This paper presents a voice-activated health monitoring system implemented using a Raspberry Pi. The prototype consists of two nodes: a medical sensor node with the user and a processing node, which communicates via a radio link. This system relies on speech recognition as the basis of its voice-activated design. It uses Mel-frequency cepstral coefficients for feature extraction and a dynamic time-warping algorithm for keyword recognition and comparing reference patterns. The system offers a stable, secure, and cloud-independent solution, achieving a keyword spotting accuracy of 95%.

Internet of Things (IoT) technology and its incorporation in healthcare have improved their coverage and effectiveness. Now, medical research aims to enhance its industry with the next technological trend. Another paradigm shift started with Large Language Models (LLMs). Notably, its ability to produce intelligent and reliable information through contextual learning has shown the potential to create more responsive patient support systems. Incorporating LLMs trained with the proper medical data alleviates the need for constant medical consultations by providing an effective alternative through their deployment in the IoT network. However, this combination runs into issues with scalability and security due to the vulnerabilities of the IoT network and the high processor demands of the LLM. This work presents an edge and private blockchain-based medical LLM deployment platform to address these concerns and create a secure and scalable patient support system. This work tests the feasibility of these design approaches by measuring their responsiveness compared to other configurations. The results showed the platform’s scalability and security using the edge-based and private blockchain approach.

The Internet of Things (IoT) has enabled many services and applications that benefit the urban landscape. One of these services is urban noise monitoring. Noise pollution is an issue that can disrupt the lifestyle and well-being of the populace. An urban noise monitoring system can help analyze the noise levels around the different areas in the city to isolate and address disruptive locations. However, it is a service that heavily relies on its training data. It must remain secure to keep the monitoring system accurate and reliable. So, we propose a secure urban noise monitoring system using a private blockchain and Federated Learning (FL)-based edge-IoT platform. It combines the immutability of blockchain technology with the privacypreserving capabilities of FL, reinforcing the security of training data within an IoT network. Based on our tests, our proposed platform was able to preserve the security, privacy, and integrity of the urban noise monitoring system.

IoT networks have become widespread in different technological industries due to the development of 6G networks. In this paradigm shift, industries like healthcare and autonomous vehicle research have incorporated Large Language Models (LLMs) into their applications and services. This combination has improved the effectiveness of Internet of Thing (IoT)-driven applications requiring intelligent interactions between humans and machines, bridging these wireless services to real-time intractability. However, as the IoT network grows, scalability and security issues arise. We present a private blockchain-based edge IoT platform to address these concerns in IoT-based LLM services. We evaluated our design's feasibility by testing its responsiveness and analyzing its security contributions. The results show the potential of our platform to improve the scalability of the IoT network through edge computing and reinforce its security through the private blockchain.

Integrating smart technologies in vehicles has brought rise to connected and autonomous vehicles (CAVs), One of the services impacted by this paradigm shift is driving assistance. Most systems use learning-based approaches such as object recognition to improve transportation quality. So, these services require exchanging and sharing data along the CAV network, which raises security issues. Within this work is a federated learning (FL)-based platform as a step towards secure CAV systems. It uses FL to secure client data locally and alleviate pressure on CAV servers and services against targeted attacks. A testbed evaluates the platform's feasibility in preserving the integrity of its implemented classifier while keeping a level of security of its training data. According to experimental results, the FL-based implementation can maintain the integrity of the classifier's accuracy even after introducing its distributive scheme. Also, security evaluations present the benefits of FL reinforcing network security and improving client data privacy. Based on the results, the proposed platform proves its feasibility as an integrity-preserving and secure option for object recognition-based driving assistance services in CAVs.

Healthcare has moved towards integrating wireless technology to create and enable more services for the patients' convenience. Among the new and popular services is the formulation of wearable Internet of Things (IoT)-enabled telemedicine. However, when the number of IoT devices entering the network increases, it creates concerns about the service's ability to keep its data secure and preserve its real-time capabilities. To address these issues, in this work, we propose a fog-based IoT platform incorporating blockchain technology and smart contracts. We evaluated our design's ability to keep the real-time capabilities of wearable IoT-enabled telemedicine services through experimentation. According to the results, the introduced platform can effectively reduce the overall latency of data transactions compared to other standard network configurations. We further evaluated and observed the contributions of our design to the security of the telemedicine service. Through experimentation, we prove the feasibility of the proposed platform in addressing the highlighted issues of wearable IoT-enabled telemedicine services.

In recent years, healthcare systems have included the Internet of Things (IoT) technology in their services, such as in remote patient monitoring systems. Wearable IoT devices can provide information regarding the patient's health that are accurate and time-sensitive. However, vulnerabilities are evident as more IoT devices connect to the network. For healthcare services, the security of patient data is an issue. At the same time, with real-time data transmissions, the network runs into manageability concerns. In this work, we propose a private blockchain-based Wireless Body Area Network (WBAN) platform to aid wearable IoT devices in healthcare services. We chose this blockchain technology due to its strengths in security. Then, we enable a distributive architecture using WBANs to introduce a decentralized configuration that can ensure privacy among wearable IoT devices within the network. To evaluate the feasibility of the proposed platform in terms of latency and throughput, we conducted experiments with several wearable IoT devices. The results show that integrating a WBAN to create a fog server improves the network performance with an increasing number of IoT devices and packet size. Also, the blockchain showed its ability to address security threats in healthcare services. We evaluate our proposed platform through a performance test and a STRIDE threat model, and we prove its feasibility in improving the security and manageability of wearable IoT devices in healthcare.

Over the years, the popularity and usage of wearable Internet of Things (IoT) devices in several healthcare services are increased. Among the services that benefit from the usage of such devices is predictive analysis, which can improve early diagnosis in e-health. However, due to the limitations of wearable IoT devices, challenges in data privacy, service integrity, and network structure adaptability arose. To address these concerns, we propose a platform using federated learning and private blockchain technology within a fog-IoT network. These technologies have privacy-preserving features securing data within the network. We utilized the fog-IoT network’s distributive structure to create an adaptive network for wearable IoT devices. We designed a testbed to examine the proposed platform’s ability to preserve the integrity of a classifier. According to experimental results, the introduced implementation can effectively preserve a patient’s privacy and a predictive service’s integrity. We further investigated the contributions of other technologies to the security and adaptability of the IoT network. Overall, we proved the feasibility of our platform in addressing significant security and privacy challenges of wearable IoT devices in predictive healthcare through analysis, simulation, and experimentation.

The number of embedded devices that connect to a wireless network has been growing for the past decade. This interaction creates a network of Internet-of-Things (IoT) devices where data travel continuously. With the increase of devices and the need for the network to extend via fog computing, we have fog-based IoT networks. However, with more endpoints introduced to it, the network becomes open to malicious attackers. This work attempts to protect fog-based IoT networks by creating a platform that secures the endpoints through public-key encryption. The servers are allowed to mask the data packets shared within the network. To be able to track all of the encryption processes, we incorporated the use of permissioned blockchains. This technology completes the security layer by providing an immutable and automated data structure to function as a hyper ledger for the network. Each data transaction incorporates a handshake mechanism with the use of a public-key pair. This design guarantees that only devices that have proper access through the keys can use the network. Hence, management is made convenient and secure. The implementation of this platform is through a wireless server–client architecture to simulate the data transactions between devices. The conducted qualitative tests provide an in-depth feasibility investigation on the network’s levels of security. The results show the validity of the design as a means of fortifying the network against endpoint attacks.

With the rise of IoT-based sensor networks, there is an increasing need for proper security mechanisms and efficient management of crucial resources, such as energy. At the same time, as more services are integrated, more sensors are introduced into the network. As a consequence, the architecture that manages these sensors need to be improved. Blockchains can be a solution in terms of data security. To cater to the inexpensive devices used as sensors in most IoT-based sensor networks, usually, permissioned blockchains are used as its base data management structure. However, due to the diverse collection of sensors that can be incorporated into a network, a static database is not enough. A more sustainable and automative system is needed as its service administrator. Therefore, we chose to integrate smart contracts to enable adaptive and dynamic automation. Lastly, to manage the reception of all the incoming data, a proper interface is required. Therefore, we chose to encapsulate the blockchain within a REST API to enable a systematic allocation of network resources. With these technologies, we propose a low-cost platform to address the management issues of current IoT-based sensor networks. We tested our design against a commercial REST API service and standard socket communication to test its feasibility. The testing metrics were latency and throughput. Based on the results, our platform proved to be the most stable and proficient among the configurations. Therefore, our proposed design shows promise as a low-cost, secure, and systematic means of managing IoT-based sensor networks.

An Internet of Things (IoT) network can have different components such as servers, gateways, and the end devices. An important source of performance constraint in such an IoT network is found in the limitations of its gateway. The capability of a gateway can dictate the effectiveness of a network and its services. The capacity, power consumption, and security of an IoT gateway are revealed as sources of network bottlenecks and service constraints. Blockchain technology can create a decentralized structure that can offload these strains. To unify these nodes as gateways under the same network, we need an effective means of communication. This paper proposes a setup that makes use of the decentralized capabilities of private blockchain technology partnered with the low-powered and secure connection of Bluetooth Low Energy (BLE). This provides a more secure means of wireless communication and prevents the nodes from being concentrated within an area. The architecture was compared against a standard WiFi network (2.4GHz) to prove its feasibility in effectively carrying out its functionality. In an experiment that used 4 gateway nodes, BLE proved to be more feasible than WiFi by yielding a better verification packet rate of 14 per minute compared to its counterpart that measured 4 per minute. Also, it showed to be more efficient in terms of power consumption with an average of 1095.40 mW, while the WiFi setup was measured to be 1191.83 mW. These results show promise in using BLE paired with blockchain technology to solve the capacity, power and security issues in IoT networks.

Health care services have become a high demand due to the rise in medical technology. As a result, related resources are being depleted. Hospitals no longer have any space to accommodate for incoming patients. Remote Patient Monitoring (RPM) is a solution to this issue by creating a convenient and easy to access healthcare service. However, RPM systems are constrained by concerns on patient privacy, response time, and patient-service interaction. Patients emphasize their privacy, which requires health care services to maintain the confidentiality of their patient’s information. Wearable health monitors continuously transmit data. This feature results in high volumes of data transmissions towards the servers. In the current state of wearable devices, there is a lack of giving the patient an integrated means of interacting with the healthcare centre and vice versa. In this paper, we propose an architecture that uses fog computing and Internet of Things (IoT) devices to an already existing RPM system and addresses these challenges. The introduced system enables the health care providers to verify any of their data through a local server before it is reported to the main server. Also, this design incorporates a data filter that controls the outgoing data to maintain patient privacy. Finally, the inclusion of a local server offloads the extra data processing that is required from the server for a better flow of data. Tests in latency were executed to investigate the feasibility of a scalable fog architecture against a standard cloud-device setup. The results show that the proposed fog setup yielded significantly lower latencies under an increasing number of RPM rooms compared to the cloud setup. Results further support the fog and IoT-based architecture as a potential option for a scalable RPM.