scholarly journals Hybrid Training with Binary Search Protocol for Wireless Sensor Networks

2007 ◽  
Vol 3 (3-4) ◽  
pp. 233-249 ◽  
Author(s):  
Ruzana Ishak ◽  
Qingwen Xu ◽  
Stephan Olariu ◽  
Shaharuddin Salleh
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Binary Search ◽  
Wireless Sensor ◽  
Training Procedure ◽  
Location Awareness ◽  
Distributed Sensor ◽  
Number Of Cycles ◽  
And Training ◽  
Training Protocol

Locationing problem in Wireless Sensor Networks(WSNs) can be viewed as a general distributed sensor problem. It is with sensors that can discover other nodes or estimate ranges between nodes, that serve as position references. In this paper, we show that sensors acquire coarse-grain location awareness by the training protocol. The training protocol which hybrids the synchronization and training procedure. In this protocol, synchronization and training are combined into one scheme. The sink node sends two beacons in each slot instead of one. In the training, sensor searching for its location using a binary search scheme. Our simulation results shown less number of cycles needed for training.

An Intelligent Sensor Placement Method to Reach a High Coverage in Wireless Sensor Networks

2013 ◽  
pp. 168-183
Author(s):  
Shirin Khezri ◽  
Karim Faez ◽  
Amjad Osmani
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Sensor Placement ◽  
New Method ◽  
Wireless Sensor ◽  
Sensor Deployment ◽  
Distributed Sensor Networks ◽  
Distributed Sensor ◽  
Grid Points ◽  
Adequate Coverage

Adequate coverage is one of the main problems for Sensor Networks. The effectiveness of distributed wireless sensor networks highly depends on the sensor deployment scheme. Optimizing the sensor deployment provides sufficient sensor coverage and saves cost of sensors for locating in grid points. This article applies the modified binary particle swarm optimization algorithm for solving the sensor placement in distributed sensor networks. PSO is an inherent continuous algorithm, and the discrete PSO is proposed to be adapted to discrete binary space. In the distributed sensor networks, the sensor placement is an NP-complete problem for arbitrary sensor fields. One of the most important issues in the research fields, the proposed algorithms will solve this problem by considering two factors: the complete coverage and the minimum costs. The proposed method on sensors surrounding is examined in different area. The results not only confirm the successes of using the new method in sensor placement, also they show that the new method is more efficiently compared to other methods like Simulated Annealing(SA), PBIL and LAEDA.

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 Detecting Suspicious Behavior From Only Positional Data With Distributed Sensor Networks

2005 ◽  
Author(s):  
Neil C. Rowe
Keyword(s):  
Mathematical Model ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Anomalous Behavior ◽  
Wireless Sensor ◽  
Distributed Sensor Networks ◽  
Distributed Sensor ◽  
Sample Data ◽  
Positional Data

Wireless sensor networks are increasingly popular, and are being used to measure simple properties of their environment. In many applications such as surveillance, we would like them to distinguish “suspicious” behavior automatically. We distinguish here between suspicious and anomalous behavior, and develop a mathematical model which we illustrate on some sample data. We show the model predicts six classic deception strategies. We conclude with analysis of more sophisticated deceptions that exploit system responses to simpler deceptions.

An Intelligent Sensor Placement Method to Reach a High Coverage in Wireless Sensor Networks

2011 ◽  
Vol 3 (3) ◽  
pp. 54-68
Author(s):  
Shirin Khezri ◽  
Karim Faez ◽  
Amjad Osmani
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Sensor Placement ◽  
New Method ◽  
Wireless Sensor ◽  
Sensor Deployment ◽  
Binary Particle Swarm Optimization ◽  
Distributed Sensor Networks ◽  
High Coverage ◽  
Distributed Sensor

Adequate coverage is one of the main problems for Sensor Networks. The effectiveness of distributed wireless sensor networks highly depends on the sensor deployment scheme. Optimizing the sensor deployment provides sufficient sensor coverage and saves cost of sensors for locating in grid points. This article applies the modified binary particle swarm optimization algorithm for solving the sensor placement in distributed sensor networks. PSO is an inherent continuous algorithm, and the discrete PSO is proposed to be adapted to discrete binary space. In the distributed sensor networks, the sensor placement is an NP-complete problem for arbitrary sensor fields. One of the most important issues in the research fields, the proposed algorithms will solve this problem by considering two factors: the complete coverage and the minimum costs. The proposed method on sensors surrounding is examined in different area. The results not only confirm the successes of using the new method in sensor placement, also they show that the new method is more efficiently compared to other methods like Simulated Annealing(SA), PBIL and LAEDA.

Clock Drift Management Using Nature Inspired Algorithms

2012 ◽  
Vol 5 (4) ◽  
pp. 48-62
Author(s):  
Prakash Tekchandani ◽  
Aditya Trivedi
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Time Synchronization ◽  
Synchronization Error ◽  
Wireless Sensor ◽  
Clock Skew ◽  
Clock Drift ◽  
Distributed Sensor ◽  
Network Applications ◽  
Nature Inspired Algorithms

Time Synchronization is common requirement for most network applications. It is particularly essential in a Wireless Sensor Networks (WSNs) to allow collective signal processing, proper correlation of diverse measurements taken from a set of distributed sensor elements and for an efficient sharing of the communication channel. The Flooding Time Synchronization Protocol (FTSP) was developed explicitly for time synchronization of wireless sensor networks. In this paper, we optimized FTSP for clock drift management using Particle Swarm Optimization (PSO), Variant of PSO and Differential Evolution (DE). The paper estimates the clock offset, clock skew, generates linear line and optimizes the value of average time synchronization error using PSO, Variant of PSO and DE. In this paper we present implementation and experimental results that produces reduced average time synchronization error using PSO, Variant of PSO and DE, compared to that of linear regression used in FTSP.

Sign in / Sign up

Export Citation Format

Share Document