What is a smart device? - a conceptualisation within the paradigm of the internet of things

Internet of Things (IoT) networks are being designed to connect billions of sensors/devices for an application-specific environment to provide services and carry out special tasks. It enables the virtual connection of the real-world physical devices with Internet cyberspace. While, there is an exponentially growing trend in the use of IoT technology for diverse applications such as smart agriculture, smart industries, smart hospitals, smart homes, etc., there are pressing design issues which affect the IoT network performance in the long run, for example, replacement of batteries for increasing the lifetime of devices to maintain the network longevity. To tackle this challenge, various wireless powered-based energy transfer paradigms were proposed over time. Backscatter communication (BackCom) is likewise one of the energy-efficient solutions proposed, which can satisfy the stringent green communication aspects of IoT networks. To achieve massive connectivity in BackCom-enabled IoT network, selecting a suitable multiple access scheme is also a crucial part. Most multiple access schemes proposed for BackCom systems are based on frequency or timeslots sharing techniques. Multiple access techniques proposed for BackCom IoT system can reap the gain of non-orthogonal multiple access (NOMA) principle. In NOMA, multiple end nodes (ENs) of sensors network is served in the same resource block, thereby, increasing the spectrum efficiency of BackCom IoT system. In this chapter, we discuss the prospects of backscatter technique with application of NOMA for green communication in IoT network.

BackgroundWith the projected upsurge in the percentage of people with some form of disability, there has been a significant increase in the need for assistive mobility devices. However, for mobility aids to be effective, such devices should be adapted to the user’s needs. This can be achieved by improving the confidence of the acquired information (interaction between the user, the environment, and the device) following design specifications. Therefore, there is a need for literature review on the adaptability of assistive mobility devices.ObjectiveIn this study, we aim to review the adaptability of assistive mobility devices and the role of the internet of medical things in terms of the acquired information for assistive mobility devices. We review internet-enabled assistive mobility technologies and non–internet of things (IoT) assistive mobility devices. These technologies will provide awareness of the status of adaptive mobility technology and serve as a source and reference regarding information to health care professionals and researchers.MethodsWe performed a literature review search on the following databases of academic references and journals: Google Scholar, ScienceDirect, Institute of Electrical and Electronics Engineers, Springer, and websites of assistive mobility and foundations presenting studies on assistive mobility found through a generic Google search (including the World Health Organization website). The following keywords were used: assistive mobility OR assistive robots, assistive mobility devices, internet-enabled assistive mobility technologies, IoT Framework OR IoT Architecture AND for Healthcare, assisted navigation OR autonomous navigation, mobility AND aids OR devices, adaptability of assistive technology, adaptive mobility devices, pattern recognition, autonomous navigational systems, human-robot interfaces, motor rehabilitation devices, perception, and ambient assisted living.ResultsWe identified 13,286 results (excluding titles that were not relevant to this study). Then, through a narrative review, we selected 189 potential studies (189/13,286, 1.42%) from the existing literature on the adaptability of assistive mobility devices and IoT frameworks for assistive mobility and conducted a critical analysis. Of the 189 potential studies, 82 (43.4%) were selected for analysis after meeting the inclusion criteria. On the basis of the type of technologies presented in the reviewed articles, we proposed a categorization of the adaptability of smart assistive mobility devices in terms of their interaction with the user (user system interface), perception techniques, and communication and sensing frameworks.ConclusionsWe discussed notable limitations of the reviewed literature studies. The findings revealed that an improvement in the adaptation of assistive mobility systems would require a reduction in training time and avoidance of cognitive overload. Furthermore, sensor fusion and classification accuracy are critical for achieving real-world testing requirements. Finally, the trade-off between cost and performance should be considered in the commercialization of these devices.

Modified genetic algorithm and fine-tuned long short-term memory network for intrusion detection in the internet of things networks with edge capabilities

An efficient Clustered IoT (CIoT) routing protocol and control overhead minimization in IoT network

The Internet of Things (IoT) refers to a network where different smart devices are interconnected through the Internet. This network enables these devices to communicate, share data, and exert control over the surrounding physical environment to work as a data-driven mobile computing system. Nevertheless, due to wireless networks' openness, connectivity, resource constraints, and smart devices' resource limitations, the IoT is vulnerable to several different routing attacks. Addressing these security concerns becomes crucial if data exchanged over IoT networks is to remain precise and trustworthy. This study presents a trust management evaluation for IoT devices with routing using the cryptographic algorithms Rivest, Shamir, Adleman (RSA), Self-Adaptive Tasmanian Devil Optimization (SA_TDO) for optimal key generation, and Secure Hash Algorithm 3-512 (SHA3-512), as well as an Intrusion Detection System (IDS) for spotting threats in IoT routing. By verifying the validity and integrity of the data exchanged between nodes and identifying and thwarting network threats, the proposed approach seeks to enhance IoT network security. The stored data is encrypted using the RSA technique, keys are optimally generated using the Tasmanian Devil Optimization (TDO) process, and data integrity is guaranteed using the SHA3-512 algorithm. Deep Learning Intrusion detection is achieved with Convolutional Spiking neural network-optimized deep neural network. The Deep Neural Network (DNN) is optimized with the Archimedes Optimization Algorithm (AOA). The developed model is simulated in Python, and the results obtained are evaluated and compared with other existing models. The findings indicate that the design is efficient in providing secure and reliable routing in IoT-enabled, futuristic, smart vertical networks while identifying and blocking threats. The proposed technique also showcases shorter response times (209.397 s at 70% learn rate, 223.103 s at 80% learn rate) and shorter sharing record times (13.0873 s at 70% learn rate, 13.9439 s at 80% learn rate), which underlines its strength. The performance metrics for the proposed AOA-ODNN model were evaluated at learning rates of 70% and 80%. The highest metrics were achieved at an 80% learning rate, with an accuracy of 0.989434, precision of 0.988886, sensitivity of 0.988886, specificity of 0.998616, F-measure of 0.988886, Matthews Correlation Coefficient (MCC) of 0.895521, Negative predictive value (NPV) of 0.998616, False Positive Rate (FPR) of 0.034365, and False Negative Rate (FNR) of 0.103095.

With the continuous evolution of smart environments powered by Internet of Things (IoT) networks and smart devices, there becomes a crucial need to address and ensure privacy and security. Intrusion Detection Systems (IDSs) that are specially designed for use in IoT networks play a vital role in strengthening the security posture of an IoT network and system by safeguarding and preventing attacks against smart environments. This research paper presents a comparative study of IDSs for IoT networks, with a focus on signature-based, anomaly-based, and specification-based IDS detection methods while highlighting the significance of IDSs in protecting IoT networks and smart environments, which have become recent targets for attackers due to their integration with modern and advanced technologies and their involvement with large volumes of data. The study investigates the mentioned IDS methods covering the strengths and weaknesses of each method in safeguarding smart environments and networks. This paper dives into the characteristics that make IDS decision-making more effective primarily in terms of security, considering privacy and performance. The findings of this study contribute to the hardening of IoT network security by offering recommendations for IDS selection for enhancing IoT overall security, specifically through the adoption of adaptive-based IDSs.

In this paper, a Smart Device (SD) localization method, based on the Path Loss (PL) model of Macro Base Station (MBS) and femtocells, using the convex optimization method is discussed for an Internet of Things (IoT) networks. Localization plays a major role for smart city, smart agriculture, and smart health applications in IoT networks. Global Positioning System (GPS) works well for outdoor positioning but fails to provide accurate locations in an indoor environment and non-line-of-sight (NLOS) paths. We propose the Convex optimization (CO) method that uses the combined effects of the Received Signal Strength (RSS) from macrocells and femtocells. The method requires no additional infrastructure and localizes a Smart Device (SD) in an IoT environment. The Cramèr-Rao Lower Bound (CRLB) is also evaluated to analyze the performance of the estimator. Extensive simulations demonstrate that our proposed method provides an accurate location as compared to Least Square method.

The Internet of Things (IoT) has revolutionized innovation to collect and store the information received from physical objects or sensors. The smart devices are linked to a repository that stores intelligent information executed by sensors on IoT-based smart objects. Now, the IoT is shifted from knowledge-based technologies to operational-based technologies. The IoT integrates sensors, smart devices, and a smart grid of implementations to deliver smart strategies. Nowadays, the IoT has been pondered to be an essential technology. The transmission of information to or from the cloud has recently been found to cause many network problems to include latency, power usage, security, privacy, etc. The distributed intelligence enables IoT to help the correct communication available at the correct time and correct place. Distributed Intelligence could strengthen the IoT in a variety of ways, including evaluating the integration of different big data or enhancing efficiency and distribution in huge IoT operations. While evaluating distributed intelligence in the IoT paradigm, the implementation of distributed intelligence services should take into consideration the transmission delay and bandwidth requirements of the network. In this article, the distributed intelligence at the Edge on IoT Networks, applications, opportunities, challenges and future scopes have been presented.

In recent years, the number of mobile device users has increased at a significant rate due to the rapid technological advancement in mobile technology. While mobile devices are providing more useful features to its users, it has also made it possible for cyber threats to migrate from desktops to mobile devices. Thus, it is important for mobile device users to be aware that their mobile device could be exposed to cyber threats and that users could protect their devices by employing cyber security measures. This study discusses how users in responded to the smart mobile devices (SMD) breaches. A number of behavioural model theories are used to understand the user behaviour towards security features of smart mobile devices. To assess the impact of smart mobile devices (SMD) security and privacy, surveys had been conducted with users, stressing on product preferences, user behaviour of SMD, as well as perceptions on the security aspect of SMD. The results was very interesting, where the findings revealed that there were a lack of positive relationships between SMD users and their level of SMD security awareness. A new framework approach to securing SMD is proposed to ensure that users have strong protection over their data within SMD.

Mobile-edge computing (MEC)-enabled Internet of Things (IoT) networks have been deemed a promising paradigm to support massive energy-constrained and computation-limited IoT devices. Energy harvesting (EH) further enhances the operating capabilities of IoT devices that normally only possess very limited energy support. Nevertheless, many studies show that IoT devices using EH can experience uncertainty and unpredictability, which can complicate the EH-based IoT network design. Furthermore, with many new services in 5G and the forthcoming 6G eras, such as autonomous driving and vehicular communications, mobility consideration in IoT networks becomes more and more important. In this article, we study the computing offloading and resource allocation problems in an IoT network that supports both mobility and EH. The long-term average sum service cost of all the mobile IoT devices (MIDs) is minimized by optimizing the harvested energy, task-partition factors, the central process unit frequencies, the transmit power, and the association vector of MIDs. An online mobility-aware offloading and resource allocation (OMORA) algorithm is proposed based on the Lyapunov optimization and semidefinite programming (SDP). This online algorithm optimizes the offloading scheme without the need to have prior knowledge of the user mobility, EH model, and channel condition. Theoretical analysis shows that the proposed OMORA algorithm can achieve asymptotic optimality. Simulation results demonstrate that the proposed algorithm can effectively balance the system service cost and energy queue length, and outperform other offloading benchmark algorithms on the system service cost and packet losses.

Security and privacy in Internet of things: methods, architectures, and solutions

Internet of Things (IoT) as a prospective platform to develop mobile applications, is facing with significant challenges posed by the tension between resource-constrained mobile smart devices and low-latency demanding applications. Recently, mobile edge computing (MEC) is emerging as a cornerstone technology to address such challenges in IoT. In this paper, by leveraging social ties in human social networks, we investigate the optimal dynamic computation offloading mode selection to jointly minimize the total tasks' execution latency and the mobile smart devices' energy consumption in MEC-aided low-latency IoT. Different from the previous studies, which mostly focus on how to exploit social tie structure among mobile smart device users to construct the permutation of all the feasible modes, we consider dynamic computation offloading mode selection with social awareness-aided network resource assignment, involving both the computing resources and transmit power from heterogeneous mobile smart devices. On the one hand, we formulate the dynamic computation offloading mode selection into the infinite-horizon time-average renewal-reward problems subject to time average latency constraints on a collection of penalty processes. On the other hand, an efficient solution is also developed, which elaborates on a Lyapunov optimization-based approach, i.e., drift-plus-penalty (DPP) algorithm. Numerical simulations are provided to validate the theoretical analysis and assess the performance of the proposed dynamic social-aware computation offloading mode selection method considering different configurations of the IoT network parameters.

Congestion control in Internet of Things: Classification, challenges, and future directions

A New Vision Over Agile Project Management in the Internet of Things Era

Internet of Things (IoT) aims to build a smart environment by using various smart devices, including servers, mobile devices, and sensors to enhance people's life. Understanding how to use different hardware and software components—programming languages, libraries, tools, and configuration setups—is required to build IoT applications due to the heterogeneity of IoT devices. As a result, building IoT applications can add unnecessary burdens to developers because they need to focus not only on the application logic that meets clients' requirements but also on the details of implementing and configuring the services. In this paper, we propose SMIILE, Smart Module Integration for IoT Logic and Environment, to address this issue. In this programming model, IoT developers use the SMIILE language to describe their application logic and prescribe the programming configuration. Then the SMIILE compiler analyzes the description and prescription to generate deployable configuration and source code files and the documentation necessary to guide IoT programmers in building the applications effectively. In our deployment model, IoT programmers can use SMIILE to supplement their application development process by customizing the libraries to meet their needs or incorporating the SMIILE generated code into their programming environment.