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Wireless acoustic sensor networks represent an attractive solution that can be deployed for animal detection and recognition in a monitored area. A typical configuration for this application would be to transmit the whole acquired audio signal through multi-hop communication to a remote server for recognition. However, continuous data streaming can cause a severe decline in the energy of the sensors, which consequently reduces the network lifetime and questions the viability of the application. An efficient solution to reduce the sensor's radio activity would be to perform the recognition task at the source sensor then to communicate the result to the remote server. This approach is intended to save the energy of the acoustic source sensor and to unload the network from carrying, probably, useless data. However, the validity of this solution depends on the energy efficiency of performing on-sensor detection of a new acoustic event and accurate recognition. In this context, this paper proposes a new scheme for on-sensor energy-efficient acoustic animal recognition based on low-complexity methods for feature extraction using the Haar wavelet transform. This scheme achieves more than 86% in recognition accuracy while saving 71.59% of the sensor energy compared with the transmission of the raw signal.

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Health insurance has become a crucial component of people’s lives as the occurrence of health problems rises. Unaffordable healthcare problems for individuals with little income might be a problem. In the case of a medical emergency, health insurance assists individuals in affording the costs of healthcare services and protects them financially against the possibility of debt. Security, privacy, and fraud risks may impact the numerous benefits of health insurance. In recent years, health insurance fraud has been a contentious topic due to the substantial losses it causes for individuals, commercial enterprises, and governments. Therefore, there is a need to develop mechanisms for identifying health insurance fraud incidents. Furthermore, a large quantity of highly sensitive electronic health insurance data are generated on a daily basis, which attracts fraudulent users. Motivated by these facts, we propose a smart healthcare insurance framework for fraud detection and prevention (SHINFDP) that leverages the capabilities of cutting-edge technologies including blockchain, 5G, cloud, and machine learning (ML) to enhance the health insurance process. The proposed framework is evaluated using mathematical modeling and an industrial focus group. In addition, a case study was demonstrated to illustrate the SHINFDP’s applicability in enhancing the security and effectiveness of health insurance. The findings indicate that the SHINFDP aids in the detection of healthcare fraud at early stages. Furthermore, the results of the focus group show that SHINFDP is adaptable and simple to comprehend. The case study further strengthens the findings and also describes the implications of the proposed solution in a real setting.

Existing solutions designed for acoustic target localization based on the received signal energy in WSN suffer either from low localization accuracy due to suboptimal convergence or high computations requirements due to the application of complex algorithms. The main contribution of this paper is the design of a new lightweight energy-based acoustic source localization solution intended for implementation in wireless sensors. The novelty of this solution resides in devising lightweight computation of the cost function in the location estimator algorithm and the application of the Tabu metaheuristic method to enhance the localization accuracy. In terms of localization accuracy, the proposed scheme outperforms many existing solutions with reduced localization error by up to 88% while using only three energy measurements, which saves around 85% of node energy. Furthermore, the performance analysis results showed a stable behavior of the proposed algorithm across a wide range of parameters variations of the energy-decay model, which demonstrates the robustness of the scheme.