The rapid expansion of wireless communication and the Internet of Everything (IoE) has transformed modern technology, necessitating secure and efficient Resource Allocation (RA) to optimize system performance. However, the increasing number of IoE devices introduces security vulnerabilities, particularly from Non-Legitimate Devices (NLDs) that threaten network integrity, data confidentiality, and system availability. This study proposes an RA model based on cyber threat intelligence (CTI) to detect and mitigate malicious devices, integrating a two-state Hidden Markov Model (HMM) for NLD identification and encryption/decryption mechanisms for secure task communication. The model is designed for IoE-Edge fog-based networks, reducing dependency on external cloud servers while leveraging 6G-enabled device clustering for optimized task distribution. A novel CTI-based RA mechanism, namely the Secure-Intelligent Main Task Off-loading Scheduling Algorithm (Sec- i MTOSA), is introduced to enhance intelligent scheduling and secure RA. Experimental results demonstrate that Sec- i MTOSA achieves an average of 93.7% accuracy in detecting NLDs while maintaining a secure RA process with only an average of 7.2% increase in end-to-end delay compared to non-secure traditional methods. These results validate the effectiveness of the model, demonstrating a high accuracy rate in identifying legitimate NLDs while maintaining a low computational overhead suitable for lightweight IoE-Edge environments. Although Sec- i MTOSA introduces minor end-to-end delays due to its embedded security features, it remains efficient for real-time IoE-Edge deployments. These findings establish CTI-driven RA as a scalable and secure approach for next-generation IoE-Edge networks.

The integration of Large Language Models (LLM) with multimodal agentic AI within the Medical Internet of Things (MIoT) ecosystem is redefining modern healthcare intelligence. This convergence enables continuous patient observation, adaptive clinical decision-making, and context-aware interaction between humans and machines across various biomedical data modalities. Healthcare systems generate a wide range of multimodal data, including textual records such as EHRs, prescriptions, and pathology notes; medical imagery such as CT, MRI, fundus, and radiographs; spoken data from consultations and transcriptions; video streams for rehabilitation and physiotherapy monitoring; and sensor readings such as ECG, SpO \({}_{2}\) , and glucose levels. Conventional unimodal algorithms fall short in interpreting this diversity, whereas LLM-augmented agentic frameworks fuse and reason over these heterogeneous sources, grounding their outputs in medical ontologies and coordinating task-specific agents to enhance real-world clinical workflows. This article presents a comprehensive overview of multimodal agentic AI powered by LLM for MIoT-enabled healthcare systems. Introduces a 6D unified taxonomy that covers multimodal input channels, fusion mechanisms, core LLM reasoning capabilities, agentic coordination models, computational deployment layers, and ethical governance frameworks. To contextualize this taxonomy, the discussion includes a Virtual Hospital case study centered on cancer that demonstrates how multimodal signals such as imaging, genomics, patient dialogues, and clinical updates integrate through intelligent agents to enable personalized diagnosis, automated documentation, home rehabilitation, and rapid intervention in emergencies. The survey also consolidates current progress on datasets, benchmarks, and evaluation protocols for AI in multimodal and agentic healthcare. The survey identifies critical research gaps, such as the lack of longitudinal multimodal datasets, standardized evaluation frameworks for multi-agent reasoning, and reliable methods to assess trustworthiness in clinical AI. Furthermore, it examines security and compliance issues such as adversarial manipulation, data leakage, and accountability across distributed agent networks, and it proposes countermeasures through federated data governance, secure MCP-oriented orchestration, and privacy-aware edge deployment strategies. By situating recent advances within the Virtual Hospital paradigm and oncology workflows, this study provides a systematic foundation for developing scalable, secure, and ethically aligned multimodal agentic systems based on LLMs, guiding the next generation of intelligent MIoT-driven healthcare ecosystems.

Recent advancements of the Industrial Internet of Things (IIoT) have revolutionized modern urbanization and smart cities. While IIoT data contain rich events and objects of interest, processing a massive amount of IIoT data and making predictions in real-time are challenging. Recent advancements in artificial intelligence (AI) allow processing such a massive amount of IIoT data and generating insights for further decision-making processes. In this article, we propose several key aspects of AI-enabled IIoT data for smart city monitoring. First, we have combined a human-intelligence-enabled crowdsourcing application with that of an AI-enabled IIoT framework to capture events and objects from IIoT data in real time. Second, we have combined multiple AI algorithms that can run on distributed edge and cloud nodes to automatically categorize the captured events and objects and generate analytics, reports, and alerts from the IIoT data in real time. The results can be utilized in two scenarios. In the first scenario, the smart city authority can authenticate the AI-processed events and assign these events to the appropriate authority for managing the events. In the second scenario, the AI algorithms are allowed to interact with humans or IIoT for further processes. Finally, we will present the implementation details of the scenarios mentioned above and the test results. The test results show that the framework has the potential to be deployed within a smart city.

introduction Share on Special Section on Edge-AI for Connected Living Editors: M. Shamim Hossain King Saud University, Saudi Arabia King Saud University, Saudi ArabiaView Profile , Changsheng Xu Chinese Academy of Sciences, China Chinese Academy of Sciences, ChinaView Profile , Josu Bilbao IKERLAN, Spain IKERLAN, SpainView Profile , Md. Abdur Rahman University of Prince Mugrin, KSA University of Prince Mugrin, KSAView Profile , Abdulmotaleb El Saddik University of Ottawa, Canada University of Ottawa, CanadaView Profile , Mohamed Bin Zayed University of Artificial Intelligence, UAE & University of Ottawa, Canada University of Artificial Intelligence, UAE & University of Ottawa, CanadaView Profile Authors Info & Claims ACM Transactions on Internet TechnologyVolume 22Issue 3August 2022 Article No.: 55epp 1–3https://doi.org/10.1145/3514196Published:14 March 2022Publication History 0citation176DownloadsMetricsTotal Citations0Total Downloads176Last 12 Months176Last 6 weeks24 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 Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

Capturing psychological, emotional, and physiological states, especially during a pandemic, and leveraging the captured sensory data within the pandemic management ecosystem is challenging. Recent advancements for the Internet of Medical Things (IoMT) have shown promising results from collecting diversified types of such emotional and physical health-related data from the home environment. State-of-the-art deep learning (DL) applications can run in a resource-constrained edge environment, which allows data from IoMT devices to be processed locally at the edge, and performs inferencing related to in-home health. This allows health data to remain in the vicinity of the user edge while ensuring the privacy, security, and low latency of the inferencing system. In this article, we develop an edge IoMT system that uses DL to detect diversified types of health-related COVID-19 symptoms and generates reports and alerts that can be used for medical decision support. Several COVID-19 applications have been developed, tested, and deployed to support clinical trials. We present the design of the framework, a description of our implemented system, and the accuracy results. The test results show the suitability of the system for in-home health management during a pandemic.

Medical IoT devices are rapidly becoming part of management ecosystems for pandemics such as COVID-19. Existing research shows that deep learning (DL) algorithms have been successfully used by researchers to identify COVID-19 phenomena from raw data obtained from medical IoT devices. Some examples of IoT technology are radiological media, such as CT scanning and X-ray images, body temperature measurement using thermal cameras, safe social distancing identification using live face detection, and face mask detection from camera images. However, researchers have identified several security vulnerabilities in DL algorithms to adversarial perturbations. In this article, we have tested a number of COVID-19 diagnostic methods that rely on DL algorithms with relevant adversarial examples (AEs). Our test results show that DL models that do not consider defensive models against adversarial perturbations remain vulnerable to adversarial attacks. Finally, we present in detail the AE generation process, implementation of the attack model, and the perturbations of the existing DL-based COVID-19 diagnostic applications. We hope that this work will raise awareness of adversarial attacks and encourages others to safeguard DL models from attacks on healthcare systems.

Vehicle-to-vehicle communication assists road-side information exchange granting ease of access and sharing between users. The communication between the vehicles is short-lived due to interference and data congestion in the resource constraint medium. This manuscript introduces a linear adaptive congestion control (LACC) augmenting the benefits of greedy routing and data dissemination model (DDM). LACC focuses on selecting beneficiary vehicle by assessing its end-to-end service capacity and link stability preference. Different from the conventional greedy approach, routing is aided by a linear integer programming module for smart decisions on neighbor selection. The interrupts in data transmission and forwarding due to non-localized vehicles, congested routing paths and paused transmissions are addressed using LACC as a series of linear optimization. This helps to improve the performance of vehicular communication estimated using delay, message delivery, outage, and beacon messages.

In this article, we share our experiences in designing and developing a suite of deep neural network–(DNN) based COVID-19 case detection and recognition framework. Existing pathological tests such as RT-PCR-based pathogen RNA detection from nasal swabbing seem to display low detection rates during the early stages of virus contraction. Moreover, the reliance on a few overburdened laboratories based around an epicenter capable of supplying large numbers of RT-PCR tests makes this testing method non-scalable when the rate of infections is high. Similarly, finding an effective drug or vaccine with which to combat COVID-19 requires a long time and many clinical trials. The development of pathological COVID-19 tests is hindered by shortages in the supply chain of chemical reagents necessary for testing on a large scale. This diminishes the speed of diagnosis and the ability to filter out COVID-19 positive patients from uninfected patients on a national level. Existing research has shown that DNN has been successful in identifying COVID-19 from radiological media such as CT scans and X-ray images, audio media such as cough sounds, optical coherence tomography to identify conjunctivitis and pink eye symptoms on the ocular surface, body temperature measurement using smartphone fingerprint sensors or thermal cameras, the use of live facial detection to identify safe social distancing practices from camera images, and face mask detection from camera images. We also investigate the utility of federated learning in diagnosis cases where private data can be trained via edge learning. These point-of-care modalities can be integrated with DNN-based RT-PCR laboratory test results to assimilate multiple modalities of COVID-19 detection and thereby provide more dimensions of diagnosis. Finally, we will present our initial test results, which are encouraging.

Once a subject is diagnosed with cancer, a patient goes through a series of diagnosis and tests, referred to as after cancer treatment. Due to the nature of the treatment and side effects on regular lifestyles, maintaining quality of life in the home environment is a challenging task. Sometimes within a home environment, a cancer patient's situation changes abruptly, as the functionality of certain organs deteriorate, which affects their quality of life. In this paper, we propose a Blockchain and off-chain based framework which will allow multiple medical and ambient intelligent IoT sensors to capture quality of life information from one's home environment and securely share it with one's community of interest. Using our proposed framework, both transactional records and multimedia big data - consisting of a user's physiological as well as mental states - can be shared with an oncologist or palliative care unit for real-time decision support. We have also developed Blockchain-based data analytics, which will allow a clinician to visualize the immutable history of the patient's data available from an in-home secure monitoring system for a better understanding of a patient's current or historical states. We further designed a generic oncologist smart contract and digital wallet for different stakeholders to automate the treatment plan of a particular patient. Finally, we will present our current implementation status, which provides significant encouragement for further development.

Blockchain's properties in addressing trust in highly decentralized environments can make it an enabler for novel sharing economy services. In this paper, we demonstrate the practicality of blockchain-based Secure IoT as a Service (SIoTaaS), where an IoT device can be rented from a service provider, securely and in a privacy-preserving fashion. Our framework allows the simultaneous operations of distinct providers of IoT-based sharing economy services at a large scale. Multiple parties can securely share text and multimedia in the context of location and point-of-interest sharing, perform financial transactions by hiding true identity of parties involved in various online transactions, perform user and IoT registration, transfer value transactions via Ethereum tokens between providers and consumers, as well as raw IoT data payload. This can turn smart room IoT devices, such as smart locks, light bulbs, air conditioning and fans into rentable business entities within a secure sharing economy platform. We will demonstrate such a proof of concept IoT sharing economy framework, which is specifically designed to support the temporary IoT needs of very large numbers of users, such as Hajj pilgrims concentrating for a short period of time at a single area in Saudi Arabia.

Crowdsourcing offers great opportunities to recognise user context and prescribe relevant services for both offline and real-time activities. In this work, we present a zoning model that leverages spatio-temporal dimensions and then employs different contexts to recommend necessary customised services. The context model takes into consideration three context sets: fully restricted, fully unrestricted and semi-restricted with respect to both spatial and temporal dimensions. As a proof of concept, we apply this zoning model in a scenario where a very large crowd get together to perform spatio-temporal activities. The user context of the heterogeneous crowd is captured using the carried smartphones, i.e. via crowdsourcing. Depending on the context sets and zone, the system can recommend a set of services to each user. The system has been deployed since 2014 to support the spatio-temporal activities of a very large crowd. We present our implementation details and the user feedback, which is very encouraging.

Social Internet of Things is assumed to provide health services by incorporating social networks and the Internet of Things (IoT). Although, much development in therapy monitoring has been observed recently, few advancements have been achieved in the domain of in-home therapy. Existing industrial and medical solutions require complex and expensive hardware and software that are impractical for home use. Another challenge for in-home therapy is that therapists cannot confirm whether patients are conducting the therapy correctly and for the prescribed number of times. To address these challenges, we propose the multisensor therapy (m-Therapy) framework, in which multiple gesture-tracking sensors and environmental sensors are used to collect therapy and ambient data. The m-Therapy framework compresses the collected data and uploads to a big data server. The framework uses a model of the therapy to guide a patient performing therapy exercises outside medical institutions and even at home. Ambient IoT sensors can help maintain an appropriate ambient environment, which is generally maintained at the medical institutions. We have developed analytics that can provide live or statistical kinematic data, including rotational and angular range of motion of the joints of interest, and ambient environmental data, which can be shared with therapists and caregivers. We present our findings, which shows that the proposed m-Therapy monitoring system can be deployed in real-life scenarios.

Recommending an optimized path for a large crowd poses a unique challenge to existing routing algorithms due to the interactions between users and the dynamic changes over road networks. Industries, researchers and end users show an enormous interest in crowdsourced data comprising social networks and user-generated content to remain updated with their concerns. In this paper, we present a data collection framework that helps users to find optimized routes in a dynamic environment. We have developed a data collection framework to collect dynamic road conditions via a set of location-based services to support a very large Hajj crowd by capturing their locations using smartphones. We also collect geotagged social network data that provides more details about road conditions. The system leverages geotagged crowdsourced information to identify constraints such as accidents, congestions, and roadblocks. Moreover, by continuously collecting real-time geotagged data of moving users, the system can also find the flow of traffic and road conditions. We propose a spatial grid index to compute the optimized path, and to identify the affected users within impact zones. The plan is to test the whole application and back-end server during Hajj 2016, where over three million pilgrims from all over the world gather to perform their rituals.

Crowdsourced multimedia data poses several challenges when it is collected, stored, indexed, retrieved, and visualized. Examples of crowd source multimedia data are social sensors, vehicle sensors, physical sensors, human sensors, etc. Analyzing such multimodal and diversified crowdsourced data provides very rich understanding about the need of individuals within a crowd. Such understanding makes it possible to tailor services to individuals' needs, also called context-aware services. In this paper, we propose a spatial multimedia big data framework that can collect multimedia data from 1) a very large crowd equipped with multi-sensory smartphones, 2) vehicles, and 3) social networks. A set of multimedia services are offered to users to support their spatio-temporal activities. These include but not limited to 1) simple user interfaces to utilize multimedia services for instant guidance, 2) navigation to points of interests (POI), and 3) efficient and cost effective intra-city rides to users. The big data framework is designed to handle a very large number of multimedia spatio-temporal queries in real-time. The system is a pilot project and will be deployed during the event of Hajj 2015 when over three million pilgrims from all over the world will visit Makkah, Saudi Arabia to perform their Hajj rituals.

Social networks, such as Facebook, Instagram, and Twitter, provide intuitive ways to share a variety of information including geotagged multimedia data within users' communities of interest (COI) or publicly in real-time. Real-time geotagged multimedia data can provide semantics to conventional spatial queries in order to enrich the user navigation experience. In this paper, we introduce a mechanism to process spatio-temporal queries by leveraging geotagged multimedia data such as images, audio, video, and text, in order to add semantics to the conventional queries. Our framework collects, stores, and spatially tags multimedia data shared by users through social networks or through our developed mobile application. The system then uses such data in order to enhance the conventional routing services by resolving existing usability issues and by providing semantics to the routes in terms of enriched points of interest while taking dynamic road conditions into account. A proof of concept of the system will be demonstrated with the following spatio-temporal queries on road networks: 1) multimedia-enhanced shortest path queries; 2) multimedia-enhanced k-nearest neighbor queries; and 3) multimedia-enhanced range queries. Finally, a novel technique for finding lost individuals using geotagged multimedia data is also introduced. The results are tailor-made to the users' smartphone bandwidth and resolution requirements

The proliferation of networked home appliances, coupled with the popularity of low cost gesture detection sensors has made it possible to create smart home environments where users can manipulate devices of daily use through gestures. In this paper, we present a multi-sensory environment that allows a disabled person to control actual devices around the house that are presented to her in a mixed reality environment, as part of occupational therapy exercises. An analytical engine processes the motion data collected by gesture recognition sensors, measures recovery metrics, such as joint range of motion and speed of movement, etc. and presents the results to the therapist or the patient in the form of graphs and exercise statistics. We have incorporated intuitive 2D and 3D user interfaces into the environment to make the therapy experience more engaging and immersive for the disabled people. From the multimedia occupational therapy exercise data analysis, a therapist can get determine the ability of a patient to perform daily life activities without the help of others.

Traditional routing algorithms for calculating the fastest or shortest path become ineffective or difficult to use when both source and destination are dynamic or unknown. To solve the problem, we propose a novel semantic routing system that leverages geo-tagged rich crowdsourced multimedia information such as images, audio, video and text to add semantics to the conventional routing. Our proposed system includes a Semantic Multimedia Routing Algorithm (SMRA) that uses an indexed spatial big data environment to answer multimedia spatio-temporal queries in real-time. The results are customized to the users' smartphone bandwidth and resolution requirements. The system has been designed to be able to handle a very large number of multimedia spatio-temporal requests at any given moment. A proof of concept of the system will be demonstrated through two scenarios. These are 1) multimedia enhanced routing and 2) finding lost individuals in a large crowd using multimedia. We plan to test the system's performance and usability during Hajj 2015, where over four million pilgrims from all over the world gather to perform their rituals.

Logging on to a system using a conventional keyboard may not be feasible in certain environments, such as, in a surgical operation theatre or in an industrial manufacturing facility. We have developed a multi-sensory gesture based login system that allows a user to access secure information using body gestures. The system can be configured to use different types of gestures according to the type of sensors available to the user. We have proposed a simple scheme to represent all alphanumeric characters required for password entry as gestures within the multi-sensory environment. Our scheme is scalable enough to support sensors that detect a large number of gestures to those that can only accept a few. This allows the system to be used in a variety of situations such as usage by disabled persons with limited ability to perform gestures. We are in the midst of deploying our developed system in a clinical environment.

Traditional routing algorithms for calculating the fastest or shortest path become ineffective or difficult to use when both source and destination are dynamic or unknown. To solve the problem, we propose a novel semantic routing system that leverages geo-tagged rich crowdsourced multimedia information such as images, audio, video and text to add semantics to the conventional routing. Our proposed system includes a Semantic Multimedia Routing Algorithm (SMRA) that uses an indexed spatial big data environment to answer multimedia spatio-temporal queries in real-time. The results are customized to the users' smartphone bandwidth and resolution requirements. The system has been designed to be able to handle a very large number of multimedia spatio-temporal requests at any given moment. A proof of concept of the system will be demonstrated through two scenarios. These are 1) multimedia enhanced routing and 2) finding lost individuals in a large crowd using multimedia. We plan to test the system's performance and usability during Hajj 2015, where over four million pilgrims from all over the world gather to perform their rituals.

This paper presents a system that enables efficient and scalable real-time user and vehicle discovery using textual, audio and video mechanisms. The system allows users to group together for shared intra-city transportation with the aid of multimedia that helps individuals to 1) find community of common interest (CoCI), 2) locate individual users in a large crowd and 3) locate vehicles for mobility in an efficient and cost effective manner. The system is a pilot project and will be deployed during Hajj 2015 when over three million pilgrims from all over the world visit Makkah, Saudi Arabia.

We propose a context aware framework that offers a set of cloud-based services to support a very large Hajj and Umrah crowd by capturing their contexts using smartphones. The proposed framework captures the individual's context, provides a set of adapted services, and allows being in touch with a subset of one's community of interest. We leverage the spatiotemporal sensory data captured by our framework to define users' contexts for optimized services. Our proposed framework is also envisioned to assist the Hajj and Umrah authorities to (1) improve Hajj & Umrah documentation, (2) improve Hajj organization through better understanding of pilgrims' (individual and crowd) spatial and temporal behavior and needs, and (3) protect pilgrims' environment through environmental monitoring. In particular, the developed methods, techniques, and algorithms will support the pilgrimage quality of experience. We have tested our system through end-user subjects and due to apply for the upcoming Hajj events. We present our implementation details and the general impression of end users about our system.

In this paper, we present a novel idea of a map-based therapy environment for people with Hemiplegia. The therapy environment is designed according to the suggestions of therapists, which consists of a spatial map browsing serious game augmented with our novel multi-sensory natural user interface (NUI). The NUI is based on 3D motion sensors that can recognize different hand and body gestures used for browsing a 3D or 2D map. The 3D motion sensors work in a non-invasive way; hence, they do not require any wearable body attachments and can be used at home without assistance from the therapists. The map-browsing environment provides an immersive experience to the disabled users, which helps in performing therapy in an interesting and entertaining manner. We have developed analytics for measuring certain quality of health improvement metrics from each type of spatial map browsing movements. The 3D motion sensors have been tested with Nokia, Google, ESRI, and a number of other maps that allow a subject to visualize and browse the 3D and 2D maps of the world. The map browsing session data shows the nature of big data; hence, the session data is stored in a cloud environment. Our developed serious game environment is web-based; thus anyone having the appropriate low cost sensor hardware can plug it in and start experiencing a natural way of hands free map browsing. We have deployed our framework in a hospital that treats Hemiplegic patients. Based on the feedback obtained, the developed platform shows a huge potential for use in hospitals that provide physiotherapy services as well as at patients' home as an assistive therapeutic service.

Recently, the use of non-invasive ways to track joint motion in the human body has drawn significant attention in the therapy domain. One of the reasons for this popularity is due to availability of economically priced 3D motion sensors. In this paper, we present a web-based 3D interactive serious game interface that uses non-invasive methods to recognize the movements of the body. Motion data of a subject is collected through two motion sensors, a Kinect and a LEAP, in a non-invasive manner. Joint motion along with inverse kinematic joint information is displayed in 3D environment in a live manner or recorded for offline replaying and data analysis. To facilitate the complex therapy authoring process, the system incorporates an authoring tool that allows a therapist to design a complex therapy in terms of primitive therapies and assign it to a patient. The subject as well as other members of the community of interest such as therapists, parents and caregivers can view the results at any time and can follow up with patient's progress.

This paper presents a multimedia e-health framework to conduct therapy sessions by collecting live therapeutic data of patients in a non-invasive way. Using our proposed framework, a therapist can model complex gestures by mapping them to a set of primitive actions and generate high-level therapies. Two inexpensive 3D motion tracking sensors, a Kinect and a Leap, are used to collect motion data of a given subject. Data can then be displayed on screen in a live manner or recorded for offline replaying and analysis. The system incorporates an intelligent authoring tool for therapy design, and produces live plots that show the quality of improvement metrics for a given patient.

In this paper we propose a multimedia environment that can capture kinematic data from live gestures of a child having Hemiplegia disability and generate live analytical results to be useful for decision making system of a therapist. The kinematic data is obtained from some clinically suggested therapy modules that are used to monitor quality of improvement of a disabled child, which includes exercises involving the affected joints and muscles. The proposed environment uses the 3D depth sensing Microsoft Kinect device to detect, recognize and track the movement of different key joints of the body and deduce kinematic data from these movements. The method is non-invasive as the child does not need to wear any external devices in the body. The proposed environment incorporates Second Life serious game environment where the live therapeutic movement of child, therapist and one's community of interest is synchronized between physical and virtual world. Finally, we share our preliminary test data, which is validated by the therapists from three different disability hospitals that treat children with Hemiplegia.

In this paper, we present an interactive multimedia environment that can be used to effectively complement the role of a therapist in the process of rehabilitation for disabled children. We use Microsoft Kinect 3D depth sensing camera with the online Second Life virtual world to record rehabilitation exercises performed by a physiotherapist or a disabled child. The exercise session can be played synchronously in Second Life. The physical activities of the users are synchronized with their virtual counterparts in the Second Life. The exercises can be recorded as well and made available for downloading to facilitate offline playback. A disabled child can follow the exercise at home in the absence of the therapist, since the system can provide visual guidance for performing the exercise in the right manner. Using the proposed system, parents at home can also assist the disabled child in performing therapy sessions in the absence of a therapist. Following the suggestions of therapists, the developed prototype can track several gestures of children who have mobility problems. Using a single Kinect device, we can capture high resolution joint movement of the body, without the need for any complicated hardware set up. The initial joint-based angular measurements show promising potential of our prototype to be deployed in real physiotherapy sessions.

Prior art in the expression of digital rights using XML is demonstrated to require a process of interpretation or parsing, as is characteristic with language processing, and in addition to be subject to the cross-domain scripting problem. The expression of digital rights using JSON, however, represents a novel approach, bypassing the need for language processing, and in addition, solving the cross-domain scripting problem.

In this paper, we present a web-based framework in which users can annotate tactile feeling to a YouTube video and experience the tactile feeling by wearing a tactile device while watching\annotating the video. The tactile device is embedded into a wearable garment, a haptic jacket and a haptic arm band in this paper, and has a rectangular layout like a video screen. Therefore, the tactile information is represented as a sequence of rectangular arrays with time stamps and stored in XML format. Each element of the array represents a tactile intensity, a magnitude of actuation. In the framework we provide a web-based authoring tool to add tactile feeling while navigating a video and setting tactile intensity in a time line. We also introduce a web browser in which a tactile device driver is embedded to activate the tactile device based on the annotated tactile information.

Body sensor networks (BSN) can capture physical phenomena from a human body, contextual information from the environment and high level events of a person. Associating contextual information and events with the captured raw sensory data can serve as a crucial input for many applications such as e- Health. For example, to accurately and timely monitor an elderly person with several physical disabilities while he is at home or outdoors, the context and event information along with raw sensory data needs to be reached to an e-Health service provider to assist in taking time critical decision. Such process includes receiving the sensory data, analyzing it to trigger necessary services such as sending an alert message to the family physician, hospital, emergency service, his immediate caregiver, family members, friends and so on. A BSN also allows members of one's community of interest, referred to as a social network, to query real-time sensory, contextual and event data. Combining the social network with BSN is envisioned to enhance the current state of the art in e-Health applications. In this paper, we propose a framework, called SenseFace, that can dynamically pass sensory data from one's BSN to his/her social network and vice versa. Finally, we illustrate the design and implementation of the framework.

An ant colony-based routing protocol is presented in this paper that is specifically designed to route many-to-one sensory data in a multi-hop Wireless Sensor Network (WSN). Because a many-to-one routing paradigm generates lots of traffic in a multi-hop WSN resulting in greater energy wastage, higher end-to-end delay and packet loss, the proposed routing protocol also comes with a lightweight congestion control mechanism, which is capable of handling both event-based and periodic upstream sensory data flow to the base station. The proposed protocol works in two-phases. During the first phase, the protocol uses ant-based intelligence to find and enforce the shortest path and in the second phase, when the actual many-to-one sensory data transmission takes place, the protocol combines the knowledge gained during the first phase with the congestion control mechanism to avoid packet loss and traffic while routing the sensory data. When compared with the related algorithms, the proposed algorithm shows promising results.

Navigation in 3D world has reached its pinnacle with the advent of several technologies like JAVA, XML, WEB SERVICES, VRML, X3D etc. A lot of efforts have been given to visualize and navigate in virtual mall, cities, digital libraries etc. Most of them visualize only static objects. We designed a Virtual Reality (VR) Tool, called Learning Object Repository Navigation and Authoring in Virtual environment (LORNAV) that extracts Learning Object Metadata (LOM) dynamically from repositories and creates 3D representation of these objects. The proposed tool provides several facilities such as personalized navigation allowing a user to view content that is of interest to him. It also helps users to create new aggregated Learning Objects (LOs) from existing ones in a 3D Environment to be presented in SMIL format.