RSS-based Monte Carlo localisation for mobile sensor networks

With the proliferation of the Internet of Things (IoT), employing Received Signal Strength (RSS) as a metric to determine the location of a target (e.g., person or mobile device) is of great interest in terms of cost and ease of implementation. Indeed, RSS measurements can be easily obtained for most off-the-shelf devices, such as WiFi- or ZigBee compatible devices or sensors. This paper deals with the indoor localization problem in wireless sensor networks (WSNs) and proposes a new approach for radio signal propagation modelling and localization estimation, that accounts for the imperfection of RSS measurements and the reliability of RSS sources by using the Dempster-Shafer Theory (DST). In the signal propagation modelling, key information regarding the geometry of indoor environment that is divided into zones separated by walls (zoning), are considered. Based on the number of walls, the RSS irregularities are estimated using different distance intervals, which are weighted by a probability density determined experimentally. To estimate the location of a target node, the PCR6 rule is used to combine the belief masses of the positions obtained from the probability density. In order to evaluate the performance of the proposed approach, an experimental WSN has been deployed in a living apartment. The obtained results demonstrate that the proposed approach improves the localization accuracy compared to the case without zoning. Moreover, the obtained localization mean error proves the feasibility of a precise localization of humans in indoor environments in the case of Ambient Assisted Living and Social Robotics applications.

For wireless sensor networks, received signal strength (RSS) and proximity (also known as connectivity) measurements have been proposed as simple and inexpensive means to estimate range between devices and sensor location. While RSS measurements are recognized to suffer from errors due to the random nature of the fading channel, proximity measurements, ie., knowing only whether or not two devices are in communication range, are often discussed without considering that they are affected by the same fading channel. Proximity measurements are actually just a binary quantization of RSS measurements. We use the Cramér-Rao bound (CRB) to compare the minimal attainable variances of unbiased sensor location estimators for the cases of RSS and proximity measurements. For completeness, we also present the CRB for sensor localization with systems using K-level quantized RSS (QRSS) measurements, of which proximity measurements are the special case: K=2. Examples are presented for the case of one unknown-location sensor, and for the case of a 5 by 5 grid of sensors. These examples show that lower bounds for standard deviation in proximity-based systems are, as a rule of thumb, about 50% higher than the bounds for RSS-based systems. Furthermore, results are presented which show how many bits of quantization are necessary for a QRSS-based system to nearly achieve the bounds of an unquantized RSS system.

Determining the physical positions of sensors has been a fundamental and crucial problem in wireless sensor networks (WSNs). Due to the inherent characteristics of WSNs, extremely limited resources available at each low-cost and tiny sensor node, connectivity-based range-free solutions could be a better choice to feature a low overall system cost. However, localization by means of mere connectivity may underutilize the proximity information available from neighborhood sensing. Although received signal strength (RSS) values are irregular and highly dynamic, it might provide heuristic information about which neighboring nodes are closer and which are further. We propose a distributed Mobile-beacon-assisted localization scheme based on RSS and Connectivity observations (MRC) with a specific trajectory in static WSNs. To ensure that MRC performs as true to reality as possible, we propose two improved approaches based on MRC to consider irregular radio scenario in the noisy environment. Comparing the performance with three typical range-free localization methods in static WSNs, our lightweight MRC algorithm with limited computation and storage overhead is more suitable for very low-computing power sensor nodes that can efficiently outperform better with only basic arithmetic operations in the simulation and physical environments.

Localization of wireless ad hoc and sensor networks has gained much research attention for several years. This paper proposes a hybrid localization scheme which exploits Received Signal Strength (RSS)-based ranging and Self Organizing Maps (SOM)-based range free localization methods to obtain the tradeoff between cost, power and location accuracy. Distance information from RSS measurement has been utilized in the learning steps of SOM-based localization algorithm to get more accurate location estimates while reducing the number of learning steps. Methods on RSS uncertainty reduction and obstacle filtering are also incorporated in the proposed RSS-SOM scheme. Results from extensive simulations prove that our proposed hybrid solution outperforms several existing localization algorithms in both isotropic and anisotropic network environments with lower anchor utilization.

The paper focuses on the performance analysis of the timing acquisition scheme based on received signal strength (RSS) measurements for ultra-wideband (UWB) communication systems. To our knowledge, few paper discusses the performance of the timing acquisition scheme based on RSS measurements for UWB in details. The timing acquisition scheme based on RSS measurements has many advantages, such as low cost, high speed and robustness. However, low transmission power limitation and complicated indoor multipath propagation environments make it difficult to measure accurate RSS at the receiver and affect the accuracy of the time delay offset estimation for the local template signals. In addition, the time variation of the indoor channel state can form a multiple-to-one mapping relation between the RSS measurements and the distance, which largely limits the method application. In the paper, we analyze the accuracy of time delay offset estimation for this method by deriving the Cramer-Rao lower bound (CRLB). The number results show that the accuracy of the timing acquisition method based on RSS measurements suffers from both the UWB signal transmission speed and the varying distance between the transmitter and receiver. It is noted that though the accuracy of the method can hardly fulfil the fine timing acquisition requirement for UWB systems, it will largely reduce the coarse timing acquisition time and improve the performance for UWB timing acquisition systems if combined with other timing acquisition ones

Advances in miniaturized wireless and sensing technologies have enabled the construction of cheap, low-powered, portable wireless devices capable of forming ad hoc networks. While these networks have shown enormous potential in applications such as remote sensing and target tracking, these applications require the devices to determine their own location. Additionally, devices capable of self-localization can also be used to implement location-based services or to improve coordination between first-responders to disaster sites or infantry in tactical situations. Existing techniques such as GPS may not be available due to design or environmental constraints, so other methods need to be devised. Previous works have proposed methods for wireless devices to self-localize based on received signal strength (RSS), but these methods offer limited accuracy due to the large error in RSS measurements. Recognizing the trend for these portable wireless devices to contain acceleration sensors, we propose an algorithm to combine these acceleration measurements with RSS readings to achieve accurate localization. We apply a distributed extended Kalman filter to track position based on these two measurements and a kinematic node movement model. This algorithm is able to take advantage of correlations between successive location estimates to improve estimation accuracy. We calculate the posterior Cramer-Rao bound for this algorithm and analyze it through simulation. Our analysis shows that by utilizing the acceleration information, the network is able to self-localize despite the large inaccuracy in RSS readings.

To address three-dimensional (3D) localization problems in the wireless sensor networks (WSNs) of mobile communications, a closed-form algorithm by utilizing hybrid information extracted from the received-signal-strength (RSS) with unknown signal powers and the angle-of-arrival (AOA) is proposed in this paper. Firstly, the distance of the target is expressed as the linear equation with respect to coordinates of target and trigonometric functions of angles. Then, the RSS measurement is rewritten as the ratios of distances between the target and different receiving sensors. Furthermore, the set of pseudo-linear equations for the location of target is derived with respect to the AOA and nonlinear RSS measurements. Under the condition of the small variance of measurements, the first-order Taylor expansion around the value of measurement is taken to the set of pseudo-linear equations and the first-order perturbation errors is also obtained. Finally, the solution of the target localization is derived utilizing the weighted least square (WLS). Theoretical analysis and simulation results show that the performance of proposed algorithm converge to the CRLB in the situation of the moderate measurement errors.

accurate localization of sensor nodes is essential for the effective operation of Underwater Acoustic Sensor Networks (UASNs). Conventional wireless sensor network techniques cannot be directly applied underwater due to severe attenuation of radio waves and the difficulty of achieving precise time synchronization. To address these challenges, this study employs Received Signal Strength (RSS) as the primary distance-estimation metric, as it does not require clock synchronization and provides more reliable measurements in acoustic environments. We propose an enhanced localization algorithm capable of operating under clock asynchronization and node mobility. The algorithm estimates inter-node distances using RSS with the support of the Lambert-W function, enabling robust computation even in nonlinear conditions. Node mobility is incorporated by calculating velocity through the Durbin algorithm, after which initial coordinates and velocities are used to predict the final node positions at any given time. The improved approach meets the critical requirements of UASNs, including higher localization accuracy, efficient processing for dynamic nodes, and reduced computational cost, making it a practical and effective solution for real-time underwater applications.

To achieve secure communications in wireless sensor networks, sensor nodes should be able to establishsecret shared keys with neighboring parties. The location information of sensor nodes can beused to generate a shared key. In this paper, we propose yet another location-based key managementscheme for wireless sensor networks. After reviewing the existing location-based key managementschemes, we focus on the scheme called LDK because of its pros and cons. To solve a communicationinterference problem in LDK and its similar methods, we devise the new key revision processthat incorporates the grid-based location information. We also propose the key establishment processusing the grid information. For analysis, we conducted a simulation and confirmed that our methodcan increase the connectivity while decrease the compromise ratio when the minimum number ofcommon keys required for key establishment is high. Finally, we also found that hexagonal deploymentof anchor nodes can save network costs in our method.

Localization based on the received signal strength (RSS) is by far the cheapest and simplest option. By constructing the appropriate path-loss model, the RSS information can be converted to the distance estimates which can determine the position of the target node with Linear Least Square (LLS) algorithm. In this paper, the LLS algorithm can be directly applied by subtracting a reference node equation from the remaining ones rather than defining a new variable, which selects the reference node with the maximal RSS among all the RSS measurements. Furthermore, based on the latest research results about the estimates of the squared distance, we investigate the unbiased and linear minimum mean square error (MMSE) estimates of the squared distance, and the covariance matrixes about the two estimates with the proposed LLS algorithm are derived, respectively. Then the weighted LLS localization algorithms are presented by utilizing the covariance matrixes. Simulation results demonstrate that the localization accuracy of the linear MMSE estimate with the proposed LLS algorithm is superior to the unbiased estimate. Moreover, the proposed weighted LLS algorithms outperform the traditional LLS localization algorithms in terms of localization accuracy.

Source localization using RSS (received signal strength) measurements has received considerable attention recently. However, there are few works focusing on the problem of positioning multiple directional sources. To provide a benchmark for evaluating localization algorithms, this paper derives the Cramer-Rao lower bounds (CRLB) for estimating locations, orientations, transmit powers, beam widths of multiple directional sources and the path loss exponent (PLE) of the environment. Meanwhile, in order to facilitate the computation and expression, we also present the relationship of the CRLB among different quantities of sources when estimating their locations and orientations. Given that the maximum likelihood (ML) estimator for the localization problem is highly non-convex and non-linear, this paper proposes an Expectation Maximization (EM) like algorithm to estimate the multiple parameters of each of the sources iteratively. Moreover, the complexity of the presented ML-EM algorithm is significantly reduced via linear representation among the parameters to be estimated. Numerical and simulation results demonstrate the performance of the proposed methods and the derived bounds in terms of estimation error, noise robustness and number of sources.

An efficient Dynamic Addressing based routing protocol for Underwater Wireless Sensor Networks

Received Signal Strength (RSS) localization is widely used due to its simplicity and availability in most mobile devices. The RSS channel model is defined by the propagation losses and the shadow fading. These parameters might vary over time because of changes in the environment. In this paper, the problem of tracking a mobile node by RSS measurements is addressed, while simultaneously estimating a two-slope RSS model. The methodology considers a Kalman filter with Interacting Multiple Model architecture, coupled to an on-line estimation of the observation's variance. The performance of the method is shown through numerical simulations in realistic scenarios.

Wireless sensor networks are being used in a variety of ways - from reconnaissance and detection in military to biomedical applications and a wide variety of commercial endeavors. We introduce a position-adaptive direction finding method using mobile sensor networks and present recent experimental results in localization of a non-cooperative sensor node using static and mobile sensor networks. Electromagnetic (EM) direction finding is a technique in which a group or a swarm of Micro-Aerial Vehicles (MAVs) cooperate their sensing missions, adapt their position in real-time autonomously, and localize an unknown, hidden EM source based on optimal detection algorithms. The MAVs are equipped with IRIS wireless sensor nodes that serve as the sensing agents by providing mobility to the otherwise stationary nodes. In order to localize the transmitter, we use the Received Signal Strength Indicator (RSSI) data to approximate distance from the transmitter to the revolving receivers. We provide an algorithm for on-line estimation of the Path Loss Exponent (PLE) estimation that is used in modeling the distance based on received signal strength (RSS) measurements. The emitter position estimation is calculated based on surrounding sensors RSS values using Least-Square Estimation (LSE).

Sensor localization based on Received Signal Strength Indication (RSSI) in Wireless Sensor Networks (WSNs) takes a place in research interest because received signal strength can be measured without additional hardware. However, RSSI based localization requires static configuration for anchor nodes to achieve high localization accuracy in reasonable time. Furthermore, when sufficient number of mobile nodes are chosen as anchors, RSSI based localization accuracy is radically influenced by measurement errors of signal strength in physical environments. As a result, RSSI based localization error may increase due to anchor mobility such as a case of close anchors and a case of approximately linear anchors. In this paper, a new localization scheme is proposed to solve such problems. In addition, the proposed scheme is enhanced to work in low anchor density to minimize additional equipment cost for anchor nodes. Performance comparison and simulation results show that the proposed scheme achieves better localization accuracy in fading environments (i.e. urban, suburban or rural) compared to existing RSSI solutions.