The low Earth orbit (LEO) satellite megaconstellation can provide ubiquitous coverage and high-performance connectivity, supporting multi-slice applications with various key performance indicator (KPI) requirements. However, due to the dynamic nature of LEO satellites, limited resources, and the diverse demands of different slices, managing user association (UA) and resource allocation becomes an increasingly challenging task in areas with overlapping satellite coverage. This paper proposes a joint optimization model for UA and resource allocation in satellite networks (SLSNs). Based on mixed-integer non-linear programming (MILP), our model minimizes the total propagation delay and optimizes the demand satisfaction ratio (DSR) using a Max-Min approach to ensure each slice meets its unique throughput requirements. In addition, a visibility-aware component is incorporated to prioritize longer satellite visibility, reduce handovers, and improve network stability. Due to the computational complexity of the MILP model, we propose a heuristic-based balanced association with delay-aware bandwidth distribution (B-DAD) approach. B-DAD operates in two phases: the initial UA phase selects satellites based on a combined metric of delay, load, and visibility duration, while the residual bandwidth distribution phase reallocates unused bandwidth among associated users proportionally. Extensive simulations demonstrate that our approaches significantly improve DSR, propagation delays, transmission delays, and network stability compared to the widely adopted benchmark maximum sum of data rate (Max-SR) and Greedy methods under varying elevation angles. Our findings highlight the effectiveness of the MILP model in achieving optimal solutions and the efficiency of B-DAD as a scalable alternative for large-scale scenarios.

The modern Internet supports diverse applications with heterogeneous quality of service (QoS) requirements. Low Earth orbit (LEO) satellite constellations offer a promising solution to meet these needs, enhancing coverage in rural areas and complementing terrestrial networks in urban regions. Ensuring QoS in such networks requires joint optimization of routing, bandwidth allocation, and dynamic queue scheduling, as traffic handling is critical for maintaining service performance. This paper formulates a joint routing and bandwidth allocation problem where QoS requirements are treated as soft constraints, aiming to maximize user experience. An adaptive scheduling approach is introduced to prioritize flow-specific QoS needs. We propose a Monte Carlo tree search (MCTS)-inspired method to solve the NP-hard route and bandwidth allocation problem, with Lyapunov optimization-based scheduling applied during reward evaluation. Using the Starlink Phase 1 Version 2 constellation, we compare end-user experience and fairness between our proposed DSROQ algorithm and a benchmark scheme. Results show that DSROQ improves both performance metrics and demonstrates the advantage of joint routing and bandwidth decisions. Furthermore, we observe that the dominant performance factor shifts from scheduling to routing and bandwidth allocation as traffic sensitivity changes from latency-driven to bandwidth-driven.

Internet of Things (IoT) devices have become increasingly ubiquitous with applications not only in urban areas but remote areas as well. These devices support industries such as agriculture, forestry, and resource extraction. Due to the device location being in remote areas, satellites are frequently used to collect and deliver IoT device data to customers. As these devices become increasingly advanced and numerous, the amount of data produced has rapidly increased potentially straining the ability for radio frequency (RF) downlink capacity. Free space optical communications with their wide available bandwidths and high data rates are a potential solution, but these communication systems are highly vulnerable to weather-related disruptions. This results in certain communication opportunities being inefficient in terms of the amount of data received versus the power expended. In this paper, we propose a deep reinforcement learning (DRL) method using Deep Q-Networks that takes advantage of weather condition forecasts to improve energy efficiency while delivering the same number of packets as schemes that don't factor weather into routing decisions. We compare this method with simple approaches that utilize simple cloud cover thresholds to improve energy efficiency. In testing the DRL approach provides improved median energy efficiency without a significant reduction in median delivery ratio. Simple cloud cover thresholds were also found to be effective but the thresholds with the highest energy efficiency had reduced median delivery ratio values.

Software-defined networking (SDN) has emerged as a promising approach for managing traditional satellite communication. This enhances opportunities for future services, including integrating satellite and terrestrial networks. In this paper, we have developed an SDN-enabled framework for Low Earth Orbit (LEO) satellite networks, incorporating the Open-Flow protocol, all within an OMNeT++ simulation environment. Dynamic controller assignment is one of the most significant challenges for large LEO constellations. Due to the movement of LEO satellites, satellite-controller assignments must be updated frequently to maintain low propagation delays. To address this issue, we present a dynamic satellite-to-controller assignment (DSCA) optimization problem that continuously adjusts these assignments. Our optimal DSCA (Opt-DSCA) approach minimizes propagation delay and optimizes the number of active controllers. Our preliminary results demonstrate that the DSCA approach significantly outperforms the static satellite-to-controller assignment (SSCA) approach. While SSCA may perform better with more controllers, this scheme fails to adapt to satellite movements. Our DSCA approach consistently improves network efficiency by dynamically reassigning satellites based on propagation delays. Further, we found diminishing returns when the number of controllers is increased beyond a certain point, suggesting optimal performance with a limited number of controllers. Opt-DSCA lowers propagation delays and improves network performance by optimizing satellite assignments and reducing active controllers.

For decades, satellites have facilitated remote internet of things (IoT) services. However, the recent proliferation of increasingly capable sensors and a surge in the number deployed, has led to a substantial growth in the volume of data that needs to be transmitted via satellites. In response to this growing demand, free space optical communication systems have been proposed, as they allow for the use of large bandwidths of unlicensed spectrum, enabling high data rates. However, optical communications are highly vulnerable to weather-induced disruptions, thereby limiting their high potential. This paper proposes the use of high altitude platform station (HAPS) systems in conjunction with delay-tolerant networking techniques to increase the amount of data that can be transmitted to the ground from satellites when compared to the use of traditional ground station network architectures. The architectural proposal is evaluated in terms of delivery ratio and buffer occupancy, and the subsequent discussion analyzes the advantages, challenges and potential areas for future research.

We present a custom integrated circuit (IC) for use in miniaturized genetic test devices with optical coupling with either an optical relay lens or direct surface contact micro-fluidic channels. This IC is capable of high-voltage generation in the 70-300 V range at 10 μA. High-voltage switching circuits for mm-scale capillaries channels with up to 4 electrodes are present. Optical detection with an optical power resolution of 0.46 pW across the diode surface is available for sample detection through a 13-bit analog-to-digital converter. All systems and supporting devices communicate and are powered by a USB connection and fall within the USB 2.0 power budget of 2.5 W.

Pseudo-NMOS level-shifters consume large static current making them unsuitable for portable devices implemented with HV CMOS. Dynamic level-shifters help reduce power consumption. To reduce on-current to a minimum (sub-nanoamp), modifications are proposed to existing pseudo-NMOS and dynamic level-shifter circuits. A low power three transistor static level-shifter design with a resistive load is also presented.