With the integration of communication and computing, it is expected that part of the computing is transferred to the transmitter side. In this paper we address the general problem of Frequency Modulation (FM) for function approximation through a communication channel. We exploit the benefits of the Discrete Cosine Transform (DCT) to approximate the function and design the waveform. In front of other approximation schemes, the DCT uses basis of controlled dynamic, which is a desirable property for a practical implementation. Furthermore, the proposed modulation allows to recover both the measurement and the function in a single transmission. Our experiments show that this scheme outperforms the double side-band (DSB) modulation in terms of mean squared error (MSE). This can also be implemented with an agnostic receiver, in which the function is unkown to the receiver. Finally, the proposed modulation is compatible with some of the existing transmission technologies for sensor networks.
In high-resolution Earth observation imagery, Low Earth Orbit (LEO) satellites capture and transmit images to ground to create an updated map of an area of interest. Such maps provide valuable information for meteorology and environmental monitoring, but can also be employed for realtime disaster detection and management. However, the amount of data generated by these applications can easily exceed the communication capabilities of LEO satellites, leading to congestion and packet dropping. To avoid these problems, the Inter-Satellite Links (ISLs) can be used to distribute the data among multiple satellites and speed up processing. In this paper, we formulate a satellite mobile edge computing (SMEC) framework for real-time and very-high resolution Earth observation and optimize the image distribution and compression parameters to minimize energy consumption. Our results show that our approach increases the amount of images that the system can support by a factor of 12× and 2× when compared to directly downloading the data and to local SMEC, respectively. Furthermore, energy consumption was reduced by 11% in a real-life scenario of imaging a volcanic island, while a sensitivity analysis of the image acquisition process demonstrates that energy consumption can be reduced by up to 90%.
Modern satellites deployed in low Earth orbit (LEO) accommodate processing payloads that can be exploited for edge computing. Furthermore, by implementing inter-satellite links, the LEO satellites in a constellation can route the data end-toend (E2E). These capabilities can be exploited to greatly improve the current store-and-forward approaches in Earth surveillance systems. However, they give rise to an NP-hard problem of joint communication and edge computing resource management (RM). In this paper, we propose an algorithm that allows the satellites to select between computing the tasks at the edge or at a cloud server and to allocate an adequate power for communication. The overall objective is to minimize the energy consumption at the satellites while fulfilling specific service E2E latency constraints for the computing tasks. Experimental results show that our algorithm achieves energy savings of up to 18% when compared to the selected benchmarks with either 1) fixed edge computing decisions or 2) maximum power allocation.
Modern satellites deployed in low Earth orbit (LEO) accommodate processing payloads that can be exploited for edge computing. Furthermore, by implementing inter-satellite links, the LEO satellites in a constellation can route the data end-toend (E2E). These capabilities can be exploited to greatly improve the current store-and-forward approaches in Earth surveillance systems. However, they give rise to an NP-hard problem of joint communication and edge computing resource management (RM). In this paper, we propose an algorithm that allows the satellites to select between computing the tasks at the edge or at a cloud server and to allocate an adequate power for communication. The overall objective is to minimize the energy consumption at the satellites while fulfilling specific service E2E latency constraints for the computing tasks. Experimental results show that our algorithm achieves energy savings of up to 18% when compared to the selected benchmarks with either 1) fixed edge computing decisions or 2) maximum power allocation.
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