A Historical Account of Four Women who Made the Internet of Things Possible

A Historical Account of Four Women who Made the Internet of Things Possible

The Internet of Things as a field has been continuously growing since 1982, when it was first thought of. Such is its speed of growth, however, that according to predictions there will be over 50 billion devices as a part of the IoT by 2020. This makes it tempting, in speaking of the field, to only focus on its present and on its future development, but I reckon it is always wise to take a moment to also reflect on the past, and to remember the people who pioneered it.

An old and heteronormative saying claims that “Behind every successful man, there is a woman”.  As a woman in CS myself, I don’t like that saying, but I espouse the thought of a similar one:  “Behind every successful innovation, there is also a woman“.  Given our modern ideals of gender equality and progress, it is not always enough to generically look back at the people who paved the way for the IoT; sometimes we have to specifically remember the media-overlooked women who did so, and to give them credit where it’s due.

The Internet of Things refers to the intelligent interconnection of various devices and machines to a larger network, or the Internet. While it comes with its  own set of inherent risks, as does any technological innovation, it certainly aspires to make our lives simpler.

This was not the work of merely one man or one woman. The IoT came into existence because of the efforts of many different people, including women. Each person discovered or created something that enabled us to move one step closer to the Internet of Things as we know it today. For this simple reason, I have decided to dedicate this essay to not just one, but four different revolutionary female computer scientists, all of whom, I believe, were instrumental to the development of the IoT.

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“Big data” challenges for software engineering evolution

In software engineering the “big data” catchphrase refers to in-homogeneous large-scale data that can stem from all software development cycles. Such data can be: source code, software bugs and errors, system logs, commits, issues from backtracking systems, discussion threads from consulting sites (e.g. stackoverflow.com), emails from mailing-lists, as well as developers’ demographic data and characteristics and user requirements and reviews. Software engineering can benefit from the aforementioned data in many ways, but there are several challenges regarding the handling of such data.

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What is topic modeling?

Topic modeling is an Information Retrieval (IR) technique that discovers representative topics from a collection of documents. Thus, we expect that logically related words will co-exist in the same document more frequently than words from different topics. For example, in a document about the space, it is more possibly to find words such as: planet, satellite, universe, galaxy, and asteroid. Whereas, in a document about the wildlife, it is more likely to find words such as: ecosystem, species, animal, and plant, landscape. But why text classification is so useful? In this blog post, we try to explain the importance of topic modeling and its use in software engineering.

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Why do mobile applications crash?

Mobile devices have become an indispensable part of our everyday lives, mastering our on-line transactions and influencing our communication with others. However, most smartphone users experience application crashes once in a while. A crash manifests when, for example, you are using your favorite application and it suddenly stops working properly or closes. Sometimes, this can be really troublesome, especially when you try to send an important message or proceed with a financial transaction. There are many reasons that can lead mobile applications to crashes—and the causes are not always tractable. This blog entry discusses the causes of application crashes in mobile devices, based on the examination of a corpus of crash reports from Android applications [1].

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