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Nowadays, it is obvious that technology has revolutionised our lives by supporting us to do complicated jobs. The Internet of Things (IoT) is one of the emerging technologies. One of the most significant current research topics in the IoT is smart city. The smart city includes several applications such as smart home, smart industry and smart mobility. The smart car parking system is an aspect of smart mobility and an important application in smart city projects, because of the rapidly increasing number of cars in urban areas. However, most of the current proposals in smart car parking systems manage the data on the cloud side which is a problem since the system needs to send the raw data from sensor to cloud and receive instructions back: this is expensive in terms of energy and data transmission cost. To tackle this issue we present a proposal to save energy and to reduce the amount of data that is transmitted over the network to cloud by processing closer to source in this paper. The architecture is demonstrated through a case study.

This paper presents a cloud approach for low cost capacity planning evaluations. To perform these evaluations we have to specify and measure the workload on the target system to discover issues and make the necessary adjustments. However, due to high costs, these evaluations are usually done using simulations, which does not consider stochastic effects. We propose to use a tool named PEESOS, a generic and flexible approach to apply real workloads and measure used resources on these real systems. As a proof of concept, our case study use a real ticket sales service to evaluate the influence of scalability in the resource provisioning to show how PEESOS can lower the cost of such real evaluations. The results show the efficiency and savings that we can obtain using PEESOS for large-scale capacity planning evaluations before the real services are deployed. This approach can avoid several problems that real services faces when they launch.

Classic request-response Service-oriented architecture (SOA) has reached a level of maturity where SOA inspired extensions are enabling new and creative domains like the Internet of Things, real-time business or real-time Web. These new domains impose new requirements on SOA, such as a huge data volume, mediation between various data structures and a large number of sources that need to be procured, processed and provided with almost zero latency. Service selection is one of the areas where decisions have to be made based on consumer requests and service offerings. Processing this data requires typical SOA behavior combined with more elaborate approaches to process large amounts of data with near-zero latency. The approach presented in this paper combines pub-sub approaches for processing service offerings and mediations with classical request-response SOA approaches for consumer requests facilitated by Complex Event Processing (CEP). This paper presents a novel approach for subscribing to dynamic service properties and receiving up-to-date information in real-time. Therefore, we are able to select services with near-zero latency since there is no need to pull for property values anymore. The paper shows how to map requests to streaming data, how to process and answer complex requests with low latency and how to enable real-time service selection.