Context aware systems are able to adapt their behavior according to the environment in which the user is. They can be integrated into an Internet of Things (IoT) infrastructure, allowing a better perception of the user’s physical environment by collecting context data from sensors embedded in devices known as smart objects. An IoT extension called the Internet of Mobile Things (IoMT) suggests new scenarios in which smart objects and IoT gateways can move autonomously or be moved easily. In a comprehensive view, Quality of Context (QoC) is a term that can express quality requirements of context aware applications. These requirements can be those related to the quality of information provided by the sensors (e.g., accuracy, resolution, age, validity time) or those referring to the quality of the data distribution service (e.g, reliability, delay, delivery time). Some functionalities of context aware applications and/or decision-making processes of these applications and their users depend on the level of quality of context available, which tend to vary over time for various reasons. Reviewing the literature, it is possible to verify that the quality of context support provided by IoT-oriented middleware systems still has limitations in relation to at least four relevant aspects: (i) quality of context provisioning; (ii) quality of context monitoring; (iii) support for heterogeneous device and technology management; (iv) support for reliable data delivery in mobility scenarios. This paper presents two main contributions: (i) a state-of-the-art survey specifically aimed at analyzing the middleware with quality of context support and; (ii) a new middleware with comprehensive quality of context support for Internet of Things Applications. The proposed middleware was evaluated and the results are presented and discussed in this article, which also shows a case study involving the development of a mobile remote patient monitoring application that was developed using the proposed middleware. This case study highlights how middleware components were used to meet the quality of context requirements of the application. In addition, the proposed middleware was compared to other solutions in the literature.
Several new applications of mobile computing environments, such as Intelligent Transportation Systems, Fleet Management and Logistics, and integrated Industrial Process Automation share the requirement of remote monitoring and high performance processing of huge data streams produced by large sets of mobile nodes. Two key requirements for the deployment and operation of such mobile infrastructures are the handling of large and variable numbers of wireless connections to the monitored mobile nodes regardless of their current use or locations, and to automatically adapt to variations in the volume of the mobile data streams. This article describes the design, implementation, and evaluation of an autonomic mechanism for load balancing of mobile data streams. The autonomic capability has been incorporated into a scalable middleware system based on a Data Centric Publish Subscribe approach using the OMG Data Distribution Service (DDS) standard and aimed at real-time and adaptive handling of mobile connectivity and data stream processing for great sets of mobile nodes. A significant amount of evaluation experiments of the proposed infrastructure is presented, reinforcing its viability and the benefits arising from the use of an autonomic approach to handle the requirements of high variability and scalability.
The current ubiquity of smart phones with mobile Internet and several short-range wireless interfaces (NFC, Bluetooth, Bluetooth Smart) and the fact that these devices are carried almost anytime and anywhere by users, enables potentially new pervasive sensing applications where smartphones can act as universal hubs for interaction with sensors (or sensor networks) that have only short-range wireless connectivity. Thus, in next years we can expect an increasing number of long-term and large-scale deployments for various crowd-sourced monitoring applications, such as environment monitoring, domestic utility meter reading, urban monitoring, etc. In this paper, we present the implementation and initial performance results with our mobile-cloud middleware that enables such opportunistic mobile sensing. One of the singular features of our middleware is the capability to discover, dynamically download and install sensor-specific transcoding modules on the mobile phone according to the encountered sensor type and make.
Ambient Assisted Living (AAL) main goal is the development of health monitoring systems for patients with chronic diseases and elderly people through the use of body, home, and environmental sensors that increase their degree of independence and mobility. A comprehensive software infrastructure for AAL systems should be able to cover scenarios involving several patient mobility levels, locations, and physical and cognitive abilities. Cloud computing can provide to AAL systems the ability to extend the limited processing power of mobile devices, but its main role is to integrate all stakeholders through the storage and processing of health data and the orchestration of healthcare business logic. On the other hand, the Internet of Things (IoT) provides the ability to connect sensors and actuators, integrating and making them available through the Internet. This paper presents the Mobile-Hub/Scalable Data Distribution Layer, a middleware for AAL based on cloud computing and IoT. We discuss how this middleware can handle the requirements of the main health monitoring scenarios and present results that demonstrate the ability to opportunistically discover and connect with sensors in a timely manner and the scalability necessary for handling the connection and data processing of many connected patients. KEYWORDSambient assisted living, cloud computing, health monitoring systems, internet of things (IoT) 1 range of other applications for AAL systems, such as rescue and emer-gency response systems, fall detection, video surveillance systems, etc.Nowadays, AAL systems are regarded as a trend in a context of increasing awareness of how the Internet can be used to personal healthcare. Ambient Assisted Living systems are composed of several technologies: sensors and actuators, portable/wearable devices, heterogeneous wireless networks, medical applications executing on mobile devices (handhelds), personal computers, or in a cloud computing infrastructure. Among the variety of low-level sensors that can be applied in AAL systems, there are the wearable medical sensors, able to collect data from physiological signals (e.g., Electrocardiogram [ECG], Electromyogram, heart rate, and oxygen consumption) or data reflecting the body movement (e.g., accelerometer). Personal mobile devices, such as smartphones, are also usually equipped with motion and location sensors (e.g., accelerometer and GPS). Environmental sensors can also be used, as they collect information that helps determine if environmental conditions (e.g., temperature, light, humidity, and carbon dioxide levels) favor or not the patient's health. In addition to gathering data Concurrency Computat: Pract Exper. 2017;29:e4043. wileyonlinelibrary.com/journal/cpe
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