The increasing adoption of electric vehicles (EVs) presents voltage stability challenges in low-voltage (LV) residential distribution networks. EV aggregators can mitigate these issues by coordinating EV charging and discharging while enhancing economic returns through participation in day-ahead market with ancillary services. This paper proposes a novel multi-objective optimization (MOO) approach to maximize the aggregator’s profit including revenues from energy arbitrage, reserve capacity, regulation services and battery degradation costs while simultaneously minimizing voltage deviations within the distribution network. An augmented epsilon-constraint (AUGMENCON) method is implemented to explore the optimal trade-offs between profitability and voltage stability. The implemented method outperforms the Non-dominated Sorting Genetic Algorithm II (NSGA-II) in producing a more non-dominated Pareto front. The methodology is validated on an IEEE 33-bus LV residential network using the MATPOWER toolbox in MATLAB 2024b, demonstrating the feasibility and effectiveness of balancing economic incentives with voltage regulation constraints.

Real-time mobile applications such as AR/VR, cloud gaming, and collaborative robotics rely on edge computing to maintain ultra-low latency, yet they can suffer noticeable service interruptions when users move because the application's session state must migrate to a new edge site. We present EdgeWarp, a system that delivers instant mobility for stateful edge applications over 5G while laying the groundwork for 6G. EdgeWarp employs a two-step synchronization protocol that proactively mirrors application state at edge sites predicted to serve the user next, sharply reducing transfer delays, and it signals each session's latency budget to the 5G control plane so that critical flows are prioritized during handover. Experiments with real applications and 4G/5G radio traces show that EdgeWarp cuts mobility-induced downtime by up to 15.4×, charting a path toward zero-downtime edge computing—a prerequisite for emerging 6G scenarios such as holographic telepresence, tactile Internet, and large-scale digital twins. We have made our anonymized code publicly accessible here.

Electricity smart meters have been widely adopted primarily for billing purposes. These meters provide a net-metering of the residential unit without distinguishing between the energy consumption or generation. As the penetration of distributed solar photovoltaic is expected to account for at least one-fourth of the energy mix in 2050, the visibility of the distributed energy resources to distribution network operators is vital. The disaggregation of behind-themeter net metering estimates the energy consumption and generation of residential units which enhances the observability of the low voltage network and provides analytics to support decision making. In this paper, we train a feedforward artificial neural network (ANN) to disaggregate the net metering data into energy consumption and generation. This model can be integrated as part of the distribution network operator tools for supporting decision making. The results show that ANN can disaggregate energy consumption and generation with an average RMSE and MAE performance of 0.1 when applied on real datasets.

5G and next-generation cellular networks aim to support tactile internet to enable immersive and real-time applications by providing ultra-low latency and extremely high reliability. This imposes new requirements on the design of cellular core networks. A key component of the cellular core is the control plane. Time to complete cellular control plane operations (e.g., mobility handoff, service establishment) directly impacts the delay experienced by end-user applications. In this paper, we design Neutrino, a cellular control plane that provides users an abstraction of reliable access to cellular services while ensuring lower latency. Our testbed evaluations based on real cellular control traffic traces show that Neutrino provides an improvement in control procedure completion times by up to$3.1\times $without failures, and up to$5.6\times $under control plane failures, over existing 5G. We also show how these improvements translate into improving end-user application performance: for AR/VR applications and self-driving cars, Neutrino improves performance by up to$2.5\times $and$2.8\times $, respectively.