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Abstract-As small satellites become more capable through miniaturized electronics and on-board processing, constellations of low-cost satellites lunched in Low-Earth Orbit (LEO) become feasible. The increase in the number of LEO satellites drives the need for frequency coexistence between the LEO constellation systems with the already existing geostationary (GSO) satellite networks. In this context, it is crucial to design the communication links paying special attention to interference analysis. This is particularly true when the LEO satellite constellation exploit inter-satellite communication links (ISL). In this paper, a radio frequency interference analysis based on simulation of the dynamic satellite constellation is presented and the design parameters of the inter-satellite links are analyzed. The results suggest that carefully choosing the design parameters of the intersatellite links, spectrum coexistence of LEO and GSO networks may be possible.

Monitoring spatio-temporal continuous fields using wireless sensor networks (WSNs) has emerged as a novel solution. An efficient data-driven routing mechanism for sensor querying and information gathering in large-scale WSNs is a challenging problem. In particular, we consider the case of how to query the sensor network information with the minimum energy cost in scenarios where a small subset of sensor nodes has relevant readings. In order to deal with this problem, we propose a Pheromone-based In-Network Processing (PhINP) mechanism. The proposal takes advantages of both a pheromone-based iterative strategy to direct queries towards nodes with relevant information and query-and response-based innetwork filtering to reduce the number of active nodes. Additionally, we apply reinforcement learning to improve the performance. The main contribution of this work is the proposal of a simple and efficient mechanism for information discovery and gathering. It can reduce the messages exchanged in the network, by allowing some error, in order to maximize the network lifetime. We demonstrate by extensive simulations that using PhINP mechanism the query dissemination cost can be reduced by approximately 60% over flooding, with an error below 1%, applying the same in-network filtering strategy.

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