Monitoring Architecture Research Articles

Latency-Adjustable Cloud/Fog Computing Architecture for Time-Sensitive Environmental Monitoring in Olive Groves

The emerging and vast adoption of the Internet of Things (IoT) has sprung a plethora of research works regarding the potential benefits in smart agriculture. A popular implementation involves the deployment of Wireless Sensor Networks (WSNs), which embed low energy consumption sensory nodes to capture the critical environmental parameters prevailing on the farms. However, to manage the ever-increasing volumes of raw data successfully, new approaches must be explored. Under this scope, current work reports on the design and development of an IoT system, having in mind the case of olive groves, which are considered the dominant sector for agricultural activity in the Mediterranean Basin. The system incorporates the cloud/fog computing paradigm to equip the olive growers with a low-cost solution for accurate, reliable, and almost real-time monitoring of their crops. Its core is based on a three-layered network architecture, capable of dynamically balancing the generated load, by pushing cloud-elastic resources to the underlying fog network. As such, the premise of the approach lies in the conforming character of the system that allows for targeted alterations to its operational functionality to meet stringent latency and traffic load environmental monitoring constraints. To evaluate the performance of the proposed architecture, a demo prototype is developed and deployed in the facilities of the Ionian University. Experimental results illustrate the efficiency, flexibility, and scalability of the approach in terms of latency, achieving response time reduction across all platforms, a subject of the utmost importance when it comes to precision agriculture of the future. Moreover, it is shown that the system is capable of dynamic functionality adaptation, to meet network traffic load constraints, achieving high throughput (on average 95%) and addressing potential environmental dangers to olive oil production.

Experimental validation of a dual-receiver radar architecture for snowpack monitoring

AbstractMicrowave radars can be used to monitor the internal structure of the snowpack, delivering real-time and non-destructive measurements. Recently, the working principle of an innovative radar architecture able to identify some of the most important snowpack parameters, without external aids, has been demonstrated. A key point of this new architecture is the use of two independent receiving antennas, and one transmitting antenna. This paper presents a comparison between two different implementations, either based on one physical antenna miming two receiving antennas, or based directly on two physical receiving antennas. The different advantages and disadvantages of both solutions are discussed, highlighting the superior accuracy achieved by the implementation based on two physical receiving antennas. Then, this paper also presents the field results achieved by this type of radar architecture, on the grounds of a 5-day experimental campaign that took place in winter 2019 in the Italian Alps on dry snow. The comparison between the radar measurements and the ground truth (manual snowpit analysis, in terms of snowpack depth, dielectric constant, bulk density, and snow water equivalent) is provided. Overall, a root mean square error of around 3.5 cm, 0.05, 27 kg/m3, and 2.5 cm is achieved, respectively.

A model-based architecture for interactive run-time monitoring

We present a model-based architecture for monitoring executions of models of real-time and embedded systems. This architecture is highly configurable and allows for the combination of various run-time monitoring tools, not only for observing the system execution, but also for interacting with it. Using a variety of case studies, we illustrate the use of the architecture for connecting the code generated from a model with a range of external tools for different purposes, including execution animation and run-time verification. However, the external tool can not only consume information from the execution, but also generate input for it and thus influence and steer it.

An abstract reasoning architecture for privacy policies monitoring

Privacy is a topic of increasing interest not only for scientific communities, but also for public opinion and for regulatory bodies from all Countries. The problem is that privacy management and enforcement are very dynamic activities and monitoring known fallacies of systems is really difficult, since their number grows up day by day. Moreover, monitoring systems for yet unknown vulnerabilities is, of course, even more difficult. Here we want to present a model and an architecture, based on semantics and reasoning, which is able to detect privacy problems by abstract reasoning on monitored information. The methodology we present here fetches data and evidences from logs and other files, applying model transformation techniques in order to populate semantics repository for enacting reasoning actions. We provide here some experimental results in order to prove the strength of the proposed approach.

Research on Data Sharing Architecture for Ecological Monitoring Using Iot Streaming Data

The rapid development of Internet of Things (IoT) technology and the widespread deployment of various sensors around the world have produced a large number of data streams. Thus, current computing systems face the challenge of quickly receiving and managing these large-scale streaming data. This study builds an efficient distributed database based on Greenplum (GP) and focuses on solving the problem of the low efficiency of structured data queries for observed ecological data collected from fragile areas in Northwest China's desert oasis. First, a distributed database is designed and deployed at the physical storage structure level. A database table structure is then established based on the characteristics of the streaming data. On this basis, the data storage strategy is optimized at the data table level. Additionally, the query efficiency of the distributed database is compared with the query efficiency of traditional standalone databases. The results show that the distributed database significantly improves the data query efficiency. The greater the amount of data stored, the better the improvement in efficiency. Finally, based on the optimized distributed database, we develop a data sharing system for streaming data from ecologically fragile areas in the desert oasis in Northwest China, which provides a new approach for the efficient sharing of massive amounts of IoT streaming data for ecological monitoring. Our storage system is still currently working normally, which is highly important to both data managers and users.

IoT Architecture for Interoperability and Monitoring of Industrial Nodes

Rapid advances in industrialization methods with information and communication technologies, sensor networking, ubiquitous computing, and more recently the Internet of Things, have spurred progress in developing the next generation of manufacturing technology. This article addresses the problem of incompatibility between the components required by the automation and robotics industry. The architecture model and implementation responds to the needs identified by the industry, facilitating interoperability and measurability of industrial technologies. In the industry value chain, monitoring for intelligent maintenance plays a key role, that of maintaining productive availability and allowing the use of assets throughout their life cycle, at the lowest operational cost. Based on the advances in frameworks and middleware defined by robotics software, a IoT architecture for Industrial environments (IIoT) is proposed, based on reference models for Industry 4.0, which provides edge layers for field devices, fog for gateways and supervisors, and cloud for business logic and services according to the devices or industrial nodes. The validation for IIoT architecture presents the results of experimental analysis of sensor tests applied in a manufacturing environment to enable integration and sharing of resources. Finally, we discuss the real contributions of research to the high cohesion and low coupling of the architecture based on interoperability between the industrial nodes.

An Inexpensive Upgradation of Legacy Cameras Using Software and Hardware Architecture for Monitoring and Tracking of Live Threats

Surveillance through digital cameras is increasing exponentially. A majority of these cameras are not smart cameras; therefore, they send their video stream to a central server where it is processed and analyzed for any threats. Typically, human operators or machine learning algorithms at the cloud analyzed and processed the post-event videos to track and locate the perpetrator or victim. The centralized approach leads to two primary shortcomings: 1) the high cost of cloud infrastructure; 2) lack of instant tracking and detection of the threat. One solution is to replace these legacy cameras with the smart cameras so they can process information locally. Although the solution is costly, it could solve the real-time threat detection issues. However, the need for a central server remains there, to construct the path of threat, when threat moves from one camera view to another. The existing distributed architectures for threat tracking shifts the load of threat capturing and processing from a central server to the edge nodes, which in turn reduces the computational power but does not remove the role of the central server completely. These architectures don’t equip each camera of processing and communicating with each other. Further, in the existing distributed architectures, the local cameras are not able to store the path of the threat individually and just transmit the captured trajectory to the central body. This research proposed a second alternative that makes use of legacy cameras through additional hardware and software components such that they can process information and collaborate locally. The research addresses the challenge by introducing a low cost distributed threat tracking framework that allows the single camera to identify the threat and communicate its information to other cameras without involving the central server. The framework stores the information in a lightweight architecture that is inspired by the blockchain storage algorithm. The system also allows querying the path traveled by the threat at any stage. To evaluate the system, we performed two simulated experiments: one with a central server and another with the proposed distributed system. The results of the experiments showed that the time to track the threat through the proposed system was lower than the existing centralized system. Moreover, the proposed system predicted the paths of threats with an accuracy of 85.49%. In the future, the technique may be improved with reinforcement learning and other machine learning techniques.

Health Monitoring Based on Internet of Medical Things: Architecture, Enabling Technologies, and Applications

Recently, wearable devices have got increasing popularity in wide applications in medical and disaster rescue efforts to ensure the health and safety of users, which facilitates the development of the Internet of Medical Things (IoMT). Due to the posture alteration and mobility of users, the topology of the IoMT changes frequently, which increases the difficulty for resource allocation and routing strategy. In this paper, we respectively probe into the health monitoring architectures of the IoMT for both individual and group, allowing the monitored users to move at will. Furtherly, combined with the diversity of disaster rescuers, we build an IoMT-based disaster rescuer health monitoring system with searchers, doctors and porters. For each application, we point out the enabling technologies and demonstrate the existing researches. It is worth noting that the complexity of environment and high mobility of rescuers increase the probability of route breakage. Thus, this paper creatively addresses effective routing repair solutions for route breakage in IoMT-based disaster rescuer health monitoring system by exploiting the mobility of rescuers. Finally, we forecast three most likely directions in the field of IoMTs.

Service Broker-Based Architecture Using Multi-Criteria Decision Making for Service Level Agreement

With the on-going trends of the telecom services, the number of service providers with similar functionalities is undergoing a rapid growth. The customers face the difficulty to decide which service provider can satisfy their needs and full their requirements. Negotiating contracts between involved parts, and hiding heterogeneity in the distributed network environment has been challenging for telecom operators and service providers. Different languages exist to describe the Service Level Agreement (SLA), which is a contract between a service provider and a customer. However, since each service provider expresses his SLA in his own way, it disrupts the customer's choice of the best service provider, and leads to a bad contract management. In this respect, we propose a novel architecture for service selection, and SLA management between different stakeholders in our network architecture. The idea is to set up a smart broker where we implemented a Multi-Criteria Decision Making (MCDM) method to maximize utility function so that the customer can choose services with required QoS performances. We also came up with the idea of settling a negotiation model for the SLA, and a context based SLA contract ontology in IP Multimedia Subsystem (IMS) network is also proposed to provide users with a clear model to express their requirements and preferences. Moreover, we used the New Generation Operations Systems and Software (NGOSS) Framework to model and analyze networks and services actions. To better understand the relationship and the projection of NGOSS Framework and IMS platform, we introduce an SLA management and monitoring architecture in IMS network.

A Lightweight Blockchain Based Framework for Underwater IoT

The Internet of Things (IoT) has facilitated services without human intervention for a wide range of applications, including underwater monitoring, where sensors are located at various depths, and data must be transmitted to surface base stations for storage and processing. Ensuring that data transmitted across hierarchical sensor networks are kept secure and private without high computational cost remains a challenge. In this paper, we propose a multilevel sensor monitoring architecture. Our proposal includes a layer-based architecture consisting of Fog and Cloud elements to process and store and process the Internet of Underwater Things (IoUT) data securely with customized Blockchain technology. The secure routing of IoUT data through the hierarchical topology ensures the legitimacy of data sources. A security and performance analysis was performed to show that the architecture can collect data from IoUT devices in the monitoring region efficiently and securely.

A Lightweight Blockchain Based Framework for Underwater IoT

The Internet of Things (IoT) has facilitated services without human intervention for a wide range of applications, including underwater monitoring, where sensors are located at various depths, and data must be transmitted to surface base stations for storage and processing. Ensuring that data transmitted across hierarchical sensor networks are kept secure and private without high computational cost remains a challenge. In this paper, we propose a multilevel sensor monitoring architecture. Our proposal includes a layer-based architecture consisting of Fog and Cloud elements to process and store and process the Internet of Underwater Things (IoUT) data securely with customized Blockchain technology. The secure routing of IoUT data through the hierarchical topology ensures the legitimacy of data sources. A security and performance analysis was performed to show that the architecture can collect data from IoUT devices in the monitoring region efficiently and securely.

Image Transmission over Cognitive Radio Networks for Smart Grid Applications

Today, Smart Grids (SGs), as the goal of the next-generation power grid system, span extremely wide aspects from power generation to end-user utilities. In smart grids, Energy and Information flows are mutually dependent and performance degradation of one side may have a high impact on the other side. In this work, we introduce our architecture for monitoring of Wind Turbine (WT) farms in smart grids. In our proposed system an industrial camera is embedded on a Wireless Cognitive Radio node for each WT to capture appropriate images and stream videos to the cognitive coordinator. Any packet loss in transmission between an embedded cognitive node and the coordinator can degrade peak signal-to-noise ratio (PSNR) of the received images. The image streaming is a delay sensitive transmission which should be done in harsh environments in SGs. To tackle these challenging issues, we introduce our efficient model, called JOPSS, for joint optimization of both packet size and Number of Spectrum Sensing Iterations (NSSI) during image transmission in time-restricted conditions. We define our proposed objective function as the quotient of the Overhead Time and the Effective Transmission Time (ETT). In addition, we introduce our methods based on the Minimum of Overhead Time Channel Selection (MOTS) for the efficient channel selection along with Dynamic Parameter Updating Procedure (DPUP) to benefit different strategies in Mandatory and Proactive Handoffs (MHO/PHO). The obtained results show that noticeable improvements in both PSNR and feature-similarity (FSIM) can be achieved on our models JOPSS and JOPSS-SAFE, respectively.

A Framework for the Evaluation of Trainee Performance in Cyber Range Exercises

This paper proposes a novel approach for the evaluation of the performance achieved by trainees involved in cyber security exercises implemented in modern cyber ranges. Our main contributions include: the definition of a distributed monitoring architecture for gathering relevant information about trainees activities; an algorithm for modeling the trainee activities using directed graphs; novel scoring algorithms, based on graph operations, that evaluate different aspects (speed, precision) of a trainee during an exercise. With respect to previous work, our proposal allows to measure exactly how fast a user is progressing towards an objective and where he does wrong. We highlight that this is currently not possible in the most popular cyber ranges.

A Cloud-computing and big data based wide area monitoring of power grids strategy

This paper first analyzes the process and characteristics of big data and cloud computing. A cloud-based architecture for cloud-based power grid wide-area monitoring is proposed. This architecture uses parallel computing and big data mining to provide relevant auxiliary decision-making for the grid. The cloud computing architecture of this paper is based on Hadoop and a brief description of the Hadoop architecture is demonstrated. Finally, two scenarios of grid visualization and grid scheduling decision are briefly described.

Latency Analysis for Sequential Detection in Low-Complexity Binary Radio Systems

We consider the problem of making a quick decision in favor of one of two possible physical signal models while the numerical measurements are acquired by sensing devices featuring minimal digitization complexity. Therefore, the digital data streams available for statistical processing are binary and exhibit temporal and spatial dependencies. To handle the intractable multivariate binary data model, we first consider sequential tests for exponential family distributions. Within this generic probabilistic framework, we identify adaptive approximations for the log-likelihood ratio and the Kullback-Leibler divergence. The results allow designing sequential detectors for binary radio systems and analyzing their average run-time along classical arguments of Wald. In particular, the derived tests exploit the spatio-temporal correlation structure of the analog sensor signals engraved into the binary measurements. As an application, we consider the specification of binary sensing architectures for cognitive radio and GNSS spectrum monitoring where our results characterize the sequential detection latency as a function of the temporal oversampling and the number of antennas. Finally, we evaluate the efficiency of the proposed algorithms and illustrate the accuracy of our analysis via Monte-Carlo simulations.

PerfSONAR: An Importance of Multi-Domain Network Monitoring

Research community interacts by means of multi-domain networks. Operating policies, set of hardware components, customers, bandwidth configuration varies from network to network. Hard failures like fiber cut, power failures are easy to detect while soft failures which result in packet loss and degrade the throughput are difficult to detect. Present network monitoring tools are subject to single administrative domain. Hence, network cannot be monitored over multiple domains. Performance focused Service Oriented Network monitoring ARchitecture (perfSONAR) is designed and developed by open source community which supports advanced level of network monitoring across multi domain network. In this paper, I have focused on the importance of multi-domain network monitoring and reviewed the work of perfSONAR proposed by various researchers.

SCADA Based Remote Monitoring and Control of Pressure & Flow in Fluid Transport System Using IMC-PID Controller

Ubiquitous sensing enabled for monitoring and control in the industrial sector are mostly WSN (Wireless Sensor Network) based systems or SCADA systems which all suffer from noteworthy limitations. WSN based systems are not homogenous or incompatible systems and they lack coordinated communication and transparency among regions and processes. Hence in order to enhance the proprietary of remote monitoring and control technology, this paper emphasizes the development of IoT based architecture with local intelligent using IMC based PID controller can be integrated with DCS/SCADA and this research work spectacle only the development and application of Local Intelligence present in the proposed IoT architecture for remote monitoring and control of concerned field parameters. This proposed local intelligence tactic governs the input and output from a hardware platform positioned on a process plant, organize added processing power for meticulous analysis in the control center by the use of software integration, data acquisition and logging on a created hybrid database, and spectacle significant processed post-data statistics to operators via a standard DCS/SCADA HMI. The established local intelligence is experimentally validated in the lab scale experimental fluid transport system to monitor and control pressure and flow rate parameters remotely by incorporating with CENTUM CS 3000.The simulation and experimental results of Local Intelligence using an IMC-PID controller on a lab scale fluid transport system are conveyed with its numerical data.

The CARINA Metacognitive Architecture

Metacognition has been used in artificial intelligence to increase the level of autonomy of intelligent systems. However, the design of systems with metacognitive capabilities is a difficult task due to the number and complexity of processes involved. The main objective of this article is to introduce a novel metacognitive architecture for monitoring and control of reasoning failures in artificial intelligent agents. CARINA metacognitive architecture is based on precise definitions of structural and functional elements of metacognition as defined in the MISM meta-model. CARINA can be used to implement real-world cognitive agents with the capability for introspective monitoring and meta-level control. Introspective monitoring detects reasoning failure (for example, when expectation is violated). Metacognitive control selects strategies to recover from failures. The article demonstrates a CARINA implementation of reasoning failure detection and recovery in an intelligent tutoring system called FUNPRO.

Strengthening IoT-WSN Architecture for Environmental Monitoring

Saving the environment is the alarming red-hot topic of this trendy world. A proper arrangement is required to monitor different environmental pollutions. Many researchers and volunteers are developing and deploying Wireless Sensor Networks (WSN) for this purpose. Internet-of-Things (IoT) is one of the widely used cost-effective modern technologies to design wireless sensor nodes. Three fresh functional modules are introduced in this work to constitute the proposed work named as ‘Strengthening IoT-WSN Architecture for Environmental Monitoring’ (SIAEM) which is indented to overcome some impuissance of applying generalized wireless sensor network architecture in the field of environmental pollution monitoring. The functional modules introduced in this work are Customized Clustering of IoT-WSN Nodes (CCIN) and Energy Aware State Change Routing Protocol (EASCRP). The objective of this proposed IoT-WSN architecture is to reduce the Latency, Jitter, End-to-End delay and Power Consumption whereas, improving the performance parameters such as Throughput and Packet Delivery Ratio. The impact of proposed method in the performance of IoT-WSN network is measured and stated using benchmark network simulator.

A Study on Monitoring and Control Architecture for Smart Lighting System in IoT Environment

A Study on Monitoring and Control Architecture for Smart Lighting System in IoT Environment