The use of simultaneous wireless information and power transfer (SWIPT) is a key enabler of achieving convenience and prolonging the energy supply lifetime of wireless networks. To address the low efficiencies of far-field power transfer, a reconfigurable intelligent surface (RIS) is adopted to enhance the energy harvesting (EH) performance, which can construct a favorable wireless propagation environment. In this paper, we consider an RIS-assisted SWIPT system with wireless transfer from the access point (AP) to multiple-antenna receivers, which include information receivers (IRs) and energy receivers (ERs). First, we formulate the problem of maximizing the minimum rates of the IRs as a nonconvex constrained optimization problem. For ideal and nonideal channels, we propose two different solutions. Second, we simplify the objective function and decompose the problem into several subproblems by using sorting and iterative optimization algorithms. Moreover, under optimal boundary and Karush-Kuhn-Tucker (KKT) conditions, we successfully solve this problem. The simulation results illustrate a promising approach for wireless communication by comparing ideal RIS and unsatisfactory situations with no RIS cases under various conditions.

Mobile edge computing (MEC) has emerged as a promising solution to alleviate mobile devices' (MUs') computational burden by offloading part or all of their computational tasks to a nearby edge server. To promote the wide deployment of MEC, charging the MUs and rewarding the edge server is a good mechanism to motivate the edge server to offer computing service. Current related literature usually assume a linear pricing strategy, in which the unit price in time is a linear function with the served computing capability. In this work, however, we explore the nonlinear pricing strategy for the first time, as the cost of a CPU presents super-linear feature with the computing capability. A MEC system with multiple MUs and a single edge server is considered and a two-level Stackelberg game with the leader being the edge server and the followers being MUs is formulated, whereby edge server's revenue is maximized in the upper level via optimizing the nonlinear pricing function while the defined cost function of individual MUs is minimized by deciding the amount of data for offloading and computing capability to purchase from the edge server in the lower level. Through analysis, steps of transformations, and relaxation, closed-form optimal solution for lower-level problem is derived, and the solution for upper-level problem is presented although it is non-convex. Numerical results verify the superiority of our proposed pricing strategy over traditional linear pricing strategy.

To achieve maximum throughput in Named Data Networking (NDN) with consideration of fairness among each source-sink pair, we model the routing problem in NDN as a mixed integer maximum concurrent flow problem. Taking advantage of the smart forwarding framework in NDN, we obtain an approximate optimal routing and forwarding solution with our improved Fleischer algorithm and Random Rounding (FRR) algorithm. We analyze the complexity of the Fleischer algorithm more comprehensively and give the best scaling method to achieve the least iterations. In addition, we further reduce the complexity of Fleischer algorithm so that it iterates only once, thus getting a heuristic algorithm which is called Multicommodity Multi-Path (MCMP). Using ndnSIM-based simulation, we compare our proposed routing algorithms with the existing routing algorithms, including flooding, k-shortest path, and best single path routing, and the simulation results prove that FRR and MCMP can outperform other algorithms both in throughput and latency, and it can also be concluded that the best number of paths for k-shortest path routing should be close to and less than the average node degree of the network. For studying routing optimization with caching, we conduct simulations with different in-network cache capacity and different caching policies. The simulation results show that although the LRU can achieve the best performance under the same cache capacity, the cache capacity plays a decisive role. And all routing algorithms produce better performance with caching, but the FRR still achieves the best performance, and follows by MCMP.

Low Earth Orbit (LEO) satellite networks are promising for satellite-based cloud platforms. Due to frequent link switching and long transmission distances in LEO satellite networks, applying the TCP/IP architecture introduces challenges such as packet loss and significant transmission delays. These issues can trigger excessive retransmissions, leading to link congestion and increased data acquisition delay. Deploying Named Data Networking (NDN) with connectionless communication and link-switching tolerance can help address these problems. However, the existing congestion control methods in NDN lack support for congestion avoidance, lossless forwarding, and tiered traffic scheduling, which are crucial for achieving low-delay operations in satellite-based cloud platforms. In this paper, we propose a Congestion Control method with Lossless Forwarding (CCLF). Addressing the time-varying nature of satellite networks, CCLF implements zero packet loss forwarding by monitoring output queues, aggregating packets, and prioritizing packet scheduling. This approach overcomes traditional end-to-end bottleneck bandwidth limitations, enhances network throughput, and achieves low-delay forwarding for critical Data packets. Compared with the Practical Congestion Control Scheme (PCON), the CCLF method achieves lossless forwarding at the network layer, reduces the average flow completion time by up to 41%, and increases bandwidth utilization by up to 57%.