Optimal Compression and Transmission Rate Control for Node-Lifetime Maximization

We consider a system that is composed of an energy constrained sensor node and a sink node, and devise optimal data compression and transmission policies with an objective to prolong the lifetime of the sensor node. While applying compression before transmission reduces the energy consumption of transmitting the sensed data, blindly applying too much compression may even exceed the cost of transmitting raw data, thereby losing its purpose. Hence, it is important to investigate the trade-off between data compression and transmission energy costs. In this paper, we study the joint optimal compression-transmission design in three scenarios which differ in terms of the available channel information at the sensor node, and cover a wide range of practical situations. We formulate and solve joint optimization problems aiming to maximize the lifetime of the sensor node whilst satisfying specific delay and bit error rate (BER) constraints. Our results show that a jointly optimized compression-transmission policy achieves significantly longer lifetime (90% to 2000%) as compared to optimizing transmission only without compression. Importantly, this performance advantage is most profound when the delay constraint is stringent, which demonstrates its suitability for low latency communication in future wireless networks.Comment: accepted for publication in IEEE Transactions on Wireless Communicaiton

Energy efficiency analysis of collaborative compressive sensing scheme in cognitive radio networks

In this paper, we investigate the energy efficiency of conventional collaborative compressive sensing (CCCS) scheme, focusing on balancing the tradeoff between energy efficiency and detection accuracy in cognitive radio environment. In particular, we derive the achievable throughput, energy consumption and energy efficiency of the CCCS scheme, and then formulate an optimization problem to determine the optimal values of parameters which maximize the energy efficiency of the CCCS scheme. The maximization of energy efficiency is proposed as a multi-variable, non-convex optimization problem, and we provide approximations to reduce it to a convex optimization problem. We highlight that errors due to these approximations are negligible. Subsequently, we analytically characterize the tradeoff between dimensionality reduction and collaborative sensing performance of the CCCS scheme, i.e., the implicit tradeoff between energy saving and detection accuracy. It is shown that the resulting loss due to compression can be recovered through collaboration, which improves the overall energy efficiency of the system

Team One Carbon Catcher Design Report

Overview The burning of fossil fuels largely contributes to the increase of CO2 in the atmosphere. The US Department of Transportation alone contributed almost 6 million metric tons of carbon dioxide emissions in 2018 (EIA). Due to this, this report proposes recycling captured CO2 into a base for cleaner burning fuel in order to reduce emissions from the transportation industry and many others, which has the potential to impact many areas. Extraction of atmospheric CO2 is possible through a membrane filtration system based on traditional nitrogen generation. The passive filtration system autonomously separates the CO2 from other air components, thereby reducing energy consumption. The system's working sensors and actuators utilize similar energy saving strategies, such as distributing cloud-computing services over multiple servers and mainframes to reduce computing power. The movement of air is directed by a scalable fan device, which is presented as a modular design to allow customization of fan parts to specific size and installation requirements. As an integrated device, Team 1’s Carbon Catcher operates with a high efficiency in order to maximize the commercial opportunity of converting captured CO2 into cleaner fuel while also reducing CO2 emissions and the greenhouse effect. Goal The goal of Team 1’s Carbon Catcher project proposal is to design a cost-effective, scalable, and modular atmospheric carbon dioxide removal system that is capable of being utilized in a range of urban environments and may fit a variety of different customer requirements or requests

Distributed Training Large-Scale Deep Architectures

Scale of data and scale of computation infrastructures together enable the current deep learning renaissance. However, training large-scale deep architectures demands both algorithmic improvement and careful system configuration. In this paper, we focus on employing the system approach to speed up large-scale training. Via lessons learned from our routine benchmarking effort, we first identify bottlenecks and overheads that hinter data parallelism. We then devise guidelines that help practitioners to configure an effective system and fine-tune parameters to achieve desired speedup. Specifically, we develop a procedure for setting minibatch size and choosing computation algorithms. We also derive lemmas for determining the quantity of key components such as the number of GPUs and parameter servers. Experiments and examples show that these guidelines help effectively speed up large-scale deep learning training