Self-configuration and smart binding control on IOT applications

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.

Nowadays, location awareness becomes the key to numerous Internet of Things (IoT) applications. Among the various methods for indoor localisation, received signal strength indicator (RSSI)-based fingerprinting attracts massive attention. However, the RSSI fingerprinting method is susceptible to lower accuracies because of the disturbance triggered by various factors from the indoors that influence the link quality of radio signals. Localisation using body-mounted wearable devices introduces an additional source of error when calculating the RSSI, leading to the deterioration of localisation performance. The broad aim of this study is to mitigate the user’s body shadowing effect on RSSI to improve localisation accuracy. Firstly, this study examines the effect of the user’s body on RSSI. Then, an angle estimation method is proposed by leveraging the concept of landmark. For precise identification of landmarks, an inertial measurement unit (IMU)-aided decision tree-based motion mode classifier is implemented. After that, a compensation model is proposed to correct the RSSI. Finally, the unknown location is estimated using the nearest neighbour method. Results demonstrated that the proposed system can significantly improve the localisation accuracy, where a median localisation accuracy of 1.46 m is achieved after compensating the body effect, which is 2.68 m before the compensation using the classical K-nearest neighbour method. Moreover, the proposed system noticeably outperformed others when comparing its performance with two other related works. The median accuracy is further improved to 0.74 m by applying a proposed weighted K-nearest neighbour algorithm.

This work presents an indoor localization service with the aid of received signal strength indicator (RSSI) from IoT. The RSSI emmitted from the IoT are converted into the relative distance between the IoT nodes to determine the current location. In order to enhance the accuracy, the area of interest is further divided into few zones. The RSSI from the nearby IoT devices (BLE Beacons) detected in each zones are collected. This enable the system to first identify the correct zone located, and utilizing the relative distance from the RSSI at the zone to further acquire the current position inside the zone. A demo system has been developed with the Bluetoothe Low Energy (BLE) to evaluate the indoor localization service with this zone system.

In this paper, we experimentally investigate the combined effect of human, antenna orientation in elevation direction and the ground effect on the Received Signal Strength Indicator (RSSI) parameter in the Wireless Sensor Network (WSN). In experiment, we use MICAz motes and consider different scenarios where antenna of the transmitter node is tilted in elevation direction. The motes were placed on the ground to take into account the ground effect on the RSSI. The effect of one, two and four persons on the RSSI is recorded. For one and two persons, different walking paces e.g. slow, medium and fast pace, are analysed. However, in case of four persons, random movement is carried out between the pair of motes. The experimental results show that some antenna orientation angles have drastic effect on the RSSI, even without any human activity. The fluctuation count and range of RSSI in different scenarios with same walking pace are completely different. Therefore, an efficient human activity algorithm is need that effectively takes into count the antenna elevation and other parameters to accurately detect the human activity in the WSN deployment region.

Wireless sensor network (WSN) indoor location systems are based on effective distance estimation between the target node and anchors to find the node's precise location. Almost all low-cost wireless devices use the received signal strength indicator (RSSI) metric for estimating distance. This study aims to minimise the error in the estimated distance between the anchor nodes and the target node by proposing a model based on an Artificial Neural Network (ANN) for determining the distance using the RSSI. The proposed model was used to apply 3D indoor localisation. ANN is implemented in an Esp32 embedded system to make it compatible with WSN and IoT applications by using the TinyML approach for designing and implementing an efficient base station. The results show that the time required for the indoor localisation process does not exceed 40 ms, which is a very promising result for real-time localisation applications.

This paper is concerned with a wireless receiving link evaluation method using statistical means of received signal strength indicator (RSSI) and link quality indicator (LQI) based on the IEEE 802.15.4 protocol for wireless sensor networks. Traditional methods using single RSSI and single LQI based on the IEEE 802.11 protocol have the disadvantage of the inaccurate evaluation. In this paper, we carry out a quantitative emulation experiment via computing statistical means of RSSI and LQI based on wireless sensor networks protocol of IEEE 802.15.4. Tested numerical values are analyzed using MATLAB and SPSS by defining the wireless link evaluation sensitivity. Result curves of RSSI to packet reception rate (PRR) and LQI to PRR we finally derive are shown that statistical means of RSSI and LQI can obtain the status information of receiving links more accurately, compared with the traditional wireless link evaluation using single RSSI and single LQI.

Dementia is an irreversible disease, its prevalence increases with age, the elderly with dementia increasing with years become a nonnegligible population, the government and the public shall be prepared for this tide. The information and communication technology is improved continuously in recent years, the Internet of Things technology becomes mature increasingly, which is helpful to the life aspects of home, traffic and shopping, and it can be combined with clinical knowledge and experience for the environment of health care, promoting the senile dementia treatment field to face how to use the complete architecture of Internet of Things to provide an effective adjuvant therapy mechanism. The early dementia temporal orientation training mechanism can be built by using the concept of health care Internet of Things. The infrastructure proposed in this study combines xBeacon sensing equipment with novel hybrid operation modes, including Received Signal Strength Indication (RSSI) positioning, event analysis method and intelligent cutting algorithm, to reduce the slightly disabled patients’ troubles in direction judgment and the probably derived anxiety and unease. The effective record and analysis of routine behavior pattern support the elderly to maintain the mobility in daily life independently, promoting the “home-based care for the aged” and “Aging in Place” visions and the attainment of objectives effectively.

The number of physical devices connected to the Internet such as vehicles, household appliances and other "things" has been steadily increasing in recent years, thus forming the basis for the Internet of Things (IoT). IoT ecosystem extends beyond country borders and application domains, combining thousands of versatile devices that differ in terms of their structures, capabilities, and available resources. It is therefore not surprising that the landscape of wireless communication technologies and the degree of IoT devices available today is excessively broad and diverse. Interference between networks leads to frame collisions and consequent packet loss. Frame collisions occur when two or more packets overlap in time and frequency and use the same Long Range (LoRa) parameters, i.e. the same Spreading Factor (SF), Bandwidth (BW) and Carrier Frequency (CF). When most devices use the same configuration, collision probability is higher. The probability of frame collisions is also affected by traffic characteristics, particularly the periodicity of the transmission and the payload size. Larger payload sizes and more frequent transmissions accumulate with higher Time on Air (ToA) and channel occupancy [1]. Transmission powers and the location of the gateways also influences this situation. The aim of this experimental work is to verify the effects of collisions and interference in Long Range Wide Area Network (LoRaWAN), regarding the periodicity of transmissions and the payload sizes in a context of an inter-SF interference scenario. And to validate measuring tools like Received Signal Strength Indicator (RSSI), ToA and Packet Delivery Ratio (PDR) in the reliability of a network. We validate our conclusions by analyzing ToA according with RSSI and different data sizes, the RSSI for different SFs and different data sizes and finally the packet loss through the PDR for a better understanding of the reliability of packet transmission in our link. In the end we conclude that in an environment with inter-SF interference, the choice of SFs and packet lengths impacts ToA. When dealing with multiple devices near gateways, different SFs and varying packet lengths can lead to increased latency and longer ToA. Consequently, the risk of packet loss becomes more significant, especially if the signal-to-interference ratio (SIR) is low. This limitation will therefore affect scalability, particularly in Non-Line-Of-Sight (NLOS) scenarios.

Wireless sensor networks (WSN) have gradually integrated into the concept of the Internet of Things (IoT) and become one of the key technologies. This paper studies the optimization algorithm in the field of artificial intelligence (AI) and effectively solves the problem of node location in WSN. Specifically, we propose a hybrid algorithm WOA-QT based on the whale optimization (WOA) and the quasi-affine transformation evolutionary (QUATRE) algorithm. It skillfully combines the strengths of the two algorithms, not only retaining the WOA’s distinctive framework advantages but also having QUATRE’s excellent coevolution ability. In order to further save optimization time, an auxiliary strategy for dynamically shrinking the search space (DSS) is introduced in the algorithm. To ensure the fairness of the evaluation, this paper selects 30 different types of benchmark functions and conducts experiments from multiple angles. The experiment results demonstrate that the optimization quality and efficiency of WOA-QT are very prominent. We use the proposed algorithm to optimize the weighted centroid location (WCL) algorithm based on received signal strength indication (RSSI) and obtain satisfactory positioning accuracy. This reflects the high value of the algorithm in practical applications.

We proposed a localization algorithm named LSARSSI for mobile node based on RSSI (received signal strength indicator) between locating sensor node with inertia module built-in and the single anchor. Instead of directly mapping RSSI values into physical distance, contrasting RSSI values received from anchor in different visited locations, LSARSSI utilizes the geometric relationship of perpendicular intersection to compute node positions. Given that the values of RSSI among two visited locations are equal, we regard that their distances to anchor node are equal. After obtaining several sets of such visited locations, the relative location of mobile node and anchor node can be calculated. Because of the limitations of LSARSSI, we put forward an improved algorithm named ILSARSSI. Our scheme uses only one location-known anchor which is useful in low density environment without using additional hardware. The simulations show that LSARSSI achieves high accuracy and ILSARSSI performs high stability and feasibility.

An indoor localization system in wireless sensor networks has become a hot development area. Received signal strength indicator (RSSI)-based localization is a promising technique since it requires a relatively low configuration, battery power and easy control. However, the received signal strength is influenced by channel interference and propagation environments. This characteristic affects channel stability and location accuracy in RSSI-based localization. As a result, we propose a novel indoor localization system consisting of a preprocessing method and a post-processing method. To improve channel stability, the pre-processing method selects an optimal channel in terms of the smallest distance error. The optimal channel is less affected to IEEE 802.11. To develop location accuracy, the post-processing method performs maximum likelihood estimation-based location tracking scheme considering the reliability of the RSSI value measurement. We apply three methods to the existing MLE to improve the reliability of the RSSI value. By using this indoor localization system with preprocessing and post-processing, the location error can be reduced. We also proved the performance of the indoor localization system by conducting experiments in a real indoor environment.

Human Positioning Estimation Method Using Received Signal Strength Indicator (RSSI) in a Wireless Sensor Network

On account of wide combination of continuous applications Wireless Sensor Network (WSN) is the most sweltering examination field in the domain of PC sort out. Remote Sensor Network includes little, self-overseeing sensor centers passed on in a remote region to distinguish, assemble and system data and transmit it to the customer. In such framework centers can move and synchronize with the neighbors. As a result of conveyability of center points, mastermind changes dynamically and centers get included and emptied. In this paper, we are going to consider unmistakable cream coordinating shows in WSN. Different mutt coordinating shows perform well in different circumstance and their introduction is seen reliant on changed estimations. Moreover, we have proposed Energy Efficient Hybrid Routing Protocol (EEHRP) in WSN. There are different sensor center points in a Wireless Sensor Network (WSN) that have constrained imperativeness, amassing and the limit with regards to getting ready. A limited asset in a WSN is the power. The presentation of these structures can be improved by using clustering. The fundamental statute on which all the clustering estimations are based is that the framework essentialness is used in picking and gathering head for the most amazing imperativeness capability and the data is epitomized in the pack dependent on the center points that submit to the gathering head. The picked CH sends data from a concentrated center point. The prescribed work proposed LEACH/Low Energy Adaptive Clustering Hierarchy and Received Signal Strength Indicator (RSSI) which are essentialness capable controlling shows in remote sensor frameworks. The past is an auto dealing with, flexible method wherein the sensor center points mastermind themselves into sets known as gatherings. These sets select one cluster head so the essentialness usage is improved. Data gathering is also united with the objective that the amount of messages that are traded to the Base Station (BS) is lessened. This redesigns the framework future. While, in RSSI, the division information is used for evaluating the point of confinement of the battery. The partition between the center points is assessed for finding the circumstance of the motional articles in constrained regions. It has been exhibited that better execution is cultivated by the proposed frameworks.

The routing protocol is one of the most important aspects of the wireless sensor network, determining network connectivity, delay, energy balancing, reliability and other factors. Furthermore, a minimum hop-count does not mean minimum delay and energy consumption. To overcome the routing protocol's shortcomings, we proposed an energy-efficient expected transmission count routing decision strategy (E3TX) for wireless sensor networks. E3TX makes use of two physical parameters, received signal strength indicator (RSSI) and battery voltage, to obtain the final decision via our proposed decision strategy. In our strategy, the received signal strength indicator is used to predict the future packet reception rate (PRR), and the battery voltage is used to estimate the residual energy of network nodes to balance their load. In order to estimate the packet reception rate via the received signal strength indicator with greater accuracy, we performed multiple experiments to build the relationship model between RSSI and PRR. In this article, we use NS-2.35 to evaluate and compare the performance of E3TX with AODV, AOMDV and BIETX. Our simulation results show that our proposed E3TX performs well when compared to the previous studies, not only in terms of energy consumption, but also in reliable data transmission and end-to-end delay.

Increasing the location accuracy of the Indoor Positioning System (IPS) is an important research area in localization.Utilizing mobile beacons in IPS environment has made localization more accurate and cost-effective.This paper proposes an Filtered RSSI and Beacon Weight Approach (FRBW) based on improved Received Signal Strength Indicator (RSSI) values using a Kalman filter.This algorithm takes both the distance and improved RSSI measurements between beacon nodes into consideration.Kalman filter applied on the RSSI measurements that eliminate noise of the signal and then applied on FRBW positioning algorithm.The proposed algorithm was applied using eight beacons.The results show that this FRBW approach has better positioning accuracy and minimum location error, and can be applied in IoT applications in smart city.