Opportunities of hybrid random access protocols for M2M communications in LTE/LTE-A networks

Machine-to-machine (M2M) communications on Long-term evolution (LTE) networks form a substantial part for the Internet-of-things (IoT). The random access procedure is the first step for M2M devices to access network resources. Many researchers have attempted to improve the efficiency of the random access procedure. This work revisits the performance of the hybrid random access protocols which combine congestion control techniques with collision resolution techniques. In particular, we investigate two hybrid protocols. The first one combines the pre-backoff (PBO) with tree random access (TRA), and the second one combines dynamic access barring (DAB) with TRA. The probability analysis is presented for both protocols. The performance is evaluated based on the access success rate, the mean throughput, the mean delay, the collision rate and the mean retransmissions. The simulation results show that the hybrid protocols achieve the highest success rate and throughput with moderate delay and low collision rates with a lower mean number of retransmissions compared to three benchmarks that apply either a congestion control or a collision resolution. The opportunities of future developments of hybrid protocols are listed at the end of this paper to highlight the issues that could be investigated to improve the performance of hybrid random access protocols.

Social: An Algorithmic Stable Coin for Social Influence

Social is an algorithmic stable coin for social influence, built off of UMA or Universal Market Access Protocols, CloutContracts (CCS), and on the Celo network. It is based off of quantitative algorithmic stabilization provided by a social coin's perceived cryptographic value. Social is a utility and this technology is conceptual. How Social works, is by integrating statistical averages for CCS and social tokens staked within its network. It integrates universal market access in regards to CCS data (CloutContracts is a smart contracts platform for social influencers and creators). Social as an algorithmically pegged stable coin, will eventually create a standard for social influence. Decentralized finance applications might even peg cryptographic value to Social as a utility. Social can integrate decentralized oracles in order to process data much quicker over time, once the network becomes large enough. Since CloutContracts integrates social networks across various different places such as DeSo/BitClout, Minds, Peepeth, Steemit, etc., one can eventually establish some sort of cryptographic metric in regards to social influence, and develop complex algorithms centered around social physics and human behavioral processes. The same type of mathematical models that apply to quantitative algorithms in the stock market for example, can apply to social influence. The same also applies for mathematical models modeled after games like chess and go. Social as a stable coin, creates another complexity that CloutContracts can use to create new mathematical standards around market access data that it already has, and could be quite critical. Social influence as a market, and as some sort of utility, can then be looked at as either a metric, mathematical bet, or speculative model for new forms of political and human societies.

An Essay on the Challenges of Doing Education Research in Canada

In this essay, I discuss the challenges faced by Canadian researchers in trying to undertake research, particularly in the area of education. I begin by focusing on the issue of data availability (with focus on the lack of race data in Canada) and the extreme limitations that these issues place on the potential for research on important Canadian education issues and then discuss what I regard as hypervigilant data access protocols for Canadian data sets. I then turn to practical issues that arise when comparing education data across cities and countries and the process of “harmonizing” the data. I address the compromises that must be made when attempting to make data comparable across different sites. I conclude by discussing how the larger context in which education occurs must be considered when understanding observed comparative differences between educational outcomes.

Hard Numbers: Open Consumer Price Database

We document a new source of consumer price microdata. The new database allows researchers studying consumer price behaviour to access current and granular raw statistical observations. The range of observed prices fully covers goods and services of the Rosstat’s CPI sample and extends beyond it. In this paper, we pursue two objectives. First, we describe the data collection mechanism, data structure, and their access protocols, as well provide four complete illustrations of their application using open API: i) training machine models of product classification based on text labels, ii) real-time tracking of product prices, iii) estimating hedonic regressions for product groups, and iv) calculating arbitrary analytical price indices. Second, we share a set of basic skills and technologies for the benefit of researchers interested in creating their own sources of alternative data.