Multipath fading in wireless sensor mote Vaishali M.Tech (VLSI), IMSEC, Ghaziabad/MTU, Noida Abstract: In this paper we study about the new technology as to transfer the data with the help of smart device, Sensor mote. In this paper, we study about the Multi path fading implications (to transfer the data) that can happen in smart device, Smart Dust. Smart dust is so small that everyone cannot see it but yet it performs the efficient working. During data transfer processing, when information reaches at the receiver end, then we get some loss of information because of obstructions. This paper aims at studying multi path fading and path loss incurred by information during data transfer from Smart Dust to Central Receiver system. There are many questions arises in everyone s mind that is it working? Now the answer is yes. Yes, it is working with the best result. It gives us exact and original information. It sends original images with the help of sensors. Index terms: Wireless sensor mote, Smart dust, Wireless communication, MATLAB. 1. INTRODUCTION The purpose of this paper is to analyse techniques for designing wireless communication systems for millimeter scale sensing and communication known as Assembly of particles-sensor mote. A smart mote element is a selfcontained sensing and communication system that can be combined into roughly a cubic-millimeter mote to perform integrated, massively distributed sensor networks. Smart dust can consist of hundreds to thousands of dust motes, each containing the capability of sensing and monitoring environmental conditions and communicating to other devices. Figure 1 shows the conceptual diagram of a smart dust mote. Each mote contains one or more sensors, a power supply, analog circuitry, bi-directional communication, and a programmable microprocessor. Advances in miniaturization, integration, and energy management in digital circuit, optical communications and Micro Electro-Mechanical Systems. Figure 1. Smart dust mote with sensor, optical receiver, passive and active transmitter, signal-processing and control circuitry and power sources. (MEMS) led to the manufacturing of small sensors, optical communication components, and power supplies, whereas microelectronics provides increasing functionality in smaller areas, with lower energy consumption. Actual smart dust motes are being developed by professor Kris Pister at the University of California, Berkeley, as part of a program to produce the smallest possible devices that have a viable way of communicating with each other. At present, each mote is about 5 mm long as shown in Figure2. Figure 2. 63mm 3 mote with optic chips having CCR.
Due to advantages in discreet size, substantial functionality, connectivity and low cost, smart dust will provide new methods to sense and interact with the environment. Each sensor mote can be equipped with many different sensors depending on specific purposes: temperature, light, humidity, pressure, 3 axis magnetometers, 3 axis accelerometers and other sensors. Dust motes can be mounted to the objects that one wishes to monitor, or a large collection of motes can just be deployed over the environment at random. The motes obtain the desired information from the surrounding environment and report these via a communication network. Depending on the application, dust motes can be made to only communicate directly with a base station transceiver, or peer-to-peer communication can be performed between dust motes. The applications of sensor motes are numerous for industry as well as the military. Useful applications for smart dust are listed below. Collecting data for meteorological, geophysical, or planetary research Tracking the movements of birds, small animals, and even insects Virtual keyboard (Acceleration sensing glove) 2 axis accelerometer on top of the each finger on a data glove Smart office space Temperature, humidity, and environmental comfort sensors 1) Monitoring product quality temperature, humidity, pressure sensors 2) Defense-related sensor networks - acoustic, vibration, and magnetic field sensors The functions of a smart dust component required to design a fully autonomous smart dust system are described: power supplies, sensor suites, and communication conditions. With those requirements in mind, we will realize the important design parameters to design a reliable smart dust system. Several possible communication systems will be described including free space optical links with active and passive transmitters, fibre-optic links and RF links. 1.1 Mote Sensor Network To compensate for the limitations on power, reliability, communication range and sensor fidelity, it is necessary to deploy a large number of motes throughout the target environment to form a sensor network leveraging the motes tiny volume. A sensor network could consist of either redundant sensors monitoring the same parameter or disparate sensors monitoring the same target from different parameters. As motes are equipped with several sensors and wireless communication modules, they are an excellent choice for distributed sensor networks. In addition, motes computational capability can enable more sophisticated sensor networks. Comparing to a single expensive high fidelity sensor, a network of mote sensors using appropriate algorithms is superior in providing local information as well as global knowledge, and is much more robust against failure. 1.2 Imaging Receiver A large entrance aperture of the receiver lens creates a large field of view to focus into an image. By employing a large entrance aperture, the imaging receiver can obtain more captured power from each CCR and this will improve the link SNR as shown in fig3 Figure 3 The active optical image stabilization of the imaging receiver helps the imaging receiver track jittering dust mote images. CCD and CMOS are main technologies in the imaging system. CMOS imaging systems, with active pixel sensors (APS) have many advantages over CCD imaging systems such as lower cost, increased on-chip functionality, lower power requirements, and miniaturization. Additionally, a CMOS imaging system has the ability to process incoming signals by on chip/ on-pixel processing and this will make high-data-rate reception possible. Since the pulse of the interrogating laser can be synchronized with the image sensor frame clock, the frame rate should be lower than the uplink bit rate. This frame rate should also be higher than the frame rate of the most image sensor arrays.
Figure 4. Image sensor architecture. Figure 4. shows the image sensor architecture. The image sensor array contains 105 pixels, so a representative offchip data transfer rate can be found by multiplying a total number of pixels by the bit rate of transmission. An aggregate bit rate of the information that comes out of the image sensor array is around several tens of Mbps and this information contains the bit streams and the locations of the corresponding active pixels. 2. MULTIPATH FADING IN WIRELESS CHANNEL The major problems encountered in a wireless channel are path loss and multipath fading. The path loss of the channel severally attenuates the transmitted signal and sets a lower bound on the signal strength the receiver can expect. Multipath fading does a few things. With path loss, it attenuates the transmitted signal. Attenuation introduced by multipath fading adds to the attenuation introduced by path loss. Together with AWGN originally present in the channel, this attenuation in the received signal strength sets a limit on the SNR required of the demodulator to achieve a certain BER. The second impairment brought about by multipath fading, distortion, introduces ISI, which limits the BER. The Doppler shift introduces phase impairment to the modulated signal received under multipath condition and is another error source that can limit the achievable BER. To investigate these problems, we need to develop more complex channel models, channels having randomly time varying impulse responses. The channel model for wireless communication and its impact on receiver front end design by considering path loss and multipath fading in a channel. As shown in Figure 4., the pixel array contains 1000 fixed clusters and each cluster has 100 pixels. Each cluster employs simple local circuitry that can detect active pixels, decode corresponding data, and transfer their data and location to a synchronous data bus. Figure 6: Received power under path loss and multipath fading. Figure5. Early sensor mote made from component As shown in above figure, the received power s variation in distance from the transmitter can be understood by observing its average value at a given distance from the transmitter as well as its local variation in spatial proximity to that location. The first is characterised by path loss and
second by multipath fading. Path loss describes large scale propagation and multipath fading describes small- scale propagation. Received power variation between the transmitter and receiver, there are many propagation paths and signals travelling through these different paths interfere with one another. To illustrate this, we can draw a simple picture that incorporates four of these paths. Figure7: Four paths to differentiate the effect of interference caused by global and local reflectors. 3. PATH ENVIRONMENT To classify the paths according to the reflection is based on whether they suffer from global (large) reflection or from local (small) reflection. In global reflection, paths 1 and 2 do not go through global reflection, whereas paths 3 and 4 go through such reflections. In local reflection, paths 1 and 3 do not go through local reflections, whereas paths 2 and 4 do go through such reflections. 3.1 Global reflection Let the signal going through path1 (with distance between Rx and Tx antenna equal to d ) will arrive at the Rx antenna directly. A replica of this signal follows path3, where it bounds off a global reflector (e.g. a hill) before it arrives at the Rx antenna (with distance between Rx and Tx antenna equal to d ). There it interferes with the signal that goes through path 1 at the Rx antenna. At the receive antenna, these two signals will have a phase difference between proportional to d -d. Specifically, d -d is very small compared with lambda, the wavelength of the carrier. The interference is always destructive in nature because the phase difference is small, movement of receive antenna results a small variation of received power. The reflector is due to global objects (like a hill), which move/change, the interference in nature. The interference are formed as a result of reflection due to global objects and are deterministic in nature. 3.2 Local Reflection In this section, we focus on small-scale reflection. The signal going through path1 and path 2 which is redrawn in the below figure. The signal goes along path1 from the Rx antenna. A replica of this signal travels along path2. The Rx antenna bounds off a local reflector before it arrive at the Rx antenna. It interferes with the signal that travels along path 1. The received antenna is situated with respect to Tx antenna, this interference can be destructive or constructive in variation of received power. Large phase difference results large variation of received power. The interference patterns in the present case are formed as a result of reflection due to local objects and are random in nature. 3.3 Path loss: A First glance Large scale propagation is of the order of 5 λ to 50 λ hence this path loss is described by interference effects going on between signals propagating through paths 1, 2 and paths 3, 4. The path1 is line of sight (LOS) and path 3 is non - line-of-sight (NLOS). Interference between paths 1 and 4, paths 2 and 3 and paths 2 and 4 shows similar characteristics. Characteristics of the path loss include the following: 1. It involves large scale propagation in the local averaged power over a region of 5 to 50.it is a constant power over this region, with its value set equal to average. 2. Path loss is attributed to interference between paths 1,2 and paths 3,4, we can conclude that the received power goes down as transmitter/ receiver separation increases. The effect of movement is a simple loss in signal strength received by receiver. The signal loss to first order is a simple function of distance. Path loss is due to free space propagation and reflection and also by diffraction and scattering. 3. The number of relevant path is small, boiled down to one or two. Path loss include signal loss in the received signal, which lowers the SNR and hence BER. From the other wireless devices we get the black/white, blurred or sharpened images as shown in figure8.
[2] K. S. J. Pister, J.M. Kahn, B. E. Boser, Wireless Networks of Millimeter-Scale Sensor Nodes, 1998, [3] Kris Pister, Smart Dust: Autonomous sensing and communication in a cubic millimeter, Figure8. The blurred and black/ white images displayed by other wireless devices. but with the help of Sensor mote, we get the exact and original images as shown in figure9. [4] B. Warneke, B. Atwood, K. S. J. Pister, Smart Dust Mote Forerunners, Proc. IEEE Inter. Conference on MEMS, 2001. [5] E. A. Hollar, COST Dust, M. S. Thesis in UC Berkeley, 2000. [6] B. Warneke, M. Last, B. Liebowitz, K. S. J. Pister, Smart Dust: Communicating With a Cubic Millimeter Computer, IEEE, 2001. [7] J.M. Kahn, R.H. Katz, K. S. J. Pister, Next Century Challenges: Mobile Networking for Smart Dust, Figure9. The original image is received by sensor mote. 4. CONCLUSION In this paper we present an overview of the sensor mote and study how the sensor mote works actually. In this paper we discussed about how the signal, message and information transfer from one mote to another mote and to the base station to receiver end. In this paper, I have discussed that at the receiver end how we can get the original images From the other wireless devices we get the blurred or sharpened images but with the help of Sensor mote, we get the exact and original images. My search is going on over it. REFERENCES: [1] V. S. Hsu, J.M. Kahn, K. S. J. Pister, Wireless Communication for Smart Dust, 1998,