Localisation algorithm with node selection under power constraint in software‐defined sensor networks

Zhu, Yaping; Xing, Song; Zhang, Yueyue; Yan, Feng; Shen, Lianfeng

doi:10.1049/iet-com.2017.0077

Cited by 13 publications

(3 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They proposed a centralized scheme based on software-defined networking (SDN) to minimize active anchors and prolong network lifetime. Simulations show this scheme reduces active anchors, ensures desired number of anchors, and reduce energy usage; but the plan can slightly reduce localization accuracy, after that Y. Zhu et al [11] developed an improved localization algorithm for softwaredefined sensor networks (SDSNs) using a node-selection strategy under network power constraints. This algorithm uses the Software-Defined Networking (SDN) technique for centralized control and uses the Cramer-Rao lower bound (A-CRLB) value to evaluate each node's contribution to localization accuracy.…”

Section: Introductionmentioning

confidence: 99%

Improved Accurate Localization using PSO and the Weighted Dijkstra Algorithm in Software Defined Wireless Sensor Networks

Baraa Abbas Shahal

2023

IJRITCC

View full text Add to dashboard Cite

Wireless Sensor Networks (WSNs) are crucial in various fields, including monitoring the environment, surveillance, and healthcare. They rely on localization services for accurate data transfer and optimal network performance. Traditional WSN techniques struggle to adapt to dynamic environmental changes beyond the intended task scope. A synergy between Software-Defined Networking (SDN) and WSN has been suggested to address this issue. This research paper presents proposed approach for machine learning-based localization in Software Defined Wireless Sensor Networks (SDWSNs) using Particle Swarm Optimization (PSO) technique and the Weighted Dijkstra algorithm. PSO technique is used for clustering, the weighted Dijkstra algorithm (WDA) for finding the shortest path and sending data packets, and machine learning algorithms like AdaBoost and Naïve Bayes for data classification. The effectiveness of the proposed approach is measured using energy consumption, throughput, network lifespan, and packet delivery ratio, outperforming existing models like OEERP, LEACH, DRINA, and BCDCA. The machine learning algorithms' performance is also evaluated, with Naïve Bayes achieving the highest accuracy of 78.24% and AdaBoost 98.90%.

show abstract

Section: Introductionmentioning

confidence: 99%

Improved Accurate Localization using PSO and the Weighted Dijkstra Algorithm in Software Defined Wireless Sensor Networks

Baraa Abbas Shahal

2023

IJRITCC

View full text Add to dashboard Cite

show abstract

“…The localization algorithms use few location-aware sensor nodes that are called reference nodes and apply different methods to estimate the proximity between location unknown nodes and location-aware reference nodes [14]. The obtained proximity measures are further processed using different algorithms such as convex algorithm [15], optimized distance [16], swarm intelligence algorithms [17], Hessian regularization regression [18], self localization protocol [19], deep neural network [20], sine cosine algorithm [21].…”

Section: Introductionmentioning

confidence: 99%

Localization of isotropic and anisotropic wireless sensor networks in 2D and 3D fields

Bhat

Santhosh

2021

Telecommun Syst

View full text Add to dashboard Cite

Internet of Things (IoT) has changed the way people live by transforming everything into smart systems. Wireless Sensor Network (WSN) forms an important part of IoT. This is a network of sensor nodes that is used in a vast range of applications. WSN is formed by the random deployment of sensor nodes in various fields of interest. The practical fields of deployment can be 2D or 3D, isotropic or anisotropic depending on the application. The localization algorithms must provide accurate localization irrespective of the type of field. In this paper, we have reported a localization algorithm called Range Reduction Based Localization (RRBL). This algorithm utilizes the properties of hop-based and centroid methods to improve the localization accuracy in various types of fields. In this algorithm, the location unknown nodes identify the close-by neighboring nodes within a predefined threshold and localize themselves by identifying and reducing the probable range of existence from these neighboring nodes. The nodes which do not have enough neighbors are localized using the least squares method. The algorithm is tested in various irregular and heterogeneous conditions. The results are compared with a few state-of-the-art hop-based and centroid-based localization techniques. RRBL has shown an improvement in localization accuracy of 28% at 10% reference node ratio and 26% at 20% reference node ratio when compared with other localization algorithms.

show abstract

“…Advances in Micro-Electro-Mechanical System (MEMS) technology, wireless communication, and digital electronics have led to the proliferation of WSNs [3]. With the advancement of sensor technologies, WSNs are envisioning a wide variety of promising services in many fields such as search-and-rescue operations, environmental monitoring, precision agriculture, smart transportation, military surveillance, event detection (fires, floods, and hailstorms), to name a few [4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Is Localization of Wireless Sensor Networks in Irregular Fields a Challenge?

Bhat

Santhosh

2020

Wireless Pers Commun

View full text Add to dashboard Cite

Wireless sensor networks have been considered as an emerging technology for numerous applications of cyber-physical systems. These applications often require the deployment of sensor nodes in various anisotropic fields. Localization in anisotropic fields is a challenge because of the factors such as non-line of sight communications, irregularities of terrains, and network holes. Traditional localization techniques, when applied to anisotropic or irregular fields, result in colossal location estimation errors. To improve location estimations, this paper presents a comparative analysis of available localization techniques based on taxonomy framework. A detailed discussion on the importance of localization of sensor nodes in irregular fields from the reported real-life applications is presented along with challenges faced by existing localization techniques. Further, taxonomy based on techniques adopted by localization methods to address the effects of irregular fields on location estimations is reported. Finally, using the designed taxonomy framework, a comparative analysis of different localization techniques addressing irregularities and the directions towards the development of an optimal localization technique is addressed.

show abstract

Localisation algorithm with node selection under power constraint in software‐defined sensor networks

Cited by 13 publications

References 28 publications

Improved Accurate Localization using PSO and the Weighted Dijkstra Algorithm in Software Defined Wireless Sensor Networks

Improved Accurate Localization using PSO and the Weighted Dijkstra Algorithm in Software Defined Wireless Sensor Networks

Localization of isotropic and anisotropic wireless sensor networks in 2D and 3D fields

Is Localization of Wireless Sensor Networks in Irregular Fields a Challenge?

Contact Info

Product

Resources

About