Wireless sensor networks (WSNs) can provide new methods for information gathering for a variety of applications. In order to ensure the network quality of service, the quality of the measurements has to be guaranteed. Distributed fault detection and isolation schemes are preferred to centralized solutions to diagnose faulty sensors in WSNs. Indeed the first approach avoids the need for a central node that collects information from every sensor node, and hence it limits complexity and energy cost while improving reliability.In the case of state estimation over distributed architectures, the sensor faults can be propagated in the network during the information exchanging process. To build a reliable state estimate one has to make sure that the measurements issued by the different sensors are fault free. That is one of the motivations to build a distributed fault detection and isolation (FDI) system that generates an alarm as soon as a measurement is subject to a fault (has drift, cdots ). In order to diagnose faults with small magnitude in wireless sensor networks, a systematic methodology to design and implement a distributed FDI system is proposed. It resorts to distinguishability measures to indicate the performance of the FDI system and to select the most suitable node(s) for information exchange in the network with a view to FDI. It allows one to determine the minimum amount of data to be exchanged between the different nodes for a given FDI performance. In this way, the specifications for FDI can be achieved while the communication and computation cost are kept as small as possible. The distributed FDI systems are designed both in deterministic and stochastic frameworks. They are based on the parity space approach that exploits spacial redundancy as well as temporal redundancy in the context of distributed schemes. The decision systems with the deterministic method and the stochastic method are designed not only to detect a fault but also to distinguish which fault is occurring in the network. A case study with a WSN is conducted to verify the proposed method. The network is used to monitor the temperature and humidity in a computer room. The distributed FDI system is validated both with simulated data and recorded data. / Doctorat en Sciences de l'ingénieur et technologie / info:eu-repo/semantics/nonPublished

Localization and coverage are two important and closely related problems in wireless ad hoc networks. Localization aims to determine the physical locations of devices in a network, while coverage determines if a region of interest is sufficiently monitored by devices. Localization systems require a high degree of coverage for correct functioning, while coverage schemes typically require accurate location information. This thesis investigates the relationship between localization and coverage such that new schemes can be devised which integrate approaches found in each of these well studied problems. This work begins with a thorough review of the current literature on the subjects of localization and coverage. The localization scheduling problem is then introduced with the goal to allow as many devices as possible to enter deep sleep states to conserve energy and reduce message overhead, while maintaining sufficient network coverage for high localization accuracy. Initially this sufficient coverage level for localization is simply a minimum connectivity condition. An analytical method is then proposed to estimate the amount of localization error within a certain probability based on the theoretical lower bounds of location estimation. Error estimates can then be integrated into location dependent schemes to improve on their robustness to localization error. Location error estimation is then used by an improved scheduling scheme to determine the minimum number of reference devices required for accurate localization. Finally, an optimal coverage preserving sleep scheduling scheme is proposed which is robust to localization error, a condition which is ignored by most existing solutions. Simulation results show that with localization scheduling network lifetimes can be increased by several times and message overhead is reduced while maintaining negligible differences in localization error. Furthermore, results show that the proposed coverage preserving sleep scheduling scheme results in fewer active devices and coverage holes under the presence of localization error.

Sensor networks comprise of a collection of resource-constrained, low cost, sometimes fragile wireless motes which have the capability to gather information about their surroundings through the use of sensors, and can be conceived as a distributed computing platform for applications ranging from event detection to environmental monitoring. A Sensor Network Query Processor (SNQP) is a means of collecting data from sensor networks where the requirements are defined using a declarative query language with a set of Quality of Service (QoS) expectations. As sensor networks are often deployed in hostile environments, there is a high possibility that the motes could break or that the communication links between the motes become unreliable. SNQP Query Execution Plans (QEPs) are often optimised for a specific network deployment and are designed to be as energy efficient as possible whilst ensuring the QEPs meet the QoS expectations, yet little has been done for handling the situation where the deployment itself has changed since the optimisation in such a way as to make the original QEP no longer efficient, or unable to operate. In this respect, the previous work on SNQPs has not aimed at being resilient to failures in the assumptions used at compilation/optimisation time which result in a QEP terminating earlier than expected. This dissertation presents a collection of approaches that embed resilience into a SNQP generated QEPs in such a way that a QEP operates for longer whilst still meeting the QoS expectations demanded of it, thereby resulting in a more reliable platform that can be applicable to a broader range of applications. The research contributions reported here include (a) a strategy designed to adapt to predictable node failures due to energy depletion; (b) a collection of strategies designed to adapt to unpredictable node failures; (c) a strategy designed to handle unreliable communication channels; and (d) an empirical evaluation to show the benefits of a resilient SNQP in relation to a representative non-resilient SNQP.

The agricultural sector is an emerging application area for Wireless Sensor Networks (WSNs). This requires sensor nodes to be deployed in the outdoor environment so as to monitor pertinent natural features, such as soil condition or pest infestation. Limited energy supply and subsequent battery replacement are common issues for these agricultural sensor nodes. One possible solution is to use energy harvesting, where the ambient energy is extracted and converted into usable electrical form to energise the wireless sensors. The work presented in this thesis investigates the feasibility of using Radio Frequency (RF) energy harvesting for a specific application; that is powering a generic class of wireless ground-level, agricultural sensor networks operating in an outdoor environment. The investigation was primarily undertaken through a literature study of the subject. The first part of the thesis examines several energy harvesting/ wireless energy transfer techniques, which may be applicable to power the targeted agricultural WSN nodes. The key advantages and limitations of each technique are identified, and the rationale is being given for selecting far-field RF energy harvesting as the investigated technique. It is then followed by a theoretical-based system analysis, which seeks to identify all relevant design parameters, and to quantify their impact on the system performance. An RF link budget analysis was also included to examine the feasibility of using RF energy harvesting to power an exemplar WSN node - Zyrox2 Bait Station. The second part of the thesis focuses on the design of two energy harvesting antennas. The first design is an air-substrate-based folded shorted patch antenna (FSPA) with a solid ground plane, while the second design is a similar FSPA structure with four pairs of slot embedded into its ground plane. Both antennas were simulated, fabricated and tested inside an anechoic chamber, and in their actual operating environment - an outdoor field. In addition, a power harvester circuit, built using the commercially available off-the-shelf components, was tested in the laboratory using an RF signal generator source. The results from both the laboratory and field trial were analysed. The measurement techniques used were reviewed, along with some comments on how to improve them. Further work on the RF energy harvester, particularly on the improvement of the antenna design must be carried out before the feasibility and viable implementations for this application can be definitively ascertained.

Power management is an active area of research in wireless sensor networks (WSNs). Efficient power management is necessary because WSNs are battery-operated devices that can be deployed in mission-critical applications. From the communications perspective, one main approach to reduce energy is to maximize throughput so the data can be transmitted in a short amount of time. Frame fragmentation techniques aim to achieve higher throughput by reducing retransmissions. Using experiments on a WSN testbed, we show that frame fragmentation helps to reduce energy consumption. We then study and compare recent frame fragmentation schemes to find the most energy-efficient scheme. Our main contribution is to propose a new frame fragmentation scheme that is optimized to be energy efficient, which is originated from the chosen frame fragmentation scheme. This new energy-efficient frame fragmentation protocol is called (Green-Frag). Green-Frag uses an algorithm that gives sensor nodes the ability to transmit data with optimal transmit power and optimal frame structure based on environmental conditions. Green-Frag takes into consideration the channel conditions, interference patterns and level, as well as the distance between sender and receiver. The thesis discusses various design and implementation considerations for Green-Frag. Also, it shows empirical results of comparing Green-Frag with other frame fragmentation protocols in terms of energy efficiency. Green-Frag performance results shows that it is capable of choosing the best transmit according to the channel conditions. Subsequently, Green-Frag achieves the least energy consumption in all environmental conditions.

Security is a key challenge for any IIoT network and more so for constrained IWSN deployments. Novel methods are thus required to enhance security, taking into consideration the lossy and low power nature of the IWSN. The use of ICMP packets is proposed as a method to generate fingerprinting information for IWSN devices. The ICMP based method uses the round-trip time information in the ICMP header as a fingerprinting metric. The results showed that the effect of the physical layer can be averaged out of the measurement if enough samples are available. A linear relationship was found between hop count and round-trip time for a static network which can be used in the design phase of the IWSN network or alternatively as a method to fingerprint routing anomalies in real-time. The ICMP method was able to differentiate between devices from different vendors, but unable to fingerprint devices from the same vendor due to physical layer interference. The work shows that fingerprinting in an IWSN using the ICMP method is possible if the timing delta under investigation is an order of magnitude larger than the timing variation introduced by the physical layer while maintaining a reasonable signal-to-noise ratio. / Dissertation (MEng (Computer Engineering))--University of Pretoria, 2021. / Electrical, Electronic and Computer Engineering / MEng (Computer Engineering) / Unrestricted

Wireless sensor networks enable inexpensive distributed monitoring systems that are the backbone of smart cities. In this dissertation, we are interested in wireless sensor networks for traffic monitoring and an emergency flood detection to improve the safety of future cities. To achieve real-time traffic monitoring and emergency flood detection, the system has to be continually operational. Accordingly, an energy source is needed to ensure energy availability at all times. The sun provides for the most inexpensive source of energy, and therefore the energy is provided here by a solar panel working in conjunction with a rechargeable battery. Unlike batteries, solar energy fluctuates spatially and temporally due to the panel orientation, seasonal variation and node location, particularly in cities where buildings cast shadows. Especially, it becomes scarce whenever floods are likely to occur, as the weather tends to be cloudy at such times when the emergency detection system is most needed. These considerations lead to the need for the optimization of the energy of the sensor network, to maximize its sensing performance. In this dissertation, we address the challenges associated with long term outdoor deployments along with providing some solutions to overcome part of these challenges. We then introduce the energy optimization problem, as a distributed greedy approach. Motivated by the flood sensing application, our objective is to maximize the energy margin in the solar powered network at the onset of the high rain event, to maximize the network lifetime. The decentralized scheme will achieve this by optimizing the energy over a time horizon T, taking into account the available and predicted energy over the entire routing path. Having a good energy forecasting scheme can significantly enhance the energy optimization in WSN. Thus, this dissertation proposes a new energy forecasting scheme that is compatible with the platform’s capabilities. This proposed prediction scheme was tested on real data and compared with state-of-theart forecasting schemes on on-node WSN platforms. Finally, to establish the relevance of the aforementioned schemes beyond theoretical formulations and simulations, all proposed protocols and schemes are subjected to hardware implementation.

Recently, wireless sensor networks (WSNs), consisted of battery-powered sensor nodes, are widely adopted by various civilian/military applications for implementing real-time monitoring or long-term surveillance tasks. One of the critical issues of WSNs is energy efficiency. Due to the limited battery capacity, the network lifetime and performance of WSNs are constrained. Also, once the sensor is deployed into a risky/remote environment, the replacement of its battery is hard. Therefore, how to improve the energy efficiency of the WSN is a critical issue and has gained tremendous attention from researchers around the world. To address this problem, by taking advantage of the emerging high-mobility devices (e.g., unmanned aerial vehicle (UAV)), we propose energy-efficient mobile device-assisted schemes in different-scale WSNs. Thanks to the rapid development of wireless techniques, two emerging approaches, i.e., data gathering technique and wireless charging technique, are beneficial to balance the workloads among all sensors or replenish energy to achieve the semi-permanent WSN. First, we design data gathering schemes using the mobile data collector. In order to meet the performance requirements of systems with different scales, our algorithms have two working modes: single- and multiple-data-collector scenarios. For the small-scale system, a single data collector is adopted to access and collect data from the deployed node, and we propose single mobile data collector-assisted (SDCA) data collection schemes for small-scale WSNs. For the large-scale system, multiple data collectors are utilized to gather sensed data from deployed nodes, and two-mode multiple mobile data collector-assisted (MDCA) data collection scheme is designed for balancing between the system energy consumption and the data forwarding latency. Second, the joint data collection and energy charging scheme is developed by adopting mobile chargers (MCs) as mobile devices that are responsible for energy charging and data collection simultaneously. For facing the different performance requirements of systems, a two-mode MC scheduling algorithm is presented. To evaluate our works, extensive simulation experiments are conducted on the OMNeT++ simulator. The results demonstrate that the proposed algorithms achieve better performance than the control group regarding system-wide energy efficiency, network lifetime and average end-to-end delay.

The emergence of the Internet of Things (IoT) brought about the widespread of Body Sensor Networks (BSN) that continuously monitor patients using a collection of tiny-powered and lightweight bio-sensors offering convenience to both physicians and patients in the modern health care environment. Unfortunately, the deployment of bio-sensors in public hacker-prone settings means that they are vulnerable to various security threats exposing the security and privacy of patient information. This thesis presents an authentication scheme for each of two applications of medical sensor networks. The first is an ECC based authentication scheme suitable for a hospital-like setting whereby the patient is hooked up to sensors connected to a medical device such as an ECG monitor while the doctor needs real-time access to continuous sensor readings. The second protocol is a Chebyshev chaotic map-based authentication scheme suitable for deployment on wearable sensors allowing readings from the lightweight sensors connected to patients to be sent and stored on a trusted server while the patient is on the move. We formally and informally proved the security of both schemes. We also simulated both of them on AVISPA to prove their resistance to active and passive attacks. Moreover, we analyzed their performance to show their competitiveness against similar schemes and their suitability for deployment in each of the intended scenarios.

The aim of this thesis is to design and implement modules for wireless sensor network ZigBee. These modules could communicate together and share information about measured values. The network will be managed by coordinator of network, which will process data from sensor modules and will decide about next steps around the network.