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.

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 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.

A growing number of smart items are entering our daily lives as the Internet of Things becomes increasingly prevalent. ICT device miniaturization introduces a brand-new material type called Communicating Material (CM). The term “communications material” refers to a single system that includes a material equipped with communication devices. In this paper, the main limitation of CM was studied - the issue of energy consumption. Due to the limited battery capacity of sensor nodes, the issue of network lifetime comes to the fore, emphasizing the importance of power management and optimization for each sensor node. The first and most important step in tackling this problem is to precisely estimate and calculate each node's power usage. In addition, the WSN's embeddedness in the material makes it challenging to replace batteries and measure network power consumption, necessitating the development of a different approach to power consumption estimation. Thus, our work explores all the different approaches to energy estimation in WSN and tries to choose the best method that fits our WSN platform.

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