Modern Clustering Techniques in Wireless Sensor Networks

Wireless sensor networks (WSNs) are employed in various applications from healthcare to military. Due to their limited, tiny power sources, energy becomes the most precious resource for sensor nodes in such networks. To optimize the usage of energy resources, researchers have proposed several ideas from diversified angles. Clustering of nodes plays an important role in conserving energy of WSNs. Clustering approaches focus on resolving the conflicts arising in effective data transmission. In this chapter, we have outlined a few modern energy-efficient clustering approaches to improve the lifetime of WSNs. The proposed clustering methods are: (i) fuzzy-logic-based cluster head election, (ii) efficient sleep duty cycle for sensor nodes, (iii) hierarchical clustering, and (iv) estimated energy harvesting. Classical clustering approaches such as low energy adaptive clustering hierarchy (LEACH) and selected contemporary clustering methods are considered for comparing the performance of proposed approaches. The proposed modern clustering approaches exhibit better lifetime compared to the selected benchmarked protocols

Thermodynamic magnetization of a strongly correlated two-dimensional electron system

We measure thermodynamic magnetization of a low-disordered, strongly correlated two-dimensional electron system in silicon. Pauli spin susceptibility is observed to grow critically at low electron densities - behavior that is characteristic of the existence of a phase transition. A new, parameter-free method is used to directly determine the spectrum characteristics (Lande g-factor and the cyclotron mass) when the Fermi level lies outside the spectral gaps and the inter-level interactions between quasiparticles are avoided. It turns out that, unlike in the Stoner scenario, the critical growth of the spin susceptibility originates from the dramatic enhancement of the effective mass, while the enhancement of the g-factor is weak and practically independent of the electron density.Comment: As publishe

Genome-Wide Association Study for Type 2 Diabetes in Indians Identifies a New Susceptibility Locus at 2q21

Indians undergoing socioeconomic and lifestyle transitions will be maximally affected by epidemic of type 2 diabetes (T2D). We conducted a two-stage genome-wide association study of T2D in 12,535 Indians, a less explored but high-risk group. We identified a new type 2 diabetes–associated locus at 2q21, with the lead signal being rs6723108 (odds ratio 1.31; P = 3.32 3 1029 ). Imputation analysis refined the signal to rs998451 (odds ratio 1.56; P = 6.3 3 10212) within TMEM163 that encodes a probable vesicular transporter in nerve terminals. TMEM163 variants also showed association with decreased fasting plasma insulin and homeostatic model assessment of insulin resistance, indicating a plausible effect through impaired insulin secretion. The 2q21 region also harbors RAB3GAP1 and ACMSD; those are involved in neurologic disorders. Forty-nine of 56 previously reported signals showed consistency in direction with similar effect sizes in Indians and previous studies, and 25 of them were also associated (P , 0.05). Known loci and the newly identified 2q21 locus altogether explained 7.65% variance in the risk of T2D in Indians. Our study suggests that common susceptibility variants for T2D are largely the same across populations, but also reveals a population-specific locus and provides further insights into genetic architecture and etiology of T2D

Magnetization of a strongly interacting two-dimensional electron system in perpendicular magnetic fields

Applied Science

Conductivity of a spin-polarized two-dimensional electron liquid in the ballistic regime

Applied Science