Multimedia sensors enable monitoring applications to obtain more accurate and detailed information. However, the development of efficient and lightweight solutions for managing data traffic over wireless multimedia sensor networks (WMSNs) has become vital because of the excessive volume of data produced by multimedia sensors. As part of this motivation, this paper proposes a fusion-based WMSN framework that reduces the amount of data to be transmitted over the network by intra-node processing. This framework explores three main issues: 1) the design of a wireless multimedia sensor (WMS) node to detect objects using machine learning techniques; 2) a method for increasing the accuracy while reducing the amount of information transmitted by the WMS nodes to the base station, and; 3) a new cluster-based routing algorithm for the WMSNs that consumes less power than the currently used algorithms. In this context, a WMS node is designed and implemented using commercially available components. In order to reduce the amount of information to be transmitted to the base station and thereby extend the lifetime of a WMSN, a method for detecting and classifying objects on three different layers has been developed. A new energy-efficient cluster-based routing algorithm is developed to transfer the collected information/data to the sink. The proposed framework and the cluster-based routing algorithm are applied to our WMS nodes and tested experimentally. The results of the experiments clearly demonstrate the feasibility of the proposed WMSN architecture in the real-world surveillance applications.
For many knowledge-intensive applications, it is important to develop an environment that permits flexible modeling and fuzzy querying of complex data and knowledge including uncertainty. With such an environment, one can have intelligent retrieval of information and knowledge, which has become a critical requirement for those applications. In this paper, we introduce a fuzzy knowledge-based (FKB) system along with the model and the inference mechanism. The inference mechanism is based on the extension of the Rete algorithm to handle fuzziness using a similarity-based approach. The proposed FKB system is used in the intelligent fuzzy object-oriented database (IFOOD) environment, in which a fuzzy object-oriented database is used to handle large scale of complex data while the FKB system is used to handle knowledge of the application domain. Both the fuzzy object-oriented database system and the fuzzy knowledge-based system are based on the object-oriented concepts to eliminate data type mismatches. The aim of this paper is mainly to introduce the FKB system of the IFOOD environment.Index Terms-Flexible querying, fuzzy set theory, inference mechanism, knowledge-based systems, object-oriented databases, uncertainty.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.