Can you give example applications using single bin sliding DFT?
I do not want to do FFT since I only need to see a single frequency bin and also FFT is not suited well for real-time applications. I see that there are sliding Goertzel and sliding single bin DFT methods


FFT is just an implementation of the DFT. So, for the same reasons you don't want the FFT you wouldn't want any other DFT. 

Goertzel is basically a way of getting the same result any DFT would have given you, but only for a single bin, exactly. Goertzel's latency, when implemented in parallel hardware, is the same as the overall DFT's, just with less ressource usage. In software, you'd use a bunch of operations.

To detect a single tone, my recommendation here would simply be a correlator (which happens to be the same as a matched filter) with the tone of interest. Basically, take the tone you're interested in, and use it as filter taps (apply windowing to taste). You can easily shift in single samples into such a filter. If you don't window (==rectangular window) and convolve a complex sinusoid with a whole number of samples as period, you get what you'd get as a single bin DFT: it's the same formula.

The whole truth, though, is that you don't really get a useful estimate for the presence of a periodic signal with every new sample – no matter how you twist and turn this, it's always a rate-reducing operation ($n$ samples go in, 1 goes out).

So, you might want to consider your definition of "real-time detection". How could one sample change the fact that there's a let's say 200-samples-long periodicity in the signal?

Depending on what you implement this on (an FPGA? ASIC? DSP? MCU? PC-style computer?), you'd want to pick a different implementation, and you'd be getting samples in buffers, anyway – so, I'll go out on a limb here and claim that if you are writing software, you can probably get away with a simple narrowband FIR (and you'd tune that bandwidth and transition width to make your detector robust to slight deviations in frequency), and it's often most efficient to implement that using fast convolution based on the FFT.