This paper studies the complexity of operations on finite automata and the complexity of their decision problems when the alphabet is unary. Let $n$ denote the maximum of the number of states of the input finite automata considered in the corresponding results. The following main results are obtained: (1) Given two unary NFAs recognising $L$ and $H$, respectively, one can decide whether $L \subseteq H$ as well as whether $L = H$ in time $2^{O((n \log n)^{1/3})}$. The previous upper bound on time was $2^{O((n \log n)^{1/2})}$ as given by Chrobak (1986), and this bound was not significantly improved since then. (2) Given two unary UFAs (unambiguous finite automata) recognising $L$ and $H$, respectively, one can determine a UFA recognising $L \cup H$ and a UFA recognising complement of $L$, where these output UFAs have the number of states bounded by a quasipolynomial in $n$. However, in the worst case, a UFA for recognising concatenation of languages recognised by two $n$-state UFAs, uses $2^{Θ((n \log^2 n)^{1/3})}$ states. (3) Given a unary language $L$, if $L$ contains the word of length $k$, then let $L(k)=1$ else let $L(k)=0$. Let $ω_L$ be the $ω$-word $L(0)L(1)\ldots$ and let $\cal L$ be a fixed $ω$-regular language. The last section studies how difficult it is to decide, given an $n$-state UFA or NFA

Testbeds remain indispensable instruments for experimentally evaluating IoT-oriented low-power wireless networking solutions. With the evolution of the field, they are increasingly expected to match envisioned deployment conditions of such solutions, notably in terms of scale. However, large-scale testbeds are scarce, likely because they have been believed to be expensive. This paper argues that this belief need no longer be justified by presenting the architecture and basic properties of 1KT, our new smart-building IoT testbed for solutions utilizing IEEE 802.15.4 and Bluetooth Low Energy. It comprises 1000 experimental devices deployed directly in human spaces of 168 rooms on all 5 floors of a sizable building. At the same time, its cost is relatively low considering the scale.

Quantified crowd, a vision in which on-body sensors of nearby people collaborate to detect various phenomena within a crowd and produce feedback, is an emerging research area. One of the issues that impedes progress in this new area is a lack of a broadly applicable experimental platform, such as the platforms that enabled research on wireless sensor networks. In this paper, we aim to address this issue by presenting such an experimental hardware-software platform. Not only does the platform introduce custom badge-form low-power devices that feature a number of sensors relevant to quantified crowd, but it also ensures that these devices can interoperate with commercial off-the-shelf smartphones, wristbands, and other devices for self-quantification. In effect, it constitutes a powerful experimental instrument, as we show in a preliminary real-world evaluation.