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The sensing-throughput trade-off and the sensing-energy trade-off for cooperative spectrum sensing have been the subject of recent research. Two important, but often conflicting, design objectives for low-power devices are throughput and energy efficiency. These have not been comprehensively investigated jointly in cognitive radio networks for the design of spectrum sensing algorithms. This paper examines the energy-throughput trade-off for cooperative spectrum sensing and formulates an optimisation problem for the trade-off between energy and throughput for secondary users based on spectrum sensing efficiency. The objective is to minimise the energy consumed in spectrum sensing, reporting cooperative decisions to a central entity and data transmission while satisfying reliability constraints and providing a given throughput to secondary users. A heuristic solution is developed that determines the optimal sensing, reporting and transmission duration. Analysis and simulation results reveal the optimal value for sensing, reporting and transmission duration in order to achieve the best trade-off between energy consumption and throughput for secondary users.

The successful deployment of dynamic spectrum access requires cognitive radio (CR) to more accurately find the unoccupied portion of the spectrum. An accurate spectrum sensing technique can reduce the probability of false alarms and misdetection. Cooperative spectrum sensing is usually employed to achieve accuracy and improve reliability, but at the cost of cooperation overhead among CR users. This overhead can be reduced by improving local spectrum sensing accuracy. Several signal processing techniques for transmitter detection have been proposed in the literature but more sophisticated approaches are needed to enhance sensing efficiency. This article proposes a two-stage local spectrum sensing approach. In the first stage, each CR performs existing spectrum sensing techniques, i.e., energy detection, matched filter detection, and cyclostationary detection. In the second stage, the output from each technique is combined using fuzzy logic in order to deduce the presence or absence of a primary transmitter. Simulation results verify that our proposed technique outperforms existing local spectrum sensing techniques. The proposed approach shows significant improvement in sensing accuracy by exhibiting a higher probability of detection and low false alarms. The mean detection time of the proposed scheme is equivalent to that of cyclostationary detection.

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We propose a method for building a simple electronic nose based on commercially available sensors used to sniff in the market and identify spoiled/contaminated meat stocked for sale in butcher shops. Using a metal oxide semiconductor-based electronic nose, we measured the smell signature from two of the most common meat foods (beef and fish) stored at room temperature. Food samples were divided into two groups: fresh beef with decayed fish and fresh fish with decayed beef. The prime objective was to identify the decayed item using the developed electronic nose. Additionally, we tested the electronic nose using three pattern classification algorithms (artificial neural network, support vector machine and k-nearest neighbor), and compared them based on accuracy, sensitivity, and specificity. The results demonstrate that the k-nearest neighbor algorithm has the highest accuracy.

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The research on the Internet of Things (IoT) has made huge strides forward in the past couple of years. IoT has its applications in almost every walk of life, and it is being regarded as the next big thing that can change the way humans perceive about their daily life. Smart IoT devices of heterogeneous nature make an essential part of modern day IoT-based systems. The security of these devices is of paramount importance as they handle an enormous amount of critical data and its breach can lead to potentially life-threatening situations. To secure the IoT devices of heterogeneous nature, we formulated a weighted optimization problem in this work. The objective function of this problem is to secure the IoT devices while finding the best trade-off between their resource usage and throughput. To achieve the objective, we consider a pool of five different implementations of Advanced Encryption Standard (AES) cryptographic schemes that offer varied resources and throughput numbers. These implementation schemes are mapped to IoT devices of heterogeneous nature. The mapping is performed through a novel adaptive framework that can consider different weights for resources and throughput to eventually find the best trade-off between the resources and throughput of an IoT-based system. This framework considers the resource and throughput requirements of different IoT devices and uses the Hungarian algorithm to adaptively map different AES implementations on them. Extensive experimentation is performed where the best trade-off is found through varying resource and throughput weight combinations. The comparison of the proposed framework with random and greedy approaches is also performed. Comparison results show that the proposed framework adaptively secures the IoT-based system while providing better resource usage and throughput results. The proposed framework provides, on average, 11% and 17% better throughput and 3% and 13% better resource usage results as compared to random and greedy approach, respectively.