تکنیک های سنسور (حسگر) جستجو جهت دریافت (سنجش) به عنوان یک معماری سرویس در اینترنت اشیا Sensor Search Techniques for Sensing as a Service Architecture for the Internet of Things

THE number of sensors deployed around the world is increasing at a rapid pace. These sensors continuously generate enormous amounts of data. However, collecting data from all the available sensors does not create additional value unless they are capable of providing valuable insights that will ultimately help to address the challenges we face every day (e.g. environmental pollution management and traffic congestion management). Furthermore, it is also not feasible due to its large scale, resource limitations, and cost factors. When a large number of sensors are available from which to choose, it becomes a challenge and a time consuming task to select the appropriate1 sensors that will help the users to solve their own problems. The sensing as a service [1] model is expected to be built on top of the IoT infrastructure and services. It also envisions that sensors will be available to be used over the Internet either for free or by paying a fee through midddleware solutions. Currently, several middleware solutions that are expected to facilitate such a model are under development. OpenIoT [2], GSN [3], and xively (xively.com) are some examples. These middleware solutions strongly focus on connecting sensor devices to software systems and related functionalities [2]. However, when more and more sensors get connected to the Internet, the search functionality becomes critical. This paper addresses the problem mentioned above as we observe the lack of focus on sensor selection and search in existing IoT solutions and research. Traditional web search approach will not work in the IoT sensor selection and search domain, as text based search approaches cannot capture the critical characteristics of a sensor accurately. Another approach that can be followed is that of metadata annotation. Even if we maintain metadata on the sensors (e.g. stored in a sensor’s storage) or in the cloud, interoperability will be a significant issue. Furthermore, a user study done by Broring et al. [4] has described how 20 participants were asked to enter metadata for a weather station sensor using a simple user interface. Those 20 people made 45 mistakes in total. The requirement of reentering metadata in different places (e.g. entering metadata on GSN once and again entering metadata on OpenIoT, etc.) arises when we do not have common descriptions. Recently, the W3C Incubator Group released Semantic Sensor Network XG Final Report, which defines an SSN ontology [5]. The SSN ontology allows describing sensors, including their characteristics. This effort increases the interoperability and accuracy due to the lack of manual data entering. Furthermore, such mistakes can be avoided by letting the sensor hardware manufactures produce and make available sensor descriptions using ontologies so that IoT solution developers can retrieve and incorporate (e.g. mapping) them in their own software system. Based on the arguments above, ontology based sensor description and data modeling is useful for IoT solutions. This approach also allows semantic querying. Our proposed solution allows the users to express their priorities in terms of sensor characteristics and it will search and select appropriate sensors. In our model, both quantitative reasoning and semantic querying techniques are employed to increase the performance of the system by utilizing the strengths of both techniques. In this paper, we propose a model that can be adopted by any IoT middleware solution. Moreover, our design can be run faster using MapReduce based techniques, something which increases the scalability of the solution. Our contributions can be summarized as follows. We have developed an ontology based context framework for sensors in IoT which allows capturing and modeling context properties related to sensors. This information allows users to search the sensors based on context. We have designed, implemented and evaluated our proposed CASSARAM model and its performance in a comprehensive manner. Specifically, we propose a ComparativePriority Based Weighted Index (CPWI) technique to index and rank sensors based on the user preferences. Furthermore, we propose a Comparative-Priority Based Heuristic Filtering (CPHF) technique to make the sensor search process more effi- cient. We also propose a Relational-Expression based Filtering (REF) technique to support more comprehensive searching. Finally, we propose and compare several distributed sensor search mechanisms. The rest of this paper is structured as follows: In Section II, we briefly review the literature and provide some descriptions of leading IoT middleware solutions and their sensor searching capabilities. Next, we present the problem definitions and motivations in Section III. Our proposed solution, CASSARAM, is presented with details in Section IV. Data models, the context framework, algorithms, and architectures are discussed in this section. The techniques we developed to improve CASSARAM are presented in Section V. In Section VI, we provide implementation details, including tools, software platforms, hardware platforms, and the data sets used in this research. Evaluation and discussions related to the research findings are presented in Section VII. Finally, we present a conclusion and prospects for future research in Section VIII.