The implementation of indoor localization based on an experimental study of RSSI using a wireless sensor network

Amine, Cheriet Mohammed El; Ouslim, Mohamed; Boualam, Belaidi

doi:10.1007/s12083-015-0372-9

Cited by 11 publications

(8 citation statements)

References 24 publications

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“…Although this method saves the time and labor of fingerprint location in measuring RSSIs in many locations, as well as facilitating deployment in WSNs, there is a big deviation between the model of RSSI and distance established by it and the relationship between RSSI and distance in the actual environment, especially in a multipath environment. Aside from LNSM, other models have been used to describe the relationship between RSSI and distance, such as the free space propagation model (FSPM) [34] and the two-ray ground reflection model (TGRM) [8]. FSPM establishes the relationship between RSSI and distance when a wireless signal propagates in open space, so it does not consider the reflection effect of the ground and obstacles on the signal.…”

Section: Related Workmentioning

confidence: 99%

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An Optimal Multi-Channel Trilateration Localization Algorithm by Radio-Multipath Multi-Objective Evolution in RSS-Ranging-Based Wireless Sensor Networks

Fang

Chen

2020

Sensors

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The Global Positioning System (GPS) is unable to provide precise localization services indoors, which has led to wireless sensor network (WSN) localization technology becoming a hot research issue in the field of indoor location. At present, the ranging technology of wireless sensor networks based on received signal strength has been extensively used in indoor positioning. However, wireless signals have serious multipath effects in indoor environments. In order to reduce the adverse influence of multipath effects on distance estimation between nodes, a multi-channel ranging localization algorithm based on signal diversity is herein proposed. In real indoor environments, the parameters used for multi-channel localization algorithms are generally unknown or time-varying. In order to increase the positioning accuracy of the multi-channel location algorithm in a multipath environment, we propose an optimal multi-channel trilateration positioning algorithm (OMCT) by establishing a novel multi-objective evolutionary model. The presented algorithm utilizes a three-edge constraint to prevent the traditional multi-channel localization algorithm falling into local optima. The results of a large number of practical experiments and numerical simulations show that no matter how the channel number and multipath number change, the positioning error of our presented algorithm is always smaller compared with that of the state-of-the-art algorithm.

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Section: Related Workmentioning

confidence: 99%

“…The WSNs are a kind of self-organizing network composed of many nodes with wireless transceivers, microprocessors, and sensors [4][5][6]. The received signal strength indication (RSSI) is the easiest to measure by a wireless transceiver, so it is often used to estimate distances and locations between nodes [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

An Optimal Multi-Channel Trilateration Localization Algorithm by Radio-Multipath Multi-Objective Evolution in RSS-Ranging-Based Wireless Sensor Networks

Fang

Chen

2020

Sensors

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“…The study by Sevtsuk et al [36] showed that WiFi counts can be collected and mapped to visually represent occupant spatial intensities which can be used by universities to identify space utilization rates. El Amine et al [37] and Mardini et al [38] proposed a more sophisticated and advanced WiFi-based occupant detection solution. These studies estimated occupants' location using ZigBee, and XBee networks and developed algorithms to estimate RSS Indication (RSSI) fingerprinting which detected occupants' locations within 0.8 m. On the other hand, the study by Martani et al [7] showed that only 40% of building occupants were connected to the WiFi network, raising concerns about this solution's reliability.…”

Section: Wireless Network-based Solutionsmentioning

confidence: 99%

Effectiveness of using WiFi technologies to detect and predict building occupancy

et al. 2017

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Abstract. This paper presents findings of a case-study demonstrating the effectiveness of using WiFi networks to detect occupancy as opposed to CO 2 sensors, commonly used for demand-controlled heating, ventilation and air conditioning (HVAC) systems. The study took place in one building at the University of Manitoba Fort Garry campus in Canada. In a classroom, the number of WiFi connections was collected on an hourly basis over one-week, simultaneously with CO 2 concentration levels at 10-min intervals. The number of occupants in this classroom was also counted on an hourly basis over the same study period. Data analysis showed that WiFi counts predicted actual occupancy levels more accurately than CO 2 concentration levels, thus validating the use of this technology to track occupancy. This study was the first to use both CO 2 concentration and WiFi counts simultaneously as indicators for occupancy. Results demonstrated the possibility of using WiFi counts in large buildings for controlling HVAC systems at a higher accuracy and lower cost than other sensor technologies. Implications and influences: Given the large contribution of HVAC systems to overall buildings' energy consumption, this study presents a new method for efficiently operating HVAC systems. Results highlighted the accuracy of using WiFi connections as predictors for occupancy patterns to be used for controlling HVAC systems instead of CO 2 sensors. These findings provide a foundation for further research on using WiFi networks to manage and operate HVAC systems in new buildings. Efficient operation of these systems based on real-time occupancy as opposed to static schedules provides facility managers with an opportunity for significant energy savings at a relatively low cost.Keywords: buildings energy management / occupancy and energy consumption / smart buildings systems / green buildings / sensor-based HVAC systems

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“…Large-scale positioning experiments indicated that the algorithm is effective and accurate. Another novel algorithm using RSSI mapping N°3 20 has been proposed where the localization accuracy is improved by multi-powerful classifiers. Meanwhile, there were some state-of-the-art localization algorithms reported.…”

Section: Introductionmentioning

confidence: 99%

Single anchor node real-time positioning algorithm based on the antenna array

2017

International Journal of Distributed Sensor Networks

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This article presents an anchor node based on directional antenna array and reports the design of an automatic test system. The antenna radiation characteristics and the wireless channel model are studied first, followed by the evaluation of a single anchor node localization algorithm for wireless sensor network real-time positioning on the base of antenna radiation modeling and radio channel propagation model research then. Finally, the algorithm is evaluated by experiments in real environments. Experimental results show that the algorithm positioning accuracy could reach 1 m, and angular positioning accuracy error is less than 20°with no data exchange required during the positioning process. Hence, the wireless data transmission protocol complexity is reduced with improved real-time performance. This study is expected to provide technical references for the method research of wireless sensor network real-time positioning and new positioning system in practical applications.

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The implementation of indoor localization based on an experimental study of RSSI using a wireless sensor network

Cited by 11 publications

References 24 publications

An Optimal Multi-Channel Trilateration Localization Algorithm by Radio-Multipath Multi-Objective Evolution in RSS-Ranging-Based Wireless Sensor Networks

An Optimal Multi-Channel Trilateration Localization Algorithm by Radio-Multipath Multi-Objective Evolution in RSS-Ranging-Based Wireless Sensor Networks

Effectiveness of using WiFi technologies to detect and predict building occupancy

Single anchor node real-time positioning algorithm based on the antenna array

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