Locating node technology, as the most fundamental component of wireless sensor networks (WSNs) and internet of things (IoT), is a pivotal problem. Distance vector-hop technique (DV-Hop) is frequently used for location node estimation in WSN, but it has a poor estimation precision. In this paper, a multi-objective DV-Hop localization algorithm based on NSGA-II is designed, called NSGA-II-DV-Hop. In NSGA-II-DV-Hop, a new multi-objective model is constructed, and an enhanced constraint strategy is adopted based on all beacon nodes to enhance the DV-Hop positioning estimation precision, and test four new complex network topologies. Simulation results demonstrate that the precision performance of NSGA-II-DV-Hop significantly outperforms than other algorithms, such as CS-DV-Hop, OCS-LC-DV-Hop, and MODE-DV-Hop algorithms.

Background: Intracranial atherosclerotic stenosis (ICAS) is one of the leading causes of stroke worldwide.Current diagnostic evaluations and treatments remain insufficient to assess the vulnerability of intracranial plaques and reduce the recurrence of stroke in symptomatic ICAS. On the other hand, asymptomatic ICAS is associated with an increased risk of cognitive impairment. The pathogenesis of ICAS related cognitive decline is largely unknown. The aim of SICO-ICAS study (stroke incidence and cognitive outcomes of ICAS) is to elucidate the pathophysiology of stroke and cognitive impairment in ICAS population, comprehensively evaluating the complex interactions among life-course exposure, genomic variation, vascular risk factors, cerebrovascular burden and coexisting neurodegeneration.Methods: SICO-ICAS is a multicenter, prospective, observational cohort study. We aim to recruit 3,000 patients with symptomatic or asymptomatic ICAS (>50% or occlusion) who will be followed up for ≥12 months. All participants will undergo pre-designed magnetic resonance imaging packages, blood biomarkers testing, as well as detailed cognitive domains assessment. All participants will undergo clinical visits every 6 months and telephone interviews every 3 months. The primary outcome measurement is ischemic stroke or cognitive impairment within 12 months after enrollment.Discussion: This study will establish a large prospective ICAS cohort, hopefully discover new biomarkers associated with vulnerable intracranial plaques, identify subjects at high risk for incident ischemic stroke or cognitive impairment, and eventually propose a precise diagnostic and treatment strategy for ICAS population.

Wireless sensor location is a challenging task issue in the Internet of Things (IoT). Distance vector-hop (DV-hop) algorithm provides a range-free positioning scheme, but its position prediction method based on least square method brings a large positioning error. To overcome this issue, this paper constructs a three-dimensional (3D) many-objective positioning model. Specifically, we consider many factors, such as the error characteristics of the estimated distance, the distribution characteristics of nodes and the computational cost. Based on these factors, we propose a many-objective 3D-DV-hop positioning model, and propose a data preprocessing strategy and outlier removal strategy. Finally, a fashionable many-objective optimization algorithm is employed to solve the model. The experimental results show that the model proposed in this paper has great advantages in accuracy and robustness, and is superior to the current single and multi-objective positioning model.