Connectivity Based DV-Hop Localization for Internet of Things

Gui, Linqing; Xiao, Fu; Zhou, Yang; Shu, Feng; Val, Thierry

doi:10.1109/tvt.2020.2998093

Cited by 42 publications

(22 citation statements)

References 28 publications

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“…One common group of range-free approaches are those based on DV-Hop. In 2020, Gui et al [6] introduced a decentralized, range-free approach based on the DV-Hop family of localization approaches. Centralized Connectivity based DV-Hop (CCDV-Hop) and Distributed Connectivity based DV-Hop (DCDV-Hop) have relatively low computational complexity and achieve low localization error.…”

Section: B Range-free Approachesmentioning

confidence: 99%

“…Different types of localization algorithms exist. They can be mainly divided into range-based [4], [12], [18], [19], [21] and range-free approaches [6]- [8], [15]. In range-based approaches, unknown nodes (non-anchor nodes trying to localize themselves) must actively determine the distance to anchor nodes or the angles of incoming radio signals.…”

Section: Introductionmentioning

confidence: 99%

“…(5) Thorough experimental evaluation including ablation experiments are used to verify the favorable properties of the scheme, such as low localization error at low anchor densities and resistance against attacks. (6) An optimized version of the original MCL [8] simulator-including implementations of SA-MCL [7] and RESA-MCL-is provided to ease future evaluation and comparison with other approaches.…”

Section: Introductionmentioning

confidence: 99%

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Robustness Enhanced Sensor Assisted Monte Carlo Localization for Wireless Sensor Networks and the Internet of Things

Bochem

Zhang

2022

IEEE Access

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With distributed sensor systems commonly found in Wireless Sensor Networks or the Internet of Things, knowing the location sensor data was acquired from is very important, especially in scenarios with mobile sensors. Range-free Monte Carlo Localization based approaches are very energy efficient and do not require additional hardware beyond a radio, which is found on sensor nodes anyways. However, the use of motion sensor data based dead reckoning greatly improves the accuracy of location estimates and increases robustness against faulty or malicious actors within the network. In this work, we propose Robustness Enhanced Sensor Assisted Monte Carlo Localization (RESA-MCL). We show RESA-MCL's effectiveness with respect to both general localization accuracy and robustness against malicious attacks or malfunctioning nodes. To evaluate and compare our scheme against existing approaches, we introduce three attack models based on malicious anchor nodes. The performance of RESA-MCL is evaluated under these attack models and our approach outperforms existing schemes in both very low and higher anchor node density environments, achieving a localization error of 0.5 with an anchor density of 0.33. Overall, RESA-MCL outperforms comparable approaches at lower anchor densities with up to 48 % lower localization error and demonstrates strongly increased robustness against attacks with minimal computational overhead.

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Section: B Range-free Approachesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Robustness Enhanced Sensor Assisted Monte Carlo Localization for Wireless Sensor Networks and the Internet of Things

Bochem

Zhang

2022

IEEE Access

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“…To calculate the position of the moving nodes with the absolute distance or angle information between the nodes, range-based localization methods adopt different algorithms, such as trilateration, triangulation, least squares, and maximum likelihood estimation [ 7 ]. At the same time, the range-free localization algorithms make use of some information such as network connectivity and estimated distance between nodes to realize positioning, which includes the distance vector-hop (DV-hop) positioning algorithm, the centroid algorithm, the approximate point-in-triangulation test (APIT) positioning algorithm, the convex optimization positioning algorithm [ 8 , 9 ], etc. In addition, according to the information of the WSN nodes processed in different sites, the localization algorithms can be divided into centralized localization and distributed localization algorithms [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

A Distributed Localization Method for Wireless Sensor Networks Based on Anchor Node Optimal Selection and Particle Filter

Luo

Liu

Yan

et al. 2022

Sensors

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In wireless sensor networks, due to the significance of the location information of mobile nodes for many applications, location services are the basis of many application scenarios. However, node state and communication uncertainty affect the distance estimation and position calculation of the range-based localization method, which makes it difficult to guarantee the localization accuracy and the system robustness of the distributed localization system. In this paper, we propose a distributed localization method based on anchor nodes selection and particle filter optimization. In this method, we first analyze the uncertainty of error propagation to the least-squares localization method. According to the proportional relation between localization error and uncertainty propagation, anchor nodes are selected optimally in real-time during the movement of mobile nodes. Then we use the ranging and position of the optimally selected anchor nodes to obtain the location information of the mobile nodes. Finally, the particle filter (PF) algorithm is utilized to gain the optimal estimation of the localization results. The experimental evaluation results verified that the proposed method effectively improves the localization accuracy and the robustness of the distributed system.

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“…Various measurement techniques such as Time of Arrival (TOA), Angle of Arrival (AOA), Received Signal Strength (RSS), connectivity, etc are used for this purpose [11], [12]. Among the available techniques, connectivity based localization does not need any additional hardware and hence is popularly used for WSN applications [13], [14]. Hop based localization is one such type of method where proximity to reference nodes is measured using the Average Hop Distance (AHD) between any two neighboring nodes [15], [16].…”

Section: Introductionmentioning

confidence: 99%

A Method for Fault Tolerant Localization of Heterogeneous Wireless Sensor Networks

Bhat

Santhosh

2021

IEEE Access

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Location information of nodes in Wireless Sensor Networks (WSN) is essential to identify the origins of events and to act on them. Several localization algorithms are developed for this purpose. In this work, we have considered hop based localization algorithms, which are popularly used in WSN applications. These algorithms use a few reference nodes with location information and localize other nodes with reference to these nodes. But, in practical scenarios, some reference nodes may turn faulty and report incorrect location information to other nodes. This reduces the localization accuracy of the entire network. Therefore, it is essential to identify and filter out faulty reference nodes from the localization process. But, in Heterogeneous Wireless Sensor Networks (HWSN), since both faulty nodes and heterogeneous nodes modify hop distances, it becomes even more challenging to identify only faulty nodes among a set of heterogeneous nodes. In this work, we have reported a fault filtering method that can be used with any of the existing hop based localization algorithms for fault-tolerant localization. This method first normalizes the distance estimations using the communication radius of nodes and then uses the Jenks Natural Breaks algorithm for filtering out the nodes producing inconsistent distance estimations. The reported method is incorporated into existing localization algorithms and tested in 2D/3D, isotropic/anisotropic environments. The results show an improvement of 14%, 52%, and 51% in localization accuracy when tested with DV-Hop, Weighted DV-Hop, and HHO-AM algorithms, respectively.

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Connectivity Based DV-Hop Localization for Internet of Things

Cited by 42 publications

References 28 publications

Robustness Enhanced Sensor Assisted Monte Carlo Localization for Wireless Sensor Networks and the Internet of Things

Robustness Enhanced Sensor Assisted Monte Carlo Localization for Wireless Sensor Networks and the Internet of Things

A Distributed Localization Method for Wireless Sensor Networks Based on Anchor Node Optimal Selection and Particle Filter

A Method for Fault Tolerant Localization of Heterogeneous Wireless Sensor Networks

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