Smart IoT devices and applications in smart cities exchange important real-time information with their environment. However, a subset of these systems may face limitations in analyzing and processing the required large amounts of data to meet ultra-low-latency criteria. This limitation could be attributed to factors such as constrained CPU and battery resources. Thanks to the 5G and edge computing capabilities, a viable solution involves migrating a subset of these latency-sensitive and computationally intensive tasks to edge nodes and servers. This strategic service placement ensures a safe continuity of the application. In this paper, autonomous cars operating in smart cities, engaging in continuous data exchange with their external environment to meet real-time and latency-sensitive requirements, serve as an illustrative example of smart applications. The car’s decision service is strategically placed on edge nodes through a proactive (re)placement approach designed for dynamic and mobile environments. This approach uses a quality of service (QoS) metric prediction degradation module, which leverages Exponential smoothing methods to identify a suitable edge node for hosting the car’s decision module, with latency as a key criterion. Multiple configurations for outlier detection techniques are evaluated. A proof-of-concept validates the chosen model by comparing it to the AutoRegressive Integrated Moving Average (ARIMA) and the proposed proactive service (re)placement approach. This approach ensures the continuity of the placed module, suggesting the feasibility of locating non-critical modules on edge nodes.

Abstract Service providers provision more and more Internet-of-Things (IoT) services in the cloud for dynamicity and cost-effectiveness purposes. This is made possible thanks to the introduction of edge computing that brings additional computing and resources for analytics close to the data sources and thus enables meeting the low latency requirement. Edge nodes should support (i) the heterogeneity of IoT devices (e.g., sensor, actuator) and (ii) characteristics (e.g., mobility, location awareness). IoT is already integrated to the hybrid cloud/edge environment. However, the ecosystem lacks of automation due to the previously mentioned characteristics. Indeed, edge nodes are often manually selected during deployment time, and most of the regular quality-of-service (QoS) management procedures remain difficult to implement. This paper introduces a comprehensive semantic model called EdgeOnto. It encompasses all concepts related to IoT applied in the context of edge computing. The ultimate goal of EdgeOnto is to automate the several steps that make up the IoT services lifecycle in hybrid cloud/edge environment. On the one hand, semantics enable an automatic discovery of the relevant edge nodes that are suitable to host and execute IoT services considering their requirements. On the other hand, it allows supporting the specific QoS procedures that are related to such setting (e.g., low latency, mobility, jitter). The core ontology was designed with the Protégé open-source tool. A smart strawberry farming use case was implemented and evaluated for illustration purposes. The results validate the accuracy and the precision of the designed semantic matchmaker.

Abstract Mobile health care systems highly depend on collected physiological data through medical sensors to provide high‐quality care services. However, inaccurate physiological data from sensors pose a major challenge for health care providers when making decisions, whereas an erroneous decision can affect the user's life. We propose, in this paper, an anomalous data detection and isolation approach for mobile health care systems. Our approach, called AUDIT, detects inaccurate measurements in real time and distinguishes between faults or errors and health events. To do so, we propose reduced time and space complexities algorithms based on dimension reduction within the context of resource constraints. Furthermore, a decision algorithm is proposed while exploring the spatio‐temporal correlation between physiological attributes. First, we describe our approach. Then, we give its implementation details. Finally, to demonstrate the effectiveness of our approach, we show different experiments related to its detection performances and its time and space complexities.

Mobile healthcare applications highly depend on healthcare data, which is collected from wearable or implantable sensors. However, sensor readings may be inaccurate due to resource-constrained devices, sensor misplacement, patient with smearing, and other environmental related causes. Analyzing healthcare data is of paramount importance to provide high quality-care services and reduce false medical diagnosis. In this paper, we propose an online approach to detect inaccurate measurements and to raise alerts only when patients seem to be in emergency situations. The proposed approach is based on robust principal component analysis and adaptive threshold for multivariate anomaly detection, and on contribution plots for univariate anomaly diagnosis. We apply our proposed approach on real medical dataset. Our experimental results prove the effectiveness of our approach in detecting and diagnosing anomalous physiological measurements. The reduced time and space complexities of our approach make it useful and efficient for real time mobile health applications.

Real time mobile Health applications highly depend on sensor readings to provide high-quality health services. However, real-time sensor readings may be inaccurate and cause abnormal physiological measurements due to internal and external factors. Thus, abnormal readings have a significant impact on the reliability of such applications and consequently affect the patient's life. This paper addresses the following issue by proposing a robust approach for online detection of abnormal medical measurements. The proposed approach is based on robust Principal Component Analysis (PCA) to analyze collected physiological measurements from sensors and detect the occurrence of multivariate anomalies based on squared prediction error at runtime. We apply our proposed approach on real medical dataset. Our simulation results prove the effectiveness of our approach in achieving good recall with a low false alarm rate. The reduced time and space complexity of our approach make it useful and efficient for real time settings.

An important issue in distributed systems is to improve the information dissemination, especially in the Publish/Subscribe systems deployed in mobile ad hoc networks (MANET). In fact, the achievement of this goal needs to take into account various problems such as the failure of the communication system and the degradation of the quality of service (QoS). We focus on QoS problem, we address possible solutions to ensure efficient and reliable communication systems. We propose reconfiguration actions rules which are applicable to a network having QoS degradation. We use graphs to model the distributed system and graph transformation engine (GMTE) to execute rules of reconfiguration. The generated solution is selected according to various evaluation criteria in order to satisfy the requirements of system in terms of QoS.

Medical data are crucial for providing reliable mobile health services. However, its reliability may be degraded due to internal factors related to performance variations of mobile technologies used in the application and other external ones such as the environmental ones. This paper addresses this problem by proposing an ontology-based approach to deal with data and reliability management in mobile health applications. To this end, the proposed ontology model is inspired from the IBM autonomic paradigm which encompasses four concepts namely Monitor, Analyzer, Planner and Executor. The monitor concept includes the major reusable concepts that provide the basic infrastructure to build an ontology model for mobile health applications and the major reusable concepts to build an ontology model for data reliability management procedure. Besides, we defines analysis rules using SWRL (Semantic Web Rule Language) in order to analyze the reliability of each incoming medical datum et generate alerts regarding abnormal behaviors. We implement the proposed ontology model using Protg as an ontology editor.

In this paper, we propose to employ a statistical prediction model to assess medical data accuracy. To this end, the purpose of this paper is to perform medical data prediction using the Auto Regressive (AR) model. The experimental results which prove the efficiency of the proposed approach are reported based on three performance criteria namely Root Mean Square Error, Mean Absolute Error and the Theil Inequality Coefficient.

This paper presents an analytical model for monitoring and analyzing QoS of publish/subscribe systems on MANET. The proposed customizable model ensures the user a good quality of service requirements and combines both proactive and reactive statistical analysis. In fact, the reactive analysis, suitable for multimedia applications, aims to detect failures by approximating latency series with a Gumbel distribution. Besides, the proactive analysis, suitable for crisis management applications, forecasts failures occurrence relaying on the Autoregressive Integrated Moving Average Formula. Finally, a hybrid analysis was proposed by dynamically switching from reactive to predictive forms of analysis whenever QoS violations are noticed. The correlation method was also used to identify source failure causes once they were detected or predicted. The efficiency and accuracy of the proposed scheme were validated by performing simulation.

In this paper, we propose an analytical framework for monitoring and analyzing QoS of publish/subscribe systems on MANET. Our framework copes with intermittent connectivity frequently occurring in MANET and provides statistical methods allowing detecting QoS degradations affecting links between brokers at the middleware layer. Besides, our analytical framework identifies QoS degradation source which enables to repair the system. The proposed framework is extensively evaluated with simulation experiments. Simulations results show its efficiency.

In this paper, we propose a failure prediction methodology for quality of service (QoS) degradation prediction for publish/subscribe systems on MANET. Our propose is to use the Auto Regressive Integrated Moving Average (ARIMA) method to predict failure occurrence in the system and to provide optimal QoS provision of applications. Besides, our forecasting algorithm looks for the source behind QoS degradation using the Correlation method. Simulations results are performed to prove the efficiency of the proposed approach. A comparison is done proving that our proposal outperforms the Auto Regression (AR) based prediction approach.

In this paper, we propose a decentralized and adaptive approach for QoS aware publish/subscribe systems deployed on MANET. It provides monitoring and analysis modules acting at the middleware layer to detect QoS degradations between brokers. The paper presents a statistical method allowing for a permanent measurement of QoS parameters and an instantaneous detection of link failures. The statistical method used resorts to the Extreme Values Theory (EVT) and particularly uses the Gaussian and Gumbel distributions to approximate empirical distributions of the measured QoS parameter. Simulation results show the efficiency of the proposed approach.