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In this paper, we present a modified Otsu 's algorithm for solving the automatic threshold estimation problem in energy detection based Cognitive Radio (CR) application. The modified algorithm was tested extensively and compared with other known algorithms using both simulated and real datasets. In particular, our findings reveal that the modified algorithm provides an averagely lower false alarm rate than the other techniques compared with in this paper. Furthermore, the results obtained show that the algorithm is independent of the bandwidth's size, while having a total complexity of O(V), where V is the total sample size. Thus, from the results of this paper, full and effective automatic blind spectrum sensing using an Energy Detector is possible in CR. This can be achieved at a Signal-to-Noise Ratio of 5 dB to meet the IEEE 802.22 draft standard of P >90% and P <10%.

New advancements in the technology of wireless sensors have contributed to the development of special protocols which are unique to sensor networks where minimal energy consumption is vital and very important. As a result, the focus and effort of researchers is on designing better routing algorithms for a given application and network architecture of interest. Flat-based routing protocols have been found to be less advantageous to clustering routing protocols when their performance are compared in a large-scale wireless sensor network scenario. This is due to the fact that clustering operation reduces the amount of redundant messages that are transmitted all over the network when an event is detected. This paper is an investigation of cluster-based routing protocols for wireless sensor networks.

This paper presents a new algorithm for detecting and characterizing potholes and bumps directly from noisy signals acquired using an Accelerometer. A wavelet transformation based filter was used to decompose the signals into multiple scales. These coefficients were correlated across adjacent scales and filtered using a spatial filter. Road anomalies were then detected based on a fixed threshold system, while characterization was achieved using unique features extracted from the filtered wavelet coefficients. Our analyses show that the proposed algorithm detects and characterizes road anomalies with high levels of accuracy, precision and low false alarm rates.

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