Emotion recognition from physiological signals has substantial potential for applications in mental health and emotion-aware systems. However, the lack of standardized, large-scale evaluations across heterogeneous datasets limits progress and model generalization. We introduce FEEL (Framework for Emotion Evaluation), the first large-scale benchmarking study of emotion recognition usingelectrodermal activity (EDA) and photoplethysmography (PPG) signals across 19 publicly available datasets. We evaluate 16 architectures spanning traditional machine learning, deep learning, and self-supervised pretraining approaches, structured into four representative modeling paradigms. Our study includes both within-dataset and cross-dataset evaluations, analyzing generalization across variations in experimental settings, device types, and labeling strategies. Our results showed that fine-tuned contrastive signal-language pretraining (CLSP) models (71/114) achieve the highest F1 across arousal and valence classification tasks, while simpler models like Random Forests, LDA, and MLP remain competitive (36/114). Models leveraging handcrafted features (107/114) consistently outperform those trained on raw signal segments, underscoring the value of domain knowledge in low-resource, noisy settings. Further cross-dataset analyses reveal that models trained on real-life setting data generalize well to lab (F1 = 0.79) and constraint-based settings (F1 = 0.78). Similarly, models trained on expert-annotated data transfer effectively to stimulus-labeled (F1 = 0.72) and self-reported datasets (F1 = 0.76). Moreover, models trained on lab-based devices also demonstrated high transferability to both custom wearable devices (F1 = 0.81) and the Empatica E4 (F1 = 0.73), underscoring the influence of heterogeneity. Overall, FEEL provides a unified framework for benchmarking physiological emotion recognition, delivering insights to guide the development of generalizable emotion-aware technologies. Code implementationis available at https://github.com/alchemy18/FEEL. More information about FEEL can be found on our website https://alchemy18.github.io/FEEL_Benchmark/.

Contemporary applications in AR/VR, connected vehicles, Industry 4.0/5.0, and smart-buildings, have been recently gaining traction. These applications require orchestration of composite services in soft real-time to meet user Quality of Experience (QoE) performance. Furthermore, it is necessary to support seamless connectivity and computation at scale, despite uncertainties due to mobility and heterogeneity. Service composition orchestration solutions that are based on centralized orchestrators suffer from scalability challenges due to handling of data transmission between sub-services. The flexible forwarding and in-network caching capabilities of Named Data Networking (NDN) open the door to new distributed composition orchestration techniques. This paper develops Cache-Assisted Service Composition Orchestration leveraging NDN (CaSCON), aiming to reduce the latency of composite services through distributed orchestration. CaSCON allows services to forward results to succeeding services in the composite workflow, parallelizing execution based on data dependencies, and caching intermediate results of the pruned workflow within the network for potential future use. Experimental results through simulation, emulation, and hardware test-beds, show that these techniques are able to reduce communication and orchestration latency in the evaluated scenarios by 53% on average, and reduce transmitted packets by 52% on average, when compared to centralized orchestration.

Physiological signals hold immense potential for ubiquitous emotion monitoring, presenting numerous applications in emotion recognition. However, harnessing this potential is hindered by significant challenges, particularly in the collection of annotations that align with physiological changes since the process hinges heavily on human participants. In this work, we set out to study human participants' perspectives in the emotion data collection procedure. We conducted a lab-based emotion data collection study with 37 participants using 360° virtual reality video stimulus followed by semi-structured interviews with the study participants. Our findings presented that intrinsic factors like participants' perception, experiment design nuances, and experiment setup suitability impact their emotional response and annotation within lab settings. Drawing from our findings and prior research, we propose recommendations for incorporating participants' context into annotations and emphasizing participant-centric experiment designs. Furthermore, we explore current emotion data collection practices followed by AI practitioners and offer insights for future contributions leveraging physiological emotion data.

The shift away from multigenerational families to nuclear families in India has created a growing need to support older adults living independently. While technology can help address this gap, older adults' limited exposure to newer technology restricts the adoption of such solutions. However, they remain comfortable with long-standing technologies like television (TV). This study explores their daily technology usage and challenges, aiming to determine whether TV can be leveraged to improve their quality of life. We examined how TV systems could be enhanced to assist older adults with tasks such as staying connected, receiving health alerts, and ensuring security. Using a participatory design approach, we developed video probes using the prototype of the TV-based application and interviewed 27 older adults to assess its acceptance and usability. Our findings demonstrate older adults' strong interest in a TV-based solution and a preference for familiar technology to support security, independence, and wellbeing.

Traditional localization systems often rely on a network of external sensors, making the setups cumbersome, expensive, and requiring significant calibration effort. The advent of Bluetooth 5.1 and later versions brought enhancements that enable precise localization using constant tone extension (CTE) in the signal through Angle of Arrival (AoA) and Angle of Departure (AoD) techniques. This work examines the capacity of a single Bluetooth Low-Energy (BLE) locator with an antenna array based on AoA in terms of performance, efficiency, and latency in real-time indoor positioning. While traditional neural networks train measured entities to match calculated distances, we utilize the azimuth and elevation angle components in the AoA measured and train neural networks to match their theoretical counterparts. We conducted extensive experiments in a real-world lab environment, providing ablation studies in the design. The results demonstrate the system’s capability in real-time with many potential interference variables. Under lab conditions, our results show the capacity of a single locator wanes past 4m with the best average accuracy of 0.09m error in positioning within a 5m radius to as much as ∼ 1m of error beyond 6m up to the maximum possible measuring distance in the lab.

EEVR (Emotion Elicitation in Virtual Reality) is a novel dataset specifically designed for language supervision-based pre-training of emotion recognition tasks, such as valence and arousal classification. It features high-quality physiological signals, including electrodermal activity (EDA) and photoplethysmography (PPG), acquired through emotion elicitation via 360-degree virtual reality (VR) videos.Additionally, it includes subject-wise textual descriptions of emotions experienced during each stimulus gathered from qualitative interviews. The dataset consists of recordings from 37 participants and is the first dataset to pair raw text with physiological signals, providing additional contextual information that objective labels cannot offer. To leverage this dataset, we introduced the Contrastive Language Signal Pre-training (CLSP) method, which jointly learns representations using pairs of physiological signals and textual descriptions. Our results show that integrating self-reported textual descriptions with physiological signals significantly improves performance on emotion recognition tasks, such as arousal and valence classification. Moreover, our pre-trained CLSP model demonstrates strong zero-shot transferability to existing datasets, outperforming supervised baseline models, suggesting that the representations learned by our method are more contextualized and generalized. The dataset also includes baseline models for arousal, valence, and emotion classification, as well as code for data cleaning and feature extraction. Further details and access to the dataset are available at https://melangelabiiitd.github.io/EEVR/.

We propose LATR-lazy TLB coherence-a software-based TLB shootdown mechanism that can alleviate the overhead of the synchronous TLB shootdown mechanism in existing operating systems. By handling the TLB coherence in a lazy fashion, LATR can avoid expensive IPIs which are required for delivering a shootdown signal to remote cores, and the performance overhead of associated interrupt handlers. Therefore, virtual memory operations, such as free and page migration operations, can benefit significantly from LATR's mechanism. For example, LATR improves the latency of munmap() by 70.8% on a 2-socket machine, a widely used configuration in modern data centers. Real-world, performance-critical applications such as web servers can also benefit from LATR: without any application-level changes, LATR improves Apache by 59.9% compared to Linux, and by 37.9% compared to ABIS, a highly optimized, state-of-the-art TLB coherence technique.

We propose Solros---a new operating system architecture for heterogeneous systems that comprises fast host processors, slow but massively parallel co-processors, and fast I/O devices. A general consensus to fully drive such a hardware system is to have a tight integration among processors and I/O devices. Thus, in the Solros architecture, a co-processor OS (data-plane OS) delegates its services, specifically I/O stacks, to the host OS (control-plane OS). Our observation for such a design is that global coordination with system-wide knowledge (e.g., PCIe topology, a load of each co-processor) and the best use of heterogeneous processors is critical to achieving high performance. Hence, we fully harness these specialized processors by delegating complex I/O stacks on fast host processors, which leads to an efficient global coordination at the level of the control-plane OS.

Processing a one trillion-edge graph has recently been demonstrated by distributed graph engines running on clusters of tens to hundreds of nodes. In this paper, we employ a single heterogeneous machine with fast storage media (e.g., NVMe SSD) and massively parallel coprocessors (e.g., Xeon Phi) to reach similar dimensions. By fully exploiting the heterogeneous devices, we design a new graph processing engine, named Mosaic, for a single machine. We propose a new locality-optimizing, space-efficient graph representation---Hilbert-ordered tiles, and a hybrid execution model that enables vertex-centric operations in fast host processors and edge-centric operations in massively parallel coprocessors.

Opportunistic network created among mobile devices in challenged environments can be effectively exploited to provide application services. However, data and services may be subject to space and time constraints in challenged environments where it is critical to complete application services within given spatiotemporal limits. This paper discusses an analytical framework that takes into account human mobility traces and provides quantitative measures of the spatiotemporal requirements for service sharing and composition in challenged opportunistic environments. The analytical results provide estimates on feasibility of service sharing and service compositions for various mobility models. To validate the framework, we conduct simulation experiments using multiple human mobility and synthesized datasets. In these experiments, we analyze service composition feasibility, service completion rate and time for resource utilization.

Inferring abstract contexts and activities from heterogeneous data is vital to context-aware ubiquitous applications but still remains one of the most challenging problems. Recent advances in Bayesian nonparametric machine learning, in particular the theory of topic models based on Hierarchical Dirichlet Process (HDP), has provided an elegant solution towards these challenges. However, limited existing methods have addressed the problem of inferring latent multifaceted activities and contexts from heterogeneous data sources such as those collected from mobile devices. In this paper, we extend the original HDP to model heterogeneous data using a richer structure of the base measure being a product-space. The proposed model, called product-space HDP (PS-HDP), naturally handles the heterogeneous data from multiple sources and identify the unknown number of latent structures in a principle way. Although this framework is generic, our current work primarily focuses on inferring (latent) threefold activities of who-when-where simultaneously, which corresponds to inducing activities from data collected for identity, location and time. We demonstrate our model on synthetic data as well as on a real-world dataset – the StudentLife dataset. We report results and provide analysis on the discovered activities and patterns to demonstrate the merit of the model. We also quantitatively evaluate the performance of PS-HDP model using standard metrics including F1-score, NMI, RI, purity, and compare them with well-known existing baseline methods.

To achieve scalable, high-throughput, low-latency packet processing, TCP implementations are parallelized across cores in multicore platforms. This, however, significantly affects the order in which packets from different flows are delivered to application processing. Our measurements record up to 75% of the packets are delivered to applications in a way that does not match the order in which they are received on the network interface. For many important classes of applications, such as financial services, bidding and trading engines, game engines, this cross-flow packet reordering affects their ability to provide fairness guarantees. To address this gap, we propose TCP-Ordo - a TCP stack which provides strict ordering of packets across multiple flows, as well as flexibility to control the degree to which ordering is enforced. TCP-Ordo outperforms existing TCP implementations in both latency and throughput. The current prototype is implemented as a user-level TCP stack for Mellanox Connectx3 NICs with 40Gbps Ethernet interfaces. Without ordering guarantees TCP-Ordo delivers one way latency of 4.75usec (a 5x improvement over the Linux kernel) for 150B packets and throughput of 22Gbps (a 2x improvement over mTCP) for 1500B packets. Furthermore, with TCP-Ordo applications are provided with strict packet order delivery, with performance that continues to be superior to the state-of-the-art, even when enforcing packet order across 800,000 connections on a 12-core platform. Finally, TCP-Ordo supports the notion of `ordered domains' that offer flexibility in the degree of ordering that an application will experience, and pay for.

Opportunistic networks are characterized by the dynamic connectivity created when mobile devices encounter each other, as they are within close proximity. During these transient opportunities, devices are typically within one-hop wireless range of their neighbors. Opportunistic networks are an effective way, in terms of bandwidth and battery consumption to distribute large volume content among peers. Many existing proposals consider opportunistic networks as a best-effort content delivery approach, which limits their applications. We exploit characteristics of human mobility to derive an effective data forwarding scheme that achieves Combined Optimal Stability and Capacity (COSC) for opportunistic networks. COSC includes a path selection algorithm to maximize the utility of link capacity and stability. We validate theoretical findings with rigorous simulation studies using synthetic and real-world mobility traces. When compared with other approaches, COSC shows significant improvement due to the consideration of link capacity and stability.

Pervasive networks formed by users' mobile devices have the potential to exploit a rich set of distributed service components that can be composed to provide each user with a multitude of application level services. However, in many challenging scenarios, opportunistic networking techniques are required to enable communication as devices suffer from intermittent connectivity, disconnections and partitions. This poses novel challenges to service composition techniques. While several works have discussed middleware and architectures for service composition in well-connected wired networks and in stable MANET environments, the underlying mechanism for selecting and forwarding service requests in the significantly challenging networking environment of opportunistic networks has not been entirely addressed. The problem comprises three stages: i) selecting an appropriate service sequence set out of available services to obtain the required application level service; ii) routing results of a previous stage in the composition to the next one through a multi-hop opportunistic path; and iii) routing final service outcomes back to the requester. The proposed algorithm derives efficiency and effectiveness by taking into account the estimated load at service providers and expected time to opportunistically route information between devices. Based on this information the algorithm estimates the best composition to obtain a required service. It is shown that using only local knowledge collected in a distributed manner, performance close to a real-time centralized system can be achieved. Applicability and performance guarantee of the service composition algorithm in a range of mobility characteristics are established through extensive simulations on real/synthetic traces.

Knowledge of user movement in mobile environments paves the way for intelligent resource allocation and event scheduling for a variety of applications. Existing schemes for estimating user mobility are limited in their scope as they rely on repetitive patterns of user movement. Such patterns may not exist, or are difficult to recognize in soft-real time, in open environments such as parks, malls, or streets. We propose a novel scheme for Real-time Mobility Estimation for Mobile Environments (MEME). MEME employs the concept of temporal distances and uses logistic regression to make real time estimations about user movement. MEME relies only on opportunistic message exchange and is fully distributed, scalable, and requires neither a central infrastructure nor Global Positioning System. MEME has been tested on real world and synthetic mobility traces - makes predictions about direction and count of users with up to 90% accuracy, enhances successful video downloads in shared environments.

In shared physical spaces such as parks, streets, tourist places etc., content generated at user's devices tends to lose its relevance with distance from the immediate spatio-temporal neighborhood. Here, content includes sensory information as well as user generated content. In such participatory environments, content generated at the producers' devices are often sought by applications seeking information on devices carried by consumers. Spatio-temporal context shared by producers and consumers is often a driving factor in determining the suitability of content to share in shared public spaces. In addition, mobile devices suffer from resource constraints. While there exist many middleware mechanisms to aid content sharing in participatory environments, there is need for a context-aware middleware that can provide an effective matching between consumer needs and content generated at producers under dynamically varying spatio-temporal as well as resource contexts. With this goal in mind, we propose a novel content based context-aware middleware called ConCon for dynamic participatory environments. ConCon employs a publish-subscribe architecture, with stable nodes called providers that serve as intermediaries between producers and consumers. ConCon takes into account content semantics, as well as context attributes such as location, residual energy, residual bandwidth etc. Through extensive simulation experiments performed on both real-world and synthetic traces we show that ConCon greatly enhances information quality by increasing diversity of shared content. Furthermore, ConCon reduces cost of sharing content in terms of resource usage in participatory environments.

Exploiting opportunistic contacts between mobile devices to enable deployment of real applications through reliable and efficient data transfers poses a significant research challenge. Indeed, accurate prediction of contact volume, defined as the maximum amount of data transferable during a contact, can improve performance of deployments. However, existing schemes for estimating contact volume that make use of preconceived patterns or contact time distributions may not be applicable in uncertain environments. In this paper, we propose a novel scheme called PCV that predicts contact volume in soft real-time to enable efficient and reliable data transfers in opportunistic networks. An Android Application that learns data rate profiles has been developed to facilitate PCV. In addition, an analytical model has been developed to depict variable data rates between mobile devices. Extensive simulations are carried out on both synthetic and real world mobility traces to validate the usefulness of PCV. Experimental results show the effectiveness of our approach in terms of reliable data transfers.

As a key enabling technology for pervasive computing, opportunistic networks have attracted intensive research efforts recently. In this paper, we present a new routing scheme for opportunistic networks that aims at forwarding messages to a destination location/area, instead of forwarding to specific nodes. Our routing scheme, termed LOOP for LOcation based routing for OPportunistic networks, exploits the regularity embedded in human moving pattern. As human movements often exhibit a high degree of repetition including regular visits to certain places and regular contacts during daily activities, we can predict a mobile node's future locations based on its mobility trace with high confidence. We formulate the movement pattern mining as a multi-label classification problem and construct a Bayes' predictive model to explore the mobility history and learn the movement pattern. This movement pattern will then be used to predict the node's future movement. Based on the prediction, the ability of the node to deliver a message to the destination is quantified through defined metrics. These metrics will be the determining factor for choosing proper relaying nodes in several proposed strategies. Our scheme can preserve privacy as no information, including location information, needs to be exchanged among nodes. At the same time, our scheme achieves total distributed control as each node can choose its individual forwarding strategy without involving network wide changes. Our analytical and simulation results show that LOOP can achieve significant performance gains over well known existing strategies for routing in opportunistic networks.

In opportunistic environments, tasks such as content sharing and service execution among remote devices are facilitated by relays (devices with short-range wireless connectivity) that receive data, move around, and then forward the data. To achieve high throughput, it is important to secure forwarding and provide incentives for participation by relays. However, it is extremely challenging to monitor the behavior of relays in an opportunistic network due to sparse connectivity. Existing schemes do not work when selfish/malicious relays collude with each other to forge routing metrics, drop useful data, flood the network, or earn extra reward.

Opportunistic networks (ONs) exploit mobility of devices to route messages and distribute information. Unfortunately, the fleeting and intermittent connections between pairs of devices make many traditional computer collaboration paradigms, such as distributed shared memory (DSM), very difficult to realize. DSM systems, developed for traditional networks, rely on relatively stable, consistent connections among participating nodes. We propose a novel delay tolerant lazy release consistency (DTLRC) mechanism for implementing distributed shared memory in opportunistic networks. DTLRC permits mobile devices to remain independently productive while separated, and provides a mechanism for nodes to regain coherence of shared memory if and when they meet again. Simulations using experimentally determined data traces demonstrate that DTLRC is a viable concept for enhancing cooperation among mobile wireless devices in opportunistic networking environments.

A series of opportunistic contacts in space and time among devices carried by mobile users, can be utilized to forward messages from one user to another in the absence of an end-to end connected path. Existing routing metrics exhibit efficient performance in either homogeneous (users have similar mobility characteristics) or specific heterogeneous (users exhibit varied mobility characteristics) scenarios. However, in practice, behavior of users changes at different locations and times, making it hard to generalize any one routing algorithm. The adaptive forwarding scheme, ProxiMol, proposed in this paper leverages two simple facts some users have better likelihood of message delivery due to higher mobility, while others do due to their location's proximity to destination. Key contributions of ProxiMol include: i) a model to infer user's location over time from its diffusion (a measure of mobility); ii) an analytical result to estimate distance between users; and iii) an empirical method to estimate diffusion of a user. These are used to compute the likelihood of delivery taking into account both the mobility of a user and her proximity to destination. In addition to this robust forwarding scheme, a novel concept of disconnected distance that captures partial paths in networks with moderate levels of connectivity is introduced. ProxiMol improves delivery ratios (10-20%) and reduces delays by up to 50%, when compared against previously proposed algorithms, in user environments that range from relatively homogeneous to highly heterogeneous settings.

Pervasive networks formed by users' mobile devices have the potential to exploit a rich set of distributed service components that can be composed to provide each user with a multitude of application level services. However, mobile and pervasive networks suffer from intermittent connectivity, disconnections and partitions, such that opportunistic networking techniques are required to enable communication. This poses novel challenges to service composition techniques. While several works have discussed middleware and architecture for service composition in well-connected wired networks and in stable MANET environments, the underlying mechanism for selecting and forwarding service requests in the significantly challenging networking environment of opportunistic networks has not been addressed. The problem comprises three stages: i) selecting an appropriate service sequence set out of available services; ii) forwarding service inputs to the device hosting the next service in the composition; and iii) routing final service outcomes back to the requester. The proposed algorithm derives efficiency and effectiveness by taking into account the service load and location of devices providing the services, as well as intermittent connectivity, to select a particular service set. Through extensive simulations on real and synthetic traces, we show that by using only local knowledge collected in a distributed manner, performance close to a real-time centralized system can be achieved.

Opportunistic networks are created dynamically by exploiting contacts between pairs of mobile devices that come within communication range. While forwarding in opportunistic networking has been explored, investigations into asynchronous service provisioning on top of opportunistic networks are unique contributions of this paper. Mobile devices are typically heterogeneous, possess disparate physical resources, and can provide a variety of services. During opportunistic contacts, the pairing peers can cooperatively provide (avail of) their (other peer's) services. This service provisioning paradigm is a key feature of the emerging opportunistic computing paradigm. We develop an analytical model to study the behaviors of service seeking nodes (seekers) and service providing nodes (providers) that spawn and execute service requests, respectively. The model considers the case in which seekers can spawn parallel executions on multiple providers for any given request, and determines: 1) the delays at different stages of service provisioning; and 2) the optimal number of parallel executions that minimizes the expected execution time. The analytical model is validated through simulations, and exploited to investigate the performance of service provisioning over a wide range of parameters.

Most of the routing and replication algorithms assume the network to be large and therefore, the architecture and algorithms are designed to be scalable. These algorithms however may not perform well with limited number of nodes in a network of UAVs. It is better to design and simulate such algorithms to increase the efficiency in a small network as scalability is no longer an issue. For such networks, we present design and simulation of some effective routing and replication algorithms to route packets, disseminate information, and replicate data among nodes.

Opportunistic computing has emerged as a new paradigm in computing, leveraging the advances in pervasive computing and opportunistic networking. Nodes in an opportunistic network avail of each others' connectivity and mobility to overcome network partitions. In opportunistic computing, this concept is generalised, as nodes avail of any resource available in the environment. Here we focus on computational resources, assuming mobile nodes opportunistically invoke services on each other. Specifically, resources are abstracted as services contributed by providers and invoked by seekers. In this paper, we present an analytical model that depicts the service invocation process between seekers and providers. Specifically, we derive the optimal number of replicas to be spawned on encountered nodes, in order to minimise the execution time and optimise the computational and bandwidth resources used. Performance results show that a policy operating in the optimal configuration largely outperforms policies that do not consider resource constraints.

Effective security mechanisms are essential to the widespread deployment of pervasive systems. Much of the research focus on security in pervasive computing has revolved around distributed trust management. While such mechanisms are effective in specific environments, there is no generic framework for deploying and extending these mechanisms over a variety of pervasive systems. We present the design and implementation of a novel framework called distributed trust toolkit (DTT), for implementing and evaluating trust mechanisms in pervasive systems. The DTT facilitates the extension and adaptation of trust mechanisms by abstracting trust mechanisms into interchangeable components. Furthermore, the DTT provides a set of tools and interfaces to ease implementation of trust mechanisms and facilitate their execution on a variety of platforms and networks. In addition to the adaptability and extensibility provided by this design, we demonstrate through simulation that use of DTT improves utilization of resources and enhances performance of existing trust mechanisms in pervasive systems. We are currently developing an implementation of the DTT that can be easily deployed in pervasive environments.

This paper examines the recovery of user context in indoor environmnents with existing wireless infrastructures to enable assistive systems. We present a novel approach to the extraction of user context, casting the problem of context recovery as an unsupervised, clustering problem. A well known density-based clustering technique, DBSCAN, is adapted to recover user context that includes user motion state, and significant places the user visits from WiFi observations consisting of access point ID and signal strength. Furthermore, user rhythms or sequences of places the user visits periodically are derived from the above low level contexts by employing a state-of-the-art probabilistic clustering technique, the Latent Dirichlet Allocation (LDA), to enable a variety of application services. Experimental results with real data are presented to validate the proposed unsupervised learning approach and demonstrate its applicability.

In open environments, such as mobile peer-to-peer systems, participants may need to access resources from unknown users. A critical security concern in such systems is the access of faulty resources, thereby wasting the requester's time and energy and possibly causing damage to her system. A common approach to mitigating the problem involves reputation mechanisms; however, since reputation relies on cooperation, a reputation mechanism's effectiveness can be significantly diminished in hostile environments. Reputation systems also require substantial communication among peers leading to: i) vulnerability to errors caused by intermittent connectivity; ii) message delivery disruptions caused by malicious peers; and iii) energy sapping message overheads. In this paper, we present AREX, a low-cost, adaptive mechanism designed to provide security for peers in hostile and uncertain environments, which are common in mobile P2P systems. AREX features an adaptive exploration strategy that increases the system's utility for benign peers and decreases the systempsilas utility for malicious peers. AREX reduces vulnerabilities and energy costs by operating without communication between peers. Through simulation, we demonstrate AREX's ability to reduce energy costs, protect benign peers, and diminish malicious peers' motivation to attack in a variety of hostile environments.

Research on security in peer-to-peer (P2P) systems is dominated by reputation-based solutions. These solutions propagate opinions about other peers in order to help identify the best set of peers to utilize. In this paper, we model peers with utility functions and use those functions to examine the case in which an individual peer participates in an unfamiliar and untrusted system, similar to one in which a mobile peer can enter when moving into a new location. We additionally introduce a novel security mechanism for P2P systems called resource exploration in order to mitigate the problems inherent in reputation-based systems and analyze its effect on a 2-player game (between an attacker and the benign peer).

Presents the introductory welcome message from the conference proceedings.

In wireless sensor networks, resource-constrained nodes are expected to operate in unattended highly dynamic environments. Hence, the need for adaptive and autonomous resource/task management in wireless sensor networks is well recognized. We present distributed independent reinforcement learning (DIRL), a Q-learning based framework to enable autonomous self-learning/adaptive applications with inherent support for efficient resource/task management. The proposed scheme based on DIRL, learns the utility of performing various tasks over time using mostly local information at nodes and uses the utility value along with application constraints for task management by optimizing global system-wide parameters like total energy usage, network lifetime etc. We also present an object tracking application design based on DIRL to exemplify our framework. Finally, we present results of simulation studies to demonstrate the feasibility of our approach and compare its performance against other existing approaches. In general for applications requiring autonomous adaptation, we show that DIRL on average is about 90% more efficient than traditional resource management schemes like static scheduling without losing any significant accuracy/performance.

In recent years, ubiquitous computing applications span such areas as telemedicine, banking, and transportation that require user privacy protection. The realization of context-aware ubiquitous computing exasperates existing privacy concerns. Ubiquitous computing applications demand new privacy enhancing technologies for the information and communication environments where the users are equipped with flexible and portable applications to support the capture, communication, recall, organization and reuse of diverse information. In this paper, we develop a novel scheme for the infusion of privacy into context-aware ubiquitous computing. We present Precision, a system for privacy enhanced context-aware information fusion in ubiquitous computing environments. In our scheme, privacy is defined as a set of parameters encapsulated in composite data entities called privons, through which, we aim at infusing privacy into Precision. We evaluate our proposed scheme through real interactions in implementation of privons.

The paper proposes a novel improvised architecture for providing fundamental services through exploitation of node heterogeneity so that relatively stable and capable devices cooperate and assist resource-poor devices. The novel hierarchical architecture called NeVo (Network of Volunteers) uses a small subset of the volunteer nodes to perform basic services such as QoS provisioning, service discovery, mobility management, and trust management in pervasive environments. Service discovery protocols and trust management schemes that utilize the NeVo architecture are presented. The utility of NeVo as a flexible and adaptable architecture for creating and provisioning fundamental services is validated through extensive simulation studies.

Service-oriented architectures (SOAs) promise to provide transparency to resource access by exposing the resources available as services. SOAs have been employed within pervasive computing systems to provide essential support to user tasks by creating services representing the available resources. The mechanism of combining two or more basic services into a possibly complex service is known as service composition. Existing solutions to service composition employ a template-matching approach, where the user needs are expressed as a request template, and through composition, a system would identify services to populate the entities within the request template. However, with the dynamism involved in pervasive environments, the user needs have to be met by exploiting available resources, even when an exact match does not exist. In this paper, we present a novel service composition mechanism for pervasive computing. We employ the service-oriented middleware platform called Pervasive Information Communities Organization (PICO) to model and represent resources as services. The proposed service composition mechanism models services as directed attributed graphs, maintains a repository of service graphs, and dynamically combines multiple basic services into complex services. Further, we present a hierarchical overlay structure created among the devices to exploit the resource unevenness, resulting in the capability of providing essential service-related support to resource-poor devices. Results of extensive simulation studies are presented to illustrate the suitability of the proposed mechanism in meeting the challenges of pervasive computing-user mobility, heterogeneity, and the uncertain nature of involved resources.

As the tremendous growth in peer-to-peer (P2P) applications, the issues related to file consistency become critical. In this paper, an algorithm for file Consistency Maintenance through Virtual servers (CMV) is proposed for unstructured and decentralized P2P systems. In CMV, consistency of each dynamic file is maintained by a virtual server (VS). A file update can only be accepted through the VS to ensure the one-copy serializability. The VS of a file is a logical network composed of multiple replica peers (RPs) that have replicas of the file. Mathematical analysis is performed to determine the optimal parameter selections that achieve minimum overhead messages for maintaining file consistency. The numerical results indicate that CMV is well suited for efficient file consistency maintenance in P2P systems.

Increasing popularity of the pervasive computing paradigm on one hand and the technological developments on the other, have paved the way for development and deployment of pervasive services in the everyday habitat. The service providers, who build, operate and manage services would want to maximize their revenues, and at the same time, the users would seek guarantees to support their applications. While there are a number of mechanisms proposed to build and operate service oriented architectures (SOAs) in pervasive computing arena, very little work has been done in terms of evaluating such schemes. The work presented in this paper proposes mechanisms to evaluate SOAs in pervasive computing. We also present a mechanism to evaluate service composition techniques, which are an effective way of delivering services to the end user. In earlier work, we proposed a service composition mechanism in pervasive computing environments called seamless service composition (SeSCo). We evaluate and compare SeSCo with a discover+match mechanism for the deployment of pervasive services within an automobile. We also present performance results of the two approaches through simulation.

Caching frequently accessed data objects at the local buffer of a mobile user (MU) has been found to be very effective in improving information availability in mobile wireless environments. Several mechanisms have been proposed in the literature to address the challenging problem of cache consistency in cellular wireless networks. However, these mechanisms are limited to single cell systems. In this paper, we develop a novel Dynamic Scalable Asynchronous Cache Consistency Scheme (DSACCS) that can adaptively maintain mobile data objects globally or locally depending on the minimum consistency maintenance cost in multi-cell systems. The cost function is derived by taking into account each data object's update frequency, MUs' access pattern and roaming frequency, number of cells and number of MUs in the system. Extensive simulation studies demonstrate that DSACCS outperforms three existing cache strategies extended to multi-cell environments. The three cache consistency strategies are homogeneous invalidation reports (IRs), inhomogeneous IR without roaming check, and inhomogeneous IR with roaming check. Finally, an improvisation of DSACCS, called DSACCS-G, is proposed for grouping cells in order to facilitate effective cache consistency maintenance in multi-cell systems.

Digital multimedia has gained popularity due to the huge success of the Internet. Users acquire and disseminate multimedia information using different types of devices and communication channels. On the other hand, personal devices, such as laptops, PDAs and smart phones, are not only capable of producing and rendering multimedia, but also support associated computing and communication tasks. However, there is a lack of support for ensuring automated, continuous access to multimedia in the presence of dynamicity and heterogeneity. Pervasive computing concepts can be effectively employed to provide anytime anywhere multimedia support to users over heterogeneous wireless environments. We present a novel approach to support adaptive services for multimedia delivery in heterogeneous wireless networks. The service adaptation and composition mechanism exploits middleware tools developed for pervasive information communities of software agents. We also demonstrate the feasibility of the proposed scheme through an intuitive scenario, and present our prototype results.

Summary form only given. In mobile distributed systems, vital resources like battery power and wireless channel bandwidth impose significant challenges in ubiquitous information access. We propose a novel energy and bandwidth efficient data caching mechanism, called greedydual least utility (GD-LU), that enhances dynamic data availability while maintaining consistency. The proposed utility-based caching mechanism considers several characteristics of mobile distributed systems, such as connection-disconnection, mobility handoff, data update and user request patterns to achieve significant energy savings in mobile devices. Based on the utility function derived from an analytical model, we propose a cache replacement algorithm and a passive prefetching algorithm to cache and prefetch data objects. Our comprehensive simulation experiments demonstrate that the proposed mechanism achieves more than 10% energy saving and near-optimal performance tradeoff between access latency and energy consumption.

Caching techniques have been successfully employed to overcome some of the problems posed by disconnection and/or limited bandwidth in mobile environments. Demand driven and prefetching techniques to maintain optimal caches have been found to have limited success in such environments. We develop profile based methods to maintain and enhance data availability at mobile client's caches. Profiling techniques determine data items to be prefetched and cached depending on a user group and context. We have developed a prototype system that demonstrates the applicability of the proposed scheme in a crisis management situation. Results of implementation of the above technique in experimental mobile environments show that profile based caching can significantly enhance data availability in mobile and ubiquitous environments.

We propose a novel algorithm, called update propagation through replica chain (UPTReC), to maintain file consistency in decentralized and unstructured peer-to-peer (P2P) systems. In UPTReC, each file has a logical replica chain composed of all replica peers (RPs) which are defined as peers that have replicas of the file. Each RP acquires partial knowledge of the bi-directional chain by keeping a list of information about k nearest RPs, called probe peers, in each direction. When an RP initiates an update, it pushes the update to all possible online (active) RPs through the replica chain. A reconnected RP pulls an online RP to synchronize the replica status and the information of the probe peers. An analytical model is derived to evaluate the performance of the UPTReC algorithm. The analytical results provide a better understanding of the system in choosing the system parameters for probabilistically guaranteed file consistency with minimum overheads. Simulation experiments are conducted to compare the performance with an existing update propagation algorithm based on the rumor spreading scheme. The experimental results show that the UPTReC can significantly reduce (up to 70%) overhead messages and also achieve smaller stale query ratio for files prone to frequent updates.

Due to deterministic and fast lookup performance, ternary content addressable memory (TCAM) has recently been gaining popularity in general policy filtering (PF) for packet classification in high-speed networks. However, the PF table update poses significant challenges for efficient use of TCAM. To avoid erroneous and inconsistent rule matching, the traditional approach is to lock the PF table during the rule update period, but table locking has a negative impact on data path processing. In this paper, we propose a novel scheme, called Consistent Policy Table Update Algorithm (CoPTUA), for TCAM. Instead of minimizing the number of rule moves to reduce the locking time, CoPTUA maintains a consistent PF table throughout the update process, thus eliminating the need for locking the PF table while-ensuring correctness of rule matching. Our analysis and simulation show that, even for a PF table with 100,000 rules, an arbitrary number of rules can be updated simultaneously within 1 second in the worst case, provided that 2 percent of the PF table entries are empty. Thus, CoPTUA enforces any new rule in less than 1 second for practical PF table size with high memory utilization and without impacting data path processing.

In the literature, there exit two types of cache consistency maintenance algorithms for mobile computing environments: stateless and stateful. In a stateless approach, the server is unaware of the cache contents at a mobile user (MU). Even though stateless approaches employ simple database management schemes, they lack scalability and ability to support user disconnectedness and mobility. On the other hand, a stateful approach is scalable for large database systems at the cost of nontrivial overhead due to server database management. We propose a novel algorithm, called Scalable Asynchronous Cache Consistency Scheme (SACCS), which inherits the positive features of both stateless and stateful approaches. SACCS provides a weak cache consistency for unreliable communication (e.g., wireless mobile) environments with small stale cache hit probability. It is also a highly scalable algorithm with minimum database management overhead. The properties are accomplished through the use of flag bits at the server cache (SC) and MU cache (MUC), an identifier (ID) in MUC for each entry after its invalidation, and estimated time-to-live (TTL) for each cached entry, as well as rendering of all valid entries of MUC to uncertain state when an MU wakes up. The stale cache hit probability is analyzed and also simulated under the Rayleigh fading model of error-prone wireless channels. Comprehensive simulation results show that the performance of SACCS is superior to those of other existing stateful and stateless algorithms in both single and multicell mobile environments.

In mobile environments, efficient binding update delivery results in fast adaptation to the effects of migration. The scheme discussed performs the update using IP multicast. The proposed active delivery scheme/sup +/ (ADS/sup +/) is an extension of the ADS earlier proposed by the authors. Unlike ADS, ADS/sup +/ does not assume the use of additional network hardware and yet provides a quick, deployable, bandwidth conserving solution to the delivery of binding updates.

A large fraction of Internet users access network resources through Web clients/browsers. The quality of service (QoS) experienced by their Internet users has gained great importance in recent years. Furthermore, new Web applications require the delivery of multimedia data in real time (e.g., streaming stored video and audio) and information transfer through the Internet is becoming one of the principal paradigms for business: electronic sales, banking, finance, collaborative work are a few examples of this. The QoS perceived by its users is thus becoming a dominant factor for the success of an Internet-based Web service. The principal QoS attributes users perceive include those related to the service "responsiveness", i.e., the service availability and timeliness.

Speculative prefetching has been proposed to improve the response time of network access. Previous studies in speculative prefetching focus on building and evaluating access models for the purpose of access prediction. This paper investigates a complementary area which has been largely ignored, that of performance modeling. We analyze the performance of a prefetcher that has uncertain knowledge about future accesses. Our performance metric is the improvement in access time, for which we derive a formula in terms of resource parameters (time available and time required for prefetching) and speculative parameters (probabilities for next access). We develop a prefetch algorithm to maximize the improvement in access time. The algorithm is based on finding the best solution to a stretch knapsack problem, using theoretically proven apparatus to reduce the search space. An integration between speculative prefetching and caching is also investigated.

A novel multiple access protocol, called DAMMA, is proposed for efficient handling of heterogenous traffic encountered in emerging wireless multimedia communications. To minimise contention collisions, DAMMA adopts random time slot selection in addition to the adaptive procedure for maintaining sufficient time slots available for contention. With DAMMA, time slots are selectively allocated to traffic of different service classes and priority levels to meet the diverse QoS requirements. Computer simulation results indicate that DAMMA can significantly enhance the throughput performance of data traffic. Under a given condition, the DAMMA protocol can achieve a maximum throughput of 91%, which compares well with 70% for the master-slave dynamic rate access (MSDRA) protocol adopted in the General Radio Packet Service (GPRS) system.

Providing real-time or continuous media (CM) application ser-vices in wireless networks poses a significant challenge, as it requires timely delivery of data in a best-effort network. In this paper, we propose a cache-based scheme for mobility-aware, CM applications. The proposed scheme exploits a previously proposed caching strategy to complement Mobile-IP by plac-ing services closer to migrated mobile nodes. The central idea of this work is based on the migration of sessions in order to facilitate uninterrupted delivery of CM in mobile environments. The performance of the proposed scheme is investigated by sim-ulation studies. In particular, the effect of the proposed scheme on several QoS parameters under varying conditions of mobil-ity and CM data is measured.

Previous studies in speculative prefetching focus on building and evaluating access models for the purpose of access prediction. This paper on the other hand investigates the performance of speculative prefetching. When prefetching is performed speculatively, there is bound to be an increase in the network load. Furthermore, the prefetched items must compete for space with existing cache occupants. These two factors-increased load and eviction of potentially useful cache entries-are considered in the analysis. We obtain the following conclusion: to maximise the improvement in access time, prefetch exclusively all items with access probabilities exceeding a certain threshold.

In this paper we propose AMTRee, an active network (AN) based multicast tree that is bidirectional, optimizable on demand and adaptive to source migration. We show how AN can be leveraged to enable a multicast tree to be modified and optimized efficiently after handoff. By filtering unnecessary signaling messages, maintaining minimal storage at routers and incorporating features of shared-tree methods we are able to achieve a scalable solution. Furthermore we introduce an AN-based optimization algorithm that is executed on demand by receivers. The performance of AMTree is compared to that of the bidirectional home agent (HA) method and the remote subscription method. We found that compared to the bidirectional HA method AMTree has a much lower handoff and end-to-end latency. The AMTree approach does not require a new multicast tree to be built after each handoff and yet the end-to-end latency is comparable to that of the remote subscription method.

Wireless communication offers flexible ways of enabling access to electronic information from anywhere, anytime. However, wireless communication suffers from many restrictions due to the inherent limitations of the wireless media and existing wireless communication protocols. Limited radio frequency spectrum (or bandwidth) is one of the major issues in wireless communication. In this paper, we propose a frequency re-allocation scheme, message ring-based channel re-allocation (MRCR) that improves channel utilization in wireless communication systems. MRCR uses software agents to circulate and maintain a message ring that carries bandwidth information. We present results of simulation studies to demonstrate the effectiveness of the MRCR scheme.

A1 Department of Computer Science, Curtin University of Technology, GPO Box U 1987 Perth WA 6845, Australia Email: [email protected]

A multistage bus network (MEN) is proposed to overcome some of the shortcomings of the conventional multistage interconnection networks (MINs), single bus, and hierarchical bus interconnection networks. The MBN consists of multiple stages of buses connected in a manner similar to the MINs and has the same bandwidth at each stage. A switch in an MBN is similar to that in a MIN switch except that there is a single bus connection instead of a crossbar. MBNs support bidirectional routing and there exists a number of paths between any source and destination pair. The authors develop self routing techniques for the various paths, present an algorithm to route a request along the path with minimum distance, and analyze the probabilities of a packet taking different routes. Further, they derive a performance analysis of a synchronous packet-switched MBN in a distributed shared memory environment and compare the results with those of an equivalent bidirectional MIN (BMIN). Finally, they present the execution time of various applications on the MBN and the BMIN through an execution-driven simulation. They show that the MBN provides similar performance to a BMIN while offering simplicity in hardware and more fault-tolerance than a conventional MIN.

In this paper, we discuss the design aspects of a dynamic distributed directory scheme (DDS) to facilitate efficient and transparent access to information files in mobile environments. The proposed directory interface enables users of mobile computers to view a distributed file system on a network of computers as a globally shared file system. In order to counter some of the limitations of wireless communications, we propose improvised invalidation schemes that avoid false sharing and ensure uninterrupted usage under disconnected and low bandwidth conditions.

Various Artificial Neural Networks (ANNs) have been proposed in recent years to mimic the human brain in solving problems involving human-like intelligence. Efficient mapping of ANNs comprising of large number of neurons onto various distributed MIMD architectures is discussed in this paper. The massive interconnection among neurons demands a communication efficient architecture. Issues related to the suitability of MIMD architectures for simulating neural networks are discussed. Performance analysis of ring, torus, binary tree, hypercube, and extended hypercube for simulating artificial neural networks is presented. Our studies reveal that the performance of the extended hypercube is better than those of ring, torus, binary tree, and hypercube topologies.

We describe the design of a directory-based shared memory architecture on a hierarchical network of hypercubes. The distributed directory scheme comprises two separate hierarchical networks for handling cache requests and transfers. Further, the scheme assumes a single address space and each processing element views the entire network as contiguous memory space. The size of individual directories stored at each node of the network remains constant throughout the network. Although the size of the directory increases with the network size, the architecture is scalable. The results of the analytical studies demonstrate superior performance characteristics of our scheme compared with those of other schemes.

A new interconnection topology-the extended hypercube-consisting of an interconnection network of k-cubes is discussed. The extended hypercube is a hierarchical, expansive, recursive structure with a constant predefined building block. The extended hypercube retains the positive features of the k-cube at different levels of hierarchy and at the same time has some additional advantages like reduced diameter and constant degree of a node. The paper presents an introduction to the topology of the extended hypercube and analyzes its architectural potential in terms of message routing and executing a class of highly parallel algorithms. Topological properties and performance studies of the extended hypercube are presented.>