Suhinthan Maheswararajah

Microbial and viral metagenomes of a subtropical freshwater reservoir subject to climatic disturbances

Extreme climatic activities, such as typhoons, are widely known to disrupt our natural environment. In particular, studies have revealed that typhoon-induced perturbations can result in several long-term effects on various ecosystems. In this study, we have conducted a 2-year metagenomic survey to investigate the microbial and viral community dynamics associated with environmental changes and seasonal variations in an enclosed freshwater reservoir subject to episodic typhoons. We found that the microbial community structure and the associated metagenomes continuously changed, where microbial richness increased after typhoon events and decreased during winter. Among the environmental factors that influenced changes in the microbial community, precipitation was considered to be the most significant. Similarly, the viral community regularly showed higher relative abundances and diversity during summer in comparison to winter, with major variations happening in several viral families including Siphoviridae, Myoviridae, Podoviridae and Microviridae. Interestingly, we also found that the precipitation level was associated with the terrestrial viral abundance in the reservoir. In contrast to the dynamic microbial community (L-divergence 0.73±0.25), we found that microbial metabolic profiles were relatively less divergent (L-divergence 0.24±0.04) at the finest metabolic resolution. This study provides for the first time a glimpse at the microbial and viral community dynamics of a subtropical freshwater ecosystem, adding a comprehensive set of new knowledge to aquatic environments.

Sensor Scheduling for Target Tracking by Suboptimal Algorithms

We analyze the problem of tracking a single target, from which measurements are taken using noisy sensors. Each measurement is associated with the measurement error, usage cost, and physical and computational constraints. As it is acceptable for many real applications that target dynamics are modeled as a linear system that is impaired by white Gaussian noise. Moreover, it is assumed that sensor measurements are linearly distributed with white Gaussian noise. Optimal sensor scheduling is achieved by finding the sensor sequence that minimizes the total cost, which consists of the measurement error and sensor usage cost for the entire time horizon subject to specific system constraints. To handle this discrete optimization problem, we propose well-performing suboptimal methods for different energy constraints in sensor nodes. First, we propose a suboptimal method called the best step look-ahead technique, which performs very well when the energy constraints can safely be removed due to their negligible influence on the overall system. We also show that, under certain assumptions, the Viterbi algorithm can be applied as a suboptimal method to obtain attractive results. Second, the energy constraints are relaxed using Lagrangian multipliers to formulate the problem as a min-max optimization problem. We use particle swarm optimization to tune the Lagrangian multipliers and Viterbi algorithm to find the optimal sensor sequence. To illustrate the effectiveness of our algorithms in realistic settings, we study a numerical problem of single target tracking with several noisy sensors and convincingly show that the proposed methods perform better than existing methods.

ViQuaS: An improved reconstruction pipeline for viral quasispecies spectra generated by next-generation sequencing

MOTIVATION The combined effect of a high replication rate and the low fidelity of the viral polymerase in most RNA viruses and some DNA viruses results in the formation of a viral quasispecies. Uncovering information about quasispecies populations significantly benefits the study of disease progression, antiviral drug design, vaccine design and viral pathogenesis. We present a new analysis pipeline called ViQuaS for viral quasispecies spectrum reconstruction using short next-generation sequencing reads. ViQuaS is based on a novel reference-assisted de novo assembly algorithm for constructing local haplotypes. A significantly extended version of an existing global strain reconstruction algorithm is also used. RESULTS Benchmarking results showed that ViQuaS outperformed three other previously published methods named ShoRAH, QuRe and PredictHaplo, with improvements of at least 3.1-53.9% in recall, 0-12.1% in precision and 0-38.2% in F-score in terms of strain sequence assembly and improvements of at least 0.006-0.143 in KL-divergence and 0.001-0.035 in root mean-squared error in terms of strain frequency estimation, over the next-best algorithm under various simulation settings. We also applied ViQuaS on a real read set derived from an in vitro human immunodeficiency virus (HIV)-1 population, two independent datasets of foot-and-mouth-disease virus derived from the same biological sample and a real HIV-1 dataset and demonstrated better results than other methods available.

Sensor Scheduling For Target Tracking Using Particle Swarm Optimization

This paper presents a new solution to the problem of optimal sensor scheduling for tracking a target with several noisy sensor measurements. The state of the target is modeled as a linear Gaussian model and the measurements are assumed linearly related to the state model and impaired by Gaussian noise. The state and the mean square error (MSE) of the estimated state can be calculated recursively by Kalman filtering technique. Each measurement is associated with measurement error, usage cost and physical and computational constraints. We consider the sensor scheduling problem as finding the optimal sequence of the sensors in order to minimize the measurement error and sensor usage cost for the entire time horizon subject to satisfying the constraints under consideration. In this paper we use the particle swarm optimization to find a sub-optimal sensor schedule. We study a numerical problem with tracking a vehicle with three noisy sensors and results show that the sensor scheduling obtained from the proposed method is very close to the optimal solution within a reasonable number of iterations

Mobile Sensor Management For Target Tracking

In sensor networks, the problem of coverage is a fundamental issue for randomly distributed sensor nodes. In target tracking, it is important to gather a sufficient number of measurements from the sensors to estimate the target trajectory. This paper presents a new approach to improve the tracking accuracy by using mobile sensors with restricted movements. The state of the target and sensors are modeled as a linear Gaussian model and the measurements are assumed non linearly related to the state model and impaired by Gaussian noise. Extended Kalman filtering (EKF) technique is used to estimate the predicted mean square error (MSE) of the estimated target state. We attempt to find the optimal sensor movement and sensor sequence in order to minimize the predicted estimation error subject to satisfying the constraints. Simulation results show that the proposed approach improves the tracking performance

Adaptive wireless sensor networks powered by hybrid energy harvesting for environmental monitoring

Due to the cost-effective nature and deployment flexibility of wireless sensor network (WSN), it has been extensively used in many real world applications. Sensor nodes are relatively inexpensive and capable of data processing and wireless communication with some level of intelligence, they play a key role in real world applications. Precision irrigation in agriculture is a key application of wireless sensor network. Typically, a sensor node is powered by its on-board battery source. This limitation fully or partially contributes to causing many problems in the network such as the loss of connectivity of a sensor node known as orphaned-node. Moreover, available number of sensor types in a sensor node is typically limited and it requires a significant modification in hardware and software interfaces to extend the number of sensor types. In this paper, we propose an adaptive sensor node system combining a flexible hardware prototype and innovative energy harvesting techniques to optimise the performance of the network operating in a large farming environment.

Management of Orphaned-Nodes in Wireless Sensor Networks for Smart Irrigation Systems

Wireless sensor networks may have unconnected nodes known as orphaned nodes due to their failure in obtaining a network address from a router-capable parent node in the network initialization process. The existence of a large number of orphaned nodes adversely affects the performance of wireless sensor network applications such as real time sensor-based automated irrigation control systems. We investigated the optimal management of orphaned nodes in a sensor network deployed in an automated irrigation system. In practice, it is unavoidable that sensor measurements contain random noise. The presence of orphan nodes adds to the effect of measurement noise further reducing the precision of irrigation management. However, reconnecting/restoring orphaned nodes to the sensor network may require some compromises to be made since the parent nodes are restricted in the maximum number of children they can possess. Optimal restoration can be achieved by finding the optimal parent node for each orphaned node that improves irrigation management. We propose two algorithms to suboptimally restore the orphaned nodes to the network, satisfying network constraints for small and large areas of farming. Numerical examples are presented to demonstrate the performance of the proposed methods.

Energy Adaptive Sensor Scheduling for Noisy Sensor Measurements

In wireless sensor network applications, sensor measurements are corrupted by noises resulting from harsh environmental conditions, hardware and transmission errors. Minimising the impact of noise in an energy constrained sensor network is a challenging task. We study the problem of estimating environmental phenomena (e.g., temperature, humidity, pressure) based on noisy sensor measurements to minimise the estimation error. An environmental phenomenon is modeled using linear Gaussian dynamics and the Kalman filtering technique is used for the estimation. At each time step, a group of sensors is scheduled to transmit data to the base station to minimise the total estimated error for a given energy budget. The sensor scheduling problem is solved by dynamic programming and one-step-look-ahead methods. Simulation results are presented to evaluate the performance of both methods. The dynamic programming method produced better results with higher computational cost than the one-step-look-ahead method.

Energy Aware Sensor Group Scheduling to Minimise Estimated Error from Noisy Sensor Measurements

In wireless sensor network applications, sensor measurements are corrupted by noise resulting from harsh environmental conditions, hardware and transmission errors. Minimising the impact of noise in an energy constrained sensor network is a challenging task. We study the problem of estimating environmental phenomena (e.g., temperature, humidity, pressure) based on noisy sensor measurements to minimise the estimation error. An environmental phenomenon is modeled using linear Gaussian dynamics and the Kalman filtering technique is used for the state estimation. At each time step, a group of sensors is scheduled to transmit data to the base station to minimise the total estimated error for a given energy budget. We considered a diverse solution for scheduling sensors from heuristic-based Particle Swarm Optimisation (PSO) to dynamic programming and one-step-look-ahead methods. The simulation results show that PSO outperforms all other proposedmethods and it requires less computational time than dynamic programming.