WSN using CNt......... just have a look on the proposal

The combination of recent technological advances in electronics, nanotechnology, wireless communications, computing, networking, and robotics has enabled the development of Wireless Sensor Networks (WSNs), a new form of distributed computing where sensors (tiny, lowcost and low-power nodes, colloquially referred to as ”motes”) deployed in the environment communicate wirelessly to gather and report information about physical phenomena. WSNs have been successfully used for a variety of applications, such as environmental monitoring, object and event detection or military surveillance. The increasing expectations and demands for greater functionality and capabilities from these devices often result in greater software complexity for applications. Sensor and actor programming is a tedious error-prone task usually carried out from scratch. In order to facilitate the software development of this kind of system new tools and methodologies must be proposed. Middleware can simplify the creation and configuration of WSAN applications offering a set of high level services. The component-oriented paradigm seems to be a good alternative to define middleware. Software components offer several features (reusability, adaptability, ...) very suitable for WSANs which are dynamic environments with rapidly changing situations. Moreover, there is an increasing need to abstract and encapsulate the different middleware and protocols used to perform the interactions between nodes. However, the classical designs of component models and architectures (.NET, COM, JavaBeans) either suffer from extensive resource demands (memory, communication bandwidth, ...) or dependencies on the operating system,protocol or middleware. For this reason we propose to use UM-RTCOM , a previous development focused on software components for real-time distributed systems adapted to the unique characteristics of WSANs. The model is platform independent and uses light-weight components which make the approach very appropiate for this kind of system. In addition UM-RTCOM uses an abstract model of the components which allows different analysis to be performed, including real-time analysis. In order to facilitate the development of WSAN applications we propose a novel middleware called MWSAN. It is specified with UM-RTCOM allowing us to define real time characteristics such as, establishing timing requirements (priority, periods,...) in the services and then to improve the temporal behavior of applications, attending to the most critical events first. MWSAN is composed of several components that provide a set of primitives related to sensor/actor interconnection, quality of service (QoS) and the actor coordination. These issues are very important and necessary and we think that high level primitives will simplify the work of the designers. The middleware is highly configurable in order to fit in with the limitations of sensorsand actors. For example, the middleware for motes does not include the component for actor coordination thereby reducing the required memory space. In order to implement our proposals, automatic tools will map the UM-RTCOM specification of MWSAN to RT Java source code using an implementation called jRate for actors. jRate allows us to meet the timing specifications of UM-RTCOM, implementing a priority based application where the highest priority events received by actors will be executed first. In the case of motes, due to the limited resources, tools will map MWSAN to nesC, a component language for sensorson TinyOS.
Some approaches have incorporated the componentoriented paradigm to deal with WSAN programming. The most used is possibly nesC. This is an event-driven component-oriented programming language for networked embedded systems. Our proposal, UM-RTCOM, presents a higher level concurrency model and a higher level shared resource access model. In addition, real-time requirements of WSAN applications can be directly supported by means of the model primitives and the abstract model provided,reflecting the component behavior, which allows real-time analysis to be performed. Other languages such as Esterel, Signal, and Lustre target embedded, hard realtime, control systems with strong time guarantees but they are not general purpose programming languages. Much work has targeted the development of coordination models and the supporting middleware in the effort to meet the challenges of WSNs. However, since the above listed requirements impose stricter constraints, they may not be suitable for application to WSANs. In UM-RTCOM is used to specify a coordination model based on tuple channels. The use of this component model allowed us to include real time characteristics in the tuple channels improving the application performance. 1.2 Operational Setting Our reference operational setting is based on an architecture where there is a dense deployment of stationary sensors forming clusters, each one governed by a (possibly mobile) actor. Communication between a cluster actor and the sensors is carried out in a single-hop way. Although singlehop communication is inefficient in WSNs due to the long distance between sensors and the base station, in WSANs this may not be the case, because actors are close to sensors. Our approach, however, does not preclude the possibility of having one of these sensors acting as the “root”of a sensor “sub-network” whose members communicate in a multi-hop way. Therefore, this root sensor cannot send only its sensor measurements to the actor but also the information collected from the multi-hop sub-network. On the other hand, several sensors may form a redundancy group inside a cluster. The zone of the cluster monitored by a redundancy group will still be covered in spite of failures or sleeping periods of the group members. Several actors may form a (super-)cluster which is governed by one of them, the so-called cluster leader actor. It takes centralized decisions related to the task assignment or QoS for the actors. Moreover, clustering together with the single-hop communication scheme minimizes the eventtransmission time from sensors to actors, which helps to support the real-time communication required in WSANs.

Three-Dimensional Electrostatic Effects of CNTs -my research work

SINGLE wall carbon nanotubes (CNTs) are of great interest for future electron device applications because of their excellent electrical properties. Electrostatics are an important factor in transistor performance and therefore need to be carefully studied. It is well known that the electrostatics of CNT devices can be significantly different from bulk devices due to the one-dimensional (1-D) channel geometry. Previous theoretical studies of CNT-FET electrostatics assumed a coaxial geometry . These studies thoroughly described the role of the two–dimensional (2-D) coaxial environment on the electrostatics of the 1-D channel. The coaxial geometry provides good gate control with subthreshold swings very close to 60 mV/decade, and the oxide thickness and dielectric constant both play an important role by determining the gate capacitance of the device. The contact geometry also plays an important role in the transfer of charge from the metal contact to the CNT. For low Schottky barriers (SB), a large charge transfer can be achieved if the contact has a large surface area and the oxide dielectric constant is large. Thin oxides and small contact areas can achieve very short electrostatic scaling lengths (the distance by which the source and drain fields penetrate into the channel) and therefore good electrostatic behavior. While a coaxial geometry provides important qualitative insights into the behavior of experimental devices (which typically have a planar top or bottom gated geometry), a full threedimensional (3-D) treatment of planar devices is needed. In this paper, we performed a careful study of the 3-D electrostatics of planar-gated CNTFETs. Our objective is to provide both qualitative and quantitative insights valid for realistic devices. Several of the results are analogous to those that occur in a coaxial geometry, but we show quantitatively how they play out in a realistic planar geometry. The results should be useful for interpreting experiments and for designing high-performance CNTFETs. Among the several techniques available to treat 3-D electrostatics, we find the method of moments (also known as the boundary element method) well suited for simulating planargate CNTFETs. This method is computationally inexpensive because it uses grid points only on the surfaces where charge exists and not in the entire 3-D domain. The computational domain is thus reduced only to the important device regions: the channel, the contacts, and the gate. The problem of boundary conditions in the open areas and termination of the simulation domain does not appear at all. Since all the boundary elements are assumed to be point charges, the electrostatic potential of the system decays to zero at infinity, where both the potential and electric field are zero. In this way, the method of moments inherently assumes a zero-field boundary condition as the distance , whichfacilitates the simulation of devices with electrostatically open boundaries, (e.g., the back-gated CNTFET).

We examine the effect of the oxide thickness, the oxide dielectric constant, and the contact geometry for two different device geometries, the bottom-gated (BG) and top-gated (TG) devices shown in Fig. 1.We will show that for a CNTFET with either of these two geometries, the scaling length is mostly determined by the gate oxide thickness. The geometry of the source and drain contacts can also play an important role. The BG device is more sensitive to the contact width rather than the contact height because a wider device more effectively screens the gate field and prevents it from terminating on the CNT.We will also show that high-k dielectric materials do not offer a significant advantage for the BG device because the oxide thickness plays the dominant role. Both the effects of contact height and high-k materials are, however, more pronounced for the TG device, where the high-k dielectric in the upper region increases not only the gate to CNT coupling, but also the contact-to-CNT coupling and the contact-to-gate parasitics as well. When the contacts are thick and can screen the gate field and prevent it from terminating on the channel, high-k dielectrics can actually degrade the electrostatic performance of the device. A careful geometry optimization for the contacts of planar short channel devices is, therefore, important. A properly designed TG device with very thin gate oxide can provide near ideal subthreshold behavior, similarly to what is predicted for coaxial geometries. Finally, we find that one-dimensional “needle-like” contacts offer the best electrostatic performance. In practice, however, this advantage would have to be balanced against the increased series resistance.

WSN and nanotechnology

IN RECENT years, advances in miniaturization, low-power circuit design, simple and reasonably efficient wireless communication equipment, and improved small-scale energy supplies have combined with reduced manufacturing costs to make a new technological vision possible, i.e., wireless sensor networks. These networks combine simple wireless communication infrastructure, minimal computation facilities, and minimally invasive sensors to form a network that can be deeply embedded in our physical environment to create an information world. Typical sensing tasks for such a device could be temperature, light, vibration, sound, and radiation. The desired size would be a few cubic millimeters or even smaller. The target price should be less than US$ 1, including radio front end, microcontroller, power supply, and the actual sensor. All these components are integrated together in a single device to form a “sensor node.” While these networks of sensor nodes share many commonalities with existing ad hoc network concepts, there are also a number of fundamental differences and specific challenges. While designing and deploying a wireless sensor network, we found several major limitations that must be addressed.

BACKGROUND AND APPLICATIONS

wireless sensor networks have benefited from advances in both microelectromechanical systems (MEMS) and networking technologies. Such environments may have many inexpensive wireless nodes, each capable of collecting, storing, and processing environmental information, and communicating with neighboring nodes. A sensor node is made up of four basic components, as shown in Fig. 1, namely 1) a “sensing unit,” 2) a “processing unit,” 3) a “transceiver unit,” and 4) a “power unit.” They may also have additional application-dependent components such as a “location finding system,” “power generator,” and “mobilizer.” Sensing units are usually composed of two subunits, namely 1) sensors and 2) analog-to-digital converters (ADCs). The analog signals produced by the sensors in response to the observed phenomenon are converted to digital signals by ADC and then fed into the processing unit. The processing unit, which is generally associated with a small storage unit, manages the procedures that make the sensor node collaborate with the other nodes to carry out the assigned sensing tasks. A transceiver unit connects the node to the network. One of the most important components of a sensor node is the power unit. Power units may be supported by power scavenging units such as solar cells. There are also other subunits that are application dependent. Most of the sensor network routing techniques and sensing tasks require knowledge of location with high accuracy. Thus, it is common that a sensor node has a location finding system. A mobilizer may sometimes be needed to move sensor nodes when it is required to carry out their assigned tasks. All of these subunits may need to fit into a matchbox-sized module or even smaller. In the past, sensors are connected by wire lines. Today, this environment is combined with the novel ad hoc networking and wireless technologies to facilitate intersensor communication, which greatly improves the flexibility of installing and configuring a sensor network. Sensor nodes coordinate among themselves to produce high-quality information about the physical environment. A base station (the sink) may be a fixed node or a mobile node capable of connecting the sensor network to an existing communications infrastructure or to the Internet where a user can have access to the reported data. Networking unattended sensor nodes may have profound effects on the efficiency of many military and civil applications such as target field imaging, intrusion detection, weather monitoring, security, tactical surveillance, and distributed computing; on detecting ambient conditions such as temperature, movement, sound, and light; or the presence of certain objects, inventory control, and disaster management. Deployment of a sensor network in these applications can be in random fashion (e.g., dropped from an airplane) or can be planted manually (e.g., fire alarm sensors in a facility). For example, in a disaster management application, a large number of sensors can be dropped from a helicopter. These networked sensors can assist rescue operations by locating survivors, identifying risky areas, and making the rescue team more aware of the overall situation in the disaster area, such as tsunami, earthquake, etc. Sensor nodes are ensely deployed in close proximity or embedded within the medium to be observed. Therefore, they usually work unattended in remote geographic areas. They may be working in the interior of large machinery, at the bottom of an ocean continuously, in a biologically or chemically contaminated field, in a battlefield beyond enemy lines, and in a home or large building. Driven by all these exciting and demanding applications of wireless sensor network, several critical requirements have been addressed actively from the network prospective for supporting viable deployments, as follows:

• long longevity;
• noninvasive form factor;
• optimal sensing coverage and connectivity;
• high sensing resolution.