Routing Optimization Based on OSPF in Multi-Layer Satellite Network

Multi-layer satellite network has become a hot spot for its wider coverage and higher bandwidth level. However, due to the frequent link changes and complexity of network, it is hard to find out a mechanism to handle well on long delay and high packet loss level. This paper proposes an optimized OSPF protocol called OOWLP to eliminate unnecessary routing convergence to optimize the packet loss level and delay ultimately. Link plan table, which records link contacting plan, will be used to update the link state database periodically so that we can eliminate the flooding procedure caused by scheduled link changes. On the other hand, Constrained Shortest Path First (CSPF) will be used to get business differentiated routes in multi-layer satellite network to optimized the throughput capacity in congestion scenario. We divide the sending packets into different businesses and get the routes for each business with longer duration limited by remaining bandwidth. Simulation results show that in normal scenario, average packet loss rate and delay performance are improved 17.42%, 51.44ms respectively, average packet loss rate and throughput capacity performance are optimized 79.05%, 9.81Mbps respectively in congestion scenario compared to standard OSPF. As a result, the proposed mechanism is able to shorten the average delay and lower the packet loss level in multi-layer satellite network.

A Review of Routing Algorithms Based on Multi-Layer Satellite Networks

Compared with single layer satellite network, satellite distribution of multi-layer satellite networks in the double layer or multilayer orbital plane, combines all the advantages of engaging subjects of the satellite, therefore contains its low vulnerability, good robustness, high stability, high spectrum efficiency and system throughput, channel characteristics such as large capacity, in the satellite network is a very promising direction. Multilayer satellite network also has some inevitable disadvantages, that is, the number of nodes and the number of links is large, so it has the characteristics of frequent changes in network topology, which requires higher routing algorithm. There are many researches on routing algorithms based on multi-layer satellite networks at home and abroad. This paper introduces the general situation of multi-layer satellite network, classifies the existing routing algorithms of multi-layer satellite network according to different standards, analyzes several typical routing algorithms in detail, points out the advantages and disadvantages, and summarizes the future development trend.

Real-time alerts from AI-enabled camera traps using the Iridium satellite network: a case-study in Gabon, Central Africa

Efforts to preserve, protect, and restore ecosystems are hindered by long delays between data collection and analysis. Threats to ecosystems can go undetected for years or decades as a result. Real-time data can help solve this issue but significant technical barriers exist. For example, automated camera traps are widely used for ecosystem monitoring but it is challenging to transmit images for real-time analysis where there is no reliable cellular or WiFi connectivity. Here, we present our design for a camera trap with integrated artificial intelligence that can send real-time information from anywhere in the world to end-users. We modified an off-the-shelf camera trap (Bushnell™) and customised existing open-source hardware to rapidly create a 'smart' camera trap system. Images captured by the camera trap are instantly labelled by an artificial intelligence model and an 'alert' containing the image label and other metadata is then delivered to the end-user within minutes over the Iridium satellite network. We present results from testing in the Netherlands, Europe, and from a pilot test in a closed-canopy forest in Gabon, Central Africa. Results show the system can operate for a minimum of three months without intervention when capturing a median of 17.23 images per day. The median time-difference between image capture and receiving an alert was 7.35 minutes. We show that simple approaches such as excluding 'uncertain' labels and labelling consecutive series of images with the most frequent class (vote counting) can be used to improve accuracy and interpretation of alerts. We anticipate significant developments in this field over the next five years and hope that the solutions presented here, and the lessons learned, can be used to inform future advances. New artificial intelligence models and the addition of other sensors such as microphones will expand the system's potential for other, real-time use cases. Potential applications include, but are not limited to, wildlife tourism, real-time biodiversity monitoring, wild resource management and detecting illegal human activities in protected areas.

Reinforcement Learning-Based Routing Algorithm in Satellite-Terrestrial Integrated Networks

Satellite-terrestrial integrated network (STIN) is an indispensable component of the Next Generation Internet (NGI) due to its wide coverage, high flexibility, and seamless communication services. It uses the part of satellite network to provide communication services to the users who cannot communicate directly in terrestrial network. However, existing satellite routing algorithms ignore the users’ request resources and the states of the satellite network. Therefore, these algorithms cannot effectively manage network resources in routing, leading to the congestion of satellite network in advance. To solve this problem, we model the routing problem in satellite network as a finite-state Markov decision process and formulate it as a combinatorial optimization problem. Then, we put forth a Q-learning-based routing algorithm (QLRA). By maximizing users’ utility, our proposed QLRA algorithm is able to select the optimal paths according to the dynamic characteristics of satellite network. Considering that the convergence speed of QLRA is slow due to the routing loop or ping-pong effect in the process of routing, we propose a split-based speed-up convergence strategy and also design a speed-up Q-learning-based routing algorithm, termed SQLRA. In addition, we update the Q value of each node from back to front in the learning process, which further accelerate the convergence speed of SQLRA. Experimental results show that our improved routing algorithm SQLRA greatly enhances the performance of satellite network in terms of throughput, delay, and bit error rate compared with other routing algorithms.