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Peoples are naturally communicators but devices are not. In the Internet of Things (IoT) architecture, the smart devices (SDs), sensors, programs and association of smart objects are connected together to transfer information among them. The SD is designed as physical device linked with computing resources that are capable to connect and communicat...
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... the whole world is becoming more and more depending on the mobility services and wireless communication. The drawback of wireless communication is now clear as wireless networking is growing. According to statistica [20] website, by 2020, It is expected that the total number of smart devices connected together will reach up to 50 billion. According to Siemens research, up to 2020, near about 26 billion physical objects will be connected together on the internet (See figure 1). That time is not far away when billions of physical things linked together in real time. They can communicate each other and forwarding and process required data on the cloud. But there is a lack of technical standardization security perspective on the internet of smart thing. According to Statistica [20] report, in 2025, the total number of connected devices in the world will be approximately 75.44 billion. See figure 1. The main factor of this growth is not the population of the world but the smart devices. The integrated technologies are playing big role to connect the physical things together and exchange the information among them [11], [12]. This environment where the machine can talk to another machine (Machine-to-machine) and human can talk to machine. The IoT is integration of physical things, smart devices, smart buildings, smart vehicles, embedded objects including electronics, programs, sensors, actuators and network connections to exchange information among each other [13]. The The IoT represents the interconnected physical things that are uniquely identified with sensors [4]. Many researchers moved in the area of security and reliability in IoT day by day. The reliability can be measured through smart device integrity. The IoT requires an understanding of the connectivity between devices, the development of standards for the transmission of information and tools that enable the autonomous behavior of objects according to the functions to be met and the instructions received from the network that interconnects [16]. Transport and logistics have already long incorporated these technologies, particularly to improve service delivery, and the next evolution is towards the personal and professional environment. The following is the observation that can be happen in next few ...
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p>Peoples are naturally communicators but devices are not. In the Internet of Things (IoT) architecture, the smart devices (SDs), sensors, programs and association of smart objects are connected together to transfer information among them. The SD is designed as physical device linked with computing resources that are capable to connect and communic...
p>Peoples are naturally communicators but devices are not. In the Internet of Things (IoT) architecture, the smart devices (SDs), sensors, programs and association of smart objects are connected together to transfer information among them. The SD is designed as physical device linked with computing resources that are capable to connect and communic...
Citations
... Unfortunately, security sometimes takes a backseat as businesses race to create new IoT devices with innovative uses. Businesses could employ outdated security requirements [49]. ...
Because providing billions of objects with network connectivity, the Internet of Things (IoT) enables the collection and transfer of real-time data for intelligent applications. Therefore, IoT enables remote access and control of connected devices when there is a sufficient network infrastructure. Additionally, the introduction of software-defined networking (SDN) presents capabilities that allow internet providers and users to control and connect network equipment wirelessly, even as enabling a global perspective on the network, that has previously become a soaring interest area due to its extensive use for various applications and systems, including wireless sensor nodes, medical equipment, delicate home sensor systems, and some other connected IoT devices. In order to create worldwide connectivity between the Internet of Things (IoT) and based on the SDN architecture in the medical contexts, this paper's contribution is to outline some relevant directions. Additionally, we provide a model based on software defined network principles that depicts interactions between a group of people each of whom have a Nano network within their bodies and the medical services via the local network of a medical institution. For everybody electrical engineers to data engineers the requirement to integrate everything in a global setting is a significant problem. As a result, the cloud is useful for handling the instantaneous sharing of information. In terms of health care, the effort is also stated in terms of IoT architecture and services. IoT's current prospects for the healthcare sector are quite promising. Due to its capacity for sensing and measuring, it is also highly well-liked. From the smallest sensor to the massive amounts of data gathered, this revolution is completely changing how we view healthcare.
... The need for computation continues to grow with the widespread use of the Internet of Things devices and the increase in artificial intelligence (AI)-centric and cloud services [1]. To meet these needs, traditional processor architectures had been formed at the centre of computing for a long time. ...
... T Acc with Communication = T Acc + T setup + T transfer ·len data (1) where len data is the length of the transferred data between the memory and the accelerator, the communication between the CPU and the accelerator (T setup ), and the transfer time per data unit (from/to memory) (T transfer ) for memory operations. To evaluate the observed real performance by the LAA, we compare T Acc with Communication to T CPU . ...
Lookaside accelerators (LAAs) enable opportunities to asynchronously offload computationally intensive tasks from the general-purpose processors to the accelerator in order to improve the overall performance of a given system. However, the communication overhead involved in moving data between the LAA and the CPU can be significant and can impact anticipated performance, diminishing the benefits of a LAA. This paper examines the communication overhead between LAAs and CPUs, focusing on the communication setup and data transmission components. We develop a mathematical model to analyse and capture the problem and investigate the conditions under which hardware accelerators provide effective performance. To verify and validate the proposed model, we carried out studies using an ARM-based platform. Experimental results reveal that the length of data send to the LAA significantly impacts the overall offload benefit of the LAA when compared with software-only solutions. Communication overhead, specifically communication setup latency, has a diminishing impact on smaller data lengths. The presented mathematical model and experimental results clearly demonstrate that the targeted data length for offload is a critical parameter when designing LAAs.
... Our society is continuously exposed to an increased risk of cybersecurity threats due to the ongoing digitization in the modern world [1]. The never-ending growing number and variety of interconnected devices, including critical systems such as power grids, does not only expand the attack surface for a malicious actor but is also negatively affecting the possible consequences in case of a successful attack [2]. Furthermore, the increasing generated load on existing security monitoring systems is exceeding single system capabilities and challenging their scalability to detect threats in near realtime [3]. ...
An intrusion detection system (IDS), traditionally an example of an effective security monitoring system, is facing significant challenges due to the ongoing digitization of our modern society. The growing number and variety of connected devices are not only causing a continuous emergence of new threats that are not recognized by existing systems, but the amount of data to be monitored is also exceeding the capabilities of a single system. This raises the need for a scalable IDS capable of detecting unknown, zero-day, attacks. In this paper, a novel multi-stage approach for hierarchical intrusion detection is proposed. The proposed approach is validated on the public benchmark datasets, CIC-IDS-2017 and CSE-CIC-IDS-2018. Results demonstrate that our proposed approach besides effective and robust zero-day detection, outperforms both the baseline and existing approaches, achieving high classification performance, up to 96% balanced accuracy. Additionally, the proposed approach is easily adaptable without any retraining and takes advantage of n-tier deployments to reduce bandwidth and computational requirements while preserving privacy constraints. The best-performing models with a balanced set of thresholds correctly classified 87% or 41 out of 47 zero-day attacks, while reducing the bandwidth requirements up to 69%.
... IoT stands for the 'internet of things', which represents physical devices with modern technologies [7]. These devices use the internet or other communication networks to connect and communicate with other devices and exchange information [8]. For example, smart doors use cards to read the data within them as a key and verification method. ...
In the past few years, the number of IoT devices has grown substantially, and this trend is likely to continue. An increasing amount of effort is being put into developing software for the ever-increasing IoT devices. Every IoT system at its core has software that enables the devices to function efficiently. But security has always been a concern in this age of information and technology. Security for IoT devices is now a top priority due to the growing number of threats. This study introduces best practices for ensuring security in the IoT, with an emphasis on guidelines to be utilized in software development for IoT devices. The objective of the study is to raise awareness of the potential threats, emphasizing the secure software development lifecycle. The study will also serve as a point of reference for future developments and provide a solid foundation for securing IoT software and dealing with vulnerabilities.
... This concept makes communication between humans and things easier and thus leads to the implementation of new smart cities [6,7]. IoT has shown a very fast growing, 75.44 billion devices are estimated to be connected to the internet [8] by the end of 2025. Moreover, IoT technology plays an important role in improving human life by providing helpful intelligent applications, such as smart home, smart vehicle, smart learning and smart healthcare. ...
Users’ security is one of the most important issues in Internet of Things (IoT) due to the high number of IoT devices involved in different applications. Security threats are evolving at a rapid pace that make the current security and privacy measures unsuitable. Therefore, several researchers have been attracted by this domain with the aim of proposing either new or improved solutions to address the problem of security in IoT. Blockchain technology is a relatively new invention in modern IoT applications to solve the security issue. It is based on the use of a public immutable ledger called a blockchain. After conducting a verification process, several parts on a network encode transactions into this ledger. Moreover, Machine learning (ML) algorithms have been used as emerging solutions to improve IoT security. Reinforcement learning (RL) is the most popular machine learning technique proposed to secure IoT systems. Unlike other ML methods, RL can observe, learn and interact with the environment even if it has minimum information about the considered parameters. Various researches have been proposed to treat security problem in IoT based on either RL technique or Blockchain technology or a combination of both techniques. Therefore, we believe there is a need for a comprehensive survey on works proposed in recent years that address security issues using these techniques. In this paper, we provide a summary of research efforts made in the past few years, from 2018 to 2021, addressing security issues using RL and blockchain techniques in the IoT domain.
... According to [9], the number of connected online devices have over the years risen from 300,000 in 1990 to about 16.4 billion in 2022 and is predicted to reach 75.44 billion in 2025. Since early 2014, the mobile has overtaken the personal computer (desktop/laptop) as the leading device used to navigate the Net. ...
... While the number of IoT tasks varied from 100 to 300, the number of IoT tasks for different experiments was constantly set equal to 20. The CPU processing energy of each FN was evenly distributed from 2000 to 6000 MIPS to ensure the heterogeneity of FNs, while its energy consumption was randomly generated in the active state at [80-200] W. 51 Thus, a propagation delay of [1][2][3] ms was assumed between FNs while a value of 1000 Mbps was considered for the bandwidth of the communication links. In order to consider a significant correlation between the required deadline time of the tasks and their size, two different types of tasks were taken into consideration: 22 1. ...
... The input and output file sizes for both types were randomly selected at [100-10 000] and [1-1000] KB, respectively. The packets' arrival time was generated randomly within the interval of [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. ...
The rapid advancement of the “Internet of Things” (IoT) devices has led to the emergence of different types of IoT applications that need immediate response and low delay to operate. The emergence of fog computing has provided a proper platform to process fast‐emerging IoT applications. Nevertheless, to name the disadvantages of fog computing devices, it can be said that they are typically distributed, dynamic, and resource‐limited. Therefore, it seems a substantial challenge to schedule fog computational resources effectively to perform heterogeneous and delay‐sensitive IoT tasks. The problem of scheduling tasks aimed at minimizing the energy consumption of fog nodes is formulated in this article, while meeting the requirements of the quality of service (QoS) of IoT tasks, including response time. Minimizing the deadline time and balancing the network load are also considered in the mathematical model. In the next stage, a new algorithm is introduced based on a wavefront cellular learning automata (WCLA) called the wavefront cellular learning automata improved by genetic algorithm (WCLA + GA). WCLA + GA is indeed a modified version of WCLA that has been improved using the genetic algorithm. In this version, the WCLA reinforcement signal is regulated by a genetic algorithm that accelerates the automata convergence rate. WCLA + GA is then utilized to schedule fog tasks. Simulating the proposed method followed by comparing it with other methods demonstrates that WCLA + GA performs task scheduling significantly better in terms of response time, energy consumption, and percentage of tasks that meet their deadline.
... These "Internet of things (IoT)" devices can be used anywhere, such as monitoring a person with a pacemaker, pastured livestock, and traffic control [21]. As the number of IoT devices increases [22], electric power supply becomes difficult, particularly in deserted areas, such as mountains or seaborne areas. However, owing to the adverse effects of carbon dioxide emission on global warming, the importance of various energy-harvesting methods using green, sustainable, and renewable sources, such as sunlight, wind, heat, and vibration, continues to increase. ...
This study investigates energy harvesting by a deionized (DI) water droplet flow on an epitaxial graphene film on a SiC substrate. We obtain an epitaxial single-crystal graphene film by annealing a 4H-SiC substrate. Energy harvesting of the solution droplet flow on the graphene surface has been investigated by using NaCl or HCl solutions. This study validates the voltage generated from the DI water flow on the epitaxial graphene film. The maximum generated voltage was as high as 100 mV, which was a quite large value compared with the previous reports. Furthermore, we measure the dependence of flow direction on electrode configuration. The generated voltages are independent of the electrode configuration, indicating that the DI water flow direction is not influenced by the voltage generation for the single-crystal epitaxial graphene film. Based on these results, the origin of the voltage generation on the epitaxial graphene film is not only an outcome of the fluctuation of the electrical-double layer, resulting in the breaking of the uniform balance of the surface charges, but also other factors such as the charges in the DI water or frictional electrification. In addition, the buffer layer has no effect on the epitaxial graphene film on the SiC substrate.
... The common ways in which computer systems battle cybersecurity concerns are by using firewalls, encryption, and intrusion detection systems, but since IoTs are fundamentally different from computers, the various security measure cannot be directly ported onto IoT devices [17][18][19]. There has also been a boom in IoT devices, with about 23.14 billion connected devices in 2018, and it is projected to be as high as 75.44 billion connected devices in 2025 [20]. IDS are software applications that detect intrusions in network policy violations or malicious activities [21]. ...
The dominant intrusion detection models in internet of things industrial internet of things cybersecurity use network-based datasets. The Modbus protocol is one of the most often targeted protocols and cyberattacks against IoT/IIoT devices have grown to be a major threat in recent years. Due to the intricacy of the protocol and the quick evolution of cyber threats, detecting these attacks using conventional techniques might be difficult. This paper proposes an architecture that consistently outperforms the state-of-the-art methods of performing intrusion Detection that includes binary classification of whether an intrusion occurred or not and multi-class classification that classifies the different types of attacks using an embedding layer in a neural network to model the register values. The best accuracy results were obtained with a convolutional neural network, with an accuracy of 98.91% in the Modbus Binary dataset, a fully connected neural network with an accuracy of 98.06% in the multi-class classification of the Modbus dataset, and long short-term memory neural networks with an accuracy of 99.97%, 99.7%, and 80.20% in Binary, multi-class, and multi-class sub-categories, respectively which conclude that the proposed architecture performs consistently better than the control NN. Three NN are designed with and without the proposed architecture. All experiments performed in this paper conclude that the proposed architecture performs consistently better than the control NN. This paper shows that a NN with an embedding function can effectively be used to model whether an attack occurred on a device and the class of attack that occurred. This network can be utilized in the future to lessen DoS attacks and other types of network attacks. The network will be able to protect itself against a lot of damage if attacks can be predicted either before they occur or at the same moment they are launched.
... The number of Internet of Things (IoT) devices has rapidly increased due to advancements in IoT technology and new wireless communication technologies like 5G. As of 2020, over 30.7 billion IoT devices have been deployed worldwide, and it is projected that by 2025, this number will reach 75.4 billion [1]. These devices are used in homes and industrial environments and significantly impact various aspects of daily life. ...
