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In this paper, we propose a time-efficient indoor navigation and evacuation (TINE) framework to minimize moving time for mobile users based on Internet of Things (IoT) technologies. In normal time, the proposed TINE framework can estimate the density of mobile users in each area and determine the moving speeds to pass through different areas. Based on the determined moving speed of each area, an indoor navigation path can be planned to provide the shortest moving time for a mobile user. In emergent time, TINE can accurately estimate the escaping time for groups of mobile users by jointly considering the length and moving time of passageways, the capacity of passageways/doors/exits, the present distribution and parallel moving of mobile users, and the possible congestion caused by other groups. Based on the estimated escaping time, TINE can efficiently alleviate the congestion of all passageways/exits and evenly distribute the evacuation load among exits to minimize the total escaping time. According to our review of relevant research, this is the first solution that can both provide the fastest navigation path to arbitrary target places and evacuate all groups of mobile users to safe places in the shortest escaping time. Simulation results show that TINE outperforms existing schemes and can significantly reduce the total walking and escaping times of indoor navigation and evacuation, respectively. In particular, an Android-based prototype with iBeacon IoT localization is implemented to verify the feasibility of our TINE system.

This paper designs and implements a smart proactive iBeacon system, called GuidingAll, using smartphones to detect abnormal events, guide mobile users, and find moving targets through Internet of Things (IoT) technologies. The GuidingAll system consists of the smartphone APP, guiding server, fixed iBeacon, and our designed smart iBeacon. The smartphone APP is used for real-time guiding and crowdsourced sensing through fixed iBeacons and nearby smartphones. The guiding server is employed for fastest path planning and moving target localization based on the sensing data reported by participating smartphones. The fixed iBeacons are deployed to provide indoor positioning information to smartphones with Bluetooth Low Energy (BLE) scanning, whereas the smart iBeacon is designed to enable proactive event detection and target BLE broadcasting through integrated sensors and associated iBeacon, respectively. In particular, mobile users with smartphones can form crowdsourced sensing networks in places with fixed iBeacons, and GuidingAll can continuously monitor and cooperatively find a specific moving target equipped with the smart iBeacon. An Android-based prototype with fixed and smart iBeacons is implemented to verify the feasibility and correctness of our GuidingAll system.

Along with the flourishing development of Internet of Things, vehicles which assist move goods are developed as well, for example, automatic guided vehicle applied in manufacturing plants. Vehicles vary with pattern of goods to be moved. If it moves along magnetic tapes, it would lose its flexibility in moving directions. To make vehicles more dynamic and convenient, this study designs and implements automatic following technology of vehicles. Through relative position between vehicles and objects to be followed positioned by satellite and laser radar installed on vehicles which can detect relative distance, vehicles are able to automatically follow objects to be followed.

In this paper, we propose a smart campus care and guiding framework with deep learning-based face recognition, called DeepGuiding, for students through Internet of Things technologies. The DeepGuiding framework can construct the dedicated video trajectory of a campus student, where the recorded video for each student can be automatically classified to achieve efficient footprint review as necessary. In addition, DeepGuiding can provide time-efficient indoor and outdoor guiding in a campus to quickly reach places, meet friends, and find students. To the best of our knowledge, DeepGuiding is the first campus care and guiding system which provides the following features: 1) it achieves the seamless outdoor and indoor navigation between buildings in a campus; 2) it keeps additional construction cost low by utilizing existing surveillance cameras in a campus; and 3) it reduces the total searching time for finding a specific event/target in a campus by alleviating time-consuming labor overhead to review a huge amount of video data. An Android-based prototype using iBeacon indoor localization and global positioning system outdoor positioning with surveillance cameras is implemented to verify the feasibility and superiority of our DeepGuiding framework. The Experimental results show that DeepGuiding outperforms existing face recognition methods and can achieve high recognition accuracy for students not close to surveillance cameras.

In this paper, we propose a travel time estimation and prediction (TTEP) framework to enhance the driving efficiency on highways through the Internet of Vehicles (IoV). Highway travel time estimation and prediction are important for the drivers in a long-distance traveling. The accurate travel time information on highways is the key to improve the efficiency of transportation systems. When current flow status is collected through the IoV, TTEP can accurately estimate and predict highway travel time by the proposed weighted root-mean-square similarity (Weighted-RMSS) method. In addition, when current flow status is unavailable at the present time, we propose the multiple slope-based linear regression (Multi-SBLR) method to predict highway travel time only using historical traffic data. Furthermore, the spatiotemporal mobilities of vehicles on highways are analyzed and explored to improve the prediction accuracy of the proposed Weighted-RMSS and Multi-SBLR methods. To verify the feasibility and superiority of TTEP, we adopt the open Electronic Toll Collection data of highways in Taiwan to evaluate the prediction accuracy of our approaches. Experimental results show that our approaches outperform existing methods and can significantly reduce the prediction errors of highway travel time. In particular, we further implement the Android-based and web-based systems of TTEP to predict and compare travel time at different departure times and locations for highway drivers.

This paper proposes a group-based framework with dedicated path planning for emergency guiding based on Internet of Things (IoT) technologies. The proposed framework can model the spatiotemporal mobility of indoor people to determine and relieve the congestion of corridors and exits. A dedicated path can be determined to provide the shortest evacuation time for each group of nearby people. The corridor and exit capacities, corridor lengths, clustering motion of a group, concurrent moving of different groups, and up-to-date distribution of group people are considered together to accurately estimate the evacuation time for each group. Based on the estimated evacuation time, evacuation load can be evenly distributed among corridors and exits to alleviate the congestion of all corridors and exits for minimizing total evacuation time. The performance of the proposed framework is evaluated by conducting mathematical analysis and computer simulations, which outperforms existing schemes and can achieve the shortest evacuation time for group-based emergency guiding. In addition, an Android-based prototype with indoor IoT localization technologies is implemented to verify the feasibility of our framework.

This paper proposes a lane-level beacon-less, infrastructure-less, and GPS-less cooperative collision avoidance (BIG-CCA) framework for preventing rear-end collisions. The BIG-CCA framework applies vehicular sensor networks to prevent chain vehicle collisions, which are common road accidents that occur when vehicles make sudden stops. BIG-CCA enables vehicles equipped with only onboard sensors to prevent such accidents. Based on extensive research, BIG-CCA is the first lane-level CCA solution that provides the following features: 1) BIG-CCA does not maintain a list of neighboring vehicles through beacons and, thus, the overall signaling overhead can be reduced to conserve bandwidth; 2) BIG-CCA does not use the GPS positions of vehicles to facilitate collision avoidance and, thus, the inaccuracy and unavailability of a GPS can be avoided; and 3) BIG-CCA does not rely on costly roadside infrastructures but employs only vehicle-to-vehicle communications to form warning groups for vehicles driving along in the same lane. BIG-CCA consists of a distributed grouping mechanism and a receiver-based forwarding scheme. Vehicles join or leave a warning group through only single-hop transmissions with the low overhead of group maintenance. When sudden braking occurs in a warning group, the proposed receiver-based forwarding scheme can specify a single forwarder without message contention. The performance of BIG-CCA is evaluated by conducting mathematical analysis and computer simulations, which outperform existing methods. An Android-based prototype is also implemented to verify the feasibility of BIG-CCA.

This paper designs and implements a mobile children monitoring and finding system, called iBaby, using wearable devices and nearby smartphones to detect unexpected holding and find missing children through Internet of Things (IoT) technologies, respectively. In the monitoring mode, the iBaby system can prevent young children from taking away by strangers/people with bad intentions. In the finding mode, the iBaby system can cooperatively find missing children equipped with hand wearable devices consisting of the mobile iBeacon and 3-axis accelermeter through crowdsourced sensing networks formed by smartphone users with outdoor GPS and indoor IoT localization. To accurately detect stranger holding behaviors, multi-feature based, artificial neural network based, and convolutional neural network based posture recognition methods are designed to improve recognition success rates of iBaby as much as possible. In particular, an iOS-based prototype with Arduino wearable devices and static iBeacon nodes is implemented to verify the feasibility and correctness of our iBaby system.

This paper designs and implements a mobile crowd-sourced guiding system, called EasyFind, using smartphones to guide indoor people and find lost items through Internet of Things (IoT) technologies. In normal time, the EasyFind system can provide the fastest guiding path with the shortest moving time to a destination place based on the density of indoor people in each area. In addition, in emergency time, the EasyFind system can evacuate all people in the shortest total escaping time through modeling spatial and temporal mobilities of indoor people. Furthermore, the EasyFind system can cooperatively find lost items equipped with mobile iBeacon nodes through participatory sensing networks formed by mobile users with smartphones in places with static iBeacon nodes. To precisely localize the lost item, six item localization cases are addressed to reduce the positioning errors with different numbers of smartphones detecting the lost item and different numbers of fixed iBeacon nodes nearby these item-detecting smartphones. An Android-based prototype with static and mobile iBeacon nodes is implemented to verify the feasibility and correctness of our EasyFind system.

In this letter, we design and analyze an infrastructure-less framework for lane positioning, tracking, and requesting through vehicle-to-vehicle communications. The proposed framework involves applying vehicular sensor networks to localize the lane position of vehicles on road segments. It enables vehicles equipped with image sensors to detect the current lane location without using a global positioning system, which may give an inaccurate location, and without the need for roadside infrastructures, which can be costly. Through image sensors, the proposed framework can maintain the recognition of the lane position of a vehicle. By using vehicle-to-vehicle communications, the proposed framework enables lane position information to be requested from other vehicles in the same lane when required. The proposed framework facilitates the development of innovative applications for the next-generation intelligent transportation systems that rely on high positioning accuracy, such as lane-level cooperative collision avoidance, dynamic traffic control, and intelligent personal navigation systems. An analytical model is derived for estimating the positioning success ratio of the proposed framework. The analytical and simulation results show that the proposed framework can significantly improve the positioning success ratios by cooperative lane requesting.

In this paper, we propose a Cooperative Energy-Efficient Localization Framework (CELF) for power saving in mobile long-thin networks (MLTNs). We use the cycling ad hoc network with fleet cyclists using mobile phones along a common cycling route as an example (our results could be applicable to other scenarios as well, such as the mountaineering party and army troops). As cyclists need to upload their position data and download global fleet information, all power-consuming GPS receivers have to be switched on for obtaining their current locations. In CELF, distributed localization among cyclists without central control is designed to minimize the energy consumption of positioning. To the best of our knowledge, CELF is the first distributed solution for localization which provides the following features: (i) CELF does not rely on costly roadside infrastructures but only employs cyclist-to-cyclist communications; (ii) CELF can supply group members with the up-to-date locations without activating GPS receivers; (iii) CELF can estimate member positions only based on single mobile anchor instead of multiple static anchors. Simulation and experimental results show that CELF outperforms existing works, which can significantly reduce the energy consumption for localization while keeping the positioning accuracy similar to GPS.

This paper proposes a load-balancing framework for distributed emergency guiding based on wireless sensor networks. A load-balancing guiding scheme is designed and an analytical model is derived to reduce the total evacuation time of people indoors. The guiding scheme can provide the fastest path for people to reach an exit according to the evacuation time estimated using the analytical model. Based on thorough research, this is the first distributed solution in which corridor capacity and length, exit capacity, and the concurrent movement and distribution of people are considered in estimating the evacuation time and planning escape paths. Using the proposed framework, congestion in corridors and at exits can be eased to substantially reduce the total evacuation time. Analytical and simulation results show that this approach outperforms existing schemes and can prevent people from following local-optimal guiding directions that increase the evacuation time. A prototype called the Load-balancing Emergency Guiding System (LEGS) is implemented; this system can be used to compare the evacuation times and guiding directions provided by existing schemes and the proposed scheme for various distributions of people.

In this paper, we propose a cyber-physical signage interacting framework for the interaction between digital signage and mobile users using smart handheld devices, such as smartphones and tablets. The proposed framework can provide diverse multimedia/feedback services to mobile users interacting with digital signage through face detection, classification, and recognition techniques based on mobile cloud computing. Mobile users only need to click and drag the interested service over the face of advertising celebrities/endorsers displayed on digital signage and can obtain supplementary multimedia information or feedback related comments. Using the intuitive way of gesture-based operations, mobile users can directly interact with digital signage through their handheld devices. Our framework reveals an innovative human–machine interface for signage interacting between digital signage and mobile users. In addition, we integrate our framework with a face cache mechanism that can make the interaction delay as small as possible for popular signage. Furthermore, an Android-based signage interacting system is implemented to verify the feasibility and superiority of our framework. Experimental results show that our approach outperforms the existing methods and can significantly reduce the average consumption time of obtaining interested multimedia contents from digital signage.

Mobile ad hoc networks (MANETs) have gained a lot of interests in research communities for the infrastructure-less self-organizing nature. A MANET with fleet cyclists using smartphones forms a two-tier mobile long-thin network (MLTN) along a common cycling route, where the high-tier network is composed of 3G/LTE interfaces and the low-tier network is composed of IEEE 802.11 interfaces. The low-tier network may consist of several path-like networks. This work investigates cooperative sensing data collection and distribution with packet collision avoidance in a two-tier MLTN. As numbers of cyclists upload their sensing data and download global fleet information frequently, serious bandwidth and latency problems may result if all members rely on their high-tier interfaces. We designed and analyzed a cooperative framework consisting of a distributed grouping mechanism, a group merging and splitting method, and a sensing data aggregation scheme. Through cooperation between the two tiers, the proposed framework outperforms existing works by significantly reducing the 3G/LTE data transmission and the number of 3G/LTE connections.

In this paper, we design an audience-aware signage sensing and interacting system, called Cyber-Physical Ad (CPAd), for individuals using smartphones based on Internet of Things technologies. In the CPAd system, a digital signage can sense surrounding audiences via built-in sensors and the signage content can be adaptive to interact with nearby audiences through real-time multimedia contents. In addition, audiences can use smartphones to click and drag a dedicated service on the touchscreen to an endorser face on the digital signage. An Android-based prototype of CPAd is implemented to sense approaching audiences and interact with these audiences in a cyber-physical manner to achieve effective advertising and marketing.

In this paper, we propose a video-based indoor positioning system (VIPS) to provide centimeter-grade localization for the Internet of Things (IoT). The VIPS system integrates installed surveillance cameras with the IoT to provide highly-accurate positioning information to indoor people, which efficiently enables microlocation-aware IoT applications and services in smart environments. VIPS can estimate the precise position of an individual and detect the facing direction for each individual. To the best of our knowledge, VIPS is the first indoor positioning system which provides the following features: 1) it achieves the centimeter-grade positioning accuracy for the IoT that can detect the facing direction of individuals, 2) it employs existing surveillance cameras that can keep additional construction cost low, and 3) it establishes an innovative infrastructure for next-generation IoT applications that can make centimeter-accuracy individual-based path planning, group-based emergency evacuation, and microlocation-based geofencing possible. With the IoT, the estimated positions can be transmitted to corresponding individuals for interacting with nearby smart objects. With surveillance cameras, the facing direction of an individual can be detected even if her/his current position is not changed. This paper demonstrates our current prototype.

In this paper, we design and implement a face-based signage interacting system, called AdYou, for mobile users using smartphones. By using the AdYou system, mobile users only need to drag the service icon on the face of a particular target in a digital signage. AdYou is able to provide diverse services to mobile users through face detection and recognition techniques. Using the intuitive way of gesture-based operations, mobile users can directly interact with digital signage. We further integrate the AdYou server with a face cache mechanism that can make the system response time as small as possible for popular signage. AdYou reveals an innovative user interface for signage interacting between mobile users and digital signage. We have implemented an Android-based AdYou system that can be interacted by mobile users as fast as possible.

Mobile communication and handheld devices are currently extremely popular, and provide people with convenient and instant platforms for social networking. However, existing social networking services cannot offer efficient human-machine interfaces or intuitive user experiences. Mobile users must manually input account information and find targets from search results when attempting to add someone to their friend list on social networking sites, such as Facebook and Twitter. Additionally, mobile users may not be able to identify correct targets because some usernames are identical. Typos may occur during the input process due to unfamiliar identifiers, further increasing the total operation time. To encourage social initiation between mobile users, we design an instant social networking framework, called SocialYou, to minimize the startup time based on mobile cloud computing. SocialYou proposes an efficient architecture and innovative human-machine interfaces to alleviate the complexity and difficulty for mobile users using handheld devices. In particular, we implement an Android-based prototype to verify the feasibility and superiority of SocialYou. The experimental results show that SocialYou outperforms the existing methods and saves substantial amounts of operation time for mobile social networking.

Summary Most methods of file transfer between mobile devices have to take considerable time to obtain and input target identifiers. To speed up file sharing of mobile devices, an intelligent file transfer framework is designed based on mobile cloud computing. Only single-finger action is performed in the proposed framework, which just drags the file to the target face on the touchscreen of a mobile device. The dragged file can be transmitted to the target receiver who can either get the file immediately as her/his mobile device is online or receive the file later after connecting to the Internet. Our framework provides the following features: (i) users do not need to know the target identifier in advance; (ii) users do not need to input identity information by themselves; and (iii) users can select a specific target among multiple candidates from the camera of a mobile device. The intelligent file transfer framework reveals an efficient architecture and innovative user interfaces to transfer files between mobile devices, which can significantly reduce the complexity and difficulty of file sharing. An Android-based prototype is implemented to verify the feasibility and superiority of our framework. Experimental results show that our approach outperforms existing schemes and can save large amounts of time in file sharing for mobile users. Copyright © 2015 John Wiley & Sons, Ltd.