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Acoustic-Based Electrodynamic Energy Harvester for Wireless Sensor Nodes Application

Acoustic-Based Electrodynamic Energy Harvester for Wireless Sensor Nodes Application

sources, acoustic energy is also abundantly available in the surroundings of wireless sensor nodes. For wireless sensor nodes, the transduction of ambient acoustic energy into electrical energy is suitable for applications such as non-destructive health monitoring of machines (such as, turbines, rotary compressors, electrical generators and motors) and vehicles (automobiles and aircrafts). In the surrounding of the aircraft, the sound pressure level (SPL) above 80 dB is obtained over a frequency range of 20 Hz to 20 kHz [6]. In vehicles, such as automobiles, the SPL ranges from 70 dB to 90 dB over a frequency band that range from 1 to 100 Hz [7]. In the surrounding of the turbofan engines, the SPL of about 150 dB is obtained over a frequency range of 20 Hz to 20 kHz [8], [9]. Moreover, in automobiles without engine muffler, the SPL reaches up to 194 dB and its frequency is spread over a range of 1 to 20 Hz [10]. In car air conditioning system, acoustic energy exits, in supply duct, return duct and exhaust duct with SPLs that reaches up to 71.9, 65.1 and 47.7 dB respectively over a frequency range of 20 Hz to 20 kHz [11].
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A Query Driven Routing Protocol for Wireless Sensor Nodes in Subsurface

A Query Driven Routing Protocol for Wireless Sensor Nodes in Subsurface

Traditionally in WSN network application, communications within nodes have seen a lot of development and changes. Generally WSNs communication depends on two major factors viz data to be collected and energy available. We have seen many different routing protocols for conventional applications of WSNs, which have been developed or wished-for to solve the challenges posed by these networks. The existing protocols are based on diverse assumptions as regards the application background of the concerned network as well as operational manners. Routing mechanisms have been defined for traditional applications as well as for underwater operations too. But none have been developed or proposed for subsurface exploration at the time of listing this paper. Routing in WSNs, even for traditional applications is intrinsically challenging owing to its distinctiveness, which separates it from other wireless networks like ad-hoc mobile networks (MANET), cellular network or simple Wi- Fi systems. It has been discussed and debated that for a large number of nodes to be deployed, it is not feasible to build an IP based global addressing scheme, since it would lead to a mammoth ID overhead maintenance. This being the reason that traditional IP based routing cannot be used for any of the WSNs applications. In addition to this, unlike our traditional communication networks, application of sensors within subsurface requires to flow accumulated data based on various parameters to the base station. This could be done in single hop (as in Direct Reporting) or via multiple hops (as in Directed Diffusion). The other apparent factor to be considered is the resource constraints within wireless sensor nodes. These nodes have limited energy, processing and storage capabilities. Keeping in mind the resource constraints, we have proposed Query Driven data reporting model, since it requires transmission only on “as and when required” basis.
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SmartGate – connecting wireless sensor nodes to the Internet

SmartGate – connecting wireless sensor nodes to the Internet

Abstract. Wireless sensor nodes inside buildings are used to read out sensor data and to control actuators. The nodes need to operate for a long time with a single battery. Often the sensor data should be accessible via Internet from every point of the world. When using a standard Wi-Fi connection, the battery of the node would be depleted after a few hours due to idle currents in receive state. Using sensor nodes with included wake-up receivers can prolong the lifetime of the sensor network to several years. However, no gateway exists that can, on the one hand, connect itself to the Internet and on the other hand can send out the special coded wake-up signal needed by the wake-up receivers on the nodes. In this work we want to bridge this gap by introducing the SmartGate. It is a gateway that has two transceivers incorporated on a single printed circuit board (PCB). A Wi-Fi module connects itself to an existing Wi-Fi network and listens for incoming messages. A CC430 microcontroller analyzes the incoming Wi-Fi messages and builds up the corresponding wake-up signal with included 16-bit address coding. The wake-up signal is sent out using the integrated CC1101 transceiver core from Texas Instruments. A woken-up node will read out its sensor data and will transmit it back to the gateway, where it will be packed into a TCP / IP packet and sent back to the user. The use of the gateway allows the implementation of a wireless sensor network with wake-up receivers that can be accessed via Internet from every point of the world.
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Measurement And Validating Energy Harvested In Wireless Sensor Nodes

Measurement And Validating Energy Harvested In Wireless Sensor Nodes

3 Wireless sensor networks (WSNs) have attracted a great research interest in recent years. Since wireless sensor nodes can provide information from previously inaccessible locations and from previously unachievable number of locations, many new application areas are emerging, such as environmental sensing, structural monitoring and human body monitoring. Although wireless sensor nodes are easy to deploy, the lack of physical connection means they must have their own energy supply. Because batteries have limited lifetime and are environmentally hazardous, it has become widely agreed that energy harvesters are needed for long-lasting sensor nodes. The idea is to use energy harvester to capture small amounts of energy from the environment and use the generated energy to power the nodes in wireless sensor networks.
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Design and implementation of a DSP architecture for wireless sensor 
		nodes

Design and implementation of a DSP architecture for wireless sensor nodes

This paper proposes two different architectures to reduce power in wireless sensor nodes. Along with these two architectures, carry looks ahead adder logic and SAD Algorithms using folded tree architecture are also explained. The energy needed for the wireless communication is very high. Radio communication has highest energy consumption. Power parallel prefix technique is used in this paper to reduce the energy and power. Parallel prefix adders have the best performance in VLSI Design. The main aim of this paper is to design and implementation of newly proposed folded tree architecture. Trunk and twig phase are the two different phases of the folded tree architecture. The energy consumption can be significantly reduced by employing a more appropriate processing element. There are different types of computations in microcontrollers. Folded tree architecture is based on the on the node data processing. Measurements of the silicon implementation show an improvement of 10-20 × in terms of energy as compared to traditional modern micro controllers found in sensor nodes.
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Wave monitoring using wireless sensor nodes

Wave monitoring using wireless sensor nodes

The goal of this master’s research project is to develop a system that can be used to monitor wave characteristics with high spatial resolution. Pioneering work with wireless sensor nodes is done to perform the measurements used to determine these wave characteristics. The demand for such a system, based on wireless sensor nodes originates from the needs of marine scientists at AIMS, the Australian Institute of Marine Science. These scientist need detailed information on the delicate ecosystem they observe and protect, the Great Barrier Reef (GBR), Australia. As waves are an important physical force on the reef, measuring this force will help understand the complex dynamics of the reef. Section 1.1 elaborates on why wireless sensor nodes are used and what problems arise when using these for wave monitoring.
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Recharging of Wireless Sensor Nodes for Real Time Transmission

Recharging of Wireless Sensor Nodes for Real Time Transmission

Energy harvesting is a process that captures small amounts of energy that would otherwise be lost as heat, light and sound. This captured energy is used to power lower electronic devices like sensors. Wireless sensor nodes are central elements in a wireless sensor network, these does sensing, processing and communication. It stores and executes the communication protocols and data processing algorithms. Energy is the most important parameter for WSN. Batteries are installed in a sensor to power the sensor node. UAV's are the flying robots to deploy number of sensor in an area like forest and military areas. UAVs can fly in air with altitude and these can move on the earth to collect the data.
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MAXIMIZING SYSTEM LIFETIME OF WSN BY SCHEDULING OF WIRELESS SENSOR NODES

MAXIMIZING SYSTEM LIFETIME OF WSN BY SCHEDULING OF WIRELESS SENSOR NODES

More clearly that if the set of nodes in the network is X , then its subset node of X should be selected for the active state so that it can cover the entire area or network and the remaining node are put to sleep so that they can turn on later in activation state to continue their work And to check the coverage of whole area is not the easy task it is as tough as another task of this research. This is difficult because someone has to monitor a large number of points in the field to become sure of all the point is being covered during our task. That’s why some of the authors just proposed some methods of converting the area coverage problem into the point coverage. And in this research of ours we just convert the area coverage problem into the target coverage and goes on. And then we just divide the area into the region which is of square shaped and to check the entire coverage of area or square region we have to just check the region is under the coverage of at least one of the wireless sensor nodes. We assume that sensor nodes are location-aware and are able to locally determine the cells they can cover. Furthermore, we assume that the sensing ranges of all nodes are equal and the monitoring environment is a two dimensional rectangular area, through which nodes are deployed randomly. In this thesis title is “Maximizing the lifetime of system of WSN by scheduling of wireless sensor nodes” so we got this problem statement that how to select a active node so that it can perform consistently. So practically we achieve this problem statement. By this we can able to maintain the order of nodes and we are reliable to use the node as active state and sleep state otherwise there is big confusion about that and certainly it can happen a node battery backup is there and we are not using it since we have it so there should be proper use of batteries so that we can achieve our goal.
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IMPROVING THE LIFETIME OF THE WIRELESS SENSOR NODES USING PPSS PROTOCOL

IMPROVING THE LIFETIME OF THE WIRELESS SENSOR NODES USING PPSS PROTOCOL

MCTA algorithm presented in [12] is just an example of kinematics-based prediction. Another example is the Pre-diction-based Energy Saving scheme (PES) introduced in [14]. It only uses simple models to predict a specific location without considering the detailed moving probabilities. Bayesian estimation methods estimate the target state by incorporating new measures to modify the prior states as well as predict the posterior ones. For example, information-driven sensor querying (IDSQ) [28] optimizes the sensor selection to maximize the information gain while minimizing the communication and resource usage. The enhancement of energy efficiency is not achieved by sleep scheduling, but by minimizing the communication energy. On the contrary, PPSS aims at improving the overall performance on energy efficiency and tracking performance using sleep scheduling. Another example of Bayesian estimation methods is the particle filtering [23]. In [13], the authors predict the target location using a particle filter, then schedule the sleep patterns of nodes based on the prediction result. Similar to Circle scheme, they schedule the sleep patterns based on the distance only.
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A survey of railway condition monitoring by ZIGBEE wireless sensor nodes

A survey of railway condition monitoring by ZIGBEE wireless sensor nodes

Three types of track-borne electromagnetic device were compared. The rail vibration velocity had the direct correlations with the generated voltage of electromagnetic devices. The signals are nearly same in amplitude profile and opposite in phase. system locates away and no longer scales linearly. We could engage manually such nonlinearities through adjusting then on-linear parameter β and damping ratio of the magnetic. It should be noted that the proposed energy harvester can generate enough power for ZigBee end device when the vehicle passes; in case of no vehicle passing through, there is no wheel set/track interaction energy for enabling the system. Therefore, an energy storage circuit with batteries and supplementary power source (e.g. wind turbine) are necessary for round-the- clock and long-term monitoring. The energy harvester can charge the batteries (either by under-track wind turbine or by electromagnetic devices) and extend the applications. At a distance above20 m, the communication between the ZigBee end device and coordinator became unstable Unlike the wireless monitoring application in the Bridge and elevated highway, whose mounting height of nodes is very large; the track-borne ZigBee nodes are connected to the rail foot and the railway track is quite close to the ground foundation, so the effects of RSSI in relation to mounting eight should also be considered. Requirement of a feasible railway condition monitoring system includes: 1) Reduced maintenance; 2) Save cost;3) Constructability: the ease and efficiency with which the monitoring system can be mounted; and 4) Maintenance strategy: relied on the prediction of failure rather than maintenance based on a regular schedule.
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A Survey on Routing Protocols in Wireless Sensor Nodes

A Survey on Routing Protocols in Wireless Sensor Nodes

Designing suitable routing algorithms for different applications, fulfilling the different performance demands has been considered as an important issue in wireless sensor networks. In these context many routing algorithms have been proposed to improve the performance demands of various applications through the network layer of the wireless sensor networks protocol stack [3, 4], but most of them are based on single-path routing. In single-path routing approach basically source selects a single path which satisfies the performance demands of the application for transmitting the load towards the sink. Though the single path between the source and sink can be developed with minimum computation complexity and resource utilization, the other factors such as the limited capacity of single path reduces the available throughput [5]. Secondly, considering the unreliable wireless links single path routing is not flexible to link failures, degrading the network performance. Finding an alternate path after the primary path has disrupted to continue the data transmission will cause an extra overhead and increase delay in data delivery. Due to these factors single path routing cannot be considered effective technique to meet the performance demands of various applications.
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Clustering Nodes in Wireless Sensor Network: A Survey

Clustering Nodes in Wireless Sensor Network: A Survey

While designing a cluster algorithm there are many design issue that should be taken into consideration. Wireless sensor nodes have limited energy capacity and once their battery is discharged it cannot be replaced or recharged, hence the clustering algorithm should be energy efficient so as increase the network life time. Application dependency should taken into consideration as the level data aggregation may be application dependent. Even though most of the clustering algorithms are designed with energy efficiency is view, the quality of service should also be taken care of. As the quality of service is mostly an application specific requirement, it should not deteriorate below the required level. Especially in military tracking where even a small delay is unacceptable.
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Communication and self location of Wireless
Sensor Network or Nodes using Wireless
System: A Survey

Communication and self location of Wireless Sensor Network or Nodes using Wireless System: A Survey

A wireless ad-hoc network is a decentralized type of wireless network. A WSN is dynamically self-organized and self- configured, with the nodes in the network automatically Wireless sensor nodes are low power, battery operated devices with limited computation and transmission. A medium access control (MAC) protocol is required to coordinate the access to the channel, while ensuring good throughput, fairness, low latency at a reasonable energy cost. In the first part of our work, we propose a distributed Channel allocation scheme to exploit this multi-channel capacity in sensor networks while taking into interference avoidance. The problem of channel allocation is similar to the code assignment problem in Code Division Multiple Access (CDMA). Earlier works on CDMA code allocation have approached this as a graph coloring problem, where colors can be repeated only at 3 hops or more, unlike traditional graph coloring as surveyed in 1-hop clustering structure is in place: the non-linear relationship between energy and distance makes a single bit transmission more energy efficient using several short, intermediate hops instead of one longer hop.
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Abstract: Wireless sensor network (WSN) are composed of a large number of sensor nodes which

Abstract: Wireless sensor network (WSN) are composed of a large number of sensor nodes which

A wireless sensor network is a highly complex distributed system comprising huge number of tiny wireless sensor nodes and base station (BS). Each wireless sensor node consists of sensor, processor, memory, RF transceiver (radio), peripherals, and power supply unit (battery) [1]. The basic components [1] of a node are a sensor unit, an ADC (Analog to Digital Converter), a CPU (Central processing unit), a power unit and a communication unit. Sensor nodes are micro-electro-mechanical systems [2] (MEMS) that produce a measurable response to a change in some physical condition like temperature and pressure. Sensor nodes sense or measure physical data of the area to be monitored. The continual analog signal sensed by the sensors is digitized by an analog-to-digital converter and sent to controllers for further processing. Sensor nodes are of very small size, consume extremely low energy, are operated in high volumetric densities, and can be autonomous and adaptive to the environment. The spatial density of sensor nodes in the field may be as high as 20 nodes/m3.As wireless sensor nodes are typically very small electronic devices, they can only be equipped with a limited power source [3]. Each sensor node has a certain area of coverage for which it can reliably and accurately report the particular quantity that it is observing. Several sources of power consumption in sensors are: (a) signal sampling and conversion of physical signals to electrical ones; (b) signal conditioning, and (c) analog-to-digital conversion.
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Optimal Route Planning with Mobile Nodes in Wireless Sensor Networks

Optimal Route Planning with Mobile Nodes in Wireless Sensor Networks

The introduction of mobility through mobile nodes in WSNs is beneficial for multiple different reasons such as for connectivity, cost lowering, reliability, energy efficiency and area coverage. For a dense WSN with many nodes, connectivity is usually not a large issue, since the nodes are closely packed together and are able to communicate with multiple other nodes. The difficulty lies in sparse WSN where static nodes cannot cope with isolated regions or large coverage holes. A sparse WSN architecture becomes much more feasible when mobility is added to sensor nodes. Furthermore, with the addition of mobility, fewer stationary nodes are necessary to cover areas in which mobile nodes may route through. Thus less sensor nodes are needed which decreases the cost. Even though the addition of mobility to a node increases the cost of those individual mobile nodes, a WSN which uses mostly static nodes and a few mobile nodes to fill in the imperfect coverage areas is a key strategy to lower cost and improving performance.
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Detection of False Data Distribution Nodes in Wireless Sensor Networks

Detection of False Data Distribution Nodes in Wireless Sensor Networks

Node compromise attack is a serious threat in success of wireless sensor networks. Many methods [5]-[13] have been used to detect node compromise attack. Roughly speaking, these techniques can be categorized into two classes: detection in the second stage [5]; and detection in third stage [6]-[12]. Detection in the second stage in [5], Song et al. make the first attempt to detect node compromise in the second stage. Their motivation is that for some applications, an adversary may not be able to precisely deploy the compromised sensors back into their original positions. Then, the detection of location change will become an indication of a potential node compromise. Detection in the third stage. In [6] to handle the MAC layer misbehavior, Kyasanur and Vaidya propose modifications to IEEE 802.11 MAC protocol to simplify misbehavior detection. Once the sensor nodes are compromised, they could launch false data injection attack. Thus several en-route filtering schemes [7] [8] have been proposed to drop the false data en-route before they reach the sink. Nevertheless, these schemes only mitigate the threats. Thus in [9], ye et al. propose a probabilistic nested marking scheme to locate colluding compromised nodes in false data injection attacks. Recently several software-based attestation schemes [10] [11] for node compromise detection in sensor networks also have been proposed. However, they are not readily applied into regular sensor networks due to several limitations [12]. In [12], Yang et al. present two distributed schemes towards making software based attestation more practical. In these schemes, neighbors of a suspicious node collaborate in the attestation process to make a joint decision. Different from the above previously reported schemes, this proposed scheme attempts to detect the node compromise attack in the first stage.
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ZigBee Wireless Sensor Network Model for Supporting Mobile Nodes

ZigBee Wireless Sensor Network Model for Supporting Mobile Nodes

Step 4: The devices receive the beacon frame, and select the router or coordinator which has the smallest hop count and has the capability of receiving the child nodes as its parent node from the neighbor table based on the parent node selection algorithm, then send the network request frame and start the timer to wait for access reply frame.

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A Survey on Enhancing Network Lifetime of Nodes in Wireless Sensor Networks

A Survey on Enhancing Network Lifetime of Nodes in Wireless Sensor Networks

A sensor network monitors the information from surroundings and transmits the data to the base station. Since transmissions consume the majority of the energy available to a sensor node it becomes important to limit their usage while maintaining reliable communication with the sink node. Routing in WSNs is very challenging due to the following reasons [15]: first, it is not possible to build global addressing and routing algorithms exactly as in typical communication networks. Second, the data flows from multiple sources to the sink. Third, the generated data may be redundant due to the physical closeness of the neighboring sensor nodes. Last, the sensor nodes are tightly constrained in terms of transmission power, on-board energy, processing capacity and storage. Routing protocols for wireless sensor networks must take into consideration metrics such as reliability, throughput, latency, storage requirement, and overhead and network lifetime.
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3D Localization and Visualization of Sensor Nodes and Data Sensing in Underwater Wireless Sensor Networks

3D Localization and Visualization of Sensor Nodes and Data Sensing in Underwater Wireless Sensor Networks

Water constitutes 70% of the earth's surface. As such, it provides natural resources and is also an essential mode of civilian and military transportation. Therefore, UWSNs can be used for applications such as fish finding, seismic monitoring of oil fields, detecting submarines, monitoring pollution and assisted navigation [1]. These sensor nodes are deployed underwater to obtain required information. The sensor nodes can either be stationary or mobile and can be performed to transmit information using acoustic wireless connectivity [2]. However, acoustic communication has several challenges, one of them being the range of communication. Therefore, nodes are deployed over a wide area to have recursive communication. There is the movement of sensor nodes that occurs with the ocean currents affecting signal transmission. The performance of the sensor network may be affected by other numerous factors such as the temperature of water, signal attenuation, water dynamics and noise. The characteristics of Underwater Sensor Networks are fundamentally different from that of terrestrial networks. Finding the location of every sensor in UWSNs is a
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Approximating the Lifespan of Wireless Sensor Networks Based in Mobile Nodes

Approximating the Lifespan of Wireless Sensor Networks Based in Mobile Nodes

To lessen the blind zone in organize scope; we propose a scope advancement calculation of remote sensor arrange in view of portable hubs. This calculation figures the inconsistency of visually impaired zone in organize scope and acquires the base estimated numerical arrangement by using the quantitative connection between vitality utilization of related hubs and the position of the versatile hubs. In the wake of deciding the ideal relative position of the versatile hubs, the issue of visually impaired zone between the static hubs is tended to. Recreation result demonstrates that the proposed calculation has high powerful flexibility and can address the issue of visually impaired zone maximally. Other than expanding the system scope, the calculation additionally lessens the system vitality utilization; streamlines organize scope control and shows high joining. Keywords: Mobile node; Wireless Sensor Network; network coverage rate; static nodes; the blind zone
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