Distance Estimation Based on RSSI Improvements of Orientation Aware Nodes

Ivanic, Mirko; Mezei, Ivan

doi:10.1109/zinc.2018.8448660

Cited by 16 publications

(10 citation statements)

References 7 publications

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“…4.6. RSSI RSSI corrupts as the inverse square of distance amid receiver and sender based on Fris [35]. It can be modeled as shown in Equation ( 26), wherein B refers to distance and is computed as shown in Equation (27).…”

Section: Trustmentioning

confidence: 99%

Optimal Cluster Head Selection in WSN with Convolutional Neural Network-Based Energy Level Prediction

Gurumoorthy¹,

Subhash

Prado

et al. 2022

Sensors

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Currently, analysts in a variety of nations have developed various WSN clustering protocols. The major characteristic is the Low Energy Adaptive Clustering Hierarchy (LEACH), which attained the objective of energy balance by sporadically varying the Cluster Heads (CHs) in the region. Nevertheless, because it implements an arbitrary number system, the appropriateness of CH is complete with suspicions. In this paper, an optimal cluster head selection (CHS) model is developed regarding secure and energy-aware routing in the Wireless Sensor Network (WSN). Here, optimal CH is preferred based on distance, energy, security (risk probability), delay, trust evaluation (direct and indirect trust), and Received Signal Strength Indicator (RSSI). Here, the energy level is predicted using an improved Deep Convolutional Neural Network (DCNN). To choose the finest CH in WSN, Bald Eagle Assisted SSA (BEA-SSA) is employed in this work. Finally, the results authenticate the effectiveness of BEA-SSA linked to trust, RSSI, security, etc. The Packet Delivery Ratio (PDR) for 100 nodes is 0.98 at 500 rounds, which is high when compared to Grey Wolf Optimization (GWO), Multi-Objective Fractional Particle Lion Algorithm (MOFPL), Sparrow Search Algorithm (SSA), Bald Eagle Search optimization (BES), Rider Optimization (ROA), Hunger Games Search (HGS), Shark Smell Optimization (SSO), Rider-Cat Swarm Optimization (RCSO), and Firefly Cyclic Randomization (FCR) methods.

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Section: Trustmentioning

confidence: 99%

Optimal Cluster Head Selection in WSN with Convolutional Neural Network-Based Energy Level Prediction

Gurumoorthy¹,

Subhash

Prado

et al. 2022

Sensors

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“…14 Field environment monitoring, rehabilitative therapy, and battlefield research have all made extensive use of wireless sensor networks (WSNs). 15,16 For an optimal clustering process, the most appropriate response regarding every possible result is optimization. To identify the optimal solution, many optimization methodologies may be applied.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Mobile Ad Hoc Network cluster based routing: Energy prediction via improved deep learning technique

Murugan,

Unhelkar

2024

Int J Communication

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SummaryMobile Ad Hoc Network (MANETs) are indeed autonomous, fast‐deployable wireless networks that are ideal for communications in areas with limited radio infrastructure, outdoor events, military operations, and disaster relief. But the primary problem is with routing because of the nodes' mobility. This paper proposes a cluster‐based routing under energy prediction via deep learning techniques. Here, proposes a new model termed as Concatenation of Convolutional with Max‐Avg Pooling layer in Deep Convolutional Neural Network (CCMAP‐DCNN) for energy prediction, in which additional layers are inserted into the extant DCNN structure to ensure the effectiveness of energy prediction. As a result, clusters are created by grouping the nodes together. Constraints like energy, trust, distance, and latency are taken into account while choosing the cluster leader. For cluster head selection and optimal routing, proposes a new hybrid Namib Beetle Upgraded Jellyfish Search Optimization (NBUJSO) algorithm that utilizes the NBO strategy to the JSO algorithm making the selection process more optimal. The best route is then chosen by the optimum routing method, which takes into account variables like network quality and mobility to send data packets from source to destination as efficiently as possible. Finally, the data aggregation process is followed for eliminating the redundant transmission.

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“…In the case of RF-based localisation systems the fundamentals ranging techniques to estimate the distance from two nodes are: Received Signal Strength (RSS); Time of Arrival (ToA); Time Difference of Arrival (TDoA) and Angle of Arrival (AoA). RSS is the simplest approach to estimate the distance between two nodes [23], [24]. In this case, the path loss model is exploited to estimate the distance by measuring the signal's received power.…”

Section: Introductionmentioning

confidence: 99%

Scale up to infinity: the UWB Indoor Global Positioning System

Santoro

Nardello

Brunelli

et al. 2021

2021 IEEE International Symposium on Robotic and Sensors Environments (ROSE)

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Determining assets position with high accuracy and scalability is one of the most investigated technology on the market. The accuracy provided by satellites-based positioning systems (i.e., GLONASS or Galileo) is not always sufficient when a decimeter-level accuracy is required or when there is the need of localising entities that operate inside indoor environments. Scalability is also a recurrent problem when dealing with indoor positioning systems. This paper presents an innovative UWB Indoor GPS-Like local positioning system able to tracks any number of assets without decreasing measurements update rate. To increase the system's accuracy the mathematical model and the sources of uncertainties are investigated. Results highlight how the proposed implementation provides positioning information with an absolute maximum error below 20 cm. Scalability is also resolved thanks to DTDoA transmission mechanisms not requiring an active role from the asset to be tracked.

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Distance Estimation Based on RSSI Improvements of Orientation Aware Nodes

Cited by 16 publications

References 7 publications

Optimal Cluster Head Selection in WSN with Convolutional Neural Network-Based Energy Level Prediction

Optimal Cluster Head Selection in WSN with Convolutional Neural Network-Based Energy Level Prediction

Optimizing Mobile Ad Hoc Network cluster based routing: Energy prediction via improved deep learning technique

Scale up to infinity: the UWB Indoor Global Positioning System

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