Cost Minimization of Sensor Placement and Routing in Wireless Sensor Networks

2016 ◽  
pp. 115-134
Author(s):  
Tata Jagannadha Swamy ◽  
Garimella Rama Murthy
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Cost Minimization ◽  
Real Life ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Wireless Sensor Nodes ◽  
Technological Advances ◽  
Strongly Connected ◽  
Placement And Routing

Wireless Sensor Nodes (WSNs) are small in size and have limited energy resources. Recent technological advances have facilitated widespread use of wireless sensor networks in many real world applications. In real life situations WSN has to cover an area or monitor a number of nodes on a plane. Sensor node's coverage range is proportional to their cost, as high cost sensor nodes have higher coverage ranges. The main goal of this paper is to minimize the node placement cost with the help of uniform and non-uniform 2D grid planes. Authors propose a new algorithm for data transformation between strongly connected sensor nodes, based on graph theory.

Cost Minimization of Sensor Placement and Routing in Wireless Sensor Networks

2020 ◽  
pp. 1286-1301
Author(s):  
Tata Jagannadha Swamy ◽  
Garimella Rama Murthy
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Cost Minimization ◽  
Real Life ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Wireless Sensor Nodes ◽  
Technological Advances ◽  
Strongly Connected ◽  
Placement And Routing

Wireless Sensor Nodes (WSNs) are small in size and have limited energy resources. Recent technological advances have facilitated widespread use of wireless sensor networks in many real world applications. In real life situations WSN has to cover an area or monitor a number of nodes on a plane. Sensor node's coverage range is proportional to their cost, as high cost sensor nodes have higher coverage ranges. The main goal of this paper is to minimize the node placement cost with the help of uniform and non-uniform 2D grid planes. Authors propose a new algorithm for data transformation between strongly connected sensor nodes, based on graph theory.

Non Uniform Grid Based Cost Minimization and Routing in Wireless Sensor Networks

2012 ◽  
Vol 2 (1) ◽  
pp. 16-28
Author(s):  
Tata Jagannadha Swamy ◽  
Jayant Vaibhav Srivastava ◽  
Garimella Ramamurthy
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Target Location ◽  
Cost Minimization ◽  
Sensor Placement ◽  
Real Life ◽  
Minimum Cost ◽  
Wireless Sensor ◽  
Uniform Grid ◽  
Technological Advances

Recent technological advances have facilitated the widespread use of wireless sensor networks in many applications. In real life situations we have to cover or monitor a lot of points/places on plane. Sensor’s range is proportional to their cost, as high cost sensors have higher ranges. In this paper the authors developed a new algorithm for sensor placement for target location with cost minimization and coverage to non-uniform plane. Sensor placement for target location implies that they are given different type of sensors with different cost and range for given points on plane, which are to be covered with minimum cost. Then the authors discuss how information can be passed from one node to another.

scholarly journals Calculating the Number of Cluster Heads Based on the Rate-Distortion Function in Wireless Sensor Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-7
Author(s):  
Mingxin Yang ◽  
Jingsha He ◽  
Yuqiang Zhang
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Data Fusion ◽  
Rate Distortion ◽  
Distortion Function ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Wireless Sensor Nodes ◽  
Cluster Heads

Due to limited resources in wireless sensor nodes, energy efficiency is considered as one of the primary constraints in the design of the topology of wireless sensor networks (WSNs). Since data that are collected by wireless sensor nodes exhibit the characteristics of temporal association, data fusion has also become a very important means of reducing network traffic as well as eliminating data redundancy as far as data transmission is concerned. Another reason for data fusion is that, in many applications, only some of the data that are collected can meet the requirements of the sink node. In this paper, we propose a method to calculate the number of cluster heads or data aggregators during data fusion based on the rate-distortion function. In our discussion, we will first establish an energy consumption model and then describe a method for calculating the number of cluster heads from the point of view of reducing energy consumption. We will also show through theoretical analysis and experimentation that the network topology design based on the rate-distortion function is indeed more energy-efficient.

scholarly journals Wireless Sensor Networks for Indoor Environment Monitoring Using LabVIEW

2011 ◽  
pp. 87-90
Author(s):  
Mrutyunjay Rout ◽  
Dr. Harish Kumar Verma ◽  
Subhashree Das
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Indoor Environment ◽  
Environmental Parameters ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Environment Monitoring ◽  
Wireless Sensor Nodes ◽  
Local Decision ◽  
Mems Technology

Wireless sensor networks (WSNs) have gained worldwide attention in recent years, particularly with the rapid progress in Micro-Electro-Mechanical Systems (MEMS) technology which has facilitated the development of smart sensors. These sensors are small, with limited processing and computing resources, and they are inexpensive compared to traditional sensors. These sensor nodes can sense, measure, and gather information from the environment and, based on some local decision process, they can transmit the sensed data to the user. WSNs are large networks made of a numerous number of sensor nodes with sensing, computation, and wireless communication capabilities. In present work we provide a brief summary of the state-ofthe- art in wireless sensor networks, investigate the feasibility of indoor environment monitoring using crossbow wireless sensor nodes. Here we used nesC programming language and TinyOS operating system for programming Crossbow sensor nodes and LabVIEW GUI is used for displaying different indoor environmental parameters such as temperature, humidity and light acquired from different Wireless sensor nodes. These sensor readings can help building administrators to monitor the physical conditions of the environment in a building for creating optimized energy usage.

Basic Concepts of Wireless Sensor Networks

2011 ◽  
pp. 57-64
Author(s):  
Lina M. Pestana Leão de Brito ◽  
Laura M. Rodríguez Peralta
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Low Cost ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Distributed Sensor Networks ◽  
Distributed Sensor ◽  
Technological Advances ◽  
The Cost ◽  
Communication Equipment

As with many technologies, defense applications have been a driver for research in sensor networks, which started around 1980 due to two important programs of the Defense Advanced Research Projects Agency (DARPA): the distributed sensor networks (DSN) and the sensor information technology (SensIT) (Chong & Kumar, 2003). However, the development of sensor networks requires advances in several areas: sensing, communication, and computing. The explosive growth of the personal communications market has driven the cost of radio devices down and has increased the quality. At the same time, technological advances in wireless communications and electronic devices (such as low-cost, low-power, small, simple yet efficient wireless communication equipment) have enabled the manufacturing of sensor nodes and, consequently, the development of wireless sensor networks (WSNs).

scholarly journals DESIGN ISSUES AND CLASSIFICATION OF WSNS OPERATING SYSTEMS

2013 ◽  
pp. 281-285
Author(s):  
ANIL KUMAR SHARMA ◽  
SURENDRA KUMAR PATEL ◽  
GUPTESHWAR GUPTA
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Operating Systems ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Design Issues ◽  
Wireless Sensor Nodes ◽  
Computing Environments ◽  
Computational Resources

Wireless Sensor Networks is an emerging area of research. Wireless Sensor networks (WSNs) face lot of problems that do not arise in other types of wireless networks and computing environments. Limited computational resources, power constraints, low reliability and higher density of sensor nodes (motes) are just some basic problems that have to be considered when designing or selecting a new operating system in order to evaluate the performance of wireless sensor nodes (motes). In this paper we focused on design issues, challenges and classification of operating systems for WSNs.

scholarly journals WSN Based Flood Estimations and Early Cautioning System using IoT

2019 ◽  
Vol 8 (10) ◽  
pp. 3916-3919
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Physical Phenomenon ◽  
Real Life ◽  
Network Connectivity ◽  
Weather Conditions ◽  
Environmental Data ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
The Internet

Wireless Sensor Networks (WSN ) provides virtual layer where knowledge regarding actual world can be retrieved by any computational arrangement as these operate as digital skin. These are irreplaceable possessions used for comprehending ideas of IoT as they are used to gather information about physical phenomenon. IoT offers virtual interpretation through Internet Protocol towards a huge variation of real-life objects from buses to saucer, from building to trees in woods. Its appeal is the universal widespread access to the status and location of anything we may be interested in. The Internet of Things (IoT) is the network of physical objects, devices, vehicles, buildings and other items which are embedded with electronics, software, sensors, and network connectivity, which enables these objects to collect and exchange data. WSNs are combined into the “IoT”, where sensor nodes join the Internet vigorously and use it to collaborate and carry out their tasks. Wireless sensor networks (WSN) are well suited for longterm environmental data acquisition for IoT representation. Weather conditions monitoring is made by gathering quantifiable information regarding prevailing condition of atmospheric procedure to venture how will it progress in that location

scholarly journals MAXIMIZING THE LIFETIME AND SECURITY OF WIRELESS SENSOR NETWORKS

2013 ◽  
pp. 19-22
Author(s):  
SARANYA. S ◽  
GOWRI. V
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Security ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Fuzzy Approach ◽  
Biological Detection ◽  
Technological Advances ◽  
Military Healthcare

Recent technological advances have facilitated the widespread use of wireless sensor networks in many applications such as battle field surveillance, environmental observations, biological detection and industrial diagnostics. In wireless sensor networks, sensor nodes are typically power-constrained with limited lifetime, and so it’s necessary to understand however long the network sustains its networking operations. We can enhance the quality of monitoring in wireless sensor networks by increasing the WSNs lifetime. At the same time WSNs are deployed for monitoring in a range of critical domains such as military, healthcare etc. Accordingly, these WSNs are vulnerable to attacks. Now this proposed work concentrate on maximizing the security of WSNs with the already existing approach (i.e. combination of A* and fuzzy approach) for maximizing the lifetime of WSNs. This paper ensures sensed data security by providing authenticity, integrity, confidentiality. So, this approach provides more effective and efficient way for maximizing the lifetime and security of the WSNs.

scholarly journals Future Trends in Wireless Sensor Network (WSN)

2019 ◽  
Vol 8 (6S2) ◽  
pp. 480-482
Keyword(s):  
Wireless Sensor Networks ◽  
Wireless Sensor Network ◽  
Sensor Networks ◽  
Sensor Network ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Future Trends ◽  
Wireless Sensor Nodes ◽  
Military Application ◽  
Remote Environment

In part years wireless sensor networks (WSNs) have shown great improvement and also have become trusted areas in research. A wireless sensor networks (WSNs) is made up of many wireless sensor nodes that provides the source field and sink of a wireless network. The ability to sense the surrounding nodes, computing and connecting to other nodes wirelessly provide the wireless sensor network s(WSNs).the application of WSN is seen in many areas like military application, tracking, monitoring remote environment, surveillance, healthcare department and so on. Because of wide application the challenges for better developed technology and improvement have increased .this paper discuss some of the recent and future trends of Wireless sensor network. [1],[ 3],[5]

scholarly journals Characterization and Modeling of Received Signal Strength and Charging Time for Wireless Energy Transfer

2015 ◽  
Vol 2015 ◽  
pp. 1-15 ◽  
Author(s):  
Uthman Baroudi ◽  
Amin-ud-din Qureshi ◽  
Samir Mekid
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Transfer ◽  
Sensor Networks ◽  
Signal Strength ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Wireless Energy Transfer ◽  
Wireless Sensor Nodes ◽  
Charging Time ◽  
Indoor And Outdoor

Wireless sensor networks can provide effective means for monitoring and controlling a wide range of applications. Recently, tremendous effort was directed towards devising sensors powered from ambient sources such as heat, wind, and vibration. Wireless energy transfer is another source that has attractive features that make it a promising candidate for supplying power to wireless sensor nodes. This paper is concerned with characterizing and modeling the charging time and received signal strength indicator for wireless energy transfer system. These parameters play a vital role in deciding the geometry of sensor network and the routing protocols to be deployed. The development of communication protocols for wireless-powered wireless sensor networks is also improved with the knowledge of such models. These two quantities were computed from data acquired at various coordinates of the harvester relative to a fixed position of RF energy source. Data was acquired for indoor and outdoor scenarios using the commercially available PowerCast energy harvester and evaluation board. Mathematical models for both indoor and outdoor environments were developed and analyzed. A few guidelines on how to use these models were suggested. Finally, the possibility of harvesting the energy from the ambient RF power to energize wireless sensor nodes was also investigated.

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