Throughput capacity of two-hop relay MANETs under finite buffers

Since the seminal work of Grossglauser and Tse [1], the two-hop relay algorithm and its variants have been attractive for mobile ad hoc networks (MANETs) due to their simplicity and efficiency. However, most literature assumed an infinite buffer size for each node, which is obviously not applicable to a realistic MANET. In this paper, we focus on the exact throughput capacity study of two-hop relay MANETs under the practical finite relay buffer scenario. The arrival process and departure process of the relay queue are fully characterized, and an ergodic Markov chain-based framework is also provided. With this framework, we obtain the limiting distribution of the relay queue and derive the throughput capacity under any relay buffer size. Extensive simulation results are provided to validate our theoretical framework and explore the relationship among the throughput capacity, the relay buffer size and the number of nodes

Forest composition and growth in a freshwater forested wetland community across a salinity gradient in South Carolina, USA

Tidal freshwater forested wetlands (TFFW) of the southeastern United States are experiencing increased saltwater intrusion mainly due to sea-level rise. Inter-annual and intra-annual variability in forest productivity along a salinity gradient was studied on established sites. Aboveground net primary productivity (ANPP) of trees was monitored from 2013 to 2015 on three sites within a baldcypress (Taxodium distichum) swamp forest ecosystem in Strawberry Swamp on Hobcaw Barony, Georgetown County, South Carolina. Paired plots (20 × 25-m) were established along a water salinity gradient (0.8, 2.6, 4.6 PSU). Salinity was continuously monitored, litterfall was measured monthly, and growth of overstory trees ⩾10 cm diameter at breast height (DBH) was monitored on an annual basis. Annual litterfall and stem wood growth were summed to estimate ANPP. The DBH of live and dead individuals of understory shrubs were measured to calculate density, basal area (BA), and important values (IV). Freshwater forest communities clearly differed in composition, structure, tree size, BA, and productivity across the salinity gradient. The higher salinity plots had decreased numbers of tree species, density, and BA. Higher salinity reduced average ANPP. The dominant tree species and their relative densities did not change along the salinity gradient, but the dominance of the primary tree species differed with increasing salinity. Baldcypress was the predominant tree species with highest density, DBH, BA, IV, and contribution to total ANPP on all sites. Mean growth rate of baldcypress trees decreased with increasing salinity, but exhibited the greatest growth among all tree species. While the overall number of shrub species decreased with increasing salinity, wax myrtle (Morella cerifera) density, DBH, BA, and IV increased with salinity. With rising sea level and increasing salinity levels, low regeneration of baldcypress, and the invasion of wax myrtle, typical successional patterns in TFFW and forest health are likely to change in the future

Meta-Reinforcement Learning for Timely and Energy-efficient Data Collection in Solar-powered UAV-assisted IoT Networks

Unmanned aerial vehicles (UAVs) have the potential to greatly aid Internet of Things (IoT) networks in mission-critical data collection, thanks to their flexibility and cost-effectiveness. However, challenges arise due to the UAV's limited onboard energy and the unpredictable status updates from sensor nodes (SNs), which impact the freshness of collected data. In this paper, we investigate the energy-efficient and timely data collection in IoT networks through the use of a solar-powered UAV. Each SN generates status updates at stochastic intervals, while the UAV collects and subsequently transmits these status updates to a central data center. Furthermore, the UAV harnesses solar energy from the environment to maintain its energy level above a predetermined threshold. To minimize both the average age of information (AoI) for SNs and the energy consumption of the UAV, we jointly optimize the UAV trajectory, SN scheduling, and offloading strategy. Then, we formulate this problem as a Markov decision process (MDP) and propose a meta-reinforcement learning algorithm to enhance the generalization capability. Specifically, the compound-action deep reinforcement learning (CADRL) algorithm is proposed to handle the discrete decisions related to SN scheduling and the UAV's offloading policy, as well as the continuous control of UAV flight. Moreover, we incorporate meta-learning into CADRL to improve the adaptability of the learned policy to new tasks. To validate the effectiveness of our proposed algorithms, we conduct extensive simulations and demonstrate their superiority over other baseline algorithms

Stability analysis of differential scheme for dynamic equations of mooring cable system

The mooring cable system in plane motion can be modeled as two coupled partial differential equations, which can be numerical solved by finite difference method directly. The difference scheme is analyzed, and parameters selection for time-marching of displacement and velocity are deduced. The stability condition of the scheme is analyzed through Fourier series method, and parameters range which match stable scheme is given. Then, the parameters range is verified by a numerical example