Stress analysis of perforated graphene nano-electro-mechanical (NEM) contact switches by 3D finite element simulation

Microsystem Technologies - In this article, we report the finite element method (FEM) simulation of the suspended double-clamped graphene beam-based NEM switches with standard and perforated beam...     

QoS Aware Trust Based Routing Algorithm for Wireless Sensor Networks

Wireless Personal Communications - In Wireless Sensor Network (WSN), the lifetime optimization based on minimal energy consumption and security are the crucial issues for the effective design of...     

Data dissemination with interoperability in IoT network

Internet of Things (IoT) is connected to heterogeneous devices. Efficient adaptive scheduling with encoding and decoding of data is an unaddressed issue in IoT. This paper processes the data under three major hierarchy: namely, adaptability, scheduling of data, and network coding for that data. The reliable access to the information is ensured by a device which is a primary eminence in IoT. Device must be able to adapt itself according to the changes in the network and to maintain its reliability as well as transparency and seamless access to the resources. To enhance the performance of the data dissemination, the scheduling process is investigated using the spatial grouping in IoT devices; this is achieved by joint spatial and code domain scheduling scheme, and the novel preconfigured access scheme is coined in order to minimize the collision rate of arbitrary access; during the data dissemination, the erasure coding scheme is used for the encoding and decoding of packets which provides optimal redundancy. We carried the simulation using Contiki and it shows the proposed Polymorphic Erasure Coding with Markov decision Adaptability and Neural networks (PECMAN) improves in terms of cost, overhead, and delay when compared with Multi‐user Shared Access (EMUSA), Polynomial‐time Optimal Storage Allocation (OSA) scheme, and Event‐Aware Back pressure Scheduling Scheme (EABS).     

Stacking of nanocrystalline graphene for nano-electro-mechanical (NEM) actuator applications

Graphene nano-electro-mechanical switches are promising components due to their excellent switching performance such as low pull-in voltage and low contact resistance. Mass fabrication with an appropriate counter electrode remains challenging. In this work, we report the stacking of nanocrystalline graphene (NCG) with a 70-nm dielectric separation layer. The buried NCG layer is contacted through the formation of vias and acts as actuation electrode. After metallization, the top 7.5-nm thin NCG layer is patterned to form double-clamped beams, and the structure is released by hydrofluoric acid etching. By applying a voltage between the top and buried NCG layer, a step-like current increase is observed below 1.5 V, caused by the contact of the movable beam with the buried NCG. No pull-out is observed due to the thin sacrificial layer and high beam length, resulting in low mechanical restoring force. We discuss the possible applications of the NCG stacking approach to realize nano-electro-mechanical contact switches and advanced logical components such as a AND logic.