We are witnessing the rise of a novel class of wearable devices equipped with various sensing capabilities as well as further miniaturization of sensing components that are nowadays being integrated into mobile devices. The inherent mobility of such devices has the capacity to produce dense and rich spatiotemporal information about our environment creating the mobile Internet of Things (IoT). The management of mobile resources to enable sensor discovery and seamless integration of mobile geotagged sensor data with cloud-based IoT platforms creates new challenges due to device dynamicity, energy constraints, and varying sensor data quality. The paper presents an ecosystem for mobile crowdsensing applications which relies on the CloUd-based PUblish/Subscribe middleware (CUPUS) to acquire sensor data from mobile devices in a context-aware and energy-efficient manner. The ecosystem offers the means for location management of mobile Internet-connected objects and adaptive data acquisition from such devices. In addition, our solution enables filtering of sensor data on mobile devices in the proximity of a data producer prior to its transmission into the cloud. Thus it reduces both the network traffic and energy consumption on mobile devices. We evaluate the performance of our mobile CUPUS application to investigate its performance on mobile phones in terms of scalability and CPU, memory and energy consumption under high publishing load.

Continuous processing of top-k queries over data streams is a promising technique for alleviating the information overload problem as it distinguishes relevant from irrelevant data stream objects with respect to a given scoring function over time. Thus it enables filtering of irrelevant data objects and delivery of top-k objects relevant to user interests in real-time. We propose a solution for distributed continuous top-k processing based on the publish/subscribe communication paradigm-top-k publish/subscribe over sliding windows (top-k/w publish/subscribe). It identifies k best-ranked objects with respect to a given scoring function over a sliding window of size w, and extends the publish/subscribe communication paradigm by continuous top-k processing algorithms coming from the field of data stream processing.In this paper, we introduce, analyze and evaluate the essential building blocks of distributed top-k/w publish/subscribe systems: First, we present a formal top-k/w publish/subscribe model and compare it to the prevailing Boolean publish/subscribe model. Next, we outline the top-k/w processing tasks performed by publish/subscribe nodes and investigate the properties of supported scoring functions. Furthermore, we explore potential routing strategies for distributed top-k/w publish/subscribe systems. Finally, we experimentally evaluate model properties and provide a comparative study investigating traffic requirements of potential routing strategies.

With the advent of Smart Cities, public transport authorities are more and more interested in Intelligent Transport System (ITS) applications that allow to process a large amount of static and real time data in order to make public transport smarter. However, deploying such applications in a large scale distributed environment is challenging and requires an automated, scalable, flexible, elastic, loosely-coupled communication models in order to dynamically link information providers and consumers. To this end, Publish/Subscribe (Pub/Sub) systems offer an asynchronous, dynamic, decoupled interaction scheme that is perfectly suitable for developing upto-date, large-scale distributed applications within the ITS domain. In addition, cloud computing offers computational resources as services to utility driven model regardless of considering geographical locations in a scalable, elastic, fault tolerant and cost-effective way. In this work, we build an ITS application "Real-time Public Transit Tracking" on top of a Mobile Pub/Sub System (MoPS), and deploy it over an open source cloud platform, OpenStack, in order to achieve high performance and flexible management. We conduct a set of experiments to evaluate the performance of the implemented ITS application in terms of scalability, resource usage, and efficiency of the underlying matching algorithm under automated mobility of the subscribers. Our experimental results show that the ITS application can handle a large number of subscribers and publishers with proper reliability and negligible notification delay under real-time constraints. Further, we present a measurement study to characterize the impact of different workloads on the performance of OpenStack.

No abstract