Cell-free massive MIMO (CF-mMIMO) ensures uniform service across large areas by jointly serving user equipments (UEs) with densely distributed access points (APs). Dynamic cooperation clustering (DCC) provides a scalable framework for CF-mMIMO, but extending it to multiple central processing units (CPUs) increases complexity to the order of the total APs, leading to excessive signaling overhead. To address this, we propose multi-CPU DCC (MCC), a scalable approach that reduces large-scale fading coefficient (LSFC) exchanges from the total APs to a single LSFC. Using this shared LSFC, MCC estimates remaining AP–UE LSFCs to form a user-centric AP cluster while limiting signaling. Experimental results show that MCC achieves 99% of the performance of an idealized DCC while reducing UE–AP, AP–CPU, CPU–CPU, and total signaling by 74.06%, 51.72%, 65.56%, and 57.93%, respectively. Thus, MCC enhances scalability in multi-CPU environments while maintaining performance and minimizing signaling overhead.

Safe motion planning algorithms are necessary for deploying autonomous robots in unstructured environments to prevent harm to humans and avoid damage to nearby objects. Generating these motion plans in real-time is also important to ensure that the robot can adapt to sudden changes in its environment. Many trajectory optimization methods introduce heuristics that balance safety and real-time performance, potentially increasing the risk of the robot colliding with its environment. This paper addresses this challenge by proposing Conformalized Reachable Sets for Obstacle Avoidance With Spheres (CROWS). CROWS is a novel real-time, receding-horizon trajectory planner that generates probablistically-safe motion plans. Offline, CROWS learns a novel neural network-based representation of a sphere-based reachable set that overapproximates the swept volume of the robot's motion. CROWS then uses conformal prediction to compute a confidence bound that provides a probabilistic safety guarantee on the learned reachable set. At runtime, CROWS performs trajectory optimization to select a trajectory that is probabilstically-guaranteed to be collision-free. We demonstrate that CROWS outperforms a variety of state-of-the-art methods in solving challenging motion planning tasks in cluttered environments while remaining collision-free. Code and video demonstrations can be found at https://roahmlab.github.io/crows/.

Cell-free massive multiple-input multiple-output (CF-mMIMO) can provide uniformly great service to user equipment (UE) by utilizing a large number of distributed access points (APs) connected to a single central processing unit (CPU). For addressing scalability and feasibility issues inherent in single-CPU CF-mMIMO systems, multiple-CPU CF-mMIMO (MC-CF-mMIMO) has been considered. However, the MC-CF-mMIMO system inevitably encounters performance degradation at the boundary area of the CPUs, referred to as the CPU edge. Cooperation among CPUs can improve the performance in the CPU edge region; however, scalability problems resurface owing to inter-CPU information sharing during the cooperation process. In this study, we propose a scalable CPU cooperation scheme that focuses on power control to address the performance degradation issue in the CPU edge region. Initially, we propose a policy estimation scheme for other CPUs to reduce the overhead of information sharing. Based on these estimated policies, each CPU can independently derive power control policies for the max-min optimization problem without simultaneous information sharing. Simulation results show that the proposed power control scheme achieves a performance improvement of up to 4.3234 dB in terms of CPU edge region performance and minimum spectral efficiency compared to baseline schemes based on the degree of CPU cooperation.

Cell-free massive MIMO (CF-mMIMO) provides flattened spectral efficiency throughout the network. Recently, multi-CPU CF-mMIMO (MC-CF-mMIMO) has been considered for addressing a scalability problem. However, MC-CF-mMIMO now suffers from a performance degradation problem in the boundary of the CPU overage, referred the CPU-edge. In this study, we analyze and improve the performance of MC-CF-mMIMO, focusing on the CPU-edge. First, we present a simple CPU cooperation scheme to mitigate the performance degradation problem on the CPU-edge. Then, we provide the closed-form expression of the downlink spectral efficiency according to the precoding schemes. Experimental results indicate that performance without CPU cooperation on the CPU-edge is degraded by 28.66% compared with CPU cooperation.

One significant security challenge in vehicular networks is defending against malicious members’ attacks, including insiders and compromised authorities. Insiders are legitimate vehicles who have passed the registration process. Since they can exploit all the information related to the network and other members’ communication, it is easier to perform various attacks with a high impact. In addition, an authority takes charge of registering and managing legitimate vehicles. Thus, if the authority is compromised, it will cause significant damage to the system, including the leaking of private information, such as identity, location, and membership. Many authentication schemes have been proposed to protect vehicular communication from these security issues. However, most existing schemes still face the vulnerability of malicious members. Furthermore, most conventional schemes require additional interactions between the vehicles and infrastructure for authentication, which can cause communication overheads. To overcome these issues, we propose a novel blockchain-based one-time authentication scheme to protect vehicular communication against malicious members. One-time authentication provides higher security and efficiency as every message is authenticated with different proof at a time. We use publicly verifiable secret sharing with blockchain for this property, which brings two benefits. First, it prevents even an authority from obtaining members’ identities by distributing encrypted shares instead of their real identities. Second, it enables robust vehicular communication against insiders’ attacks by allowing a vehicle to send unique proof generated from its private information with messages. Receivers can authenticate the messages by comparing attached values to the information through the blockchain in a noninteractive manner. Security analysis shows that our scheme assures secure vehicle-to-everything communication against insider attacks, and efficiency analysis shows how both authentication and consensus delay change.

Group Key Exchange (GKE) is an important tool to develop secure multi-user applications such as group text messages, ad-hoc networks, and so on. Most of the currently deployed GKE schemes are synchronous, i.e., they require all the participants to be online during their execution. However, with more battery-powered devices being used in such applications, the synchronicity requirement is challenging to fulfill. To fill the gaps, asynchronous GKE schemes have been introduced in the literature. Nevertheless, the currently available asynchronous and synchronous GKE schemes rely on Trusted Third Parties (TTPs) for key establishment and management. To this end, reliance on TTPs is a serious shortcoming since TTPs are well known to be the single point of failure. Furthermore, the existing GKE schemes require participants to perform all computations, which can degrade the performance of resource-constrained devices such as Internet of Things (IoT) devices. To solve these problems, in this paper, we propose an asynchronous GKE scheme that uses blockchain and smart contracts to store the security keys-related material and reduce the computational load of the participants. Furthermore, our proposed scheme provides Perfect Forward Secrecy (PFS) and Post-Compromised Security (PCS). Our implementation on Ethereum shows that the proposed scheme can scale to more than 100 participants when combined with a distributed storage system.

Group Key Agreement (GKA) mechanisms play a crucial role in realizing various applications in different networks, such as sensor networks and the Internet of Things (IoT). To be suitable for IoT, a GKA must satisfy several critical requirements. First, a GKA must be robust against a compromised device attack and satisfy essential secrecy definitions without the existence of a Trusted Third Party (TTP). TTP is often used by IoT devices to establish ad hoc networks securely, and usually, these devices are resource-constrained. Second, the GKA must be able to distribute session keys successfully, even with offline devices. Third, a GKA must reduce the burden of heavy cryptographic computations for IoT devices. Based on these observations, we propose a new GKA scheme that satisfies all the requirements above. The proposed scheme leverages smart contracts to alleviate the computational and storage overheads on IoT devices induced by cryptographic functions. It also brings the advantage of asynchronism such that offline devices will be able to compute the group key once they are online.