Load Balanced Clusters for Efficient Mobile Computing

Mobile computing is distributed computing that involves components with dynamic position during computation. It bestows a new paradigm of mobile ad hoc networks (MANET) for organizing and implementing computation on the fly. MANET is characterized by the flexibility to be deployed and functional in “on-demand” situations, combined with the capability to ship a wide spectrum of applications and buoyancy to dynamically repair around broken links. The underlying issue is routing in such dynamic topology. Numerous studies have shown the difficulty for a routing protocol to scale to large MANET. For this, such network relies on a combination of storing some information about the position of the Mobile Unit (MU) at selected sites and on forming some form of clustering. But the centralized Clusterhead (CH) can become a bottleneck and possibly lead to lower throughput for MANET. We propose a mechanism in which communication outside the cluster is distributed through separate CHs. We prove that the overall averaged throughput increases by using distinct CHs for each neighboring cluster. Although increase in throughput, reduces after one level of traffic rates due to overhead induced by “many” CHs

Data science and innovations for intelligent systems

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A Bayesian reanalysis of the Standard versus Accelerated Initiation of Renal-Replacement Therapy in Acute Kidney Injury (STARRT-AKI) trial

Background Timing of initiation of kidney-replacement therapy (KRT) in critically ill patients remains controversial. The Standard versus Accelerated Initiation of Renal-Replacement Therapy in Acute Kidney Injury (STARRT-AKI) trial compared two strategies of KRT initiation (accelerated versus standard) in critically ill patients with acute kidney injury and found neutral results for 90-day all-cause mortality. Probabilistic exploration of the trial endpoints may enable greater understanding of the trial findings. We aimed to perform a reanalysis using a Bayesian framework. Methods We performed a secondary analysis of all 2927 patients randomized in multi-national STARRT-AKI trial, performed at 168 centers in 15 countries. The primary endpoint, 90-day all-cause mortality, was evaluated using hierarchical Bayesian logistic regression. A spectrum of priors includes optimistic, neutral, and pessimistic priors, along with priors informed from earlier clinical trials. Secondary endpoints (KRT-free days and hospital-free days) were assessed using zero–one inflated beta regression. Results The posterior probability of benefit comparing an accelerated versus a standard KRT initiation strategy for the primary endpoint suggested no important difference, regardless of the prior used (absolute difference of 0.13% [95% credible interval [CrI] − 3.30%; 3.40%], − 0.39% [95% CrI − 3.46%; 3.00%], and 0.64% [95% CrI − 2.53%; 3.88%] for neutral, optimistic, and pessimistic priors, respectively). There was a very low probability that the effect size was equal or larger than a consensus-defined minimal clinically important difference. Patients allocated to the accelerated strategy had a lower number of KRT-free days (median absolute difference of − 3.55 days [95% CrI − 6.38; − 0.48]), with a probability that the accelerated strategy was associated with more KRT-free days of 0.008. Hospital-free days were similar between strategies, with the accelerated strategy having a median absolute difference of 0.48 more hospital-free days (95% CrI − 1.87; 2.72) compared with the standard strategy and the probability that the accelerated strategy had more hospital-free days was 0.66. Conclusions In a Bayesian reanalysis of the STARRT-AKI trial, we found very low probability that an accelerated strategy has clinically important benefits compared with the standard strategy. Patients receiving the accelerated strategy probably have fewer days alive and KRT-free. These findings do not support the adoption of an accelerated strategy of KRT initiation