Shrinkage Based Particle Filters for Tracking in Wireless Sensor Networks with Correlated Sparse Measurements

This thesis focuses on the development of mobile tracking approaches in wireless sensor networks (WSNs) with correlated and sparse measurements. In wireless networks, devices have the ability to transfer information over the network nodes via wireless signals. The strength of a wireless signal at a receiver is referred as the received signal strength (RSS) and many wireless technologies such as Wi-Fi, ZigBee, the Global Positioning Systems (GPS), and other Satellite systems provide the RSS measurements for signal transmission. Due to the availability of RSS measurements, various tracking approaches in WSNs were developed based on the RSS measurements. Unfortunately, the feasibility of tracking using the RSS measurements is highly dependent on the connectivity of the wireless signals. The existing connectivity may be intermittently disrupted due to the low-battery status on the sensor node or temporarily sensor malfunction. In ad-hoc networks, the number of observation of the RSS measurements rapidly changing due to the movements of network nodes and mobile user. As a result, the tracking algorithms have limited data to perform state inference and this prevents accurate tracking. Furthermore, consecutive RSS measurements obtained from nearby sensor nodes exhibit spatio-temporal correlation, which provides extra information to be exploited. Exploiting the statistical information on the measurements noise covariance matrix increases the tracking accuracy. When the number of observations is relatively large, estimating the measurement noise covariance matrix is feasible. However, when they are relatively small, the covariance matrix estimation becomes ill-conditioned and non-invertible. In situations where the RSS measurements are corrupted by outliers, state inference can be misleading. Outliers can come from the sudden environmental disturbances, temporary sensor failures or even from the intrinsic noise of the sensor device. The outliers existence should be considered accordingly to avoid false and poor estimates. This thesis proposes first a shrinkage-based particle filter for mobile tracking in WSNs. It estimates the correlation in the RSS measurement using the shrinkage estimator. The shrinkage estimator overcomes the problems of ill-conditioned and non-invertibility of the measurement noise covariance matrix. The estimated covariance matrix is then applied to the particle filter. Secondly, it develops a robust shrinkage based particle filter for the problem of outliers in the RSS measurements. The proposed algorithm provides a non-parametric shrinkage estimate and represents a multiple model particle filter. The performances of both proposed filters are demonstrated over challenging scenarios for mobile tracking

Effect of Distance and Direction on Distress Keyword Recognition using Ensembled Bagged Trees with a Ceiling-Mounted Omnidirectional Microphone

Audio surveillance can provide an effective alternative to video surveillance in situations where the latter is impractical. Nevertheless, it is essential to note that audio recording raises privacy and legal concerns that require unambiguous consent from all parties involved. By utilizing keyword recognition, audio recordings can be filtered, allowing for the creation of a surveillance system that is activated by distress keywords. This paper investigates the performance of the Ensemble Bagged Trees (EBT) classifier in recognizing the distress keyword "Please" captured by a ceiling-mounted omnidirectional microphone in a room measuring 4.064m (length) x 2.54m (width) x 2.794m (height). The study analyzes the impact of different distances (0m, 1m, and 2m) and two directions (facing towards and away from the microphone) on recognition performance. Results indicate that the system is more sensitive and better able to identify targeted signals when they are farther away and facing toward the microphone. The validation process demonstrates excellent accuracy, precision, and recall values exceeding 98%. In testing, the EBT achieved a satisfactory recall rate of 86.7%, indicating moderate sensitivity, and a precision of 97.7%, implying less susceptibility to false alarms, a crucial feature of any reliable surveillance system. Overall, the findings suggest that a single omnidirectional microphone equipped with an EBT classifier is capable of detecting distress keywords in a low-noise enclosed room measuring up to 4.0 meters in length, 4.0 meters in width, and 2.794 meters in height. This study highlights the potential of employing an omnidirectional microphone and EBT classifier as an edge audio surveillance system for indoor environments

AHP-TOPSIS based handover algorithm with distance prediction for 5G networks

5G is growing globally, and the handover performance is needed to be updated and improved to adapt to new changes in telecommunication. 5G are considered small cell networks that are anticipated to have a short dwell time for users that move at high speed, like a vehicle traversing the 5G cell at a rate of 40km/h and above. It induces unnecessary handover that causes poor user experience and waste of network resources. This research tackles the problem by proposing a new handover algorithm that integrates a travel distance prediction method with AHP-TOPSIS (Analytic Hierarchy Process - Techniques for Order Preference by Similarity for an Ideal Solution) decision making. The proposed algorithm has successfully reduced the unnecessary handover in 5G networks up to 89.75% compared to the conventional TOPSIS method

Handover Decision-Making Algorithm for 5G Heterogeneous Networks

The evolution of 5G small cell networks has led to the advancement of vertical handover decision-making algorithms. A mobile terminal (MT) tends to move from one place to another and, as the 5G network coverage is small, user network access will change frequently and lead to a high probability of unnecessary handover, which is a waste of network resources and causes degradation of service quality. This paper aims to reduce the number of unnecessary handovers in 5G heterogeneous networks by proposing a handover decision-making algorithm that integrates the dwelling time prediction technique and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The proposed algorithm reduces the number of unnecessary handovers by estimating the connection time to a small cell network using the dwell time prediction technique. The TOPSIS evaluates the network quality and chooses the best network based on user preference. The result shows that the proposed handover algorithm reduces the number of unnecessary handovers to small cell networks in high-speed scenarios. It also saves the network connection cost by up to 27.51% compared with the TOPSIS-based handover algorithm. As for throughput achievement, the proposed algorithm yields an improvement of 5.12%. The proposed algorithm significantly reduces the number of unnecessary handovers in the high-speed scenario while fulfilling user preferences

Matlab Codes for the Shrinkage based Particle Filter

It contains the Matlab codes for the developed shrinkage based particle filter. The shrinkage based particle filter combined the shrinkage estimator and the particle filter to jointly estimate the shadowing noise covariance matrix of measurements and the state of the mobile user

Cluster Heads Distribution of Wireless Sensor Networks via Adaptive Particle Swarm Optimization

Abstract-Wireless sensor networks consists of hundreds or thousands of sensor nodes supported by small capacity battery. For environmental monitoring purposes, sensor nodes must have high endurance capabilities. Therefore, selecting suitable cluster heads (CH) location becomes a challenging issue. In this work, cluster heads distribution based on adaptive particle swarm (PSO) is proposed. PSO is one of the swarm intelligence methods designed to find optimum solution by mimicking the behavior of bird flocking and fish schooling. Adaptive cognitive and social learning factor can achieve better convergence speed and particles reselection mechanism can reduce the chances of getting trapped in local maximum. The performance of the proposed method is compared with low energy adaptive cluster hierarchical (LEACH). Simulation result shows that proposed method outperforms LEACH in terms of first node die (FND) round, total data received by base station and energy consume per round

Design and development of an aquaponic system with a self-cleaning drainage pipe and real time pH monitoring system

This paper introduces a prototype of a sustainable aquaponic system with a self-cleaning drainage pipe design to control the water level and thus solves the problem of waste accumulation in the fish tank. An Internet of Things (IoT) based monitor system was designed to monitor in real-time the pH value of the water in the aquaponic system. This system is designed for local communities particularly small urban households or for educational demonstration purposes. The result shows that the prototype significantly reduces the waste accumulation, and therefore maintains the water pH levels between 6.5 to 8.0 which is ideal for fish growth. With the help of the self-cleaning mechanism and real-time pH monitoring capabilities, the plant growth was up to 18% better compared to 6% without using the system, and fish growth was 27% better compared to 10.2% to the one not using the system. The implementation of an IoT monitoring system and self-cleaning pipe installation had proven the success of the small-scale aquaponic system as shown by the healthy growth rate of the fish and vegetables

An extended adaptive mechanism of evolutionary based channel assignment via reinforcement

Current development in the field of wireless mobile communication is extremely limited by the capacity constraints of the available frequency spectrum. Hence proper utilisation of channel allocation techniques which are capable of ensuring efficient channel assignment is essential in order to solve the non-deterministic polynomial-time hard (NP-hard) channel assignment problem. The process of channel assignment must satisfy hard-constraints such as electromagnetic compatibility (EMC) and the demand of channels in a cell. Initial channel assignment parameters are obtained using self-learning scheme and evolutionary algorithms is used to fine-tune the estimated parameters from reinforcement learning algorithm to optimise the channel assignment problem in wireless mobile networks. Particle reselection and dynamic inertia approach in particle-swarm-optimisation (PSO) is shown to have 8 % improvement over the standard PSO algorithm. Subsequently, the introduction of PSO showed 70 -75 % power saving advantage over suboptimal resource allocation techniques

Non-invasive blood pressure and heart rate sensing using photoplethysmogram sensor

The non-invasive and cuffless blood pressure (BP) and heart rate (HR) monitoring device is designed to help people to monitor their BP and HR regularly. A Photoplethysmogram (PPG) sensor was used to obtain the PPG signal needed for the BP and HR calculations. The performance of the BP and HR monitoring device was tested based on the accuracy of the readings obtained by comparing readings from the proposed device with the readings obtained by clinical instruments. The factors that are affecting the accuracy of the sensor are also discussed in this paper