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Summary: Wireless Multimedia Sensor Networks (WMSNs) are composed of a large number of sensor nodes that are distributed in a region to collect and transmit data. Video transmission is one of the most important applications of WMSNs because it can provide critical information about monitored areas. WMSNs face challenges related to energy consumption, bandwidth usage, and network congestion related to huge amounts of data collected by sensors. To tackle this problem, this paper proposes the Distributed Video Transmission Reduction Approach for Energy Saving in WMSN (DiViTRA). The method involves two phases: sensing and transmission phases. DiViTRA achieves frame rate adaptation to reduce the number of captured video frames and save energy during the sensing phase. In the transmission phase, three effective techniques, ORB (Oriented FAST and Rotated BRIEF), Brute‐Force (BF) Matcher, and Grid‐based Motion Statistics (GMS) are applied to decide whether to transmit the current captured frame or remove it and adjust the frame capturing rate of the video sensor accordingly. In the case of frame transmission, the DiViTRA approach compresses the frame using two data reduction approaches: PCA (Principal Component Analysis) and Huffman encoding. Through simulations, DiViTRA demonstrates a 12% reduction in energy consumption, and 71% is a ratio of reduction in sent frames while preserving stream quality. The approach has been validated in scenarios involving critical events, showcasing its efficacy in maintaining data integrity during transmission. [ABSTRACT FROM AUTHOR]

Summary: In the modern world, it will be necessary to deploy a large number of sensor devices to sense everything around us in order to detect changes, risks, and hazards and to mitigate them. This increasing number of sensor devices represents an essential data provider in the Internet of Things (IoT). The devices generate and transmit a huge amounts of data which requires a large amount of storage and high processing power to come real‐time processing and speed up the network. It also leads to an increase in high energy consumption. Thus, it is important to remove redundant data to reduce the data transmission before sending it to the gateway while maintaining a good level of data quality. In this paper, a distributed energy‐efficient data reduction (DEDaR) approach based on prediction and compression to minimize the data transmission in IoT Networks is proposed. The DEDaR is used in periods to make decision. In each period, the autoregressive prediction (ARP) is used to predict the data of the next period and make a decision on whether to send the data of the current period to the gateway or not. In the case of data transmission, the redundant data are eliminated using an efficient compression approach based on adaptive piecewise constant approximation (APCA), symbolic aggregate approximation (SAX), and finally fixed code dictionary (FCD) based on Huffman encoding. The simulation results based on real‐sensed data show that the proposed DEDaR approach outperforms the other recent methods in terms of data reduction percentage, transmitted data size, energy consumption, and data accuracy. [ABSTRACT FROM AUTHOR]

Breast cancer has been recently considered as one of the broadly spread diseases that causes death among women. Early disease diagnosis is a critical aim in building the treatment policies and is extremely related to safety of patient. Therefore, there is a necessity for computer aided detection (CAD) in order to provide accurate and rapid diagnosis for breast cancer. Recently, many classification models utilizing machine learning approaches have been adopted and modified to diagnose breast cancer disease. Moreover, the performance of each model depends on different compositions such as the number and type of data features and the parameters of model. In order to enhance the performance of classification model, this research proposes a model using modified K-means algorithm to create a new training dataset of breast cancer which can highly improve the performance of support vector machine model. A modified K-means algorithm is also proposed to build a high quality training dataset that contributes significantly to reduce the training time of classifiers, and improve the performance of classifier. The proposed model handles the noise and irregularity in data and produce high quality dataset which represents all the cases of disease. The two recognized datasets Wisconsin Breast Cancer (WBC) and Wisconsin Diagnostic Breast Cancer (WDBC) have been used to examine and appraise the performance of the proposed model. The experimental results show that the proposed model has a significant performance compared to other previous works and with accuracy level of 98.067%, sensitivity of 100%, specificity of 94.811%, precision of 97.011% and finally with area under the curve related to the receiver operating characteristic of 97.406%. [ABSTRACT FROM AUTHOR]

The necessity of developing sufficient systems to monitor health conditions has increased due to the aging of the population and the prevalence of chronic diseases, creating a demand for remote health care systems that make use of biosensors. This article proposes an energy‐saving multisensor data sampling and fusion with decision‐making for the monitoring of patient health risk in wireless body sensor networks (WBSNs). The work consists of three steps: energy‐efficient sampling rate adaptation, multisensor data fusion, and decision‐making. The sampling is performed in each biosensor and it adapts its rate based on the local risk and the global risk in which global risk computed at the coordinator, where the data is fused afterward. Finally, decisions are made according to the risk level of the patient. The processing of these functions enables in real‐time the adoption of the biosensor sampling rates based on the dynamic risk level of each biosensor, and a corresponding decision is made whenever an emergency is detected. The performance of the suggested approach is evaluated using actual health datasets, and some of its aspects are put into comparison with an existing approach, such as the data reducing and energy‐consuming rates. The acquired results illustrate a decrease in the volume of gathered data, thus a significant energy saving has been made while preserving data accuracy and integrity. Moreover, presenting a data fusing model at the coordinator level by means of an early warning score system has assessed the health condition of patients and took an appropriate decision when detecting emergencies. [ABSTRACT FROM AUTHOR]

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Periodic Sensor Networks (PSNs) represent one of the essential elements of emerging Cyber-Physical System (CPS) designs because of their using in multiple applications. PSNs are one of the major contributors of the big data in the future. One fundamental challenge in PSNs is to periodically collect the large volume of data in an energy efficient way and then transmit them to the sink so as to enhance the network lifetime. Since sensor batteries have a limited lifetime, therefore, adaptive sampling method to periodic data collection is required to support energy-efficient data gathering and fusion of CPS. In this research, we suggest a protocol, called Distributed Adaptive DAta Collection protocol (DADAC), which collects periodically sensor readings and prolong the lifetime of a Periodic Sensor Network (PSN). The lifetime of DADAC protocol is divided into cycles. Each cycle is composed of four stages. First, data collection. Second, dimensionality reduction using Piecewise Aggregate Approximation (PAA) technique to reduce the amount of data collected by each sensor. Third, Frequency Reduction using SAX (Symbolic Aggregate approXimation) approach in order to remove the redundant data before send them to sink. Fourth, sampling rate adaptation based PAA similarity to acclimate its rate of sampling according to the dynamic modification of the monitored environment. DADAC allows each sensor to remove the redundant collected data and adapts its sampling rate in accordance with the monitored environment conditions. We conduct extensive simulation experiments on real sensor data by applying OMNeT++ network simulator to explain the effectiveness of the DADAC protocol in comparison with two other existing methods. [ABSTRACT FROM AUTHOR]

In Wireless Sensor Network (WSN), the dense sensor nodes deployment in the sensing field can be exploited in conserving the energy of the whole network, where the data of these nodes can be highly correlated. Therefore, it is necessary to turn off the unnecessary nodes that sense similar sensor readings so as to reduce the redundant sensed readings and decrease the communication overhead thus extend the WSN lifetime. This article suggests a Sensor Activity Scheduling (SAS) protocol for lifetime improvement of WSNs. SAS works in a periodic way. It exploits the spatial correlation among sensed sensor data in order to produce the best sensor activities schedule in WSNs. SAS composed of three phases: data collection, decision-based optimization, and sensing. SAS measures the similarity degree among the sensed data that collected in the first phase. It makes a decision of which sensors stay active during the sensing phase in each period and put the other nodes into low power sleep whilst keeping a good accuracy level to the received data at the sink to conserve the power and enhance the lifetime of the WSN. Several experiments based on real sensed data and by using OMNeT++ simulator demonstrate that SAS can save energy and extend the WSN lifetime efficiently compared with the other methods. [ABSTRACT FROM AUTHOR]

The most important problem in a Wireless Sensor Network (WSN) is to optimize the use of its limited energy provision, so that it can fulfil its monitoring task as long as possible. Among known available approaches that can be used to improve power management, lifetime coverage optimization provides activity scheduling which ensures sensing coverage while minimizing the energy cost. In this article an approach called Perimeter-based Coverage Optimization protocol (PeCO) is proposed. It is a hybrid of centralized and distributed methods: the region of interest is first subdivided into subregions and the protocol is then distributed among sensor nodes in each subregion. The novelty of the approach lies essentially in the formulation of a new mathematical optimization model based on the perimeter-coverage level to schedule sensors' activities. Extensive simulation experiments demonstrate that PeCO can offer longer lifetime coverage for WSNs compared to other protocols. [ABSTRACT FROM AUTHOR]