Mobile devices have been increasingly deployed in large-scale cyber-physical systems (CPS) to traverse the field and retrieve various data measurements from designated physical entities with stringent performance requirements. This work studies the Availability-constrained real-time Fresh Data Retrieval problem in CPS with a Speed Adjustable mobile device (AFDR-SA). The goal is to maintain the temporal validity of the real-time data with different priorities to be retrieved in the system while meeting the data availability constraints imposed by the communication range between the mobile device and the physical entities. The general case of the AFDR-SA problem is proved to be NP-hard. A dynamic programming (DP)-based optimal algorithm is proposed for a special scenario where the retrieval times of individual data items with the same priority are of the same length. For the general case where data items can have arbitrary retrieval times and different priorities, another different DP-based scheme is proposed, which is proved to be optimal given the retrieval order. A fast heuristic with low complexity is also proposed for the general problem to improve the computational efficiency. The experimental results show that the proposed schemes for the general case outperform the state-of-the-art methods and have close performance compared to the optimal solution while incurring much less computational overhead.

This work studies the non-uniform multi-source data update problem for real-time monitoring systems, where a set of heterogeneous data sources transmit their updates to a Base Station (BS) through wire or wireless channel(s). The performance metric called Age of Information (AoI) - which measures the time elapsed since the last data update of each source received by the BS - is commonly used to quantify the freshness of the data updates. However, most existing work on minimizing AoI of multi-source data updates assume that all sources have a uniform size of data updates which unnecessarily reduces their applicability. This work explores a more general model where individual sources can have non-uniform sizes of data updates, and provides thorough analysis to optimize both peak and average AoI of the target system. Based on these analysis, an optimal scheme to minimize the peak AoI is first developed by guaranteeing the delivery frequency of each source proportional to the function determined by its data size. A$(2+\delta)$-approximation algorithm based on random sampling (RS) and a heuristic called Ratio-driven Maximum Age First (RMAF) are further proposed to minimize the average AoI. Our extensive experiments validate the bound of RS, and show that RMAF can achieve close performance to the lower bound of the minimum time-average AoI and outperforms the state-of-the-art schemes.

Applying high-speed real-time wireless technologies in industrial applications has the great potential to reduce the deployment and maintenance costs compared to their wired counterparts. Wireless technologies enhance the mobility and reduce the communication jitter and delay for mobile industrial equipment, such as mobile collaborative robots. Unfortunately, most existing wireless solutions employed in industrial fields either cannot support the desired high-speed communications or cannot guarantee deterministic, real-time performance. A more recent wireless technology, RT-WiFi, achieves a good balance between high-speed data rates and deterministic communication performance. It is however developed on commercial-of-the-shelf (COTS) hardware, and takes considerable effort and hardware expertise to maintain and upgrade. To address these problems, this paper introduces the software-defined radio (SDR)-based RT-WiFi solution which we call SRT-WiFi. SRT-WiFi provides full-stack configurability for high-speed real-time wireless communications. We present the overall system architecture of SRT-WiFi and discuss its key functions which achieve better timing performance and solve the queue management and rate adaptation issues compared to COTS hardware-based RT-WiFi. To achieve effective network management with rate adaptation in multi-cluster SRT-WiFi, a novel scheduling problem is formulated and an effective algorithm is proposed to solve the problem. A multi-cluster SRT-WiFi testbed is developed to validate the design, and extensive experiments are performed to evaluate the performance at both device and system levels.

Smart factory automation has an ongoing trend to employ high-speed real-time wireless technologies to interconnect heterogeneous industrial assets to perform various sensing and control services, and support mobile equipment to conduct designated tasks in a collaborative fashion. Applications in automation industries usually have stringent requirements on both high data rates and deterministic real-time performance. Existing efforts, however, either cannot meet the performance requirements or are based on commercial-off-the-shelf (COTS) hardware and cannot provide full-stack configurability [1].

Applying emerging wireless powered communication (WPC) technologies in wireless networks has the potential to significantly reduce the maintenance cost of battery replacement and facilitate device deployment in extreme environments. This paper presents the design, implementation and performance evaluation of a real-time wireless powered communication network (RT-WPCN). We present the overall system architecture of RTWPCN and discuss the design challenges of its key hardware and software components, including the wireless powered user device, the beamforming-based hybrid access point (HAP), and the timedivision multiple access (TDMA) based data link layer protocol. We present our solutions to address each of the challenges and describe their implementation details on real hardware. Based on the developed RT-WPCN system, the minimal user throughput maximization problem is formulated and an effective algorithm is proposed to solve it. Extensive expenments are conducted to validate the design principles of RT-WPCN, and thoroughly evaluate its performance at both the component and system levels.

Wireless sensing devices compose the major physical components of Internet-of-Things (IoT) systems which in recent years we have witnessed their rapid deployment in both consumer and industrial applications to support diverse sensing and control services [1]. Most of those sensing devices however are battery powered, and they require significant economic and human resources to replace or charge the batteries frequently to maintain proper operations. To overcome this issue, an ever-growing number of IoT devices are now being powered by harvesting ambient energy sources, which are mostly unreliable and not controllable. By contrast, emerging RF-based energy harvesting technologies [2] provide key benefits in terms of being wireless and controllable to provide sustainable energy supply. The IoT systems relying on RF-based energy harvesting technologies are in general called wireless powered communication networks (WPCN).

Mobile devices have been increasingly deployed in large-scale cyber-physical systems (CPS) to traverse the field and retrieve data measurements from designated physical entities with stringent performance requirements. This work studies the availability-constrained real-time data retrieval problem in CPS with a speed adjustable mobile device (AFDR-SA). The goal is to maintain the temporal validity of the real-time data to be retrieved in the system while meeting the data availability constraints imposed by the communication range between the mobile device and the physical entities. A dynamic programming (DP)-based optimal algorithm is proposed for a special but commonly presented scenario where the retrieval times of individual data items are of the same length. Based on this optimal algorithm, an effective heuristic method is further developed for the general case where data items can have arbitrary retrieval times. The effectiveness of the proposed methods are validated through extensive experiments. Our results demonstrate the optimality of the DP-based algorithm, and show that the heuristic method outperforms the state-of-the-art schemes and performs close to the optimal solution obtained by the exhaustive search with much less computational overhead.

In this article, we present an advanced coherent receiver for the IEEE 802.15.4 offset quadrature phase-shift keying (OQPSK) physical layer and provide an open-source implementation in the GNU Radio framework. Simulation and field test results show that the proposed receiver achieves about 11-dB power gain over an existing GNU Radio receiver, which treats OQPSK as minimum-shift-keying (MSK) for low-complexity processing. While suitable modules can be added to the MSK-based receiver for performance enhancement, the proposed receiver still maintains a 6-dB power gain. The proposed coherent receiver is attractive for IoT applications where a powerful software-defined-radio (SDR)-based gateway is deployed to interact with various sensors.

This paper studies how to maintain real-time data freshness in the emerging wireless powered communication networks (WPCNs). In a WPCN, one or multiple hybrid access points (HAPs) with constant power supply transfer the energy and receive the status update through wireless to and from a set of distributed sensor nodes simultaneously. We utilize the concept of Age of Information (AoI) to quantitatively measure the freshness of the sensor data, and formulate the AoI optimization problem. Based on this problem formulation, we first explore the optimal time allocation method to minimize the average AoI for sensor nodes in single-hop WPCNs. This method is then extended to derive the optimal time allocation for two-hop WPCNs, where some sensor nodes may transmit status update to the HAP through relay nodes. In this more complex scenario, we apply a two-phase method to 1) identify all the two-hop node candidates and their associated relay nodes, and 2) determine the best assignment and the corresponding time allocation to optimize the average AoI. A 5-node WPCN testbed is developed to validate the functional correctness of the proposed methods. Extensive simulations are also conducted for performance evaluation under more comprehensive settings. The experimental results show that the proposed methods can reduce the average AoI by 83.4% on average compared to the state-of-the-art methods.

Along with the rapid growth of Industrial Internet-of-Things (IIoT) applications and their penetration into many industry sectors, real-time wireless networks (RTWNs) have been playing a more critical role in providing real-time, reliable and secure communication services for such applications. A key challenge in RTWN management is how to ensure real-time Quality of Services (QoS) especially in the presence of unexpected external and internal disturbances. Most prior work takes a centralized approach for handling disturbances, which is slow and subject to single-point failure, and does not scale. To overcome these drawbacks, this paper presents a fully distributed packet scheduling framework called FD-PaS. FD-PaS aims to provide guaranteed fast response to unexpected disturbances while dropping a minimum number of packets for meeting the deadlines of all critical tasks. To combat the scalability challenge, FD-PaS incorporates several key advances in both algorithm design and data link layer protocol design to enable individual nodes to make on-line decisions locally without any centralized control. Our extensive simulation and testbed results have validated the correctness of the FD-PaS design and demonstrated its effectiveness in providing fast response for handling disturbances.