Localization by Salp Swarm Optimization with Doppler Effect in Wireless Sensor Networks

Wireless sensor networks (WSNs) have lately been widely used due to its abundant practice in methods that have to be spread over a large range. In any wireless application, the position precision of node is an important core component. Node localization intends to calculate the geographical coordinates of unknown nodes by the assistance of known nodes. In a multidimensional space, node localization is well-thought-out as an optimization problem that can be solved by relying on any metaheuristic’s algorithms for optimal outputs. This paper presents a new localization model using Salp Swarm optimization Algorithm with Doppler Effect (LOSSADE) that exploit the strengths of both methods. The Doppler effect iteratively considers distance between the nodes to determine the position of the nodes. The location of the salp leader and the prey will get updated using the Doppler shift. The performance validation of the presented approach simulated by MATLAB in the network environment with random node deployment. A detailed experimental analysis takes place and the results are investigated under a varying number of anchor nodes, and transmission range in the given search area. The obtained simulation results are compared over the traditional algorithm along with other the state-of-the-art methods shows that the proposed LOSSADE model depicts better localization performance in terms of robustness, accuracy in locating target node position and computation time.

Enhance the Performance of Wireless Sensor Network based on Software Define Network and Kalman Filter

The paper proposes a methodology for estimating packet flowing at the sensor level in SDN-WSN based on the partial congestion controller with Kalman filter. Furthermore, the actual purpose of proposing such methodology for predicting in advance the subsequent step of packet flow, and that will consequently contribute in reducing the congestion that might happen. The model proposed (SDN with Kalman filter) is optimized using congestion controller, the methodology of proposed work, the first step random distributed of random node, the apply the Kmean cluster of select the head cluster node in, the connected the network based on LEACH protocol. in this work proposed SDN with Kalman filter for control on network and reduce error of data, where achieve by add buffer memory for each nodes and head cluster to store the data, and SDN control on transmit ion data and receiver data, before transmit apply the Kalman filter on data to reduce error data. The proposed technique, according to simulation findings, extends the network's lifetime by over 30% more than typical WSNs, the reduce the average density of memory to 20% than traditional WSN, and the increase the average capacity of memory to 20% than traditional WSN.

Detection of Email Spam using Natural Language Processing Based Random Forest Approach

An unsolicited means of digital communications in the internet world is the spam email, which could be sent to an individual or a group of individuals or a company. These spam emails may cause serious threat to the user i.e., the email addresses used for any online registrations may be collected by the malignant third parties (spammers) and they expose the genuine user to various kinds of attacks. Another method of spamming is by creating a temporary email register and receive emails that can be terminated after some certain amount of time. This method is well suited for misusing those temporary email addresses for sending free spam emails without revealing the spammers real account details. These attacks create major problems like theft of user credentials, lack of storage, etc. Hence it is essential to introduce an efficient detection mechanismthrough feature extraction and classification for detecting spam emails and temporary email addresses. This can be accomplished through a novel Natural Language Processing based Random Forest (NLP-RF) approach. With the help of our proposed approach, the spam emails are reduced and this method improves the accuracy of spam email filtering, since the use of NLP makes the system to detect the natural languages spoken by people and the Random Forest approach uses multiple decision trees and uses a random node for filtering the spams.

The Surprising Power of Graph Neural Networks with Random Node Initialization

Graph neural networks (GNNs) are effective models for representation learning on relational data. However, standard GNNs are limited in their expressive power, as they cannot distinguish graphs beyond the capability of the Weisfeiler-Leman graph isomorphism heuristic. In order to break this expressiveness barrier, GNNs have been enhanced with random node initialization (RNI), where the idea is to train and run the models with randomized initial node features. In this work, we analyze the expressive power of GNNs with RNI, and prove that these models are universal, a first such result for GNNs not relying on computationally demanding higher-order properties. This universality result holds even with partially randomized initial node features, and preserves the invariance properties of GNNs in expectation. We then empirically analyze the effect of RNI on GNNs, based on carefully constructed datasets. Our empirical findings support the superior performance of GNNs with RNI over standard GNNs.

Sampling Graphlets of Multiplex Networks: A Restricted Random Walk Approach

Graphlets are induced subgraph patterns that are crucial to the understanding of the structure and function of a large network. A lot of effort has been devoted to calculating graphlet statistics where random walk-based approaches are commonly used to access restricted graphs through the available application programming interfaces (APIs). However, most of them merely consider individual networks while overlooking the strong coupling between different networks. In this article, we estimate the graphlet concentration in multiplex networks with real-world applications. An inter-layer edge connects two nodes in different layers if they actually belong to the same node. The access to a multiplex network is restrictive in the sense that the upper layer allows random walk sampling, whereas the nodes of lower layers can be accessed only through the inter-layer edges and only support random node or edge sampling. To cope with this new challenge, we define a suit of two-layer graphlets and propose novel random walk sampling algorithms to estimate the proportion of all the three-node graphlets. An analytical bound on the sampling steps is proved to guarantee the convergence of our unbiased estimator. We further generalize our algorithm to explore the tradeoff between the estimated accuracy of different graphlets when the sample budget is split into different layers. Experimental evaluation on real-world and synthetic multiplex networks demonstrates the accuracy and high efficiency of our unbiased estimators.

Orthodontic Treatment Based on Wearable Mirror-Type Oral Prosthetic Tongue Flap without Bracket Correction

White teeth can make people full of confidence and satisfy the concept of modern life from the love of beauty. Due to the fusion of computer-aided design and teeth, invisible orthodontics has become the focus of research. Invisible orthodontic treatment technology can predict the results of orthodontics. How to automatically calculate the position and posture of the teeth in the middle stage of orthodontics is the key point of the treatment technology. In order to solve this problem, this article is divided into two parts to start research. Aiming at the problem of tooth orthodontic path planning, quaternion is used to define the tooth posture, combined with the initial posture and target posture of the tooth. A two-stage method is given to plan a collision-free path for the orthodontic tooth. In the first stage, the quaternion spherical linear interpolation and position linear interpolation are used to obtain the intermediate posture of the tooth during orthodontics, and the initial value of the orthodontic stage is obtained, and the obtained intermediate posture is used as a sampling node to apply to the next stage. In the second phase, considering the problem of orthodontic collision and interference, a scheme for calculating the priority of orthodontics is proposed, and the random node expansion part in the RRT (Rapid-exploration Random Tree) algorithm is improved. The initial value of the orthodontic phase is used to calculate the initial value of the iteration. Finally, a path with no collision and the least number of orthodontic stages is searched from the random tree of each tooth node. The experimental results and analysis show that this method can quickly and effectively solve the orthodontic path of teeth, and it is used clinically. The clasp-free invisible correction technology pushes the molars far away to leave gaps for treating patients with mild to moderate overcrowding. The treatment time should be reduced by at least 30%; the stability of the gaps and the long-term healing effect of the treatment provide a reference.

Farmer Dissemination of Seeds (FDS) Deployment Method Applied to Simulated Leach Protocol

Recent communication technologies in the Wireless Sensor Networks WSN enable us to implement and construct various physical sensing nodes with electronic circuits for transmitting and receiving tasks. Low-Energy Adaptive Clustering Hierarchy LEACH is a well-known routing protocol used and implemented in researches and articles; also, there are various attempts from the researchers to modify it to achieve the best results. Since almost all of the articles considered deployment either predefined or randomly depleted for the whole reign of interest RoI. A sophisticated random node deployment method is proposed, named Farmer Disseminating the Seeds FDS, the farmer walks with almost uniform steps and parallel lines to cover the whole RoI. A formation of a uniform grid with deviated random local distances from grid crossings considered as a predefined number of normal nodes with one advance node that has double battery energy. FDS is used to improve the importance of deployment methods as an additive parameter in estimating lifetime and energy consumption in routing protocols. Traditional random deployment and FDS methods are compared.

Network hubs cease to be influential in the presence of low levels of advertising

Attempts to find central “influencers,” “opinion leaders,” “hubs,” “optimal seeds,” or other important people who can hasten or slow diffusion or social contagion has long been a major research question in network science. We demonstrate that opinion leadership occurs only under conventional but implausible scope conditions. We demonstrate that a highly central node is a more effective seed for diffusion than a random node if nodes can only learn via the network. However, actors are also subject to external influences such as mass media and advertising. We find that diffusion is noticeably faster when it begins with a high centrality node, but that this advantage only occurs in the region of parameter space where external influence is constrained to zero and collapses catastrophically even at minimal levels of external influence. Importantly, nearly all prior agent-based research on choosing a seed or seeds implicitly occurs in the network influence only region of parameter space. We demonstrate this effect using preferential attachment, small world, and several empirical networks. These networks vary in how large the baseline opinion leadership effect is, but in all of them it collapses with the introduction of external influence. This implies that, in marketing and public health, advertising broadly may be underrated as a strategy for promoting network-based diffusion.