The document presents a self-organizing time synchronization protocol (STSP) for wireless sensor networks. STSP achieves time synchronization across a network in a decentralized manner without requiring nodes to track information about their neighboring nodes. It utilizes an adaptive value tracking method to dynamically adjust clock speeds based on differences between a node's clock and those received from neighbors. Simulation results show STSP achieves tighter synchronization than an existing method, especially in denser networks, while keeping memory and processing requirements constant regardless of network density. STSP provides scalable and adaptive time synchronization suitable for dynamic wireless sensor networks.
Design and Implementation of Spatial Localization Based on Six -axis MEMS SensorIJRES Journal
This paper focuses on the 3-axis MEMS gyroscope, 3-axis MEMS accelerometer study spatial
orientation. In order to avoid the influence of the environment on the positioning of the text based on physical
principles established sports model, combining coordinate transformation method, the microcontroller STM32
platform with integrated 3-axis MEMS gyroscope, 3-axis MEMS accelerometer chip MPU60x0 designed a new
space positioning system, and using I2C protocol to transfer information. The system is highly integrated, simple
circuit, small size, low power consumption, easy expansion, easy maintenance, etc., can be used as an adjunct to
a wireless network based positioning, improve positioning accuracy, precision can also be positioned relatively
low areas applications.
Abstract - Positioning is a fundamental component of human life to make meaningful interpretations of the environment. Without knowledge of position, human beings are like machines and have very limited capabilities to interact with the environment. Even machines in today’s world can be made smarter if positioning information is made available to them. Indoor positioning of pedestrians is the broad area considered in this thesis. A foot mounted pedestrian tracking device has been studied for this purpose. Systems which utilize foot mounted inertial navigation system has been in the literature for more than two decades. However very few real time implementations have been possible. The purpose of this thesis is to benchmark and improve the performance of one such implementation.
Evolution of a shoe-mounted multi-IMU pedestrian dead reckoning PDR sensoroblu.io
Shoe-mounted inertial navigation systems, aka pedestrian dead reckoning or PDR sensors, are being preferred for pedestrian navigation because of the accuracy offered by them. Such shoe sensors are, for example, the obvious choice for real time location systems of first responders. The opensource platform OpenShoe has reported application of multiple IMUs in shoe-mounted PDR sensors to enhance noise performance. In this paper, we present an experimental study of the noise performance and the operating clocks based power consumption of multi-IMU platforms. The noise performances of a multi-IMU system with different combinations of IMUs are studied. It is observed that four-IMU system is best optimized for cost, area and power. Experiments with varying operating clocks frequency are performed on an in-house four-IMU shoe-mounted inertial navigation module (the Oblu module). Based on the outcome, power-optimized operating clock frequencies are obtained. Thus the overall study suggests that by selecting a well-designed operating point, a multi-IMU system can be made cost, size and power efficient without practically affecting its superior positioning performance.
Despite being around for almost two decades, footmounted inertial navigation only has gotten a limited spread. Contributing factors to this are lack of suitable hardware platforms and difficult system integration. As a solution to this, we present an open-source wireless foot-mounted inertial navigation module with an intuitive and significantly simplified dead reckoning interface. The interface is motivated from statistical properties of the underlying aided inertial navigation and argued to give negligible information loss. The module consists of both a hardware platform and embedded software. Details of the platform and the software are described, and a summarizing description of how to reproduce the module are given. System integration of the module is outlined and finally, we provide a basic performance assessment of the module. In summary, the module provides a modularization of the foot-mounted inertial navigation and makes the technology significantly easier to use.
Inertial Sensor Array Calibration Made Easy !oblu.io
Ultra-low-cost single-chip inertial measurement units (IMUs) combined into IMU arrays are opening up new possibilities for inertial sensing. However, to make these systems practical, calibration and misalignment compensation of low-cost IMU arrays are necessary and a simple calibration procedure that aligns the sensitivity axes of the sensors in the array is needed. Team at KTH suggests a novel mechanical-rotation-rig-free calibration procedure based on blind system identification and a platonic solid (Icosahedron) printable by a contemporary 3D-printer. Matlab-scripts for the parameter estimation and production files for the calibration device are made available.
IRJET- Tool: Segregration of Bands in Sentinel Data and Calculation of NDVIIRJET Journal
This document discusses using satellite imagery and vegetation indices to analyze and map vegetation. It summarizes several research papers on using the Normalized Difference Vegetation Index (NDVI) with different sensors and techniques. Specifically, it examines calculating NDVI from mountain terrain satellite data, using selected bands from Sentinel-2 satellite data for agriculture applications, extracting buildings from satellite images using shadow detection, and applying NDVI to unmanned aerial system multispectral remote sensing for post-disaster assessment. The document also discusses preprocessing techniques and algorithms like random forests and support vector machines for satellite image classification.
Massive Sensors Array for Precision Sensingoblu.io
More than a billion smartphones being sold annually and growing with CAGR of 16%, the smartphone industry has become a driving force in the development of ultralow-cost inertial sensors. Unfortunately, these ultra low-cost sensors do not yet meet the needs of more demanding applications like inertial navigation and biomedical motion tracking systems. However, by adapting a wisdom of the crowd’s thinking and design arrays consisting of hundreds of sensing elements, one can capitalize on the decreasing cost, size, and power-consumption of the sensors to construct virtual high-performance low-cost inertial sensors. Team at KTH, Sweden and WUSTL, USA share findings and challenges.
Wireless Sensor Network using Particle Swarm Optimizationidescitation
Wireless sensor network (WSN) is becoming
progressively important and challenging research area. A
Wireless sensor network (WSN) consists of spatially
distributed autonomous sensors to monitor physical and
environmental conditions and to co-operatively pass their data
through the network to a main location. Wireless sensor
consists of small low cost sensor nodes, having a limited
transmission range and their processing, storage capabilities
and energy resources are limited. The main task of such a
network is to gather information from a node and transmit it
to a base station for further processing.WSN has different
issues such as optimal sensor deployment, node localization,
base station placement, location of target nodes, energy aware
clustering and data aggregation. Recently researchers around
the world are applying bio-inspired optimization algorithm
known as particle swarm optimization (PSO) for increasing
efficiency in the WSN issues. This paper describes the use of
PSO algorithm for optimal sensor deployment in WSN.
Design and Implementation of Spatial Localization Based on Six -axis MEMS SensorIJRES Journal
This paper focuses on the 3-axis MEMS gyroscope, 3-axis MEMS accelerometer study spatial
orientation. In order to avoid the influence of the environment on the positioning of the text based on physical
principles established sports model, combining coordinate transformation method, the microcontroller STM32
platform with integrated 3-axis MEMS gyroscope, 3-axis MEMS accelerometer chip MPU60x0 designed a new
space positioning system, and using I2C protocol to transfer information. The system is highly integrated, simple
circuit, small size, low power consumption, easy expansion, easy maintenance, etc., can be used as an adjunct to
a wireless network based positioning, improve positioning accuracy, precision can also be positioned relatively
low areas applications.
Abstract - Positioning is a fundamental component of human life to make meaningful interpretations of the environment. Without knowledge of position, human beings are like machines and have very limited capabilities to interact with the environment. Even machines in today’s world can be made smarter if positioning information is made available to them. Indoor positioning of pedestrians is the broad area considered in this thesis. A foot mounted pedestrian tracking device has been studied for this purpose. Systems which utilize foot mounted inertial navigation system has been in the literature for more than two decades. However very few real time implementations have been possible. The purpose of this thesis is to benchmark and improve the performance of one such implementation.
Evolution of a shoe-mounted multi-IMU pedestrian dead reckoning PDR sensoroblu.io
Shoe-mounted inertial navigation systems, aka pedestrian dead reckoning or PDR sensors, are being preferred for pedestrian navigation because of the accuracy offered by them. Such shoe sensors are, for example, the obvious choice for real time location systems of first responders. The opensource platform OpenShoe has reported application of multiple IMUs in shoe-mounted PDR sensors to enhance noise performance. In this paper, we present an experimental study of the noise performance and the operating clocks based power consumption of multi-IMU platforms. The noise performances of a multi-IMU system with different combinations of IMUs are studied. It is observed that four-IMU system is best optimized for cost, area and power. Experiments with varying operating clocks frequency are performed on an in-house four-IMU shoe-mounted inertial navigation module (the Oblu module). Based on the outcome, power-optimized operating clock frequencies are obtained. Thus the overall study suggests that by selecting a well-designed operating point, a multi-IMU system can be made cost, size and power efficient without practically affecting its superior positioning performance.
Despite being around for almost two decades, footmounted inertial navigation only has gotten a limited spread. Contributing factors to this are lack of suitable hardware platforms and difficult system integration. As a solution to this, we present an open-source wireless foot-mounted inertial navigation module with an intuitive and significantly simplified dead reckoning interface. The interface is motivated from statistical properties of the underlying aided inertial navigation and argued to give negligible information loss. The module consists of both a hardware platform and embedded software. Details of the platform and the software are described, and a summarizing description of how to reproduce the module are given. System integration of the module is outlined and finally, we provide a basic performance assessment of the module. In summary, the module provides a modularization of the foot-mounted inertial navigation and makes the technology significantly easier to use.
Inertial Sensor Array Calibration Made Easy !oblu.io
Ultra-low-cost single-chip inertial measurement units (IMUs) combined into IMU arrays are opening up new possibilities for inertial sensing. However, to make these systems practical, calibration and misalignment compensation of low-cost IMU arrays are necessary and a simple calibration procedure that aligns the sensitivity axes of the sensors in the array is needed. Team at KTH suggests a novel mechanical-rotation-rig-free calibration procedure based on blind system identification and a platonic solid (Icosahedron) printable by a contemporary 3D-printer. Matlab-scripts for the parameter estimation and production files for the calibration device are made available.
IRJET- Tool: Segregration of Bands in Sentinel Data and Calculation of NDVIIRJET Journal
This document discusses using satellite imagery and vegetation indices to analyze and map vegetation. It summarizes several research papers on using the Normalized Difference Vegetation Index (NDVI) with different sensors and techniques. Specifically, it examines calculating NDVI from mountain terrain satellite data, using selected bands from Sentinel-2 satellite data for agriculture applications, extracting buildings from satellite images using shadow detection, and applying NDVI to unmanned aerial system multispectral remote sensing for post-disaster assessment. The document also discusses preprocessing techniques and algorithms like random forests and support vector machines for satellite image classification.
Massive Sensors Array for Precision Sensingoblu.io
More than a billion smartphones being sold annually and growing with CAGR of 16%, the smartphone industry has become a driving force in the development of ultralow-cost inertial sensors. Unfortunately, these ultra low-cost sensors do not yet meet the needs of more demanding applications like inertial navigation and biomedical motion tracking systems. However, by adapting a wisdom of the crowd’s thinking and design arrays consisting of hundreds of sensing elements, one can capitalize on the decreasing cost, size, and power-consumption of the sensors to construct virtual high-performance low-cost inertial sensors. Team at KTH, Sweden and WUSTL, USA share findings and challenges.
Wireless Sensor Network using Particle Swarm Optimizationidescitation
Wireless sensor network (WSN) is becoming
progressively important and challenging research area. A
Wireless sensor network (WSN) consists of spatially
distributed autonomous sensors to monitor physical and
environmental conditions and to co-operatively pass their data
through the network to a main location. Wireless sensor
consists of small low cost sensor nodes, having a limited
transmission range and their processing, storage capabilities
and energy resources are limited. The main task of such a
network is to gather information from a node and transmit it
to a base station for further processing.WSN has different
issues such as optimal sensor deployment, node localization,
base station placement, location of target nodes, energy aware
clustering and data aggregation. Recently researchers around
the world are applying bio-inspired optimization algorithm
known as particle swarm optimization (PSO) for increasing
efficiency in the WSN issues. This paper describes the use of
PSO algorithm for optimal sensor deployment in WSN.
Discovering adaptive wireless sensor network using eSAT Journals
Abstract When we consider the standard Bellman-Ford algorithm, it uses static values of link cost function and distance function. These static values are stored in sink node so that the sink node requires memory to keep data safe. Therefore the space, message and time complexity of a network and node increases. To overcome this we discover Fast Time Dependent Shortest Path algorithm with message and used in network with β synchronizer. The FTSP algorithm uses dynamic values of link cost function and distance function and to store these values we are using vector compression method so that there is no need to store the data into the sink node. Because of this the message, time and space complexity of node will be decreases. Keywords- Duty cycle, Time dependent, β synchronizer
Design of Real-time Self Establish Wireless Sensor For Dynamic NetworkIJTET Journal
Abstract— Wireless sensor network in the recent trend engaged with high speed responsive real time system. This type of real time system requires reliable and compatible sensor to work in an environment where the sensor is dynamic in nature. Sensor network is to design to perform a set of high level information processing tasks such as detection, tracking or classification. Application of sensor networks is wide ranging and can vary significantly in application requirements, modes of deployment, sensing modality, power supply. Dynamic configuring of wireless sensor involves timing constraints to configure the sensor or to switch an adaptive sensor when working node failure due to energy, data rate, packet loss and range of the sensor. So the network, with such dynamic nature needs a background sensor which is able to be switched when the active sensor has a problem and improper functioning due to the network deploy environment. The background sensor lies inactive inside the range of the active sensor; ensure that the sensor is about to die and make sure the last data transfer successful find delay time to switch. Fault tolerance is achieved by switching the background sensor with the active sensor, where the background sensor self establish themselves in the network and perform similar routing metrics and configure them self with the network as soon they are switched. Once, the actual sensor retained back to the active condition then the background sensor will go to inactive state during this switching process the sensor will not loss data packet.
Adaptive Neighbor Discovery for Mobile and Low Power Wireless Sensor Networks Dimitrios Amaxilatis
Adaptive neighbor discovery is a technique that adapts beaconing rates in wireless sensor networks based on neighborhood changes. It uses a stability metric to determine if nodes can relax beaconing. Stable nodes with consistent neighborhoods reduce beaconing, while unstable nodes increase beaconing to update neighbors. Simulations show it reduces beacons by 90% in stable environments. Real world tests on a testbed show it extends network lifetime by 20% and handles mobility better than fixed neighbor discovery approaches. Further work includes evaluating duty cycling and using it with other network protocols.
A survey on various time synchronization techniques in underwater sensor netw...IAEME Publication
This document summarizes a survey on time synchronization techniques for underwater sensor networks. It discusses how traditional time synchronization methods for terrestrial wireless sensor networks cannot be directly applied to underwater sensor networks due to different challenges like higher propagation delays, limited bandwidth, and node mobility. It then reviews several time synchronization protocols that have been proposed specifically for underwater sensor networks, including Mobi-Sync, DA-Sync, MU-Sync, MC-Sync, TSHL, and CLUSS. It provides a comparative analysis of these protocols and discusses their strengths and limitations. The document also outlines the network architecture and major sources of error that need to be addressed for time synchronization in underwater sensor networks.
Protocols For Self Organisation Of A Wireless Sensor NetworkSaatviga Sudhahar
The document proposes protocols for self-organization of wireless sensor networks. It discusses challenges including energy consumption and localized algorithms. It presents SMACS for link layer organization, EAR for mobility management, and SAR for multihop routing. Cooperative signal processing algorithms like SWE and MWE are introduced to reduce data communication costs through local processing. The algorithms aim to address energy efficiency while allowing scalability in wireless sensor networks.
- Mobile ad hoc networks (MANETs) are autonomous systems of wireless nodes that can dynamically change topology as nodes move. Routing must adapt to these changes.
- There are two main categories of routing protocols: table-driven protocols proactively maintain consistent, up-to-date routing tables whereas on-demand protocols only determine routes when needed.
- Examples of protocols include DSDV as a table-driven protocol and AODV as an on-demand protocol, with AODV using route requests and replies to discover routes only when transmitting data.
The document discusses various data dissemination protocols in wireless sensor networks. It describes flooding, gossiping, rumor routing, sequential assignment routing, direct diffusion, SPIN, and geographic hash table protocols. Flooding broadcasts packets to all neighbors, causing implosion and resource blindness issues. Gossiping sends packets randomly to one neighbor to avoid implosion. Rumor routing and direct diffusion use flooding initially and then optimize routing. SPIN uses data advertisements before transmission. Geographic hash table hashes node locations to optimize routing.
This document discusses power aware routing protocols for wireless sensor networks. It begins by describing wireless sensor networks and how they are used to monitor environmental conditions. It then classifies routing protocols for sensor networks based on their functioning, node participation style, and network structure. Specific examples are provided for different types of routing protocols, including LEACH, TEEN, APTEEN, SPIN, Rumor Routing, and PEGASIS. Chain-based and clustering routing protocols are also summarized.
This document provides an overview of wireless sensor networks. It discusses wireless communication technologies, the need for wireless communication, and defines wireless sensor networks. It describes the characteristics, architecture, operating systems, applications, and technical challenges of wireless sensor networks. Finally, it discusses some companies that manufacture wireless sensor network products, including Cisco, IBM, and Libelium.
Wireless sensor networks combine sensing, computation and communication capabilities into small sensor nodes. A wireless sensor network consists of multiple sensor nodes that communicate wirelessly to perform distributed sensing tasks. Each sensor node contains components for power, computation, sensing and communication. Security is important for wireless sensor networks due to their widespread applications and vulnerabilities like traffic analysis attacks and Sybil attacks. Common security techniques for wireless sensor networks include encryption, cryptography and access control protocols.
This document provides an overview of wireless sensor networks and their applications. It discusses that a sensor network is comprised of sensing, computing, and communication elements that allow an administrator to instrument, observe and react to events in an environment. There are typically four basic components: sensors, an interconnecting network, a central point for information clustering, and computing resources to handle the data. Common applications of sensor networks include military surveillance, environmental monitoring, and infrastructure/facility monitoring.
The document discusses wireless sensor networks and describes their key characteristics. It notes that wireless sensor networks consist of low-power smart sensor nodes distributed over a large field to enable wireless sensing and data networking. The sensor nodes contain sensors, processors, memory, and radios. Wireless sensor networks can be either unstructured with dense node distribution or structured with few scattered nodes.
This document discusses wireless sensor networks. It outlines their applications such as environmental monitoring, health care, and military uses. It also examines factors that influence sensor network design like fault tolerance, scalability, production costs, and power consumption. The communication architecture of sensor networks is presented, including the application, transport, network, data link, and physical layers. Sensor networks have the potential to be widely used in many applications due to their flexibility and fault tolerance.
Distributed Approach for Clock Synchronization in Wireless Sensor NetworkEditor IJMTER
Time synchronization is an important service in WSNs. existing time synchronization algorithms
provide on average good synchronization between arbitrary nodes, however, as we show in this paper, close-by
nodes in a network may be synchronized poorly. We propose the Distributed Time Synchronization Algorithm
(DTSA) which is designed to provide accurately synchronized clocks between nearest-neighbours. DTSA works
in a completely decentralized fashion: Every node periodically broadcasts its time information. Synchronization
messages received from direct neighbours are used to calibrate the logical clock. The algorithm requires neither a
tree topology nor a reference node, which makes it robust against link and node failures.
This document contains two papers. The first paper summarizes a study that designed a prototype smoke detection device for a student dormitory at Klabat University using a microcontroller, MQ-7 and UV-Tron sensors, buzzer, and SMS gateway to detect cigarette smoke and notify users. The second paper proposes a wireless sensor network design for environmental monitoring applications to measure temperature, humidity, CO2, and other factors.
Variable neighborhood Prediction of temporal collective profiles by Keun-Woo ...EuroIoTa
Temporal collective profiles generated by mobile network users can be used to predict network usage, which in turn can be used to improve the performance of the network to meet user demands. This presentation will talk about a prediction method of temporal collective profiles which is suitable for online network management. Using weighted graph representation, the target sample is observed during a given period to determine a set of neighboring profiles that are considered to behave similarly enough. The prediction of the target profile is based on the weighted average of its neighbors, where the optimal number of neighbors are selected through a form of variable neighborhood search. This method is applied to two datasets, one provided by a mobile network service provider and the other from a Wi-Fi service provider. The proposed prediction method can conveniently characterize user behavior via graph representation, while outperforming existing prediction methods. Also, unlike existing methods that utilize categorization, it has a low computational complexity, which makes it suitable for online network analysis.
Effective Audio Storage and Retrieval in Infrastructure less Environment over...IRJET Journal
1) The document proposes a system called SAoD for effective audio storage and retrieval in infrastructure-less wireless sensor networks.
2) SAoD uses a time-division cooperative recording technique to segment audio files into chunks stored across multiple sensors. It encodes chunk metadata into Bloom filters and replicates the filters to reduce communication costs.
3) The system estimates the network size using a gossip algorithm. This allows audio chunks to be replicated probabilistically across the network, guaranteeing high retrieval success rates with low communication overhead.
Energy efficient and scheduling techniques for increasing life time in wirele...IAEME Publication
This document summarizes an article that proposes techniques to improve energy efficiency and extend network lifetime in wireless sensor networks. It discusses object tracking using a prediction-based technique to reduce energy consumption. It also introduces a backbone scheduling algorithm that forms multiple overlapping backbone networks to route traffic. These backbones work alternately to distribute energy usage evenly among sensors and prolong overall network lifetime. The scheduling allows some sensors to forward messages as backbone nodes while others can power down their radios to save energy.
Simulative Performance Evaluation of a Free Space Optical Communication Link ...Editor IJCATR
FSO enables to provide last mile transmission reach
with some advantages such as inherent security and
no electrical hazards without laying any fiber or cable.
In this paper we have analyzed the performance of
free space optics communication system by
employing five transmitters (CSNRZ CSRZ DBNRZ
MDBNRZ and MDBRZ) for different attenuation
values at 10Gbps up to the transmission distance of
1100m. The effect of beam divergence, transmitter
losses, receiver losses and receiver aperture diameter
is also calculated on the performance of proposed
hybrid modulation format based free space optical
communication system.
Discovering adaptive wireless sensor network using eSAT Journals
Abstract When we consider the standard Bellman-Ford algorithm, it uses static values of link cost function and distance function. These static values are stored in sink node so that the sink node requires memory to keep data safe. Therefore the space, message and time complexity of a network and node increases. To overcome this we discover Fast Time Dependent Shortest Path algorithm with message and used in network with β synchronizer. The FTSP algorithm uses dynamic values of link cost function and distance function and to store these values we are using vector compression method so that there is no need to store the data into the sink node. Because of this the message, time and space complexity of node will be decreases. Keywords- Duty cycle, Time dependent, β synchronizer
Design of Real-time Self Establish Wireless Sensor For Dynamic NetworkIJTET Journal
Abstract— Wireless sensor network in the recent trend engaged with high speed responsive real time system. This type of real time system requires reliable and compatible sensor to work in an environment where the sensor is dynamic in nature. Sensor network is to design to perform a set of high level information processing tasks such as detection, tracking or classification. Application of sensor networks is wide ranging and can vary significantly in application requirements, modes of deployment, sensing modality, power supply. Dynamic configuring of wireless sensor involves timing constraints to configure the sensor or to switch an adaptive sensor when working node failure due to energy, data rate, packet loss and range of the sensor. So the network, with such dynamic nature needs a background sensor which is able to be switched when the active sensor has a problem and improper functioning due to the network deploy environment. The background sensor lies inactive inside the range of the active sensor; ensure that the sensor is about to die and make sure the last data transfer successful find delay time to switch. Fault tolerance is achieved by switching the background sensor with the active sensor, where the background sensor self establish themselves in the network and perform similar routing metrics and configure them self with the network as soon they are switched. Once, the actual sensor retained back to the active condition then the background sensor will go to inactive state during this switching process the sensor will not loss data packet.
Adaptive Neighbor Discovery for Mobile and Low Power Wireless Sensor Networks Dimitrios Amaxilatis
Adaptive neighbor discovery is a technique that adapts beaconing rates in wireless sensor networks based on neighborhood changes. It uses a stability metric to determine if nodes can relax beaconing. Stable nodes with consistent neighborhoods reduce beaconing, while unstable nodes increase beaconing to update neighbors. Simulations show it reduces beacons by 90% in stable environments. Real world tests on a testbed show it extends network lifetime by 20% and handles mobility better than fixed neighbor discovery approaches. Further work includes evaluating duty cycling and using it with other network protocols.
A survey on various time synchronization techniques in underwater sensor netw...IAEME Publication
This document summarizes a survey on time synchronization techniques for underwater sensor networks. It discusses how traditional time synchronization methods for terrestrial wireless sensor networks cannot be directly applied to underwater sensor networks due to different challenges like higher propagation delays, limited bandwidth, and node mobility. It then reviews several time synchronization protocols that have been proposed specifically for underwater sensor networks, including Mobi-Sync, DA-Sync, MU-Sync, MC-Sync, TSHL, and CLUSS. It provides a comparative analysis of these protocols and discusses their strengths and limitations. The document also outlines the network architecture and major sources of error that need to be addressed for time synchronization in underwater sensor networks.
Protocols For Self Organisation Of A Wireless Sensor NetworkSaatviga Sudhahar
The document proposes protocols for self-organization of wireless sensor networks. It discusses challenges including energy consumption and localized algorithms. It presents SMACS for link layer organization, EAR for mobility management, and SAR for multihop routing. Cooperative signal processing algorithms like SWE and MWE are introduced to reduce data communication costs through local processing. The algorithms aim to address energy efficiency while allowing scalability in wireless sensor networks.
- Mobile ad hoc networks (MANETs) are autonomous systems of wireless nodes that can dynamically change topology as nodes move. Routing must adapt to these changes.
- There are two main categories of routing protocols: table-driven protocols proactively maintain consistent, up-to-date routing tables whereas on-demand protocols only determine routes when needed.
- Examples of protocols include DSDV as a table-driven protocol and AODV as an on-demand protocol, with AODV using route requests and replies to discover routes only when transmitting data.
The document discusses various data dissemination protocols in wireless sensor networks. It describes flooding, gossiping, rumor routing, sequential assignment routing, direct diffusion, SPIN, and geographic hash table protocols. Flooding broadcasts packets to all neighbors, causing implosion and resource blindness issues. Gossiping sends packets randomly to one neighbor to avoid implosion. Rumor routing and direct diffusion use flooding initially and then optimize routing. SPIN uses data advertisements before transmission. Geographic hash table hashes node locations to optimize routing.
This document discusses power aware routing protocols for wireless sensor networks. It begins by describing wireless sensor networks and how they are used to monitor environmental conditions. It then classifies routing protocols for sensor networks based on their functioning, node participation style, and network structure. Specific examples are provided for different types of routing protocols, including LEACH, TEEN, APTEEN, SPIN, Rumor Routing, and PEGASIS. Chain-based and clustering routing protocols are also summarized.
This document provides an overview of wireless sensor networks. It discusses wireless communication technologies, the need for wireless communication, and defines wireless sensor networks. It describes the characteristics, architecture, operating systems, applications, and technical challenges of wireless sensor networks. Finally, it discusses some companies that manufacture wireless sensor network products, including Cisco, IBM, and Libelium.
Wireless sensor networks combine sensing, computation and communication capabilities into small sensor nodes. A wireless sensor network consists of multiple sensor nodes that communicate wirelessly to perform distributed sensing tasks. Each sensor node contains components for power, computation, sensing and communication. Security is important for wireless sensor networks due to their widespread applications and vulnerabilities like traffic analysis attacks and Sybil attacks. Common security techniques for wireless sensor networks include encryption, cryptography and access control protocols.
This document provides an overview of wireless sensor networks and their applications. It discusses that a sensor network is comprised of sensing, computing, and communication elements that allow an administrator to instrument, observe and react to events in an environment. There are typically four basic components: sensors, an interconnecting network, a central point for information clustering, and computing resources to handle the data. Common applications of sensor networks include military surveillance, environmental monitoring, and infrastructure/facility monitoring.
The document discusses wireless sensor networks and describes their key characteristics. It notes that wireless sensor networks consist of low-power smart sensor nodes distributed over a large field to enable wireless sensing and data networking. The sensor nodes contain sensors, processors, memory, and radios. Wireless sensor networks can be either unstructured with dense node distribution or structured with few scattered nodes.
This document discusses wireless sensor networks. It outlines their applications such as environmental monitoring, health care, and military uses. It also examines factors that influence sensor network design like fault tolerance, scalability, production costs, and power consumption. The communication architecture of sensor networks is presented, including the application, transport, network, data link, and physical layers. Sensor networks have the potential to be widely used in many applications due to their flexibility and fault tolerance.
Distributed Approach for Clock Synchronization in Wireless Sensor NetworkEditor IJMTER
Time synchronization is an important service in WSNs. existing time synchronization algorithms
provide on average good synchronization between arbitrary nodes, however, as we show in this paper, close-by
nodes in a network may be synchronized poorly. We propose the Distributed Time Synchronization Algorithm
(DTSA) which is designed to provide accurately synchronized clocks between nearest-neighbours. DTSA works
in a completely decentralized fashion: Every node periodically broadcasts its time information. Synchronization
messages received from direct neighbours are used to calibrate the logical clock. The algorithm requires neither a
tree topology nor a reference node, which makes it robust against link and node failures.
This document contains two papers. The first paper summarizes a study that designed a prototype smoke detection device for a student dormitory at Klabat University using a microcontroller, MQ-7 and UV-Tron sensors, buzzer, and SMS gateway to detect cigarette smoke and notify users. The second paper proposes a wireless sensor network design for environmental monitoring applications to measure temperature, humidity, CO2, and other factors.
Variable neighborhood Prediction of temporal collective profiles by Keun-Woo ...EuroIoTa
Temporal collective profiles generated by mobile network users can be used to predict network usage, which in turn can be used to improve the performance of the network to meet user demands. This presentation will talk about a prediction method of temporal collective profiles which is suitable for online network management. Using weighted graph representation, the target sample is observed during a given period to determine a set of neighboring profiles that are considered to behave similarly enough. The prediction of the target profile is based on the weighted average of its neighbors, where the optimal number of neighbors are selected through a form of variable neighborhood search. This method is applied to two datasets, one provided by a mobile network service provider and the other from a Wi-Fi service provider. The proposed prediction method can conveniently characterize user behavior via graph representation, while outperforming existing prediction methods. Also, unlike existing methods that utilize categorization, it has a low computational complexity, which makes it suitable for online network analysis.
Effective Audio Storage and Retrieval in Infrastructure less Environment over...IRJET Journal
1) The document proposes a system called SAoD for effective audio storage and retrieval in infrastructure-less wireless sensor networks.
2) SAoD uses a time-division cooperative recording technique to segment audio files into chunks stored across multiple sensors. It encodes chunk metadata into Bloom filters and replicates the filters to reduce communication costs.
3) The system estimates the network size using a gossip algorithm. This allows audio chunks to be replicated probabilistically across the network, guaranteeing high retrieval success rates with low communication overhead.
Energy efficient and scheduling techniques for increasing life time in wirele...IAEME Publication
This document summarizes an article that proposes techniques to improve energy efficiency and extend network lifetime in wireless sensor networks. It discusses object tracking using a prediction-based technique to reduce energy consumption. It also introduces a backbone scheduling algorithm that forms multiple overlapping backbone networks to route traffic. These backbones work alternately to distribute energy usage evenly among sensors and prolong overall network lifetime. The scheduling allows some sensors to forward messages as backbone nodes while others can power down their radios to save energy.
Simulative Performance Evaluation of a Free Space Optical Communication Link ...Editor IJCATR
FSO enables to provide last mile transmission reach
with some advantages such as inherent security and
no electrical hazards without laying any fiber or cable.
In this paper we have analyzed the performance of
free space optics communication system by
employing five transmitters (CSNRZ CSRZ DBNRZ
MDBNRZ and MDBRZ) for different attenuation
values at 10Gbps up to the transmission distance of
1100m. The effect of beam divergence, transmitter
losses, receiver losses and receiver aperture diameter
is also calculated on the performance of proposed
hybrid modulation format based free space optical
communication system.
Improving the performance of free space optical systems: a space-time orthogo...IJECEIAES
Free space optical (FSO) communication systems are known for high-level capacity and information security. The overall system performances of FSO systems are however significantly affected by atmospheric turbulence induced fading. This paper, therefore, proposes a space-time code (STC) technique to mitigate this effect through the introduction of an additional degree of error correction capacity by exploiting the spectral dimension in the coding space. A space-time trellis coded orthogonal frequency division modulation (OFDM) scheme was developed, simulated and evaluated for free space optical communication through a gamma-gamma channel. The evaluation of the coding gain obtained from the simulation results, the mathematical analysis and the truncation error analysis shows that the proposed technique is a promising and viable technique for improving the error correction performance on optical communication links.
Clock Synchronization using Truncated Mean and Whale Optimization for Cluster...IJCNCJournal
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Self-Organizing Time Synchronization in Wireless Sensor Networks with Adaptive Value Trackers
1. Self-Organizing Time Synchronization of WSNs
with Adaptive Value Trackers
Önder GÜRCAN
1,2
& Kasm Sinan YILDIRIM
1
1
Department of Computer Engineering, Ege University, Turkey
2
Institut de Recherche en Informatique de Toulouse, Paul Sabatier University, France
Seventh IEEE International Conference on Self-Adaptive and
Self-Organizing Systems, September, 2013
Önder GÜRCAN (Ege University) Self-Org. Time Syn. of WSNs with AVTs SASO 2013 1 / 11
2. Need for Time Synchronization
in Wireless Sensor Networks (WSN)
Tiny sensor nodes with limited battery, memory, computation capability.
read-only hardware clock
provides local time notion
frequently drifts apart due to aging,
battery level, temperature etc.
WSN applications such as target tracking require global time
notion.
There is a need for time synchronization where...
a logical clock value (representing the global time) is calculated
Önder GÜRCAN (Ege University) Self-Org. Time Syn. of WSNs with AVTs SASO 2013 2 / 11
3. Why Self-Organization for Time Synchronization
in WSNs?
When frequent topological changes node failures in WSNs are
considered
local interactions (peer-to-peer)
decentralized control (no reference node)
simple behaviors (no hierarchical topology)
global organization (network-wide synchronization)
dynamic adaptivity (reaction to topological changes)
Önder GÜRCAN (Ege University) Self-Org. Time Syn. of WSNs with AVTs SASO 2013 3 / 11
4. Why Self-Organization for Time Synchronization
in WSNs?
When frequent topological changes node failures in WSNs are
considered
local interactions (peer-to-peer)
decentralized control (no reference node)
simple behaviors (no hierarchical topology)
global organization (network-wide synchronization)
dynamic adaptivity (reaction to topological changes)
and these are...
...the properties of self-organizing systems! [Serugendo et al., 2011]
Several self-organizing synchronization solutions in the literature!
[Babaoglu et al., 2007, Tyrrell and Auer, 2008, Leidenfrost and Elmenreich, 2009, Tyrrell et al., 2010,
Pagliari and Scaglione, 2011, Klinglmayr and Bettstetter, 2012, Zhang et al., 2012]
either not designed for WSNs
or provide only synchronicity, not Global Time Notion =
Sychronized Time
Önder GÜRCAN (Ege University) Self-Org. Time Syn. of WSNs with AVTs SASO 2013 3 / 11
5. Why we should want a new protocol?
Besides, there are practical synchronization protocols
a special node acting as a time reference
[Elson et al., 2002, van Greunen and Rabaey, 2003, Ganeriwal et al., 2003, Dai and Han, 2004,
Maróti et al., 2004, Sun et al., 2006, Kusy et al., 2006, Lenzen et al., 2009, Schmid et al., 2009,
Schmid et al., 2010, Ferrari et al., 2011, Yildirim and Kantarci, 2013b, Yildirim and Kantarci, 2013a]
building a communication infrastructure (e.g., a spanning tree)
[van Greunen and Rabaey, 2003, Ganeriwal et al., 2003, Dai and Han, 2004, Sun et al., 2006]
can only provide synchronicity [Werner-Allen et al., 2005, Yu and Tirkkonen, 2008]
keeping track of the time information of neighboring nodes
[Sommer and Wattenhofer, 2009, Schenato and Fiorentin, 2011]
causes memory overhead
which neighbors to track? which ones to discard?
a big problem especially in densely connected networks
[Dousse and Thiran, 2004]
Önder GÜRCAN (Ege University) Self-Org. Time Syn. of WSNs with AVTs SASO 2013 4 / 11
6. Why we should want a new protocol?
Besides, there are practical synchronization protocols
a special node acting as a time reference
[Elson et al., 2002, van Greunen and Rabaey, 2003, Ganeriwal et al., 2003, Dai and Han, 2004,
Maróti et al., 2004, Sun et al., 2006, Kusy et al., 2006, Lenzen et al., 2009, Schmid et al., 2009,
Schmid et al., 2010, Ferrari et al., 2011, Yildirim and Kantarci, 2013b, Yildirim and Kantarci, 2013a]
building a communication infrastructure (e.g., a spanning tree)
[van Greunen and Rabaey, 2003, Ganeriwal et al., 2003, Dai and Han, 2004, Sun et al., 2006]
can only provide synchronicity [Werner-Allen et al., 2005, Yu and Tirkkonen, 2008]
keeping track of the time information of neighboring nodes
[Sommer and Wattenhofer, 2009, Schenato and Fiorentin, 2011]
causes memory overhead
which neighbors to track? which ones to discard?
a big problem especially in densely connected networks
[Dousse and Thiran, 2004]
What we want is to achieve ...
self-organizing time synchronization without keeping track of
neighbors.
Önder GÜRCAN (Ege University) Self-Org. Time Syn. of WSNs with AVTs SASO 2013 4 / 11
7. Self-Organizing Time Sychronization Protocol
(STSP)
STSP Algorithm
Periodically broadcast the logical clock value to the neighbors
(beacon period)
Upon receiving the logical clock value of a neighbor
1 calculate the dierence (clock skew) between my logical clock and
the received value
2 add clock skew / 2 to my logical clock
3 if clock skew is lower than the max possible skew
1 if clock skew 0, speed up my logical clock
2 if clock skew 0, slow down my logical clock
3 else the speed of my logical clock is said to be good
Önder GÜRCAN (Ege University) Self-Org. Time Syn. of WSNs with AVTs SASO 2013 5 / 11
8. But how this speed adjustment is done?
Figure: Let's begin with v0 and try to nd the best value between vmin and vmax .
Önder GÜRCAN (Ege University) Self-Org. Time Syn. of WSNs with AVTs SASO 2013 6 / 11
9. STSP Simulation Setup
networks consisting of 100 sensor nodes
with densities 10, 20 and 50
clock drifts - uniformly distributed [-10−4, 10−4]
delays on the communication links - gaussian random variable
beacon period of 30 seconds.
AVT params: vmin = −10−4,vmax = 10−4, ∆min = 10−10,∆max = 10−5
Önder GÜRCAN (Ege University) Self-Org. Time Syn. of WSNs with AVTs SASO 2013 7 / 11
10. STSP Simulation Setup
networks consisting of 100 sensor nodes
with densities 10, 20 and 50
clock drifts - uniformly distributed [-10−4, 10−4]
delays on the communication links - gaussian random variable
beacon period of 30 seconds.
AVT params: vmin = −10−4,vmax = 10−4, ∆min = 10−10,∆max = 10−5
compared with Gradient Time Synchronization Protocol (GTSP)
[Sommer and Wattenhofer, 2009]
converges to the average clock value/speed of all neighbors
allocates memory to keep track of each neighboring node
lots of computation for each neighbor
Evaluation metrics: instantaneous synchronization errors
global skew (arbitrary nodes)
local skew (neighboring nodes)
Önder GÜRCAN (Ege University) Self-Org. Time Syn. of WSNs with AVTs SASO 2013 7 / 11
11. Low Density Results - 100 nodes 10 neighbors
Önder GÜRCAN (Ege University) Self-Org. Time Syn. of WSNs with AVTs SASO 2013 8 / 11
12. Midium Density Results - 100 nodes 20 neighbors
Önder GÜRCAN (Ege University) Self-Org. Time Syn. of WSNs with AVTs SASO 2013 9 / 11
13. High Density Results - 100 nodes 50 neighbors
Önder GÜRCAN (Ege University) Self-Org. Time Syn. of WSNs with AVTs SASO 2013 10 / 11
14. Why STSP is meaningful for you?
Scalable
the denser the network, the tighter the synchronization was
memory CPU requirements remain the same.
Adaptive, not adaptable.
adaptive - maintains some stable states (e.g., any global time value)
adaptable - maintains particular organization (e.g., a specic global
time value)
Adaptivity is provided using Adaptive Value Trackers (AVTs)
requires quite a few arithmetic operations
parameters to be set carefully, e.g. high precision (∆min ) is not good
Önder GÜRCAN (Ege University) Self-Org. Time Syn. of WSNs with AVTs SASO 2013 11 / 11
15. Why STSP is meaningful for you?
Scalable
the denser the network, the tighter the synchronization was
memory CPU requirements remain the same.
Adaptive, not adaptable.
adaptive - maintains some stable states (e.g., any global time value)
adaptable - maintains particular organization (e.g., a specic global
time value)
Adaptivity is provided using Adaptive Value Trackers (AVTs)
requires quite a few arithmetic operations
parameters to be set carefully, e.g. high precision (∆min ) is not good
Can AVTs improve robustness against faulty behavior?
Yes. Because the ∆ values converge ∆min
But successive erroneous feedbacks would increase the ∆ values
exponentially
Önder GÜRCAN (Ege University) Self-Org. Time Syn. of WSNs with AVTs SASO 2013 11 / 11
16. References
Babaoglu, O., Binci, T., Jelasity, M., and Montresor, A. (2007).
Firey-inspired heartbeat synchronization in overlay networks.
In Self-Adaptive and Self-Organizing Systems, 2007. SASO '07. First
International Conference on, pages 7786.
Dai, H. and Han, R. (2004).
Tsync: A lightweight bidirectional time synchronization service for
wireless sensor networks.
ACM SIGMOBILE Mobile Computing and Communications Review,
8:125139.
Dousse, O. and Thiran, P. (2004).
Connectivity vs capacity in dense ad hoc networks.
In INFOCOM 2004. Twenty-third AnnualJoint Conference of the IEEE
Computer and Communications Societies, volume 1, pages 476486.
Elson, J., Girod, L., and Estrin, D. (2002).
Fine-grained network time synchronization using reference broadcasts.
SIGOPS Oper. Syst. Rev., 36(SI):147163.
Önder GÜRCAN (Ege University) Self-Org. Time Syn. of WSNs with AVTs SASO 2013 11 / 11
17. Ferrari, F., Zimmerling, M., Thiele, L., and Saukh, O. (2011).
Ecient network ooding and time synchronization with glossy.
In Information Processing in Sensor Networks (IPSN), 2011 10th
International Conference on, pages 7384. IEEE.
Ganeriwal, S., Kumar, R., and Srivastava, M. B. (2003).
Timing-sync protocol for sensor networks.
In SenSys'03: Proc. of the 1st int. conf. on Embedded networked
sensor systems, pages 138149, New York, NY, USA. ACM.
Klinglmayr, J. and Bettstetter, C. (2012).
Self-organizing synchronization with inhibitory-coupled oscillators:
Convergence and robustness.
ACM Trans. Auton. Adapt. Syst., 7(3):30:130:23.
Kusy, B., Dutta, P., Levis, P., Maroti, M., Ledeczi, A., and Culler, D.
(2006).
Elapsed time on arrival: A simple and versatile primitive for canonical
time synchronisation services.
Int. J. Ad Hoc Ubiquitous Comput., 1(4):239251.
Önder GÜRCAN (Ege University) Self-Org. Time Syn. of WSNs with AVTs SASO 2013 11 / 11
18. Leidenfrost, R. and Elmenreich, W. (2009).
Firey clock synchronization in an 802.15.4 wireless network.
EURASIP J. Embedded Syst., 2009:7:17:17.
Lenzen, C., Sommer, P., and Wattenhofer, R. (2009).
Optimal Clock Synchronization in Networks.
In 7th ACM Conference on Embedded Networked Sensor Systems
(SenSys), Berkeley, California, USA.
Maróti, M., Kusy, B., Simon, G., and Lédeczi, A. (2004).
The ooding time synchronization protocol.
In SenSys'04: Proc. of the 2nd int. conf. on Embedded networked
sensor systems, pages 3949, New York, NY, USA. ACM.
Pagliari, R. and Scaglione, A. (2011).
Scalable network synchronization with pulse-coupled oscillators.
Mobile Computing, IEEE Transactions on, 10(3):392405.
Schenato, L. and Fiorentin, F. (2011).
Average timesynch: a consensus-based protocol for time
synchronization in wireless sensor networks.
Önder GÜRCAN (Ege University) Self-Org. Time Syn. of WSNs with AVTs SASO 2013 11 / 11
19. Automatica, 47(9):18781886.
Schmid, T., Charbiwala, Z., Anagnostopoulou, Z., Srivastava, M. B.,
and Dutta, P. (2010).
A case against routing-integrated time synchronization.
In Proc. of the 8th ACM Conf. on Embedded Networked Sensor
Systems, SenSys '10, pages 267280, New York, NY, USA. ACM.
Schmid, T., Charbiwala, Z., Shea, R., and Srivastava, M. (2009).
Temperature compensated time synchronization.
Embedded Systems Letters, IEEE, 1(2):37 41.
Serugendo, G., Gleizes, M.-P., and Karageorgos, A., editors (2011).
Natural Computing Series. Springer.
Sommer, P. and Wattenhofer, R. (2009).
Gradient Clock Synchronization in Wireless Sensor Networks.
In 8th ACM/IEEE Inter. Conf. on Information Processing in Sensor
Networks (IPSN), San Francisco, USA.
Sun, K., Ning, P., and Wang, C. (2006).
Önder GÜRCAN (Ege University) Self-Org. Time Syn. of WSNs with AVTs SASO 2013 11 / 11
20. Tinysersync: secure and resilient time synchronization in wireless
sensor networks.
In CCS '06: Proceedings of the 13th ACM conference on Computer
and communications security, pages 264277, New York, NY, USA.
ACM.
Tyrrell, A. and Auer, G. (2008).
Decentralized inter-base station synchronization inspired from nature.
In Vehicular Technology Conference, 2008. VTC 2008-Fall. IEEE 68th,
pages 15.
Tyrrell, A., Auer, G., and Bettstetter, C. (2010).
Emergent slot synchronization in wireless networks.
Mobile Computing, IEEE Transactions on, 9(5):719732.
van Greunen, J. and Rabaey, J. (2003).
Lightweight time synchronization for sensor networks.
In WSNA '03: Proceedings of the 2nd ACM international conference
on Wireless sensor networks and applications, pages 1119, New York,
NY, USA. ACM.
Önder GÜRCAN (Ege University) Self-Org. Time Syn. of WSNs with AVTs SASO 2013 11 / 11
21. Werner-Allen, G., Tewari, G., Patel, A., Welsh, M., and Nagpal, R.
(2005).
Firey-inspired sensor network synchronicity with realistic radio eects.
In SenSys '05: Proceedings of the 3rd international conference on
Embedded networked sensor systems, pages 142153, New York, NY,
USA. ACM.
Yildirim, K. S. and Kantarci, A. (2013a).
External gradient time synchronization in wireless sensor networks.
Parallel and Distributed Systems, IEEE Transactions on,
99(PrePrints):1.
Yildirim, K. S. and Kantarci, A. (2013b).
Time synchronization based on slow ooding in wireless sensor
networks.
IEEE Transactions on Parallel and Distributed Systems,
99(PrePrints):1.
Yu, J. and Tirkkonen, O. (2008).
Self-organized synchronization in wireless network.
Önder GÜRCAN (Ege University) Self-Org. Time Syn. of WSNs with AVTs SASO 2013 11 / 11
22. In Self-Adaptive and Self-Organizing Systems, 2008. SASO '08. Second
IEEE International Conference on, pages 329338.
Zhang, H., Llorca, J., Davis, C. C., and Milner, S. D. (2012).
Nature-inspired self-organization, control, and optimization in
heterogeneous wireless networks.
Mobile Computing, IEEE Transactions on, 11(7):12071222.
Önder GÜRCAN (Ege University) Self-Org. Time Syn. of WSNs with AVTs SASO 2013 11 / 11