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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.

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.

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.

For 5G wireless communications, the 3GPP Narrowband Internet of Things (NB-IoT) is one of the most promising technologies, which provides multiple types of resource unit (RU) with a special repetition mechanism to improve the scheduling flexibility and enhance the coverage and transmission reliability. Besides, NB-IoT supports different operation modes to reuse the spectrum of LTE and GSM, which can make use of bandwidth more efficiently. The IoT application grows rapidly; however, those massive IoT devices need to operate for a very long time. Thus, the energy consumption becomes a critical issue. Therefore, NB-IoT provides discontinuous reception operation to save devices’ energy. But, how to further reduce the transmission energy while ensuring the required ultra-reliability is still an open issue. In this paper, we study how to guarantee the reliable communication and satisfy the quality of service (QoS) while minimizing the energy consumption for IoT devices. We first model the problem as an optimization problem and prove it to be NP-complete. Then, we propose an energy-efficient, ultra-reliable, and low-complexity scheme, which consists of two phases. The first phase tries to optimize the default transmit configurations of devices which incur the lowest energy consumption and satisfy the QoS requirement. The second phase leverages a weighting strategy to balance the emergency and inflexibility for determining the scheduling order to ensure the delay constraint while maintaining energy efficiency. Extensive simulation results show that our scheme can serve more devices with guaranteed QoS while saving their energy effectively.

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.

IEEE 802.11 wireless network supports multiple link rates at the physical layer. Each link rate is associated with a certain required Signal-to-Interference-and-Noise Ratio (SINR) threshold for successfully decoding received packets. On transmission failures, the 802.11 DCF performs a binary exponential backoff mechanism to discourage channel access attempts, hoping to reduce congestion. When traditional link adaptation is applied, both rate reduction and binary backoff represent double penalties for this wireless link, which may cause overly conservative transmission attempts. On the other hand, once transmission succeeds, 802.11 DCF resets the backoff contention window to the minimum value to encourage channel access attempts. At the same time, traditional link adaptation may also decide to increase the data rate, which leads to overly aggressive transmission attempts. We observe this improper interaction of link rate and backoff mechanism that harms the 802.11 system performance, due to separate consideration of those two parameters. In this paper, we propose to jointly adapt the rate and backoff parameters. Specifically, an Enhanced Adaptation of link Rate and Contention window, abbreviated as EARC, is devised. EARC is a closed-loop (receiver-assisted) link rate adaptation protocol that jointly considers the backoff mechanism. With only one extra byte carried by the DATA packet, EARC incurs little controlling overhead despite its receiver-assisted nature. Moreover, since SINR information commonly utilized by receiver-assisted protocols is not precisely supported in real devices, we introduce a rate selection reference (RSR) table empirically derived by constantly monitoring the environmental energy level and reception behavior. The RSR table then guides the receiver to select the best sustainable rate for the transmitter. Simulation results demonstrate the RSR table is a practical option for making the rate decision, and the proposed EARC approach is effective in maintaining high system throughput, compared to other link adaptation algorithms.