This work studies the challenge of scheduling real-time control commands in Networked Control Systems (NCS), where control actions rely on the freshness of data collected from multiple sources. In dynamic environments, ensuring that control commands in an NCS are accurate and frequent is essential for maintaining the system responsiveness. For this aim, we introduce a new metric, Age of Result (AoR), which quantifies the time elapsed since the last control command was generated and executed. This metric reflects the system’s capability to adapt to real-time changes in the operational environment by considering both data freshness and control command frequency. We conduct a detailed analysis of AoR in NCS, paying special attention to the dependencies between sensing and computing phases. We first address computation-intensive and network-intensive scenarios, proposing random sampling (RS)-based approximate algorithms for each case. Subsequently, we develop another RS-based algorithm and a heuristic approach for the general model. Simulation results demonstrate that our approach can effectively minimize AoR and significantly enhance the system performance and real-time adaptability compared to existing strategies.

This work studies the Age-of-Information (AoI) optimization problem in the information-gathering wireless network systems, where time-sensitive data updates are collected from multiple information sources, and each source is equipped with a battery and harvests energy from ambient energy, such as solar, wind, etc. The arrival of the harvested energy can be modeled as the stochastic process, and an information source can deliver its data update only when 1) there is energy in the battery, and 2) this source is selected to transmit its data update based on the transmission policy. This work analyzes how the energy arrival pattern of each source and the transmission policy jointly influence the average AoI among multiple sources. To the best of our knowledge, this is the first work that formally develops the closed-form expression of average AoI in the Stationary Randomized Sampling (SRS) policy space and proposes approximation schemes with constant ratios in multi-source systems under a stochastic energy harvesting model. More specifically, under the perfect wireless channel, the closed-form expression of AoI under the SRS policy space with arbitrary finite battery size is developed. Based on the result, we propose the Max Energy-Aware Weight (MEAW) policy, which is proven to achieve 2-approximation in the full policy space. Under the uncertain wireless channel, we develop the closed-form expression of Whittle’s index to address the target problem. Based on the result, we propose the Energy-aware Whittle’s index policy (EWIP) and prove its approximate performance by using the Lyapunov optimization techniques. Experimental results show that MEAW under the perfect channel setting and EWIP under the uncertain channel setting both perform close to the theoretical lower bound and outperform the state-of-the-art schemes.

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.

Energy harvesting techniques have been increasingly employed in both consumer and industrial applications to provide clean energy supply. Among the many available energy harvesting techniques, ambient energy harvesting (AEH) is a promising one as it harvests free energy from the environment and, thus, is economically efficient. AEH techniques, however, heavily depend on the dynamic environment and are thus uncontrollable and unstable. More recently, the wireless power transfer (WPT) technique has attracted significant attentions due to its highly controllable feature when powering low-cost devices. Unfortunately, WPT faces strict regulatory limitations to provide high power density and requires charging infrastructures installed to perform effective wireless energy transfer. The pros and cons of the two techniques motivate this work to design a hybrid energy harvesting method by charging a device using a combination of AEH and WPT to maximize the throughput of a wireless system. Specifically, this work first proposes an optimal offline charging scheme to maximize the point-to-point data throughput of a wireless system by fully utilizing the ambient energy and providing extra power supply through WPT to determine the transmission rates. An online heuristic algorithm is further proposed to improve the computational efficiency for practical scenarios when the system has the estimation of future AEH patterns. Our experimental results show that the proposed approaches are effective in maximizing the data throughput when compared to the state of the art.

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.