This brief presents a compact model to capture the major difference between high-voltage (HV) and low-voltage MOSFETs, i.e., the carrier velocity saturation effect in the drift region of HV MOSFETs. We discuss the numerical and behavioral issues that can arise in SPICE simulations with the existing current-dependent formulation in Berkeley-Short-Channel-IGFET model (BSIM) for HV transistors. We then demonstrate how a voltage-dependent formulation can mitigate them without losing simplicity and accuracy. We also validate the proposed model against experimental data of HV transistors.

We enhance the capability of industry standard compact model BSIM6 to model the parasitic current$ I_{{\text {edge}}}$at the shallow trench isolation edge. Accurate, efficient, and scalable model for$ I_{{\text {edge}}}$is developed by finding the key differences between$ I_{{\text {edge}}}$and main device drain current ($ I_{{\text {main}}}$). It is found that$ I_{{\text {edge}}}$has a different sub-threshold slope, body-bias coefficient, and short-channel behavior as compared to$ I_{{\text {main}}}$. These important effects along with their dependencies on device geometry, bias conditions, and temperature are accounted for in the model. The model is in excellent agreement with experimental data verifying its scalability and readiness for production level usage.

Due to various operational constraints on the unmanned autonomous vehicle (AUxV) networks operating in an adversarial environment, a fault-tolerant routing scheme is an imperative need. Looking at the risk involved in their applications such as search and rescue, threat surveillance, chemical and biohazard sampling, even a fault of minor nature in the system software/hardware may result in destructive consequences. The AUxV network member nodes vary in architecture, capability, application and power of their internal systems. In such a case it is important that the fault-tolerant scheme should take into consideration the kind of heterogeneity involved and should be able to perform in such a scenario as well. Therefore, to address these issues, in this paper we propose a cross-layer and learning automata (LA) based fault-tolerant routing algorithm for AUxVs, named as ULARC (Unmanned Vehicle Network with LA based Routing using Cross Layer Design). We use the theory of LA for the selection of optimal path for routing between source and destination. In this paper, we also focus on making our proposed strategy an energy-efficient one by using a cross-layer architecture for sleep scheduling of nodes. Further, we have devised an α-based scheduling scheme which further adds to the energy efficiency of our protocol by reducing the overhead on the network.

Internet of Things (IOT) is a wireless ad-hoc network of everyday objects collaborating and cooperating with one other in order to accomplish some shared objectives. The envisioned high degrees of association of humans with IOT nodes require equally high degrees of reliability of the network. In order to render this reliability to IOT networks, it is necessary to make them tolerant to faults. In this paper, we propose mixed cross-layered and learning automata (LA)-based fault-tolerant routing protocol for IOTs, which assures successful delivery of packets even in the presence of faults between a pair of source and destination nodes. As this work concerns IOT, the algorithm designed should be highly scalable and should be able to deliver high degrees of performance in a heterogeneous environment. The LA and cross-layer concepts adopted in the proposed approach endow this flexibility to the algorithm so that the same standard can be used across the network. It dynamically adopts itself to the changing environment and, hence, chooses the optimal action. Since energy is a major concern in IOTs, the algorithm performs energy-aware fault-tolerant routing. To save on energy, all the nodes lying in the unused path are put to sleep. Again this sleep scheduling is dynamic and adaptive. The simulation results of the proposed strategy shows an increase in the overall energy-efficiency of the network and decrease in overhead, as compared to the existing protocols we have considered as benchmarks in this study.