Estimation problems in wireless sensor networks (WSNs) typically involve collecting and processing data from distributed sensors at the fusion center to infer the state of an environment. However, not all measurements contribute equally to estimation accuracy. In this work, we incorporate the concept of ordered transmission into sequential estimation to select the most informative measurements from different sensors, while ensuring the desired estimation quality. We analyze a general estimation problem with different estimator choices and derive stopping rules for collecting measurements. Then, we derive the expected number of transmissions required for our ordered transmission-based sequential estimation scheme and compare it with that of a conventional sequential estimation scheme with unordered transmissions. To validate the proposed protocol, we apply it to a radar-based WSN for target localization and velocity estimation, designing an ordered transmission strategy and a sequential stopping rule. Simulation results show that our protocol requires fewer transmissions compared to conventional sequential estimation while maintaining similar estimation accuracy in general WSNs. In a radar-based WSN, the proposed protocol achieves reliable estimation with reduced communication overhead and improved response time.

Millimeter wave (mmWave) systems, currently employed in 5G and IEEE 802.11ad/ay devices, enable high data rates through wide bandwidths and directional communication. However, high carrier frequencies used in these systems result in a higher phase noise than lower frequency systems. This paper investigates the problem of spatial channel estimation in the presence of severe phase noise, which manifests as partially coherent phase perturbations in the observed channel measurements. In this model, phase noise remains relatively constant within a packet but varies substantially across packets. Under such partially coherent phase noise, we first develop two computationally efficient on-grid algorithms to estimate narrowband mmWave channels: Partially Coherent Matching Pursuit (PCMP) and Enhanced Partially Coherent Matching Pursuit (EPCMP), assuming a known channel sparsity. Both algorithms exploit the sparse structure in mmWave channels, enabling a significant reduction in training overhead while achieving good estimation performance. The main difference between PCMP and EPCMP is how the sparse channel support is identified. The EPCMP algorithm can achieve better estimation performance at the cost of increased computational complexity compared to the PCMP algorithm. We then relax the known-sparsity assumption, adapt the proposed algorithms accordingly, and further extend them to the wideband case for an unknown sparsity. Additionally, we derive sufficient conditions to recover a support element with proposed algorithms. Simulation results demonstrate the advantages of our methods over comparable channel estimation benchmarks.

When designing wireless sensor networks (WSNs), energy efficiency and security are two crucial aspects to consider. The deployment of low-cost battery-operated sensors and limited communication bandwidths require the networks to be energy efficient. Meanwhile, the distributed nature of WSNs necessitates the networks to be secure. In this chapter, we consider the effect of Byzantine attacks, one of the most important security threats faced by WSNs, on one kind of promising energy-efficient decision-making schemes, called ordered transmission-based (OT-based) schemes. In energy-efficient OT-based schemes, less informative data is omitted from transmission for energy efficiency. Due to intermittent data reception from the sensors, the fusion center cannot fully learn the behavior of all the sensors. The problem of how Byzantine attacks affect the decision-making performance of energy-efficient OT-based schemes is investigated, and some possible countermeasures are discussed.

Light detection and ranging (LiDAR) plays a crucial role in machine perception for advanced driver assistance systems. Existing LiDARs, however, do not adapt their sensing strategy to complement driver's perception. We demonstrate a novel LiDAR prototype that dynamically adapts its range and resolution over the field of view, according to real-time driver gaze. Our gaze-aware LiDAR emphasizes scanning peripheral zones the driver may overlook, i.e., critical areas during driving. Our demonstration showcases enhanced perception, highlighting the potential of hybrid human-machine sensing for safer driving.

In this article, we develop a communication-control co-design framework in a wireless networked control system with multiple geographically separated controllers and controlled systems, modeled via a Poisson point process. Each controlled system consists of an actuator, plant, and sensor. Controllers receive state estimates from sensors and design control inputs, which are sent to actuators over a shared wireless channel, causing interference. Our co-design includes control strategies at the controller based on sensor measurements and transmission acknowledgments from the actuators for both rested and restless systems - systems with and without state feedback, respectively. In the restless system, controllability depends on consecutive successful transmissions, while in the rested system, it depends on total successful transmissions. We use both classical and block ALOHA protocols for channel access, optimizing access based on sensor data and acknowledgments. A statistical analysis of control performance is followed by a Thompson sampling-based algorithm to optimize the ALOHA parameter, achieving sublinear regret. We show how the ALOHA parameter influences control performance and transmission success.