Event-driven wireless sensor networks (WSNs) are equipped with the sensor nodes which are able to capture the vital changes in the measurements and report them to the base station. Due to the nature of harsh and unattended environments, it is impossible to install sensor nodes manually. Therefore, a random deployment fashion such as by aircraft is required. However, with high probability, sensor nodes will not have uniform distribution in the environment which will cause “hole” in the network. In another issue, sensor nodes are limited in energy supply and transmission range, and thus an appropriate technique is essential to calculate energy efficient routes to relay data from the sensor nodes to the base station. In hierarchical routing category, clustering techniques attempt to partition the sensor nodes in the network to appropriate groups and select the cluster heads to contact with the base station directly. However, in large scale WSNs, most of the sensor nodes are far from the base station and cannot contact directly. Most of the clustering techniques do not consider how cluster heads can reach to the base station. In our research, A Delaunay triangulation approach is employed to detect holes in the network. Then, due to the overhead of clustering methods to define cluster areas, a virtual gridding scheme is applied to define cluster areas. To overcome uncertainties in the environment, a fuzzy logic-based approach is designed to select appropriate cluster heads and hop-nodes in a distributed manner. The experimental results prove the effectiveness and accuracy of our proposed model and applicability to large scale WSNs.

We study the problem of employing a mobile-sink into a large-scale Event-Driven Wireless Sensor Networks (EWSNs) for the purpose of data harvesting from sensor-nodes. Generally, this employment improves the main weakness of WSNs that is about energy-consumption in battery-driven sensor-nodes. The main motivation of our work is to address challenges which are related to a network’s topology by adopting a mobile-sink that moves in a predefined trajectory in the environment. Since, in this fashion, it is not possible to gather data from sensor-nodes individually, we adopt the approach of defining some of the sensor-nodes as Rendezvous Points (RPs) in the network. We argue that RP-planning in this case is a tradeoff between minimizing the number of RPs while decreasing the number of hops for a sensor-node that needs data transformation to the related RP which leads to minimizing average energy consumption in the network. We address the problem by formulating the challenges and expectations as a Mixed Integer Linear Programming (MILP). Henceforth, by proving the NP-hardness of the problem, we propose three effective and distributed heuristics for RP-planning, identifying sojourn locations, and constructing routing trees. Finally, experimental results prove the effectiveness of our approach.

Background: Long COVID is a heterogeneous post-viral condition in which persistent neurological, autonomic, cognitive, and psychometric symptoms often occur without clear isolated biomarker abnormalities. This mismatch suggests that disease persistence may be driven not only by changes in individual markers, but by longitudinal reorganization of biological and clinical interactions. Materials and Methods: This observational longitudinal study evaluated patients with persistent symptoms after confirmed SARS-CoV-2 infection at 3 and 6 months. Clinical assessment included neurological examination, Hospital Anxiety and Depression Scale, Beck Depression Inventory, and COMPASS-31. Biomarkers representing hypoxia signaling, oxidative/redox stress, endothelial and renin–angiotensin system activity, glycation-related processes, and complement regulation were analyzed. Correlation analysis, association-level biomarker–clinical network modeling, and complementary Graphical LASSO-based sparse network estimation were used to compare network density, community organization, centrality, and edge rewiring between time points. Results: Conventional paired analysis identified HIF-1$\alpha$ as the only continuous variable with a statistically significant longitudinal change (Wilcoxon statistic = 610.0, $p=0.000350$), whereas association-level network analysis revealed a broader systems-level signal. The association-level biomarker–clinical network preserved a similar global size at 3 and 6 months, with 16 nodes, 27 versus 26 edges, and densities of 0.225 versus 0.217. However, this apparent stability concealed substantial rewiring: 19 edges were shared, 8 were lost, and 7 emerged. Complementary Graphical LASSO analysis with 1000 bootstrap resamples supported this pattern by identifying a conservative sparse conditional-dependency core, including seven shared conditional-dependency edges across time points and selective weakening of four early conditional dependencies. The C3–C4 relationship reversed from negative to positive correlation ($r=-0.618$ to $r=0.618$), indicating marked remodeling of complement-associated regulation. A psychometric–autonomic module involving Beck, HADS I, HADS II, and COMPASS-31 remained stable across both assessments. Conclusions: Long COVID progression was characterized by dynamic remodeling of immune, endothelial/RAS, oxidative-redox, hypoxia-related, autonomic, and psychometric interactions. Longitudinal network analysis identified a systems-level interaction structure that was not captured by isolated biomarker comparisons alone and that was further supported by complementary sparse conditional-dependency analysis.