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Autonomous Networking/notes/2 RFID.md

@@ -144,6 +144,7 @@ Often the number of tags in the system **is not known!**
 
 -  The total number of tags is estimated according to the outcome of the previous frame (based on Chebyshevʼs inequality)
 	 ![[Pasted image 20240928181304.png]]
+I look for the n (number of tags) value that minimizes the difference!
 
 Given N and a possible value of n, the expected number of slots with r tags is estimated as:
 ![[Pasted image 20240928181424.png]]

Autonomous Networking/notes/8.md

@@ -0,0 +1,75 @@
+### The 10-arms testbed
+- we compare different strategies to assess the relative effectiveness
+- 10 actions along the X axis
+- Y axis shows the distribution of rewards
+
+-  Each reward is sampled from a normal distribution with some mean q*(a) and variance=1
+- Each q*(a) is drawn from a normal distribution with mean=0 and variance=1
+![[Pasted image 20241025084609.png]]
+
+- q* is randomly sampled from a normal distribution
+- rewards are randomly sampled based on q
+- actions are randomly taken on exploration steps
+- to fairly compare different methods we need to perform many independent run
+- for any learning method we measure its performance over 2000 independent runs
+
+![[Pasted image 20241025084755.png]]
+
+.. add siled ...
+![[Pasted image 20241025084830.png]]
+
+#### Experiments
+- run experiments for different epsilons
+- 0
+- 0.01
+- 0.1
+
+![[Pasted image 20241025084938.png]]
+- exploring more I find the best actions
+- exploring less it will converge slowly
+- not exploring may never find the best action(s)
+
+Let's do the same experiment starting with optimistic initial values
+- we start with a high value for the rewards
+- we set q1(a) = +5 for all actions
+![[Pasted image 20241025085237.png]]
+as we can see, the system explores more at the beginning, which is good as it will find the best actions to take sooner!
+
+**Optimistic initial value method:**
+- explores more at the beginning
+- only effective for stationary problems
+	- for non-stationary problems we have to use eps-greedy
+
+### Optimism in the Face of Uncertainty
+- ...
+- easy problem:
+	- two arms, one always good and one always bad
+	- try both and done
+- hard problem:
+	- arm much better than other one but there is much noise
+	- takes really long time to disambiguate
+
+![[Pasted image 20241025085759.png]]
+which actions should we peek?
+- greedy would peek the green one
+- eps-greedy too
+- optimism in the face of uncertainty says:
+	- the more uncertain we are about an action-value, the more it is to explore that action, as it could turn out to be the best!
+	- principle: *do not take the arm you believe is best, take the one which has the most potential to be the best*
+
+![[Pasted image 20241025090344.png]]
+the brackets represent a confidence interval around q*(a). The system is confident that the value lies somewhere in the region.
+
+If region is very small, we are very certain!
+
+![[Pasted image 20241025090549.png]]
+In this situation we chose Q2 as estimated value is the highest.
+
+#### Action selection
+![[Pasted image 20241025090625.png]]
+... check slides for formula explaination ...
+
+
+- to systematically reduce uncertainity, UCB explores more at the beginning
+- UCB's exploration reduces over time, eps-greedy continues to take a random action 10% of the time
+

Autonomous Networking/notes/q&a.md

@@ -0,0 +1,54 @@
+#### Q: explain the problem of energy consumption in sensor networks
+As sensor run on batteries, energy consumption is a serious problem as we want sensors' batteries to last as long as possible. It's often challenging to replace or recharge the batteries.
+Energy consumption is caused by a several things:
+- overhearing
+- overemitting
+- idle listening
+- collisions
+- overhead caused by control packets
+- continuous operation
+- transmission distance
+
+To achieve a low energy consumption is very important to define good MAC and routing strategies.
+For MAC we can use protocols such as S-MAC, allows sensor to sleep most of the time when they are not communicating.
+S-MAC works by letting sensors do the carrier sense only for a small fraction of the time while idle. To make this work, neighbor nodes needs to be synchronized to each-other, to be able to do carrier sensing at the same time. ecc.
+
+
+#### Q: Challenges of routing in wireless sensor networks
+routing protocols must be:
+- scalable to support networks with very different sizes, and performance should not degrade increasing the size
+- wide range of node density
+- limited resources for each node
+	- low computation capability
+	- can not use too much energy
+	- nodes may even not have a global ID
+- fault tollerant
+	- a node failure should not destroy the entire network
+- support mobility as some nodes may be mobile
+A good routing protocol should also guarantee that the network will have a long lifetime, as long as possible.
+Energy consumption is very important as we seen before, for this reason, based on the needs, we can have different kind of routing protocols:
+- proactive
+- reactive
+- geo-based
+
+#### Q: Explain the difference between Framed Slotted Aloha and Tree Slotted Aloha protocols in RFID system
+Both protocols are based on Slotted ALOHA: a frame is divided in time slots, and a tag randomly choses a slot to answer, in a way to reduce collisions.
+In Frame Slotted Aloha, the number of slots in a frame is always the same. If two (or more) nodes decide to take the same slot, they create a collision. To try to address the collision, a new query is issued by the reader.
+In TSA instead, for each collision slot s, a new child frame with a smaller slot number is issued. And only the tag that decided to transmit in slot s will transmit in the same frame.
+The TSA protocol improves the system efficiency as the probability of having a collision is lower. But for both protocols, to have good performance is important to have an estimate of the number of tags to identify as we need to chose the number of slots based on it. If we have too many slots, we will have a lot of time wasted in idle slots, if we have too few slots, we will have a lot of collisions.
+
+#### Q: in a slotted aloha protocol for RFID system how is the estimated tag population participating into intermediate frames?
+Main issues:
+- total number of tags to identify is not known
+- initial frame size is set to a predefined value (e.g. 128)
+- the size of the following frames is estimated by:
+$$tags\_per\_collision\_slot=\frac{(estimated\_total\_num\_of\_tags) - (identified\_tags)}{collision\_slots}$$
+The key issue is that we don't know the total number of tags! We can estimate it with the Chebyshev inequality. The problem is that for very large tag number, it can be inaccurate.
+
+#### Q: explain the binary splitting protocol for RFID systems (discuss its performance)
+All the tags have a counter set initially to 0. When the reader sends the first query every tag responds.
+Every time a collision is generated, rags randomly increments their counter. The process repeat until a single tag or no tag responds. In this case all tags will decrement the counter.
+As each time the tags are split into two sets, we can "see" it as a binary tree, so we can count the node of the tree to get an estimation.
+$$BS_{tot}(n)=\begin{cases}1,n\le1\\ 1+\sum_{k=0}^{n}\binom{n}{k}\left(\frac12\right)^{k}\left(1-\frac12\right)^{n-k}\left(BS_{tot}\left(k\right)+BS_{tot}\left(n-k\right)\right),n>1\end{cases}$$
+
+#### Q: explain the differences between proactive and reactive routing in sensor networks. Discuss the advantages and disadvantages