In this paper, lossless time-series data compression scheme with bit-back asymmetric numeral systems (BB-ANS) is proposed for massive smart meter environment. As smart meters increase in deployment and connectivity, an efficient compression method is needed to transmit and save big data. Bit-back coding was introduced as a novel compression method using bayesian inference modeling. Recently, bit-back coding is combined with asymmetric numeral systems (ANS) which are stack-like structures, showing significant compression gains on several cases. ANS is an approach for entropy coding combining Huffman coding and arithmetic coding which has a first-in-last-out (FILO) form suited for bit-back coding. Compared to other compression methods, bit-back coding effectively shares priorly learned probabilistic models for encoder and decoder. In this study, variational auto-encoder (VAE) is customized to jointly learn approximate posterior and likelihood between message and latent variables. Thus, the smart meter data can be efficiently updated within finite length time-intervals. The proposed scheme is evaluated with the actual smart meter dataset and the results demonstrate the superiority of BB-ANS in compression ratios over other state-of-the-art lossless compression schemes. To the best of our knowledge, this study is the first attempt to apply bit-back coding to time-series smart metering data, enabling efficient data compression with deep generative models.

For machine type communication with random access (RA) protocol, finding optimal policy using deep reinforcement learning (DRL) is being actively investigated for various quality of service requirements. In particular, it was shown that throughput-based reward function in DRL can derive policies that outperform conventional exponential backoff (EB)-based algorithms in terms of throughput and fairness. However, age of information (AoI), which is a measure of the freshness of data, was not addressed in the process of training the DRL agent and only used as a measure of fairness. It has been theoretically proven that, even in the simplest queuing system, maximizing throughput does not guarantee minimizing AoI. In this paper, we proposed a novel DRL scheme to directly optimize AoI in a slotted ALOHA RA channel. By taking into account urgency and packet age as extra local information for a reward, AoI could be improved while preserving the throughput. Numerical simulations showed that the proposed DRL approach could achieve an improvement of 22.04% in AoI performance, with a marginal throughput loss of around 3.95%, compared to the existing throughput-based DRL method.