QoS-Aware Stream Federation and Optimization Based on Service Composition

QoS-Aware Stream Federation and Optimization Based on Service Composition

Feng Gao (Insight Centre for Data Analytics, National University of Ireland, Galway, Ireland), Muhammad Intizar Ali (Insight Centre for Data Analytics, National University of Ireland, Galway, Ireland), Edward Curry (Insight Centre for Data Analytics, National University of Ireland, Galway, Ireland) and Alessandra Mileo (Insight Centre for Data Analytics, National University of Ireland, Galway, Ireland)
Copyright: © 2016 |Pages: 25
DOI: 10.4018/IJSWIS.2016100103
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Abstract

The proliferation of sensor devices and services along with the advances in event processing brings many new opportunities as well as challenges. It is now possible to provide, analyze and react upon real-time, complex events in urban environments. When existing event services do not provide such complex events directly, an event service composition maybe required. However, it is difficult to determine which event service candidates (or service compositions) best suit users' and applications' quality-of-service requirements. A sub-optimal service composition may lead to inaccurate event detection, lack of system robustness etc. In this paper, the authors address these issues by first providing a quality-of-service aggregation schema for complex event service compositions and then developing a genetic algorithm to efficiently create near-optimal event service compositions. The authors evaluate their approach with both real sensor data collected via Internet-of-Things services as well as synthesised datasets.
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2. Motivation Scenario

Event services can be applied in different Smart City scenarios. In this paper, we study the scenario of smart urban travel planning, as regular citizens, developers and city stakeholders rank it highly1. The city of Aarhus in Denmark has deployed a set of street-level traffic sensors. These sensors are paired as start nodes and end nodes. Each pair is capable of monitoring the average vehicle speed v and vehicle count n on a street segment (from the start node to the end node). Combined with the distance d between the two sensors, the estimated travel time t = d/v and congestion level c = n/d can be derived and published regularly as traffic report events.

Figure 1 shows some traffic sensor nodes (depicted as red dots) on the Aarhus city map. Suppose a user, Alice, has an important appointment in 15 minutes, and she has to travel from home (on segment A in Figure 1) to her work place (on segment F in Figure 1) within the time frame. Alice decides not to pick a route randomly since it is rush hour and there's a good chance that she may experience traffic congestion. Instead, Alice uses a travel planner application on her smartphone to select the fastest route. Alice would like to receive live traffic condition reports during her trip in case some traffic incidents happen on the selected route and a detour is necessary. To do that, she specifies the start and end location of the travel. She also wants to be sure that the time estimation is accurate, so she sets some non-functional constraints such as the accuracy of the estimated travel time above 90%.

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