As a possible implementation of a low-power wide-area network (LPWAN), Long Range (LoRa) technology is considered to be the future wireless communication standard for the Internet of Things (IoT) as it offers competitive features, such as a long communication range, low cost, and reduced power consumption, which make it an optimum alternative to the current wireless sensor networks and conventional cellular technologies. However, the limited bandwidth available for physical layer modulation in LoRa makes it unsuitable for high bit rate data transfer from devices like image sensors. In this paper, we propose a new method for mangrove forest monitoring in Malaysia, wherein we transfer image sensor data over the LoRa physical layer (PHY) in a node-to-node network model. In implementing this method, we produce a novel scheme for overcoming the bandwidth limitation of LoRa. With this scheme the images, which requires high data rate to transfer, collected by the sensor are encrypted as hexadecimal data and then split into packets for transfer via the LoRa physical layer (PHY). To assess the quality of images transferred using this scheme, we measured the packet loss rate, peak signal-to-noise ratio (PSNR), and structural similarity (SSIM) index of each image. These measurements verify the proposed scheme for image transmission, and support the industrial and academic trend which promotes LoRa as the future solution for IoT infrastructure.

It is well-known that clustering partitions network into logical groups of nodes in order to achieve energy efficiency and to enhance dynamic channel access in cognitive radio through cooperative sensing. While the topic of energy efficiency has been well investigated in conventional wireless sensor networks, the latter has not been extensively explored. In this paper, we propose a reinforcement learning-based spectrum-aware clustering algorithm that allows a member node to learn the energy and cooperative sensing costs for neighboring clusters to achieve an optimal solution. Each member node selects an optimal cluster that satisfies pairwise constraints, minimizes network energy consumption and enhances channel sensing performance through an exploration technique. We first model the network energy consumption and then determine the optimal number of clusters for the network. The problem of selecting an optimal cluster is formulated as a Markov Decision Process (MDP) in the algorithm and the obtained simulation results show convergence, learning and adaptability of the algorithm to dynamic environment towards achieving an optimal solution. Performance comparisons of our algorithm with the Groupwise Spectrum Aware (GWSA)-based algorithm in terms of Sum of Square Error (SSE), complexity, network energy consumption and probability of detection indicate improved performance from the proposed approach. The results further reveal that an energy savings of 9% and a significant Primary User (PU) detection improvement can be achieved with the proposed approach.

The development of new technological applications is driving the need for deterministic network communication that exhibits reliable behavior. This is particularly relevant for applications like automation and autonomous vehicles, which require real-time network responsiveness. This has led to the development of various technologies, including Time-Sensitive Networking (TSN). Recent studies have explored the optimization of scheduling and routing in TSN, as this is crucial for meeting TSN’s objectives. Current methods primarily focus on scheduling Time-Triggered (TT) transmissions, often overlooking the scheduling of lower-priority traffic types. Many of these methods do not optimize routing, and only a few consider reliability. In this paper, we present the Optimized Hybrid Deterministic Scheduling and Routing Plus (OHDSR+) approach. This method prioritizes communications, optimizing both scheduling and routing of TT communications, while also addressing the requirements of lower-priority best-effort (BE) communications, and ensuring the reliability of TT communications. We evaluated our approach using various problem instances. The results demonstrate our approach’s effectiveness in managing different traffic types and ensuring reliable transmission for TT traffic. Our model achieves significant improvements compared to the state-of-the-art REU-CP-R method, with reductions in total latency of up to 1.03%, total response time of up to 34.70%, and scheduling response time of up to 63.97%. Notably, OHDSR+ exhibits superior scalability, successfully finding solutions for all tested network sizes with a 100% success rate, while REU-CP-R achieved only an 87.5% success rate. This highlights OHDSR+’s ability to provide optimal solutions for networks of various sizes with minimal latency and response times.

Real-time communications characterized by low-latency, deterministic, and reliable behavior are crucial for the advancement of emerging technologies. Consequently, Time-Sensitive Networking (TSN) has been developed to address the distinct demands of sectors such as automation and autonomous vehicles applications. This is currently achieved through various methods that emphasize the scheduling of critical data traffic. However, many of these methods determine routes independently, potentially impacting the schedulability of transmissions. Additionally, there is a noticeable lack of emphasis on the scheduling and routing of low-priority transmissions within TSN. In this paper, we introduce the optimized hybrid deterministic scheduling and routing (OHDSR) approach. This method takes into account the priority of communications to jointly optimize the scheduling and routing of time-triggered (TT) communications, while also catering to low-priority best-effort (BE) communications. Extensive experimental evaluations show the high efficiency of our proposed method. It ensures not only the prompt delivery of TT communications but also the delivery of BE communications within suitable time frames, with a maximum difference of 14.29% from TT communications, meeting their respective deadlines. Moreover, the evaluation demonstrates the high scalability of the proposed approach, providing improved response times compared to the latest work for both routing and scheduling.

Predictive maintenance (PdM) identifies the equipment conditions and forecasts when maintenance is required to minimize downtime, whis is crucial for medical equipment. This study developed a machine learning-based PdM for a Computed Tomography (CT) scan machine using Internet of Things (IoT) sensors to monitor temperature, humidity, current, radiation, and XY-axis acceleration. Data were collected from January to December 2023 at a hospital in the Klang Valley, Malaysia. The readings were preprocessed to follow a normal distribution, representing the typical working conditions of the machine. Owing to limited faulty condition data, synthetic data were generated by expanding the tails of the data distribution and using a Gaussian noise generator. These synthetic data are vital for training robust machine learning models. An artificial neural network (ANN) was designed to predict the machine’s breakdown risk using all sensor parameters as inputs. The ANN model achieved an impressive prediction accuracy of 97.58%, proving its relibility in forecasting breakdowns. The model consistently predicted a high breakdown risk in November 2023, which was confirmed by a repair report that indicating maintenance was required in early December 2023. This study demonstrated that integrating IoT sensors with ANN models can significantly enhance the PdM of medical equipment, reduce downtime, and improve operational efficiency. These promising results suggest the potential application of this approach in other critical medical devices.

Dynamic Frame Slotted ALOHA (DFSA) is a de facto algorithm in the EPC Global Class-1 Generation-2 protocol for Radio Frequency Identification (RFID) tag collision problem. DFSA fails when the UHF RFID tag deployment becomes dense like in Internet of Things (IoT). Existing works do not provide readers prior tag estimates. Most algorithms assume a collision slot means two tag collision. But in dense IoT applications, much more than two tags can constitute a collision slot. Moreover, research proves collision slot might occur due to other reasons such as error-prone channel. This paper proposes a RFID anti-collision algorithm, kg-DFSA that equips the reader with prior information on accurate tag estimate. In kg-DFSA, tag identification is divided into two stages – initialization and identification. In the initialization stage, the reader uses improved K-means clustering running concurrently with a tag counter algorithm to cluster tags into K groups using tags’ RN16 while the counter returns an accurate tag number estimate. In the identification stage, the tags are read only in frame chunks that match their group IDs while a new frame size look up table is developed to boost efficiency. Variants of the proposed kg-DFSA, traditional DFSA and another grouping based DFSA algorithm (FCM-DFSA) were implemented in MATLAB. Extensive Monte Carlo simulation shows the proposed kg-DFSA edges DFSA in terms of success rate 50%, system efficiency 65% and identification time 28%. The proposed model is useful in enhancing the existing MAC protocol to support dense IoT deployment of RFID.