An IoT-Based Encapsulated Design System for Rapid Model Identification of Plant Development

Actual and upcoming climate changes will evidently have the largest impact on agriculture crop cultivation in terms of reduced harvest, increased costs, and necessary deviations from traditional farming. The aggravating factor for the successful applications of precision and predictive agriculture is the lack of granulated historical data due to slow, year-round cycles of crops, as a prerequisite for further analysis and modeling. A methodology of plant growth observation with the rapid performance of experiments is presented in this paper. The proposed system enables the collection of data with respect to various climate conditions, which are artificially created and permuted in the encapsulated design, suitable for further correlation with plant development identifiers. The design is equipped with a large number of sensors and connected to the central database in a computer cloud, which enables the interconnection and coordination of multiple geographically distributed devices and related experiments in a remote, autonomous, and real-time manner. Over 40 sensors and up to 24 yearly harvests per device enable the yearly collection of approximately 750,000 correlated database entries, which it is possible to independently stack with higher numbers of devices. Such accumulated data is exploited to develop mathematical models of wheat in different growth stages by applying the concepts of artificial intelligence and utilizing them for the prediction of crop development and harvest.

XGB-RF: A Hybrid Machine Learning Approach for IoT Intrusion Detection

In the past few years, Internet of Things (IoT) devices have evolved faster and the use of these devices is exceedingly increasing to make our daily activities easier than ever. However, numerous security flaws persist on IoT devices due to the fact that the majority of them lack the memory and computing resources necessary for adequate security operations. As a result, IoT devices are affected by a variety of attacks. A single attack on network systems or devices can lead to significant damages in data security and privacy. However, machine-learning techniques can be applied to detect IoT attacks. In this paper, a hybrid machine learning scheme called XGB-RF is proposed for detecting intrusion attacks. The proposed hybrid method was applied to the N-BaIoT dataset containing hazardous botnet attacks. Random forest (RF) was used for the feature selection and eXtreme Gradient Boosting (XGB) classifier was used to detect different types of attacks on IoT environments. The performance of the proposed XGB-RF scheme is evaluated based on several evaluation metrics and demonstrates that the model successfully detects 99.94% of the attacks. After comparing it with state-of-the-art algorithms, our proposed model has achieved better performance for every metric. As the proposed scheme is capable of detecting botnet attacks effectively, it can significantly contribute to reducing the security concerns associated with IoT systems.

A Node Placement Algorithm Utilizing Mobile Nodes in WSN and IoT Networks

The operation of the Internet of Things (IoT) networks and Wireless Sensor Networks (WSN) is often disrupted by a number of problems, such as path disconnections, network segmentation, node faults, and security attacks. A method that gains momentum in resolving some of those issues is the use of mobile nodes or nodes deployed by mobile robots. The use of mobile elements essentially increases the resources and the capacity of the network. In this work, we present a Node Placement Algorithm with two variations, which utilizes mobile nodes for the creation of alternative paths from source to sink. The first variation employs mobile nodes that create locally-significant alternative paths leading to the sink. The second variation employs mobile nodes that create completely individual (disjoint) paths to the sink. We then extend the local variation of the algorithm by also accounting for the energy levels of the nodes as a contributing factor regarding the creation of alternative paths. We offer both a high-level description of the concept and also detailed algorithmic solutions. The evaluation of the solutions was performed in a case study of resolving congestion in the network. Results have shown that the proposed algorithms can significantly contribute to the alleviation of the problem of congestion in IoT and WSNs and can easily be used for other types of network problems.

A Novel Dual-Band Implantable Antenna for Pancreas Telemetry Sensor Applications

In this study, a novel implantable dual-band planar inverted F-antenna (PIFA) is proposed and designed for wireless biotelemetry. The developed antenna is intended to operate on the surface of the pancreas within the Medical Device Radiocommunications Service (MedRadio 401–406 MHz) and the industrial scientific and medical band (ISM, 2.4–2.5 GHz). The design analysis was carried out in two steps, initially inside a canonical model representing the pancreas, based on a finite element method (FEM) numerical solver. The proposed antenna was further simulated inside the human body taking into account the corresponding dimensions of the tissues and the electrical properties at the frequencies of interest using a finite-difference time-domain (FDTD) numerical solver. Resonance, radiation performance, electrical field attenuation, total radiated power, and specific absorption rate (SAR), which determines the safety of the patient and the maximum permissible input power and other electromagnetic parameters, are presented and evaluated.

Backscattered Visible Light Sensing of Retroreflective Foils Utilizing Random Forest Based Classification for Speed and Movement Direction Determination and Identification of an Indoor Moving Object

Making the Internet of Things “green” has become a major research focus in recent years. The anticipated massive increase in the numbers of sensor and communication devices makes this endeavor even more important, resulting in various solution approaches ranging from energy harvesting to energy efficient routing schemes. In this work, we propose a system that can perform some of the main tasks of the Internet of Things, namely identification and sensing of an indoor moving object, by the means of visible light sensing in combination with off-the-shelf retroreflective foils, without the necessity to place any actively powered components on the object itself. By utilizing the supervised machine learning approach of random forest, we show that these two tasks can be fulfilled with up to 99.96% accuracy. Based on our previous findings in this regard, we propose some advancements and improvements of the overall system, yielding better results in parallel with an increased complexity of the system. Furthermore, we expand the number of performable tasks toward additional movement direction determination. The achieved results demonstrate the applicability of visible light sensing and its potentials for a “green” Internet of Things.

Call Blocking Probabilities under a Probabilistic Bandwidth Reservation Policy in Mobile Hotspots

In this paper we study a mobility-aware call admission control algorithm in a mobile hotspot. To this end, a vehicle is considered which has an access point with a fixed capacity. The vehicle alternates between stop and moving phases. When the vehicle is in the stop phase, it services new and handover calls by prioritizing them via a probabilistic bandwidth reservation (BR) policy. Based on this policy, new handover calls may enter the reservation space with a predefined probability. When the vehicle is in the moving phase, it services new calls only. In that phase, two different policies are considered: (a) the classical complete sharing (CS) policy, where new calls are accepted in the system whenever there exists available bandwidth, and (b) the probabilistic BR policy. Depending on the selected policy in the moving phase, we propose the probabilistic BR loss model (if the CS policy is selected) and the generalized probabilistic BR loss model (if the probabilistic BR policy is selected). In both stop and moving phases, where the call arrival process is Poisson, calls require a single bandwidth unit in order to be accepted in the system, while the service time is exponentially distributed. To analytically determine call blocking probabilities and the system’s utilization, we propose efficient iterative algorithms based on two-dimensional Markov chains. The accuracy of the proposed algorithms is verified via simulation.

A Novel System-Theoretic Matrix-Based Approach to Analysing Safety and Security of Cyber-Physical Systems

Cyber-Physical Systems (CPSs) are getting increasingly complex and interconnected. Consequently, their inherent safety risks and security risks are so intertwined that the conventional analysis approaches which address them separately may be rendered inadequate. STPA (Systems-Theoretic Process Analysis) is a top-down hazard analysis technique that has been incorporated into several recently proposed integrated Safety and Security (S&S) analysis methods. This paper presents a novel methodology that leverages not only STPA, but also custom matrices to ensure a more comprehensive S&S analysis. The proposed methodology is demonstrated using a case study of particular commercial cloud-based monitoring and control system for residential energy storage systems.

A Survey on Traffic Prediction Techniques Using Artificial Intelligence for Communication Networks

Much research effort has been conducted to introduce intelligence into communication networks in order to enhance network performance. Communication networks, both wired and wireless, are ever-expanding as more devices are increasingly connected to the Internet. This survey introduces machine learning and the motivations behind it for creating cognitive networks. We then discuss machine learning and statistical techniques to predict future traffic and classify each into short-term or long-term applications. Furthermore, techniques are sub-categorized into their usability in Local or Wide Area Networks. This paper aims to consolidate and present an overview of existing techniques to stimulate further applications in real-world networks.

A Survey from Real-Time to Near Real-Time Applications in Fog Computing Environments

In this article, we present a comprehensive survey on time-sensitive applications implemented in fog computing environments. The goal is to research what applications are being implemented in fog computing architectures and how the temporal requirements of these applications are being addressed. We also carried out a comprehensive analysis of the articles surveyed and separate them into categories, according to a pattern found in them. Our research is important for the area of real-time systems since the concept of systems that respond in real time has presented various understandings and concepts. This variability of concept has been due to the growing requirements for fast data communication and processing. Therefore, we present different concepts of real-time and near real-time systems found in the literature and currently accepted by the academic-scientific community. Finally, we conduct an analytical discussion of the characteristics and proposal of articles.

Intelligent Interference Management in UAV-Based HetNets

Unmanned aerial vehicles (UAVs) can play a key role in meeting certain demands of cellular networks. UAVs can be used not only as user equipment (UE) in cellular networks but also as mobile base stations (BSs) wherein they can either augment conventional BSs by adapting their position to serve the changing traffic and connectivity demands or temporarily replace BSs that are damaged due to natural disasters. The flexibility of UAVs allows them to provide coverage to UEs in hot-spots, at cell-edges, in coverage holes, or regions with scarce cellular infrastructure. In this work, we study how UAV locations and other cellular parameters may be optimized in such scenarios to maximize the spectral efficiency (SE) of the network. We compare the performance of machine learning (ML) techniques with conventional optimization approaches. We found that, on an average, a double deep Q learning approach can achieve 93.46% of the optimal median SE and 95.83% of the optimal mean SE. A simple greedy approach, which tunes the parameters of each BS and UAV independently, performed very well in all the cases that we tested. These computationally efficient approaches can be utilized to enhance the network performance in existing cellular networks.