Dynamic window secured implicit geographic forwarding for wireless sensor network.

Routing security is a major concerned in Wireless Sensor Network since a large scale of unattended nodes is deployed in ad hoc fashion with no possibility of a global addressing due to a limitation of node's memory and the node have to be self organizing when the systems require a connection with the other nodes. It becomes more challenging when the nodes have to act as the router and tightly constrained on energy and computational capabilities where any existing security mechanisms are not allowed to be fitted directly. These reasons thus increasing vulnerabilities to the network layer particularly and to the whole network, generally. In this paper, a Dynamic Window Secured Implicit Geographic Forwarding (DWSIGF) routing is presented where a dynamic time is used for collection window to collect Clear to Send (CTS) control packet in order to find an appropriate hoping node. The DWIGF is expected to minimize a chance to select an attacker as the hoping node that caused by a blackhole attack that happen because of the CTS rushing attack, which promise a good network performance with high packet delivery ratios

The impact of window's size in DWSIGF routing protocol.

In this study, different collection window's size is been analyzed to investigate the impact on network performance: packet delivery ratio, message overhead and end to end delay on the Dynamic Window Secured Implicit Geographic Forwarding (DWIGF) routing protocol where this protocol is based on a dynamic collection window approached. Its method on using dynamic window's size has minimized the probability of selecting attackers and guaranteed high packet delivery ratios when there is a blackhole attack in the communication link. The DWSIGF is then compared with the best chosen window's size to analyze the network performance with and without attacker in the communication line, respectively. The DWIGF is able to minimize a Clear To Send (CTS) rushing attack that leads to a blackhole and selectively forwarding attack with a guaranteed of high packet delivery ratios where a selection of a failed trade and all attacker is minimized, respectively. As a result, this routing protocol is promising a dynamic and secured communication without inserting any existing security mechanism inside

Comparison of melt flow index of propylene polymerisation in loop reactors using first principles and artificial neural network models

The inability to measure product quality in polymerisation industries on-line causes major difficulties. There are no on-line instruments to measure resin characteristics that define polymer quality, such as melt flow index (MFI) and density. MFI always often have to be evaluated in a time consuming and manpower intensive lab analysis. In most plants, MFI is measured only several times a day using a manual analytical test. An on-line MFI measurement is essential in fulfilling customer requirements and preventing losses. This paper presents models for soft sensors to measure MFI in industrial polypropylene loop reactors using first principle (FP) model and artificial neural network (ANN) model. For the FP model, two industrial interconnected loop reactors for propylene polymerisation are modelled as two continuous stirred tank reactors (CSTRs) in series. The mathematical models of nonlinear differential equations which describe the polymerisation process were solved numerically. The ANN model of the two loop reactors are developed by employing the concept of Feed- Forward Back Propagation (FFBP) network architecture using Levenberg-Marquardt training method. The ANN model act as estimator to predict the polymer MFI. Both models are developed and simulated in MATLAB. The simulation results of the MFI between FPM and ANN model are compared and analysed. The prediction of the ANN model is found to be more accurate compare to the MFI calculated by the FP model. The ANN model prediction is good within the range of training data. The CPU time recorded that ANN model is much faster than FP model

How do first year Malaysian chemical engineering students approach learning?

Dynamic technology development and innovation of the 21st century have challenged chemical engineers in their working aspects. Future engineers are required to have high competitiveness in knowledge and skills in a fast changing world, requiring them to adapt and learn fast at a deep level. Consequently, education becomes a crucial means in developing matured learners who can efficiently adapt and acquire new knowledge and skills. Determining the students' approach to learning as early as possible, whether deep or surface learning, is important to assist the students in their learning. Their approach to learning can reflect their academic performance. The objective of this study is to determine the learning approach of first year chemical engineering students in a Malaysian research university. To achieve this objective, a pre-post quasiexperimental design was used to determine the approach to learning of 57 first year chemical engineering students. Revised Study Process Questionnaire (RSPQ-2F), a 20-item instrument developed by John Biggs and colleagues was used to measure the student's learning approach at the beginning and at the end of the first semester. The quantitative data were analyzed using pair-sample t-Test to measure the mean RSPQ-2F scores. A p-value < .05 was considered as significant. The findings show that the students mostly use deep approach compared to surface approach to learning at the beginning of the semester. At the end of the semester, there was a slight increase in their deep approach to learning, although the increase is not significant. Similarly, there was a slight decrease in surface learning, although the decrease is not significant

Optimising traffic control for a congested intersectton

ln congested road networks, a signalised intersection that determines cycle time and green time splits based on current traffic demand will greatly improve traffic flows. Vehicle actuated traffic signals are very commonly used to control isolated intersections due to their ability to respond to the actual traffic demand. However it was observed that unnecessary stop delays occur because the given green time allocations were more than that required. This is due to position of the detector which is located in the vicinity of the stop line. A significant reduction in vehicle delay was observed when the detectors were located 30 - 40 meters from the stop lines. Also, the signal performance was observed to deteriorate when most arms of the junction were congested. The reason for this deterioration was that the pre-set maximum green times were not optimised for the actual traffic demand. ln this study the genetic algorithm technique was utilised to optimise traffic control by minimising a performance index which, in this case, is the stop-delay. ln the event of over congestion, the traffic signal will control the traffic by emulating manual control by traffic marshals by prioritising selected arms or giving more green times based on queue length or a combination of both

International Journal of Electrical and Computer Engineering 5:4 2010 Performance of a Connected Random Covered Energy Efficient Wireless Sensor Network

Abstract—For the sensor network to operate successfully, the active nodes should maintain both sensing coverage and network connectivity. Furthermore, scheduling sleep intervals plays critical role for energy efficiency of wireless sensor networks. Traditional methods for sensor scheduling use either sensing coverage or network connectivity, but rarely both. In this paper, we use random scheduling for sensing coverage and then turn on extra sensor nodes, if necessary, for network connectivity. Simulation results have demonstrated that the number of extra nodes that is on with upper bound of around 9%, is small compared to the total number of deployed sensor nodes. Thus energy consumption for switching on extra sensor node is small. Keywords—Wireless sensor networks, energy efficient network, performance analysis, network coverage. I

Video detection system for traffic Iight sensor

The point based inductive loop is widely used in conventional traffic light sensors. The sensor is used either to detect the presence of vehicles or : to measure the gap or headway of the arriving vehicle in the vehicle-actuated system or to count the traffic volume and to determine the queue length in a coordinated adaptive system. In a more sophisticated system, the sensor is also used to detect any traffic incident. However, the rising cost of installing the loops and disruption of traffic flows during installation or maintenance has resulted in the video detection system becoming more attractive. In addition, the cost of equipment for the video detection system has reduced substantially in the past l0 years. This paper describes the utilisation of a video camera and image processing to detect the presence of vehicles, to count the volume of approaching traffic, to measure queue length and to detect traffic incidents at the approach road of a signalised intersection. Neural networks were used to detect the presence of the vehicles, to detect the traffic incident and to measure the queue length by identifying whether the road surface was occupied by vehicles and whether these vehicles were moving or stationary for a specified duration of time. The number of arriving vehicles was counted by observing the fluctuation of the selected pixels values in the middle of the traffic lane. A single camera which was developed in this study is able to capture the above mentioned parameters simultaneously from a multi-lane road approach