Internet of Things (IoT) enables a myriad of applications by interconnecting software to physical objects. The objects range from wireless sensors to robots and include surveillance cameras. The applications are often critical (e.g. physical intrusion detection, fire fighting) and latency-sensitive. On the one hand, such applications rely on specific protocols (e.g. MQTT, COAP) and the network to communicate with the objects under very tight timeframe. On the other hand, anomalies (e.g. communication noise, sensors' failures, security attacks) are likely to occur in open IoT systems and can result by sending false alerts or the failure to properly detect critical events. To address that, IoT systems have to be equipped with anomaly detection processing in addition to the required event detection capability. This is a key feature that enables reliability and efficiency in IoT. However, anomaly detection systems can be themselves object of failures and attacks, and then can easily fall short to accomplish their mission. This paper introduces a Reliable Event and Anomaly Detection Framework for the Internet of Things (READ-IoT for short). The designed framework integrates events and anomalies detection into a single and common system that centralizes the management of both concepts. To enforce its reliability, the system relies on a reputation-aware provisioning of detection capabilities that takes into account the vulnerability of the deployment hosts. As for validation, READ-IoT was implemented and evaluated using two real life applications, i.e. a fire detection and an unauthorized person detection applications. Several scenarios of anomalies and events were conducted using NSL-KDD public dataset, as well as, generated data to simulate routing attacks. The obtained results and performance measurements show the efficiency of READ-IoT in terms of event detection accuracy and real-time processing.

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.

Internet of Things (IoT) applications can play a critical role in business and industry. Industrial IoT (IIoT) refers to the use of IoT technologies in manufacturing. Enabling IIoT applications in cloud environments requires the design of appropriate IIoT Platform as-a-Service (IIoT PaaS) to support and ease their provisioning (i.e., development, deployment and management). This paper critically reviews the IIoT PaaS architectures proposed so far in the relevant literature. It only surveys the architectures that are suitable for IIoT applications provisioning and it excludes regular IoT solutions from its scope. The evaluation is based on a set of well-defined architectural requirements. It also introduces and discusses the future challenges and the research directions. The critical review discusses the PaaS solutions that focus on the whole spectrum of IoT verticals and also the ones dealing with specific IoT verticals. Existing limitations are identified and hints are provided on how to tackle them. As critical research directions, the mechanisms that enable the secure provisioning, and IIoT PaaS interaction with virtualized IoT Infrastructure as-a-Service (IaaS) and fog computing layer are discussed.

With the explosive growth of the Internet of Things (IoT) devices deployed in edge networks, the functionalities of IoT devices are typically encapsulated as IoT services, and user requests can be achieved through the composition of data and/or computation-intensive IoT services. Considering the prediction-uncertainty of forthcoming requests, certain IoT services may (i) not be hosted currently by appropriate IoT devices, or (ii) such an IoT service exists, but its non-functional properties may hardly be satisfied with respect to certain constraints prescribed by requests. To address this challenge, this paper proposes an efficiency-aware service Migration Scheduling (denoted eMS) mechanism in edge networks, in order to migrate IoT services on-demand, and thus, to optimally settle non-satisfiable constraints. Specifically, IoT services are re-scheduled, such that certain IoT services are migrated from their hosting IoT devices to neighboring ones, while minimizing the energy consumption and average delay caused by this service re-scheduling operation. We formulate this service re-scheduling as a multi-objective and multi-constraint optimization problem, which is solved through integrating the greedy algorithm into the fast non-dominated sorting and crowded-comparison operators as the hybrid genetic algorithm (G-NSGA-II). Based on real-life datasets provided by an oil pipeline monitoring project, extensive experiments are conducted, and evaluation results show that our eMS is promising in reducing the energy consumption and average delay of service re-scheduling in comparison with the state-of-art’s techniques.

Study of mobility on Cloud infrastructures and platforms shows that they present limitation related to deployment and running of service-based applications that are built from heterogeneous services (heterogeneous programming languages, communication protocols and/or hosting frameworks). In this paper, we present mobile service-containers that are able to host services on the Cloud and migrate from one virtual machine to another independently of the programming languages and/or bindings of the deployed services. Mobile micro-containers are extensions from our already developed micro-containers for hosting service-based applications on the Cloud. Several approaches were considered and discussed to provide mobility for service micro-containers. Experiments are conducted to compare these approaches but also to determine the overhead of the added mobility.

The Internet of Things ( IoT ) has been widely deployed to support versatile applications, where an application can be satisfied by functionally compatible and non-functionally satisfiable IoT services. Considering the fact that the capacities of IoT devices may change dynamically, whether or not, and to what extent, certain constraints can be satisfied during their execution, are to be explored. This observation motivates us to formalize the interpretation of qualitative and quantitative satisfaction for prescribed constraints, and thus, to achieve IoT service monitoring at runtime. Specifically, we formulate the problem of IoT service monitoring as a constraint satisfaction problem, where multiple constraints, including spatial-temporal constraints, energy limitation, and capacity restrictions, are considered. Specification-based monitoring is developed based on S ignal T emporal L ogic ( STL ), where a novel accumulative robustness metric is proposed, denoted A ccumulative STL ( ASTL ), to emphasize the robust satisfaction over the entire time domain. Thereafter, IoT service monitoring is converted to ASTL formulae, and its constraint satisfaction is interpreted with qualitative and quantitative semantics at runtime. Case studies and extensive evaluations are conducted upon publicly-available datasets, where various influential factors are considered. Experimental results show that our ASTL performs better than the state-of-the-art's techniques with more robust satisfaction.