Improved Recursive DV-Hop Localization Algorithm with RSSI Measurement for Wireless Sensor Networks

Messous, Salma; Liouane, Hend; Cheikhrouhou, Omar; Hamam, Habib

doi:10.3390/s21124152

Cited by 33 publications

(19 citation statements)

References 42 publications

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“…In the indoor visible light positioning system, the LED lamp is fixed to the ceiling as a signal transmitter to provide illumination during data transmission, led lighting emits a light signal with identification information (ID), the receiver recovers the original signal after processing the collected signal, and then uses the visible light positioning algorithm to calculate the receiver's [ 13 ] specific location.…”

Section: Visible Light Positioning System Analysismentioning

confidence: 99%

Research on Compression Sensing Positioning Algorithm of Indoor Complex Environment Visible Light Indoor Based on Hybrid APIT

2022

Computational Intelligence and Neuroscience

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In today’s highly urbanized world, indoor space is becoming more extensive and more complex, and under the increasingly urgent needs, indoor positioning has attracted people’s attention. With the rapid development of LED lighting technology, indoor positioning technology based on visible light communication has many advantages over traditional indoor positioning technology. Aiming at the influence of environmental factors such as noise and reflected light on the positioning accuracy, the compression perception theory is applied to the localization of visible light. The position of the receiving end in the positioning space is defined as a sparse variable in the discrete space. The power measurement matrix is expressed as the product of the observation matrix, and the sparse matrix and sparse vector in the compression perception theory are expressed. The traditional APIT algorithm is easy to misjudge unknown nodes in the triangle, resulting in low positioning accuracy of the algorithm. In this study, an indoor visible positioning algorithm based on hybrid APIT is proposed, which uses the area relationship of the triangle to determine the initial position of the unknown node, and then uses the tangent circle to further narrow the area where the unknown node may be located, and uses the hybrid centroid localization algorithm to obtain the estimated position of the unknown node.

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Section: Visible Light Positioning System Analysismentioning

confidence: 99%

Research on Compression Sensing Positioning Algorithm of Indoor Complex Environment Visible Light Indoor Based on Hybrid APIT

2022

Computational Intelligence and Neuroscience

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“…However, the drawback is that it needs homogeneously positioned IoT networks and the uniform amplitude reduction of signal strength in every direction. To overcome this drawback, improvements have been proposed in the literature [33][34][35]. Minimal mean square error measure for modifying mean hop distance [33] works well for isotropic topologies and improves location precision.…”

Section: Hop Count-basedmentioning

confidence: 99%

“…To overcome this drawback, improvements have been proposed in the literature [33][34][35]. Minimal mean square error measure for modifying mean hop distance [33] works well for isotropic topologies and improves location precision. However, for non-isotropic and anisotropic environments, this modification generates more errors while estimating the distance.…”

Section: Hop Count-basedmentioning

confidence: 99%

Survey of Localization for Internet of Things Nodes: Approaches, Challenges and Open Issues

2021

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With exponential growth in the deployment of Internet of Things (IoT) devices, many new innovative and real-life applications are being developed. IoT supports such applications with the help of resource-constrained fixed as well as mobile nodes. These nodes can be placed in anything from vehicles to the human body to smart homes to smart factories. Mobility of the nodes enhances the network coverage and connectivity. One of the crucial requirements in IoT systems is the accurate and fast localization of its nodes with high energy efficiency and low cost. The localization process has several challenges. These challenges keep changing depending on the location and movement of nodes such as outdoor, indoor, with or without obstacles and so on. The performance of localization techniques greatly depends on the scenarios and conditions from which the nodes are traversing. Precise localization of nodes is very much required in many unique applications. Although several localization techniques and algorithms are available, there are still many challenges for the precise and efficient localization of the nodes. This paper classifies and discusses various state-of-the-art techniques proposed for IoT node localization in detail. It includes the different approaches such as centralized, distributed, iterative, ranged based, range free, device-based, device-free and their subtypes. Furthermore, the different performance metrics that can be used for localization, comparison of the different techniques, some prominent applications in smart cities and future directions are also covered.

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“…Wireless sensor networks are less expensive, have lower power consumption, and are self-con gurable sensor nodes [1]. By considering these parameters, WSNs are used in areas like healthcare, for smart transportation, home monitoring, military tracking, environmental monitoring, national security purpose, and indoor navigation [2]. e sensing nodes sense the location and accordingly disseminate the data.…”

Section: Introductionmentioning

confidence: 99%

“…e range-based technique is based on pattern matching or connectivity measurements. It is categorized as (1) time of arriving (TOA), (2) time difference of arriving (TDOA), (3) received signaling strength indicator (RSSI), and (4) angle of arriving (AOA) [2].…”

Section: Introductionmentioning

confidence: 99%

Improved DV-Hop Algorithm Based on Swarm Intelligence for AI and IoT-Federated Applications in Industry 4.0

Zhang

2022

Mathematical Problems in Engineering

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In the Internet of Things (IoT) ecosystem, localization is critical for tracking and monitoring targets via nodes. The distance vector-hop (DV-Hop) technique is a good choice for localizing neighborhood in IoT networks. The conventional DV-Hop algorithm is a distributed localization approach that does not consider the distribution of the nodes into full deliberation when calculating the hop count from the source to destined nodes. The transfer distance and node positions thus do not attain higher efficiency while ascertaining the distance between sources and destined nodes. The study aims to resolve the pitfalls in the traditional algorithm by making enhancements in controlling the original DV-Hop algorithm’s hop count and transfer distance method by utilizing the particle swarm to estimate the node positions. Error rate in the distance between beacon nodes and unseen nodes is effectively reduced with the proposed technique that calculate error factors with corrections in a reversed fashion to revise hop counts. An escape factor is introduced to take control of updating particles’ velocity in the system, and the inertia weight is defined by a piecewise function to enlarge search space. This mechanism increases the diversity of the particle populations and mitigates the tendency of estimations on node positions to be trapped into local optima under stationary state. Also, the improved DV-Hop algorithm described in the paper has a better convergence speed due to the presence of random inertia weight logarithmic method. Finally, the problem of premature convergence is also tackled as a variation factor is adopted in collaboration with a fitness function that affects the particles’ movement range and assists in global convergence. The overall performance of improved DV-Hop is evaluated by statistical metrics and also compared with the traditional DV-Hop algorithm under simulated environment with the data collected from real-world scenarios. Industry 4.0 is fully dependent upon IoT and the count of hops is very important for deciding the routing from the source to destination for speedy transmission of data. The improved DV-hop algorithm can achieve better results and has reduced error rate by more than 30%. The DV-Hop algorithm plays an important role in IoT-enabled environment especially in Industry 4.0.

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Improved Recursive DV-Hop Localization Algorithm with RSSI Measurement for Wireless Sensor Networks

Cited by 33 publications

References 42 publications

Research on Compression Sensing Positioning Algorithm of Indoor Complex Environment Visible Light Indoor Based on Hybrid APIT

Research on Compression Sensing Positioning Algorithm of Indoor Complex Environment Visible Light Indoor Based on Hybrid APIT

Survey of Localization for Internet of Things Nodes: Approaches, Challenges and Open Issues

Improved DV-Hop Algorithm Based on Swarm Intelligence for AI and IoT-Federated Applications in Industry 4.0

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