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In parcel delivery, the “last mile” from the parcel hub to the customer is costly, especially for time-sensitive delivery tasks that have to be completed within hours after arrival. Recently, crowdshipping has attracted increased attention as a new alternative to traditional delivery modes. In crowdshipping, private citizens (“the crowd”) perform short detours in their daily lives to contribute to parcel delivery in exchange for small incentives. However, achieving desirable crowd behavior is challenging as the crowd is highly dynamic and consists of autonomous, self-interested individuals. Leveraging crowdshipping for time-sensitive deliveries remains an open challenge. In this paper, we present an agent-based approach to on-time parcel delivery with crowds. Our system performs data stream processing on the couriers’ smartphone sensor data to predict delivery delays. Whenever a delay is predicted, the system attempts to forge an agreement for transferring the parcel from the current deliverer to a more promising courier nearby. Our experiments show that through accurate delay predictions and purposeful task transfers many delays can be prevented that would occur without our approach.
Nowadays, smartphones and sensor devices can provide a variety of information about a user’s current situation. So far, many recommender systems neglect this kind of information and thus cannot provide situationspecific recommendations. Situation-aware recommender systems adapt to changes in the user’s environment and therefore are able to offer recommendations that are more appropriate for the current situation. In this paper, we present a software architecture that enables situation awareness for arbitrary recommendation techniques. The proposed system considers both (semi-)static user profiles and volatile situational knowledge to obtain meaningful recommendations. Furthermore, the implementation of the architecture in a museum of natural history is presented, which uses Complex Event Processing to achieve situation awareness.
Smart Cities require reliable means for managing installations that offer essential services to the citizens. In this paper we focus on the problem of evacuation of smart buildings in case of emergencies. In particular, we present an abstract architecture for situation-aware evacuation guidance systems in smart buildings, describe its key modules in detail, and provide some concrete examples of its structure and dynamics.
Complex Event Processing (CEP) has been established as a well-suited software technology for processing high-frequent data streams. However, intelligent stream based systems must integrate stream data with semantical background knowledge. In this work, we investigate different approaches on integrating stream data and semantic domain knowledge. In particular, we discuss from a software engineering per- spective two different architectures: an approach adding an ontology access mechanism to a common Continuous Query Language (CQL) is compared with C-SPARQL, a streaming extension of the RDF query language SPARQL.
In recent years, multiple efforts for reducing energy usage have been proposed. Especially buildings offer high potentials for energy savings. In this paper, we present a novel approach for intelligent energy control that combines a simple infrastructure using low cost sensors with the reasoning capabilities of Complex Event Processing. The key issues of the approach are a sophisticated semantic domain model and a multi-staged event processing architecture leading to an intelligent, situation-aware energy management system.
In huge warehouses or stockrooms, it is often very difficult to find a certain item, because it has been misplaced and is therefore not at its assumed position. This position paper presents an approach on how to coordinate mobile RFID agents using a blackboard architecture based on Complex Event Processing.
