Great interest is directed toward real-time applications to determine the exact location of sensor nodes deployed in an area of interest. In this paper, we present a novel approach using a combination of the Kalman filter and regularized bounding box method for localizing unknown nodes in an area using an FPGA-enhanced edge computing UAV whose trajectory is known and is represented as the position of many anchors. The UAV is equipped with a GPS system that allows it to gather location data of sensor nodes as it moves around its environment. We employ a regularized bounding box to predict the positions of the unknown nodes using regularization factors and we use the Kalman filter algorithm to smooth and improve the accuracy of the sensor nodes to be localized. In order to localize the unknown nodes, the UAV receives the number of hops from each node and uses this information as input to the localization algorithm. Furthermore, the use of an FPGA board allows for real-time processing of sensory data, enabling the UAV to make fast and accurate decisions in dynamic environments. The localization algorithm was implemented on the FPGA board “Zynq MiniZed 7007s evaluation board” using Xilinx blocks in Simulink, and the generated code was converted into VHDL using Xilinx System Generator. The algorithm was simulated and synthesized using “Vivado” software. In fact, the proposed system was evaluated by comparing the performances achieved through two different implementations: Hardware and Software implementation. In effect, the performance of FPGA hardware implementation presents a new achievement in localization due to its easy testing and fast implementation. Our results show that this approach can efficiently locate unknown nodes with good latency and high accuracy. In fact, the execution time of the FPGA-integrated algorithm is reduced by about 60 times compared to the software implementation and the power consumption is about 100 mW, which proves the suitability of FPGA for localization in WSNs, offering a promising solution for various mobile WSN applications.

Abstract As localization represents the main core of various wireless sensor network applications, several localization algorithms have been suggested in wireless sensor network research. In this article, we put forward an iterative bounding box algorithm enhanced by a Kalman filter to refine the unknown node’s estimated position. In fact, several research efforts are currently in progress to extend the 2D positioning algorithm in WSNs to 3D that reflects reality and the most practical applications. Subsequently, we replace a large number of GPS-equipped anchors with a single mobile anchor. In our studies, we consider the type of range-free sensor network exploiting the wireless sensors connectivity. We assess the performance of our algorithm using exhaustive experiments on several isotropic and anisotropic topologies. Our proposed algorithm can fulfill the joint goals of algorithm transparency and accuracy for various scenarios by evaluating parameters such as localization accuracy whilst changing other simulation parameters such as the effect of communication range, mobile anchor node position and sensor node deployment topology. It has been proven by the results of the experiments that the proposed algorithm effectively reduces the location error without requiring more equipment or increasing the communication cost.

Abstract An automated computer‐assisted medical diagnosis that combines latest medical approaches and the advanced machine learning algorithms is a very crucial multidisciplinary technology, generating correct and noninvasive diagnoses of multiple diseases like breast cancer. The work proposed in this article focuses on the development of a biomedical computer‐assisted diagnosis model that can classify digital mammography as normal (healthy) or abnormal, and further, as malignant or benign. The proposed approach employs the discrete Chebyshev transform to extract the features. Then, the kernel principal component analysis technique is used to extract the discriminating features from the original feature vector. Subsequently, an optimized kernel extreme learning machine is proposed as a classifier to detect the tumors present in the mammographic images. Because the efficiency of the proposed classifier depends on its characteristic kernel variable, the main idea of the present work is to choose the most appropriate features from the downsized feature set and simultaneously obtain the optimized value of the aforementioned parameter. To validate the efficiency of the proposed work, the proposed scheme is performed on two publicly available data sets, namely the Mammographic Image Analysis Society data set and the INbreast data set. From the experimental analysis and its results, it is showed that for both normal–abnormal and malignant–benign classification, the proposed approach results in accuracy of 100% for the first data set. However, in the case of malignant–benign classification, the proposed approach gives an accuracy of 99.93% for the second data set. Further, it is also observed that the proposed approach exhibits highest performance as compared to that of the other approaches. Additionally, the ANOVA test is evaluated to demonstrate that the achievement of the proposed approach is significantly good than that of the other existing approaches.

Localization of cerebral electrical activity of emotional states on the basis of electrophysiological recordings is an important area of investigation in recent years. This field was explored to locate the sources of the emotions in the cortex. The theory that every emotion can have a unique trigger center in the cortex was followed. A precise and accurate method was used, the Trilateration, recognized in the GPS networks, which deduces the points of interest from the distances. This method gave the exact coordinates of the generating points of emotions in the surface area of cortex under the influence of modulating thalamic nuclei. It was found that the energies are stronger in the occipital and parietal part of the brain. Moreover the frontal part plays the role of inhibitor and stimulator of emotions.

Many applications in wireless sensor network (WSN) involve the execution of multiple computationally heavy in-network processing tasks. Collaborative in-network processing among different sensor nodes is critical due to the limited capability of a single node. Task allocation is required to assign efficiently the workload of each task. In this paper, Logic Gate-based Evolutionary Algorithm (LGEA) is introduced which implements the logic gate mechanism in order to solve the task allocation problem. Each individual stands for a potential task allocation solution. The overall problem is formulated as a binary multiobjective optimization problem. The task workload and connectivity are the constraints that must be satisfied. The aim is to minimize the number of active nodes, the computation and the communication load distribution. Simulations confirm the potential of the logic gate mechanism to find the optimized task allocation scheme. The LGEA also outperforms the binary approach based on the Binary Particle Swarm Optimization (BPSO). Therefore, the Logic operation represents an original strategy and an efficient procedure for the binary optimization.

Most scheduling problems are highly complex combinatorial problems. However, stochastic methods such as genetic algorithm yield good solutions. In this paper, we present a controlled genetic algorithm (CGA) based on fuzzy logic and belief functions to solve job-shop scheduling problems. For better performance, we propose an efficient representational scheme, heuristic rules for creating the initial population, and a new methodology for mixing and computing genetic operator probabilities.