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Under strict social-distancing directives during the COVID-19 pandemic, one of the most impacted businesses is probably the catering industry, which has been forced to earn their revenue mainly from delivery and takeout services. However, traditional takeouts require patrons to wait in line, order and pick up their meals, incurring unnecessary human contact and service inefficiency. We observe these drawbacks and propose a contactless meal order and takeout service (Mots) automated system realized by AI-assisted smart robots to address the issue. In our Mots system, we develop a bump-free schedule based on the Welsh-Powell coloring algorithm for grouping robots into several non-colliding moving batches. Simulation results show that our Mots solution can effectively improve takeout efficiency and promote service accuracy, boosting business profits up to 95.4% under simulated cases for various cafeteria scales and shop popularity differences, compared to the traditional takeout method. Our experiments suggest that Mots is also capable of accommodating a sudden surge of arriving patrons within a short period of time. Furthermore, we have implemented a proof-of-concept prototype to demonstrate our Mots automated operations.

Cloud-RAN (C-RAN) is a promising network architecture to provide 5G broadband services. In C-RAN, the base stations (BSs) are separated as the computation entities (i.e., Baseband Units (BBUs) ) and the radio entities (i.e., Remote Radio Heads (RRHs) ), where the BBUs are put in a centralized cloud to realize centrally control and the RRH are left at cell sites for signal transmission. With C-RAN, multiple collaborative cells can transmit data to user equipments (UEs) by Joint Transmission (JT) technology to improve network efficiency. On the other hand, 3GPP standard has defined the Discontinuous Reception (DRX) mechanism, which allows UEs to turn off their radio interfaces periodically to save their energy. However, how to cooperate DRX with JT under the C-RAN architecture is still an open issue. Therefore, this letter studies how to optimize UEs’ DRX parameters while considering their quality-of-service (QoS) in C-RAN with JT. To solve this problem, we propose an energy-efficient JT scheduling scheme. The main idea of our scheme is to well pair UEs and BSs with the consideration of joint transmission quality and then optimizes DRX parameters to further save energy. Extensive simulation results show that our scheme can improve system throughput, well utilize resource and save UEs’ energy consumption.

In 5G mobile communications, Cloud-RAN (C-RAN) (Camps-Mur et al. in IEEE Commun Mag 57:99–105, 2019) is proposed to provide broadband services. It separates computation entities, i.e., baseband units (BBUs), from base stations and puts all BBUs in a centralized cloud. Existing researches have investigated how to enhance user equipments (UEs) to receive data from multiple collaborative cells by leveraging the dynamic point selection(DPS) technology. Collaborative transmission may cause higher energy consumption for UEs. Although 3GPP has defined the discontinuous reception (DRX) mechanism by regulating UEs to turn off their radio interfaces in a periodical manner, how to well coordinate DRX with DPS under the C-RAN architecture is still left as an open issue. This paper is the first one addressing this resource optimization problem in C-RAN with DPS, which asks how to optimize DRX parameters of UEs by considering their quality-of-service (QoS). We prove this problem to be NP-complete, and then propose two effective and efficient DPS solutions, called serving-ratio(SR) scheme and cost-aware (CA) scheme. SR serves UEs based on a special ‘serving ratio’ to ensure UEs receiving continuous subframes, especially for those in cell intersections. On the other hand, CA exploits the strategies of minimal scheduling costs to balance energy and throughput efficiency in a perspective way. Extensive simulation results validate that our schemes can successfully achieve higher system throughput, provide better resource utilization, and serve more UEs while guaranteeing their QoS and saving considerable energy as compared to existing schemes.

Drones have been applied to a wide range of security and surveillance applications recently. With drones, Internet of Things are extending to 3D space. An interesting question is: Can we conduct person identification (PID) in a drone view? Traditional PID technologies such as RFID and fingerprint/iris/face recognition have their limitations or require close contact to specific devices. Hence, these traditional technologies can not be easily deployed to drones due to dynamic change of view angle and height. In this work, we demonstrate how to retrieve IoT data from users’ wearables and correctly tag them on the human objects captured by a drone camera to identify and track ground human objects. First, we retrieve human objects from videos and conduct coordination transformation to handle the change of drone positions. Second, a fusion algorithm is applied to measure the correlation of video data and inertial data based on the extracted human motion features. Finally, we can couple human objects with their wearable IoT devices, achieving our goal of tagging wearable device data (such as personal profiles) on human objects in a drone view. Our experimental evaluation shows a recognition rate of 99.5% for varying walking paths, and 98.6% when the drone’s camera angle is within 37°. To the best of our knowledge, this is the first work integrating videos from drone cameras and IoT data from inertial sensors.

With the rise of Artificial Intelligence and Internet of Things, more and more applications are designed to bring benefit for our daily life. Voice is an easily generated input method by human. The process of converting voice into text is speech recognition. Online tools could provide high accuracy for speech recognition. In some applications, e.g., factory, Internet is forbidden and the number of specific voice commands are limited. It becomes necessary to be able to perform speech recognition in real-time on the embedded devices without having the need to send to the remote servers. In this paper, we enhance a speech-to-text framework (PocketSphinx) on embedded devices, i.e. Raspberry PI. The enhanced PocketSphinx can execute speech-to-text on embedded devices without Internet. Moreover, it can also recognize hybrid language (English and Chinese) to control a IoT or ROS-based device.

Drones have been applied to a wide range of security and surveillance applications recently. With drones, Internet of Things are extending to3D space. An interesting question is: Can we conduct person identification(PID) in a drone view? Traditional PID technologies such as RFID and fingerprint/iris/face recognition have their limitations or require close contactto specific devices. Hence, these traditional technologies can not be easily deployed to drones due to dynamic change of view angle and height. In this work,we demonstrate how to retrieve IoT data from users’ and correctly tag themon the human objects captured by a drone camera to identify and track groundhuman objects. First, we retrieve human objects from videos and conduct coordination transformation to handle the change of drone positions. Second,a fusion algorithm is applied to measure the correlation of video data andinertial data based on the extracted human motion features. Finally, we cancouple human objects with their wearable IoT devices, achieving our goal oftagging wearable device data such as personal profiles) on human objects ina drone view. Our experimental evaluation shows a recognition rate of 98.9%.To the best of our knowledge, this is the first work integrating videos fromdrone cameras and IoT data from inertial sensors.

In this study, a novel method for the simultaneous determination and accurate quantification of abused drugs in human urine was developed. Antibody conjugated boronic acid modified magnetite nanoparticles (Fe3O4, MNPs) were prepared for the selectively purification of illicit drugs in combination with high resolution matrix-assisted laser desorption ionization time-of-flight mass spectrometer (MALDI-TOF MS) analysis. Illicit drugs, amphetamine (AM) and methamphetamine (MA), were used as model analytes to demonstrate the feasibility of our strategy. Boronic acid functionalized MNPs were first prepared via one-pot synthesis to simplify and improve the efficiency of a chemical reaction. Anti-amphetamine antibody (anti-AM antibody) and anti-methamphetamine antibody (anti-MA antibody) was conjugated onto boronic acid modified MNPs, respectively, through the formation of boronate ester bond that could maintain the correct orientation to maximally capture their antigens. The capacity of antibody conjugation to boronic acid modified MNPs was at least 24 μg antibody/mg MNPs. Antibody-conjugated MNPs were designed to specifically capture AM and MA in human urine samples, both of which can be directly eluted to MALDI target plate by adding MALDI CHCA matrix solution for the following MALDI-MS analysis. The linear range of detection of the proposed method were 25–400 ng/mL and 25–1000 ng/mL with coefficients of determination between 0.9923 and 0.9997 for AM and MA, respectively. The lowest detectable concentrations of AM and MA were 1.87 and 3.75 ng/mL, respectively. More importantly, the proposed method allows rapid and accurate quantification of AM and MA from three suspects' urine samples. The obtained results are consistent with traditional GC/MS analysis. Antibody-conjugated MNPs could thus prove to be powerful tools for important applications such as forensic science, food safety and clinical diagnosis of disease.

This paper presents a robot for people identification and tracking developed on robot operating system (ROS). It achieves modulized, light-weight, low-cost, and high-performance design goals even with the existence of distributed sensors. The key idea is to utilize wearable devices to enhance the people tracking capability of a robot through instant wireless communications and multi-sensory data fusion. Experimental results in a realistic environment demonstrate that our robot can keep tracking a specific person at a safe distance even without seeing the biological features of the person, who walks in a crowd with complex trajectories.

Both cameras and IoT devices have their particular capabilities in tracking moving objects. Their correlations are, however, unclear. In this work, we consider using a drone to track ground objects. We demonstrate how to retrieve IoT data from devices, which are attached on human objects, and correctly tag them on the human objects captured by a drone view. This is the first work correlating IoT data and computer vision from a drone camera. Potential applications of this work include aerial surveillance, people tracking, and intelligent human-drone interaction.

Software defined network (SDN) provides flexible and scalable routing by separating control plane and data plane. With centralized control, SDN has been widely used in traffic engineering, link failure recovery, and load balancing. This work considers the flow update problem, where a set of flows need to be migrated or rearranged due to change of network status. During flow update, efficiency and consistency are two main challenges. Efficiency refers to how fast these updates are completed, while consistency refers to prevention of blackholes , loops , and network congestions during updates. This paper proposes a scheme that maintains all these properties. It works in four phases. The first phase partitions flows into shorter routing segments to increase update parallelism. The second phase generates a global dependency graph of these segments to be updated. The third phase conducts actual updates and then adjusts dependency graphs accordingly. The last phase deals with deadlocks, if any, and then loops back to phase three if necessary. Through simulations, we validate that our scheme not only ensures freedom of blackholes, loops, congestions, and deadlocks during flow updates, but is also faster than existing schemes.

Good deployment of sensors empowers the network with effective monitoring ability. Different from omnidirectional sensors, the coverage region of a directional sensor is determined by not only the sensing radius (distance), but also its sensing orientation and spread angle. Heterogeneous sensing distances and spread angles are likely to exist among directional sensors, to which we refer as heterogeneous directional sensors. In this paper, we target on a bounded monitoring area and deal with heterogeneous directional sensors equipped with locomotion and rotation facilities to enable the sensors self-deployment. Two Enhanced Deployment A lgorithms, EDA-I and EDA-II, are proposed to achieve high sensing coverage ratio in the monitored field. EDA-I leverages the concept of virtual forces (for sensors movements) and virtual boundary torques (for sensors rotations), whereas EDA-II combines Voronoi diagram directed movements and boundary torques guided rotations. EDA-I computations can be centralized or distributed that differ in required energy and execution time, whereas EDA-II only allows centralized calculations. Our EDA-II outperforms EDA-I in centralized operations, while EDA-I can be adapted into a distributed deployment algorithm without requiring global information and still achieves comparably good coverage performance to its centralized version. To the best of our knowledge, this is perhaps the first work to employ movements followed by rotations for sensors self-deployment. Performance results demonstrate that our enhanced deployment mechanisms are capable of providing desirable surveillance level, while consuming moderate moving and rotating energy under reasonable execution time.

The 5G era brings the rapid development of the Internet of Things (IoT). New technologies, such as image recognition, have promoted the traditional agriculture to a new milestone. Through advanced research and development of image recognition, it can intelligently monitor the growth of crops and effectively reduce agricultural damages, while avoiding crops being stolen and reducing the manpower of farms. In this paper, we design and implement an image electronic fence based on the technology of image recognition and sensor fusion with 5G technology for smart farms. By applying cameras and beacon tags, our system can identify whether the incoming/leaving people are authorized or not at the entry and exit of farms. In addition, with the high speed and low latency of 5G technology, the video data of cameras can be transmitted in a flash and fused with the beacon detection. In this way, the incoming/leaving people can be identified efficiently so as to avoid the farms being damaged. Based on field trials, we validate that the identification accuracy of our system approaches 90% in average.

The 3GPP Narrowband Internet of Thing (NB-IoT) is the promising technology that can provide multiple types of resource unit (RU) with a special repetition mechanism to improve the scheduling flexibility and transmission reliability. Since the IoT devices need to operate for a very long time, the energy consumption becomes a critical issue. In this paper, we study how to guarantee the quality of service (QoS) while minimizing the energy consumption for IoT devices. We first model the problem and then propose an energy-efficient scheme, which consists of two stages. The first stage tries to incur the lowest energy consumption of devices and satisfy their QoS requirement. The second stage determines the scheduling order to ensure the delay constraint while maintaining energy efficiency. Simulation results show that our scheme can serve more devices while saving their energy.

For next generation mobile communications, Cloud-RAN (C-RAN) is an emerging network architecture to provide broadband services. C-RAN separates computation entities, i.e., Baseband Units (BBUs), from base stations (BSs) and puts BBUs in a cloud located in a centralized network. With C-RAN, UEs can receive data from multiple collaborative cells and thus can leverage the dynamic point selection (DPS) technology to improve network efficiency. When user equipments (UEs) enter a hotspot and can not be served due to congestion, data offloading from the hotspot to its neighboring cells may take place to balance heterogeneous cells' loads. This work shows how to integrate such DPS offloading with the Discontinuous Reception (DRX) mechanism, which allows UEs to turn off their radio interfaces in a periodical manner. We address the resource allocation problem in heterogeneously-loaded C-RAN by optimizing UEs' energy consumption based on DRX while reserving sufficient bandwidths for UEs considering their quality-of-service (QoS) through DPS. We propose an offloading-based DPS scheduling scheme by exploiting not only maximal instantaneous throughputs but also minimal energy cost. Simulation results show that our scheme can improve throughput, resource utilization, and energy consumption as compared to existing schemes.

One critical issue, for a wireless sensor network (WSN) to operate successfully, is to provide sufficient sensing coverage. Define the smart sensing environment as a sensing system with the capability to sense the environment and respond properly in an automated manner. In this paper, we target on smart sensing environments and deal with heterogeneous sensors (here sensor heterogeneity is defined as sensors having different sensing ranges) equipped with actuation facilities to assist in the sensor self-deployment. A coverage-aware sensor automation (CASA) protocol is proposed to realize an automated smart monitoring network. Two centralized algorithms are included in the CASA protocol suite: enhanced virtual forces algorithm with boundary forces (EVFA-B) and sensor self-organizing algorithm (SSOA). Unlike most previous works that tackle the deployment problem only partially, we intend to address the problem from both global deployment (EVFA-B) and local repairing (SSOA) perspectives. The EVFA-B protocol exerts weighted attractive and repulsive forces on each sensor based on predefined distance thresholds. Resultant forces then guide the sensors to their suitable positions with the objective of enhancing the sensing coverage (after a possibly random placement of sensors). Furthermore, in the presence of sensor energy depletions and/or unexpected failures, our SSOA algorithm is activated to perform local repair by repositioning sensors around the sensing void (uncovered area). This capability of local recovery is advantageous in terms of saving the communication and moving energies. Performance of the proposed sensor deployment strategies is evaluated in terms of surveillance coverage, monitoring density, network self-healing competence, and moving energy consumption. We also implement our CASA protocol suite in a real-life monitoring network (MoNet) to demonstrate the protocol feasibility and validate the MoNet detection capability of emergency events.

In modern wireless networks with multiple orthogonal (non-overlapping) channels available, one essential performance topic is how to effectively exploit channel diversity to enable parallel communications. Generally, having a radio interface hop through all available channels produces better spectrum diversity than binding it permanently to one channel, at the cost of channel switching delays and potentially compromised network connectivity. Moreover, multi-hop communications become challenging due to the lack of a common rendezvous for discovering routes and the difficulty of relaying packets from hop to hop. In this paper, we propose a multi-radio channel-hopping scheme (CHS) that preserves network connectivity. We prove that less than three radios are required by CHS in order to achieve good channel overlapping in widespread IEEE 802.11-based wireless systems. Corresponding channel-diverse routing (CDR) protocol is devised to realize efficient multi-hop communications. Simulation results demonstrate that the proposed CDR outperforms other strategies in static IEEE 802.11a multi-hop networking environments.

Machine-to-machine (M2M) communication is one of the key technologies to realize Internet of Things (IoT). Since IoT applications are mainly for smart sensing, such as metering, home surveillance, disaster detection, and e-health, their special sensing#x002F;uploading behaviors will result in periodic and#x002F;or event-driven small data transmissions, which may potentially decrease the radio resource efficiency. On the other hand, the widespread deployment of IoT raises the concurrent massive connectivity of IoT devices. How to solve these two problems is a critical issue. In this paper, we investigate an uplink resource allocation problem which considers the periodic, event-driven, and query-based IoT traffic behaviors over LTE-M. The proposed approach takes advantage of data aggregation and both spatial and temporal reuse. Our solution exploits long-term static scheduling for periodic data to ensure the latency and data rate, and employs short-term dynamic scheduling for event-driven, query-based data to improve transmission efficiency. Therefore, both small data and massive connectivity problems are relieved. Extensive simulation results show that the proposed scheme can improve resource efficiency and enlarge network capacity effectively.

For next generation wireless communications, Cloud-RAN (C-RAN) has become an emerging network architecture of mobile communications, which separates computation entities-Baseband Units (BBUs) from original base stations (BSs), and puts BBUs in the cloud then forms a centralized network architecture. With C-RAN architecture, user equipments (UEs) can receive data from multiple collaborative cells and thus can leverage dynamic point selection (DPS) technology to improve network efficiency. Note that since the UEs are powered by batteries, energy saving is always a critical issue under C-RAN architecture. In current standard of 3GPP LTE-A, it has defined Discontinuous Reception (DRX) mechanism to allow UEs to turn off their radio interfaces and go to sleep to save energy. However, how to save UEs' energy under DPS in C-RAN is still an open issue. Therefore, this paper addresses the resource allocation problem by asking how to optimize the energy conservation of UEs based on DRX while serving UEs as more as possible under the consideration of UEs' quality of service (QoS) in C-RAN with DPS. To solve this problem, we propose an energy-efficient DPS (EE-DPS) scheduling scheme. The key idea of our scheme is to serve the UEs in the intersection of cells continuously and allocate resource tightly to avoid additional wake-up intervals. Extensive simulation results show that our scheme can serve most number of UEs while achieving high throughput as well as lower energy consumption, compared to the existing schemes.