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In the field of wireless communication, transmitter localization technology is crucial for achieving accurate source tracking. However, the extant methodologies for localization face numerous challenges in wireless sensor networks (WSNs), particularly due to the constraints posed by the sparse distribution of sensors across large areas. We present DSLoc, a deep learning-based approach for transmitter localization in sparse WSNs. Our method is based on an improved high-resolution network model in neural networks. To address localization in sparse wireless sensor networks, we design efficient feature enhancement modules, and propose to locate transmitter locations in the heatmap using an image centroid-based method. Experiments conducted on WSNs with a 0.01% deployment density demonstrate that, compared to existing deep learning models, our method significantly reduces the transmitter miss rate and improves the localization accuracy by more than double. The results indicate that the proposed method offers more accurate and robust performance in sparse WSN environments. [ABSTRACT FROM AUTHOR]

Automatic program repair has drawn more and more attention since software quality is facing increasing challenges. In existing approaches, the unlimited search space is considered to be the main limitation in finding the correct patch. So how to reduce the search space to improve the efficiency of automatic program repair remains a problem to be solved. In this work, we represent a similarity-based and location-awareness-based automatic program repair (SLARepair). SLARepair takes the similarity between codes as important search information. The search space is further subdivided by the location-awareness strategy to improve search efficiency. In addition, to better guide the search process, a new fitness function is designed for genetic programming, which brings notable improvements. Moreover, the patch verification time is further reduced by utilizing the test case prioritization approach combined with test case filtering. Extensive experiments demonstrate that our SLARepair outperforms the state-of-the-art approaches on the Defects4J benchmark and achieves competitive performances. [ABSTRACT FROM AUTHOR]

Disasters are sudden and catastrophic events with fatal consequences. Time-sensitive information collection from disaster zones is crucial for improved and data-driven disaster response. However, information collection from disaster zones in a prompt way is not easy or even possible. Human-centric information provided by citizen sensors through social media platforms create an opportunity for prompt information collection from disaster zones. There is, nevertheless, limited scholarly work that provides a comprehensive review on the potential of social media analytics for disaster response. This study utilizes a systematic literature review with PRISMA protocol to investigate the potential of social media analytics for enhanced disaster response. The findings of the systematic review of the literature pieces (n = 102) disclosed that (a) social media analytics in the disaster management research domain is an emerging field of research and practice; (b) the central focus on the research domain is on the utilization of social media data for disaster response to natural hazards, but the social media data-driven disaster response to human-made disasters is an increasing research focus; (c) human-centric information intelligence provided by social media analytics in disaster response mainly concentrates on collective intelligence, location awareness, and situation awareness, and (d) there is limited scholarly research investigating near-real-time transport network management aftermath disasters. The findings inform authorities' decision-making processes as near-real time disaster response management depending on social media analytics is a critical element of securing sustainable cities and communities. [ABSTRACT FROM AUTHOR]

In this paper, we propose a three-dimensional autonomous drone exploration system (ADES) with a lightweight and low-latency saliency prediction model to explore unknown environments. Several studies have applied saliency prediction in drone exploration. However, these studies are not sufficiently mature. For example, the computational complexity and the size of the developed prediction models have not been considered. In addition, some studies have only proposed saliency prediction models without actually applying them to drones. The ADES system proposed in this paper has a small and fast saliency prediction model and uses a novel drone exploration approach based on visual-inertial odometry to solve the practical problems encountered during drone exploration, such as collisions with and the repeated exploration of salient objects. The proposed ADES system performs comparably to the state-of-the-art, multiple-discontinuous-image saliency prediction network TA-MSNet and enables drones to explore unknown environments with high efficiency. [ABSTRACT FROM AUTHOR]

Contemporary navigation systems rely upon localisation accuracy and humongous spatial data for navigational assistance. Such spatial-data sources may have access restrictions or quality issues and require massive storage space. Affordable high-performance mobile consumer hardware and smart software have resulted in the popularity of AR and VR technologies. These technologies can help to develop sustainable devices for navigation. This paper introduces a robust, memory-efficient, augmented-reality-based navigation system for outdoor environments using crowdsourced spatial data, a device camera, and mapping algorithms. The proposed system unifies the basic map information, points of interest, and individual GPS trajectories of moving entities to generate and render the mapping information. This system can perform map localisation, pathfinding, and visualisation using a low-power mobile device. A case study was undertaken to evaluate the proposed system. It was observed that the proposed system resulted in a 29 percent decrease in CPU load and a 35 percent drop in memory requirements. As spatial information was stored as comma-separated values, it required almost negligible storage space compared to traditional spatial databases. The proposed navigation system attained a maximum accuracy of 99 percent with a root mean square error value of 0.113 and a minimum accuracy of 96 percent with a corresponding root mean square value of 0.17. [ABSTRACT FROM AUTHOR]

In general, there exists numerous attractions installed in a theme park, and tourists in a theme park dynamically change their locations during a tour. Thus, a tourist may cope with the issues of selecting the attractions to visit while planning the tour route. This paper, based on the concept of location awareness, proposes a novel waiting time, called the personalized waiting time, to introduce a location-aware recommendation strategy. In addition, this paper presents an architecture of tourist service system using the proposed recommendation strategy to relieve the pressure on tourists and create the pleasant experience in their tours. The proposed location-based system consists of mobile app, ticket-reader, detecting/counting, and central subsystems, and the whole system was implemented in this study. We conducted numerous experiments and field testing results validated that the entire proposed system can correctly provide information, such as attraction introduction, recommended session time, estimated moving and waiting time, tour map, and the number of reservations. The system functions, including dynamical scheduling, attraction reservation, ticket verification, visitor detection, and visitor counting, also worked well. [ABSTRACT FROM AUTHOR]

This paper introduces a framework for inferring human activities in mobile devices by computing spatial contexts, temporal contexts, spatiotemporal contexts, and user contexts. A spatial context is a significant location that is defined as a geofence, which can be a node associated with a circle, or a polygon; a temporal context contains time-related information that can be e.g., a local time tag, a time difference between geographical locations, or a timespan; a spatiotemporal context is defined as a dwelling length at a particular spatial context; and a user context includes user-related information that can be the user's mobility contexts, environmental contexts, psychological contexts or social contexts. Using the measurements of the built-in sensors and radio signals in mobile devices, we can snapshot a contextual tuple for every second including aforementioned contexts. Giving a contextual tuple, the framework evaluates the posteriori probability of each candidate activity in real-time using a Naïve Bayes classifier. A large dataset containing 710,436 contextual tuples has been recorded for one week from an experiment carried out at Texas A&M University Corpus Christi with three participants. The test results demonstrate that the multi-context solution significantly outperforms the spatial-context-only solution. A classification accuracy of 61.7% is achieved for the spatial-context-only solution, while 88.8% is achieved for the multi-context solution. [ABSTRACT FROM AUTHOR]

Ultra-Wide Bandwidth (UWB) and mm-wave radio systems can resolve specular multipath components (SMCs) from estimated channel impulse response measurements. A geometric model can describe the delays, angles-of-arrival, and angles-of-departure of these SMCs, allowing for a prediction of these channel features. For the modeling of the amplitudes of the SMCs, a data-driven approach has been proposed recently, using Gaussian Process Regression (GPR) to map and predict the SMC amplitudes. In this paper, the applicability of the proposed multipath-resolved, GPR-based channel model is analyzed by studying features of the propagation channel from a set of channel measurements. The features analyzed include the energy capture of the modeled SMCs, the number of resolvable SMCs, and the ranging information that could be extracted from the SMCs. The second contribution of the paper concerns the potential applicability of the channel model for a multipath-resolved, single-anchor positioning system. The predicted channel knowledge is used to evaluate the measurement likelihood function at candidate positions throughout the environment. It is shown that the environmental awareness created by the multipath-resolved, GPR-based channel model yields higher robustness against position estimation outliers. [ABSTRACT FROM AUTHOR]

This paper introduces a framework for inferring human activities in mobile devices by computing spatial contexts, temporal contexts, spatiotemporal contexts, and user contexts. A spatial context is a significant location that is defined as a geofence, which can be a node associated with a circle, or a polygon; a temporal context contains time-related information that can be e.g., a local time tag, a time difference between geographical locations, or a timespan; a spatiotemporal context is defined as a dwelling length at a particular spatial context; and a user context includes user-related information that can be the user's mobility contexts, environmental contexts, psychological contexts or social contexts. Using the measurements of the built-in sensors and radio signals in mobile devices, we can snapshot a contextual tuple for every second including aforementioned contexts. Giving a contextual tuple, the framework evaluates the posteriori probability of each candidate activity in real-time using a Naïve Bayes classifier. A large dataset containing 710,436 contextual tuples has been recorded for one week from an experiment carried out at Texas A&M University Corpus Christi with three participants. The test results demonstrate that the multi-context solution significantly outperforms the spatial-context-only solution. A classification accuracy of 61.7% is achieved for the spatial-context-only solution, while 88.8% is achieved for the multi-context solution.