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The internet of things (IoT) is an ecosystem of connected objects that are accessible and available through the internet. This "thing" in the IoT could be a sensor such as a heart monitor, temperature, and oxygen rate in the blood. These sensors produce huge amounts of information that lead to congestion and an effect on bandwidth in the IoT networ...
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... general view of the proposed system The compression process in Raspberry Pi compresses the data collected by sensors using Zstandard algorithm to result in compression data (CD). The compression data on the Raspberry Pi will be sent through a wireless connection by the socket (IP address) to the server, as shown in Figure 3 implementation of the proposed system. The server will receive a CD form the Raspberry Pi. ...
Citations
... Two of these works acknowledge the existence of these algorithms but employ a different approach [8,9]. One paper [10] refers to [11], presenting an approach that uses Lempel-Ziv-Welch as one of the compression steps. There is also a highly specialized paper, with a signal processing use case, that uses Lempel-Ziv-Welch to encode data [12]. ...
... The previous papers [10,11,[14][15][16][17][18][19] on this topic have primarily focused on compression algorithms such as LZW and its variations (e.g., s-LZW) and have often employed hardware with substantial computing power, such as the Raspberry Pi 4B with 8 GB RAM. In contrast, this paper introduces, as the main contribution and novelty of our work, the following new elements: the evaluation of canonical Huffman, Lempel-Ziv 77, Lempel-Ziv 78, and Lempel-Ziv-Welch algorithms in terms of energy and time efficiency for compressing and transmitting text data and the evaluation of the Joint Photographic Experts Group algorithm in terms of energy and time efficiency for compressing and transmitting image data. ...
As the number of Internet of Things (IoT) devices continues to rise dramatically each day, the data generated and transmitted by them follow similar trends. Given that a significant portion of these embedded devices operate on battery power, energy conservation becomes a crucial factor in their design. This paper aims to investigate the impact of data compression on the energy consumption required for data transmission. To achieve this goal, we conduct a comprehensive study using various transmission modules in a severely resource-limited microcontroller-based system designed for battery power. Our study evaluates the performance of several compression algorithms, conducting a detailed analysis of computational and memory complexity, along with performance metrics. The primary finding of our study is that by carefully selecting an algorithm for compressing different types of data before transmission, a significant amount of energy can be saved. Moreover, our investigation demonstrates that for a battery-powered embedded device transmitting sensor data based on the STM32F411CE microcontroller, the recommended transmission module is the nRF24L01+ board, as it requires the least amount of energy to transmit one byte of data. This module is most effective when combined with the LZ78 algorithm for optimal energy and time efficiency. In the case of image data, our findings indicate that the use of the JPEG algorithm for compression yields the best results. Overall, our research underscores the importance of selecting appropriate compression algorithms tailored to specific data types, contributing to enhanced energy efficiency in IoT devices.
... Previous studies, that discussed interoperability and the use of reduction techniques in health applications, as far as we researched, did not conciliate both these themes. While Rubí and Gondim (2020) and Rinty, Prodhan and Rahman (2022) sought solutions to challenges related to interoperability, Kahdim and Manaa (2022) and Idrees and Idrees (2022) discuss data reduction for health applications. ...
... Extensive experiments were performed, using the Python programming language, on real data collected from patients through EEG sensors. Kahdim and Manaa (2022) designed a different method of data compression for healthcare applications. Data reduction was used to decrease the amount of data sent from the IoT sensor level to the fog level. ...
... Like Idrees and Idrees (2022), Python was the programming language chosen by Kahdim and Manaa (2022). The authors explain that the motivation for choosing this language is the fact that it is adaptable, powerful and useful for different types of tasks. ...
Interoperability and data reduction have been proven beneficial to health and medical applications that deal with large datasets. Still, the conflicts between these qualities turned out to be a problem for their conciliation. This paper presents an edge-fog-cloud architecture that offers functionalities as a service. These functionalities can guarantee certain qualities depending on the necessities of the client, such as, in our case, interoperability and data reduction. With the use of context simulators, we found that it was possible to significantly increase the output of data delivered to the servers, and decrease the size of the data that transitions in the network and is stored in the servers’ databases, without interfering with the syntactic interoperability.
... Consequently, a controller attack in an SDN environment can have a much bigger impact than a comparable attack on a traditional environment. DoS and DDoS [12], [13] attacks have reportedly been undertaken against a number of SDN controllers [14], including FloodLight, NOX, and RYU [15]- [17], among others, according to past studies. To the best of our knowledge, no in-depth research has been conducted on DoS and DDoS attacks conducted via POX and RYU controllers. ...
Distributed denial of service (DDoS) attacks have been identified as one of the greatest threats to software-defined networking (SDN) because they are highly effective, hard to detect, and easy to use, and they take advantage of vulnerabilities in which the new architecture still exists. This paper describes one technique for denying the RYU controller's services, which can cause the controller's resources to be depleted if a significant number of packets from various zombie hosts are sent to the controller using spoofed source internet protocol (IP) addresses. In order to demonstrate the impact of the attack, we measure various metrics related to RYU controllers such as its central processing unit (CPU) usage and network throughput. In this work, Mininet was used to simulate the data plane and measure metrics such as random access memory (RAM) usage, CPU load, and link latency.
... There are also a great number of usual applications for the IoT, such as sensors in hospital rooms that may keep track of a patient's vital signs throughout the day to assist medical professionals with diagnosis and treatment. The widespread distribution of COVID-19 has accelerated the development of this technology [48], [55], [123], [125], [136], [149], [204], [253], [297], [304], [319], [330], [338]- [341]. ...
The internet of things (IoT) is rapidly expanding and improving operations in a wide range of real-world applications, from consumer IoT and enterprise IoT to manufacturing and industrial IoT (IIoT). Consumer markets, wearable devices, healthcare, smart buildings, agriculture, and smart cities are just a few examples. This paper discusses the current state of the IoT ecosystem, its primary applications and benefits, important architectural stages, some of the problems and challenges it faces, and its future. This paper explains how an appropriate IoT architecture that saves data, analyzes it, and recommends corrective action improves the process’s ground reality. The IoT system architecture is divided into three layers: device, gateway, and platform. This then cascades into the four stages of the IoT architectural layout: sensors and actuators; gateways and data acquisition systems; edge IT data processing; and datacenter and cloud, which use high-end apps to collect data, evaluate it, process it, and provide remedial solutions. This elegant combination provides excellent value in automatic action. In the future, IoT will continue to serve as the foundation for many technologies. Machine learning will become more popular in the coming years as IoT networks take center stage in a variety of industries.
The authentication process in fifth-generation networks between the user equipment and the network relies on secure communication protocols, with the 5G authentication and key agreement (5G-AKA) being a prominent one. However, this protocol still has shortcomings, as identified in the 3rd Generation Partnership Project (3GPP)release, which poses vulnerabilities. Notably, a primary concern is the insufficient protection of user privacy. This deficiency allows attackers to potentially exploit weaknesses in encryption, signing, and update mechanisms, leading to the tracking of a user's identity, location, and activities. Furthermore, the 5G -AKA protocol is vulnerable to man-in-the-middle attacks, in which malicious actors can assume the identity of service providers or networks, leading to the interruption of communication between users and servers. Additionally, the protocol lacks robustness in guaranteeing mutual authentication, enabling attackers to forge authentication messages and manipulate user data. In this paper, we propose a key generation method based on the concept of post-quantum cryptography to provide robust resistance to attacks. Furthermore, we propose a key exchange method that authenticates the user's equipment and home network through the application of Kyber, where both parties possess pre-established static public keys. The proposal provides identity protection, non-traceability, mutual authentication, and confidentiality. We implement this protocol to test 5G-AKA's vulnerabilities and analyse its effectiveness against specific attacks. The experiment used Minnet simulation and Ryu controllers to implement 5G-AKA with standard-specified characteristics.
In the last decade, healthcare systems have played an effective role in improving medical services by monitoring and diagnosing patients' health remotely. These systems, either in hospitals or in other health centers, have experienced significant growth with emerging technologies. They are becoming of great interest to many countries worldwide nowadays. Portable healthcare monitoring systems (HMS) depend on internet of things (IoT) technology due to its effectiveness and reliability in several sectors, as well as in the sector of telemedicine. This paper proposes a portable healthcare system in an IoT environment controllable via a smartphone application that aims to facilitate utilization. This proposed system can track physiological indicators of a patient's body as well as the environmental conditions where the patient lives in real-time and auto-manage databases. Moreover, this paper touched on a comparison between three servers, concerning data transfer speeds from the proposed system into the servers.
Keywords:
Embedded healthcare systems; ESP32; IoT; Off-body sensors; Relational databases; Wearable sensors.
