A Multi-agent System for Outliers Accommodation in Wireless Sensor Networks

This work has been partially supported by the European Commission under the contract FP7-ICT-224282 (GINSENG) and Project CENTRO-07-ST24-FEDER-002003 (iCIS-Intelligent Computing in the Internet of Services).In monitoring applications the accuracy of data is paramount. When considering wireless sensor networks the quality of readings taken from the environment may be hampered by outliers in raw data collected from transmitters attached to nodes' analogue-to-digital converter ports. To improve the data quality sent to the base-station, a real-time data analysis should be implemented at nodes' level, while taking into account their computing power and storage limitations. This paper deals with the problem of outliers detection and accommodation in raw data. The proposed approach relies on univariate statistics within an hierarchical multi-agent framework. Results from experiments on a real monitoring scenario, at a major oil refinery plant, show the relevance of the proposed approach.publishersversionpublishe

Resilience Enhancement in Cyber-Physical Systems: A Multiagent-Based Framework

The growing developments on networked devices, with different communication platforms and capabilities, made the cyber-physical systems an integrating part of most critical industrial infrastructures. Given their increasing integration with corporate networks, in which the industry 4.0 is the most recent driving force, new uncertainties, not only from the tangible physical world, but also from a cyber space perspective, are brought into play. In order to improve the overall resilience of a cyber-physical system, this work proposes a framework based on a distributed middleware that integrates a multiagent topology, where each agent is responsible for coordinating and executing specific tasks. In this framework, both physical and cyber vulnerabilities alike are considered, and the achievement of a correct state awareness and minimum levels of acceptable operation, in response to physical or malicious disturbances, are guaranteed. Experimental results collected with an IPv6-based simulator comprising several distributed computational devices and heterogeneous communication networks show the relevance and inherent benefits of this approach

Device-Free, Activity during Daily Life, Recognition Using a Low-Cost Lidar

Device-free or off-body sensing methods, such as Lidar, can be used for location-driven Activities during Daily Life (ADL) recognition without the need for a mobile host such as a human or robot to use on-body location sensors. Because if such an attachment fails, or is not operational (powered up), when such mobile hosts are device free, it still works. Hence, this paper proposes an innovative method for recognizing ADLs using a state-of-art seq2seq Recurrent Neural Network (RNN) model to classify centimeter level accurate location data from a low-cost, 360°rotating 2D Lidar device. We researched, developed, deployed and validated the system. The results indicate that it can provide a centimeter-level localization accuracy of 88% when recognizing 17 targeted location-related daily activities

Optimal sensor placement for sewer capacity risk management

2019 Spring.Includes bibliographical references.Complex linear assets, such as those found in transportation and utilities, are vital to economies, and in some cases, to public health. Wastewater collection systems in the United States are vital to both. Yet effective approaches to remediating failures in these systems remains an unresolved shortfall for system operators. This shortfall is evident in the estimated 850 billion gallons of untreated sewage that escapes combined sewer pipes each year (US EPA 2004a) and the estimated 40,000 sanitary sewer overflows and 400,000 backups of untreated sewage into basements (US EPA 2001). Failures in wastewater collection systems can be prevented if they can be detected in time to apply intervention strategies such as pipe maintenance, repair, or rehabilitation. This is the essence of a risk management process. The International Council on Systems Engineering recommends that risks be prioritized as a function of severity and occurrence and that criteria be established for acceptable and unacceptable risks (INCOSE 2007). A significant impediment to applying generally accepted risk models to wastewater collection systems is the difficulty of quantifying risk likelihoods. These difficulties stem from the size and complexity of the systems, the lack of data and statistics characterizing the distribution of risk, the high cost of evaluating even a small number of components, and the lack of methods to quantify risk. This research investigates new methods to assess risk likelihood of failure through a novel approach to placement of sensors in wastewater collection systems. The hypothesis is that iterative movement of water level sensors, directed by a specialized metaheuristic search technique, can improve the efficiency of discovering locations of unacceptable risk. An agent-based simulation is constructed to validate the performance of this technique along with testing its sensitivity to varying environments. The results demonstrated that a multi-phase search strategy, with a varying number of sensors deployed in each phase, could efficiently discover locations of unacceptable risk that could be managed via a perpetual monitoring, analysis, and remediation process. A number of promising well-defined future research opportunities also emerged from the performance of this research

Device-Free Daily Life (ADL) Recognition for Smart Home Healthcare using a low-cost (2D) Lidar

Device-free or off-body sensing methods such as Lidar can be used for location-related Activities during Daily Life (ADL) recognition without the need for the subject to carry less accurate on-body sensors and because some subjects may forget to carry them or maintain them to be operational (powered up), i.e., users can be device free and the method still works. Hence, this paper proposes an innovative method for recognizing daily activities using a state-of-art seq2seq Recurrent Neural Network (RNN) model to classify centimeter level accurate location data from a 360-degree rotating 2D Lidar device. We deployed and validated the system. The results indicate that our method can provide a centimeter-level localization accuracy of 88% when recognizing seventeen targeted location-related daily activities

Device-Free Daily Life (ADL) Recognition for Smart Home Healthcare using a low-cost (2D) Lidar

Device-free or off-body sensing methods such as Lidar can be used for location-related Activities during Daily Life (ADL) recognition without the need for the subject to carry less accurate on-body sensors and because some subjects may forget to carry them or maintain them to be operational (powered up), i.e., users can be device free and the method still works. Hence, this paper proposes an innovative method for recognizing daily activities using a state-of-art seq2seq Recurrent Neural Network (RNN) model to classify centimeter level accurate location data from a 360-degree rotating 2D Lidar device. We deployed and validated the system. The results indicate that our method can provide a centimeter-level localization accuracy of 88% when recognizing seventeen targeted location-related daily activities

Innovative Technologies and Services for Smart Cities

A smart city is a modern technology-driven urban area which uses sensing devices, information, and communication technology connected to the internet of things (IoTs) for the optimum and efficient utilization of infrastructures and services with the goal of improving the living conditions of citizens. Increasing populations, lower budgets, limited resources, and compatibility of the upgraded technologies are some of the few problems affecting the implementation of smart cities. Hence, there is continuous advancement regarding technologies for the implementation of smart cities. The aim of this Special Issue is to report on the design and development of integrated/smart sensors, a universal interfacing platform, along with the IoT framework, extending it to next-generation communication networks for monitoring parameters of interest with the goal of achieving smart cities. The proposed universal interfacing platform with the IoT framework will solve many challenging issues and significantly boost the growth of IoT-related applications, not just in the environmental monitoring domain but in the other key areas, such as smart home, assistive technology for the elderly care, smart city with smart waste management, smart E-metering, smart water supply, intelligent traffic control, smart grid, remote healthcare applications, etc., signifying benefits for all countries