Internet of Things, Wearable, and Cloud Computing - A Survey by Avinash Magdum
Abstract Cloud computing (both infrastructure and networking) [1] and [2], Internet of things [3], involving wearable technologies [4], are becoming hot research areas to improve human life in different application areas like healthcare monitoring [5], home / office automation [6], transportation, cyber security and privacy [7], etc. In this survey I have picked up 5 problems in the areas of cloud computing, monitoring, wearable devices and security, and suggested possible solutions to these problems based on the work done by different researchers. Pervasive Healthcare systems [8] allow the patients to be monitored from their home, reducing overall healthcare expenses. It involves development and deployment of health information management through mobile devices, and health sensors [9] mounted on patient’s body. The use of mobile phone as local processor of healthcare data introduces several challenges: data storage and management, interoperability and availability of heterogeneous resources, security and privacy, unified and ubiquitous access. Healthcare monitoring systems [10] are using wearable devices to monitor different health parameters for elderly patients, while keeping their normal routines. These devices help in lowering financial burden, as the patient doesn’t have to be admitted in hospitals for monitoring his health conditions. For such wearable devices to be used effectively, “ease of use” is the most important factor. It constitutes – no obstruction, attention free, automatic device association, just-in-time information, intuitive operation, and easy to understand. Wearable computing [11] is a computing style in which a user brings and uses his / her own computer wherever he / she goes. To call a computer as wearable computer, it must have following characteristics – hands-free, always on, supporting daily life, autonomy, simplicity, flexibility, and power saving. Wireless Body Area Network (WBAN) [12] is the core technology connecting NextGeneration’s compositional elements around the human to one network. Technologies of WBAN gradually are beginning to receive the attention, and including various issues that have the close relation with their technical development, and the most important being its security. Active spaces [13] are physical spaces populated with massive numbers of specialized embedded computing devices that increase human productivity by enabling users to interact seamlessly with the surrounding environment. Since active spaces with all their entities are perceived as computing systems with massively distributed components, any breach of security can easily cause serious disturbance and damage.
Introduction Since the introduction of smart phones, it has no more remained just a communication device for making and receiving calls. A large number of small and simple applications (mobile apps) have been developed and people are trying to use smart phones as computing device for such applications. As the computing power of the smart phones [14], have been continuously getting upgraded every year, they are able to perform more and more complex applications through smart phones. Still it is way behind the computing power of desktops and laptops. There are a lot of limitation on the computation of complex tasks [15] because of resource constraints [16] of smart phones, like CPU speed, memory, storage, battery power, screen size etc. There have been continuous research happening in universities and R&D centers of software and hardware companies to address these limitations so that people can execute very large and computation intensive application using their portable devices, like smart phones. At the same time wearable devices have been getting popular for collecting and processing sensor data to provide valuable information to the users [17]. Based on the analytics, many decisions for improving quality of life are being taken, especially in the area of medical and healthcare fields, personal security field and in the applications categorized into smart homes and offices. With the combination of mobile phones and wearable devices, a new range
of applications is coming in the main stream, especially in the field of Internet of Things. The mobile phone becomes the dashboard or controller of the application while wearable device is used to get the information from sensors embedded into it. It could also have a microcontroller to do some local processing and send the data (processed or unprocessed) to mobile phone for monitoring. It then could further be sent to cloud for more analytics. All the researches in these areas can be broadly divided into 6 categories 1. 2. 3. 4. 5. 6.
Infrastructure (Cloud Computing) Monitoring (Health or environmental data) Wearable Devices Security (Personal / Infrastructure / goods) Applications (Software Development) Infrastructure (Network)
Although these are broad areas, every research problem and solution would have some elements of each of these research areas as they are very much related to each other. The 5 problems that are considered here for the survey are – 1. Cloud Computing for Pervasive Healthcare [18]: The use of mobile phone as local processor of healthcare data introduces several challenges: data storage and management (e.g., physical storage issues, availability and maintenance), interoperability and availability of heterogeneous resources, security and privacy (e.g., permission control, data anonymity, etc.), unified and ubiquitous access. So to overcome these challenges we need to deploy cloud-computing system, which communicate with mobile phones to receive the data collected by sensor and provide alerts, dashboard of health data back to mobile. 2. Ease of use in Wearable Monitoring [19]: The “ease of use” factor has 6 connotations for medical device usage and monitoring at home as below, (a) No obstruction: It implies that a device does not hinder normal user activity. Requiring a user, for example, to sit at a table to collect data is undesirable (b) Attention free: It means that the device does not impose an unnecessary awareness on the part of the patient. This type of distraction is already a problem in today’s technology-saturated society. (c) Automatic (device) association: It means that manual configuration is not required during device setup. This feature is also known as ‘plug-and-play,’ which can also imply interoperability between devices produced by different manufacturers. (d) Just-in-time information: It means that the user can receive timely device information and assistance. (e) Intuitive operation: It refers to sensible and natural sequencing of device operations. (f) Easy to understand: It implies that, whenever a device offers a user interface element, the background of the user does not impede their ability to use that element or follow up on the information provided. 3. Ensuring all the characteristics of Wearable Computing in its design [20]: Wearable computing is a computing style in which a user brings and uses his / her own computer wherever he / she goes. To call a computer as wearable computer, it must have following characteristics – (a) Hands-free: users almost always browse information without hands because they wear the computer. (b) Always on: since the wearable computer is always powered while it is worn, users can use the computer whenever they want. (c) Supports daily life: Users utilize wearable computers to support activities in their daily life. (d) Autonomy: Services provided by wearable computers, behave autonomously in response to information of user activities, situations, and environments around the user. (e) Simplicity: Users should be able to easily create / modify services. (f) Flexibility: The system configuration can be changed flexibly by adding / deleting services and adding/swapping devices.
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(g) Power saving: The system has a mechanism to save battery life such as an automatic power on/off management of attached devices. Security for Wireless Body Area Networks [21]: Wireless Body Area Network (WBAN) is the core technology connecting Next-Generation’s compositional elements around the human to one network. Technologies of WBAN [22] gradually are beginning to receive the attention, and including various issues that have the close relation with their technical development. One of the most challenging issues of WBAN is security. The health monitoring based on WBAN collects data about vital body parameter from several parts of the body and uses them for a medical treatment. These important data should be kept thoroughly in security due to the wireless networking environment that is vulnerable to attackers and stringent constrains of power, memory and computation capability. Implementing Wearable Security Services [23]: Active spaces [24] are physical spaces populated with massive numbers of specialized embedded computing devices that increase human productivity by enabling users to interact seamlessly with the surrounding environment. Since active spaces with all their entities are perceived as computing systems with massively distributed components, any breach of security can easily cause serious disturbance and damage. Further, in any active space environment, there are numerous entities that interact with one another causing a huge flow of information. These entities may be mobile, roaming between different active spaces. An entity in an active space may refer to users, objects, computers, embedded devices, and sensors that populate an active space at a given instance of time. In such a dynamic and interactive setting, it is crucial to have security services provided at the core of the system. These services include the ability to authenticate users entering a space. Entities might also need to authenticate each other using some form of mutual authentication. In some situations it is desirable to enable secure exchange of information and enforce access control. So it is essential to have lightweight, mobile and convenient security services for users that enable them to prove their identities and interact securely with the smart devices that populate the surrounding environment.
Literature Survey Research in Cloud Computing – Infrastructure The main objective of the research in this category is to use cloud as infrastructure to partly transfer the storage and computing of smart phones and mobile devices in the cloud. Lot of mobile apps, are getting developed in the cloud, and they are using smart phone as interface to access these applications. In Cloud Computing, researchers are working on problems in many areas like integrating data collected from different mobile devices for analytics, building framework for participatory sensing for performance improvement, implementing MapReduce for health data, providing additional storage and computational resources to mobile devices, improving latency issues in mobile cloud computing, improving efficiency of cloud infrastructure with low networking overheads. J Burke et al. [25] proposed a new architecture for participatory sensing to enhance and systematize existing methodology by increasing the quantity, quality and credibility of communitygathered data. Authors introduce the concept of participatory sensing, which takes everyday mobile devices, such as cellular phones, to form interactive, participatory sensor networks that enable public and professional users to gather, analyze and share local knowledge. An initial architecture to enhance data credibility, quality, privacy and ‘shareability’ in such networks is described, as well as a campaign application model that encompasses participation at personal, social and urban scales. The authors realize the need of effective participatory sensing requires more than ubiquitous mobile phones and ‘mashable’ web services. They outline core network services and an application framework that to develop applications that simultaneously protect privacy and encourage participation of handset owners. These components, implemented on ubiquitous commodity
hardware and overlaying existing network infrastructure, would enable what has been called ‘citizen sensing’, sensor network data blogging, and allow the mobile devices and wireless infrastructure to act as resources for professionals and the public to gather vital information about the built and natural environment that was previously unobservable. Continuing the work of J Burke et al. in participatory sensing, Rajib Kumar Rana et al. [26] presented the design, implementation and performance evaluation of an end-to-end participatory urban noise mapping system called Ear-Phone. The problem they worked on was, Implementing and evaluating performance of an end-to-end participatory urban noise mapping system - Ear-Phone. A noise map facilitates monitoring of environmental noise pollution in urban areas. It can raise citizen awareness of noise pollution levels, and aid in the development of mitigation strategies to cope with the adverse effects. However, state-of-the-art techniques for rendering noise maps in urban areas are expensive and rarely updated (months or even years), as they rely on population and traffic models rather than on real data. Participatory urban sensing can be leveraged to create an open and inexpensive platform for rendering up-to-date noise maps. The authors were able to provide a feasible platform for assessing noise pollution and the resources used to this computation were quite reasonable. They identified the gap in their works as the need to incorporate context-awareness in the system such that Ear-Phone only samples ambient noise when the phone is in the right environment. The problem of context awareness was partially addressed by Georgios Mylonas et al. [27], by integrating sensor data from hundreds of smart phones in the context of IoT experimentation and combine data from IoT infrastructre. They deployed a number of infrastructures in the city of Santander for Future Internet experimentation purposes, providing researchers and industry with access to large-scale IoT resources. It uses smartphones provided by volunteers to increase sensing resources and ubiquity. The system lets developers write code for Android and automatically deploy their experiments to Android devices, alongside the SmartSantander platform. The results show that researchers can avoid developing for specialized embedded platforms and instead use popular development tools for smartphones. The down side of this approach is that using such experimentation procedures could lead to an abundance of additional data, which further complicate the situation of making sense out of this data; thus, researchers might employ additional filtering and data mining efforts to produce meaningful information. Jigarkumar Contractor and Shan Lin [28] explored the opportunities to design and develop personal health care applications with cloud services. The objective was to support body network applications for secured storage, huge data and user friendly interface like speech to text service and optical to text service. The proposed a system architecture consisting of three components: a frontend body area network that collects personal health data, a mobile app that aggregates data collected and accesses cloud, and the cloud that provides data processing, storage, and other novel services. This approach has many advantages like (a) Cloud provides secure storage for large amount of medical data, accessible to caregivers; (b) Cloud services can help organize users into groups based on their interests, location etc, and facilitate fast and efficient communications within groups; (c) Cloud services provide user-friendly interfaces, such as speech to text service and optical to text service. The limitations of this approach are that it does not address exclusive security and privacy features. Also in this approach the search capabilities could be added for domain specific doctors Charalampos Doukas, and Ilias Maglogiannis [29] worked on collecting sensor data and storing in cloud, and generating alerts. They tried to address challenges of data storage and management, interoperability and availability of heterogeneous resources, unified and ubiquitous access issues in mobile pervasive healthcare technologies. They thought cloud computing could be able to address above issues. They developed a wearable – textile platform based on open hardware and software that collects motion and heartbeat data and stores them wirelessly on an open Cloud infrastructure for monitoring and further processing. This system is a dedicated solution for managing patient-related data on the cloud and that utilizes both open hardware and open software
resources for developing the hardware and software parts of the platform. It allows direct communication of the sensor devices with the Cloud application due to the lightweight API used, while it is highly scalable in the context of data stored, users and sensors supported. The open issues in this approach are, security of privacy of data and the energy efficiency of the textile sensors and microcontroller platform, for extending the system autonomy. Giancarlo Fortino [30] proposed “BodyCloud”, to support cross-disciplinary applications and specialized processing tasks by integrating BSN with sensing application services and Cloud. It is a system architecture based on Cloud Computing for the management and monitoring of body sensor data streams. It incorporates key concepts such as scalability and flexibility of resources, sensor heterogeneity, and the dynamic deployment and management of user and community applications. The system provides a platform to build and deploy applications based on body sensor networks. This work is still work in progress as for as implementation is concerned. Future work will include the development of test cases which can demonstrate the range of applications which are enabled by the system. Giancarlo Fortino et al. [31] extended the BodyCloud by , to provide programming abstractions, like, modality, workflow and view, to support the rapid and effective development of community BAN applications. They proposed an approach to design and deploy large-scale applications for body area networks (BANs). The approach is based on BodyCloud, which is a Cloudbased multi-tier application-level architecture. BodyCloud integrates a Cloud computing platform and the SPINE BAN middleware. The approach allows for rapid prototyping of BAN applications involving the management of large networks or communities of individuals. It supports the development of BAN services through the integration of (i) a flexible BAN framework, namely Android SPINE, which allows for the implementation of the on-body sensor-based application, and (ii) a SaaS-level Cloud computing framework supported by Google App Engine. The authors identify the future scope of work as (a) Extending this approach into a full-fledged development methodology supported by a CASE tool and (b) Integrating established workflow-based data mining / analysis tools into BodyCloud to provide access to a wide range of already developed algorithms for data analysis and mining. Xiping Hu et al. [32] worked on service centered contextualized vehicular (SCCV) cloud to provide high efficiently with low networking overhead on mobile devices in real-world transportation scenarios. It handles availability, connectivity, resulting in service failure. The authors propose a cloud platform to facilitate the deployment and delivery of cloud-based mobile applications over vehicular networks. SCCV cloud employs a multi-tier architecture that consists of the network, mobile device, and cloud tiers. Based on this architecture, a seamless solution for delivery of personalized mobile applications to vehicular users in an intelligent and reliable manner could be built. SCCV cloud enables the deployment and delivery of personalized mobile applications for vehicular users in an intelligent and reliable manner. Authors have presented a novel prototype mobile application developed and deployed on SCCV cloud to demonstrate its feasibility and usefulness in transportation scenarios. The results of practical experiments are presented to demonstrate the effectiveness and feasibility of SCCV cloud for real-world deployment. The limitations of this approach are that any cloud service unavailability would result in messages getting lost. Steven Bohez et al. [33] in their research work, tried to improve resource limitations of mobile devices and also reducing latency of mobile cloud computing. The authors propose “AIOLOS”, an integrated middleware platform supporting transparent distributed deployment and scaling among mobile devices and cloud infrastructures. AIOLOS is a middleware platform for mobile cloud computing. It enables easy development of component-based applications, for which components can be transparently deployed and scaled on back-end cloud datacenters, on edge-cloud nodes or locally on a mobile device. By managing applications on a component level, AIOLOS is able to (a) offload compute-intensive parts of the application from the mobile device to the cloud, (b) monitor application components and scale out in the cloud on a fine grained component level and (c) quickly
reprovision cloud Virtual Machines (VMs) by stopping, starting or migrating components. The possibilities of AIOLOS were illustrated using the Mercator use case, an application for scalable, online 3D mapping. The experiment showed that a simple scaling policy is able to use the benefits of both infrastructure level scaling as well as fast reprovisioning of components to handle the demands of Mercator. The future scope could be to expand support for other operating systems and IaaSproviders as well as develop more advanced (distributed) scaling algorithms that improve resource usage and stability. Emiliano Miluzzo [34] proposes a new model, Cloud 2.0 – a thin client approach, with most of the data analysis, processing, and storage largely delegated to the distant cloud. Main objective of proposing this model is to enhancing storage capacity of mobile devices and also offloading of high computing needs to the cloud. Cloud 2.0 augments the traditional cloud-computing framework, with a more flexible and resource-aware design and revisits the thin-client approach that shapes many services that run on our personal devices. Such devices are often underutilized in favor of more cloud centric architectures. In reality, they are perfectly capable of embracing some of the compute and storage jobs that currently require constant cloud involvement, either on their own or via local interdevice cooperation. This approach mitigates some of the shortcomings of an exclusively cloud-centric model. Local processing can reduce latency and boost quality of service (QoS) guarantees for many applications. Edge device participation in personal data storage can increase data privacy, allow faster data storage and retrieval, and reduce the monetary cost for storing data in the cloud by reducing traffic in the backbone network. Future work is needed to design the compute, storage, and data dissemination modules to take into account the complexity and dynamics of the underlying heterogeneous platform ecosystem. Also there is need to find ways to deal with some devices’ mobility and battery limitations while still guaranteeing support for an adequate user experience. Basit Qureshi [35] proposes a framework for cloud-based architecture of a digital ecosystem for storing, sharing and predictive analysis of healthcare data. The main objective is to provide analytics on standard health data, using MapReduce (big data). The proposed framework provides a cost effective digital ecosystem for collaboration, sharing and integration of Electronic Health Record systems by leveraging the benefits of cloud computing technologies. The author presents the architecture of the system combining Cloud computing technologies for centralized storing and sharing of EHR, Healthcare Predictive Analytics Kernel for processing various Machine Learning and Data Mining Algorithms for predictive analytics, and Social Media Interface for current trending topics. It also addresses standardization of EHR, PHR data acquired from Personal Health Devices, the security and the privacy of these records. The future work would focus on extension of proof-ofconcept to development of the components for the entire system. Research in Monitoring In this category, the main objective of the research is to use sensors in variety of categories like health, environment, tracking and analyze the data over a period of time to help health diagnostics and patient monitoring, take actions based on certain thresholds of environmental parameters like temperature, pressure etc. The analytics is performed typically in the cloud, and alerts are sent based on the analytics for appropriate personnel to take some actions. Under monitoring category, the areas covered by researchers are, ambient assisted living, ambient intelligence, monitoring critical health measurements, environment parameters monitoring like temperature, humidity, pollution, pressure etc. The monitoring could be done through sensors fitted on the body, for health monitoring, in the buildings, field for environmental monitoring. These sensors send data to mobile devices, aggregators, which in turn send it to cloud for analytics. Based on the analytics, alerts and other useful information is sent to mobile devices, or display monitors for patients, doctors, or other persons to take some action. Bostjan Kaluza et al. [36] worked in building latest state-of-the-art approaches, to benchmark newly developed techniques, and to advance the frontiers in ambient intelligence. The
authors propose a centralized collection of resources related to design and evaluation of contextaware systems. The main idea is to establish an online repository of datasets accompanied with the task, result and applied approach. The fundamental underpinnings in ambient intelligence applications is an effective and reliable context-aware system able to recognize and understand activities performed by a human, and context in which it happened. The main challenges in such systems are, (a) transferability, i.e., a specific implementation is tightly interrelated with a selected algorithm, available sensors, and a scenario/environment where they are employed; and (ii) comparability, i.e., there is no established benchmark problem that would enable a direct comparison of the developed context-aware systems. The authors tried to address these challenges by establishing an online repository of datasets accompanied with the task, result and applied approach. The contributors will provide the dataset with short description of the data, task and results, relevant paper, and link to resources such as implementation of the approach, preprocessing tools, and filtering. The open issues for this approach is that the automatic updating dataset is not implemented and further study needs to be continued Paolo Barsocchi et al. [37] carried similar research in the area of ambient assisted living solutions to evaluate such AAL Systems through Competitive Benchmarking, for localization and tracking. AAL system involves activities such as sensing, acting, and reasoning, which are generally implemented through various software components, such as context managers, profile and service managers, reasoners, user interfaces, and security managers. These components, are incorporated into devices spread throughout the environment, such as sensors and actuators, gateways, appliances, communication devices, and smartphones. Hence recognized evaluation methods are essential for comparing different AAL solutions. So the authors organized an annual international competition, called “Evaluating AAL Systems through Competitive Benchmarking (EvALL)”. It aimed to advance the state of the art in evaluating and comparing AAL platforms and architectures. They used the metrics like accuracy, a reference system that is transparent, repeatable and realistic, availability, installation complexity, user acceptance, and integrability in AAL systems. They covered localization, tracking and activity recognition. The future scope is to extend these activities to teleoperated robots, user interaction and interfaces, and reasoning. Hristijan Gjoreski et al. [38] continued research work by Paolo Barsocchi et al. in evaluating activity recognition system for elderly people based on recognition performance and practical utility. Agreement on a standard set of evaluation methods would help ensure that AAL approaches are valid, usable, and support real-life application development. However, developing such standard methods is difficult because AAL solutions perform diverse functions and use various sensors and other hardware. Authors present an initiative to evaluate a central function of many AAL solutions: activity recognition (AR), which is key to understanding the user’s situation and context and thus essential for creating real-life usability. This AR systems evaluation occurs in an annual competition called Evaluating AAL Systems through Competitive Benchmarking−Activity Recognition. To improve the evaluation itself, authors propose further refining the evaluation criteria, mainly the recognition delay and recognition accuracy. Even if it is important to recognize activities in a timely manner, a few seconds are usually not that important. Also a more thorough analysis of the recognition accuracy is needed, such as including per-activity analysis, as some activities are more important than others (falling versus standing). Authors also plan to improve their questionnaires. Hugo Plácido da Silva et al. [39] worked in developing interactive software and hardware system capable of sensing, processing, reacting, and interfacing the digital and analog sensor data for health monitoring. Physiological computing, poses many challenges related to the more complicated requirements of physiological data acquisition (for example, the need for higher signal-to-noise ratios or greater accuracy in the sampling rate). To address these challenges, authors present a novel development platform especially designed to consider the requirements of physiological data acquisition. This platform makes bio-signals readily available to anyone interested in exploring the field and provides a framework that we hope can drive a new wave of research and projects within the global research and engineering community. They created BITalino, a highly versatile toolkit
designed to make bio-signals available for anyone interested in innovative and creative engineering in a physiological computing framework. They also designed the analog front-end to enable BITalino to be a broad-spectrum development platform, for experimentation and rapid prototyping based on bio-signals. It facilitated to monitor ECG, EMG, EDA, ACC, LUX etc. The future work could be focusing on adding I2C support for the firmware and APIs, ultimately letting BITalino interface with accessories over a digital bus. It also involves working on accessories that can enable users to collect data from other physiological sources. It also need focus on making the MCU reprogrammable and on building up the suite of available software tools, with a special emphasis on signal processing and interpretation components. Jochen Meyer, Susanne Bol [40] propose an approach to monitor certain health parameters, using smart devices connected to an Internet service, and target primarily a lay consumer. Smart health devices fulfill three properties: they monitor certain health Parameters; are connected to an Internet service; and target primarily a lay consumer rather than the medical expert. Traditional home healthcare devices, such as blood pressure monitors, focus on chronically sick or at-risk individuals, these new health devices target people seeking an active and healthy lifestyle. These devices broadly classified into activity trackers, home care and health monitoring, and sleep monitoring. Some of the limitations of such smart devices are low precision data, and not to comprehensive analytics. Also privacy and security issues need to be addressed. Md. Golam Rabiul Alam et al. [41] proposes a suicide risk-scouting prototype by predicting mental states in cloud environment. In this system, patients’ real-time vital diseases symptoms are collected through wireless body area network (WBAN) and then analyzed the collected data in healthcare cloud platform with patient’s historical repository of diseases, habits, rehabilitations and genetics. Authors applied Viterbi, a machine-learning algorithm, to generate the most probable mental state sequence to monitor suicide risk of mentally disordered patients. They also validated the system by deploying this model on mental patients dataset. It supports to prevent some undesirable and unwanted live loosing. Early detection and monitoring of abnormal mental states can reduce the suicidal risk a significant amount. As EDA, EEG and BVP sensors are non-invasive biosensor, authors could easily setup and measure mental states of mental disordered patients. The main challenges and limitations to this approach is inadequacy of suitably structured and application oriented standard dataset of suicidal perpetrators of all nations. The enhanced model may applicable to reconnaissance of homicide risk of mental disordered patients. Research in Wearable Devices Wearable devices are getting very popular for personal health and fitness monitoring. These wearable devices could be worn on wrist, neck, head, legs etc. so that they can collect data about specific parameters and track different activities. They contain health sensors and microcontroller to process the data and provide useful information to the user. It could also sync the data with the cloud for complex processing. These devices are also sewed as part of cloths so that they don’t get noticed and the user does not even feel any obstruction in performing day-to-day activities. The areas covered by researches in wearable devices category are mobile health, sports activities tracking, human activity tracking in work campus, expressing using point lights, non-verbal communication, sedentary behavior, energy harvesting, face-to-face interaction. As the “Internet of Things” is getting penetrated in every area, wearable devices are the key elements in making it successful. Shrirang Mare et al. [42] propose to address issues of security in mobile health (mHealth).. Using mobile health (mHealth) sensing, which uses medical sensors to collect data about the patients, and mobile phones to act as a gateway between sensors and electronic health record systems, caregivers can continuously monitor the patients and deliver better care. Although some work on mHealth sensing has addressed security, achieving strong security and privacy for low power sensors remains a challenge. They propose 3 prong approach – (a) a set of two techniques that can be
applied to existing wireless protocols to make them energy efficient without compromising their security or privacy properties. The two techniques are: adaptive security, which dynamically modifies packet overhead; and MAC striping, which makes forgery difficult even for small-sized MACs. (b) Apply these techniques to an existing wireless protocol, and demonstrate a prototype on a Chronos wrist device. (c) Provide security, privacy, and energy analysis of these techniques. The authors demonstrate through the experiments that it is feasible to implement and use Adapt-lite on low power devices. Using Adaptlite makes existing privacy-preserving wireless protocols energy-efficient and hence suitable for lowpower sensors. It was also shown that the Adapt-lite techniques preserve the security and privacy properties of these protocols. The limitations to this approach is that these techniques can be applied to the protocols where only the receiver and sender share the knowledge of a unique value associated with each packet, and this unique value should not be transmitted in the packet. Jacob Sorber et al. [43] continued the research by Shrirang Mare et al. to buid a trusted wrist-worn platform called Amulet to give protection against evil, danger, or disease”. The authors propose Amulet, an mHealth architecture that provides strong security and privacy guarantees while remaining easy to use, and outline the research and engineering challenges required to realize the Amulet vision. To enable trustworthy patient-centric mHealth one need to ensure several important properties. The system must provide data confidentiality, data integrity, data authenticity, data availability and command authenticity and integrity. The system should also protect patient anonymity. Such systems must also support interoperability and modularity, and ease of use. Authors express research vision for a trusted wrist-worn platform called Amulet. An amulet is “an ornament or small piece of jewelry thought to give protection against evil, danger, or disease”. Unlike prior approaches, Amulet is designed to enable continuous sensing and actuation, requiring a wireless gateway only for occasional connectivity to back-end servers and other off-body network resources. Amulet provides a path for trusted input from and output to its user, and a secure execution platform for small “apps” that monitor the Patient’s health or manage treatment. The limitations of Amulet are that with less processing power than a mobile phone an Amulet cannot accomplish some of the things that a phone could. Protocols for energy-efficient privacy-preserving wireless communication and secure key management are heavyweight and need to be redesigned. Andres Molina-Markham et al. [44] continued the research of Jacob Sorber et al. and propose a secure system architecture for a lowpower bracelet that can run multiple applications and manage access to shared resources in a body-area mHealth network. The user can install a personalized mix of third-party applications to support monitoring of multiple medical conditions or wellness goals, with strong security safeguards. Amulet guarantees security through a robust authorization mechanism that manages resources, through application isolation, and through audit logging. Amulet is designed for use on small, light and ultralow power wearable devices that must run on a single charge for several weeks. Preliminary result show that the approach is feasible – multiple useful mHealth applications can run on low-power hardware for several weeks on a single battery charge, and the application isolation mechanism that we propose does not interfere with the implementation of such mHealth applications. The limitations of this approach are comparatively low availability, and resource shortage of Body Area Network devices. Terrell R. Bennett et al. [45] presented the MotionSynthesis Toolset (MoST) to alleviate some of the difficulties in data collection and algorithm development in Body Sensor Networks (BSNs). This toolset allows researchers to generate a sequence of movements (i.e. a diary), synthesize a data stream using real sensor data, visualize, and validate the sequence of movements and data with video and waveforms. MoST will save time and provide an opportunity for those with and without the requisite hardware to synthesize their own experiments and trials. Using this toolset allows for a variety of scenarios to be developed with authentic sensor data and straightforward validation with a variety of movements and subjects which will hasten algorithm development. Generating relevant data more quickly will allow for quicker initial analysis and faster improvement of new algorithms. Being able to select specific sensors positions and modalities will allow system designers to make
trade-offs without building multiple systems. This will speed up the validation and refinement process. The limitations identified are range of monitoring is very limited, and long term data collection is challenging because of storage limitations Chris Harrison et al. [46] build a richer set of vocabulary of lighting expression in popular use. Small point lights (e.g., LEDs) are used as indicators in a wide variety of devices today, from digital watches and toasters, to washing machines and desktop computers. Although exceedingly simple in their output – varying light intensity over time – their design space can be rich. Unfortunately, a survey of contemporary uses revealed that the vocabulary of lighting expression in popular use today is small, fairly unimaginative, and generally ambiguous in meaning. In this paper, authors work through a structured design process that points the way towards a much richer set of expressive forms and more effective communication for this very simple medium. Although the design and evaluation process presented here provides recommendations that are generally useful, the evaluations are clearly far from exhaustive in the factors that could be considered. In particular, there are many contextual factors that are particular to specific devices and circumstances that have not been brought into consideration. The POC was done for Mobile devices but in future work, it could be extended to other wearable devices and to en- compass lights with varying color, size, directionality, diffuseness, and shape. Kent Lyons et al. [47] propose BitWear, a platform for prototyping small, wireless, interactive devices. BitWear incorporates hardware, wireless connectivity, and a cloud component to enable collections of connected devices. Authors use this platform to create, explore, and experiment with a multitude of wearable and deployable physical forms and interactions. The main objective was to create a device that pushed towards the limits of small size yet retained enough functionality to be open and useful for many goals. Authors started with some of the simplest modes of interaction – pressing a button (input) and seeing an LED (output) – in a variety of physical forms. Research community, often turns to simulated or wired prototypes. Researcher could now shift into the realm where these devices can be made using standard electronics capabilities. By building, deploying, and experimenting with such devices, author intend to inform future iterations of research in this space. The identified limitations are - Latency is higher, and need to validate mass deployment. Longhan Xie et al. [48] propose to develop wearable harvesters that harness kinetic energy from human motion were developed to provide sustainable power levels for wearable computing systems. Th backpack-based harvester and insole-like harvester aim to continuously power wearable electronics, such as GPS units, via USB power sockets. Wearable devices work on the human body, which has rich, kinetic energy generation, during daily activities. If part of such mechanical energy is converted into electricity to recharge the battery, a wearable device could greatly extend its battery duration or even last indefinitely. Authors investigate energy harvesting from different types of human motion and its application in wearable electronics. In one approach, backpack-based harvester employs the external loads in the backpack to harness human trunk motion. In another approach, they embed an insole-like harvester in a shoe to scavenge kinetic energy from footsteps. Both of these harvesters improve upon earlier work in this area to efficiently harness human kinetic energy and generate considerable electricity for wearable electronics. The future scope for this work is to improve the efficiency and power output in energy harvesting and reduce their size and mass for human comfort. Mingui Sun et al. [49] present overview of their research on a new wearable computer called eButton. It is supposed to monitor health parameters like steps, calories, body motion etc. eButton has a small size and a light weight, resembling a decorative object rather than an electronic gadget. The face of the device could be designed according to individual’s age, gender, and preference. Despite its simple and personalized appearance, eButton is a complex miniature computer with a powerful CPU and an array of sensors for data collection. This paper gives an overview of the design concepts and describes a number of applications that we have explored recently, including diet and physical activity evaluation, sedentary behavior evaluation, and assistance to the elderly and blind. The limitations identified are a need to address Privacy breach. Also processing of big data is
challenging and need to be considered in future designs. Rain Ashford [50], in his research examine the possibility that wearable technology can be used to create new forms of non-verbal communication via physiological data, in particular how data can be drawn from the body and then amplified and broadcast to those interacting with the wearer. He introduces two new terms as sub-sections of the field of wearable technology called ‘responsive wearables’ and ‘emotive wearables’. He studies how responsive and emotive wearable technology is gaining the potential to evolve into new methods of investigating and amplifying our bodies through the use of various physiological sensing techniques. The research also examines ethical questions around the huge amounts of personal data that will be generated from wearables. The main concern of use of wearable technology is about privacy of personal data. Impact on social and cultural aspect of such interactions needs to be studied Research in the area of Security Advent in wearable devices, personal and environmental monitoring, cloud computing has created new types of challenges in security and privacy area. As the personal data is collected by many devices and sent to cloud for analysis, it has exposed threats of data security and privacy violation as the data can be accessed by un-authorized entities. So lot of research is getting carried out in the security and privacy area to ensure the data is encrypted, secured, and only authorized people could access it. The main areas covered are creating and implementing policies for privacy of health data, preserving user privacy, data security, network security in telecommunication, transportation and healthcare systems. As the “Internet of Things” is a new area and there is lot of diversity in the standards followed by different organizations, the protocols used for data communication. Ashfaq H. Farooqi et al. [51] have done research in the area of security requirements of cyber physical community. Cyber physical system is an emerging networking paradigm. It is applied to telecommunication, transportation, and healthcare for automation and bringing new technologies. This system is based on physical devices that communicate with each other and outside world through wired or wireless medium. Hence, it is vulnerable to various types of new and acquired threats. The authors propose the security requirements for a cyber physical community system using a case study of Vehicle cloud. Cars are one of the essential parts of our daily life. People want to utilize their travelling time and like to have something that provide them safety and some entertainment as well. V-Cloud is a novel idea that uses the benefits of newly emerging cyber physical systems and the concept of cloud computing to provide better services to the community. Authors propose vehicle cloud architecture is that assures safety of the vehicles from accidents on the road, determines vehicle or drivers condition using sensors and supply better assistance in case of abnormality, provide possible healthcare services to the passengers during travelling, discovers shortest reliable routes to the destination and provide entertainment. The further study is required to take care of new and advanced external and internal attacks Nigel Davies et al. [52] propose an approach for designing privacy-aware display personalization that avoids creation of profiles within the display infrastructure. There is increasing interest in using digital signage to deliver highly personalized content. However, display personalization presents a number of architectural design challenges – in particular, how best to provide personalization without unduly compromising viewers’ privacy. Authors present details of a system that integrates future pervasive display networks and mobile devices to support display personalization. The architecture includes mobile, display and cloud-based elements and provides comprehensive personalization features while preventing the creation of user profiles within the display infrastructure, thus helping to preserve users’ privacy. As future work, there is a need to integrate with interactive applications particularly where interaction occurs using technologies such as touch or gesture recognition on the display itself.
Greig Paul, James Irvine [53] proposed a hypothetical privacy-preserving health- monitoring platform. With waterproof monitors intended to be worn 24 hours per day, and companion smartphone applications able to offer analysis and sharing of activity data, authors investigate and compare the privacy policies of four services, and the extent to which these services protect user privacy, as these services do not fall within the scope of existing legislation regarding the privacy of health data. Authors present a set of criteria, which would preserve user privacy, and avoid the concerns identified within the policies of the services investigated. As a result of the analysis of the privacy policies of the services investigated, a hypothetical privacy-preserving health monitoring platform was described and specified, attempting to offer a very high standard of user privacy, and demonstrate that considerable privacy improvements would be possible and practical, to address some of the concerns identified in the policies of existing services. The gap identified is that, the platform being hypothetical, needs to be implemented in commercial health monitoring systems, and one can create a compliance to ensure these privacy concerns are addresses. Research in Application Specific applications are being developed for performing specific functions in Internet of Things. These are mostly mobile applications deployed in cloud to perform certain tasks like finger print matching, data analytics in specific areas like healthcare, security of premises (home, offices). The research in application area is being done in indoor positioning, proximity detection, health sensor data analytics, mobile crowd sensing, biometric recognitions like face, fingerprint, iris, etc. The main challenge is these applications need huge computing power, so they mostly deploy cloud computing, distributed computing. Martin Kurze, and Axel Roselius [54] develop an application for face recognition in a wearable display to know the details of people sitting in a big conference / sitting area. The system executes face detection and face tracking locally on the smart device and then links to the service running in the cloud to perform the actual face recognition based on the user's personal contact list. Recognized and identified persons are then displayed with names and latest social network activities. The approach is directed towards an AR ecosystem for mobile use. Therefore, open interfaces on the device are provided as well as to the service backend. Authors intend to take today's location based AR systems one step further towards computer vision based AR to really fit the needs of today's and tomorrow's users. The authors presented innovative architecture and runtime environment for Augmented Reality apps on mobile phones and other devices and a sample application: face recognition in a wearable display. It currently supports Android-OS-based devices and certain wearable displays. The future work could be to extend the support to other mobile platforms and to also other applications. Milos Stojmenovic [55] proposes developing a biometric application on mobile cloud computing, to provide real time performance. Author addresses concerns of energy consumption at the user side, along with computing and storage limitations. It consists of transmission, computation (CPU, memory), and sensing (camera, GPS) costs. Transmission challenges include unstable wireless quality, heterogeneous interfaces, and different traffic demands for real-time and delay-tolerant applications. Solutions include sleeping during idle time, predicting signal strength & traffic pattern to avoid rush hour, and sending in a burst by traffic shaping. Computation optimizations are achieved mainly by task out-sourcing to the cloud and CPU optimization. High computation and low transfer tasks are ideal candidates for outsourcing. Some tasks are always outsourced, some never, and for some it depends on bandwidth and other factors. Some of the limitations are, high energy consumption of sensors, and limitations on computation and storage on user side. Nenad Stojanovic et al. [56] propose developing a mobile application for real time complex event processing using mobile-driven distributed CEP infrastructure. Authors provide a novel infrastructure for real-time, big data driven application with distributed complex event processing that is only partially executed on the mobile devices. This tutorial paper presents a foundation for an
efficient development of such mobile applications, by introducing a mobile-driven distributed CEP infrastructure. They extended SANs to model the different goals and correlated reactions, which enable the context-aware, dynamic pattern adaptation. The pattern distribution mechanism enables optimal collaboration between the CEP engines by taking into account the available resources. The approach has been validated in a business use case involving patients suffering from hypertension. The limitations of this approach are that each topology cannot be dynamically reconfigured, when the topology is running. There has to be at least a slight downtime when the topology is reconfigured and redeployed Daisuke Taniuchi and Takuya Maekawa [57] propose a new method for automatically updating a Wi-Fi indoor positioning model on a cloud server by employing uploaded sensor data obtained from the smartphone sensors of a specific user who spends a lot of time in a given environment. Authors attempt to track the user with pedestrian dead reckoning techniques, and at the same time obtain Wi-Fi scan data from a mobile device possessed by the user. With the scan data and the estimated coordinates uploaded to a cloud server, it can automatically create a pair consisting of a scan and its corresponding indoor coordinates during the user’s daily life and update an indoor positioning model on the server by using the information. The experimental result confirmed that our method could cope with the following changes in the radio wave condition: (1) gradual changes in signal strengths from APs, (2) removal of Wi-Fi APs, and (3) sudden changes in signal strengths from APs. As future work, they plan to fuse other mobile sensing technologies such as magnetic field sensing, ambient sound sensing, and infrared positioning in addition to the Bluetooth beaconing in order to increase the availability of our framework Research in Cloud Computing - Networks Cloud computing involves grid of computers connected to each other, many times located in different geographical area, efficient and fast networking becomes most important in implementing cloud infrastructure. In the category, research is being carried out in telecommunication and transportation, of healthcare data, reliability of data transmission, personal security. Pablo Punal Pereira et al. [58] propose a holistic network architecture consisting of heterogeneous devices. The number of small embedded devices connected to the Internet is increasing. This growth is mostly due to the large number of Internet of Things (IoT) deployments, with applications such as: industrial monitoring, home automation etc. One common aspect with the majority of application areas is the lack of mobility. Most IoT devices are stationary and often use IEEE 802.15.4/6LoWPAN solutions. When a high level of mobility is required, the use of IEEE 802.15.4 is not possible without adding additional hardware for the user to carry. The proposed architecture is composed of Embedded Internet Systems (EIS) and uses standard communication protocols. One important feature is the use of the Service-oriented architecture (SOA) paradigm. The use of SOA, by utilization of the CoAP protocol and standard services, enables the proposed architecture to exchange sensor and actuator data with an Internet-based cloud as well as a user’s local cloud consisting of sensor IoT devices, smart phones and laptops. Results from experiments and real-world tests show that the proposed architecture can support sample rates of up to several kHz while enabling sensor data to be transmitted to SOA services in real time. The gaps are identified to address reliable stream of sensor data for monitoring application and security over bluetooth. Mohammad M et al. [59] work on management of vast amount of sensor data generated by pervasive healthcare applications with proper security. The integration of IoTs and cloud computing in healthcare domain have imposed several technical challenges like reliable transmission of vital sign data to cloud, dynamic resource allocation to facilitate seamless access and processing of IoT data, and effective data mining techniques. Authors propose a framework to address above challenging issues. They also discuss the possible solutions to tackle some of these challenges in smart city environment. The cloud-assisted IoT framework in pervasive healthcare domain is
scalable, cost effective and supports interoperability and lightweight access. The future research work would be to investigate the framework with real life healthcare data in a larger healthcare setting to justify its scalability, ease of use and cost effectiveness. Thomas Zachariah et al. [60] propose IoT gateway on smartphones as a software service that provides universal and ubiquitous Internet access to BLE-connected IoT devices. The current stateof-the-art requires application-layer gateways both in software and hardware that provide application specific connectivity to IoT devices. It is hard to imagine our current approach to IoT connectivity scaling to support the IoT vision. The IoT gateway problem exists in part because today’s gateways conflate network connectivity, in-network processing, and user interface functions. The authors propose an architecture that leverages the increasingly ubiquitous presence of Bluetooth Low Energy radios to connect IoT peripherals to the Internet. A worldwide deployment of IoT gateways could revolutionize application-agnostic connectivity, thus breaking free from the stove-piped architectures now taking hold. This approach utilizes the smartphone as both an IPv6 router for less resource-constrained endpoints, allowing IoT devices to communicate as IP-connected hosts and as a BLE proxy, relaying profile data from the IoT device to the cloud. The limitation of this approach is that It is siloed and requires massive deployment of new hardware ecosystem. Scalability and reliability needs to be validated.
Research Problems and Solutions Cloud Computing for Pervasive Healthcare System Implementing Cloud Computing System would help to address the challenges of data storage and management. Cloud Computing provides the facility to access shared resources and common infrastructure in a ubiquitous and transparent way, offering services on-demand, over the network, and performing operations that meet changing needs. Also, the advance of machine-to-machine communication (M2M) [61] enables the direct interaction of pervasive healthcare sensors with the Internet and by extension with Cloud Computing systems. This communication with the Internet has been recently introduced as the ‘Internet of Things’ (IoT). IoT utilizes specific protocols for interdevice and Internet communication, provides real time access to device information and allows the remote management of the devices. It also features web applications that are scalable, accessible globally and provide communication interfaces to external applications. In the case of healthcare monitoring the IoT has already been proposed as an infrastructure for medical sensor communication [62]. In this context a Cloud-based system that manages sensor data, is proposed. Wearable – textile sensors collect bio-signals from the user (like heart rate, ECG, temperature), motion data through accelerometers. Depending on the wireless technology used, the data can be forwarded to a mobile phone or directly to the Cloud infrastructure utilizing established techniques for IoT communication. Appropriate interfaces enable the data dissemination to external applications (like medical record systems or emergency detection platforms) and a web-based application provides the essential data real-time monitoring and management. This IoT based architecture [63] could be used for acquiring and managing sensor data on the Cloud.
The main components of this architecture are: • The wearable and mobile sensors that acquire patient bio-signals, motion and contextual information. • The sensor gateway that collects all the signals from the sensors and forwards them to the Internet. It can be a mobile phone or a microcontroller platform capable of communicating with the Internet. It also forwards information about the status of the sensors like proper operation, power source levels, etc. • The communication APIs are provided by the Cloud platform. These APIs are lightweight interfaces, like REST, [64] that can be used by the sensor gateways for sending sensor data and retrieving information. The API can also be utilized by external applications for data processing, alert management, etc. • The managing application consists of a web-based application getting updated in real time and provides visualization of the sensor data and important information about the patient’s context like location, activity status, etc. • The Cloud infrastructure hosts the interfaces and the managing application. It provides the essential resources like CPU, storage and application servers for deploying the web application and the interfaces that enable the communication. Every communication that takes place between the Cloud infrastructure and the rest of the components is secure by applying appropriate authentication and data encryption mechanisms. Sensors can be authenticated by unique id and data can be encrypted using symmetric encryption techniques [65]. Users and external applications can be authenticated using more sophisticated mechanisms like PKI and digital signatures [66]. The major features of the proposed architecture are its scalability, interoperability and lightweight access. It is scalable due to the fact that it relies on a Cloud infrastructure that provides resources based on utilization and demand. More users, sensors and other data sources can be added without affecting the functionality of the system or without the need for further maintenance or expansion. The web services based interfaces ensure the maximum interoperability with external applications. The Representational State Transfer (REST) API is very lightweight and can be easily accessed and implemented by wireless sensor and mobile platforms.
Ease of use in Wearable Monitoring To address the challenges in ensuring ease of use in implementing wearable devices in monitoring of healthcare systems, different types of technologies can be used. It involves creation and utilization of wearable platforms, interoperability standards, and intelligent human device interfaces that provide just-in-time, easy to understand information. Wearable Platforms: These solutions seek to provide flexible hardware/software frameworks into which medical sensors and their support software can be embedded. Wearable form factors include a wide range of bio-clothes, shoes, belts, rings, watches, etc. A common approach is to embed miniature sensors into cloth. Geogia Tech’s smart shirt [67], a “wearable motherboard,” provides a flexible computer data bus that connects the controller with multiple sensors distributed throughout the shirt. Vivometrics Inc.’s LifeShirt takes a similar approach, using off-the-shelf components to ascertain respiration, posture, movement, and parameters for the assessment of heart disease [68]. Verhaert developed baby pajamas to continuously monitor newborns in an effort to reduce the prevalence of sudden infant death syndrome [69]. The Interdepartmental Research Center at the University of Pisa, Italy, developed flexible devices using new materials called electroactive polymers, and then embedded them into smart textiles [70]. Wearable devices built into other form factors include MIT’s ring-shaped pulse oximetry sensor [71], MIT’s shoe-mounted gait monitoring sensors [72], and a wrist-wearable AMON (Advanced Medical Monitor) [73] developed in Europe. These prototypes demonstrate the feasibility of wearable medical sensors and their potential to collect health information while minimizing the effect of the devices on the user’s lifestyle (challenges A and B in the list above). Hardware, Software, and Information Standards: Standardization en route to automatic device association (challenge C) can improve interoperability between devices from different manufacturers, leading to (1) simpler device setup and (2) lower product prices through device-level competition. These improvements, with the addition of security, form a triad that will increase user acceptance of new point-of-care technology. Numerous Government, Academic, Clinical, and Industrial organizations have worked together to develop and promote medical information/device standards [74-77]. We could apply a subset of the standards as below, in the context of wearable home care systems. International medical information representation/exchange standards, IEEE/ISO 11073 (a.k.a. X73, formerly known as IEEE 1073 [77]. Embedded components adopts the IEEE/ISO 11073 standards [78, 79]. For the medical devices included in the X73 standards (i.e., the electrocardiograph and pulse oximeter), the design complies with the MIB domain information model [80] and nomenclature [81]. The IEEE/ISO 11073 standards, originally defined for acute care applications in bedside environments, have not been previously applied to wireless body-area and home networks. [82]. Due to a length limit on Bluetooth packet data, large data chunks have to be truncated at the application layer. To improve transmission efficiency, it is therefore suggested that segmentation and reassembly features be added into the IEEE/ISO 11073 session Intelligent Human/Device Interfaces (HDI): This focus area addresses challenges D, E, and F. HDIs provide intuitive operation, often through a two-way information exchange between the patient and the device, including Just-in-time, easy to understand, context sensitive help. Currently, most wearable systems are one-way: the devices collect user data and forward it to a base station or remote repository; users receive indicators from these devices (e.g., LED displays) but rarely engage in two-way dialogues with these units.
Ensuring all the characteristics of Wearable Computing in its design: For building a wearable computing system, which could have the wearable characteristic, features as hands-free, always on, supporting daily life, autonomy, simplicity, flexibility, and power saving, an Active Wearable Engine Applying Rule-based Architecture (A-WEAR) [83] is proposed. AWEAR achieves autonomy, simplicity, and service flexibility by describing the system behavior with event driven rules, and the device flexibility by using a plugin mechanism to manage attached devices. The power saving is obtained by combining rules and plugins to manage attached devices. It being wearable, operated by long-life batteries, it is hands-free and always on. Block diagram of A-WEAR is shown below,
A-WEAR manages devices via plug-ins for flexible device configuration, and services like positioning, activity monitoring are described as a set of event-driven rules to make services autonomous and simple. Services could be configured flexibly by adding / deleting / modifying rules dynamically. It achieves power saving functions with a combination of rules to keep watch on the system status and plug-ins for power management of attached devices. All services are represented as a set of ECA rules [84] in AWEAR. An ECA rule is a behavior description language in an active database system. A sample ECA rules has following structure, DEFINE Rule-ID [IN List-of-belonging-groups] [FOR Scope] [VAR Variable-name AS Variable-type]* WHEN Event-type [ (Target-of-event)] IF Conditions THEN DO Actions Rule-ID describes the name of the ECA rule. List-of-belonging-groups gives the name(s) of the group(s) that the rule belongs to. Scope describes the scope of rule execution. Variable-name describes the name of a local variable within this rule, and Variable-type specifies the type of local variable. Event-type describes the name of the event that triggers this rule, and Target-of-event describes the target of event. Conditions specify conditions for executing the following actions. A plug-in is an extension module for the system. We could use new events and actions by
adding plugins. A-WEAR uses plug-ins for device management to defuse the differences in methods for using devices and also for enhancement of system functions such as e-mail functions and multimedia functions. System structure of A-WEAR would be as follow. Also the prototype is shown in the figure at right.
We can easily construct and customize applications for wearable computing environments using AWEAR. A-WEAR significantly reduced power consumption. Security for Wireless Body Area Networks Wireless Body Area Network (WBAN) is the core technology connecting Next-Generation’s compositional elements around the human to one network. Technologies of WBAN gradually are beginning to receive the attention, and including various issues that have the close relation with their technical development. One of the most challenging issues of WBAN is security. The health monitoring based on WBAN collects data about vital body parameter from several parts of the body and uses them for a medical treatment. These important data should be kept thoroughly in security due to the wireless networking environment that is vulnerable to attackers and stringent constrains of power, memory and computation capability. A security framework [85] is proposed to address security challenges in Wireless Body Area Network. This security framework enables a wide range of utilization including the medical rehabilitation, digital IDs, the military and personal entertainment systems. The security threats in WBAN could be classified as, Eavesdropping / Interception [86], Interruption / Communication jamming [87], Modification of data [88], Unauthorized access [89], and Repudiation [90]. Typical WBAN has 3 layers – The lowest layer consists of intelligent sensors or nodes. The second level is mobile personal server that communicates with external network and temporarily stores data from the sensors or nodes. It also displays analyzed information on a screen with interactive options. The third level includes external network of remote servers, typically cloud infrastructure that provides various application services, and analytics of collected data. The WBANs have stringent requirements of high security and safety due to transmission of personal data. The proposed security architecture for WBAN, is as shown in below diagram. It also has 3 levels of security as that of WBAN levels. Security Type-1 is the security of WSAN (Wireless Sensor and Active Network) [91] including ultra low resources. Security Type-2 is the security for sensors and actuators with more resources and having local storage. Security Type-3 is the security of mobile communication between mobile server and remote servers.
The security requirements applicable to each device or communication link are as Data confidentiality - Data Integrity - Availability [92] Authentication [93], Non-repudiation [94], Access Control [95], Privacy [96], and communication flow security [97]. The security requirements in each entity (D1~D6) and relationship between devices (L1~L9) based on the proposed WBAN security model is shown in the table below.
The most important part of WBAN security is the Type-3 security. It works as part of Firewall, a personal storage of vital medical information and the point of intersection within different network protocols. The above approach would take care of the security to large extent, although a lot of scope exists for further work. Wearable Security Services To ensure lightweight, mobile and convenient security services for users that enable them to prove their identities and interact securely with the smart devices in the active space [98], a security
system is proposed which is embedded in a basic wearable device that is already being worn and used by many people on daily basis, the wristwatch. With the recent advances in mobility and miniaturization technologies, basic wearable devices with decent processing power and resources are now a reality that have already made it to the consumer market. Most people wear or carry watches to keep track of time and date. By providing lightweight security services in a “smart” wristwatch, a user need not worry about carrying or wearing additional equipment while roaming in an active space environment. A prototype could be build for wearable security services on a wristwatch using Matsucom’s OnHandTM PC wristwatch [99], and deploy it in our active space testbed. The security services that need to be built are authentication, access control, location service, and secure communication. These could be implemented as below. Authentication: It could be achieved by building lightweight and simple certificates. These certificates are issued and digitally signed by certificate authorities [100]. They are scattered throughout the active space environment. Initially, the certificate of the user is stored in his or her wristwatch. The lightweight certificates stored in the wristwatches only contain the fields – the unique ID of the user, his or her public key, the issuing CA, the digital signature of the issuing CA, extensions that is used for storing additional security attributes, or storing a “handle” for associating this certificate with additional information stored at some certificate store. The wristwatch also stores the private key of the user. The public key uses symmetric encryption along with the DiffieHellman (DH) key exchange protocol [101]. Access Control: A role-based access control model (RBAC) [102] is implemented. RBAC is a form of non-discretionary access control, which defines a hierarchy of roles. The roles are set up to mirror the organizational structure of people. Different permissions are granted to different roles and users are assigned to one or more roles depending on their title or job description. RBAC is convenient as it closely mimics the structure of organizations and allows access control policy administrators to define well-structured security policies. Location service: The location service enables users to locate other users and resources within the active space environment. This is also useful for identifying occupants of an active space. The process of registering a user in an active space can occur after authentication, or it can be a separate process where the user communicates with one of the user-registration devices in a particular active space. Once a user registers with the designated device, it invokes the presence object that handles the addition and removal of users to and from the location service. Secure Communication: Wristwatches and other portable devices carried by the user should be able to communicate securely with other entities within an active space. Every smart device in an active space should have a public / private key pair. When a user decides to communicate with some device and employs the DH key exchange protocol, a unique session key can be established between the wristwatch and the device. A protocol that offers forward and backward secrecy is implemented. Forward secrecy means that the compromise of the current key does not compromise future communication. Backward secrecy is similar, but relates to the compromise of past communication. This is achieved by introducing the concept of establishing secure channels for communication. This implementation of wearable security services providing authentication, access control, location services and secure communication in active spaces. This could be enhanced in future by implementing more powerful authentication methods like elliptical curve cryptography, and interoperating with widely used Kerberos authentication [103].
Conclusion Cloud computing implementation using open hardware and open software resources for managing patient-related data addresses the typical challenges and resource constraints of mobile devices as processor. Based on IoT functional principles and design it allows direct communication of the sensor devices with the cloud application, it uses a lightweight REST-based API used and at the same time it is highly scalable in the context of data stored, users and sensors supported.
Ease of use features are very essential for any wearable devices to become practical. Creation and utilization of wearable platforms, interoperability standards, and intelligent human device interfaces that provide just-in-time, easy to understand information ensures the required features. This ease of use features make healthcare monitoring using wearable devices very effective, and reduce the healthcare costs to a great extent. An extensible rule processing system for wearable computing, A-WEAR, uses ECA rules to describe autonomous services, and uses plug-ins to make device configuration flexible. We can easily construct and customize applications for wearable computing environments using A-WEAR. It also significantly reduces power consumption. Using A-WEAR, once can achieve all the four desired features of wearable system as – autonomy, simplicity, flexibility and power saving. A security framework proposed would support secured communication within Wireless Body Area Network. For this, first all the security threats for WBAN were analyzed and then build the framework to defuse these threats. It is realized that the most critical factor of WBAN is a mobile personal server like PDA. It works as a part of a firewall, a personal storage of vital medical information and the point of intersection within different network protocols. In the future, we need to investigate about the security of each WBAN devices, especially a mobile personal server. This survey attempts to study research done in the areas of cloud computing, wearable devices, healthcare monitoring, aspects of security and privacy and with the help of 5 research problem, overall challenges in these areas and also the possible solutions to these problems were discussed. This survey would help in understanding the different challenges and open issues in the area of wearable computing and also provide direction to build and propose appropriate solutions to the open problems.
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