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A new energy aware wireless sensor network evolution model based on complex network

A new energy aware wireless sensor network evolution model based on complex network

There had been a lot of topological structure evolution models based on energy balance and energy awareness for WSNs in the past years. In WSNs, the node energy consumption directly affects the life cycle of WSNs. Mohite [8] further proposed the combinatorial evolution model based on the scale-free model. Huang et al. [9] proposed a new evolving model of networks and pointed out that, in a certain parameter range, the network gen- erated by the new model still has the distribution charac- teristics of power law distribution. The nonlinear priority connection strategy is considered. Kumar and Ahlawat [10] proposed a new evolving model of networks, which used the node degree parameter with power index (0~1) to replace the linear relationship in the BA scale-free network model. Wang et al. [11] proposed a new model by selecting the superior and eliminating the inferior mechanism. Miyao et al. [12] proposed a weighted evolving model of networks. Gao and Yang [13] proposed a competitive weighted evolv- ing model of networks, which introduced the mechanism of the fittest rich. However, almost all existing studies do not research the energy efficiency for wireless sensor networks from the angle of complex network topology evo- lution and degree distribution.
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A New Hybrid Cryptography Technique in Wireless Sensor Network

A New Hybrid Cryptography Technique in Wireless Sensor Network

key used for encryption and decryption. In the proposed model there is a difficulty in finding the key even if the curve parameters are known. It provides better security by enhancing the security of the key. It is robust against attacks in different layer. The proposed ECC-RC4 (PER) algorithm use elliptic curve cryptography and RC4. It uses the advantages of both symmetric and asymmetric key cryptography. Elliptic curve cryptography is used for key distribution as in asymmetric key cryptography less storage is needed for key predistribution. RC4 is symmetric stream cipher used for message encryption and decryption as it needs less space to store the ciphertext as compared to symmetric encryption algorithm. RC4 algorithm is also faster among the symmetric and asymmetric algorithm. The hybrid model offers better security as compared to other as it use elliptic curve to generate shared key which is difficult to crack by attacker. The model provides faster encryption and decryption time and need less space to store the encrypted message as compared to other algorithm like AES and RSA. It provides better encryption and decryption time for shorter message when the key length is 128 bits and for longer message when the key length is 256 bits as compared to AES with same length of message. The throughput of the proposed hybrid model is better than the AES algorithm. It also consumes less power.
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Sleep Deprivation Attack Detection in Wireless Sensor Network

Sleep Deprivation Attack Detection in Wireless Sensor Network

Most devastating sleep deprivation torture comes in the form of sending useless control traffic and forces the nodes to forgo their sleep cycles so that they are completely exhausted and hence stop working. This type of attack is difficult to detect because of its apparently innocent nature. In resource constrained wireless sensor network, traditional security mechanisms fail to provide the necessary protection for sensed data. The absence of infrastructure also makes it difficult to detect security threats. Therefore security mechanisms have to be designed with efficient resource utilization, especially power. In wireless sensor network, maximum security can only be achieved by designing an effective detection model whose purpose is to provide alert about possible attacks, ideally in time to stop the attack or to mitigate the damage. It attempts to differentiate abnormal activities from normal ones, and identify malicious activities. Generally anomaly based detection mechanism has the intelligence to detect variations from normal behaviors and respond to new intrusions, whereas pattern based detection mechanisms have the capability to identify all known intrusions accurately.
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A utility based sensing and communication model for a glacial sensor network

A utility based sensing and communication model for a glacial sensor network

This paper reports on the development of a utility-based mechanism for managing sensing and communication in co- operative multi-sensor networks. The specific application considered is that of GLACSWEB, a deployed system that uses battery-powered sensors to collect environmental data related to glaciers which it transmits back to a base station so that it can be made available world-wide to researchers. In this context, we first develop a sensing protocol in which each sensor locally adjusts its sensing rate based on the value of the data it believes it will observe. Then, we detail a communication protocol that finds optimal routes for relay- ing this data back to the base station based on the cost of communicating it (derived from the opportunity cost of using the battery power for relaying data). Finally, we em- pirically evaluate our protocol by examining the impact on efficiency of the network topology, the size of the network, and the degree of dynamism of the environment. In so doing, we demonstrate that the efficiency gains of our new proto- col, over the currently implemented method over a 6 month period, are 470%, 250% and 300% respectively.
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ZigBee Wireless Sensor Network Model for Supporting Mobile Nodes

ZigBee Wireless Sensor Network Model for Supporting Mobile Nodes

ZigBee model of OPNET is used to analyze network performance of nodes under different network topology [1,2]. A new structure of ZigBee protocol is designed which increase the energy consumption of mobile nodes by 42% [3], and an energy consumption module is added for comprehensive analysis of suppressing route discovery, energy consumption, and motion trajectory of nodes[4].

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A new Measure for Optimization of Field Sensor Network with Application to LiDAR

A new Measure for Optimization of Field Sensor Network with Application to LiDAR

The first part of the dissertation aims at providing a literature review of the sensor coverage model and deployment of field sensor networks in Chapter 2. This includes reviewing the state of the art in modeling the field sensors; in particular, mechanical and solid-state LiDAR sensors in Section 2.2, followed by sensor network deployment in Section 2.3. Chapter 3 introduces two performance measures based on Infinity-norm and Frobenius-norm for construction of field sensor coverage model established in Chapter 4. These non-negative scalar functions reveal the degree of closeness of a target to the sensor considering both relative position and orientation. A performance measure is developed for the class of 2D fields sensors in Section 3.2. Utilizing the definition of star domain and considering orientation change of the target with respect to the sensor, a Frobenius-norm based measure is introduced in 3.3 which can be applied to the field sensors having in 2D/3D sensing regions. Chapter 4 presents the coverage of single (Section 4.2) and network of field sensors(Section 4.3) along with optimization of field sensor network coverage in Section 4.4 and simulations for validation of the proposed performance measure as well as coverage of field sensor network in Section 4.5.1 and 4.5.2
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A Model of Security Adaptation for Limited Resources in Wireless Sensor Network

A Model of Security Adaptation for Limited Resources in Wireless Sensor Network

The resource aware frequent item algorithm (RA-Frequent Item) calculates the number of frequent data items on the basis of the availability of memory [3]. This value is continuously updated to deal with high data rates. The algorithm represents the number of frequent items as a counter that is reset after the time limit is reached, to cope with the changing nature of the data stream. The algo- rithm receives data elements one by one and tries to find a counter for each new data, and if it succeeds, it increases the number of items for the same data. If all counters have been filled, some of the new items are ignored and the counter is reduced by one, until the algorithm reaches the specified time limit. A counter with the least frequent data is ignored, and the counter is reset to zero. If a new item is the same as a data item in memory based on the similarity threshold, the average value of both items is allocated and the counter is incremented by one. The main parameters including time threshold and number of counter items af- fect the accuracy of the algorithm. When the threshold time is reached, the algo- rithm deletes the remaining items and resets the counters [2] [18] [23] [26]. 2.5. Security Awareness
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Traffic Prevention Model for Sensor Network

Traffic Prevention Model for Sensor Network

On the other hand, a solution that tries to cover every point in an entire road network with APs (full coverage) is not very practical due to the prohibitive deployment and operational cost. They introduced a new notion of intermittent coverage for mobile users, called α-coverage, which provides worst- case guarantees on the interconnection gap while using significantly fewer APs than needed for full coverage

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Improvement the Performance Of Mobility Pattern In Mobile Ad-Hoc Sensor Network using Qualnet 5.0

Improvement the Performance Of Mobility Pattern In Mobile Ad-Hoc Sensor Network using Qualnet 5.0

Abstract––A mobile ad-hoc sensor network usually consists of large number of sensor nodes (stationary or mobile) deployed over an area to be monitored. Each sensor node is a self-contained, battery-powered device that is capable of sensing, communication and some level of computation and data processing. Due to the large amount usage of sensors in the network, it is important to keep each node small and inexpensive. This strictly restricts its resources in terms of energy, memory, processing speed bandwidth. We simulate the Mobile ad-hoc sensor network for its performance analysis. we introduced the concept of communication range remoteness for mobile nodes through random way point mobility model. We characterized the mobility of nodes with new mobility patterns through AODV protocol, without breaking the backward compatibility with earlier versions. In this paper we will proposed two model as non mobility model and mobility model. And in this paper we will compare both models using the concept of mobile ad-hoc sensor network. The performance through the multiple node of non mobility model was found better in comparison of mobility model. . than we design the concept of antenna and improve the frequency of transmitting signal for mobility model,this model is known as design mobility model. In this paper we improve the performance of design mobility in comparison of non mobility model and mobility model. The performance analysis of mobility and non-mobility and design mobility model is done through simulations on a commercial simulator called Qualnet version 5.0, software that provides scalable simulations of Wireless networks.
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An Application Oriented Network Model for Wireless Sensor Networks

An Application Oriented Network Model for Wireless Sensor Networks

To solve this problem, based on real platforms, a new perspective of PAN was investigated by us when analy- zing the fundamental causes of unreliability and asym- metry. This model takes into account factors such as link selected probability and ACK mechanism, etc. Then, an application-oriented general network model (AGNM) used for RERM research was proposed. The simulative results showed that AGNM can approximately characterize the real situation of WSNs.

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HACBA: New Approach to Hierarchical Routing for Wireless Sensor Network

HACBA: New Approach to Hierarchical Routing for Wireless Sensor Network

In the following section, an overview of related works to the protocol model is presented. Section III describes the protocol´s characteristics in order to understand its operation. Section IV presents the communication protocols used to send messages from one node to another (CSMA, TDMA, etc.). Section V shows the first comparative simulation between the HACBA protocol and the LEACH. Section VI presents the result analysis. Finally, in Section VII, the conclusions are summarized.

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Trusted and Secure Wireless Sensor Network Designs and Deployments

Trusted and Secure Wireless Sensor Network Designs and Deployments

In Reference [1], after reviewing the state-of-the-art of sensor placement for localization, authors have reached the neediness to resort to heuristic methods when deploying sensors in a complex scenario, considering complex functions for the measurement error model or focusing on a whole region of interest (ROI) instead of a single target. In the work, multi-objective evolutionary optimization is applied to obtain the optimum sensor placement. Inspired by works in the literature, the well-known non-dominated sorting genetic algorithm (NSGA-II) has been adapted to solve the sensor placement problem for target localization. The work is a research continuation of previous works from the same authors, where they used a standard multi-objective genetic algorithm to place sensors after considering multiple criteria: placing a fixed number of sensors for localization with range-difference measurements; considering a variable number of sensors, as well as non-line of sight (NLOS) conditions; etc. Applying the algorithm considering a variable number of sensors, without modifications, causes severe problems. Thus, the authors have had to modify the original NSGA-II by adding speciation and evolving subpopulations according to the size of different sensor sets. The obtained results show a considerable improvement over the standard NSGA-II.
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Patient Health Monitoring in Wireless Sensor Network

Patient Health Monitoring in Wireless Sensor Network

Wireless Sensor Network is a necessary technology for ambient assisted living . The health monitoring systems that are based on mobile and monitor PC, show the main physiological parameters; blood pressure, diabetes and heart rate on their screens. Before the use of wireless monitoring, the data was limited up to the side of patient bed. Now with the help of these systems one can transfer it to anywhere. In case of any hindrance in the transmission of data, the data is transferred with the help of 3G cellular networks. If by any chance the transmission stops and the patient is not in the range of cellular 3G, then the ad-hoc based wireless network is used. In the wireless ad-hoc network when more than one patient are sending data then multi-hop routing based scheme can be used in wireless network.
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BigEar: Ubiquitous Wireless Low Budget Speech Capturing Interface

BigEar: Ubiquitous Wireless Low Budget Speech Capturing Interface

The authors in Palafox and García-Macias [5] present a voice capture applica- tion using a wireless sensor network (WSN). The WSN nodes (each node is a MicaZ mote with a high sensitivity microphone) are grouped in clusters with a special node (cluster-head) coordinating the cluster activities in order to avoid to capture and transmit the same voice command by two or more nodes; the authors consider the command duplication unacceptable. Each cluster-head collects audio data from their cluster nodes and relays it to a base station. Each node con- tinuously senses audio signals at 2 kHz sampling frequency; if the sensed signal intensity exceeds a predetermined threshold, the node sends a notification to the cluster-head. The cluster-head selects a node to capture the command; the selected node enters into 8 kHz sampling frequency, captures three seconds of audio (with 8 bit resolution) and transfers the audio data to the cluster-head node which, in turn, relays it to the base station where a computer processes speech recog- nition tasks. The authors implement two capturing techniques: capture-and- send without coordination (consisting of human voice detection, three seconds of audio recording and transmission of the data packet to the cluster-head) and coordinate (consisting of human voice detection, node selection by the cluster- head, three seconds of audio recording and transmission of the data packet to the cluster-head). The main limit of this solution, beside having a quite high cost, is the sampling of three seconds instead of having a continuous voice detection and reconstruction. Our solution improves such limitation.
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Wireless Sensor Network of 3 – Bit ADC

Wireless Sensor Network of 3 – Bit ADC

dust or on wireless sensor networks. Sensor and process-type, autonomous, very low-powered electronic devices transmit data from one node (mote) to another in an ad-hoc network by transmitting the environment variations. The smart dust subsystems generally include analog (AI) interface, analog-to-digital (ADC), digital signal processor (DSP), digital-to-analog (DAC), and power management and communication transceiver. A clock less EDADC system is presented using the technique of CT delta modulation (DM). The ADC output is digital, time-controlled, data token. The ADC uses a DAC feedback, which is area efficient and segmented resistor string. There is a study of various R-string DAC architectures. A comparison of a component reduction to a prior art indicates that the DAC and D flip-flops in 8-bit ADC bidirectional register have reduced resistors and switches by nearly 87.5 percent using the proposed segmented DAC architecture. SNDR is 22.696 dB, 30,435 dB and 55.73 dB respectively, for the 3-bit, 4-bit and 8-bit systems, and interest ranges are as high as 220.5 kHz.
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Development of a simulation model of protected wireless sensor network

Development of a simulation model of protected wireless sensor network

Direct trust evaluation is based on matching successful and unsuccessful network events with the beta- function. Successful events take the role of α = Gi variable, while the value of β = Fi is reserved for unsuccessful events. In order to increase the significance of unsuccessful events, the penalty factor is used and weights are applied. To find out the effectiveness of using the penalty factor and weighting factors, we perform calculations for various situations where the ratio of the number of successful and unsuccessful network events changes. Such calculations were carried out for the developed method and analogs. The results of the calculations are presented in Table-1. From Table-1 we can conclude that the system RFSN = v1 does not react to the increase of unsuccessful events if the number of successful events grows as well, however the node is not always considered as trusted. The system BTMS = v2 detects an intruder, when there are twice as many unsuccessful events than successful events. The designed method = v3 detects an intruder in all the cases even if the difference between Gi and Fi is less than a half. From Table-1 it is clear that when an intruder is identified using the developed method, 40% of dropped packets are sufficient for successful intruder identification, which is not true for the other two approaches.
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Hybrid Model for Packet Scheduling in Wireless Sensor Network

Hybrid Model for Packet Scheduling in Wireless Sensor Network

The real time packet goes to priority 1 because they are having highest priority. After that there are non real time packets now which are local or from other node. So the non real time packets which are from other nodes means non local which goes to pr2. At last the non real time packets which are local which is having lowest priority which goes to pr3. So by using this we can achieve overall goals of WSN. We can also achieve fairness by preempting other tasks. If two packets having same priority then the packet which is generated at low level that packet should have higher priority. So it reduces the delay. But in DMP scheme there is one drawback of chance of occurring deadlock. When there is continuous arrival of real time packets in the network and they are having highest priority so they should be processed first. Because of this there is chance of passing the deadline of non-real time packets. There is also possibility of occurring deadlock.
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Enhanced Simulation Model of ZigBee Wireless Sensor Network

Enhanced Simulation Model of ZigBee Wireless Sensor Network

OPNET has been used to design tree self-organizing multi-hop network model based on IEEE 802.15.4 protocol, and the network formation time, data frame transmission performance, throughput, failure and recovery of equipment are simulated [1]. The medium access control (MAC) layer was optimized and designed, and the performance of a sensor network was analyzed for the MAC layer of different competition mechanisms [2,3]. The ZigBee network simulation system based on OPNET adopted ZigBee models of OPNET [4,5], which had disadvantages of difficulty in modeling, overhead, network delay, poor scalability, unguaranteed network access to all nodes, authenticity of communication radius, and other issues [6]. It is difficult to be applied in practical application areas which have high performance requirements, such as high reliability and less network delay. In addition, failures and recoveries of nodes cannot be simulated according to needs of tests, when performing a large-scale sensor network test by using ZigBee models of OPNET. When interval of frame-generating is small, and length of information has large increase, some nodes will appear continuous automatic failures or abnormal simulation.
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Wireless Sensor Network Technology and Networking Algorithms for Wireless Sensor Network Applications: A Survey

Wireless Sensor Network Technology and Networking Algorithms for Wireless Sensor Network Applications: A Survey

Abstract: Recent advances in the electronics technologies results into revolutionary developments in fields of science and technology. Due to relentless efforts of scientists and technologists the fields such as Embedded Technology, Integration technology, communication technology, smart sensor design technology etc are pervasively growing and evolving innovative fields for research and developments. On confluence of embedded technology and communication technology with computer technology, a novel field called Wireless Sensor Network is emerged. Wireless sensor network provides new paradigm for sensing and disseminating information from various environments with a great potential to serve many and diverse applications. The monitoring of various physical parameters such as temperature, fluid level, relative humidity, intensity of light, concentration of gasses dissolved in the atmosphere, vibrations, strain, soil moisture, industrial process parameters, pH and salinity of water etc plays commendable role in various sectors such as environmental pollution monitoring, high-tech agriculture, structural engineering, chemical and physical industries, transportation, military and defense, healthcare, forestry etc. The WSN is the network of smart sensor nodes, wherein the standard protocols such as Zigbee, Bluetooth, wifi, GSM etc technologies are employed to establish the RF communication. The distributed sensor nodes must be routed in the network to ensure cooperative collection of the data. Different routing protocols have been studied for their suitability to use in the WSN. It is found that, to establish the WSN, different routing protocols have been reported. Therefore, in present paper wireless sensor network technology and networking algorithms used in wireless sensor network for diverse applications are discussed.
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Design and implementation of wireless sensor network nodes based on BP neural network

Design and implementation of wireless sensor network nodes based on BP neural network

BP neural network is very sensitive to the initial network weight, with different weights initialization of network, which tends to converge to different local minimum, which is the fundamental reason for many scholars get different results of each training. The convergence speed of BP neural network algorithm is slow: because BP neural network algorithm is essentially a gradient descent method to optimize the objective function, which is very complex, therefore, will appear "sawtooth phenomenon", which makes the BP algorithm is inefficient; and because the objective function optimization is very complex, it will be close to neurons in the output of 0 or 1, some flat areas, in these regions, the weight error is little changed.
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