Unified representation of monitoring information across federated cloud infrastructures

Nowadays one of the issues hindering the potential of federating cloud-based infrastructures to reach much larger scales is their standard management and monitoring. In particular, this is true in cases where these federated infrastructures provide emerging Future Internet and Smart Cities-oriented services, such as the Internet of Things (IoT), that benefit from cloud services. The contribution of this paper is the introduction of a unified monitoring architecture for federated cloud infrastructures accompanied by the adoption of a uniform representation of measurement data. The presented solution is capable of providing multi-domain compatibility, scalability, as well as the ability to analyze large amounts of monitoring data, collected from datacenters and offered through open and standardized APIs. The solution described herein has been deployed and is currently running on a community of 5 infrastructures within the framework of the European Project XIFI, to be extended to 12 more infrastructures

Antilizer: run time self-healing security for wireless sensor networks

Wireless Sensor Network (WSN) applications range from domestic Internet of Things systems like temperature monitoring of homes to the monitoring and control of large-scale critical infrastructures. The greatest risk with the use of WSNs in critical infrastructure is their vulnerability to malicious network level attacks. Their radio communication network can be disrupted, causing them to lose or delay data which will compromise system functionality. This paper presents Antilizer, a lightweight, fully-distributed solution to enable WSNs to detect and recover from common network level attack scenarios. In Antilizer each sensor node builds a self-referenced trust model of its neighbourhood using network overhearing. The node uses the trust model to autonomously adapt its communication decisions. In the case of a network attack, a node can make neighbour collaboration routing decisions to avoid affected regions of the network. Mobile agents further bound the damage caused by attacks. These agents enable a simple notification scheme which propagates collaborative decisions from the nodes to the base station. A filtering mechanism at the base station further validates the authenticity of the information shared by mobile agents. We evaluate Antilizer in simulation against several routing attacks. Our results show that Antilizer reduces data loss down to 1% (4% on average), with operational overheads of less than 1% and provides fast network-wide convergence

An artificial intelligence-based collaboration approach in industrial IoT manufacturing : key concepts, architectural extensions and potential applications

The digitization of manufacturing industry has led to leaner and more efficient production, under the Industry 4.0 concept. Nowadays, datasets collected from shop floor assets and information technology (IT) systems are used in data-driven analytics efforts to support more informed business intelligence decisions. However, these results are currently only used in isolated and dispersed parts of the production process. At the same time, full integration of artificial intelligence (AI) in all parts of manufacturing systems is currently lacking. In this context, the goal of this manuscript is to present a more holistic integration of AI by promoting collaboration. To this end, collaboration is understood as a multi-dimensional conceptual term that covers all important enablers for AI adoption in manufacturing contexts and is promoted in terms of business intelligence optimization, human-in-the-loop and secure federation across manufacturing sites. To address these challenges, the proposed architectural approach builds on three technical pillars: (1) components that extend the functionality of the existing layers in the Reference Architectural Model for Industry 4.0; (2) definition of new layers for collaboration by means of human-in-the-loop and federation; (3) security concerns with AI-powered mechanisms. In addition, system implementation aspects are discussed and potential applications in industrial environments, as well as business impacts, are presented

The masseteric jaw‐jerk reflex in older dentate subjects and edentulous denture wearers

The aim of the present study was to investigate any variations in the jaw‐jerk reflex in edentulous subjects wearing complete dentures, compared to an age and sex‐matched dentate group. The reflex was elicited by chin taps in 22 older dentate subjects with mean age 61.3 years and in 22 denture wearers with mean age 63.1 years. Surface electromyographic recordings were obtained from the masseter muscle of the preferred chewing side during mandibular rest and at moderate clenching (40% of the individual maximum clenching masseteric EMG activity). A jaw‐jerk reflex was recorded in all subjects at least once, and its occurrence during clenching was reduced compared to rest. The occurrence of the reflex was however increased in the denture wearers in both experimental conditions, while minor differences were observed in the values for latency, duration and amplitude between the two dental status groups. These results suggest that under the present experimental conditions the periodontal ligament receptors might inhibit reflex activity. Multiple sensory interactions are expected in denture wearing. However a particular source of sensory feedback is provided by the stimulation of mucosal receptors from the acrylic denture base. Since the occurrence of the jaw‐jerk at clench in the denture wearers was also reduced compared to the rest experiments, a potential inhibitory effect of the mucosal receptors can be speculated. According to the findings in the present study the loss of teeth and the rehabilitation with complete dentures do not severely disrupt the reflex activity investigated. Copyright © 1995, Wiley Blackwell. All rights reserve

Data-driven intrusion detection for ambient intelligence

Billions of embedded processors are being attached to everyday objects and houseware equipment to enhance daily activities and enable smart living. These embedded processors have enough processing capabilities to process sensor data to produce smart insights, and are designed to operate for months without the need of physical interventions. Despite the compelling features of Internet of Things (IoT), applied at several home-oriented use cases (e.g., lighting, security, heating, comfort), due to the lack of a physical flow of information (e.g., absence of switches and cable-based gateways), the security of such networks is impeding their rapid deployment. In this work we look into IPv6 based IoT deployments, since it is the leading standard for interconnecting the wireless devices with the Internet and we propose a data-driven anomaly detection system that operates at the transport-layer of 6LoWPAN deployments. We present a comprehensive experimental evaluation carried out using both simulated and real-world experimentation facilities that demonstrates the accuracy of our system against well-known network attacks against 6LoWPAN networks. © Springer Nature Switzerland AG 2019