RESUMEN

Satellite communication is inevitable due to the Internet of Everything and the exponential increase in the usage of smart devices. Satellites have been used in many applications to make human life safe, secure, sophisticated, and more productive. The applications that benefit from satellite communication are Earth observation (EO), military missions, disaster management, and 5G/6G integration, to name a few. These applications rely on the timely and accurate delivery of space data to ground stations. However, the channels between satellites and ground stations suffer attenuation caused by uncertain weather conditions and long delays due to line-of-sight constraints, congestion, and physical distance. Though inter-satellite links (ISLs) and inter-orbital links (IOLs) create multiple paths between satellite nodes, both ISLs and IOLs have the same issues. Some essential applications, such as EO, depend on time-sensitive and error-free data delivery, which needs better throughput connections. It is challenging to route space data to ground stations with better QoS by leveraging the ISLs and IOLs. Routing approaches that use the shortest path to optimize latency may cause packet losses and reduced throughput based on the channel conditions, while routing methods that try to avoid packet losses may end up delivering data with long delays. Existing routing algorithms that use multi-optimization goals tend to use priority-based optimization to optimize either of the metrics. However, critical satellite missions that depend on high-throughput and low-latency data delivery need routing approaches that optimize both metrics concurrently. We used a modified version of Kleinrock's power metric to reduce delay and packet losses and verified it with experimental evaluations. We used a cognitive space routing approach, which uses a reinforcement-learning-based spiking neural network to implement routing strategies in NASA's High Rate Delay Tolerant Networking (HDTN) project.

Asunto(s)

RESUMEN

The Licklider transmission protocol is a point-to-point communication protocol designed for space links, which commonly involve extreme delays, disruptions, and lossy transmissions. The protocol sends application data in blocks, which in turn are sent in segments. It achieves reliable block delivery through multiple transmission rounds, each one re-sending the segments lost during the previous round. This retransmission process drives protocol performance. We derive exact and approximate methods to find the average number of rounds per block. Then, we estimate the block delivery time and other metrics using this value. We found that the common practice of matching segment lengths to the maximum transfer unit of the link layer may lead to suboptimal performance. The models provide accurate protocol performance prediction, which can help to optimize protocol parameters for specified operating conditions.

RESUMEN

Work within next generation networks considers additional network convergence possibilities and the integration of new services to the web. This trend responds to the ongoing growth of end-user demand for services that can be delivered anytime, anywhere, on any web-capable device, and of traffic generated by new applications, e.g., the Internet of Things. To support the massive traffic generated by the enormous user base and number of devices with reliability and high quality, web services run from redundant servers. As new servers need to be regularly deployed at different geographical locations, energy costs have become a source of major concern for operators. We propose a cost aware method for routing web requests across replicated and distributed servers that can exploit the spatial and temporal variations of both electricity prices and the server network. The method relies on a learning automaton that makes per-request decisions, which can be computed much faster than regular global optimization methods. Using simulation and testbed measurements, we show the cost reductions that are achievable with minimal impact on performance compared to standard web routing algorithms.

RESUMEN

This paper explores the feasibility of a spiking neural network-based approach to cognitive networking, that is potentially suitable for low-power neuromorphic chips. We discuss the design of a cognitive network controller (CNC), which can dynamically optimize the selection of resources for recurrent network tasks, based on both its assigned objectives and observations of the actual performance achieved by each resource. We present a coding strategy for the action decisions based on the time-to-fire of spikes, a learning algorithm, and a regulation method to keep synapse strengths within an adequate range. To evaluate the proposed method, we apply the CNC to a challenged network environment using simulation. In this scenario, the CNC requires to optimize the average file transfer time over a multichannel space communication link, which is available only for a time window because of orbital dynamics. Compared to conventional methods, we show that the CNC achieves its objective for a broad range of offered loads. We examine the impact of key system factors that include learning and space protocol parameters. The proposed CNC potentially fosters the development of new cognitive networking applications.

RESUMEN

This paper addresses two scalability problems related to the cognitive map of packets in ad hoc cognitive packet networks and proposes a solution. Previous works have included latency as part of the routing goal of smart packets, which requires packets to collect their arrival time at each node in a path. Such a requirement resulted in a packet overhead proportional to the path length. The second problem is that the multiplicative form of path availability, which was employed to measure resources, loses accuracy in long paths. To solve these problems, new goals are proposed in this paper. These goals are linear functions of low-overhead metrics and can provide similar performance results with lower cost. One direct result shown in simulation is that smart packets driven by a linear function of path length and buffer occupancy can effectively balance the traffic of multiple flows without the large overhead that would be needed if round-trip delay was used. In addition, energy-aware routing is also studied under this scheme as well as link selection based on their expected level of security.