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This paper considers locating an underwater target, where many sonobuoys are positioned to measure the bearing of the target's sound. A sonobuoy has very low bearing accuracy, such as 10 degrees. In practice, we can use multiple heterogeneous sonobuoys, such that the variance of a sensor noise may be different from that of another sensor. In addition, the maximum sensing range of a sensor may be different from that of another sensor. The true target must exist within the sensing range of a sensor if the sensor detects the bearing of the target. In order to estimate the target position based on bearings-only measurements with low accuracy, this paper introduces a novel target localization approach based on multiple Virtual Measurement Sets (VMS). Here, each VMS is derived considering the bearing measurement noise of each sonar sensor. As far as we know, this paper is novel in locating a target's 2D position based on heterogeneous sonobuoy sensors with low accuracy, considering the maximum sensing range of a sensor. The superiority (considering both time efficiency and location accuracy) of the proposed localization is verified by comparing it with other state-of-the-art localization methods using computer simulations.
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Partial discharge (PD) is a major indicator of various failures in power grid systems. PD exhibits a physical occurrence where a localized electrical discharge happens in insulation materials. This phenomenon causes damage to the insulating parts and, in various circumstances, leads to complete insulation breakdown. As a consequence, it can produce more costly outcomes such as abrupt outages or lost production. Therefore, PD detection plays a vital role in preventing insulation failure. In this work, we propose a system that utilizes heterogeneous sensors for the PD detection along with multi-sensor interface, real-time advanced denoise processing, flexible system operation, and Bluetooth-low-energy (BLE)-based ad hoc communication. Among the variety of heterogeneous sensors, several are developed by the application of nanomaterials and nanotechnology, thus outperforming the regular types. The proposed system successfully identifies the presence of PD from sensor signals using a microprocessor-based processing system and effectively performs an advanced denoising technique based on the wavelet transform through field-programmable-gate-array (FPGA)-based programmable logics. With the development of the system, we aim to achieve a solution with low cost, high flexibility and efficiency, and ease of deployment for the monitoring of power grid systems.
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The Internet of Things is a rapidly growing paradigm for smart cities that provides a way of communication, identification, and sensing capabilities among physically distributed devices. With the evolution of the Internet of Things (IoTs), user dependence on smart systems and services, such as smart appliances, smartphone, security, and healthcare applications, has been increased. This demands secure authentication mechanisms to preserve the users' privacy when interacting with smart devices. This paper proposes a heterogeneous framework "ADLAuth" for passive and implicit authentication of the user using either a smartphone's built-in sensor or wearable sensors by analyzing the physical activity patterns of the users. Multiclass machine learning algorithms are applied to users' identity verification. Analyses are performed on three different datasets of heterogeneous sensors for a diverse number of activities. A series of experiments have been performed to test the effectiveness of the proposed framework. The results demonstrate the better performance of the proposed scheme compared to existing work for user authentication.
Assuntos
Atividades Cotidianas , Algoritmos , Cidades , Bases de Dados como Assunto , Árvores de Decisões , Exercício Físico/fisiologia , Humanos , Smartphone , Máquina de Vetores de Suporte , Caminhada/fisiologiaRESUMO
The paper considers the connected target coverage (CTC) problem in wireless heterogeneous sensor networks (WHSNs) with multiple sensing units, termed MU-CTC problem. MU-CTC problem can be reduced to a connected set cover problem and further formulated as an integer linear programming (ILP) problem. However, the ILP problem is an NP-complete problem. Therefore, two distributed heuristic schemes, REFS (remaining energy first scheme) and EEFS (energy efficiency first scheme), are proposed. In REFS, each sensor considers its remaining energy and its neighbors' decisions to enable its sensing units and communication unit such that all targets can be covered for the required attributes and the sensed data can be delivered to the sink. The advantages of REFS are its simplicity and reduced communication overhead. However, to utilize sensors' energy efficiently, EEFS is proposed. A sensor in EEFS considers its contribution to the coverage and the connectivity to make a better decision. To our best knowledge, this paper is the first to consider target coverage and connectivity jointly for WHSNs with multiple sensing units. Simulation results show that REFS and EEFS can both prolong the network lifetime effectively. EEFS outperforms REFS in network lifetime, but REFS is simpler.
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Occupancy detection using ambient sensors has many benefits such as saving energy and money, enhancing security monitoring systems, and maintaining the privacy. However, sensors data suffers from uncertainty and unreliability due to acquisition errors or incomplete knowledge. This paper presents a new heterogeneous sensors data fusion method for binary occupancy detection which detects whether the place is occupied or not. This method is based on using neutrosophic sets and sensors data correlations. By using neutrosophic sets, uncertain data can be handled. Using sensors data fusion, on the other hand, increases the reliability by depending on more than one sensor data. Accordingly, the results of experiments applied using Random Forest (RF), Linear Discriminant Analysis (LDA), and FUzzy GEnetic (FUGE) algorithms prove the new method to enhance detection accuracy.