A Distance-Based Maximum Likelihood Estimation Method for Sensor Localization in Wireless Sensor Networks

Node localization is one of the key technologies in wireless sensor networks. Taking into account the reasons for the hardware and cost, DV-Hop algorithm is wildly used. The main DV-Hop localization error is the distance between unknown nodes and anchor nodes. In this paper, the estimation of average hop-length and RSSI value has been applied to reduce the error of measurement, which is the distance between unknown nodes and anchor nodes. Simulation results show that this localization algorithm is effective, which improves the positioning accuracy.

Currently, wireless sensor network has important in the role of the short-range wireless communication, especially in, an indoor localization. However, there are more factors of the indoor environment caused to reduce the precise localization and also increase the error of distance. This paper presents a new method to evaluate the wireless sensor network (WSN) technology for the indoor localization. The proposed methods are using received signal strength indicator (RSSI) to compute the error of distance based on weighting algorithms. Whereby the weight range localizer (WRL) and relative span exponential weight range localizer (RS-WRL) is proposed in this paper. As the results, the cumulative distribution function (CDF) probability indicates the error of distance as properly, and this method can help to increase the precision of range based localization method in an application of indoor environment.

In the wireless sensor network (WSN) localization methods based on Received Signal Strength Indicator (RSSI), it is usually required to determine the parameters of the radio signal propagation model before estimating the distance between the anchor node and an unknown node with reference to their communication RSSI value. And finally we use a localization algorithm to estimate the location of the unknown node. However, this localization method, though high in localization accuracy, has weaknesses such as complex working procedure and poor system versatility. Concerning these defects, a self-adaptive WSN localization method based on least square is proposed, which uses the least square criterion to estimate the parameters of radio signal propagation model, which positively reduces the computation amount in the estimation process. The experimental results show that the proposed self-adaptive localization method outputs a high processing efficiency while satisfying the high localization accuracy requirement. Conclusively, the proposed method is of definite practical value.

The localization of devices or nodes using wireless sensor networks is a very interesting area among researchers compared to traditionally available localization methods. The process of localization is to find out the location or position coordinates of target nodes. In this paper, a comparative analysis is done to obtain the optimized results during the estimation of the target node’s location. The distance-based localization method is used for estimating the locations of target nodes. The range measurement is done using RSSI (Received Signal Strength Indicator) based method using lognormal shadowing model (LNSM). The analysis shows that greater the number of anchor nodes will lead towards the better location estimation of the target nodes. The effect of network size and the number of target nodes are also analyzed.

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.

ABSTRACTIn large‐scale wireless sensor networks, cost‐effective and energy‐efficient localization of sensor nodes is an important research topic. In spite of their coarse accuracy, range‐free (connectivity‐based) localization methods are considered as cost‐effective alternatives to the range‐based localization schemes with specialized hardware requirements.In this paper, we derive closed‐form expressions for the average minimum transmit powers required for the localization of sensor nodes, under deterministic path loss, log‐normal shadowing, and Rayleigh fading channel models. The impacts of propagation environment and spatial density of anchor nodes on the minimum transmit power for node localization are evaluated analytically as well as through simulations. Knowledge of the minimum transmit power requirements for localizability of a sensor node enables improving energy efficiency and prolonging lifetime of the network. We also propose a novel distance metric for range‐free localization in large‐scale sensor networks. The target and anchor nodes are assumed to be positioned according to two statistically independent two‐dimensional homogeneous Poisson point processes. Analytical expression for the average distance from a target node to its kth nearest neighbor anchor node is derived and is used for estimating the target‐to‐anchor node distances for localization. The Cramér–Rao lower bound on the localization accuracy for the new distance estimator is derived. Simulation results show the accuracy of the proposed distance estimate compared with some existing ones for range‐free localization. The results of our investigation are significant for low‐cost, energy‐efficient localization of wireless sensor nodes. Copyright © 2011 John Wiley & Sons, Ltd.

Localization is one of the supporting technologies in wireless sensor networks, and accuracy is a significant criterion to evaluate the practical utility of localization algorithm. The effect of environment parameters like path loss exponent to measurement accuracy in received signal strength indicator (RSSI) is considered in this paper. According to the relationship between distance and received signal power among beacon nodes, least squares method (LSM) is introduced to acquire current environment parameters. Then the estimated distance is converted to an appropriate weight. Finally, the location information of nodes can be calculated out by weighted Centroid localization algorithm (WCLA). Simulation results in MATLAB show that our algorithm (LS-WCLA) has good self-adaptability and robustness.

As an information acquisition and processing method, wireless sensor network is also an important component of the Internet of Things. As a key core technology of wireless sensor networks, localization technology has become an important direction in the current and future research of wireless sensor networks, which is also a key issue related to the real application of wireless sensor networks and the Internet of Things. The research of localization algorithm based on intelligent computing technology is paid more attention. In this paper, a novel intelligent computing method — grey wolf optimization was used to localization in wireless sensor network and proposed a novel localization algorithm. The validity and practicability of the proposed algorithm were verified by simulation experiments. The convergence performance and localization result was discussed and compared by the classical traditional intelligent computing methods—particle swarm optimization algorithm. Moreover, the localization performance under different anchor node proportion and different communication radius were analyzesed in this paper. The simulation results show that the proposed algorithm has higher localization accuracy, and it needs fewer anchor nodes and smaller communication radius to achieve the same accuracy, thus saving cost.

As the geographical location of sensors is of great importance in the applications of a wireless sensor network, localization attracts significant research interest. Among the localization methods, received signal strength indicator (RSSI) localization algorithm is widely studied due to its low-cost. However, the radio signal transmission is affected by the factors of channel attenuation and noise, etc. Therefore, the actual location is impossible to estimate. To alleviate the effect of the RSSI measure errors, we proposed a novel error modified method for RSSI location algorithm based on neural network, short for RSSI-NN. First, we use the fitting parameters of the monitoring area to determine the parameters of RSSI model. Second, the distance between anchor node i and the unknown node Liis calculated. Then, the error modified algorithm based on neural network is proposed. Finally, we can obtain the modified value of the output layer data. Simulations demonstrate that RSSI-NN achieves lower localization error compared with conventional RSSI-based localization algorithm.

The growing awareness of the strategic value of service along with higher product performance expectations has directed many industries to adopt service centric competitive strategies in managing their supply chains. Service innovation based on currently available advanced technologies has become critical in improving enterprises’ competitive strengths. Wireless sensor network (WSN) technology, which can provide a mobile, scalable, and reliable monitoring solution, is emerging as a tool for service innovation in supply chain management. This paper provides an overview of wireless sensor network technology and discusses how it can benefit modern industries. The fundamental theories of WSN are introduced as well as an overview of the development. The impact of information technologies on supply chain management and service innovation is then briefly discussed. Much emphasis is placed on the feasibility, procedures, and critical challenges of implementing WSN in supply chain management innovation. Current and future applications of WSN are also provided, followed by a case study demonstrating the application potentials of WSN for service innovation in the healthcare industry.

This paper mainly introduces the application of multichannel communication technology in wireless sensor network. The corresponding solutions are proposed based on the analysis of the structure and characteristics of wireless sensor, and the multi-channel MAC protocol in wireless sensor network. In this paper, multi-channel communication technology in wireless sensor network (WSN) is studied, the existing problems and reasons are analysed, and some solutions to further promote the further development of the technology are put forward. With the wide application of WSN, the security of WSN has been paid more and more attention. In this paper, the existing time synchronization protocol, from the security point of view, and the fault tolerance design of the fits protocol. The protocol has an intrusion detection mechanism, which can detect the attack of malicious nodes in time synchronization. Aiming at the small deviation of slow attack and the problem of time synchronization error, information fusion is based on the quality principle, and according to the time synchronization information of different nodes, we make random weighted average correction for local time nodes. The simulation results show that the fits time synchronization protocol has good intrusion detection and fault tolerance capability.

The localization of wireless sensor nodes is one of the key technologies in wireless sensor networks (WSN). Recently, a distributed localization method has been proposed based on a novel concept of angle of departure (AOD) estimation, which can be employed by e.g., the single antenna sensors to estimate the angle information and achieve localization. Based on the AOD estimation, in this paper, we introduce a synthetic uniform circular array (SUCA) synthesized by a rotary antenna on anchor to get the distributed localization of the sensors in multi-path environments. The rotated operation of antenna could create the SUCA with a larger aperture to deal with AOD of multipath components (MPCs) and relax the condition that array aperture should be greater than the number of MPCs. This new localization method only relies on radio transceivers without requiring extra measuring equipments, and the sensor nodes conduct localization in a completely distributed manner instead of centralized processing, which is suitable for large-scale WSN. Meanwhile, the influence of carrier frequency offset (CFO) between the transceivers is also considered and it is eliminated by utilizing the signal emitted from an auxiliary antenna.

An improved received signal strength indicator positioning algorithm based on weighted centroid and adaptive threshold selection

Node Localization technology is one of key technologies in wireless sensor network. DV-Hop localization algorithm is a kind of range-free algorithm. In this paper, an improved DV-Hop algorithm aiming to enhance localization accuracy is proposed. To enhance localization accuracy, average per-hop distance is replaced by corrected value of global average per-hop distance and global average per-hop error. When calculating hop distance, unknown nodes use corresponding average per-hop distance expression according to different hop value. Comparison with DV-Hop algorithm, simulation results show that the improved DV-Hop algorithm can reduce the localization error and enhance the accuracy of sensor nodes localization more effectively.

Localization is one of the most significant technologies in wireless sensor networks (WSNs) since it plays a critical role in many applications. The main idea in most localization methods is to estimate the sensor-anchor distances that are used by sensors to locate themselves. However, the distance information is always imprecise due to the measurement or estimation errors. In this work, a novel algorithm called neighbor constraint assisted distributed localization (NCA-DL) is proposed, which introduces the application of geometric constraints to these distances within the algorithm. For example, in the case presented here, the assistance provided by a neighbor will consist in formulating a linear equality constraint. These constraints can be further used to formulate optimization problems for distance estimation. Then through some optimization methods, the imprecise distances can be refined and the localization precision is improved.