I want to implement Trust Mechanism In Wireless Sensor Networks and then calculate the residual energy of nodes.
Can anyone please help. I want to implement Subject Trust Logic as Trust Mechanism.
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Im running through the Internet, and I can't find any algorithm of counter-propagation neural network or any info about it.
Actually I need to use it in my school project, where I import the data from txt file(mostly strings) and then set the amount of neurons on every hidden layer.
Does anybody know something about that type of neural network?
Alright, this may sound completely insane, I am not an electronics guy, just asking.
As far as I'm concerned, there are two most popular technologies for close-range communication: RFID and NFC. These technologies are just emerging in the mobile phones, as you don't see a lot of devices supporting them. Is it possible to build a GSM based sensor for the purposes of detecting who's sitting at the desk?
Let me describe the context. I'm working with a mobile phone operator to build a shop as part of my studies. They are able to install any GSM transceiver anywhere, and use it legitimately.
As NFC is not quite there yet, one of my proposals to them is to let people use their phones instead of (or as they would use) any RFID card, like Oyster in London, in order to make payments, quickly change tariffs, top-up, etc. I imagine that this may be possible by making a low-power GSM base station combined with a proximity sensor.
If so, then we can get a phone user with the strongest signal, get the information from the proximity sensor to define if there is a phone in front of the sensor and thus detect who is the user. What do you think? Is it something possible?
You can definitely have low powered base stations but they would generally be for a small area in a big venue/city or for a house or room.
Any phone in the coverage area from that operator would connect to that cell if it had the best signal, so you would find it very difficult to distinguish between two users who are a couple of feet apart. I'm guessing this would not be good enough for your use case.
I want to create a functionality for suggesting connections in a social network.
In the network you can have connections and connect to other users.
I want to implement a connection suggestion functionality on the network.
I think the most basic approach to implement this is to check all my connections most occurring common connection that my user is not connected to and sugest this user to my user to connect to.
My questions is:
Is this a good basic approach for an easy connection finder?
Is there any good implementation algorithm that i can use for finding my connections most occurring user that they are connected to?
I'd try a machine learning approach for this problem.
I'll suggest two common machine learning concepts in order to solve this problem. In order for both of them to work - you need to extract features from the data (for example look at a subgraph, and friendship with each member in the subgraph is a binary feature).
The two approaches are:
Classification. In here, you are trying to find a classifier C:UserxUser->Boolean (A classifier that given two users, gives a boolean answer - should they be friends). The classification approach will require you to first manually label, or extract some classified information (A big enough set of pairs, each with a classification). The algorithm will learn this pattern, and use it to predict future inputs.
Clustering (AKA Unsupervised learning). You can try and find clusters in your graph, and suggest users to be friends with all members in their cluster.
I have to admit I never used any of these methods for friendship suggestion - so I have no idea how accurate it will be. You can use cross-validation in order to estimate the accuracy of the algorithm.
If you are interested in learning more about it - two weeks ago an on line free course has started in stanford about machine learning: https://class.coursera.org/ml-2012-002
I want to apply machine learning to a classification problem in a parallel environment. Several independent nodes, each with multiple on/off sensors, can communicate their sensor data with the goal of classifying an event as defined by a heuristic, training data or both.
Each peer will be measuring the same data from their unique perspective and will attempt to classify the result while taking into account that any neighbouring node (or its sensors or just the connection to the node) could be faulty. Nodes should function as equal peers and determine the most likely classification by communicating their results.
Ultimately each node should make a decision based on their own sensor data and their peers' data. If it matters, false positives are OK for certain classifications (albeit undesirable) but false negatives would be totally unacceptable.
Given that each final classification will receive good or bad feedback, what would be an appropriate machine learning algorithm to approach this problem with if the nodes could communicate with each other to determine the most likely classification?
If the sensor data in each individual node is generally sufficient to make a reasonable decision, they could just communicate the result and take a majority vote. If majority vote is not appropriate, you could train an additional classifier that uses the outputs of the nodes as its feature vector.
Since you want to have on-line supervised learning with feedback, you could use a neural network with backpropagation or an incremental support vector machine that adds the errors to the training set. Look into classifier biasing to deal with false-positive/false-negative trade-off.
In this instance, a neural network could be very appropriate. The inputs to the network would be each of the sensors onboard the node, along with that of its neighbors. You would calculate weights based on your feedback.
Another option (that is simpler, but can achieve good results as well) is a Gossip Algorithm. You would have to look into incorporating feedback though.
I have a data card with me.
I wanted to know, is there any way programmatically I can tell that, it is data card or it is LAN card?
I have used IFTable, I have used IListManager, INetwork manager provided by Microsoft, but not able to solve the problem.
Edited
I have one lan card and one data card connected on my system, I want to do some operation on data card, but I don't know, when my data card is connected and when my LAN card, enumerating will give me list of all connections, but how to identify LAN and Data card among them.
You should clarify your problem. What problem are you trying to solve? Why do you want to distinguish the cards?
That said, some ideas:
If you just want to identify your specific cards, every network card has a unique ID (the MAC). You can retrieve it using networking tools.
If you want to identify the type of card, you can try to get PCI information, and look for a string. But that is difficult in the general case, because there are so many different types of cards. So you'd need to clarify your question.
There is no clear distinction between the different kinds of networks anymore. On the one hand, LANs have gone wireless with WiFi. On the other hand, public networks have gone local with femtocells. Hence, it becomes increasingly difficult to distinguish the two kinds of networks, and therefore the network cards connecting to them. There is no clear boundary.