Network security challenges of the Internet of Things (IoT) appliances from a variety of suppliers used in wide areas, are rising quickly. Thus, the maintenance of these devices are extremely crucial to internet providers. However, it is important that devices are routinely tested for their smooth execution and for diagnostic security threats. But there are very few public datasets available with a large variety of devices for analysis of the behaviour of these devices. In this paper, we overcome these problems through the development of an effective dataset of IoT traffic aimed at enabling accurate IoT device identification and behaviour analysis. First, we built a smart environment with 24 diverse IoT devices and captured network traffic traces from this smart framework for 6 months duration. Secondly, we process traffic traces to extract packet-level features and flow-level features for the testing of our captured traffic traces. Third, we developed various frameworks for the security of smart environment automation by detection of IoT devices using ML as well as DL technologies. Lastly, we analysed the accuracy, significance, and flexibility of every machine learning and deep learning technique in offline mode as well as real-time mode. Our research opens up the opportunity for IoT accessibility, flexibility, and network security management in intelligent contexts without specialized devices or standards.

Nowadays, real-time object detection, which is a crucial task, is being performed through image processing and deep learning techniques. As there are several high-performance computing edge devices available, selecting the best-fit device for a particular problem is a tough task and keeping in mind the cost, performance, and weight of the device in mind. One faces several challenges while performing this task in real-time such as a lack of resources in terms of power and mobility. We have provided an insight into the computation power of devices in terms of Frames per Second (FPS) by deploying object detection models on them. This paper will provide insight into selecting the appropriate combination of device and object detection models for real-time applications. Raspberry Pi 3 (RPi3), Raspberry Pi 4 (RPi4), Intel Neural Compute Stick 2 (NCS2), and Nvidia Jetson NANO are popular devices with high computation power used for real-time applications. The memory constraints of devices along with the deployment of different You Only Look Once (YOLO) and Single-Shot Detector (SSD) are the two object detection models that have been explained in this paper. A deep learning inference optimiser, TensorRT, has been used in NANO to achieve high throughput in the performance of object detection. The precision, recall, and F1 score achieved on deploying each tested model have been presented. After observing the devices during experimentation, RPi4+NCS2 showed the best execution with the blend of factors i.e. speed, portability, and user-friendliness.

The Internet of Things is quickly taking over the world. Nevertheless, security for the IoT is becoming a more important academic topic and commercial concern because of several factors including the diverse nature of devices, protocols in use, the sensitivity of the data they carry, and security and privacy concerns. Admitting this, there appears to be a compelling need for a comprehensive survey that encompasses the entire spectrum of intrusion detection in the IoT paradigm, from foundational concepts like types of IDS, resources, and techniques for implementing IDS, to the latest technologies that can be used to enhance the performance of IDS. This study will be helpful for academic and industrial research in different ways: first, in identifying type of IDS to be used; second, in choosing various tools such as datasets and sniffing tools, and learning techniques for implementing IDS; and finally, it suggests the use of latest enabling technologies in the IoT setting to make the process of intrusion detection more secure, efficient, trustworthy, and privacy aware. We have also discussed critical challenges and research directions to help young researchers advance in their research projects.

The rapid growth of the Internet of Things (IoT) in the early 21st century has introduced complexities in delivering various services, including Quality-of-Service (QoS) management for smart agriculture sensors. Selecting optimal IoT nodes considering QoS parameters, such as energy consumption, latency, and network coverage area has become challenging. In response, this research proposes an extended form of differential evolution (DE) that incorporates a rapid adaptation approach using optimization-based design. By leveraging dynamic information from IoT devices, the proposed approach enhances exploration and exploitation capabilities, allowing for adaptive adjustment of algorithm parameters and strategies. Additionally, a novel fitness function for energy harvesting in IoT-based applications is introduced. The effectiveness of the proposed algorithm is evaluated in IoT-based applications and an IoT-service framework, with comparative analysis against state-of-the-art algorithms. The results demonstrate that the proposed approach achieves superior performance in energy harvesting QoS, delay, service cost, and maximum coverage area in the IoT-service network. This research contributes to the IoT field by offering an advanced DE algorithm that addresses limitations, providing valuable insights for QoS management in IoT-based services, particularly in the context of smart agriculture sensors.

Scene text detection is complicated and one of the most challenging tasks due to different environmental restrictions, such as illuminations, lighting conditions, tiny and curved texts, and many more. Most of the works on scene text detection have overlooked the primary goal of increasing model accuracy and efficiency, resulting in heavy-weight models that require more processing resources. A novel lightweight model has been developed in this article to improve the accuracy and efficiency of scene text detection. The proposed model relies on ResNet50 and MobileNetV2 as backbones with quantization used to make the resulting model lightweight. During quantization, the precision has been changed from float32 to float16 and int8 for making the model lightweight. In terms of inference time and Floating-Point Operations Per Second, the proposed method outperforms the state-of-the-art techniques by around 30–100 times. Here, well-known datasets, i.e., ICDAR2015 and ICDAR2019, have been utilized for training and testing to validate the performance of the proposed model. Finally, the findings and discussion indicate that the proposed model is more efficient than the existing schemes.

Recently introduced deep reinforcement learning (DRL) techniques in discrete-time have resulted in significant advances in online games, robotics, and so on. Inspired from recent developments, we have proposed an approach referred to as Quantile Critic with Spiking Actor and Normalized Ensemble (QC_SANE) for continuous control problems, which uses quantile loss to train critic and a spiking neural network (NN) to train an ensemble of actors. The NN does an internal normalization using a scaled exponential linear unit (SELU) activation function and ensures robustness. The empirical study on multijoint dynamics with contact (MuJoCo)-based environments shows improved training and test results than the state-of-the-art approach: population coded spiking actor network (PopSAN).

Internet of Things (IoT) refers to a wide variety of embedded devices connected to the Internet, enabling them to transmit and share information in smart environments with each other. The regular monitoring of IoT network traffic generated from IoT devices is important for their proper functioning and detection of malicious activities. One such crucial activity is the classification of IoT devices in the network traffic. It enables the administrator to monitor the activities of IoT devices which can be useful for proper implementation of Quality of Service, detect malicious IoT devices, etc. In the literature, various methods are proposed for IoT traffic classification using various machine learning algorithms. However, the accuracy of these machine learning algorithms depends on the data generated from various IoT devices, features extracted from network traffic, site at which IoT is deployed, etc. Moreover, the selection of features and machine learning algorithms are manual operations that are prone to error. Therefore, it is important to study the network traffic characteristics as well as suitable machine learning algorithms for accurate and optimized IoT traffic classification. In this article, we perform an in-depth comparative analysis of various popular machine learning algorithms using different effective features extracted from IoT network traffic. We utilize a public data set having 20 days of network traces generated from 20 popular IoT devices. Network traces are first processed to extract the significant features. We then selected state-of-the-art machine learning algorithms based on the recent survey papers for the IoT traffic classification. We then comparatively evaluated the performance of those machine learning algorithms on the basis of classification accuracy, speed, training time, etc. Finally, we provided a few suggestions for selecting the machine learning algorithm for different use cases based on the obtained results.

Multicast communication plays a pivotal role in Edge based Mobile Ad hoc Networks (MANETs). MANETs can provide low-cost self-configuring devices for multimedia data communication that can be used in military battlefield, disaster management, connected living, and public safety networks. A Multicast communication should increase the network performance by decreasing the bandwidth consumption, battery power, and routing overhead. In recent years, a number of multicast routing protocols (MRPs) have been proposed to resolve above listed challenges. Some of them are used for dynamic establishment of reliable route for multimedia data communication. This article provides a detailed survey of the merits and demerits of the recently developed techniques. An ample study of various Quality of Service (QoS) techniques and enhancement is also presented. Later, mesh topology-based MRPs are classified according to enhancement in routing mechanism and QoS modification. This article covers the most recent, robust, and reliable QoS-aware mesh based MRPs, classified on the basis of their operational features, and pros and cons. Finally, a comparative study has been presented on the basis of their performance parameters on the proposed protocols.

Compositional methods for Petri-net-based verification of concurrent systems are appealing because they allow the analysis of a complex system to be carried out in a piece-by-piece manner. An issue in compositional Petri-net analysis is the generation of a compact net from a labeled transition system (LTS) isomorphic to the reachability graph of the net. Several existing approaches for Petri-net generation use the concept of region, a subset of the original LTS; however, the computation of the regions in an LTS appears to be quite expensive. We report preliminary results on the generation of a Petri net from an LTS isomorphic to the net's reachability graph without requiring the computation of regions. We specifically introduce two algorithms for generating the place set and flow relation of a safe and live ordinary Petri net. The most complex of the two algorithms is nearly linear in the size of the input LTS.