An Overview of Data Aggregation Techniques with Special Sensing Intelligent Device Selection Approaches

Data Sensing Devices (DSD’s) have gained lot of traction for various use cases like border control, vehicle tracking. Data Sensing device network (DSDN) is shaped with the aid of combining lot of DSD’s across a random area. Like this multiple groups are formed. In each of the group the specific DSD is elected which is responsible for communication between two independent groups. Each of the group head has multiple attributes with first attribute based on distance, the second attribute based on remaining energy. These attributes will be input for the group head selection based on machine learning, The entire DSD’s inside a group are classified into HIGH, MEDIUM and LOW. The first priority will be given to HIGH followed by others for the primary group head selection. LEACH is a classical method used for transmission of chunks to the control center in a DSDN network. The selection of head DSD by LEACH will happen by making use of the random selection of DSD in each group using random probability selection mechanism. During the data chunk deliver the scanning process will happen from the initiator DSD to head DSD and from there the link is established with the base station (BS), the BS will then scan each group until the destination DSD is reached. The selection of head DSD by LEACH causes more holes in the DSDN because there are chances that the non-performer DSD can become a head DSD. Secondly for the transmission of chunks there is lot of back-and-forth propagation between the BS and the normal DSDs which reduces the battery level of the DSD by a large amount. The Energy based LEACH is modified on top of LEACH by measuring the energy of the DSDs and then selecting the group heads but suffers from multiple group head maintenance as well as more number of links. The proposed method will improve this by reducing the links used for end-to-end communication. In the proposed system the communication will happen based on initiator DSD, primary DSDs in different groups and then destination DSD which will avoid overhead compared to existing methods namely E-LEACH and LEACH. The proposed method is compared with LEACH and E-LEACH with respect to time taken, link count, energy consumption, residual energy measure, lifetime and overhead.

Section: ░ 3 Backgroundmentioning

confidence: 99%

A Novel Modified Energy and Throughput Efficient LOYAL UN-Supervised LEACH Method for Wireless Sensor Networks

Guruprakash

2023

“…The homes can be made intelligent system by making use of energy garner medium which can be used for transferring the gobbets to destination PSD in an effective manner [26]. The parameters like duty cycle changes, adaptation of transmission power, the quantum of data being transmitted can be controlled to have better Packet Delivery rate and can be increased by making use of energy garner grounded on solar bias [27]. When the PSDN has the PSD, whose energy has depleted also it'll be no longer usable to deliver the data gobbets, the PSD can be replaced or can recharged using a energy source which can solar or grounded on automatic energy creator [28].…”

Section: Lr-3mentioning

confidence: 99%

A Novel Buffer Packet Delivery Strategy for High Throughput and Better Health (HTBH) Method in Wireless Sensor Networks

V S,

Siddaraju

2023

There is massive call for the Packet Sender Device Network (PSDN) primarily based on tracking of areas, figuring out the consequences of climate, detection of enemy vehicles. The PSDN could have useful Packet Sender Devices (PSD’s) which can study the vicinity and then send the data from initial to receiver PSD. There are numerous constraints which have restrictions on the feature like battery, memory, and range. There is hierarchical community wherein the PSDs are spread on more than one area with every vicinity having their very own PSD’s whilst communique has to manifest among PSDs of various areas then it requires chief PSD in every vicinity which have to be elected primarily based on higher battery degree, distance to base station in addition to mobility of the PSD over a duration of time. LEACH suffers from side-to-side propagation among base station and PSDs. LEACH elects the pinnacle PSD primarily based on random possibility which has consequences of Good-Bad Ratio due to the fact there are possibilities that the PSD with low battery degree be selected as a head PSD. The proposed (HTBH) method will select the head PSD based on battery level, distance between base station and PSD, the mobility of PSD. In order to deliver the data to the processing lab PSDs of the area along with head PSD are used. The packets are classified into high and low priority so that preferences can be used during the data delivery process. Along with sending the current data packets even the data packets reside within the PSD which have to send towards the processing centre. Two policies namely single threshold and dual threshold are used to evict the packets within the PSD and then moving them towards processing centre. This study proposes a higher choice of head PSD with the aid of using considering computation of element which takes into consideration battery, distance, and mobility. The communique among the detection vicinity to receiver vicinity takes place with the assist of PSD’s and head PSD there with the aid of using decreasing range of hops. HTBH method is compared to LEACH and E-LEACH with respect various performance parameters.

“…This section provides a brief overview of current efforts to identify and categorize malignancies of the brain using MR images. The comparison of DL-based methods to the segmentation of medical imagery has showed promise in recent years [19]. A more robust feature vector can be generated by autonomously learning complex feature representations from training data.…”

Section: ░ 2 Literature Surveymentioning

confidence: 99%

MRI Intracranial Neoplasm Localization Using Convolution Neural Network Based Residual Block ResUnet: A Systematic Approach

Murthy,

Joshi,

et al. 2023

Analysis of intracranial neoplasm using multimodal MR images requires accurate and automatic segmentation. However, manually classifying tumors with similar structures or appearances in magnetic resonance imaging (MRI) with similar anatomy or appearances is more challenging, requiring experience to detect brain tumors. Precise segmentation of brain tumors gives clinicians with a foundation for surgical planning and treatment. Due to its capacity to segment brain tumor images automatically, Deep Neural Networks (DNN) have been widely used in image segmentation applications. To classify, segment and marking the occurrence of the brain tumor area accurately, we present custom Deep Convolution Neural Network (CNN) based Residual block U-Net (RB-ResUnet) architecture. Our technique is tested on publicly available Kaggle datasets utilizing quantitative metrics. Comparative results demonstrate that the custom CNN-based RB-ResUnet model can more reliably identify tumor locations and give accurate segmentation masks to tumor locations that are defined by bounding boxes. The findings of the experiment reveal that our proposed model RB-ResUnet can effectively aid in the identification, toxicological evaluation of the brain tumor and has clinical research as well as practical application.