Simulation results. 4.1. Vehicle parameters and the values used in the simulation that are not taken from the actual test vehicle (implicit):
Simulation results. This section presents some numerical examples illustrating the performances of our proposed schemes and finally compared together. For simplicity, the scenario is as- sumed with a single secondary BS serving two secondary users and a single primary cell-edge user within the cognitive cell. It is also assumed that there is one primary user per primary cell which is located in the outer part of the cognitive cell, but within the close vicinity. Note that each user is equipped with a single antenna. As shown in Fig. 3.1, secondary and primary cell-edge users within the cognitive cell are located in sector 3, i.e. q=3. The experiment is done with a single scatterer, i.e, Q = 1. The angular spread of local scatters surrounding the users is to be assumed 2 degrees. The spacing distance between the array elements is λ/2. The carrier frequency is 2 GHz. The noise variance plus the intercell interference is set to 1. In this simulation, SeDuMi solver under optimisation solver CVX [6], [89] is used to attain the optimal solution for the problems stated in (3.16) and (3.21). The azimuth directions (angle of propagation with respect to the antenna array broadside) of the users as well as the angular spread due to the local scatters cor- responding to the sector of the secondary BS can be estimated using the algorithm
Simulation results. Esitmated value g(1000,0.01,w ) Simulated value L 400 350 The number of bits leaked to Eve 300 250 200 150 100 50 0 50 100 150 200 250 300 350 400 450 500 Block length w in pass 1
Simulation results. Initialization: To initialize, Ƈ chooses a random bit from the given set {0, 1} and constructs a secure hash-digest function to respond to the queries from Ɲ, where h0=hqi be the pseudorandom function, while h1 is a random function. and ∏ Training: The Ɲ chooses the nonce Rg and NIDi in the protocol, F1: Anonymity, F2: Mutual Authentication, F3: Resist Man in the middle Attack, F4: Resist unjustified failures of login attempts, F5: Supports forward secrecy, F6: Resist impersonation attack, F7: Supports Session key security, F8: Resist Denial of service attack, F9: Biometric security (3-factor authentication) F1 × × ✓ ✓ × ✓ ✓ ✓ F2 × × ✓ × ✓ ✓ ✓ ✓ F3 × × × ✓ ✓ ✓ ✓ ✓ F4 ✓ ✓ ✓ ✓ ✓ × ✓ ✓ F5 × × × ✓ × ✓ ✓ ✓ F6 × × ✓ × × ✓ ✓ ✓ F7 × × ✓ ✓ × × ✓ ✓ F8 × × × × × ✓ × ✓ F9 × × × × × ✓ × ✓ Similarly, the event beginESP (bitstring) and event endESP(bitstring) are employed by ESP to authenticate Ui. We compute the results of queries and the order of the two Ui,S ,∏ and models ∏𝑠 𝑡 S,Ui by responding the queries, pair of events remained stable. The results in Fig. 3 depict that GN,S Execute(∏𝑠 𝑡 S,GN ) and Send(∏𝑠 ,𝑚), respectively. our scheme achieves mutual authentication and session key Ui,S Ui,S • Test (∏𝑠 ): Using this query, if qh is constructed, Ɲ selects at secrecy since the session key is robust against attackers. random v ∈{0, 1}, then it responds by returning legal session key qh in case v = 0, or any random string if v =1. Or else, Ɲ returns φ, indicating null string or emptiness. R,T • Test(∏𝑡 ): Its modeling is also similar to above query. Challenge: The Ȃ submits the Test query toward Ɲ after VI. PERFORMANCE ANALYSIS In this section, we examine the performance of proposed scheme with other contemporary authentication schemes for smart grids. Table I depicts the comparative analysis of GN,S having queried the oracle Execute (∏𝑠 𝑡 ,∏ ). S,GN features and performance efficiency between our scheme and other protocols, which manifests that the schemes [5-8, 10-12] Guess: Upon having queried 𝑇𝑒𝑠𝑡 (∏𝑠 ) or 𝑇𝑒𝑠𝑡 (∏𝑡 ), the Ȃ outputs a bit Ui,S S,Ui are unable to ensure the requisite security properties of an b as 0, if it takes the responded message as valid session key, or else it outputs b as 1. Finally, Ɲ produces the b' as 0 if b'=b, or else it will return the output as b'=1. The probability analysis for b'=b is alike the analysis performed in Lemma 1. The Ȃ could win this game if it guesses the equality for b'=b having the real experiment-ba...
Simulation results. Box and whisker plot (median, first and third quartiles, range) of estimates of the survival function minus the true quantity. Comparison of estimates when using inverse Gaussian (IG), Xxxxxxx (W), Xxxxxx Xxxxx (KM) and growth curve (GC) with a threshold models at the median failure time for each experimental factor detailed below plot.
Simulation results. All the measurements and simulations were performed on a 2 GHz Pentium dual core processor with 2 Gb RAM. We consider two cases of simulations, depending on the timeout value T for the calls to the garages (see site Timer(T ) in Table I ) : 1) No timeout (equally, T is infinite) 2) T is a finite value, which is lesser than the maximum response time of a garage. Case 1: No timeouts Based on the way delays of site calls are generated, we performed two types of simulations: those in which delays generation is done by 1) bootstrapping measured values, 2) sampling a T location-scale distribution, previously fit to measured data.
Simulation results. We compare the performance of our share-assigning scheme with the simple static scheme of allowing all ingress nodes to send at the SLA rate for different scenarios. This would allow all valid configurations of traffic mix but at the same time would introduce a lot more traffic into the network than that allowed by the SLA. In the first scenario we developed a simple star core topology as shown in figure 4a. We use three source domains S1, S2 and S3 and one destination domain. In each domain there are 30 ftp traffic sources, each one attached to a separate node. The delays inside the domains are 10usec while the delays and bandwidths in the core are shown in the figure. Thus the round-trip delays for the domains will depend on the delays in the core network. All the sources S1 30Mb 30Mb
Simulation results. The experimental results of EMPMU are compared with HPG and RC policies. For comparing these two migration policies, we have tested our data sets using two-way ANOVA test powered by SPSS IBM. Each test concludes either no difference between each groups called null hypothesis or significant difference between each groups called reject hypothesis. From this observation, we have tested our samples within groups and between groups to find a critical value. We have tested all simulation results when threshold P-value=0.05 with 95% Confidence Interval (CI). Fig. 2 represents energy consumption using EMPMU policy incorporated with different utilization threshold values. Lower Utilization Threshold (LUT) starts from 10% and gets increased upto 100% utilization. Here, 0% utilization of CPU consumes total energy consumption is 3.94224KWh and 100% utilization decreased upto 0.152155KWh. Lower bound and upper bound energy consumption of 95% CI:(0.6075, 2.4798). The mean square of energy consumption is calculated between groups 1.474 and within groups 0.942. We obtained two-way ANOVA test result with significant difference between groups 0.0155. Therefore, the result is concluded that there is no significant difference between groups when P-value>0.0155 and it belongs to null hypothesis group. Moreover, while utilization of CPU loads increases, energy consumption is reduced. Fig. 3 presents SLA violations at different intervals like energy consumption. It shows that when utilization threshold increases, SLA violation also increases. The mean square of SLA for Cloud Data Centers violation is obtained between groups 5.404 and within groups 6.914. Significant difference between groups is 0.993 with 95% CI:(0.2231, 6.2164). SLA violation result clearly demonstrates that there is a significant difference between groups. Therefore, it determines the reject hypothesis group. Fig. 2. Energy Consumption using EMPMU policy
Simulation results. In this section, we demonstrate that the proposed parameterization allows us to design beamforming vectors which attain the Pareto boundaries. We also plotted the corresponding MRT strategies and maximum sum rate points, in Section 4.8.1. In Section 4.8.2, we observe the change of sum rate optimal decoding structure when the strength of the interference channel increases. We compare the sum rate performance between the optimal sum rate and the proposed simple algorithm.
Simulation results. 4.2.1 Overall evaluations across all settings r Figure 3 shows the biases and coverage probabilities of different IRA measures compared to the “true” chance-corrected IRA K among all the 9 × 5 × 5 × 5 × 5 × 5 × 5 × 4 = 562, 500 parameter constellations in our simulation study. In the boxplots, each IRA measure’s mean bias or coverage rate over 1, 000 iterations under a single simulation setting was regarded as a data point. From left to right, the 10 IRA measures were ranked based on the magni- tudes of median bias values, and we kept this order through all the result presentations for consistency. These figures give an overall impression of how different IRA measures perform when no information about the raters or the rating task is acquired. Over the vast range of settings, Xxxx’s AC1 (median bias= −0.009), Xxxx’s Y (−0.023), and Xxxxxxx et al.’s S (−0.038) show smaller median bias across scenarios compared to other IRA methods. The overall bias performances of Xxxxxxx and Xxxxxxxx’s r11, Xxx’s ρ˜, Xxxxx’x κ, Xxx Xxxx’x I2, and Xxxxx’x π are close to each other (medians vary from −0.074 to −0.084), all of which overall underestimate the “true” XXX X. Finally, the observed proportion of agreement pˆa, without any correction of chance agreement, will always overestimate K. Regarding the overall performance of each IRA measure’s interval estimate, the coverage probabilities for AC1, Y , and S will be larger than 75% under most settings. While the interval estimate for Y seems to be too conservative in some scenarios (median coverage probability=96.9%), AC1 (88.8%) and S (88.3%) show similar coverage performances across various simulation settings. Figure 4 gives an overall assessment about the closeness among the 10 interrater agree- ment methods as well as the benchmark measure K based on their estimates/values across all simulation settings. IRA methods with similar estimates across different scenarios are agglomerated into clusters. By gauging the change of within-cluster and between-cluster variabilities along the increase of cluster numbers, 3 or 4 clusters should be an optimal cutting number. The hierarchical clustering indicates that the percent agreement pˆa itself forms a cluster, which highlights its different nature compared to other chance-corrected XXX Xxxx to K 0.4 0.2 0.0 −0.2 1.00 0.75 Coverage Probability of 95% CI IRA measures Percent agreement Gwet's AC1 Xxxx's Y Xxxxxxx et al.'x X Xxxxxxx and Xxxxxxxx's r_{11} Mak's trho Xxxxx'x kappa Xx...
