The importance of wireless sensor networks in structural health monitoring is unceasingly growing, because of the increasing demand for both safety and security in the cities. The speedy growth of wireless technologies has considerably developed the progress of structural monitoring systems with the combination of wireless sensor network technology. Wireless sensor network–based structural health monitoring system introduces a novel technology with compelling advantages in comparison to traditional wired system, which has the benefits of reducing installation and maintenance costs of structural health monitoring systems. However, structural health monitoring has brought an additional complex challenges in network design to wireless sensor networks. This article presents a contemporary review of collective experience the researchers have gained from the application of wireless sensor networks for structural health monitoring. Technologies of wired and wireless sensor systems are investigated along with wireless sensor node architecture, functionality, communication technologies, and its popular operating systems. Then, comprehensive summaries for the state-of-the-art academic and commercial wireless platform technologies used in laboratory testbeds and field test deployments for structural health monitoring applications are reviewed and tabulated. Following that, classification taxonomy of the key challenges associated with wireless sensor networks for structural health monitoring to assist the researchers in understanding the obstacles and the suitability of implementing wireless technology for structural health monitoring applications are deeply discussed with available research efforts in order to overcome these challenges. Finally, open research issues in wireless sensor networks for structural health monitoring are explored.

As we grow old, our desire for being independence does not decrease while our health needs to be monitored more frequently. Accidents such as falling can be a serious problem for the elderly. An accurate automatic fall detection system can help elderly people be safe in every situation. In this paper a waist worn fall detection system has been proposed. A tri-axial accelerometer (ADXL345) was used to capture the movement signals of human body and detect events such as walking and falling to a reasonable degree of accuracy. A set of laboratory-based falls and activities of daily living (ADL) were performed by healthy volunteers with different physical characteristics. This paper presents the comparison of different machine learning classification algorithms using Waikato Environment for Knowledge Analysis (WEKA) platform for classifying falling patterns from ADL patterns. The aim of this paper is to investigate the performance of different classification algorithms for a set of recorded acceleration data. The algorithms are Multilayer Perceptron, Naive Bayes, Decision tree, Support Vector Machine, ZeroR and OneR. The acceleration data with a total data of 6962 instances and 29 attributes were used to evaluate the performance of the different classification algorithm. Results show that the Multilayer Perceptron algorithm is the best option among other mentioned algorithms, due to its high accuracy in fall detection. 978-1-4577-1967-7/12/$26.00 ©2011 IEEE [ 131 ]

Abstract-Biological swarm is a fascinating behavior of nature that has been successfully applied to solve human problem especially for robotics application. The high economical cost and large area required to execute swarm robotics scenarios does not permit experimentation with real robot. Model and simulation of the mass number of these robots are extremely complex and often inaccurate. This paper describes the design decision and presents the development of an autonomous miniature mobile-robot (AMiR) for swarm robotics research and education. The large number of robot in these systems allows designing an individual AMiR unit with simple perception and mobile abilities. Hence a large number of robots can be easily and economically feasible to be replicated. AMiR has been designed as a complete platform with supporting software development tools for robotics education and researches in the Department of Computer and Communication Systems Engineering, UPM. The experimental results demonstrate the feasibility of using this robot to implement swarm robotic applications.Index Terms-Autonomous, AMiR, Swarm intelligent, Low-cost, Platform, Education. I. INTRODUCTIONRobots are increasingly being integrated into working tasks to replace humans. They are currently used in many fields of applications including office, military tasks, hospital operations, industrial automation, security systems, dangerous environment and agriculture [1]. Several types of mobile robots with different dimensions are designed [2][3][4][5][6][7][8][9] for various robotic applications. The autonomous swarm mobile robot is a strategy to provide a robust and flexible robotics system by exploiting a large number robot [10,11]. This strategy allows coordination of simply physical robot to cooperatively execute a single global task. Each individual robot in the swarm should have an autonomous behavior without any human intervention. The economic cost problem are often associated with swarm applications due to the large number of robots required (>100 unit). The design of AMiR has considered this issue and due to its small size, experiment could be conducted cost effectively in a small working area.Commercial robot manufacturers provide various robotics solutions and accessories for teaching, research and development [12, 13]. However, the associated cost and expandability of the robot architecture is a concern. Nearly all commercial robots does not have suitable development environment to implement swarm scenarios. Although the size of AMiR is small (6cm x 7.3cm x 4.7cm), it has been equipped with required modules such as perception, locomotion and communication. Additional small peripheral can be designed and connected as a stackable extension board with simple communication scheme.Jasmine [14, 15] is a robot platform designed specifically to implement swarm applications. Although the details of the designs are available, Jasmine is designed as a micro-robot in the size of less then 3cm cube. Hence, it could not be easily reproduced without incurr...

a b s t r a c tConventional fuzzy logic controller is applicable when there are only two fuzzy inputs with usually one output. Complexity increases when there are more than one inputs and outputs making the system unrealizable. The ordinal structure model of fuzzy reasoning has an advantage of managing high-dimensional problem with multiple input and output variables ensuring the interpretability of the rule set. This is achieved by giving an associated weight to each rule in the defuzzification process. In this work, a methodology to design an ordinal fuzzy logic controller with application for obstacle avoidance of Khepera mobile robot is presented. The implementation will show that ordinal structure fuzzy is easier to design with highly interpretable rules compared to conventional fuzzy controller. In order to achieve high accuracy, a specially tailored Genetic Algorithm (GA) approach for reinforcement learning has been proposed to optimize the ordinal structure fuzzy controller. Simulation results demonstrated improved obstacle avoidance performance in comparison with conventional fuzzy controllers. Comparison of direct and incremental GA for optimization of the controller is also presented.

This paper proposes several designs for a reliable infra-red based communication techniques for swarm robotic applications. The communication system was deployed on an autonomous miniature mobile robot (AMiR), a swarm robotic platform developed earlier. In swarm applications, all participating robots must be able to communicate and share data. Hence a suitable communication medium and a reliable technique are required. This work uses infrared radiation for transmission of swarm robots messages. Infrared transmission methods such as amplitude and frequency modulations will be presented along with experimental results. Finally the effects of the modulation techniques and other parameters on collective behavior of swarm robots will be analyzed

Achieving reliable operation under the influence of deep-submicrometer noise sources including crosstalk noise at low voltage operation is a major challenge for network on chip links. In this paper, we propose a coding scheme that simultaneously addresses crosstalk effects on signal delay and detects up to seven random errors through wire duplication and simple parity checks calculated over the rows and columns of the two-dimensional data. This high error detection capability enables the reduction of operating voltage on the wire leading to energy saving. The results show that the proposed scheme reduces the energy consumption up to 53% as compared to other schemes at iso-reliability performance despite the increase in the overhead number of wires. In addition, it has small penalty on the network performance, represented by the average latency and comparable codec area overhead to other schemes.

This paper analyzes the collective behaviors of swarm robots that play role in the aggregation scenario. Honeybee aggregation is an inspired behavior of young honeybees which tend to aggregate around an optimal zone. This aggregation is implemented based on variation of parameters values. In the second phase, two modifications on original honeybee aggregation namely dynamic velocity and comparative waiting time are proposed. Results of the performed experiments showed the significant differences in collective behavior of the swarm system for different algorithms.

This article presents an alternative approach of solving global positioning system (GPS) outages without requiring any prior information about the characteristics of the inertial navigation system (INS) and GPS sensors. INS can be used as a standalone system to bridge the outages during GPS signal loss. Kalman filter (KF) is widely used in INS and GPS integration to present a forceful navigation solution by overcoming the GPS outage problems. Unfortunately, KF is usually criticized for working under predefined models and for its observability problem of hidden state variables, sensor dependency, and linearization dependency. This approach utilizes a genetic neuro-fuzzy system (GANFIS) to predict the INS position and velocity errors during GPS signal blockages suitable for real-time application. The proposed model is able to deal with noise and disturbances in the GPS and INS output data in different dynamic environments compared to other traditional filtering algorithms such as the neural network and neuro fuzzy. Real field test results using the microelectro-mechanical system grade inertial measurement unit with an integrated GPS shows a significant improvement obtained from the integrated GPS/INS system using the GANFIS module compared to traditional methods such as Kalman filtering, particularly during long GPS satellite signal blockage.