Distance-Based Localization in Wireless Sensor Network Using Exponential Grey Prediction Model

Wireless sensor network is characterized by low power, low cost and low complexity. With these features, wireless sensor network has great potential to develop indoor positioning system. At present, Circular line of position based on Received Signal Strength Indication (RSSI) has minimum additional cost and most simple algorithm among the existing location strategies. However, radio signal propagation is easily affected by diffraction, reflections, and also scattering of radio in the building. Consequently, the location strategies need good method to improve the accuracy of position estimation system. This paper uses dynamic distance prediction algorithm in Circular line of position, and then employs Zigbee as the wireless sensor network medium. After getting original RSSI value, location node selects the raw data with distance error function, and calculates the position by low error group of RSSI. Although the method increases the number of RSSI measuring, it can effectively eliminates RSSI value caused by signal fluctuation and improves the accuracy of traditional position estimation system by data selection. Simulation results show that the accuracy of distance estimates based on dynamic distance prediction has increased about 30%.

Localization is a key issue in wireless sensor networks. The geographical location of sensors is important information that is required in sensor network operations such as target detection, monitoring, and rescue. These methods are classified into two categories, namely range-based and range-free. Range-based localizations achieve high location accuracy by using specific hardware or using absolute received signal strength indicator (RSSI) values, whereas range-free approaches obtain location estimates with lower accuracy. Because of the hardware and energy constraints in sensor networks, RSSI offers a convenient method to find the position of sensor nodes. However, in the presence of channel noise, fading, and attenuation, it is not possible to estimate the actual location. In this paper, we propose an RSSI-based localization scheme that considers the trend of RSSI values obtained from beacons to estimate the position of sensor nodes. Through applying polynomial modeling on the relationship between received RSSI and distance, we are able to locate the maximum RSSI point on the anchor trajectory. Using two such trajectories, the sensor position can be determined by calculating the intersection point of perpendiculars passing through the maximum RSSI point on each trajectory. In addition, we devised schemes to improve the localization method to perform under a variety of cases such as single trajectory, unavailability of RSSI trends, and so. The advantage of our scheme is that it does not rely on absolute RSSI values and hence, can be applied in dynamic environments. In simulations, we demonstrate that the proposed localization scheme achieves higher location accuracy compared with existing localization approaches.

To estimate the distance and localization in the wireless sensor networks (WSN), the RSSI (Received Signal Strength Indicator) value is generally assumed to have a linear relationship with the logarithm of distance communication. This is true in the absence of obstacles and wireless interference, as is the case in the free space. In this paper, the principle of nodes localization in wireless sensor networks is analyzed on the basis of RSSI as metric for estimating distances in outdoor environment. The measure of nodes distances and localization process are described. CC2530 RF module is used as a hardware platform of nodes on the location experiment. The results of localization algorithms (Trilateration and Multilateration) in Wireless Sensor Networks are presented. Test results show that the mean errors are around 4.9 m in a real-world environment for the Trilateration algorithm and around 0.5 m for Multilateration algorithm.

In the advent of Internet of Things (IoT), Wireless Sensor Network (WSN) technologies play an important role in acquisition of different physical quantities for different applications. The Received Signal Strength Indicator (RSSI) based indoor localization is a well-known localization method used in WSN technologies due to its low complexity, availability and low energy consumption. In this research we explore the possibility of applying RSSI value based Feed Forward Neural Network (FFNN) jointly to identify the correct location of a moving object or a person, which is an important requirement of IoT-based Ambient Assisted Living (AAL) applications. We setup an experimental test bed for the acquisition of RSSI data remotely, which contained two types of nodes called beacon node and the mobile node. The ESP 8266 is used as the controller for nodes, which is based on IEEE 802.11 standard. The RSSI values from the beacon nodes will be sent to a remote server via Mosquitto Message Queuing Telemetry Transport (MQTT) broker, and then the RSSI values will be secondhand utilized by the FFNN supervised learning model that we developed at the remote server. Output of FFNN model gives the location of the object or person in two dimensional (2D) space. In the end of the research, the validity is checked by using the statistical assessment models and the results substantiate the significance of using supervised learning method in RSSI based indoor positioning.

A novel handoff algorithm based on grey theory is being proposed for reducing the handoff overhead in cellular communication environment. The design of the methods is intended to be used under a log-normal fading environment. The grey prediction model is used to predict the received signal strength indicator (RSSI) values from two base stations (BS). The decision whether performing handoff is based on the predictive RSSI value, the received RSSI value and n-1 past RSSI values. By changing the handoff probability of two regions (divided by the middle point of two separated BSs), the position of first handoff to be executed will close to the middle point of two separated BSs. The simulation results show that the proposed algorithm with grey theory obtains excellent performance because it not only minimizes the number of performing handoff, but also has a very short calculating time.

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.

Based on the Zigbee-based wireless sensor network, I suggest the way to reduce errors between the short distance, improving the accuracy of the presumed distance by revising the deviation of RSSI(Received Signal Strength Indication) values is to estimate the distance using only the RF signal power without the additional hardware. In general, the graph measured by RSSI values shows the proximity values which are ideally reduced in proportion to the distance under the free outdoor space in which LOS(Line-Of-Sight) is guaranteed. However, if the result of the received RSSI values are each substituted to the formula, it can produce a larger margin of error and less accurate measurement since it is based upon the premise that this free space is not affected by reflected waves or obstacles caused by the ground and electronic jamming engendered by the environment. Therefore, the purpose of this study is to reduce the margin of errors between the distances and to measure the proximity values with the ideal type of graph by suggesting the way to revise the received RSSI values in the light of these reflected waves or obstacles and the electronic jamming. In conclusion, this study proves that errors are reduced by comparing the proposed deviation correction method to the revised RSSI value.

Distance estimation is vital for localization and many other applications in wireless sensor networks. Research has revealed that the correlation between distance and RSSI (Received Signal Strength Indication) values is the key of ranging and localization technologies in wireless sensor networks (WSNs). 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. In this paper, an Log Normal Shadowing Model (LNSM) that estimates the distance between sensor nodes in WSNs is presented. The performance of this model and proposed method are evaluated and analyzed in an indoor environment with and without considering obstacles by performing an empirical measurement using Crossbow TelosB wireless sensor motes. Our result shows that the proposed method reduced distance error and path-loss effectively than LNSM. The results of these evaluations would contribute towards obtaining accurate locations of wireless sensor nodes.

Received Signal Strength Indicator (RSSI) is used in indoor positioning for measuring object distance to the base station. However, acquiring accurate RSSI values is challenging because wireless interference factors, such as multipath decline interference, make RSSI values of the same object fluctuate over time. Therefore, instead of a single RSSI, RSSI acquisition will collect a set of RSSI values from which the most moderate RSSI is derived. For this purpose, we propose an Enhanced Gaussian Mixture Model (EGMM) to derive a more precise RSSI for improving indoor positioning accuracy. EGMM enhances Gaussian Mixture Model (GMM) by applying Akaike information criterion (AIC) to determine the best K value for GMM to divide RSSI values into K sets representing signals from different paths. Then, EGMM identifies the most appropriate set of RSSI values to derive a more precise RSSI and thus improves the accuracy of indoor positioning. Our EGMM solution performs well in an open indoor space. The experiment is conducted with iBeacon devices, and the average error distance of EGMM is about 64% of those generated by existing Gaussian filtering. The average positioning error of EGMM is about 0.48 meter, which is adequate to indoor positioning accuracy.

Localization is one of the most challenging and important issues in wireless sensor networks (WSNs), especially if cost-effective approaches are demanded. In this paper, we present intensively discuss and analyze approaches relying on the received signal strength indicator (RSSI). The advantage of employing the RSSI values is that no extra hardware (e.g. ultrasonic or infra-red) is needed for network-centric localization. We studied different factors that affect the measured RSSI values. Finally, we evaluate two methods to estimate the distance; the first approach is based on statistical methods. For the second one, we use an artificial neural network to estimate the distance.

The relationship between RSSI (Received Signal Strength Indication) values and distance is the foundation and the key of ranging and positioning technologies in wireless sensor networks. Log-normal shadowing model (LNSM), as a more general signal propagation model, can better describe the relationship between the RSSI value and distance, but the parameter of variance in LNSM is depended on experiences without self-adaptability. In this paper, it is found that the variance of RSSI value changes along with distance regu- larly by analyzing a large number of experimental data. Based on the result of analysis, we proposed the relationship function of the variance of RSSI and distance, and established the log-normal shadowing model with dynamic variance (LNSM-DV). At the same time, the method of least squares(LS) was selected to es- timate the coefficients in that model, thus LNSM-DV might be adjusted dynamically according to the change of environment and be self-adaptable. The experimental results show that LNSM-DV can further reduce er- ror, and have strong self-adaptability to various environments compared with the LNSM.

Navigation system based on indoor location estimation is one of the core technologies in mobile robot systems. Wireless sensor network has great potential in the indoor location estimation due to its characteristics such as low power consumption, low cost, and simplicity. In this paper we present an algorithm to estimate the indoor location of mobile robot based on wireless sensor network and fuzzy modeling. ZigBee-based sensor network usually uses RSSI(Received Signal Strength Indication) values to measure the distance between two sensor nodes, which are affected by signal distortion, reflection, channel fading, and path loss. Therefore we need a proper correction method to obtain accurate distance information with RSSI. We develop the fuzzy distance models based on RSSI values and an efficient algorithm to estimate the robot location which applies to the navigation algorithm incorporating the time-varying data of environmental conditions which are received from the wireless sensor network.

Localization is becoming a very important and challenging task of Wireless Sensor Networks (WSN), especially in low cost and low data-rate networks that employ a large number of nodes. Since the data collected may have no meaning if the sensor positions are unknown, it becomes of fundamental importance to be able to locate each node of the network with respect to an absolute or relative coordinate system. Various algorithms that propose strategies to determine the nodes position of a network are present in literature. They are divided into range-based and range-free algorithms. Some of the range-based algorithms estimate distance from Received Signal Strength Indicator (RSSI) measurements between the unknown node and the reference nodes. Estimators of this method appreciate the fact that it does not require additional hardware, because indicators of RSSI are generally built-in in radio chipsets now on market. It can be considered a valid solution for localization both in terms of economic cost and in terms of energy consumption. However, this solution presents high uncertainty due to the high variability that influences the RSSI measurements and to the different choices that characterize the implemented RSSI measurement methods. The authors, inspired by previous works present in literature, are aimed in exploring the influence of the most relevant causes of RSSI variability, as the multipath, and their effect on the RSSI values for WSNs. In this context they propose a simple measurement method based on the frequency diversity able to mitigate these effects.

When estimating the distance for wireless sensor networks (WSNs), we always suppose that a fixed curve model exists between the received signal strength indicator (RSSI) and communication distance. But there exist some negative factors in practice, which makes this assumption to contradict with the situation in real communication environment. It results in large distance estimation error with low efficiency. Thus, a lightweight WSN communication distance estimation method is presented, which is called distance estimation using distribution-based fingerprinting. First, we considered the uncertainty in RSSI values, and got the fingerprinting relationship in terms of RSSI data distribution, which is gained through a statistical calculation. Then, a data matching algorithm is implemented to estimate the communication distance. Finally, RSSI values in different conditions are utilized to validate this method. Experimental results demonstrated that the new method can obtain better results with high efficiency than other related methods, and can be applied in WSN localization system.

Research has revealed that the correlation between distance and RSSI (Received Signal Strength Indication) values is the key of ranging and localization technologies in wireless sensor networks (WSNs). In this paper, an RSSI model that estimates the distance between sensor nodes in WSNs is presented. The performance of this model is evaluated and analyzed in a real system deployment in an indoor and outdoor environment by performing an empirical measurement using Crossbow IRIS wireless sensor motes. Our result shows that there is less error in distance estimation in an outdoor environment compared to indoor environment. The results of these evaluations would contribute towards obtaining accurate locations of wireless sensor nodes.