Received Signal Strength Sequence Research Articles

Seeing the world from its words: All-embracing Transformers for fingerprint-based indoor localization

In this paper, we present all-embracing Transformers (AaTs) that are capable of deftly manipulating attention mechanism for Received Signal Strength (RSS) fingerprints in order to invigorate localizing performance. Since most machine learning models applied to the RSS modality do not possess any attention mechanism, they can merely capture superficial representations. Moreover, compared to textual and visual modalities, the RSS modality is inherently notorious for its sensitivity to environmental dynamics. Such adversities inhibit their access to subtle but distinct representations that characterize the corresponding location, ultimately resulting in significant degradation in the testing phase. In contrast, a major appeal of AaTs is the ability to focus exclusively on relevant anchors in RSS sequences, allowing full rein to the exploitation of subtle and distinct representations for specific locations. This also facilitates disregarding redundant clues formed by noisy ambient conditions, thus enhancing accuracy in localization. Apart from that, explicitly resolving the representation collapse (i.e., none-informative or homogeneous features, and gradient vanishing) can further invigorate the self-attention process in transformer blocks, by which subtle but distinct representations to specific locations are radically captured with ease. For that purpose, we first enhance our proposed model with two sub-constraints, namely covariance and variance losses at the Anchor2Vec. The proposed constraints are automatically mediated with the primary task towards a novel multi-task learning manner. In an advanced manner, we present further the ultimate in design with a few simple tweaks carefully crafted for transformer encoder blocks. This effort aims to promote representation augmentation via stabilizing the inflow of gradients to these blocks. Thus, the problems of representation collapse in regular Transformers can be tackled. To evaluate our AaTs, we compare the models with the state-of-the-art (SoTA) methods on three benchmark indoor localization datasets. The experimental results confirm our hypothesis and show that our proposed models could deliver much higher and more stable accuracy.

Matrix Factorization-Based RSS Interpolation for Radio Environment Prediction

This letter proposes matrix factorization (MF) based interpolation of received signal strength (RSS) from a transmitter mounted on moving robot in factory environment. For realizing the reliable wireless communication, machine learning based channel prediction methods have been intensively studied in the past decade. However, some traffic models will make the observation of RSS sequence be intermittent, and the missing values must be interpolated before input to the predictor. Classical interpolation such as linear interpolation cannot appropriately estimate the missing values because the result of the interpolation depends on the observation time. In this letter, we propose to apply an MF-based interpolation technique to RSS interpolation in order to restore the true RSS variation pattern. Moreover, the basic MF-based interpolation is improved by introducing a smoothing term in an objective function to represent the smooth variation of the RSS sequence. The simulation results show that the MF-based interpolation can improve the prediction accuracy of the machine learning based channel prediction method.

Non-Line-of-Sight Localization of Passive UHF RFID Tags in Smart Storage Systems

The UHF radio-frequency identification (RFID) has gained growing attention for tagged object localization in smart storage systems. Due to Non-Line-Of-Sight (NLOS) condition, it is challenging to accurately locate the position of tags inside closed spaces. In this paper, we propose a precise and cost-effective solution for tagged object localization in closed spaces, using only received signal strength (RSS) information. We establish a RSS profile for each tag and discover some important features of RSS profiles including uniqueness, time-variation, column-dependence and waveform-similarity. Based on these features, we propose a reference-free RSS-profile (RFRP) localization scheme. The advantage of our propose scheme is to accurately localize multiple tags in closed spaces by overcoming the challenges including the lack of pre-deployed reference tags, NLOS propagation, multi-path propagation and coupling effect. The RFRP scheme first roughly estimates tags’ coordinates based on Peak Asymmetry Factor, then acquires reference-tag substitutes through the similarity of RSS sequences. Subsequently, our scheme refines the relative positions of all tags by these substitutes. Finally all tags’ absolute positions are estimated through a RSS-ranging model. Extensive experiment results demonstrate that our approach can achieve high ordering accuracy and localization accuracy for the tags inside closed spaces.

Chase++: Fountain-Enabled Fast Flooding in Asynchronous Duty Cycle Networks

Due to limited energy supply on many Internet of Things (IoT) devices, asynchronous duty cycle radio management is widely adopted to save energy. Flooding is a critical way to disseminate messages through the whole network. Capture effect enabled concurrent broadcast is appealing to accelerate network flooding in asynchronous duty cycle networks. However, when the flooding payload's size is large, the concurrent broadcast performance is far from efficient due to the frequently unsatisfied capture effect. Intuitively, senders can send a short packet containing partial flooding payload to keep concurrent broadcast efficiency. In practice, we still face two challenges. Considering packet loss, a receiver needs an effective way to recover the entire flooding payload from several received packets as soon as possible. Moreover, considering different channel states of different senders, how a sender chooses the optimal packet length to guarantee high channel utilization is not easy. In this paper, we propose Chase++ a Fountain-code based concurrent broadcast control layer to enable fast flooding in asynchronous duty cycle networks. Chase++ uses Fountain code to alleviate the negative influence of a certain part of the flooding payload's continuous loss. Moreover, Chase++ adaptively selects packet length with the local estimation of channel utilization. Specifically, Chase++ partitions long payload into several short payload blocks, further encoded into many encoded payload blocks by Fountain-code. Then, with temporal and spatial features of the sampled RSS (received signal strength) sequence, a sender estimates the number of concurrent senders. Finally, according to the estimated number of concurrent senders, the sender determines the optimal number of encoded payload blocks in a packet and assembles the encoded payload blocks as lots of packets. Then, the concurrent broadcast layer continuously transmits these packets. Receivers can recover the original flooding payload after several independent encoded payload blocks are collected. We implement Chase++ in TinyOS with TelosB nodes. We further evaluate Chase++ on Local testbed with 50 nodes and Indriya testbed with 95 nodes. The improvement of network flooding speed can reach 23.6% and 13.4%, respectively.

On the using of Rényi’s quadratic entropy for physical layer key generation

As a promising way for establishing secret keys, physical layer key generation has been broadly studied, which typically extracts secret keys with wireless channel characteristics. The existing schemes proved that temporal channel variations, channel reciprocity and spatial de-correlation of wireless channel greatly affected the performance of physical layer key generation. In this paper, the application of collision entropy (Rényi’s quadratic entropy) for improving physical layer key generation is discussed. Specifically, after collecting sufficient channel measurements, a channel measurement evaluation mechanism based on collision entropy is implemented to determine the adequacy of these measurements. Consequently, some consecutive measurements with smaller collision entropies are dropped and the reserved measurements are applied as input of the following process. Based on the evaluation mechanism, an adaptive and efficient key generation scheme is proposed. The proposed scheme consists of five components: channel probing, the evaluation mechanism, the adaptive quantization, information reconciliation and privacy amplification. To validate this scheme, several experiments in real environments are conducted. Different from the existing schemes, the received signal strength (RSS) measurements derived by heterogeneous devices are used as channel measurements. The results show that: (1) compared with homogeneous devices, RSS sequences of heterogeneous devices have more discrepancies and these discrepancies make key generation more challenging; (2) the proposed evaluation mechanism can effectively reduce bit mismatch rate and enhance the efficiency of information reconciliation; (3) compared with other schemes, the proposed key generation scheme can efficiently generate shared secret keys between heterogeneous transceivers in real environments.

Fusing Similarity-Based Sequence and Dead Reckoning for Indoor Positioning Without Training

The traditional fingerprinting-based positioning approach usually requires a laborious training phase to collect the measurements in an environment, which is a challenge for applications involving large buildings. In this paper, we propose a novel approach to fuse similarity-based sequence and dead reckoning to track the positions of users in wireless indoor environments. The reference fingerprinting map is constructed without the need for training and is based upon the geometrical relationships of the transmitters, whose positions are known and can be obtained offline. The fingerprint used for online positioning is represented by a ranked sequence of transmitters based on the measured received signal strength (RSS), which is referred to as RSS sequence in this paper. The similarities between this sequence and the reference fingerprints are computed and embedded into a particle filter to locate a user. The displacement estimation from inertial measurement unit is then integrated into the particle filter to track a mobile user. Moreover, the proposed approach can be easily extended to include other sources of sensors. In this paper, frequency modulation radio signal measurements are used as the example to fuse with Wi-Fi measurements to achieve a better tracking accuracy. Extensive experiments are also conducted to evaluate the proposed approach.

A Robust Crowdsourcing-Based Indoor Localization System.

WiFi fingerprinting-based indoor localization has been widely used due to its simplicity and can be implemented on the smartphones. The major drawback of WiFi fingerprinting is that the radio map construction is very labor-intensive and time-consuming. Another drawback of WiFi fingerprinting is the Received Signal Strength (RSS) variance problem, caused by environmental changes and device diversity. RSS variance severely degrades the localization accuracy. In this paper, we propose a robust crowdsourcing-based indoor localization system (RCILS). RCILS can automatically construct the radio map using crowdsourcing data collected by smartphones. RCILS abstracts the indoor map as the semantics graph in which the edges are the possible user paths and the vertexes are the location where users may take special activities. RCILS extracts the activity sequence contained in the trajectories by activity detection and pedestrian dead-reckoning. Based on the semantics graph and activity sequence, crowdsourcing trajectories can be located and a radio map is constructed based on the localization results. For the RSS variance problem, RCILS uses the trajectory fingerprint model for indoor localization. During online localization, RCILS obtains an RSS sequence and realizes localization by matching the RSS sequence with the radio map. To evaluate RCILS, we apply RCILS in an office building. Experiment results demonstrate the efficiency and robustness of RCILS.

Hotspot Ranking Based Indoor Mapping and Mobility Analysis Using Crowdsourced Wi-Fi Signal

In this paper, we propose a new hotspot ranking-based indoor mapping and mobility analysis approach based on the sporadically collected crowdsourced Wi-Fi received signal strength (RSS) data. This approach aims to construct the indoor mapping, as well as achieve the mobility analysis of the users following their daily motion patterns in target environment. First, we perform the wavelet analysis with respect to each RSS sequence to mitigate the noise interference to some extent. Second, we develop a new multidimensional scaling approach to map each RSS data into a linear one in the 2-D signal space, which is followed by the density clustering approach to merge the linear ones into different clusters based on the spatial correlation property. Finally, we construct the indoor mapping from the signal into physical spaces by the concept of hotspot ranking order, as well as the transfer relations among different RSS clusters and different physical sub-areas. The experimental results demonstrate that the proposed approach can achieve the superior performance in terms of indoor mapping and mobility analysis in an unknown indoor environment.

Simultaneous pathway mapping and behavior understanding with crowdsourced sensing in WLAN environment

With the wide deployment of Wireless Local Area Network (WLAN), the WLAN Received Signal Strength (RSS) based indoor mapping has attracted significant attention for various of Location-based Services (LBSs). In this paper, we propose a new simultaneous pathway mapping and behavior understanding approach by crowdsourced sensing of WLAN RSS. First of all, a batch of WLAN RSS sequences with known locations are collected to optimize the parameters in density clustering by using the concept of Jaccard Coefficient (JC). Secondly, the wavelet analysis is considered for each RSS sequence with unknown locations to mitigate the noise interference, and then the process of multidimensional scaling approach based dimension reduction is followed to reduce the computation cost involved in density clustering. Thirdly, based on the RSS and timestamp relations of RSS measurements, the density clustering is conducted to merge the RSS measurements at unknown locations into different clusters. Finally, the pathway mapping and behavior understanding are realized according to the indoor mapping from the signal into physical space. The extensive experimental results show that the proposed approach can achieve superior pathway mapping performance compared with the well-known WILL and CIMLoc approaches.

Fingerprint and Assistant Nodes Based Wi-Fi Localization in Complex Indoor Environment

With the extensive development of Wi-Fi, indoor location services based on received signal strength (RSS) fingerprints have attracted increasing attention from researchers. In complex indoor environments, multipath and non-line-of-sight (NLOS) conditions would lead to large errors in measured values, thereby reducing indoor positioning accuracy. In this paper, we propose a Wi-Fi indoor localization method based on collaboration of fingerprint and assistant nodes. First, appropriate assistant nodes based on the similarity of RSS sequences are elaborately selected around the unknown node and distances between them are used as auxiliary information to improve the positioning accuracy. Furthermore, in the complex indoor circumstances that result in NLOS error, an adaptive Kalman filter with colored noise is used to mitigate the time-of-flight ranging error. Experiments demonstrate that in complex indoor environments, our system can outperform its counterparts with robust performance and low localization estimation error.

PRIMAL: Page Rank-Based Indoor Mapping and Localization Using Gene-Sequenced Unlabeled WLAN Received Signal Strength.

Due to the wide deployment of wireless local area networks (WLAN), received signal strength (RSS)-based indoor WLAN localization has attracted considerable attention in both academia and industry. In this paper, we propose a novel page rank-based indoor mapping and localization (PRIMAL) by using the gene-sequenced unlabeled WLAN RSS for simultaneous localization and mapping (SLAM). Specifically, first of all, based on the observation of the motion patterns of the people in the target environment, we use the Allen logic to construct the mobility graph to characterize the connectivity among different areas of interest. Second, the concept of gene sequencing is utilized to assemble the sporadically-collected RSS sequences into a signal graph based on the transition relations among different RSS sequences. Third, we apply the graph drawing approach to exhibit both the mobility graph and signal graph in a more readable manner. Finally, the page rank (PR) algorithm is proposed to construct the mapping from the signal graph into the mobility graph. The experimental results show that the proposed approach achieves satisfactory localization accuracy and meanwhile avoids the intensive time and labor cost involved in the conventional location fingerprinting-based indoor WLAN localization.

Crowd-Sourced Mobility Mapping for Location Tracking Using Unlabeled Wi-Fi Simultaneous Localization and Mapping

Due to the increasing requirements of the seamless and round-the-clock Location-based services (LBSs), a growing interest in Wi-Fi network aided location tracking is witnessed in the past decade. One of the significant problems of the conventional Wi-Fi location tracking approaches based on received signal strength (RSS) fingerprinting is the time-consuming and labor intensive work involved in location fingerprint calibration. To solve this problem, a novel unlabeled Wi-Fi simultaneous localization and mapping (SLAM) approach is developed to avoid the location fingerprinting and additional inertial or vision sensors. In this approach, an unlabeled mobility map of the coverage area is first constructed by using the crowd-sourcing from a batch of sporadically recorded Wi-Fi RSS sequences based on the spectral cluster assembling. Then, the sequence alignment algorithm is applied to conduct location tracking and mobility map updating. Finally, the effectiveness of this approach is verified by the extensive experiments carried out in a campus-wide area.

The Hybrid HMM for RSS-Based Localization in Wireless Sensor Networks

We proposea method of RSS-base localization in WSN (Wireless Sensor Network), called Hybrid HMM, to improve the stabilityof node localization basedon RSS(Received Signal Strength).This model utilizesHMM(Hidden Markov Model) to takeinto account the time factor when receiving the RSS sequence, andconverts the action of ranging into an operationof classification.For the received RSS used for localization,our Hybrid HMMwill compare it withthe preset RSS threshold value, and put the result into one of two categories for subsequent processing: If the received value is higher than the threshold value, the distance value will be drawn from the signal propagation model. If lower, the information will be obtained from a trained HMM. Experimental results show that the Hybrid HMM method can greatly improve the localization accuracy.