In the realm of energy and sensor technology, a trio of researchers from the University of Borås in Sweden have developed a novel approach to improve the efficiency and accuracy of remote state estimation in wireless sensor networks. Nho-Duc Tran, Aamir Mahmood, and Mikael Gidlund have introduced a delay-aware framework that optimizes sensor scheduling, considering the often-overlooked interplay between sensor informativeness, energy efficiency, and unpredictable delays in data transmission.
The researchers’ work, published in the IEEE Internet of Things Journal, addresses a critical challenge in wireless remote state estimation: the distortion caused by unpredictable delays in sensor-to-estimator communication. Traditional methods often rely on proxies like age of information (AoI) to gauge data freshness, but these approaches overlook the complex interplay between delay, sensor informativeness, and energy efficiency.
The team’s unified framework models this coupling explicitly and introduces a delay-dependent information gain. This motivates an information-per-joule scheduling objective that goes beyond conventional AoI proxies. To achieve this, the researchers first developed an efficient posterior-fusion update that incorporates delayed measurements without state augmentation. This provides a consistent approximation to optimal delayed Kalman updates, a common method used for estimating the state of a process based on a series of incomplete and noisy measurements.
The framework also derives tractable stability conditions, ensuring that bounded estimation error is achievable under stochastic, delayed scheduling. These conditions underscore the importance of unstable modes being observable across sensors.
Building on this foundation, the researchers cast scheduling as a Markov decision process and developed a proximal policy optimization (PPO) scheduler. This scheduler learns directly from interaction, requiring no prior delay model, and explicitly trades off estimation accuracy, freshness, sensor heterogeneity, and transmission energy through normalized rewards.
In simulations featuring heterogeneous sensors, realistic link-energy models, and random delays, the proposed method demonstrated stable learning and consistently achieved lower estimation error at comparable energy levels than random scheduling and strong reinforcement learning baselines like Deep Q-Network (DQN) and Advantage Actor-Critic (A2C). The method also showed robustness to variations in measurement availability and process/measurement noise.
For the energy sector, this research offers practical applications in optimizing sensor networks used for monitoring and controlling energy systems. By improving the efficiency and accuracy of remote state estimation, the framework can enhance the performance of smart grids, renewable energy systems, and other energy infrastructure that rely on real-time data from wireless sensors. The ability to handle heterogeneous sensors and varying delays makes the approach particularly valuable in large-scale, complex energy networks where data transmission delays and sensor characteristics can vary significantly.
This article is based on research available at arXiv.