No abstract

The nature of data in enterprises and on the Internet is changing. Data used to be stored in a database first and queried later. Today timely processing of new data, represented as events, is increasingly valuable. In many domains, complex event processing (CEP) systems detect patterns of events for decision making. Examples include processing of environmental sensor data, trades in financial markets and RSS web feeds. Unlike conventional database systems, most current CEP systems pay little attention to query optimisation. They do not rewrite queries to more efficient representations or make decisions about operator distribution, limiting their overall scalability. This paper describes the Next CEP system that was especially designed for query rewriting and distribution. Event patterns are specified in a high-level query language and, before being translated into event automata, are rewritten in a more efficient form. Automata are then distributed across a cluster of machines for detection scalability. We present algorithms for query rewriting and distributed placement. Our experiments on the Emulab test-bed show a significant improvement in system scalability due to rewriting and distribution.

Distributed content-based publish-subscribe middleware is emerging as a promising answer to the demands of modern distributed computing. Nevertheless, currently available systems usually do not provide reliability guarantees, which hampers their use in dynamic and unreliable scenarios, notably including mobile ones. In this paper, we evaluate the effectiveness of an approach based on epidemic algorithms. Three algorithms we originally proposed in [5] are thoroughly compared and evaluated through simulation in a challenging unreliable setting. The results show that our use of epidemic algorithms improves significantly event delivery, is scalable, and introduces only limited overhead.

Vehicular ad hoc networks have recently been proposed as an effective tool for improving both road safety and the comfort experienced while driving. Vehicles may propagate information about potentially dangerous events such as lane changes or sudden slowdowns to vehicles in their vicinity. Moreover they can inform vehicles approaching from farther areas about accidents and possible traffic jams. In both cases, data must be routed to specific areas, along paths determined by the underlying road traffic conditions.In this paper we propose a novel approach to address this routing problem. First, we define a message propagation function that encodes information about both target areas and preferred routes. Second, we show how this function can be exploited in several routing protocols; and finally, we evaluate the effectiveness of our approach by means of simulation. Results highlight the good performance of our routing approach in sparse as well as in dense networks.

The need of monitoring people, animals, and things in general, brings to consider mobile WSNs besides traditional, fixed ones. Moreover, several advanced scenarios, like those including actuators, involve multiple sinks . Mobility and multiple sinks radically changes the way routing is performed, while the peculiarities of WSNs make it difficult to reuse protocols designed for other types of mobile networks.\ud \ud In this paper, we describe CCBR, a Context and Content-Based Routing protocol explicitly designed for multi-sink, mobile WSNs. CCBR adopts content-based addressing to effectively support the data-centric communication paradigm usually adopted by WSN applications. It also takes into account the characteristics (i.e., context) of the sensors to filter data.\ud \ud Simulations show that CCBR outperforms alternative approaches in the multi-sink, mobile scenarios it was designed for, while providing good performance in more traditional (fixed) scenarios