With the ubiquitous deployment of wireless systems and pervasive availability smart devices, indoor localization is empowering numerous location-based services. established radio maps, WiFi fingerprinting has become one most practical approaches to localize mobile users. However, fingerprint-based algorithms are computation-intensive, with heavy dependence on both offline training phase online phase. In this paper, we propose CNNLoc, a Convolutional Neural Network (CNN) based system...

Studying growth and development of plants is central importance in botany. Current quantitative are either limited to tedious sparse manual measurements, or coarse image-based 2D measurements. Availability cheap portable 3D acquisition devices has the potential automate this process easily provide scientists with volumes accurate data, at a scale much beyond realms existing methods. However, during their development, grow new parts (e.g., vegetative buds) bifurcate different components ---...

Distributed Denial of Service (DDoS) attacks are increasingly harmful to the cyberspace nowadays. The attackers can now easily launch a bigger and more challenging DDoS attack both towards with Internet-of-Things (IoT) devices, due fast popularization them. Because characteristic overwhelming, it is important make as well accurate response attacks, real-time performance be even prevent legitimate attacks. Among methods proposed by researchers, entropy-based detection method provides...

With the ubiquitous deployment of wireless systems and pervasive availability smart devices, indoor localization is empowering numerous location-based services. established radio maps, WiFi fingerprinting has become one most practical approaches to localize mobile users. However, fingerprint-based algorithms are computationintensive, with heavy dependence on both offline training phase online phase. In this paper, we propose CNNLoc, a Convolutional Neural Network (CNN) based system...

Intelligent Transportation Systems (ITSs) have been widely deployed to provide traffic sensing data for a variety of smart applications. However, the inevitable and ubiquitous missing potentially compromises performance ITSs even undermines Therefore, accurate real-time recovery is crucial its related services, especially large-scale networks. To leverage characteristics in transportation networks recovery, we first conduct experimental explorations on dataset an ITS further quantify...

An aerial Radio Environment Map (REM) characterizes the spatial distribution of Received Signal Strength (RSS) across a geographic space interest, which is crucial for optimizing wireless communication in air. Aerial REM construction can rely on Unmanned Vehicles (UAVs) to autonomously select interesting positions sampling RSS data, enhancing quality construction. However, due lack prior information about environment, it challenging UAVs determine suitable online. Additionally, achieving...

Indoor localization has become an essential demand driven by indoor location-based services (ILBSs) for mobile users. With the rising of Internet Things (IoT), heterogeneous smartphones and wearables have ubiquitous. However, ILBSs IoT devices confront significant challenges, such as received signal strength (RSS) variances caused hardware heterogeneity, multipath reflections from complex environments, time restricted computation resources. This article proposes EdgeLoc, a robust real-time...

With the rapid development of smart cities, buildings are generating a massive amount building sensing data by equipped sensors. Indeed, provides promising way to enrich series data-demanding and cost-expensive urban mobile applications. In this paper, we study how reuse predict traffic volume on nearby roads. Nevertheless, it is non-trivial achieve accurate prediction such cross-domain with two major challenges. First, relationships between not unknown as prior, spatio-temporal complexities...

Identifying tiny objects from extremely low-resolution (LR) unmanned-aerial-vehicle-based remote sensing images is generally considered as a very challenging task, because of limited information in the object areas. In recent years, there have been attempts to approach this problem. These intend deal with LR image classification by enhancing either poor quality or representations. article, we argue that performance improvement affected inconsistency loss and learning priority on...

Wireless indoor localization has become unavoidable for industrial location-based services. Given the ubiquitous deployment of wireless access points (APs), WiFi fingerprinting Received Signal Strength (RSS) been widely adopted localization. Meanwhile, existing RSS fingerprint-based methods lack security-oriented considerations and are vulnerable to malicious attacks. When security vulnerabilities exploited, mobile users may confront mismatches, faults even system failures. In this paper, we...

Detecting objects from images captured by Unmanned Aerial Vehicles (UAVs) is a highly demanding task. It also considered very challenging task due to the typically cluttered background and diverse dimensions of foreground targets, especially small object areas that contain only limited information. Multi-scale representation learning presents remarkable approach recognizing objects. However, this strategy ignores combination sub-parts in an suffers interference feature fusion process. To...

Abstract Flower blooming is a beautiful phenomenon in nature as flowers open an intricate and complex manner whereas petals bend, stretch twist under various deformations. are typically thin structures arranged tight configurations with heavy self‐occlusions. Thus, capturing reconstructing spatially temporally coherent sequences of highly challenging. Early the process only exterior visible thus interior parts will be completely missing captured data. Utilizing commercially available 3D...

With the advancement of wireless networking technologies and communication infrastructures, mobile cloud computing has emerged as a pervasive paradigm to execute tasks for capacity-limited devices. More specifically, at network edge, resource-rich trusted cloudlet system can provide in-proximity services by executing workloads nearby Nevertheless, there are chances malicious users generate distributed denial-of-service (DDoS) flooding overwhelm servers block from legitimate users. Load...

With the unprecedented demand of location-based services in indoor scenarios, wireless localization is emerging as an essential application for mobile users. While line-of-sight GPS signal not available at spaces, WiFi fingerprinting using received strength (RSS) has become popular with its ubiquitous accessibility. Although data can be easily collected by portable devices, to achieve robust and efficient remains challenging two constraints. First, accuracy will degraded random fluctuation...

As a promising computing paradigm, Mobile Edge Computing (MEC) provides communication and capability at the edge of network to address concerns massive computation requirements, constrained battery capacity limited bandwidth Internet Things (IoT) systems. Most existing works on mobile task ignores delay sensitivities, which may lead degraded utility offloading dissatisfied users. In this paper, we study sensitivity-aware by jointly considering both user's tolerance towards execution status...

Mobile cloud computing has emerged as a pervasive paradigm to execute tasks for capacity- limited mobile devices. More specifically, at the network edge, resource-rich and trusted cloudlet system is acting 'data center in box' support compute-intensive applications. The cloudlets can provide in-proximity services by executing workloads nearby Nevertheless, load balancing of great importance, it huge impact on task response time. Existing methods basically rely strategic placement or user...

With the explosive usage of smart mobile devices, sustainable access to wireless networks (e.g., Wi-Fi) has become a pervasive demand. Most users expect seamless network connection with low cost. Indeed, this can be achieved by using an accurate received signal strength (RSS) map points. While existing methods are either costly or unscalable, recently emerged crowdsensing (MCS) paradigm is promising technique for building RSS maps. MCS applications leverage devices collaboratively collect...

The existing commodity Wi-Fi-based human gait recognition systems mainly focus on a single subject due to the challenges of multisubject walking monitoring. To tackle problem, we propose Wi-Diag, first abnormal diagnosis system that leverages only one pair off-the-shelf commercial Wi-Fi transceivers separate each subject's information and maintains an excellent performance when scenario changes. It is intelligent can release experienced doctor from heavy load work. Multisubject modeled as...

Mobile cloud computing has emerged as a promising paradigm to facilitate computation-intensive and delay-sensitive mobile applications. Computation offloading services at the edge environment are provided by small-scale infrastructures such cloudlets. While tasks in-proximity cloudlets enjoys benefits of lower latency smaller energy consumption, new issues related rising. For instance, unbalanced task distribution huge load gaps among heterogeneous becoming more challenging, concerning...

Wireless energy transfer technologies have played an important role in the development of Internet Things. Most previous studies focus on scheduling mobile chargers efficiently for rechargeable sensor nodes. In this paper, we investigate deployment problem wireless charging stations (WCSs) urban areas with respect to users detouring cost when they move candidate WCSs. With pre-known user's trajectories and given number WCSs, deploy WCSs maximize recharged guaranteed probability. We convert...