In recent years, Wireless Sensor Networks (WSNs) are attracting more attention in many fields as they are extensively used in a wide range of applications, such as environment monitoring, the Internet of Things, industrial operation control, electric distribution, and the oil industry. One of the major concerns in these networks is the limited energy sources. Clustering and routing algorithms represent one of the critical issues that directly contribute to power consumption in WSNs. Therefore, optimization techniques and routing protocols for such networks have to be studied and developed. This paper focuses on the most recent studies and algorithms that handle energy-efficiency clustering and routing in WSNs. In addition, the prime issues in these networks are discussed and summarized using comparison tables, including the main features, limitations, and the kind of simulation toolbox. Energy efficiency is compared between some techniques and showed that according to clustering mode “Distributed” and CH distribution “Uniform”, HEED and EECS are best, while in the non-uniform clustering, both DDAR and THC are efficient. According to clustering mode “Centralized” and CH distribution “Uniform”, the LEACH-C protocol is more effective.
In this paper, some commonly used hierarchical cluster techniques have been compared. A comparison was made between the agglomerative hierarchical clustering technique and the k-means technique, which includes the k-mean technique, the variant K-means technique, and the bisecting K-means, although the hierarchical cluster technique is considered to be one of the best clustering methods. It has a limited usage due to the time complexity. The results, which are calculated based on the analysis of the characteristics of the cluster algorithms and the nature of the data, showed that the bisecting K-means technique is the best compared to the rest of the other methods used.
One of the significant stages in computer vision is image segmentation which is fundamental for different applications, for example, robot control and military target recognition, as well as image analysis of remote sensing applications. Studies have dealt with the process of improving the classification of all types of data, whether text or audio or images, one of the latest studies in which researchers have worked to build a simple, effective, and high-accuracy model capable of classifying emotions from speech data, while several studies dealt with improving textual grouping. In this study, we seek to improve the classification of image division using a novel approach depending on two methods used to segment the images. The first
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Oil well logging, also known as wireline logging, is a method of collecting data from the well environment to determine subterranean physical properties and reservoir parameters. Measurements are collected against depth along the well's length, and many types of wire cabling tools depend on the physical property of interest. Well probes generally has a dynamic respon to changes in rock layers and fluid composition. These probes or well logs are legal documents that record the history of a well during the drilling stages until its completion. Well probes record the physical properties of the well, which must then be interpreted in petrographic terms to obtain the characteristics of the rocks and flui
... Show Moreole in all sta Oil well logging, also known as wireline logging, is a method of collecting data from the well environment to determine subterranean physical properties and reservoir parameters. Measurements are collected against depth along the well's length, and many types of wire cabling tools depend on the physical property of interest. Well probes generally has a dynamic respon to changes in rock layers and fluid composition. These probes or well logs are legal documents that record the history of a well during the drilling stages until its completion. Well probes record the physical properties of the well, which must then be interpreted in petrographic terms to obtain the characteristics of the rocks and fluids associated with
... Show MoreTarget tracking is a significant application of wireless sensor networks (WSNs) in which deployment of self-organizing and energy efficient algorithms is required. The tracking accuracy increases as more sensor nodes are activated around the target but more energy is consumed. Thus, in this study, we focus on limiting the number of sensors by forming an ad-hoc network that operates autonomously. This will reduce the energy consumption and prolong the sensor network lifetime. In this paper, we propose a fully distributed algorithm, an Endocrine inspired Sensor Activation Mechanism for multi target-tracking (ESAM) which reflecting the properties of real life sensor activation system based on the information circulating principle in the endocr
... Show MoreIn the presence of deep submicron noise, providing reliable and energy‐efficient network on‐chip operation is becoming a challenging objective. In this study, the authors propose a hybrid automatic repeat request (HARQ)‐based coding scheme that simultaneously reduces the crosstalk induced bus delay and provides multi‐bit error protection while achieving high‐energy savings. This is achieved by calculating two‐dimensional parities and duplicating all the bits, which provide single error correction and six errors detection. The error correction reduces the performance degradation caused by retransmissions, which when combined with voltage swing reduction, due to its high error detection, high‐energy savings are achieved. The res
... Show MoreAchieving reliable operation under the influence of deep-submicrometer noise sources including crosstalk noise at low voltage operation is a major challenge for network on chip links. In this paper, we propose a coding scheme that simultaneously addresses crosstalk effects on signal delay and detects up to seven random errors through wire duplication and simple parity checks calculated over the rows and columns of the two-dimensional data. This high error detection capability enables the reduction of operating voltage on the wire leading to energy saving. The results show that the proposed scheme reduces the energy consumption up to 53% as compared to other schemes at iso-reliability performance despite the increase in the overhead number o
... Show MoreExperimental tests were conducted to investigate the thermal performance (cooling effect) of water mist system consisting of 5μm volume median diameter droplets in reducing the heat gain entering a room through the roof and the west wall by reducing the outside surface temperature due to the evaporative cooling effect during the hot dry summer of Baghdad/Iraq. The test period
was Fifty one days during the months May, June, and July 2012. The single test day consists of 16 test hours starting from 8:00 am to 12:00 pm. The results showed a reduction range of 1.71 to 15.5℃ of the roof outside surface temperature and 21.3 to 76.6% reduction in the daily heat flux entering the room through the roof compared with the case of not using w
This study focused on spectral clustering (SC) and three-constraint affinity matrix spectral clustering (3CAM-SC) to determine the number of clusters and the membership of the clusters of the COST 2100 channel model (C2CM) multipath dataset simultaneously. Various multipath clustering approaches solve only the number of clusters without taking into consideration the membership of clusters. The problem of giving only the number of clusters is that there is no assurance that the membership of the multipath clusters is accurate even though the number of clusters is correct. SC and 3CAM-SC aimed to solve this problem by determining the membership of the clusters. The cluster and the cluster count were then computed through the cluster-wise J
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