| The Internet of Things, which enables the interconnection, interoperation, and collaboration between smart things, allows collecting data from various sources, including GPS data of vehicles, real-time traffic data of road cameras, weather data (e.g., temperature or air quality data) from environment sensors and user-generated contents (e.g., tweets, micro-blog, check-ins, photos) from mobile social APPs. In fact, sensory data have been widely available in large volume and variety nowadays. Sensory data exhibit specific characteristics, including multi-sources, heterogeneous, large-scale, real-time streaming, continuous, everexpanding, and spatial–temporal. Traditional approaches or platforms are limited in processing these sensory data, which are big data actually. The engineering and intelligence on sensory data covers the theories and technologies of different disciplines to provide efficient processing and smart analysis. Intensive research is required on sensory data engineering and intelligence. This special issue, as a dedicated forum, aims for the scientific and industrial community to present their novel models, methodologies, techniques, and solutions which can address theoretical and practical issues. It is worth mentioning that the majority of submissions are the selected papers with high quality which have been reported at the 2015 Smart World Congress. These selected papers are seriously improved and recommended to this special issue. The other submissions are from the open-call. After carefully reviewing submissions, there are 15 articles being accepted finally. A brief summary about each article is presented as follows: Internet of Things (IoT) connects billions of devices in an Internet-like structure. Each device encapsulated as a real-world service in which provides functionality and exchanges information with other devices. This large-scale information exchange results in novel interactions between things and people. Unlike traditional Web services, Internet of Services (IoS) is highly dynamic and continuously changing due to constant degradation, vanishing, and possible reappearance of the devices, and this opens a new challenge in the process of resource discovery and selection. In response to increasing numbers of services in the discovery and selection process, there is a corresponding increase in number of service consumers and consequent diversity of quality of service (QoS) available. Increase in both sides leads to the diversity in the demand and supply of services, which would result in the partial match of the requirements and offers. In the article [1], Ahmed et al. propose an IoT service ranking and selection algorithm by considering multiple QoS requirements and allowing partially matched services to be counted as a candidate for the selection process. One of the applications of IoT sensory data that attracts many researchers is transportation especially emergency and accident services which is used as a case study in this article. Experimental results from realworld services showed that the proposed method achieved significant improvement in the accuracy and performance in the selection process. |
