evaluation of the speed adaptive MIP shows that it greatly improves the performance of MIP over WLAN in rapid moving environments. Copyright © 2008 John Wiley & Sons, Ltd.

In this paper, the performance of Mobile IP (MIP) over Wireless LAN is evaluated using a testbed at different ve-hecular speeds. The result shows that current MIP proto-col is not suitable for rapidly moving environments. Through careful analysis of the relationship between per-formance and speed, and through a breaking down analy-sis of the handoff latency of MIP/WLAN, we propose a speed adaptive MIP protocol extension. In this protocol extension, a concept, handoff rate, is defined and is used to extend the MIP protocol. An evaluation of the speed adaptive MIP shows that it greatly improves the perform-ance of MIP/WLAN in rapid moving environments. 1.

In mobile environment, it is very common that mobile devices periodically stay in disconnection mode. In a networked world as today, computer users rely on network services so heavily that even a short duration of disconnection from network will dramatically distract user's normal activity. Past researches explored disconnected operation for file system and database access. In this paper, we introduce and evaluate a ubiquitous network service framework. The proposed framework combines the advantages of both disconnection operation and system virtualization in order to provide 7/24 services to mobile users. We implemented and validated our idea in both Linux and Windows platform. From our initial deployment and daily usage experiment, our architecture is highly feasible and deployable. In this paper, we will illustrate the architecture, implementation, and performance evaluation based on our implemented system.

Pervasive computing environments such as smart spaces require a mechanism to easily integrate, manage and use numerous, heterogeneous sensors and actuators into the system. However, available sensor network platforms are inadequate for this task. The goals are requirements for a smart space are very different from the typical sensor network application. Specifically, we found that the manual integration of devices must be replaced by a scalable, plug-and-play mechanism. The space should be assembled programmatically by software developers, not hardwired by engineers and system integrators. This allows for cost-effective development, enables extensibility, and simplifies change management. We found that in a smart space, computation and power are readily available and connectivity is stable and rarely ad-hoc. Our deployment of a smart house (an assistive environment for seniors) guided us to designing Atlas, a new, commercially available service-oriented sensor and actuator platform that enables self-integrative, programmable pervasive spaces. We present the design and implementation of the Atlas hardware and middleware components, its salient characteristics, and several case studies of projects using Atlas

Mobile computers require Mobile IP to preserve connectivity and properly route information while roaming over foreign networks. Registration, forwarding delay, and tunnel initialization diminishes the performance of mobility protocols during handoff. We propose a proactive rather than reactive solution to improve the performance of Mobile IP. We extend the current protocol with two new entities: ghost mobile host and ghost foreign agent. Our target is mobility scenarios in which mobile hosts are moving at a very high-speed. Our protocol extension uses the Kalman filter to determine the trajectory and speed of the mobile host and to allocate resources for Mobile IP proactively. Preliminary experiments conducted using the RAMON emulator show that the proposed predictive methods offer a promising range of 20-50% of performance improvement.

There are many navigation systems for visually impaired people but few can provide dynamic interactions and adaptability to changes. None of these systems work seamlessly both indoors and outdoors. Drishti uses a precise position measurement system, a wireless connection, a wearable computer, and a vocal communication interface to guide blind users and help them travel in familiar and unfamiliar environments independently and safely. Outdoors, it uses DGPS as its location system to keep the user as close as possible to the central line of sidewalks of campus and downtown areas; it provides the user with an optimal route by means of its dynamic routing and rerouting ability. The user can switch the system from an outdoor to an indoor environment with a simple vocal command. An OEM ultrasound positioning system is used to provide precise indoor location measurements. Experiments show an in-door accuracy of 22 cm. The user can get vocal prompts to avoid possible obstacles and step-by-step walking guidance to move about in an indoor environment. This paper describes the Drishti system and focuses on the indoor navigation design and lessons learned in integrating the indoor with the outdoor system.

The Pervasive Computing Laboratory at the University of Florida is dedicated to creating smart environments and assistants to enable elderly persons to live a longer and a more independent life at home. By achieving this goal, technology will increase the chances of successful aging despite an ailing health care system (e.g. Medicaid). One of the essential services required to maximize the intelligence of a smart environment is an indoor precision tracking system. Such system allows the smart home to make proactive decisions to better serve its occupants by enabling context-awareness instead of being solely reactive to their commands. This paper presents our hands-on experience and lessons learnt from our first phase work to build up a smart home infrastructure for the elderly. We review location tracking technology and describe the rationale behind our choice of the emerging ultrasonic sensor technology. We give an overview of the House of Matilda (an in-laboratory mock up house) and describe our design of a precision in-door tracking system. We also describe an OSGi-based robust framework that abstracts the ultrasonic technology into a standard service to enable the creation of tracking based applications by third party, and to facilitate the collaboration among various devices and other OSGi services. Finally, we describe three pervasive computing applications that use the location-tracking system which we have implemented in Matilda's house.

The growth of the wireless Internet has led to the optimization of network protocols to provide for a better performance. Most of the Internet traffic uses TCP, the de facto transport layer protocol. Unfortunately, TCP performance degrades in the mobile and wireless environments. A good amount of research has been attempted to improve its performance in the unpredictable mobile and wireless environments where link disconnections, packets losses and delays are common. Most of these proposed solutions target the case where the mobile host acts as a TCP receiver. For various reasons, almost none of the solutions have reached the stage of deployment. In this paper, we propose an interlayer collaboration model for TCP performance improvement in mobile and wireless environments. We specifically target the case where a mobile acts as a TCP sender. ILC-TCP is an end-to-end approach and does not need any special support from the base station infrastructure. ILC-TCP is evaluated against the normal TCP in various scenarios. Performance results suggest that ILC-TCP performs better than the normal TCP in many scenarios involving long disconnections, frequent disconnections and in the scenarios where a mobile host moves at considerable speeds.

The proliferation of mobile devices and the pervasiveness of wireless technology have provided a major impetus to replicate the network-based service discovery technologies in wireless and mobile networks. However, existing service discovery protocols and delivery mechanisms fall short of accommodating the complexities of the ad-hoc environment. They also place emphasis on device capabilities as services rather than device independent software services, making them unsuitable for m-commerce oriented scenarios. Konark is a service discovery and delivery protocol designed specifically for ad-hoc, peer-to-peer networks, and targeted towards device independent services in particular. It has two major aspects - service discovery and service delivery. For discovery, Konark uses a completely distributed, peer-to-peer mechanism that provides each device the ability to advertise and discover services in the network. The approach towards service description is XML based. It includes a description template that allows services to be described in a human and software understandable forms. A micro-HTTP server present on each device handles service delivery, which is based on SOAP. Konark provides a framework for connecting isolated services offered by proximal pervasive devices over a wireless medium.

In this paper, we address the problem of cache invalidation in mobile and wireless client/server environments. We present cache invalidation techniques that can scale not only to a large number of mobile clients, but also to a large number of data items that can be cached in the mobile clients. We propose two scalable algorithms: the Multidimensional Bit-Sequence (MD-BS) algorithm and the Multilevel Bit-Sequence (ML-BS) algorithm. Both algorithms are based on our prior work on the Basic Bit-Sequences (BS) algorithm. Our study shows that the proposed algorithms are effective for a large number of cached data items with low update rates. The study also illustrates that the algorithms ran be used with other complementary techniques to address the problem of cache invalidation for data items with varied update and access rates.

The proliferation of mobile devices and the pervasiveness of wireless technology have provided a major impetus to replicate the network-based service discovery technologies in wireless and mobile networks. However, existing service discovery protocols and delivery mechanisms designed for traditional infrastructure-based networks fall short of accommodating the complexities of the ad hoc environment. Konark is a service discovery and delivery protocol designed specifically for ad hoc, peer-to-peer networks, and targeted toward device-independent services in general and m-commerce oriented software services in particular. It has two major aspects-service discovery and service delivery. For discovery, Konark uses a novel decentralized, peer-to-peer mechanism that provides each device the ability to advertise and discover services in an efficient way. The approach toward service description is XML-based. It includes a description template that allows services to be described in a human and software understandable forms. A micro-HTTP server present on each device handles service delivery, which is based on SOAP. Konark provides a framework for connecting isolated services offered by proximal pervasive devices over a wireless medium.

Abstract We present an architecture for the adaptable delivery of video data under variable connection characteristics and into devices of variable capabilities. The main application of the proposed architecture is video delivery in wireless and mobile environments. The architecture is based on the Universal Multimedia Access concept and the MPEG‐7 standard. Based on the network and the mobile device, as well as constraints imposed by user preferences and the multimedia content, video is delivered through a careful application of a combination of off‐line and on‐line reductions to the video stream. We present our architecture and describe an implementation of a system based on the architecture. We present basic performance evaluation results to quantify the merit of our approach. Copyright © 2002 John Wiley & Sons, Ltd.

In wireless networks, as in many areas of engineering, simulation has been the de facto standard for testing, dimensioning and analyzing mobile protocols. Emulation, which presents a lower cost, more accurate, yet more complex engineering alternative to simulation, has not been widely used in mobile computing studies. RAMON is a software/hardware emulator tailored to mimic the realistic characteristics of wireless networks. RAMON is especially designed to study how mobile protocols cope with high vehicular speeds. The main advantage of RAMON is the rapid, cost-effective and accurate testing it provides. This ranges from proper identification of protocol bottlenecks, to testing of newly available wireless networks and hardware and software devices.

Mobile agents promise to bring in a new era in the field of World Wide Web and Internet computing. Although mobile agents have been around for sometime their full potential has not been realized due to the lack of a suitable infrastructure that would allow for their deployment and seamless integration on the Internet. In this paper we propose a system based on HTTP and existing Web server that facilitates the deployment of Java based mobile agents on the Internet. By requiring no more than Web servers as the mobile agent's environment, it will be possible to overcome the current hurdles and make mobile agents a pervasive model for Internet computing. In this paper, we present aZIMAs, a simple mobile agent environment and a potentially ubiquitous server platform.

We present a three-tier architecture of middleware that addresses challenges facing accessibility, availability, and consistency of data in mobile environments. The architecture supports the automatic hoarding of data from multiple, heterogeneous sources into possibly a variety of different mobile devices. The middle tier enables the automation of synchronization tasks in both connected mode (following disconnection) and weakly connected mode, where only intelligent and effective synchronization can be used in the presence of a low-bandwidth network. We present the three-tier architecture based on the Coda file system.

We describe a mobile collaborative system designed for wireless, ad-hoc collaboration. In recent years, mobile computing has emerged as a new discipline in the field of computer science. Due to advances in technology, portable computing devices have become more pervasive. From smart phones, and personal digital assistants (PDAs) running embedded operating systems, to portable computers running conventional desktop operating systems, these devices have increasingly provided communication capabilities that utilize wireless connections. With those communication capabilities firmly established, the next logical step is in the direction of greater interactions between mobile users equipped with such devices. However, conventional collaborative tools are ill suited for the demands of portable computers and mobile networks, especially in situations in which no fixed-network infrastructure is present. With these considerations in mind, we designed and implemented a collaborative environment and a framework API suited towards ad-hoc networks of small mobile devices. By creating such a framework, developers can easily take advantage of a decentralized and fault-tolerant collaborative environment, and rapidly develop custom collaboration spaces suited towards their specific need.

The rapid evolution of the Internet and its business tools is enabling the transformation and deployment of business processes as highly modular e-services that can be flexibly and dynamically composed to form ad-hoc workflow. This research prepares for the proliferation of automated, Internet-based workflow, by contributing a suite of protocols for self-organizing brokering communities that enables the discovery of relevant e-services. We present protocols and architecture for e-service brokering communities, and discuss their use in workbase, an Internet-based, automated workflow system over e-services. The brokering protocols are based on a three-tier architecture of agents, brokers and superbrokers. We also present an infrastructure for dynamically composing new services from exiting e-services on the Internet. An implementation using JKQML of brokering communities is provided along with the architecture and design of our e-services and workflow concepts.

Trains travel at speeds ranging from 0 to 80 m/s (0 to 288 km/hr). Providing in-train wireless Internet access to multimedia applications will require the use of a mobile networking protocol, such as Mobile-IP, to achieve uninterrupted connectivity. Although Mobile-IP represents a promising solution. its performance under "extreme" mobility is questionable. We simulated a train scenario and identified the limitations of the current Mobile-IP standard in terms of throughput, handoff and packet loss of a train moving tit different velocities. We investigated the performance of UDP- and TCP-sessions, and examined the effect of different base station interleaving distances on throughput and packet loss. The results presented are part of an investigative research into adaptive mobile networking protocols in rapidly mobile networks.

We present a Bluetooth LAN access point (LAP) simulator that we have developed to study pervasive application behavior under Bluetooth local connectivity. Our goal is to explore the impact on IP applications caused by the underlying Bluetooth protocol. Our simulator implements detailed processing of upper layers of the Bluetooth LAP stack, including PPP, RFCOMM, and L2CAP. For lower layers, it tries to capture major characteristics by performing macro-simulation. We present simulation results of a simple network configuration in Bluelooth LAP environments. Our simulator is based on the popular Network Simulator (ns) and its components.

To allow a seamless integration between wireless LANs and wireless WANs, we developed a full stack adaptation model and a simple subnet architecture that superimposes Mobile-IP on cellular-type wireless LANs. The idea is to use Mobile IP as an integrative layer atop different LAN/WAN networks. While Mobile-IP is widely used in wireless WANs, it is not known how well it performs under a wireless LAN environment, against native MAC-level handoff. Through experimentation using the 802.11 W-LAN, we found that under practical values of handoff frequencies, the performance of Mobile IP based W-LAN handoff is almost identical to the performance of W-LAN handoff. Further performance studies show the suitability of Mobile-IP as an integrative layer in this architecture.

The article proposes a scalable protocol for replication management in large-scale replicated systems. The protocol organizes sites and data replicas into a tree-structured, hierarchical cluster architecture. The basic idea of the protocol is to accomplish the complex task of updating replicated data with a very large number of replicas by a set of related but independently committed transactions. Each transaction is responsible for updating replicas in exactly one cluster and invoking additional transactions for member clusters. Primary copies (one from each cluster) are updated by a cross-cluster transaction. Then each cluster is independently updated by a separate transaction. This decoupled update propagation process results in possible multiple views of replicated data in a cluster. Compared to other replicated data management protocols, the proposed protocol has several unique advantages. First, thanks to a smaller number of replicas each transaction needs to atomically update in a cluster, the protocol significantly reduces the transaction abort rate, which tends to soar in large transactional systems. Second, the protocol improves user-level transaction response time as top-level update transactions are allowed to commit before all replicas have been updated. Third, read-only queries have the flexibility to see database views of different degrees of consistency and data currency. This ranges from global, most up to date, and consistent views, to local, consistent, but potentially old views, to local, nearest to users but potentially inconsistent views. Fourth, the protocol maintains its scalability by allowing dynamic system reconfiguration as it grows by splitting a cluster into two or more smaller ones. Fifth, autonomy of the clusters is preserved as no specific protocol is required to update replicas within the same cluster. Clusters are, therefore, free to use any valid replication or concurrency control protocols.

The goal of the InfoSleuth project at MCC is to exploit and synthesize new technologies into a unified system that retrieves and processes information in an ever-changing network of information sources. InfoSleuth has its roots in the Carnot project at MCC, which specialized in integrating heterogeneous information bases. However, recent emerging technologies such as internetworking and the World Wide Web have significantly expanded the types, availability, and volume of data available to an information management system. Furthermore, in these new environments, there is no formal control over the registration of new information sources, and applications tend to be developed without complete knowledge of the resources that will be available when they are run. Federated database projects such as Carnot that do static data integration do not scale up and do not cope well with this ever-changing environment. On the other hand, recent Web technologies, based on keyword search engines, are scalable but, unlike federated databases, are incapable of accessing information based on concepts. In this experience paper, we describe the architecture, design, and implementation of a working version of InfoSleuth. We show how InfoSleuth integrates new technological developments such as agent technology, domain ontologies, brokerage, and internet computing, in support of mediated interoperation of data and services in a dynamic and open environment. We demonstrate the use of information brokering and domain ontologies as key elements for scalability.

Article Free Access Share on The InfoSleuth Project Authors: R. J. Bayardo Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, Texas Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, TexasView Profile , W. Bohrer Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, Texas Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, TexasView Profile , R. Brice Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, Texas Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, TexasView Profile , A. Cichocki Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, Texas Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, TexasView Profile , J. Fowler Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, Texas Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, TexasView Profile , A. Halal Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, Texas Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, TexasView Profile , V. Kashyap Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, Texas Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, TexasView Profile , T. Ksiezyk Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, Texas Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, TexasView Profile , G. Martin Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, Texas Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, TexasView Profile , M. Nodine Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, Texas Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, TexasView Profile , M. Rashid Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, Texas Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, TexasView Profile , M. Rusinkiewicz Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, Texas Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, TexasView Profile , R. Shea Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, Texas Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, TexasView Profile , C. Unnikrishnan Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, Texas Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, TexasView Profile , A. Unruh Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, Texas Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, TexasView Profile , D. Woelk Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, Texas Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, TexasView Profile Authors Info & Claims SIGMOD '97: Proceedings of the 1997 ACM SIGMOD international conference on Management of dataJune 1997 Pages 543–545https://doi.org/10.1145/253260.253401Online:01 June 1997Publication History 7citation339DownloadsMetricsTotal Citations7Total Downloads339Last 12 Months2Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

A methodology for detecting failures that occur in distributed computer systems connected by a communications network is described. The methodology utilizes active polling of monitored systems. The entities polled must be service entities that function at the application layers of service providing machines. A prototype system has been implemented to test this methodology.

Among the plethora of concurrency control algorithms that have been proposed and analyzed, two-phase locking (2PL) has been adapted as the industry de facto standard concurrency control. In accord, current research in concurrency control is focusing on enhancing the scalability of 2PL performance in highly concurrent and contentious environments. This is especially needed in future on-line transaction processing systems, where thousand Transaction Per Second performance will be required.

The resiliency of distributed database systems can be realized through a collection of integrated faulttolerance mechanisms.These include clata replication techniques, failure detection, failure isolation through reconfiguration and adaptability, and non-blocking atomic commitment.Collectively, these mechanism enhance the availability and operability of the syste]n in the presence of various types of site and communication failures.In this paper, we focus on mechanisms for data replication, failure detection, and reconfiguration.We present the implementation details of each of these mechanisms along with their integration within the RAID system developed at Purdue.Data replication is implemented through the partial replication of data relations, and through the use of a library of replication control methods.An on-line replication control server (RC) provides highly available database operations through the adaptable use of these methods.Failuredetection isirriplementedvi aa reliable surveillance facility that rnonitorsthe changes in system connectivity.Such failures include site and communication failures as well as network partition.Repairs andnetwork merges are also detected by this facility, thus leading to the automatic initiation of recovery.We wilI show how failure isolation is achieved through data and server reconfiguration and by the adaptable use of replication methods.

A series of experiments is being conducted on the RAID distributed database system to study the performance and reliability implications of providing static and dynamic adaptability. The authors' studies of the cost of their adaptable implementation were conducted in the context of the concurrency controller and the replication controller. It is shown that adaptable implementations can be provided at costs comparable to those of special-purpose implementations. The experimentation with dynamic adaptability focuses on concurrency control. It is shown that dynamic adaptability can result in performance benefits and that system reconfiguration can be accomplished dynamically with less cost than stopping the system, performing reconfiguration, and then restarting the system. The authors' examination of the costs of providing greater data availability includes studying the replication control and atomicity control subsystems of RAID. The cost associated with increasing availability in an adaptable scheme of replication control and commit protocols is demonstrated.>

The behavior and performance of replica control protocols (RCP) that deal with network partitions is investigated using a quorum-based replicated database system called Seth. Seth is designed to be used as a transaction processing system, as well as a flexible experimentation tool. Seth's experimentation domain includes failures (site/link failure/repair rates), transactions (arrival rate, size, type, read/write ratio), quorum assignment, communications (communication protocols, network type, topology, number of sites), and transaction protocols (quorum-based RCPs, distributed commitment, concurrency control). The design and implementation of Seth are discussed, and two experiments are presented. The first studies the behavior of the quorum consensus protocol against different transition loads. The second experiment shows how expensive quorum-based replication is in terms of network traffic.>

The performance is studied of atomic updates across different database management systems (DBMSs). An optimistic concurrency-control algorithm is proposed that allows a subclass of global transactions to concurrently retrieve and update the multiple databases, while it places no restriction on the concurrency-control mechanisms used by each of the local DBMSs, thus maintaining local autonomy.>