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1 and L (the depth to hash each root key) The k keys preloaded on the node are such that the jth key is given by h aj (MAj) where h() denotes a one-way hash function Thus, the jth key corresponds to the Ajth root key hashed to a depth of a j This is done for each node by the TA Following this the nodes are deployed We would like to remark here that determining the keys deployed on a node as a function of the node identity will also make life easier for the adversary Using this information, the adversary can determine which nodes to compromise so as to increase his (the adversary s) knowledge of the keys from the key pool After deployment, every pair of neighboring nodes needs to gure out whether they have any common keys If the two nodes have common keys then they need to set up the session or link keys Both these steps are executed by the nodes based on information about their identities and then using the same public function F() from earlier The identity is hence the only information exchanged by the nodes We explain this again using an example Consider a node B which has to decide if it has any common keys with node A given earlier Let the identity of node B be IDB Both the nodes, A and B, exchange their identities Following this each node can calculate the sequence of k-length ordered pairs corresponding to the other Let this sequence for node B be given by F(IDB) which equals [(B1,b1), (B2,b2) , , (Bk,bk)] As in case of node A, (B1, , Bk) is a partial random permutation of integers between 1 and P (the number of root keys), while (b1, , bk) is a sequence of randomly distributed numbers between 1 and L (the depth to hash each root key) By comparing (A1 Ak) and (B1 , , Bk), the two nodes can determine the indices of the shared root keys that are present on both Let these indices of shared root keys be given as [s1, , sm] where m k Note that it is possible that m 0, which implies that the nodes do not have any common keys But otherwise if m 0, then the two nodes will have to decide the link key or the session key used to encrypt communication between themselves To do that the nodes determine di max(ai,bi) where the ai and bi values correspond to the depth of the hash for node A and node B, respectively, for the ith root key of the m shared root keys The session key between node A and node B is then obtained as K h hd1 (Ms1 )jhd2 (Ms2 )j jhdm (Msm ) We illustrate the above using a gure Consider Figure 39, where we assume that the TA has 10 root keys Further we assume that L 5 and there are three keys predeployed on every node In this gure, nodes A and C share root key indexes 5 and 10 For the key indexed 5, node A has to hash forward two times to reach the same depth as node C For the key indexed 10, node C has to hash forward one time Note that the scheme considered earlier in [17] is a special case of HARPs obtained when L 0 The authors in [18, 41] propose another probabilistic key distribution strategy The schemes here are based on the polynomial-based key predistribution protocol in [42] While the protocol in [42] was intended for group keys, the strategy has been enhanced in [18, 41] to make it feasible for a two-party case in sensor networks Consider the basic protocol in [42] applied to a two-party case In this case there exists a TA which generates a t-degree polynomial in two variables x and y denoted f(x,y) Further, this polynomial is symmetrical due to which f(x,y) f( y,x)4 The TA then computes the.

These files are created with the following naming convention: grinder_log_GID_JID ext where GID is the Grinder ID number as defined in the properties file and JID is the JVM ID number If a Grinder run specifies that there will be 2 JVMs, there will be two sets of files, one with JID 0 and another with JID 1 It is important to note that none of the timings collected by the Grinder include the sleep time between the executions of URLs The out file can be used to get a quick glance at the performance of the run for that particular Grinder/JVM run For example, the following is the summary information that will be found at the end of this file:

Final statistics for this JVM: [hostld=2,jvmld=0] (TST=Total Successful Transactions) (TPT=Total Processing Time (miliseconds)) (ART=Average Response Time (seconds))

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FIGURE 6-4c Squared-error training histories of 32 pattern-matching errors produced by a larger backpropagation network with one hidden layer and nine hidden neurons Such optimizations often converge even after temporary instabilities due to excessively large learning rates

11

(TPS=Transactions Per Second) (TUT=Total Unsuccessful Transactions) per method:

We ignore some details for simplicity such as the fact that f(x,y) is over a nite eld corresponding to a large prime number

must then find n parameters W1, W2, , Wn that minimize the sample average of g = [y(x) Y]2 This procedure is readily extended to mean-square regression of ny-dimensional pattern vectors y(x) on nx-dimensional pattern inputs x (Section 6-6) We shall approximate the desired output pattern y = Y(x) with a single neuron layer that implements

urlO, TST:90, urll, TST:90, ur!2, TST:90, ur!3, TST:90, ur!4, TST:90, ur!5, TST:90, ur!6, TST:90, ur!7, TST:90, ur!8, TST:90, TST=810 TPT=3978200 ART=044 TUT=0 Jul 12 01:53:42 TPT:1191, TPT:930, TPT:4329, TPT:6119, TPT:5731, TPT:5556, TPT:5251, TPT:4849, TPT:5826, ART:001, ART:001, ART:005, ART:007, ART-006, ART:006, ART:006, ART:005, ART:006, TPS:7557, TPS:9677, TPS:2079, TPS:1471, TPS:1570, TPS:1620, TPS:1714, TPS:1856, TPS:1545, TUT:0 TUT:0 TUT:0 TUT: 0 TUT:0 TUT:0 TUT:0 TUT:0 TUT:0

2000:

y[i]=

The dat file contains detailed information on the execution of every URL in the following format: hostld, jvmld, threadld, timesld, methodld, millis The data in this file

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