This paper presents a self-organizing hierarchical cerebellar model arithmetic computer (HCMAC) neural-network classifier, which contains a self-organizing input space module and an HCMAC neural network. The conventional CMAC can be viewed as a basis function network (BFN) with supervised learning, and performs well in terms of its fast learning speed and local generalization capability for approximating nonlinear functions. However, the conventional CMAC has an enormous memory requirement for resolving high-dimensional classification problems, and its performance heavily depends on the approach of input space quantization. To solve these problems, this paper presents a novel supervised HCMAC neural network capable of resolving high-dimensional classification problems well. Also, in order to reduce what is often trial-and-error parameter searching for constructing memory allocation automatically, proposed herein is a self-organizing input space module that uses Shannon's entropy measure and the golden-section search method to appropriately determine the input space quantization according to the various distributions of training data sets. Experimental results indicate that the self-organizing HCMAC indeed has a fast learning ability and low memory requirement. It is a better performing network than the conventional CMAC for resolving high-dimensional classification problems. Furthermore, the self-organizing HCMAC classifier has a better classification ability than other compared classifiers.

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Internet of Things (IoT) is expected to offer promising solutions to transform the operation and role of many existing systems such as transportation systems, manufacturing systems, and enables many applications in many domains. IoT aims to connect different things over the network. The goal of IoT is to provide a good and efficient service for many applications. A real-time IoT applications must react to stimuli from its environment within time intervals dictated by its environment. The instant when a result must be produced is called a deadline. Wireless Sensor Networks (WSN) have recently used been in the limelight for many domains. The IoT can be explained as a general purpose sensor network. WSNs will constitute an integral part of the IoT paradigm, spanning many different application areas. Since sensor nodes usually are developed by low-cost hardware, one major challenge in the development of many sensor-network applications is to provide high-security features with limited resources. In this paper, we propose a path generation framework with deadline considerations for real-time query processing. To meet the deadline, the framework will assign the time budget to the routing path, and then, derive a feasible path with the assigned time budget. In order to evaluate the performance of the proposed RTQP scheme, we construct a simulation model using ns 2.35. The performance of the RTQP scheme is compared with that of other related mechanisms, for which we have very encouraging results.