The two main causes of packet corruption in wireless sensor network are multipath fading and WiFi interference. Identifying the cause of the corruption can be used to improve the reliability of the transmission. If the corruption is caused by WiFi interference, the network could change the channel to a free one. If it is caused by multipath fading, the network could reroute the traffic away from the obstacles or shorten the distance. This project proposes a new method of corruption-causes-identification for the two causes mentioned. It is an immediate online diagnostic tool for IEEE 802.15.4 packets with a retransmission mechanism. It provides a statistical boundary with a processed deviation of RSSI value and the frame symbol error rate, and also a rechecking mechanism to check the decisions. In this model, the deviation of RSSI value is measured by the estimated RSSI value of correct packets and the current detected RSSI value. The benefit of the deviation design is that the statistical model hardly needs to retrain and correct the parameters in different transmission environments. The project also discusses two rechecking mechanism methods, one employs an individual secondary classification with its own characters; the other combines the neighbor packets' features to smooththe probable errors. To investigate the performance of the "frame symbol errorrate and deviation of RSSI values"-based diagnostic tool, the evaluation parts provide a comparison of different length packets. The conclusion is that this diagnostic tool provides a correct judgment of the accuracy of packet corruption caused by multipath fading up to 98.70%, and an accuracy of up to 92.99% for the WiFi-interferenced packet corruption.

Abstract This dissertation aimed at developing a multi-modal sensing u-healthcare system (MSUS), which reflects the unique properties of a healthcare application in a wireless sensor network. Together with health parameters, such as ECG, SpO2 and blood pressure, the system also transfers context-aware data, including activity, position and tracking data, in a wireless sensor network environment at home or in a hospital. Since packet loss may have fatal consequences for patients, health-related data are more critical than most other types of monitoring data. Thus, compared to environmental, agricultural or industrial monitoring, healthcare monitoring in a wireless environment imposes different requirements and priorities. These include heavy data traffic with wavelike parameters in wireless sensor network and fatal data loss due to the traffic. To ensure reliable data transfer in a wireless sensor network, this research placed special emphasis on the optimization of sampling rate, packet length and transmission rate, and on the traffic reduction method. To improve the reliability and accuracy of diagnosis, the u-healthcare system also collects context-aware information on the user’s activity and location and provides real-time tracking. Waveform health parameters, such as ECG, are normally sampled in the 100 to 400 Hz range according to the monitoring purpose. This type of waveform data may incur a heavy burden in wireless communication. To reduce wireless traffic between the sensor nodes and the gateway node, the system utilizes on-site ECG analysis implemented on the sensor nodes as well as query architecture. A 3D VRML viewer was also developed for the realistic monitoring of the user’s moving path and location. Two communication methods, an 802.15.4-based wireless sensor network and a CDMA cellular network are used by sensors placed on the users’ bodies to gather medical data, which is then transmitted to a server PC at home or in the hospital, depending on whether the sensor is within or outside the range of the wireless sensor network.

Wireless Sensor Networks (WSNs) have recently attracted much interest in the research community because of their wide range of applications. One emerging application for WSNs involves their use in healthcare where they are generally termed Wireless Medical Sensor Networks (WMSNs). In a hospital, fitting patients with tiny, wearable, wireless vital sign sensors would allow doctors, nurses and others to continuously monitor the state of those in their care. In the healthcare industry, patients are expected to be treated in reasonable time and any loss in data availability can result in further decline in the patient's condition or can even lead to death. Therefore, the availability of data is more important than security concerns. The overwhelming priority is to take care of the patient, but the privacy and confidentiality of that patient's medical records cannot be neglected. In current healthcare applications, there are many problems concerning security policy violations such as unauthorised denial of use, unauthorised information modification and unauthorised information release of medical data in the real world environment. Current WSN access control models used the traditional Role-Based Access Control (RBAC) or cryptographic methods for data access control but the systems still need to predefine attributes, roles and policies before deployment. It is, however, difficult to determine in advance all the possible needs for access in real world applications because there may be unanticipated situations at any time. This research proceeds to study possible approaches to address the above issues and to develop a new access control model to fill the gaps in work done by the WSN research community. Firstly, the adaptive access control model is proposed and developed based on the concept of discretionary overriding to address the data availability issue. In the healthcare industry, there are many problems concerning unauthorised information release. So, we extended the adaptive access control model with a prevention and detection mechanism to detect security policy violations, and added the concept of obligation to take a course of action when a restricted access is granted or denied. However, this approach does not consider privacy of patients' information because data availability is prioritised. To address the conflict between data availability and data privacy, this research proposed the Trust-based Adaptive Access Control (TBA2C) model that integrates the concept of trust into the previous model. A simple user behaviour trust model is developed to calculate the behaviour trust value which measures the trustworthiness of the users and that is used as one of the defined thresholds to override access policy for data availability purpose, but the framework of the TBA2C model can be adapted with other trust models in the research community. The trust model can also protect data privacy because only a user who satisfies the relevant trust threshold can get restricted access in emergency and unanticipated situations. Moreover, the introduction of trust values in the enforcement of authorisation decisions can detect abnormal data access even from authorised users. Ponder2 is used to develop the TBA2C model gradually, starting from a simple access control model to the full TBA2C. In Ponder2, a Self-Managed Cell (SMC) simulates a sensor node with the TBA2C engine inside it. Additionally, to enable a full comparison with the proposed TBA2C model, the Break-The-Glass Role Based Access Control (BTGRBAC) model is redesigned and developed in the same platform (Ponder2). The proposed TBA2C model is the first to realise a flexible access control engine and to address the conflict between data availability and data privacy by combining the concepts of discretionary overriding, the user behaviour trust model, and the prevention and detection mechanism.

>Magister Scientiae - MSc / The Smart Renewable Energy project at the University of The Western Cape, under the guidance of the Intelligent Systems and Advanced Telecommunication (ISAT) group, aims at developing a dynamic system that enables users to (1) design smart architectures for next generation wind and solar systems to meet African power challenges (2) use these architectures to dimension the underlying solar and wind power systems and (3) simulate, implement and evaluate the performance of such power systems. The project's existing web and mobile monitoring system will undergo a much needed upgrade to cater for monitoring of the existing system's environmental and battery bank parameters. This will be implemented by allowing users to monitor input, storage and output trends over various time frames. These time frames would include hourly, daily, weekly and monthly readings. The visual evaluation of the system will be generated by mathematical, statistical and machine learning techniques. Trends will be discovered that will allow users to optimize the system's efficiency and their usage patterns. The accompanied dimensioning system will allow users to cater for their needs in a two way fashion. Users will be able to specify the number of devices that they want to run from a solar or wind based system and their power needs will be generated. They will also be able to determine what a given system is capable of producing and the number of devices that can be used simultaneously, as a result. / National Research Foundation (NRF) and Namibia Students Financial Assistance Fund (NSFAF)

The development cost of any civil infrastructure is very high; during its life span, the civil structure undergoes a lot of physical loads and environmental effects which damage the structure. Failing to identify this damage at an early stage may result in severe property loss and may become a potential threat to people and the environment. Thus, there is a need to develop effective damage detection techniques to ensure the safety and integrity of the structure. One of the Structural Health Monitoring methods to evaluate a structure is by using statistical analysis. In this study, a civil structure measuring 8 feet in length, 3 feet in diameter, embedded with thermocouple sensors at 4 different levels is analyzed under controlled and variable conditions. With the help of statistical analysis, possible damage to the structure was analyzed. The analysis could detect the structural defects at various levels of the structure.

Real-time sensing brings the proliferation of big data that contains rich information of complex systems. It is well known that real-world systems show high levels of nonlinear and nonstationary behaviors in the presence of extraneous noise. This brings significant challenges for human experts to visually inspect the integrity and performance of complex systems from the collected data. My research goal is to develop innovative methodologies for modeling and optimizing complex systems, and create enabling technologies for real-world applications. Specifically, my research focuses on Mining Dynamic Recurrences in Nonlinear and Nonstationary Systems for Feature Extraction, Process Monitoring and Fault Diagnosis. This research will enable and assist in (i) sensor-driven modeling, monitoring and optimization of complex systems; (ii) integrating product design with system design of nonlinear dynamic processes; and (iii) creating better prediction/diagnostic tools for real-world complex processes. My research accomplishments include the following. (1) Feature Extraction and Analysis: I proposed a novel multiscale recurrence analysis to not only delineate recurrence dynamics in complex systems, but also resolve the computational issues for the large-scale datasets. It was utilized to identify heart failure subjects from the 24-hour heart rate variability (HRV) time series and control the quality of mobile-phone-based electrocardiogram (ECG) signals. (2) Modeling and Prediction: I proposed the design of stochastic sensor network to allow a subset of sensors at varying locations within the network to transmit dynamic information intermittently, and a new approach of sparse particle filtering to model spatiotemporal dynamics of big data in the stochastic sensor network. It may be noted that the proposed algorithm is very general and can be potentially applicable for stochastic sensor networks in a variety of disciplines, e.g., environmental sensor network and battlefield surveillance network. (3) Monitoring and Control: Process monitoring of dynamic transitions in complex systems is more concerned with aperiodic recurrences and heterogeneous types of recurrence variations. However, traditional recurrence analysis treats all recurrence states homogeneously, thereby failing to delineate heterogeneous recurrence patterns. I developed a new approach of heterogeneous recurrence analysis for complex systems informatics, process monitoring and anomaly detection. (4) Simulation and Optimization: Another research focuses on fractal-based simulation to study spatiotemporal dynamics on fractal surfaces of high-dimensional complex systems, and further optimize spatiotemporal patterns. This proposed algorithm is applied to study the reaction-diffusion modeling on fractal surfaces and real-world 3D heart surfaces.

In traditional Wireless Sensor network's (WSN's), the sink is the only unconditionally trusted authority. If the sink is not connected to the nodes for a period of time then the network is considered as unattended. In Unattended Wireless Sensor Network (UWSN), a trusted mobile sink visits each node periodically to collect data. This network differs from the traditional multi hop wireless sensor networks where the nodes close to the sink deplete their power earlier than the other nodes. An UWSN can prolong the life time of the network by saving the battery of the nodes and also it can be deployed in environments where it is not practical for the sink to be online all the time. Saving data in the memory of the nodes for a long time causes security problems due to the lack of tamper-resistant hardware. Data collected by the nodes has to be secured until the next visit of the sink. Securing the data from an adversary in UWSN is a challenging task. We present two non-cryptographic algorithms (DS-PADV and DS-RADV) to ensure data survivability in mobile UWSN. The DS-PADV protects against proactive adversary which compromises nodes before identifying its target. DS-RADV makes the network secure against reactive adversary which compromises nodes after identifying the target. We also propose a data authentication scheme against a mobile adversary trying to modify the data. The proposed data authentication scheme uses inexpensive cryptographic primitives and few message exchanges. The proposed solutions are analyzed both mathematically and using simulations proving that the proposed solutions are better than the previous ones in terms of security and communication overhead.

In this thesis a Proactive Risk-Aware Robotic Sensor Network (RSN) is proposed for the application of Critical Infrastructure Protection (CIP). Each robotic member of the RSN is granted a perception of risk by means of a Risk Management Framework (RMF). A fuzzy-risk model is used to extract distress-based risk features and potential intrusion-based risk features for CIP. Detected high-risk events invoke a fuzzy-auction Multi-Robot Task Allocation (MRTA) algorithm to create a response group for each detected risk. Through Evolutionary Multi-Objective (EMO) optimization, a Pareto set of optimal robot configurations for a response group will be generated using the Non-Dominating Sorting Genetic Algorithm II (NSGA-II). The optimization objectives are to maximize sensor coverage of essential spatial regions and minimize the amount of energy exerted by the response group. A set of non-dominated solutions are produced from EMO optimization for a decision maker to select a single response. The RSN response group will re-organize based on the specifications of the selected response.

Sensor failures or oversupply in wireless sensor networks (WSNs), especially initial random deployment, create both spare sensors (whose area is fully covered by other sensors) and sensing holes. We envision a team of robots to relocate sensors and improve their area coverage. Existing algorithms, including centralized ones and the only localized G-R3S2, move only spare sensors and have limited improvement because non-spare sensors, with area coverage mostly overlapped by neighbour sensors, are not moved, and additional sensors are deployed to fill existing holes. We propose a localized algorithm, called Localized Ant-based Sensor Relocation Algorithm with Greedy Walk (LASR-G), where each robot may carry at most one sensor and makes decision that depends only on locally detected information. In LASR-G, each robot calculates corresponding pickup or dropping probability, and relocates sensor with currently low coverage contribution to another location where sensing hole would be significantly reduced. The basic algorithm optimizes only area coverage, while modified algorithm includes also the cost of robot movement. We compare LASR-G with G-R3S2, and examine both single robot and multi robots scenarios. The simulation results show the advantages of LASR-G over G-R3S2.

Wireless sensor networks (WSNs) allow us to instrument the physical world in novel ways, providing detailed insight that has not been possible hitherto. Since WSNs provide an interface to the physical world, each sensor node has a location in physical space, thereby enabling us to associate spatial properties with data. Since WSNs can perform periodic sensing tasks, we can also associate temporal markers with data. In the environmental sciences, in particular, WSNs are on the way to becoming an important tool for the modelling of spatially and temporally extended physical phenomena. However, support for high-level and expressive spatial-analytic tasks that can be executed inside WSNs is still incipient. By spatial analysis we mean the ability to explore relationships between spatially-referenced entities (e.g., a vineyard, or a weather front) and to derive representations grounded on such relationships (e.g., the geometrical extent of that part of a vineyard that is covered by mist as the intersection of the geometries that characterize the vineyard and the weather front, respectively). The motivation for this endeavour stems primarily from applications where important decisions hinge on the detection of an event of interest (e.g., the presence, and spatio-temporal progression, of mist over a cultivated field may trigger a particular action) that can be characterized by an event-defining predicate (e.g., humidity greater than 98 and temperature less than 10). At present, in-network spatial analysis in WSN is not catered for by a comprehensive, expressive, well-founded framework. While there has been work on WSN event boundary detection and, in particular, on detecting topological change of WSN-represented spatial entities, this work has tended to be comparatively narrow in scope and aims. The contributions made in this research are constrained to WSNs where every node is tethered to one location in physical space. The research contributions reported here include (a) the definition of a framework for representing geometries; (b) the detailed characterization of an algebra of spatial operators closely inspired, in its scope and structure, by the Schneider-Guting ROSE algebra (i.e., one that is based on a discrete underlying geometry) over the geometries representable by the framework above; (c) distributed in-network algorithms for the operations in the spatial algebra over the representable geometries, thereby enabling (i) new geometries to be derived from induced and asserted ones, and (ii)topological relationships between geometries to be identified; (d) an algorithmic strategy for the evaluation of complex algebraic expressions that is divided into logically-cohesive components; (e) the development of a task processing system that each node is equipped with, thereby with allowing users to evaluate tasks on nodes; and (f) an empirical performance study of the resulting system.