David Murray

LoRa is a wireless technology suited for long-range IoT applications. Leveraging LoRa technology for image transmission could revolutionize many applications, such as surveillance and monitoring, at low costs. However, transmitting images, through LoRa is challenging due to LoRa’s limited data rate and bandwidth. To address this, we propose a pipeline to prepare a reduced image payload for transmission captured by a camera in a reasonably static background, which is common in surveillance settings. The main goal is to minimize the uplink payload while maintaining image quality. We use a selective transmission approach where dissimilar images are divided into patches, and a deep learning Siamese network determines if an image or patch has new content compared to previously transmitted ones. The data is then compressed and sent in constant packets via HARQ to reduce downlink requirements. Enhanced super-resolution generative adversarial networks and principal component analysis are used to reconstruct the images/patches. We tested our approach with two surveillance videos at two sites using LoRaWAN gateways, end devices, and a ChirpStack server. Assuming no duty cycle restrictions, our pipeline can transmit videos—converted to 1616 and 584 frames—in 7 and 26 min, respectively. Increased duty cycle restrictions and significant image changes extend the transmission time. At Murdoch Oval, we achieved 100% throughput with no retransmissions required for both sets. At Whitby Falls Farm, throughput was 98.3%, with approximately 71 and 266 packets needing retransmission for Sets 1 and 2, respectively.

The emerging benefits of the Internet of Things (IoT) devices have been observed within the industries. This development has introduced several issues, including increased vulnerability to potential cyberattacks and digital forensics. Forensic investigations associated with IoT need to be examined in a forensically sound manner using a standard framework. However, adopting the current traditional digital forensics tools introduces various challenges, such as identifying all IoT devices and users at the crime scene. A forensic analysis must preserve the “chain of custody” by maintaining a complete record of the evidence from the point of seizure or interception until it is tendered in court. This paper examines the suitability of a blockchain-based solution for IoT forensics for implementing tamper-resistant data storage, ensuring data integrity and immutability. Therefore, in this paper, we aim to propose a permissioned blockchain integration solution for the IoT forensics (PBCIS-IoTF) framework. Our proposed framework will focus on reviewing permissioned blockchains (Hyperledger Fabric) to enhance the IoT forensics challenges while aiming to address whether the PBCIS-IoTF could be utilised to better enhance the IoT data integrity and authenticity.

Secure Digital Evidence Management Systems (DEMSs) ae crucial for law enforcement agencies, because traditional systems are prone to tampering and unauthorised access. Blockchain technology, particularly private blockchains, offers a solution by providing a centralised and tamper-proof system. This study proposes a private blockchain using Proof of Work (PoW) to securely manage digital evidence. Miners are assigned specific nonce ranges to accelerate the mining process, called collaborative mining, to enhance the scalability challenges in DEMSs. Transaction data includes digital evidence to generate a Non-Fungible Token (NFT). Miners use NFTs to solve the puzzle according to the assigned difficulty level d, so as to generate a hash using SHA-256 and add it to the ledger. Users can verify the integrity and authenticity of records by re-generating the hash and comparing it with the one stored in the ledger. Our results show that the data was verified with 100% precision. The mining time was 2.5s, and the nonce iterations were as high as 80×103 for 𝑑=5 . This approach improves the scalability and integrity of digital evidence management by reducing the overall mining time.

This research presents a novel framework and experimental results that combine zero-knowledge proofs (ZKPs) with private blockchain technology to safeguard whistleblower privacy while ensuring secure digital evidence submission and verification. For example, whistleblowers involved in corporate fraud cases can submit sensitive financial records anonymously while maintaining the credibility of the evidence. The proposed framework introduces several key innovations, including a private blockchain implementation utilising proof-of-work (PoW) consensus to ensure immutable storage and thorough scrutiny of submitted evidence, with mining difficulty dynamically aligned to the sensitivity of the data. It also features an adaptive difficulty mechanism that automatically adjusts computational requirements based on the sensitivity of the evidence, providing tailored protection levels. In addition, a unique two-phase validation process is incorporated, which generates a digital signature from the evidence alongside random challenges, significantly improving security and authenticity. The integration of ZKPs enables iterative hash-based verification between parties (Prover and Verifier) while maintaining the complete privacy of the source data. This research investigates the whistleblower’s niche in traditional digital evidence management systems (DEMSs), prioritising privacy without compromising evidence integrity. Experimental results demonstrate the framework’s effectiveness in preserving anonymity while assuring the authenticity of the evidence, making it useful for judicial systems and organisations handling sensitive disclosures. This paper signifies notable progress in secure whistleblowing systems, offering a way to juggle transparency with informant confidentiality.

While LoRa overcomes the high-power consumption and deployment costs of GPS and mobile networks, it faces challenges in accuracy. This paper presents a method for LoRa-based localization and tracking. It uses unsupervised symbolization to analyze received signal features. We use partitioning, D-Markov machines for symbolization and the Chinese restaurant process to achieve unsupervised symbolization. In particular, a novel adaptive feature extraction technique is proposed in partitioning to overcome the problems of over-tracking and under-tracking. Mean spectral kurtosis analysis is performed across several partitioning techniques to assess their symbolization effectiveness. This enables the selection of the most appropriate partitioning technique. This enhances the localization and tracking accuracy of target objects by focusing on robustness to noise and multipath effects. The proposed method learns and estimates the distance range simultaneously, thereby eliminating the need for a separate offline training phase and the storage of reference coordinates. Experimental results using LoRa highlight the proposed method's efficacy in real-time localization, tracking, and superiority over the state-of-the-art method.

This paper proposes a long-range (LoRa)-based outdoor localization and tracking method. Our method presents an unsupervised localization approach that utilizes symbolized LoRa received signal features, such as RSSI, SNR, and path loss, where each symbol represents a system state. To identify the partitioning boundaries between the symbols in time series, we employ maximum entropy partitioning. The D-Markov machine is used to construct nondeterministic finite-state automata for extracting temporal patterns. We incorporate the Chinese restaurant process for online estimation, especially in scenarios with an unbounded number of probable areas around each LoRa gateway. An adaptive trilateration approach is then used to localize the target node from the estimated ranged radii of areas. The point-wise localization data was used for time-series continuous tracking. We collected a dataset using three LoRaWAN gateways, sensor nodes powered by single-use batteries, and a Chirpstack server on a sports oval. We thoroughly evaluated the proposed method from the perspectives of localization accuracy and tracking capability. Our method outperformed state-of-the-art machine learning-driven range-based and fingerprint-based localization techniques.

Aphids are persistent insect pests that severely impact agricultural productivity. The detection of aphid infestations is critical for mitigating their effects. This paper presents an artificial intelligence approach to detect aphids in crop images captured by consumer-grade RGB imaging cameras. In addition to detecting the presence of aphids, the size of the aphid is an important indicator of infestation severity. To address these, we present a Bayesian multi-task learning model to detect the presence of aphids and estimate their size simultaneously.

Our model employs a joint loss function, combining a classification loss and a customised size loss. The classification component aims to identify images containing aphids, whilst the customised size loss function estimates the size of the aphids. The latter is specifically designed to account for discrepancies between the estimated and actual ground truth sizes, enhancing the accuracy of the size estimation. The model utilizes a ResNet18 backbone, ensuring robustness and adaptability across various conditions.

The proposed model was evaluated using an agricultural pest dataset consisting of images of corn, rape, rice, and wheat crops. It achieved aphid presence detection accuracies of 75.77%, 66.39%, 70.01%, and 59% for corn, rape, rice, and wheat images, respectively. An in-depth evaluation of predictive uncertainties revealed areas of high confidence and potential inaccuracies for both size and presence of aphids in images, offering insight for future model refinement. We also conducted an ablation study to thoroughly analyse the contributions of each component in proposed model.

Our model offers a valuable tool that can be used in pest management strategies for facilitating more sustainable and efficient agricultural practices.

Hydroponics offers a promising approach to help alleviate pressure on food security for urban residents. It requires minimal space and uses less resources, but management can be complex. Microscale Smart Hydroponics (MSH) systems leverage IoT systems to simplify hydroponics management for home users. Previous work in nutrient management has produced systems that use expensive sensing methods or utilized lower cost methods at the expense of accuracy. This study presents a novel inexpensive nutrient management system for MSH applications that utilises a novel waterproofed, IoT spectroscopy sensor (AS7265x) in a transflective application. The sensor is submerged in a hydroponic solution to monitor the nutrients and MSH system predicts the of nutrients in the hydroponic solution and recommends an adjustment quantity in mL. A three-phase model building process was carried out resulting in significant MLR models for predicting the mL, with an R2 of 0.997. An experiment evaluated the system's performance using the trained models with a 30-day grow of lettuce in a real-world setting, comparing the results of the management system to a control group. The sensor system successfully adjusted and maintained nutrient levels, resulting in plant growth that outperformed the control group. The results of the models in actual deployment showed a strong, significant correlation of 0.77 with the traditional method of measuring the electrical conductivity of nutrients. This novel nutrient management system has the potential to transform the way nutrients are monitored in hydroponics. By simplifying nutrient management, this system can encourage the adoption of hydroponics, contributing to food security and environmental sustainability.

Effective use of GPS and mobile networks for localisation in rangeland areas is constrained by their high power consumption and high deployment costs. Long-range (LoRa), a low-power wide area network (LPWAN) technology, can be employed to mitigate these challenges. In contrast to prior research where the prevalent approaches entail multiple gateways. This work proposes a valuable methodology focused on a single mobile LoRa gateway for localisation. A particle filtering and machine learning-based pipeline is employed to map the distance between a target node and the gateway from the received signal strength indicator (RSSI). Particle filtering is used to reduce the impact of noise on the RSSI values. Then, several machine learning techniques, such as support vector machines, random forest, and k-nearest neighbour, are used on the RSSI values to estimate the distance. The estimated distance is then used for tracking using a centroid pseudo-trilateration method. The proposed method was tested in a real-world semi-line-of-sight setting, using three datasets generated by LoRaWAN-specified hardware components and a server. Two forms of experiments were performed: active searching and passive monitoring. We propose an iterative estimation process to address the dilution of precision caused by the initial positions of the gateway required for active searching applications. The results show that active searching typically requires 2 to 3 hops to reach a target node. The accuracy of passive monitoring depends on the proximity of the gateway, which varies from 20 m to 170 m. This proposed approach has the potential to open the way for localising, tracking, or monitoring target objects within sparsely populated rangeland areas, even when resources are severely constrained.

The proliferation of Internet of Things (IoT) devices has facilitated the exchange of information among individuals and devices. This development has introduced several challenges, including increased vulnerability to potential cyberattacks and digital forensics. IoT forensic investigations need to be managed in a forensically sound manner using a standard framework. However, adopting traditional digital forensics tools introduces various challenges, such as identifying all IoT devices and users at the crime scene. Therefore, collecting evidence from these devices is a major problem. This paper proposes a permissioned blockchain integration solution for IoT forensics (PBCIS-IoTF) that aims to observe data transactions within the blockchain. The PBCIS-IoTF framework designs and tests Hyperledger blockchains simulated with a Raspberry Pi device and chaincode to address the challenges of IoT forensics. This blockchain is deployed using multiple nodes within the network to avoid a single point of failure. The authenticity and integrity of the acquired evidence are analysed by comparing the SHA-256 hash metadata in the blockchain of all peers within the network. We further integrate webpage access with the blockchain to capture the forensics data from the user’s IoT devices. This allows law enforcement and a court of law to access forensic evidence directly and ensures its authenticity and integrity. PBCIS-IoTF shows high authenticity and integrity across all peers within the network.