Top PDF Implementation of a herd management system with wireless sensor networks

Implementation of a herd management system with wireless sensor networks

Implementation of a herd management system with wireless sensor networks

Domestic cattle are essentially descended from prey species from which they inherit herding traits that benefit them both socially and from a general welfare perspective. However, herds are not uniformly spaced and do not always move as a single collective. Individual animals have different social standings within herds which will influence the animals they are most likely to associate with. As a consequence, the herd may break up into independent sub-herds. This raises an additional question for a WSN which is how rapidly network topologies are likely to change and the possibilities of the nodes moving out of range of each other and of base stations. In order to anticipate the extent and rate of such changes, the behaviour of the herd needs to be captured and modelled. In previous works this has been attempted using collar mounted GPS transponders, for example [3], used GPS to assess the behaviours of 14 free ranging Zebu cows in western Niger. Samples of position were taken at 0.1Hz so that displacement and rate of position change could be used to determine grazing coverage.
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Implementation of Wireless Sensor Networks Using ZigBee

Implementation of Wireless Sensor Networks Using ZigBee

GSM module is based on SIM900A Quad-band GSM/GPRS module. SIM900 is a complete Quad-band GSM/GPRS module in a SMT type and designed with a very powerful single-chip processor integrating AMR926EJ-S core, allowing you to benefit from small dimensions and cost-effective solutions. GSM module is a compact and reliable wireless module. This module is compatible with Arduino and other MCU's. It is configured and controlled via its UART using simple AT commands. The GPRS Shield provides you a way to use the GSM cell phone network to receive data from a remote location. The shield allows you to achieve this via any of the three methods:
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Implementation of Secure Key Management Techniques in Wireless Sensor Networks

Implementation of Secure Key Management Techniques in Wireless Sensor Networks

Wireless sensor networks are networks of small, battery-powered, memory-constraint devices named sensor nodes, which have the capability of wireless communication over a restricted area [1]. Due to memory and power constraints, they need to be well arranged to build a fully functional network. Environments, where sensor nodes are deployed, can be controlled (such as home, office, warehouse, forest, etc.) or uncontrolled (such as hostile or disaster areas, toxic regions, etc.). When the environment is known and under control, deployment may be achieved manually to establish an infrastructure. However, manual deployments become infeasible or even impossible as the number of the nodes increases. If the environment is uncontrolled or the WSN is very large, deployment has to be performed by randomly scattering the sensor nodes to target area. It may be possible to provide denser sensor deployment at certain spots, but exact positions of the sensor nodes can not be controlled [2]. Thus, network topology can not be known precisely prior to deployment. Although topology information can be obtained by using mobile sensor nodes and self-deployment protocols as proposed in [3], this may not be possible for a large scale WSN.
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HDLC Implementation in Wireless Sensor Networks

HDLC Implementation in Wireless Sensor Networks

In a typical system speedup is achieved through parallelism at all stages such as multi-user, multitasking, multi-processing, multi- programming, and multi-threading. A technique used in advanced microprocessors where the microprocessor begins executing a second instruction before the first instruction has been completed. A Pipelining is a series of stages, where some work is done at each stage. To finish the work or any instruction, it has to pass through all stages. It does not reduce instruction latency i.e. the time to complete a single instruction from start to finish until it goes through all steps. Instead, it may increase latency by breaking the computation into separate steps and, the pipeline may stall further increasing latency. Pipelining is frequently used in CPUs, but avoided in real time systems, where latency is a hard constraint.
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On the implementation of distributed asynchronous non linear kernel methods over wireless sensor networks

On the implementation of distributed asynchronous non linear kernel methods over wireless sensor networks

Some works on distributed implementations have already been proposed [9, 10]. However, to our knowledge, there is no implementation description of complex non- linear kernel-based algorithms reported in the literature, like the ones in [6, 7]. Due to the important benefits of these algorithms, we have selected them as a target to study as a real implementation over a network composed by simple motes like MICAz, using the temperature field to easily illustrate the performance of the algorithm. These algorithms are quite demanding on both communication and processing capabilities. So, the main question to face is about its feasibility when implemented on an standard WSN with low resources motes. We positively answer this question by proposing a solution for this problem.
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Current Implementation of the Flooding Time Synchronization Protocol in Wireless Sensor Networks

Current Implementation of the Flooding Time Synchronization Protocol in Wireless Sensor Networks

With the increase in technological advancements, more emphasis has been put on enhancing the wireless e ffi ciency of current systems. Specifically speaking, wireless sensor networks have become the basis under which most new applications are built upon. A wireless sensor network, as the name suggests, is a collection of sensors, stationary or mobile, that are placed in various locations connected together over a wireless medium. Applications of wireless sensors extend from smart home networks, area surveillance to military operations and remote sensing [4]. They are usually placed in areas of interest where measurements are to be taken over a period of time. Initially, WSNs were used to sense and send physical and / or environmental data in order to monitor a certain behavior, for example, their use in smart home monitoring and vegetable greenhouse monitoring [5]. Recent advancements in WSNs have created an added complexity to the networks. Their uses now extend to military target tracking and surveillance, natural disaster relief, bio medical health monitoring, object and behavior tracking and automation and hazardous environment exploration [6,7] to name a few; see Figure 2.1 for more WSN applications.
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Design and Implementation of Time Synchronization Experimental System for Wireless Sensor Networks

Design and Implementation of Time Synchronization Experimental System for Wireless Sensor Networks

In the past few decades, many time synchronization protocols or methods for wire- less sensor networks have been designed [4-6]. For example, some people introduce the Internet’s time synchronization protocol standard NTP (Network Time Protocol) into the wireless sensor networks. The NTP mainly uses time deviation and delay estimation methods to synchronize the clocks on the internet platform. It contains a hierarchical tree structure formed by time servers, and the root server at the bottom is usually synchronized with the UTC (Universal Time Coordinated). Similar to NTP, some other wireless communication protocols adopt GPS (Global Positioning System) as their standard time, in order to bring about the clock synchronization [7-9]. How- ever, these methods are not suitable for wireless sensor networks, because there is a lot of energy needed to maintain consistency with the world time standard. Wireless sensor networks are usually limited to size, power, and complexity, and the sensors in network do not have enough energy to apply this kind of methods. The more popular timing protocols currently in use for wireless networks are Timing-sync Protocol for Sensor Networks (TPSN) and Flooding Time Synchronization Protocol (FTSP) [10- 12].
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Implementation of a Secured Approach using Dynamic Key Management Scheme for Node Authentication in Wireless Sensor Networks

Implementation of a Secured Approach using Dynamic Key Management Scheme for Node Authentication in Wireless Sensor Networks

Wireless sensor networks come with huge application domain but on the other hand require the same level of security. The paper discusses various authentication techniques available in wireless sensor network and analyzes them. Some techniques are very helpful but come with some disadvantages. The effort is also done to point out these difficulties. Authentication is one of the best security solutions which protects whole sensor network. The proposed security using authentication without revealing the secret information is highly secured and will not be broken. If the zero knowledge protocol is used for repeated challenges then it will be very secured and sure scheme for the security of entire network. The computational cost of this technique also appears to be very less as there are no high calculations required. So this will reduce the energy, storage requirements of the sensor node. Thus much effort should be given to develop such highly secured authentication schemes.
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Energy Effective Routing Protocol for Maximizing Network Lifetime of WSN

Energy Effective Routing Protocol for Maximizing Network Lifetime of WSN

Strictly due to the independent sleep schedules of nodes, multihop broadcast in asynchronous is relatively complicated. Hence, X-MAC-UPMA[2] is the XMAC implementation for the UMPA (Unified Power Management Architecture for Wireless Sensor Networks) package of TinyOS[3]. This is due to the ability of its two way broadcasting abilities either by the use of X-MAX-UPMA apart from its uni-cast transmissions. Nevertheless, both these schemes are highly inefficient due to the receiving of multiple copies of the message. This repetition of messages causes redundancy which increases the risk of frequent collisions and inevitable energy consumption. If multihop broadcast were able to decrease the redundant transmissions and arising collisions, it can become more efficient in an asynchronous approach.
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Wireless Sensor Networks for Disaster Management

Wireless Sensor Networks for Disaster Management

Each year floods cause loss of thousands of lives and billions worth of property in India. Last year, major loss of human lives, cattle as well as billions worth of establishments was reported in the floods in Bihar and West Bengal. Each year both Ganga and Yamuna break their boundaries and cause numerous losses. Although all these losses cannot be eradicated fully but the losses to lives and property can be reduced to barest minimum level, if the protective measures can be taken before the disaster has struck in the form of flash floods. This can be made possible with the help of communication technology employed on top of wireless sensor networks. The system development involves the various phases and of course, all phases are equally important. Starting with the first phase of data collection, level one is to deal with the physical deployment of sensing devices in the riverbanks and implementation of an effective localization scheme depending on the situation and environment. The flow path of the river, past records of water flow and future prediction of the route of the river, influence the placements of the wireless sensors. These sensors form clusters to communicate with the local base stations. The local base stations are powerful enough to communicate with one another directly using wireless communications.
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Implementation of Data Mining in Wireless Sensor
Networks: An Integrated Review

Implementation of Data Mining in Wireless Sensor Networks: An Integrated Review

Sherin Mathew et al.in 2013[3] lately, there has been a rapid growth in the wireless communication technique. Mobile sinks can be mounted upon urban vehicles with fixed trajectories supply the ideal infrastructure to effectively retrieve sensory data from such isolated WSN fields. Presented approach uses either single-hop transfer of data from SNs that lie within the MS’s range or intense involvement of network periphery nodes in data retrieval, processing, buffering, and delivering tasks. Our projected protocol aims at reducing the overall network overhead and energy expenditure associated with the multihop data recovery process while also ensuring balanced energy consumption among SNs and prolonged network lifetime. This is attained through building cluster structures consisted of member nodes that route their measured data to their assigned cluster head (CH). CHs execute data filtering upon raw data exploiting potential spatial- temporal data idleness and frontward the filtered information to appropriate finish nodes with sufficient residual energy, located in proximity to the MS’s trajectory. Simulation results specify the superior performance of our proposed algorithm to strike the appropriate performance in the power consumption and network lifetime for the wireless sensor networks.
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Energy optimization in wireless sensor networks based on genetic algorithms

Energy optimization in wireless sensor networks based on genetic algorithms

Energy Optimization in Wireless Sensor Networks based on Genetic Algorithms Energy Optimization in Wireless Sensor Networks Based on Genetic Algorithms Angela Rodriguez Intelligent Management Systems[.]

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Wireless Networks for Medical Applications

Wireless Networks for Medical Applications

sensor Networks technologies such as Zigbee(Chevrollier, Golmie, 2005) are being combined with WBANs (Jovanov, et al, 2005) to form smaller scale networks that can be placed on human clothing (or other objects) and provide unobtrusive access to their health information. Sensor Networks are also increasingly being used in natural sciences for example in monitoring wild life or other natural phenomenon. Due to lower power requirements they can be deployed for a long period of time. Due to limited range, they are deployed in large numbers and thus form a distributed network covering a large portion of space. A good example of an application of sensor networks in medical field is the CodeBlue project (Malan, etal, 2004) being developed at Harvard. Other experimental applications include forest fire detection and path tracking using adhoc sensor networks ( Fok, Roman, Chenyang , 2004). Since sensors networks device are very cheap, they can be deployed anywhere in large numbers. Some of the wireless sensor networks based devices are very sophisticated. They operate on their own operating system called the TinyOS. Therefore they can be programmed over the air, making their management very easy.
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Power Management in Wireless Sensor Networks

Power Management in Wireless Sensor Networks

using a least-squares linear regression. The interesting feature of RBS is that it records the timestamp only at the receivers. Thus, all timing uncertainties (including MAC medium access time) on the trans- mitter’s side are eliminated. This characteristic makes it especially suitable for hardware that does not provide low-level access to the MAC layer (e.g., 802.11). Although RBS synchronization only involves one hop neighbors, the mechanism can be extended to synchronize a multi-hop network [41]. In such a system, timestamps in messages can be reconciled as they are being forwarded by the intermediate nodes (according to their next-hop destination and its time difference to the current node) [42]. The main disadvantage of RBS is that it does not synchronize the sender with the receiver directly and that, when the programmers have low-level access at the MAC layer, simpler methods (e.g., TPSN) can achieve a similar precision to RBS.
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FPGA Implementation of an Agricultural Management System using wireless Networks

FPGA Implementation of an Agricultural Management System using wireless Networks

coding techniques like Binary, Gray code,Excess-3 code and One hot encoding and the utilization of devices are shown in table of comparison along with the delay and minimum period of dif[r]

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Realistic propagation effects on wireless sensor networks for landslide management

Realistic propagation effects on wireless sensor networks for landslide management

This paper presents the development of propagation models for wireless sensor networks for landslide management systems. Measurements of path loss in potential areas of landslide occurrence in Thailand were set up. The effect of the vegetation and mountain terrain in the particular area was therefore taken into account regarding the measured path loss. The measurement was carried out with short-range transmission/reception at 2400 MHz corresponding to IEEE 802.15.4 wireless sensor networks. The measurement setup was divided into two main cases, namely, the transmitting and receiving antennas installed on the ground and 1-m high above the ground. The measurement results are shown in this paper and used to develop propagation models suitable for operation of short-range wireless sensor networks of landslide management systems. The propagation model developed for the first case was achieved by fitting the averaged experimental data by the log-normal model plus the standard deviation. For the second case, the model was derived from the ray tracing theory. The mountain-side reflection path was added into the model which contained the reflection coefficient defined for the soil property. Furthermore, the resulting propagation models were employed in order to realistically evaluate the performance of wireless sensor networks via simulations which were conducted by using Castalia. In the simulations, the sensor nodes were placed as deterministic and random distributions within square simulated networks. The comparison between the results obtained from the deterministic and random distributions are discussed.
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Smart Power Management System Based On Wireless Sensor Networks

Smart Power Management System Based On Wireless Sensor Networks

The proposed system uses wireless sensors network for monitoring the voltage and current of an electrical line, which helps to protect the household appliance, and helps to make decisions depending on the previously collected data, turn on/off specific electrical lines according to is importance and priority. The proposed system seeks the house /factory holders to control the power consumed and lead to a better pick the right electrical line at the right time. A model of proposed system contains 2 sensors (current sensors and voltage sensors) that are organized within the wireless sensor network infrastructure. Microcontroller controls these sensors and the main server that is represented by raspberry pi3 contains the SQL database to support real time monitoring methods. A web interface is employed to monitor the complete system in styles of charts, numbers .Wi-Fi technology is employed to connect microcontrollers with server, and therefore the communication is achieved by using Message Queuing telemetry Transport Protocol (MQTT). The proposed system contains two primary units; the primary unit is that the receiving part that sense power by sensors and these sensors are controlled by microprocessors. The second unit is that the web server that receive the processed data via Wi-Fi communication and store it within the main database. The system structure is shown in figure below.
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Filtering method in wireless sensor network management based on EMD algorithm and multi scale wavelet analysis

Filtering method in wireless sensor network management based on EMD algorithm and multi scale wavelet analysis

In wireless sensor networks is the use of the process, there are several reasons cause the sensor network topology change: adding the new node; environmental factors or power sensor node depletion caused by the failure of the three elements; this sensor, object and the observer of the sensor network may change with mobility; the environmental conditions may result in a wireless communication link bandwidth changes, even when the phone. Application oriented network, sensor network has a very broad application prospects, for various sensor network applications for different physical quantities, so the application system for sensor networks also have a variety of requirements. The hardware platform, software systems and network protocols are very different. For each specific application of sensor networks, the sensor network design characteristics different from traditional networks.
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Data Analysis and Management Techniques in Wireless Sensor Networks

Data Analysis and Management Techniques in Wireless Sensor Networks

Today’s sensor motes (e.g: Horton et al. 2002) are full fledged computer systems, with a CPU, main memory, operating system and a suite of sensors. In the Tiny DB system (Madden et al. 2002; 2003; 2005), users connect to the sensor network using a workstation or base station directly connected to a sensor designated as the sink.

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A Distributed Trust Based Secure Communication Framework for Wireless Sensor Network

A Distributed Trust Based Secure Communication Framework for Wireless Sensor Network

The wireless sensor network is an emerging technology, which is used to sense and monitor the environment. As the nodes are deployed in an open environment, the security is one of the essen- tial factors. The cryptography techniques can ensure confidentiality, integrity and authentication. However, wireless sensor network also needs to deal with inside and outside attackers. To deal with outside attackers, attacks by compromised or malicious nodes, trust management system is suggested by many researchers in the area of wireless sensor network. Trust management system can be implemented in various applications for security management such as secure data aggre- gation, secure cluster head selection, trusted routing, access control, etc. Many researchers pro- vide different kind of solutions for these secure applications based on trust management. Howev- er, to incorporate, all such applications on a single sensor node in the network, it is essential to design and develop a trust management system, which considers various aspects and applications of wireless sensor network. As a result, in this paper, we would like to propose a parameter and trust factor based secure communication framework and design a trust management system for wireless sensor networks. Our main contribution is to identify various parameters and trust fac- tors which influences on trust in wireless sensor network and developing a framework for a trust management system based on various parameters and trust factors. The working of the proposed model is shown by simulation experiments conducted in MATLAB for the application of secure communication, data aggregation and intrusion detection in wireless sensor networks.
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