Belief Condensation Filtering for RSSI-Based State Estimation in Indoor Localization

Mehryar, Shervin; Malekzadeh, Parvin; Mazuelas, Santiago; Spachos, Petros; Plataniotis, Konstantinos N.; Mohammadi, Arash

doi:10.1109/icassp.2019.8683560

Cited by 20 publications

(7 citation statements)

References 28 publications

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“…BLE uses the same frequency spectrum as WiFi, ranging from 2.4 to 2.48 GHz, with 37 data channels and 3 advertisement channels, each with a bandwidth of 2 MHz [19]. There are several BLE-based indoor localization frameworks including but not limited to Received Signal Strength (RSS)based methods [26], Time of Arrival (ToA) [27], and Angle of Arrival (AoA) [21]. AoA-based localization is an active research field, where among all possible approaches, subspacebased angle estimation algorithms [28], [29], such as MUltiple Signal CLassification (MUSIC) and its extensions, are among the most reliable methods, especially in the presence of noise.…”

Section: Related Workmentioning

confidence: 99%

TB-ICT: A Trustworthy Blockchain-Enabled System for Indoor COVID-19 Contact Tracing

Salimibeni,

Hajiakhondi-Meybodi,

Mohammadi

et al. 2021

Preprint

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Recently, as a consequence of the COVID-19 pandemic, dependence on Contact Tracing (CT) models has significantly increased to prevent spread of this highly contagious virus and be prepared for the potential future ones. Since the spreading probability of the novel coronavirus in indoor environments is much higher than that of the outdoors, there is an urgent and unmet quest to develop/design efficient, autonomous, trustworthy, and secure indoor CT solutions. Despite such an urgency, this field is still in its infancy. The paper addresses this gap and proposes the Trustworthy Blockchain-enabled system for Indoor Contact Tracing (TB-ICT) framework. The TB-ICT framework is proposed to protect privacy and integrity of the underlying CT data from unauthorized access. More specifically, it is a fully distributed and innovative blockchain platform exploiting the proposed dynamic Proof of Work (dPoW) credit-based consensus algorithm coupled with Randomized Hash Window (W-Hash) and dynamic Proof of Credit (dPoC) mechanisms to differentiate between honest and dishonest nodes. The TB-ICT not only provides a decentralization in data replication but also quantifies the node's behavior based on its underlying credit-based mechanism. For achieving high localization performance, we capitalize on availability of Internet of Things (IoT) indoor localization infrastructures, and develop a data driven localization model based on Bluetooth Low Energy (BLE) sensor measurements. The simulation results show that the proposed TB-ICT prevents the COVID-19 from spreading by implementation of a highly accurate contact tracing model while improving the users' privacy and security.

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Section: Related Workmentioning

confidence: 99%

TB-ICT: A Trustworthy Blockchain-Enabled System for Indoor COVID-19 Contact Tracing

Salimibeni,

Hajiakhondi-Meybodi,

Mohammadi

et al. 2021

Preprint

Self Cite

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“…In [36], an optimized support vector machine (O-SVM) on the cloud for distance estimation and a Kalman filter (KF) on the edge to obtain a near true RSS value from a list of RSS measurements is proposed. Additional works experiment with the raw accuracy of BLE RSSI signals for location purposes and suggest implementing a simple smoothing algorithm to obtain better accuracy [37]- [39].…”

Section: Related Workmentioning

confidence: 99%

Improving BLE Beacon Proximity Estimation Accuracy Through Bayesian Filtering

Mackey

Spachos

Song

et al. 2020

IEEE Internet Things J.

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The interconnectedness of all things is continuously expanding which has allowed every individual to increase their level of interaction with their surroundings. Internet of Things (IoT) devices are used in a plethora of context-aware application such as Proximity-Based Services (PBS), and Location-Based Services (LBS). For these systems to perform, it is essential to have reliable hardware and predict a user's position in the area with high accuracy in order to differentiate between individuals in a small area. A variety of wireless solutions that utilize Received Signal Strength Indicators (RSSI) have been proposed to provide PBS and LBS for indoor environments, though each solution presents its own drawbacks. In this work, Bluetooth Low Energy (BLE) beacons are examined in terms of their accuracy in proximity estimation. Specifically, a mobile application is developed along with three Bayesian filtering techniques to improve the BLE beacon proximity estimation accuracy. This includes a Kalman filter, a particle filter, and a Non-parametric Information (NI) filter. Since the RSSI is heavily influenced by the environment, experiments were conducted to examine the performance of beacons from three popular vendors in two different environments. The error is compared in terms of Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). According to the experimental results, Bayesian filters can improve proximity estimation accuracy up to 30% in comparison with traditional filtering, when the beacon and the receiver are within 3 m.

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“…We present the details of the two filters below and later carry out analysis as to how much performance gain they achieve for the application of Smart Grids SE in Section III. The details of algorithmic implementation for the BCF can also be found in [24].…”

Section: B State Estimationmentioning

confidence: 99%

State Estimation for Future Energy Grids

Mehryar,

Win

2019

Preprint

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Today's power generation and distribution networks are quickly moving toward automated control and integration of renewable resources -a complex, integrated system termed the Smart Grid. A key component in planning and managing of Smart Grids is State Estimation (SE). The state-of-the art SE technologies today operate on the basis of slow varying dynamics of the current network and make simplifying linearity assumptions. However, the integration of smart readers and green resources will result in significant non-linearity and unpredictability in the network. Therefore in future Smart Grids, there is need for ever more accurate and real-time algorithms. In this work, we propose and examine new SE methods that aim to achieve these measures by approximating the true distribution of the state variables, rather than a linearized version as done for instance in Kalman filtering. Through simulations we show that in the presence of non-linearities and non-Gaussian noise, our general SE framework improves accuracy where linear and Kalman-like filters exhibit impaired performance.

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Belief Condensation Filtering for RSSI-Based State Estimation in Indoor Localization

Cited by 20 publications

References 28 publications

TB-ICT: A Trustworthy Blockchain-Enabled System for Indoor COVID-19 Contact Tracing

TB-ICT: A Trustworthy Blockchain-Enabled System for Indoor COVID-19 Contact Tracing

Improving BLE Beacon Proximity Estimation Accuracy Through Bayesian Filtering

State Estimation for Future Energy Grids

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