Energy-efficient on-demand indoor localization platform based on wireless sensor networks using low power wake up receiver

Guidara, Amir; Derbel, Faouzi; Fersi, Ghofrane; Bdiri, Sadok; Jemaa, Maher Ben

doi:10.1016/j.adhoc.2019.101902

Cited by 26 publications

(8 citation statements)

References 17 publications

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“…A summary of some of the essential works is presented in Tables 2 and 3. Based on all this knowledge and also after reviewing a few more works [35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50], it was found that there has not been more research work that focused on detecting an invalid predicted location based on the speed of the pedestrians in an indoor environment. So, a model that depends on Recurrent Neural Network and uses the speed of pedestrian navigation to adjust the predicted location label is proposed, which is discussed in Sect.…”

Section: Related Workmentioning

confidence: 99%

Fault-Tolerant indoor localization based on speed conscious recurrent neural network using Kullback–Leibler divergence

Varma

Anand

2022

Peer-to-Peer Netw. Appl.

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IoT services are the basic building blocks of smart cities, and some of such crucial services are provided by smart buildings. Most of the services like smart meters, indoor navigation, lighting control, etc., which contribute to smart buildings, need the locations of people or objects within the building. This gave rise to Indoor Localization, where only the infrastructure of the building has to be used for localization as accessing the Global Positioning System is difficult in indoor environments. Many approaches have been proposed to predict locations based on the infrastructure available indoors, and some of such techniques use Wi-Fi access points. Still, unfortunately, very few studies have concentrated on tolerating faults while being cost-effective. This work discusses hardware implementation of indoor localization. It then proposes a learning algorithm SRNN (Speed Conscious Recurrent Neural Network) that uses the RSSI (Received Signal Strength Indicator) values of available Wi-Fi access points in the building and predicts the location. Also, fault-tolerant approaches termed nearest RSSI and the most recent RSSI using Kullback-Leibler Divergence have been proposed to improve the location accuracy when access points go down and are prone to faults. Both the proposed approaches nearest RSSI and most recent RSSI along with SRNN improve the location accuracy by 4% and 2.1%, respectively, over existing techniques and contribute to optimizing predicted location's accuracy in Indoor Localization an IoT service for smart buildings.

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Section: Related Workmentioning

confidence: 99%

Fault-Tolerant indoor localization based on speed conscious recurrent neural network using Kullback–Leibler divergence

Varma

Anand

2022

Peer-to-Peer Netw. Appl.

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“…Through the hop count, DV hop detects the distance between nodes. At least three anchor nodes are involved in broadcasting coordinates with hop count through the network [17]. Hop count gets incremented when a neighbor node receives data.…”

Section: (I) Distance Vector Hopmentioning

confidence: 99%

“…In addition to the above, a new heuristic error correction mechanism suggested enhancing the positioning precision. Amir et al [17] present power saving on-demand protocol for the WSN-oriented indoor localization platform, the nodes sustained in a low energy idle listening mode by low energy built-in wake-up receiver. This starts only when a localization demand arises.…”

Section: Comparative Studymentioning

confidence: 99%

Retrospection on Localization Techniques for Positioning Nodes in Wireless Sensor Networks

Kathiroli¹,

Kanmani²

2020

IJEAT

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Wireless Sensor Networks have highly scattered, self-organized nodes that can detect, compute, and transmit the information collected at different nodes in the network. These nodes spread over a specific topographical zone. They enhance the instantaneous formation of the network. Knowing the current location of a node is a crucial and cardinal requirement for any application promulgated in WSN. Once the locations of the sensor nodes can be precisely positioned, there are ample of probabilities for the data transmission of the network to be excelling inefficiency. Location responsiveness enables essential network features such as coverage, routing, deployment, topology control, clustering, boundary discovery, target tracking, rescue, and other location services. Hence, WSN localization has become a breath and backbone arena that ostentatiously attracted significant research interest. Our work traces a compilation of all the dynamic research in sensor networks on localization techniques and emanates eminent understanding of it.

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“…Whereas, operating at low transmission power levels increases the battery lifetime but restricts the coverage area that demands a significant number of devices to be deployed to cover an AoL and locate the targets with a reasonable localization accuracy and precision. In this regard, the researchers have devised transmission power management schemes to optimize the energy efficiency and overall battery lifetime of the BLE devices [58,59,60]. Regardless of the transmission power management schemes, the change in the transmission power affects the BLE RSS.…”

Section: Bluetooth Low Energy (Ble) Based Wireless Indoor Localizamentioning

confidence: 99%

Evaluating the Implications of Varying Bluetooth Low Energy (BLE) Transmission Power Levels on Wireless Indoor Localization Accuracy and Precision

Qureshi

Umair

Hancke

2019

Sensors

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Bluetooth Low Energy (BLE) based Wireless Indoor Localization System (WILS) with high localization accuracy and high localization precision is a key requirement in enabling the Internet of Things (IoT) in today’s applications. In this paper, we investigated the effect of BLE signal variations on indoor localization caused by the change in BLE transmission power levels. This issue is not often discussed as most of the works on localization algorithms use the highest power levels but has important practical implications for energy efficiency, e.g., if a designer would like to trade-off localization performance and node lifetime. To analyze the impact, we used the established trilateration based localization model with two methods i.e., Centroid Approximation (CA) and Minimum Mean Square Error (MMSE). We observed that trilateration based localization with MMSE method outperforms the CA method. We further investigated the use of two filters i.e., Low Pass Filter (LPF) and Kalman Filter (KF) and evaluated their effects in terms of mitigating the random variations from BLE signal. In comparison to non-filter based approach, we observed a great improvement in localization accuracy and localization precision with a filter-based approach. Furthermore, in comparison to LPF based trilateration localization with CA, the performance of a KF based trilateration localization with MMSE is far better. An average of 1 m improvement in localization accuracy and approximately 50% improvement in localization precision is observed by using KF in trilateration based localization model with the MMSE method. In conclusion, with KF in trilateration based localization model with MMSE method effectively eliminates random variations in BLE RSS with multiple transmission power levels and thus results in a BLE based WILS with high accuracy and high precision.

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Energy-efficient on-demand indoor localization platform based on wireless sensor networks using low power wake up receiver

Cited by 26 publications

References 17 publications

Fault-Tolerant indoor localization based on speed conscious recurrent neural network using Kullback–Leibler divergence

Fault-Tolerant indoor localization based on speed conscious recurrent neural network using Kullback–Leibler divergence

Retrospection on Localization Techniques for Positioning Nodes in Wireless Sensor Networks

Evaluating the Implications of Varying Bluetooth Low Energy (BLE) Transmission Power Levels on Wireless Indoor Localization Accuracy and Precision

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