Few studies have investigated and proposed a middleware solution for the Internet of Mobile Things (IoMT), where the smart things (Smart Objects) can be moved, or else can move autonomously, and yet remain accessible and controllable remotely from any other computer over the Internet. Examples of mobile Smart Objects include vehicles of any kind, wearable devices, sensor tags, mobile robots, etc, anyhing with embedded sensors and/or actuators. In this context of general mobility of objects, mobile personal devices (smart phones, tablets, etc.) are well suited as the universal providers of Internet connectivity and location information for simpler smart objects that lack location sensors and have only short-range wireless interfaces. This paper describes Mobile Hub (M-Hub), a generic mobile middleware for IoMT, its design and prototype implementation for Android and Bluetooth. The Mobile Hub extends our previous mobile-cloud communication middleware SDDL, so that it is able to provide scalable and reliable mobile communication and data processing capabilities to mobile smart objects. Preliminary experiments have shown that our implementation of M-Hub delivers good mobility responsiveness and that the concept is suitable for IoT applications that require opportunistic discovery and connection to a variety of mobile Smart Objects.

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.

The majority of fatal car crashes are caused by reckless driving. With the sophistication of vehicle instrumentation, reckless maneuvers, such as abrupt turns, acceleration, and deceleration, can now be accurately detected by analyzing data related to the driver-vehicle interactions. Such analysis usually requires very specific in-vehicle hardware and infrastructure sensors (e.g. loop detectors and radars), which can be costly. Hence, in this paper, we investigated if off-the-shelf smartphones can be used to online detect and classify the driver's behavior in near real-time. To do so, we first modeled and performed an intrinsic evaluation to assess the performance of three outlier detection algorithms formulated as a data stream processing network which receives as input and processes data streams of smartphone and vehicle sensors. Next, we implemented a novel scoring mechanism based on online outlier detection to quantitatively evaluate drivers' maneuvers as either cautious or reckless. Thus, we adapted a data mining mechanism which takes into account a sensor's data rates and power to determine driver behavior in the scoring process. Finally, as the intrinsic evaluation does not necessarily reveal how well an algorithm will perform in a real-world scenario, we evaluated the algorithm that achieved the best result in a real-world case study to assess drivers' driving behavior. Our results indicate that the algorithm performs quickly and accurately; the algorithm classifies driver behavior with 95.45% accuracy. Moreover, such results are obtained within 100 milliseconds of processing time on average.

The design and development of adaptive systems brings new challenges since the dynamism of such systems is a multifaceted concern that range from mechanisms to enable the adaptation on the software level to the (self-) management of the entire system using adaptation plans or system administrator, for instance. Networked and mobile embedded systems are examples of systems where dynamic adaptation become even more necessary as the applications must be capable of discovering the computing resources in their near environment. While most of the current research is concerned with low-level adaptation techniques (i.e., how to dynamically deploy new components or change parameters), we are focused in providing management of distributed dynamic adaptation and facilitating the development of adaptation plans. In this paper, we present a middleware tailored for mobile embedded systems that supports distributed dynamic software adaptation, in transactional and non-transactional fashion, among mobile devices. We also present results of initial evaluation.

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While many systems have to provide 24 × 7 services with no acceptable downtime, they have to be able to cope with changes in their execution environment and in the requirements that they must comply, in which data stream processing is one example of system that has to evolve during its execution. On one hand, dynamic reconfiguration (i.e., the capability of evolving on-the-fly) is a desirable feature. On the other hand, stream systems may suffer with the disruption and overhead caused by the reconfiguration. Due to these conflicting requirements, safe and non-disruptive reconfiguration is still an open problem. In this paper, we propose and validate a nondisruptive reconfiguration approach for distributed data stream systems that support stateful components and intermittent connections. We present experimental evidence that our mechanism supports safe distributed reconfiguration and has negligible impact on availability and performance.