One of the big data provider in the future of the Internet of Things (IoT) is the Periodic Wireless Sensor Networks (PWSNs) because of the widespread use of this type of networks in various real life applications. The amount of data clearly grows at an unexpected rate. The high-density deployment of the sensor nodes will lead to high data redundancy in the collected readings of the sensor nodes. An energy-saving data aggregation may be an essential way to remove the data redundancy. In this article, we propose a Distributed Data Aggregation based Modified K-means (DiDAMoK) Technique for enhancement the lifetime of the PWSNs. DiDAMoK is distributed inside each sensor node. It works into periods. Each period is composed of three stages. First, the sensor readings are collected and saved in the sensor node. Second, the modified K-means is employed on these readings to convert them into clusters of readings. The number of clusters is dynamic and depends on the nature of collected readings. In the third stage, One representative reading of each cluster will be transmitted to the sink. The performance of the DiDAMoK technique is evaluated using OMNeT++ network simulator and based on real sensed data of a WSN. Simulation results explain that our DiDAMoK technique can efficiently decrease the consumed energy of the whole PWSN due to reducing the sensed readings number transmitted to the sink node while keeping a suitable data accuracy at the sink.

Popularity of wireless sensor networks (WSNs) is increasing day a day where hundreds or thousands of applications are explored. In most of such applications, the need of gathering data periodically about the monitored environment beside the limited, generally irreplaceable, power sensor sources make energy conservation and big data gathering reduction two fundamental challenges in such networks. In this paper, we propose an Adaptive Distributed Data Gathering (ADiDaG) technique for saving energy in periodic WSN applications. ADiDaG works into rounds where each round consists of three phases: data gathering, sampling decision, and transmission. These phases respectively use Map reduce, longest common subsequence similarity and grouping approach in order to search data redundancy and adapt sensor sampling rate at each round. The performance of ADiDaG is evaluated based on both simulation and experimentations where the obtained results show significant energy savings and high accurate data gathering compared to existing approaches.

Over the last years, wireless sensor networks have gained considerable popularity due to their incredible potential in monitoring a wide range of areas. In such networks, areas are monitored by a huge number of sensors via three different ways: on-demand, event-based or periodic-based. However, researchers’ effort have currently focused on the periodic monitoring due its capability of ensuring continuous real time data collection of the desired areas. Indeed, periodic monitoring, usually referred to periodic wireless sensor networks (PWSNs), impose additional challenges compared to other types of monitoring. Data management is a mainly challenging task for PWSNs due to the huge amounts of collected data required in such networks. To the best of our knowledge, this paper represents the first formal overview about PWSNs, their potential applications and their challenges. Moreover, we describe the data management as a big challenge for PWSNs while highlighting and categorizing, based on some performance measures, different algorithms designed for data collection, aggregation and correlation in such networks. Finally, open research issues of PWSNs are also suggested and put forward in this paper.

Unmanned aerial vehicles (UAVs) play pivotal roles in various applications, from surveillance to delivery services. Efficient path planning for UAVs in dynamic environments with obstacles and moving landing stations is essential to ensure safe and reliable operations. In this study, we propose a novel approach that combines the A* algorithm with the grey wolf optimizer (GWO) for path planning, referred to as GW-A*. Our approach enhances the traditional A algorithm by incorporating weighted nodes, where the weights are determined based on the distance from obstacles and further optimized using GWO. A simulation using dynamic factors such as wind direction and wind speed, which affect the quadrotor UAV in the presence of obstacles, was used to test the new approach, and we compared it with the A* algorithm using various heuristics. The results showed that GW-A* outperformed A* in most scenarios with high and low wind speeds, offering more efficient paths and greater adaptability.

Background: Health care insurance fraud is on the rise in many ways, such as falsifying information and hiding third-party liability. This can result in significant losses for the medical health insurance industry. Consequently, fraud detection is crucial. Currently, companies employ auditors who manually evaluate records and pinpoint fraud. However, an automated and effective method is needed to detect fraud with the continually increasing number of patients seeking health insurance. Blockchain is an emerging technology and is constantly evolving to meet business needs. With its characteristics of immutability, transparency, traceability, and smart contracts, it demonstrates its potential in the health care domain. In particular, self-executable smart contracts are essential to reduce the costs associated with traditional paradigms, which are mostly manual, while preserving privacy and building trust among health care stakeholders, including the patient and the health insurance networks. However, with the proliferation of blockchain development platform options, selecting the right one for health care insurance can be difficult. This study addressed this void and developed an automated decision map recommender system to select the most effective blockchain platform for insurance fraud detection. Objective: This study aims to develop smart contracts for detecting health care insurance fraud efficiently. Therefore, we provided a taxonomy of fraud scenarios and implemented their detection using a blockchain platform that was suitable for health care insurance fraud detection. To automatically and efficiently select the best platform, we proposed and implemented a decision map–based recommender system. For developing the decision-map, we proposed a taxonomy of 102 blockchain platforms. Methods: We developed smart contracts for 12 fraud scenarios that we identified in the literature. We used the top 2 blockchain platforms selected by our proposed decision-making map–based recommender system, which is tailored for health care insurance fraud. The map used our taxonomy of 102 blockchain platforms classified according to their application domains. Results: The recommender system demonstrated that Hyperledger Fabric was the best blockchain platform for identifying health care insurance fraud. We validated our recommender system by comparing the performance of the top 2 platforms selected by our system. The blockchain platform taxonomy that we created revealed that 59 blockchain platforms are suitable for all application domains, 25 are suitable for financial services, and 18 are suitable for various application domains. We implemented fraud detection based on smart contracts. Conclusions: Our decision map recommender system, which was based on our proposed taxonomy of 102 platforms, automatically selected the top 2 platforms, which were Hyperledger Fabric and Neo, for the implementation of health care insurance fraud detection. Our performance evaluation of the 2 platforms indicated that Fabric surpassed Neo in all performance metrics, as depicted by our recommender system. We provided an implementation of fraud detection based on smart contracts.

Nowadays, driving accidents are considered one of the most crucial challenges for governments and communities that affect transportation systems and peoples lives. Unfortunately, there are many causes behind the accidents; however, drowsiness is one of the main factors that leads to a significant number of injuries and deaths. In order to reduce its effect, researchers and communities have proposed many techniques for detecting drowsiness situations and alerting the driver before an accident occurs. Mostly, the proposed solutions are visually-based, where a camera is positioned in front of the driver to detect their facial behavior and then determine their situation, e.g., drowsy or awake. However, most of the proposed solutions make a trade-off between detection accuracy and speed. In this paper, we propose a novel Visual-based Alerting System for Detecting Drowsy Drivers (VAS-3D) that ensures an optimal trade-off between the accuracy and speed metrics. Mainly, VAS-3D consists of two stages: detection and classification. In the detection stage, we use pre-trained Haar cascade models to detect the face and eyes of the driver. Once the driver’s eyes are detected, the classification stage uses several pre-trained Convolutional Neural Network (CNN) models to classify the driver’s eyes as either open or closed, and consequently their corresponding situation, either awake or drowsy. Subsequently, we tested and compared the performance of several CNN models, such as InceptionV3, MobileNetV2, NASNetMobile, and ResNet50V2. We demonstrated the performance of VAS-3D through simulations on real drowsiness datasets and experiments on real world scenarios based on real video streaming. The obtained results show that VAS-3D can enhance the accuracy detection of drowsy drivers by at least $7.5\%$ (the best accuracy reached was $95.5\%$) and the detection speed by up to $57\%$ (average of $0.25$ ms per frame) compared to other existing models.