Improved low energy adaptive clustering hierarchy and its optimum cluster head selection

ABSTRACT

Wireless sensor networks (WSNs) play a vital role in present-day world, which are being used in different types of applications and occupy an important part in networking domain. The main objective of WSNs is to sense and collect the information from a given area of interest and provide the gathered data to the sink. WSN comprises of number of sensor nodes with batteries of limited energy for communication and computational activities, which are not possible to recharge the batteries after their deployment in the region of interest. Therefore, saving battery energy and utilising the limited power to the optimum level for extending network lifetime became the main factor of WSN. Hence, optimum cluster head (CH) selection will make the network to support longer lifetime and balanced energy consumption during its lifetime. Our proposed protocol selects the optimum CH and found out to be more efficient than the existing low energy adaptive clustering hierarchy. The simulated output shows better network lifetime and in some other performance metric.     

Gate leakage current reduction in IP3 SRAM cells at 45 nm CMOS technology for multimedia applications

We have presented an analysis of the gate leakage current of the IP3 static random access memory (SRAM) cell structure when the cell is in idle mode (performs no data read/write operations) and active mode (performs data read/write operations), along with the requirements for the overall standby leakage power, active write and read powers. A comparison has been drawn with existing SRAM cell structures, the conventional 6T, PP, P4 and P3 cells. At the supply voltage, V_DD = 0.8 V, a reduction of 98%, 99%, 92% and 94% is observed in the gate leakage current in comparison with the 6T, PP, P4 and P3 SRAM cells, respectively, while at V_DD = 0.7 V, it is 97%, 98%, 87% and 84%. A significant reduction is also observed in the overall standby leakage power by 56%, the active write power by 44% and the active read power by 99%, compared with the conventional 6T SRAM cell at V_DD = 0.8 V, with no loss in cell stability and performance with a small area penalty. The simulation environment used for this work is 45 nm deep sub-micron complementary metal oxide semiconductor (CMOS) technology, t_ox = 2.4 nm, V_thn = 0.22 V, V_thp = 0.224 V, V_DD = 0.7 V and 0.8 V, at T = 300 K.     

A Secure Stop and Wait Communication Protocol for Disturbed Networks

Wireless Personal Communications - Secure data communication is the need of hour today specifically when the wireless communication channel is insecure. We are proposing a protocol which can be...     

A multi-tier based clustering framework for scalable and energy efficient WSN-assisted IoT network

Wireless Networks - The effectiveness of wireless sensor network (WSN) in Internet of Thing (IoT) based large scale application depends on the deployment method along with the routing protocol. The...     

Analysis and design of optimum interleaver for iterative receivers in IDMA scheme

In this paper, we propose a new tree based interleaver (TBI) to generate different chip‐level interleaving sequences for different users in an interleave division multiple access (IDMA) system, which reduces computational complexity. This method of generation also solves the memory cost problem and reduces the amount of information exchange between mobile stations (MSs) and base stations (BSs) required to specify the interleaver. Simulation results are presented to show that the proposed interleavers perform well as compared to random interleavers in an IDMA system. Copyright © 2008 John Wiley & Sons, Ltd.     

SML-Sys: a functional framework with multiple models of computation for modeling heterogeneous system

System-on-Chip and other complex distributed hardware/software systems contain heterogeneous components. High-level modeling of such systems require frameworks that provide designers with the ability to express concepts of models of computation (MoC)s as modeling constructs. Many system-level modeling frameworks and corresponding modeling notations such as Ptolemy II and SystemC-H facilitate multi-MoC modeling but are based on imperative programming languages (C++, Java, etc). In such frameworks, the computation and communication aspects between the components of models get intertwined thereby hindering its amenability to formal analysis. In this work, we illustrate function-based semantic definitions of MoCs, which are formulated in a functional framework called SML-Sys. We illustrate through a number of examples how to create system models using this functional programming paradigm.     

MCF: A Metamodeling-Based Component Composition Framework—Composing SystemC IPs for Executable System Models

Reusing Intellectual Property (IP)-cores accompanied by automated generation of the glue-logic, and automated composability checks can help designers to create efficient system-level models quickly and correctly for fast design space exploration. Furthermore, with the rise of multiple transaction level and register-transfer level abstractions, constructing models with mixed abstraction levels is also important. A framework that allows designers to: 1) describe the structure of components, their interfaces, and their interactions, with a semantically rich visual frontend; 2) automatically select IPs from a component library-based on sound-type theoretic principles; and 3) perform constraint based checks for composability, is highly desirable in this context. A metamodel based framework brings forth further advantages. It helps in: 1) providing rigorous semantics to the visual models; 2) imposing restrictions on the model and on interactions between components through constraints expressed in a constraint language; and 3) enabling type-checking and inference-based facilities. Furthermore, using XML-based schemas to store and process meta-information about the IPs as well as the schematic visual model, allows for an IP selection and integration methodology using existing XML processing tools. With these in mind, we present MCF, a metamodeling-based component composition framework for SystemC-based IP core composition at multiple and mixed abstraction levels, with all the advantages stated above.     

Magnetodielectric Microwave Radiation Absorbent Materials and Their Polymer Composites

Functional radiation absorbent materials (RAMs) can transform incident microwave energy into heat energy, hence being essential to impede reflections of microwaves generated by modern radars in military, aerospace, and commercial applications. For such applications, use of composites is imperative to maintain an optimum bandwidth, enhance the magnetoelectric functional activity, ensure a flexible design, and reduce weight, which can be achieved by tuning the volume fractions of such materials. Use of ferrites is widely recommended for microwave (MW) suppression due to their appropriate magnetodielectric characteristics. This review first describes the requirements for an ideal MW absorber and accurate measurements for quantification of MW absorption. Then, the significance, applications, approaches, and experimental developments of magnetodielectric polymer composite RAMs are presented. Moreover, such composites facilitate exploration of nanoscale functional properties to achieve efficient RAMs. The permeability and permittivity at microwave frequencies, magnetic properties induced by unique elemental doping mechanisms, as well as physical and chemical properties of these composites are also presented. The resonance-dependent absorption condition for different families of magnetic ferrites, as well as the dependence of their magnetic properties on the resonant frequency and their absorption bandwidth (spinels up to 30 GHz, hexaferrites 1 GHz to 100 GHz), are presented for applications. Furthermore, magnetodielectric composites decorated with carbon fillers (carbon nanotubes/multiwall carbon nanotubes, graphene, reduced graphene oxide, etc.) with enhanced microwave absorption properties are discussed. Additionally, core–shell magnetodielectric materials are also discussed in detail. Finally, this review highlights the importance of magnetodielectric polymer composites decorated with conducting materials and core–shell magnetodielectric materials as effective broadband RAMs achieving the primary application requirement of broadband absorption of at least ?10 dB with reduced thickness.     

Location Estimation Based on Semi-Supervised Locally Linear Embedding (SSLLE) Approach for Indoor Wireless Networks

Location aware computing is popularized and location information use has important due to huge application of mobile computing devices and local area wireless networks. In this paper, we have proposed a method based on Semi-supervised Locally Linear Embedding for indoor wireless networks. Previous methods for location estimation in indoor wireless networks require a large amount of labeled data for learning the radio map. However, labeled instances are often difficult, expensive, or time consuming to obtain, as they require great efforts, meanwhile unlabeled data may be relatively easy to collect. So, the use of semi-supervised learning is more feasible. In the experiment 101 access points (APs) have been deployed so, the RSS vector received by the mobile station has large dimensions (i.e. 101). At first, we use Locally Linear Embedding to reduce the dimensions of data, and then we use semi-supervised learning algorithm to learn the radio map. The algorithm performs nonlinear mapping between the received signal strengths from nearby access points and the user??s location. It is shown that the proposed scheme has the advantage of robustness and scalability, and is easy in training and implementation. In addition, the scheme exhibits superior performance in the nonline-of-sight (NLOS) situation. Experimental results are presented to demonstrate the feasibility of the proposed SSLLE algorithm.