## 2 Literature Review  
Numerous researchers have explored various control architectures for civil structures that can be embedded on low-power computing cores, with a specific attempt to address the communication constraints of WSUs. In particular, these often focus on distributing traditional optimal control algorithms across a network of computing nodes and typically depend on using complex state estimators. In [8], a partially decentralized linear quadratic regulator (LQR) control scheme that leverages a Kalman filter to estimate unknown system states was validated on a full scale structure. In [9], a sparse representation of the LQR was proposed, which requires less information for decision-making than a traditional centralized approach, thereby reducing information flow requirements. In [10], the authors proposed a distributed \(H_{\infty}\) algorithm for civil infrastructure, and in [11], the authors explored distributing the \(H_{\infty}\) algorithm across multiple communication subnets of wireless sensing nodes. While these studies demonstrate the successful use of low-power computing cores in control architectures, they also highlight several challenges associated with this technology. Such challenges include increased computational requirements at the already resource-constrained sensing node and decision-making based on reduced information, resulting in some degradation in the control effectiveness. The proportional-integrator-derivative (PID) controller has also been considered in numerous studies due to its ease of implementation, which often makes it less computationally expensive. Developing the control parameters for the PID controller for seismic control of structures is challenging because the off-diagonal terms in the damping and stiffness matrices of the structure cause cross-coupling in the system. As a result, traditional empirical methods for deriving the control parameters, such as the Ziegler-Nichols method, are often ineffective. However, numerous researchers have focused on using metaheuristic algorithms, such as the particle swarm optimization [12; 13; 14; 15] and the genetic algorithm [16; 17; 18; 19], to determine the PID control parameters for a variety of applications and these can be extended to the control of civil structures. In [20], the PSO algorithm was used to derive the PID coefficients for mitigating the effect of high-impact loads on a highway bridge, modeled as a single input-single output system. In [21], a genetic algorithm was used to derive the control parameters for a PID controller for a single active tuned mass damper (ATMD) and was applied to numerically control an 11-story building. In [22], the authors combined the LQR control scheme with a PID controller using the cuckoo search algorithm and validated the controller in simulation on a seismically excited 10-story structure equipped with a single ATMD. In [23], a new evolutionary algorithm was proposed to derive the variables for a combined PID-LQR controller, which is numerically validated on a four-degree-of-freedom building that was equipped with an active control device on each floor. While all of these studies demonstrate the effectiveness of these methods, all but one is limited to a single output mechanism and they did not discuss communication considerations that would be encountered in real-world implementations of the structure. In [24], the authors did account for these communication and computational constraints by including time delays, while also exploring various metaheuristic methods for developing PID control parameters, and found that such delays could severely inhibit the control effectiveness. In this study, the authors seek to address the real-world challenges experienced when implementing seismic control of structures through consideration of communication constraints and the computational complexity of the control algorithm. In particular, we propose to use a novel sensing node that is capable of real-time spectral decomposition to alleviate computations at the controlling node.