Received Signal Strength Indicator Technique Research Articles

Enhancing Indoor Localization Accuracy in Wireless Sensor Networks: A Hybrid Approach Integrating Hierarchical Structure Poly Particle Swarm Optimization and Teaching–Learning-Based Optimization (HSPPSO-TLBO)

Background and Objective: Enhancing localization accuracy while minimizing development costs poses significant challenges in deploying and managing wireless sensor networks (WSNs). This paper presents an advanced algorithm for node localization in indoor environments, integrating sophisticated optimization techniques. The hybrid algorithm, HSPPSO-TLBO, combines Hierarchical Structure Poly Particle Swarm Optimization (HSPPSO) with Teaching– Learning-Based Optimization (TLBO). Methods: The proposed algorithm HSPPSO-TLBO aims to minimize the mean squared range error (MSRE) resulted by calculating internal distances between nodes using Received Signal Strength Indicator (RSSI). TLBO, with its robust global search capabilities, complements HSPPSO’s local search, preventing convergence to inappropriate local optima. HSPPSO-TLBO offers easy implementation and leverages the cost-free feature of RSSI, making it an attractive choice for enhancing localization precision. Results: Simulation results demonstrate the superior performance of HSPPSO-TLBO compared to other algorithms using different meta-heuristic optimization techniques. The outstanding performance of HSPPSO-TLBO is evident across various evaluation metrics, including localization error, localization rate, and simulation runtime. Conclusion: The proposed algorithm utilizing HSPPSO and TLBO is exceptionally effective in improving localization precision in indoor WSNs due to several key characteristics. These include the seamless integration and easy implementation of both HSPPSO and TLBO, along with the costfree advantage of using the RSSI technique. This combination makes the algorithm a highly functional solution for improving localization accuracy. other: N/A

Indoor visible light communication localization system utilizing received signal strength indication technique and trilateration method

Visible light communication (VLC) based on light-emitting diodes (LEDs) technology not only provides higher data rate for indoor wireless communications and offering room illumination but also has the potential for indoor localization. VLC-based indoor positioning using the received optical power levels from emitting LEDs is investigated. We consider both scenarios of line-of-sight (LOS) and LOS with non-LOS (LOSNLOS) positioning. The performance of the proposed system is evaluated under both noisy and noiseless channel as is the impact of different location codes on positioning error. The analytical model of the system with noise and the corresponding numerical evaluation for a range of signal-to-noise ratio (SNR) are presented. The results show that an accuracy of 12 dB.

Distance measurement correction based on feedback filter for RSSI localisation technique in WSNs

Localisation methods based on the received signal strength indicator (RSSI) are widely used in wireless sensor networks (WSNs). They offer an energy reduced and cost-effective solutions for wireless localisation. Due to radio irregularity, RSSI localisation is vulnerable to multipath, shadowing, interference and environmental effects, which reduces the accuracy of localisation results. In this paper, a new distance measurement correction algorithm is presented, which is based on feedback filter and hardware characteristic. It implements the feedback filter with consideration of historical values of RSSI. This enables to predict possible values of RSSI and thus nodes positions. Hardware characteristics, such as multiplication and summation capabilities are explored to improve the localisation accuracy. Real experiments were carried out on panStamp sensor nodes at two different temperatures. The new proposed RSSI technique shows a reduced localisation error compared to the simple RSSI method.

Distance measurement correction based on feedback filter for RSSI localisation technique in WSNs

Localisation methods based on the received signal strength indicator (RSSI) are widely used in wireless sensor networks (WSNs). They offer an energy reduced and cost-effective solutions for wireless localisation. Due to radio irregularity, RSSI localisation is vulnerable to multipath, shadowing, interference and environmental effects, which reduces the accuracy of localisation results. In this paper, a new distance measurement correction algorithm is presented, which is based on feedback filter and hardware characteristic. It implements the feedback filter with consideration of historical values of RSSI. This enables to predict possible values of RSSI and thus nodes positions. Hardware characteristics, such as multiplication and summation capabilities are explored to improve the localisation accuracy. Real experiments were carried out on panStamp sensor nodes at two different temperatures. The new proposed RSSI technique shows a reduced localisation error compared to the simple RSSI method.

Exploring the performance of indoor localization systems based on VLC-RSSI, including the effect of NLOS components using two light-emitting diode lighting systems

We analyze the effect of diffuse reflection on indoor localization systems based on visible light communication. The target position is estimated using a received signal strength indication technique. Two lighting systems are considered: distinct and uniform lighting systems. Each utilizes commercially available light-emitting diodes and photodiodes with an illumination level conforming to standards. We introduce a comparative study between the two lighting systems through different transmitter (Tx) and receiver (Rx) essential parameters. The results show that the uniform lighting system achieves less localization error (≤ 20.43 cm ) than a distinct lighting system (≤ 45.9 cm ). The uniform lighting system is well adapted to low-Rx field of view (FOV) and narrow radiation angle ( error = 1 mm when semiradiation angle = 5 deg ). In the case of a distinct lighting system, low-Rx FOV is also required, while the Tx semiradiation angle needs to be determined carefully ( error ≤ 3.08 cm when semiradiation angle = 20 deg ). Finally, the uniform lighting system shows flexibility in the process of Tx and Rx designs. A uniform lighting system can utilize Rxs with narrow FOVs (≥ 8.6 deg ), while a distinct lighting system is limited to Rx with a wide FOV (≥ 53.96 deg ).

A Hybrid Localization Algorithm for Wireless Sensor Networks

Abstract The continuously widening range of Wireless Sensor Networks (WSNs) applications requires exact node location which needs efficient and error free localization methods. Localization methods developed in the past are completely based on very fine numerical computation of various network parameters such as transmission range, propagation shape, transmitted or received power, sending or arrival time, connectivity information etc. These parameters are prone towards environmental situation and presence of obstacles in environment. Recently, research in localization is focused towards minimization of localization error in the available techniques. In this paper, the cause and behavior of errors in AOA and RSSI localization techniques have been mathematically analyzed. Based on the error analysis of both the existing techniques, a hybrid localization algorithm is proposed. The hybrid localization algorithm is based on existing Angle of Arrival (AOA) and Received signal strength indicator (RSSI). The algorithm is named as Minimum AOA Error with Minimum RSSI Error (MAE with MRE). Analysis of results obtained through simulation show that the hybrid localization algorithm performs better than AOA and RSSI techniques in terms of error percentage probability with varying number of known sensors, unknown sensors and shadowing effect percentage.

Enhancement of Indoor Localization by Path-Loss Reduction Using Modified RSSI Technique

Localization is one of the most challenging and important issue in wireless sensor networks (WSN), especially if cost effective approaches are demanded. Distance measurement based on RSSI (Received Signal Strength Indication) is a low cost and low complexity of the distance measurement technique, and it is widely applied in the range-based localization of the WSN. The RSS (Received Signal Strength) used to estimate the distance between an unknown node and a number of reference nodes with known co-ordinates. Location of the target node is then determined by trilateration. Log-normal shadowing model can better describe the relationship between the RSSI value and the distance. Due to the Non-line of sight and multipath transmission effects in the indoor environment, the distance error or ranging error will be large. It is a challenging task to optimize the transmission power of WSNs, in presence of path loss, because although increasing the transmission power reduces the residual energy, it also reduces the number of retransmissions required. In this paper, experimental results that are carried out to analyze the sensitivity of RSSI measurements in an indoor environment for various power levels are presented. Along with the reduction in the Distance error through improved RSSI technique, Path-loss also reduced comparatively.

The Improved Personnel Location Algorithm Based on RSSI

In the process of that based on the RSSI received signal strength indicator technique, resulting in the positioning accuracy is so low, since the simple RSSI, multipath, diffraction and non line of sight and other factors. In order to achieve higher accuracy node localization in wireless sensor, the paper is proposed based on the probability of recycling triangle centroid location algorithm in the RSSI technique,The probability of the cycle to handle triangle centroid localization algorithm. Through the Matlab simulation, compared with the traditional triangle centroid localization algorithm, the error is significantly reduced and positioning accuracy improved when the anchor point number exceeds a certain number.

Modified RSSI Technique for the Localization of Passive UHF RFID Tags in LOS Channels

This paper presents a new approach for improving the localization of passive ultra high frequency radio frequency identification (RFID) tags in line-of-sight channels using a received signal strength indicator (RSSI) technique. In practice, the complex propagation in the indoor channels and also the variability of some parameters of the RFID equipment itself introduces significant amount of errors when the operation of localization carries out the RSSI technique. Indeed, as the calculation is based on a trilateration, the incomplete knowledge of the propagation and some parameters of RFID tags leads to estimate distances which are wrong, and therefore the localization cannot be correct. In order to overcome this drawback, the proposed method takes into account the presence of unknown parameters relying on a dichotomous algorithm which includes probabilistic parameters. The presented simulation results are in good agreement with the expected theoretical results. Experimental results show that the proposed method strongly increases the accuracy of the estimated position of tags. Compared to other approaches based on the improvement of the RSSI technique, this method does not require too much complexity in terms of materials (no need for specific architecture or reference tags) and processing (fast and simple algorithm).

Enhanced Rssi-Based High Accuracy Real-Time User Location Tracking System For Indoor And Outdoor Environments

Abstract Existing researches on location tracking focus either entirely on indoor or entirely on outdoor by using different devices and techniques. Several solutions have been proposed to adopt a single location sensing technology that fits in both situations. This paper aims to track a user position in both indoor and outdoor environments by using a single wireless device with minimal tracking error. RSSI (Received Signal Strength Indication) technique together with enhancement algorithms is proposed to cater this solution. The proposed RSSI-based tracking technique is divided into two main phases, namely the calibration of RSSI coefficients (deterministic phase) and the distance along with position estimation of user location by iterative trilateration (probabilistic phase). A low complexity RSSI smoothing algorithm is implemented to minimize the dynamic fluctuation of radio signal received from each reference node when the target node is moving. Experiment measurements are carried out to analyze the sensitivity of RSSI. The results reveal the feasibility of these algorithms in designing a more accurate real-time position monitoring system