Malka N. Halgamuge

Energy efficient cluster formation in wireless sensor networks

Energy optimized cluster formation for a set of randomly scattered wireless sensors is presented. Sensors within a cluster are expected to be communicating with a cluster head only. The cluster heads summarize and process sensor data from the clusters and maintain the link with the base station. The clustering is driven by the minimization of energy for all the sensors. Recent developments in clustering are used to support the work, and a cluster visualization interface is used to observe the simulation results.

Signal-based evaluation of handoff algorithms

We propose a new framework, based on signal quality, for performance evaluation and comparison between existing handoff algorithms. It includes new call quality measures and an off-line cluster-based computationally-simple heuristic algorithm to find a near optimal handoff sequence used as a benchmark. We then compare existing handoff algorithms and identify the trade-off between signal quality and number of handoffs.

MEASUREMENT AND ANALYSIS OF ELECTROMAGNETIC FIELDS FROM TRAMS, TRAINS AND HYBRID CARS

Electricity is used substantially and sources of electric and magnetic fields are, unavoidably, everywhere. The transportation system is a source of these fields, to which a large proportion of the population is exposed. Hence, investigation of the effects of long-term exposure of the general public to low-frequency electromagnetic fields caused by the transportation system is critically important. In this study, measurements of electric and magnetic fields emitted from Australian trams, trains and hybrid cars were investigated. These measurements were carried out under different conditions, locations, and are summarised in this article. A few of the measured electric and magnetic field strengths were significantly lower than those found in prior studies. These results seem to be compatible with the evidence of the laboratory studies on the biological effects that are found in the literature, although they are far lower than international levels, such as those set up in the International Commission on Non-Ionising Radiation Protection guidelines.

Multiple Model Predictive Flood Control in Regulated River Systems with Uncertain Inflows

This paper presents a novel approach to real time automatic flood control in a managed river network that is subject to uncertain inflows. The proposed approach uses multiple models to represent inflows ranging from low to high flow. Optimal model selection is achieved in a minimum mean square error sense using a bank of Kalman filters to identify the most likely inflow characteristic. There are no a-priori probabilities assigned to the individual models. Model Predictive Control is used for water level controller design. Our Adaptive Multi Model Predictive Control (AMMPC) method is proposed as an alternative to existing techniques that also use multiple inflow models but with a-priori inflow model probabilities, either weighted or equally likely. The performance of the approach is demonstrated using a simulated river-reservoir model as well as using data collected at the Wivenhoe Dam during the 2011 floods in Queensland, Australia.

Experiences of Deploying an Indoor Building Sensor Network

Monitoring and automatic control of building environment is a crucial application of Wireless Sensor Network (WSN) in which maximizing network lifetime is a key challenge. We investigate the link quality distribution to obtain full coverage of signal strength in a single floor of building environment, experimentally. Our results confirmed the transitional region is particular concern in wireless sensor network since it accommodates high variance unreliable links. The reason due to this transitional region in inside building environment could be the obstacles including concrete/brick walls, partitions, office furniture and other items affect as additional absorption term to the path loss.

Threat analysis of portable hack tools from USB storage devices and protection solutions

Information security risks associated with Universal Serial Bus (USB) devices have been a serious issue in corporate networks after the wide adoption of USB technologies in the computing industry in 2005. Recently, the U3 USB drives have been of great interest for attackers who want to utilize USB drives as their mobile hack tools. However, beside U3 technology, attackers also have another more flexible alternative, portable application or application virtualization, which allows a wide range of hack tools to be compiled into portable format and run from USB storage devices without requiring any USB specific platform such as U3. In this paper, we provide an investigation into hack tools on U3 platform and USB platform free portable hack tools, their working mechanism, and the compilation techniques. We also provide a general description of most dangerous hack tools with their payloads which can be compiled into portable format. Finally, our proposed solution is aimed at providing the most important and concise solutions for enterprise administrators to secure their systems from portable hack tools.

A Comparative Study of Classification Algorithms using Data Mining: Crime and Accidents in Denver City the USA

In the last five years, crime and accidents rates have increased in many cities of America. The advancement of new technologies can also lead to criminal misuse. In order to reduce incidents, there is a need to understand and examine emerging patterns of criminal activities. This paper analyzed crime and accident datasets from Denver City, USA during 2011 to 2015 consisting of 372,392 instances of crime. The dataset is analyzed by using a number of Classification Algorithms. The aim of this study is to highlight trends of incidents that will in return help security agencies and police department to discover precautionary measures from prediction rates. The classification of algorithms used in this study is to assess trends and patterns that are assessed by BayesNet, NaiveBayes, J48, JRip, OneR and Decision Table. The output that has been used in this study, are correct classification, incorrect classification, True Positive Rate (TP), False Positive Rate (FP), Precision (P), Recall (R) and F-measure (F). These outputs are captured by using two different test methods: k-fold cross-validation and percentage split. Outputs are then compared to understand the classifier performances. Our analysis illustrates that JRip has classified the highest number of correct classifications by 73.71% followed by decision table with 73.66% of correct predictions, whereas OneR produced the least number of correct predictions with 64.95%. NaiveBayes took the least time of 0.57 sec to build the model and perform classification when compared to all the classifiers. The classifier stands out producing better results among all the classification methods. This study would be helpful for security agencies and police department to discover data patterns and analyze trending criminal activity from prediction rates.

Reduced growth of soybean seedlings after exposure to weak microwave radiation from GSM 900 mobile phone and base station

The aim of this work was to study possible effects of environmental radiation pollution on plants. The association between cellular telephone (short duration, higher amplitude) and base station (long duration, very low amplitude) radiation exposure and the growth rate of soybean (Glycine max) seedlings was investigated. Soybean seedlings, pre-grown for 4 days, were exposed in a gigahertz transverse electromagnetic cell for 2 h to global system for mobile communication (GSM) mobile phone pulsed radiation or continuous wave (CW) radiation at 900 MHz with amplitudes of 5.7 and 41 V m(-1) , and outgrowth was studied one week after exposure. The exposure to higher amplitude (41 V m(-1)) GSM radiation resulted in diminished outgrowth of the epicotyl. The exposure to lower amplitude (5.7 V m(-1)) GSM radiation did not influence outgrowth of epicotyl, hypocotyls, or roots. The exposure to higher amplitude CW radiation resulted in reduced outgrowth of the roots whereas lower CW exposure resulted in a reduced outgrowth of the hypocotyl. Soybean seedlings were also exposed for 5 days to an extremely low level of radiation (GSM 900 MHz, 0.56 V m(-1)) and outgrowth was studied 2 days later. Growth of epicotyl and hypocotyl was found to be reduced, whereas the outgrowth of roots was stimulated. Our findings indicate that the observed effects were significantly dependent on field strength as well as amplitude modulation of the applied field.

High Powered Cluster Heads for Extending Sensor Network Lifetime

Extension of the lifetime for a sensor network is important for most if not all applications. We propose a method to extend network lifetime by introducing several special sensors with higher battery power than normal sensors. We use these special sensors as cluster heads until their battery capacity is reduced to that of a normal sensor node before adopting a low-energy adaptive clustering hierarchy (LEACH) type method. It is shown in this work that the network lifetime can be more than doubled when battery mAh of a special sensor is kept at 1500 mAh in comparison to normal sensors of 700 mAh. Comparing our approach with an equivalent LEACH system where the initial total battery capacities are equal in both methods, we achieve an increase of 104.23% in the network lifetime

Irrigation control based on model predictive control (MPC)

This research proposes A THEORETICAL FRAMEWORK based on model predictive control (MPC) for irrigation control to minimize both root zone soil moisture deficit (RZSMD) and irrigation amount under a limited water supply. We (i) investigate means to incorporate direct measurements to MPC (ii) introduce two Robust MPC techniques - Certainty Equivalence control (CE) and Disturbance Affine Feedback Control (DA) - to mitigate the uncertainty of weather forecasts, and (iii) provide conditions to obtain two important theoretical aspects of MPC - feasibility and stability - in the context of irrigation control. Our results show that system identification enables automation while incorporating direct measurements. Both DA and CE minimize RZSMD and irrigation amount under uncertain weather forecasts and always maintain soil moisture above wilting point subject to water availability. The theoretical results are compared against the model AQUACROP, weather data and forecasts from Shepparton, Australia. We also discuss the performance of Robust MPC under different water availability, soil, crop conditions. In general, MPC shows to be a promising tool for irrigation control. MPC is used to minimize both root zone soil moisture deficit and irrigation amount.System identification incorporates direct measurements to MPC enabling automation.Uncertainty in weather forecasts is mitigated using two modified Robust MPC approaches.Optimal operation can be guaranteed through the proposed method.Guaranteed operation above wilting point at all times subject to water availability.