Location-aware Research Articles

The effect of age and proprioceptive illusion susceptibility on gait

ObjectiveAge-related gait decline has been associated with impaired proprioception, one's internal awareness of spatial location and movement. Although impaired proprioception has further been linked to greater susceptibility to proprioceptive illusions, it is unclear the impact such susceptibility has on gait and its interaction with age. The purpose of this study was to address these uncertainties. MethodsWe measured proprioceptive illusions induced by muscle spindle manipulation and gait in young and older adults. We also compared illusory measures with traditionally used joint position matching to examine if illusory susceptibility can reveal proprioceptive impairments. ResultsWe found no effect of age on proprioceptive illusion susceptibility or joint position matching error. Compared to non-perceivers, illusion perceivers across both age groups showed greater joint matching error, suggesting reduced proprioceptive acuity. Consistent with previous studies, older adults had reduced cadence, gait velocity, and step length compared to young adults in both single- and dual-task walking. Interestingly, illusion perceivers, regardless of age, showed reduced cadence and step width compared to non-perceivers. ConclusionsOur results suggest that gait impairments observed in those who perceive these proprioceptive illusions are age-independent, potentially rooted in deteriorated proprioception. SignificanceThis is the first study to examine these relations.

Enhancing Location Awareness: A Perspective on Age of Information and Localization Precision

Enhancing Location Awareness: A Perspective on Age of Information and Localization Precision

Saliency and location aware pruning of deep visual detectors for autonomous driving

Despite the remarkable achievements of deep neural networks, their high computational complexity limits their wide use in many real-world embedded applications, such as autonomous driving perception. While current neural network pruning approaches can reduce model complexity to various extents, they often adopt local or ad hoc importance measures that are not directly related to the final task. More importantly, most of them focus on classification tasks and do not take location information into consideration during pruning. To address these issues, we present a novel channel importance measure that incorporates detection-related saliency and location awareness, specifically designed for the pruning of self-driving visual detectors. Our comprehensive experiments on the KITTI and COCO_traffic datasets demonstrate that our pruning method achieves significant reductions in model size and computational operations with little performance degradation. Notably, it outperforms other state-of-the-art methods across various pruning rates and base detectors. Our pruned YOLOX-S model with 40.2% fewer parameters even improves the original model’s mAP by 1.8% on KITTI. Moreover, we experimentally highlight the potential of our pruning approach in effectively detecting small-scale objects.

Towards coordinating self-healing instances: Policy-based and non-cooperative game theory-based approaches with location awareness

Self-healing is an essential functionality in Self-Organizing cellular Network (SON). It aims to ensure service continuity by detecting, diagnosing, and compensating network outage after triggering appropriate Cell Outage Compensation (COC) functions according to the outage case. The role of self-healing is increasingly become indispensable, especially in high density network as in 5 G and beyond, in order to maintain the availability and retainability of mobile service and save revenues. However, the essence of COC entails modifications of different Network Control Parameters (NCP) which are under control of SON Functions (SONF) that are related to other self-x functionalities. In addition, it can request different types of compensation functions according to the outage case. Moreover, it requires to harmonize the objectives of the requested stand-alone SONFs over the involved Candidate Cells (CC) in order to re-build a consistent coverage model. Thus, the need for self-coordination is mandatory to guarantee conflict-free outage compensation procedure. In this paper, we propose a novel integrated coordination scheme in order to coordinate Antenna Tilt (AT)-based and Transmission Power (TXP)-based COC functions. Beside their primary targets of conflict avoidance and resolution, we prove that our location-aware coordination mechanisms, which are based on policies and Non-Cooperative Zero-Sum Game (NCZSG) theory, can collaboratively guide greedy stand-alone single-objective COC functions to reach a stable coverage model. The coordination is done even when there is no prior knowledge of the environment or the compensatory algorithms themselves. The choice of optimal Network Control Parameters Values (NCV) is determined according to the function after the coordinator's verification.

Ajna: A Wearable Shared Perception System for Extreme Sensemaking

This paper introduces the design and prototype of Ajna, a wearable shared perception system for supporting extreme sensemaking in emergency scenarios. Ajna addresses technical challenges in Augmented Reality (AR) devices, specifically the limitations of depth sensors and cameras. These limitations confine object detection to close proximity and hinder perception beyond immediate surroundings, through obstructions, or across different structural levels, impacting collaborative use. It harnesses the Inertial Measurement Unit (IMU) in AR devices to measure users’ relative distances from a set physical point, enabling object detection sharing among multiple users across obstacles like walls and over distances. We tested Ajna's effectiveness in a controlled study with 15 participants simulating emergency situations in a multi-story building. We found that Ajna improved object detection, location awareness, and situational awareness, and reduced search times by 15%. Ajna's performance in simulated environments highlights the potential of artificial intelligence (AI) to enhance sensemaking in critical situations, offering insights for law enforcement, search and rescue, and infrastructure management.

2275 Enhancing delirium awareness among patients’ relatives: a quality improvement project in elderly care

Abstract Aim This Quality Improvement Project (QIP) addressed the pressing need for increased awareness of delirium among patients’ relatives. With a 26% rise in the elderly population in Cambridgeshire, surpassing the 18.6% national average, the project provides crucial information to enhance patients’ relatives access to information about delirium, a condition affecting up to 50% of older hospitalised patients. Method This prospective QIP was conducted across seven Cambridge University Hospital (CUH) geriatric wards. Qualitative surveys determined delirium awareness, understanding, interest and perception of information availability among patients’ relatives with delirium. Measurements included data on the views of online Trust delirium information. Interventions included designing a new eye-catching delirium information poster with a QR code linked to the Trust delirium information leaflet, strategically placing QR code-enabled posters in wards, awareness campaigns targeting healthcare professionals, liaison with the inpatient Dementia team, and targeted communication at Dementia and Delirium Champion training sessions for nursing and healthcare staff. Results Pre- and post-intervention questionnaires, involving 13 relatives visiting these wards, showed a 16%, 10% and 20% respective increase in respondents’ awareness, understanding, and interest in delirium. There was a 29% increase in awareness of location of delirium information post intervention. After the interventions, views of the Trust delirium website increased by 132%. Cycles of the QIP highlighted the importance of laminated posters to comply with infection control measures and the need liaison with ward teams about strategic poster placement. Conclusion this QIP successfully demonstrated the effectiveness of QR code enabled access to patient/relative information and that a multi-faceted approach is required to facilitate effective information provision. We also demonstrated these measures increased delirium awareness in relatives. Future recommendations include continuous monitoring of the availability of posters within clinical areas, content evaluation, and work with the Dementia team to roll this out to all wards with older people within Trust.

Empowering vocational students: Exploring mobile learning for sustainable high-level cognition in authentic contexts

Early studies show that learning with mobile devices, also known as mobile learning, improves students’ learning in authentic contextual learning–i.e., learning connected to the real world. However, no empirical evidence has yet to firmly prove the effects of mobile technology on specific student skillsets such as learning scalability which means learning can be applied in various scenarios and learning sustainability which means learning can be sustained in real-world environments. Therefore, this study aims to explore the effect of learning using a mobile app called mobile Smart-Physics on learning cognitive levels, learning scalability (e.g., number of learning locations and number of experimental data), and learning sustainability (e.g., number of completed assignments). Eleventh-grade vocational high school students volunteered for this quasi-experiment and were divided into an experimental group (EG), which used Smart-Physics, and a control group (CG), which used a mobile Ubiquitous-Physics (U-Physics) app. The findings show that the EG significantly outperformed the CG concerning learning cognitive levels, learning scalability and learning sustainability. Smart-Physics features enabled the students to tackle technical and pedagogical difficulties during physical investigations in real-world environments and, in some cases, improved their task accomplishment and sustained their motivation to learn. Location awareness promoted the students’ authentic experiential learning, which sharpened their ability to apply learning in real-world environments and upload more experimental data. Feedback helped the students consolidate their physics theories and practical experiences, thereby generating more learning records with meaningful multimedia content like experimental graphs, tables, and notes in various learning locations. Therefore, we encourage practitioners to use smart learning environment features in their learning tools and activity designs.

Hybrid algorithm GA-PSO-BP in secure localization technology for wireless sensor networks

ABSTRACT Wireless sensors face problems such as poor positioning accuracy in their applications. A hybrid indoor space safety positioning technology is proposed for this purpose. This technology uses genetic models and particle swarm optimization models to optimize the parameters of neural networks. Considering the vulnerability of wireless sensors to Sybil attacks during the positioning process, communication encryption and location awareness are used for optimization. In the positioning experiment, the proposed method had maximum positioning errors of 0.23 m and 0.43 m on the first and second floors, respectively, which were superior to other methods. In the positioning security experiment, the positioning errors of the first and second floors after being attacked by Sybil were 4 m and 5 m respectively, while the proposed security positioning technology had an error range of 1 m. In terms of security comparison, when there are 31–35 attack samples, the proposed method has a maximum root mean square error and average absolute error of 0.72 m and 0.79 m, respectively, which is superior to other models. It can be seen that the proposed technology has excellent positioning effect and stability. The research content will provide important technical support for the practical deployment and security management of wireless sensor networks.

Source localization based on field signatures: Laboratory ultrasonic validation

Location awareness in wireless networks is essential for emergency services, navigation, gaming and many other applications. This article presents a method for source localization based on measuring the amplitude-phase distribution of the field at the base station. The existing scatterers in the target area create unique scattered field interference at each source location. The unique field interference at each source location results in a unique field signature at the base station which is used for source localization. In the proposed method, the target area is divided into a grid with a step of less than half the wavelength. Each grid node is characterized by its field signature at the base station. Field signatures corresponding to all nodes are normalized and stored in the base station as fingerprints for source localization. The normalization of the field signatures avoids the need for time synchronization between the base station and the source. When a source transmits signals, the generated field signature at the base station is normalized and then correlated with the stored fingerprints. The maximum correlation value is given by the node to which the source is the closest. Numerical simulations and results of experiments on ultrasonic waves in the air show that the ultrasonic source is correctly localized using broadband field signatures with one base station and without time synchronization. The proposed method is potentially applicable for indoor localization and navigation of mobile robots.

A Brief Systematics Review and Visualization of Smart Cities based on Fog and Big Data Analysis Technology

The fog computing is based on IoT, and same as clouds are on the edge of a network between a device and the cloud, which can provide very close service to the device in real time. cloud computing frameworks also extend support to emerging application paradigms such as IoT, Fog computing, Edge, and Big Data through service and infrastructure. the ubiquitous deployment of sensors in smart cities requires a high-performance computing paradigm to support big data analysis with smart technologies and communications in IoT, providing location-awareness and latency-sensitive computing near the data sources (i.e., at the edge of the network). Cloud Computing paradigms widely used in enterprises to address the emerging challenges of big data analysis because of its scalable and distributed data management scheme. However, data centers in the Cloud faces great challenges on the burden of exploding amount of big data and the additional requirements of location awareness and low latency at the edge of network necessary for smart cites. the widely distributed sensor networks generate massive volume of data, which leads to a “Big Data “analysis challenge. The machine-to-machine(M2M) communication among massive numbers of sensors will dominate future communication network traffic, namely Internet of Things, instead of traditional Internet of Contents (IoC) in human-to-human and human-to-machine communication. In this review system where we took the research article around 33 from the different database and also taken 1987 datasets form the PubMed database from 1959 to 2024 (02 June 2024). In addition, depending on the different requirement which is partially processed the data. Then the gathered data is stored at the ledger unit through the smart cities based on the fog computing and big data technology. The hashing identity-based data encryption is a public key encryption which uses a digital signature to encrypt the data to prevent the information from the third party.

A Comprehensive Analysis of the Spatial and Seasonal Shifts in Tornado Activity in the United States

Abstract Recent articles have shown that the long-portrayed “tornado alley” in the central plains is not an accurate portrayal of current tornado frequency over the United States. The greatest tornado threat now covers parts of the eastern United States. This paper shows that there has been a true spatial shift in tornado frequency, dispelling any misconceptions caused by the better visibility of tornadoes in the Great Plains versus the eastern United States. Using F/EF1+ tornadoes (the dataset least affected by increasing awareness of tornado locations or by changing rating methods), a 1° × 1° grid, and data for the two 35-yr periods 1951–85 and 1986–2020, we show that since 1951, by critical measures (tornadogenesis events, tornado days, and tornado pathlength), tornado activity has shifted away from the Great Plains and toward the Midwest and Southeast United States. In addition, tornadoes have trended away from the warm season, especially the summer, and toward the cold season since 1951. Annual trends in tornadoes by season (winter, spring, summer, and autumn) confirm this. All of the increase in F/EF1+ tornadoes in the eastern United States is due to an increase in cold season tornadoes. Tornadoes in the western United States decreased 25% (from 8451 during 1951–85 to 6307 during 1986–2020), while tornadoes in the eastern United States. increased 12% (from 9469 during 1951–85 to 10 595 during 1986–2020). The cities with the largest increases and decreases in tornado activity since 1951 are determined. Significance Statement This paper quantifies in many ways (tornadoes, tornado days, and pathlength) the geographical shift in tornadoes from the central to the eastern United States and from the warm season to the cold season, since 1951. Where and when tornadoes most frequently occur is significant not only for the research and operational meteorology communities but also for public perception and risk awareness. Some research studies have shown that tornado casualties are more likely in the eastern United States and the cold season because of preconceived notions of a “tornado alley” in the Great Plains and a “tornado season” in the spring. Publication of the results of this research might help ameliorate this problem.

LJCD-Net: Cross-Domain Jamming Generalization Diagnostic Network Based on Deep Adversarial Transfer.

Global Navigation Satellite Systems (GNSS) offer comprehensive position, navigation, and timing (PNT) estimates worldwide. Given the growing demand for reliable location awareness in both indoor and outdoor contexts, the advent of fifth-generation mobile communication technology (5G) has enabled expansive coverage and precise positioning services. However, the power received by the signal of interest (SOI) at terminals is notably low. This can lead to significant jamming, whether intentional or unintentional, which can adversely affect positioning receivers. The diagnosis of jamming types, such as classification, assists receivers in spectrum sensing and choosing effective mitigation strategies. Traditional jamming diagnosis methodologies predominantly depend on the expertise of classification experts, often demonstrating a lack of adaptability for diverse tasks. Recently, researchers have begun utilizing convolutional neural networks to re-conceptualize a jamming diagnosis as an image classification issue, thereby augmenting recognition performance. However, in real-world scenarios, the assumptions of independent and homogeneous distributions are frequently violated. This discrepancy between the source and target distributions frequently leads to subpar model performance on the test set or an inability to procure usable evaluation samples during training. In this paper, we introduce LJCD-Net, a deep adversarial migration-based cross-domain jamming generalization diagnostic network. LJCD-Net capitalizes on a fully labeled source domain and multiple unlabeled auxiliary domains to generate shared feature representations with generalization capabilities. Initially, our paper proposes an uncertainty-guided auxiliary domain labeling weighting strategy, which estimates the multi-domain sample uncertainty to re-weight the classification loss and specify the gradient optimization direction. Subsequently, from a probabilistic distribution standpoint, the spatial constraint imposed on the cross-domain global jamming time-frequency feature distribution facilitates the optimization of collaborative objectives. These objectives include minimizing both the source domain classification loss and auxiliary domain classification loss, as well as optimizing the inter-domain marginal probability and conditional probability distribution. Experimental results demonstrate that LJCD-Net enhances the recognition accuracy and confidence compared to five other diagnostic methods.

Investigating tourists’ satisfaction with infrastructures located proximal to attractions

There is limited information concerning relationship between tourists' satisfaction and awareness of infrastructure locations, particularly with regard to associated impacts to the Outstanding universal values (OUVs). The study was conducted in the Ngorongoro Conservation Area (NCA) World Heritage Site (WHS) in Tanzania employing a quantitative research approach. Theoretically, the study employed Expectancy-Disconfirmation model and Dissonance Theory on customer satisfactions. The study was guided by two null hypotheses: H01: Demographic characteristics (age, education, nationality and sex) do not influence tourist’s satisfactions with infrastructures located proximal to attractions; and H02: Tourists’ awareness of potential consequences of infrastructures located proximal to attractions do not influence their satisfactions. A multivariate logistic regression analysis was performed on the 210 responses to investigate the relationships between tourists’ demographic characteristics and awareness of potential consequences and satisfactions. Both hypotheses (H01 and H02) were rejected, indicating that tourists are satisfied with infrastructures located proximal to attractions despite being aware of the potential consequences. In addition, tourists who were aware were twice as likely as those who were not to be satisfied. However, highly educated tourists (Masters and PhD) were less likely to be satisfied of infrastructures located proximal to attractions compared to tourists with lower education levels. This study recommends that infrastructure developers and NCA WHS conservators collaborate to develop monitoring and evaluation strategies to constantly balance conservation and tourism objectives because tourists are more satisfied with infrastructure proximal to attractions. Future research should focus on new construction materials, designs and technology to safeguards WHS OUVs.

A compound framework for short-term gas load forecasting combining time-enhanced perception transformer and two-stage feature extraction

The development of accurate and reliable gas load forecasting models is critical to residential safety and gas company economics. However, since the seq2seq neural network conveys information by sequentially updating the hidden states, when processing gas load data, the information needs to be passed several times before it can affect the prediction results at subsequent time steps. This can lead to a lack of ability of the model to capture long-term dependencies. To address the above issues, we propose a novel compound framework. Time-enhanced perception transformer (TEPT) in the framework achieves parallel computation of historical data and stronger location awareness through convolutional self-attention and improved attention scores. This results in better capture of local patterns and long-term dependencies in gas loads. In addition, the framework proposes two-stage feature extraction (TSFE) for decomposing the important parts of the original information and extracting more refined features based on them. We constructed a real gas load data set from Wuxi, China to verify the effectiveness. The results show that MAE, MAPE, and RMSE of the proposed method are 3.45, 1.07%, and 4.68, which provide higher accuracy compared to other prediction methods.

A receiver-driven transport protocol using differentiated algorithms for differential congestion in datacenters

With the continuous development of applications, data centers run application traffic from various service providers, and the requirements for data center networks are also increasing. Congestion control has always been a hot topic of discussion. Prevalent sender-driven transport protocols require adjusting their sending rate after network congestion occurs, and the long feedback delay makes them susceptible to a buffer overflow. Therefore, receiver-driven transport protocols are proposed, and the receiver adopts credits to allocate downlink bandwidth to overcome network congestion. This type of scheme solves last-hop congestion and reduces congestion feedback latency. However, the existing receiver-driven transport protocol is insensitive to congestion location and thus reacts inaccurately to congestion. To address this problem, this paper proposes a congestion control algorithm with congestion location awareness. In this scheme, 1) we first design an ECN-based congestion detection method to achieve lightweight congestion types awareness, i.e., instantaneous congestion and continuous congestion. 2) Based on this, we further design a differentiated transmission control strategy. For instantaneous congestion, we adopt adaptive backoff and delay control algorithms. For continuous congestion, we employ a rate control algorithm to reduce in-network congestion. This does not require complex modifications to the switch. In our evaluation, the overall average flow completion time (FCT) of DCC is up to 21%, 89%, 1.5×, and 3.4× better than Homa, ExpressPass, Timely, and pHost, respectively.

TDoA positioning with data-driven LoS inference in mmWave MIMO communications

Location awareness is an essential feature to support various mobile services, and cooperative positioning with channel state information (CSI) in millimeter wave multiple-input multiple-output (MIMO) networks is promising. Meanwhile, strong non-line of sight (NLoS) effects in outdoor scenarios severely reduce the model-based localization accuracy, and existing fingerprint-based methods have a critical requirement for labeled data and lack scalability. To overcome these shortcomings, we propose a hybrid data-and-model driven cooperative localization scheme using uplink wideband MIMO CSI at multiple base stations (BS) as measurements. First, we design a data-driven line of sight (LoS) inference module, realized by a transformer with an attention mechanism, to estimate the statistics of LoS arrival times. Each BS is equipped with a module, and the counterpart LoS inferences are simultaneously and distributedly performed. Second, we locate the mobile user by a model-driven approximate maximum likelihood time difference of arrival algorithm with the estimated statistics. Experiment results in urban scenarios show that the proposed localization scheme is extensible and also robust to varying number of channel paths and combination of BS measurements. Considering a small training dataset, the proposed scheme significantly outperforms the data- and model-driven baselines in NLoS scenarios, in terms of positioning accuracy.

LAAKA: Lightweight Anonymous Authentication and Key Agreement Scheme for Secure Fog-Driven IoT Systems

Fog computing is a new computing model with limited resources located near the Internet of Things (IoT) devices to provide low transmission delay, mobility, and location awareness services. However, fog technology not only extends the cloud resources but also inherits its properties and security problems, such as device capture, man-in-the-middle, secret key compromise, identity impersonation, and message replay attacks. These vulnerabilities pose significant risks to the integrity of IoT-fog communications. Nevertheless, the existing mutual authentication approaches either suffer from high computational overheads or fail to provide secure mutual authentication. To address this gap, this paper introduces LAAKA, a Lightweight Anonymous Authentication and Key Agreement scheme. Considering the constrained resources of IoT and fog devices, LAAKA utilizes lightweight operations such as hash function and bitwise XOR. Its main objective is to facilitate mutual authentication and establish secure session keys between IoT devices and fog servers, making it useful for various IoT applications. We validate the robustness of LAAKA against various security threats by conducting comprehensive formal security analysis, including Burrows-Abadi-Needham (BAN) logic and Random Oracle Model (ROM), as well as informal analysis. The efficacy of our scheme is further demonstrated through evaluation with the Scyther tool. Compared to other proposed authentication approaches, the results illustrate the superior performance and efficiency of our approach in enhancing the security features, minimizing the computational cost, and optimizing storage utilization.

A Review on Intrusion Detection in IoT Networks: Existing Challenges

Advancements in computer networks has led to the inter-connectivity of different types of smart devices over the internet. Such a diversified connected network is often termed as internet of things or IoT. Off late, an ancillary of the IoT framework called the fogging or fog computing has gained tremendous prominence. . Fog computing decentralizes the infrastructure without depending on centralizing it, such as with cloud computing. Fog computing is a paradigm proposed that integrates the IoT and the cloud concept to support user mobility, low latency, and location awareness. Due to the de- centralized nature of the Fog architecture, the sharing of data among different smart devices is susceptible to security threats. In this paper, a comprehensive review on fog computing and the allied performance metrics such as coverage, error rate and throughput have been discussed. Moreover, a channel load sensing techniques utilizing the channel state information (CSI) has also been proposed with the aim to enhance the throughput and error rate of the system. Keywords: Internet of Things (IoT), Fog Computing, End Device, Error Rate, Throughput, Channel State Information.

Location privacy protection of nodes in the perception layer of the Internet of things based on Radio Frequency Identification location aware algorithm

AbstractTo ensure the security of node location privacy in the perception layer of the Internet of Things, this study proposes a location privacy protection method based on RFID location awareness algorithm. This method first introduces an adaptive multi tree anti‐collision algorithm based on collision trees when perceiving and locating the location information of nodes in the perception layer of the Internet of Things, to prevent signal collisions between multiple readers and writers. Then, based on the obtained node location results, a IoT perception layer node location privacy protection scheme based on virtual ring strategy is used to randomly initiate directed co hop routing using the virtual nodes generated after random walking, avoiding the visible area of the source node location, and transmitting data to the aggregation node in the form of the shortest routing path to protect the privacy and security of the node location. Finally, experiments are carried out to prove the progressiveness of the proposed method. The experimental results show that this method can prolong the security period of the source and aggregation nodes in the perception layer of the Internet of Things, protect the privacy and security of node positions, and has great application value.

Semi-Supervised Learning-Enhanced Fingerprint Indoor Positioning by Exploiting an Adapted Mean Teacher Model

Location awareness is crucial for numerous emerging wireless indoor applications. Deep learning algorithms have demonstrated the potential for achieving the required level of positioning accuracy in indoor environments. However, obtaining abundant labels for data-driven machine learning is costly in practical situations. As an effective solution to alleviating the insufficiency of labeled data for deep learning-based indoor positioning, deep semi-supervised learning (DSSL) can be employed to lessen the dependency on labeled data by exploiting potential patterns in unlabeled samples. In this paper, we propose an Adapted Mean Teacher (AMT) model within the DSSL paradigm for indoor fingerprint positioning by using a channel impulse response. To enhance the generalization of the trained model, we design an efficient implicit augmentation scheme for the training process in the AMT model. Furthermore, we develop a tailored residual network to efficiently extract location characteristics in the AMT framework. We conduct extensive simulation experiments for indoor scenarios with heavy non-line-of-sight conditions based on open datasets to demonstrate the effectiveness of our proposed AMT model. Numerical results indicate that the AMT model outperforms several consistency regularization methods and the pseudo-label method in terms of positioning accuracy and lower positioning latency, achieving a mean error of 90cm when using a small number of labels.