The Internet of Things envisions billions of physical devices connecting over the Internet to provide a near real-time view of the state of the world. These devices' capabilities can be abstracted as IoT services and provided on demand. To enable quality-aware service provision, Service Level Agreements (SLA) are widely used as legally binding contracts to obligate service providers to comply with a pre-negotiated Quality of Service (QoS). With a possible ever-increasing number of service providers in an IoT environment, multi-bilateral SLA negotiation is likely to be prohibitively time-consuming without an a-priori process to select trusted candidate providers with whom to negotiate. In this article, a trust model is proposed to identify trusted service providers in a dynamic IoT environment before attempting to negotiate an SLA. A trust credit that indicates both the SLA’s fulfillment and the possible negotiation success rate is derived based on historical information relating to a service’s previous negotiations and its monitored run-time performance. Indiscernibility analysis in Rough Set theory is used to predict the negotiation success rate, while Bayesian inference is applied to deduce the possibility of SLA violation according to the monitored data. The simulation results demonstrate the feasibility and efficiency of the proposed trust model.

Accurate short-term forecasts allow dynamic systems to adapt their behaviour when degradation is forecast e.g., transportation forecasting allows for alternative routing of traffic before gridlock. This rationale can be applied to service-oriented computing when creating and managing service applications. Recent approaches to improve reliability in service applications have focused on reducing the time to recovery of application using collaborative filtering-based approaches to make QoS predictions for similar users. In this article, we focus on reducing the time to detection of a failure by forecasting when a service is about to degrade in quality. Previous approaches that have focused on QoS forecasting have used traditional time-series methods that are not designed for sudden peaks caused by network congestion or battery-powered IoT devices that can reduce processing capabilities to extend battery life. More modern recurrent neural network-based approaches such as GRUs and LSTMs have long training times, which are unsuitable for dynamic environments. We propose a noisy echo state network-based approach that has been designed to reduce training time allowing the model to incorporate recent QoS values on devices at the edge. Our results show increased response time forecasting accuracy compared to state of the art approaches when tested on IoT and web services datasets.

Pervasive applications in smart cities rely on a large number of IoT devices, which are deployed in large geographic areas. Smart cities can manage these devices using Service-Oriented Architectures (e.g., micro-services) by encapsulating devices capabilities as IoT services. Distributed service discovery architectures reduce search spaces and perform discovery processes closer to consumers on edge devices. However, request management, a key task in distributed service discovery, is still challenging because requests must be forwarded through large networks where nodes have partial knowledge about other participants. Previous research has shown that social-based and bio-inspired methods can be used to manage requests in small-scale environments, but such approaches do not scale to large environments. This paper adds urban context to a social-based and bio-inspired mechanism to forward requests where they are most likely to be solved. Results show that our model has the best rate of solved requests, and intermediate latency.

This document introduces the artifacts that implement the request manager proposed in the paper "An Urban-driven Service Request Management Model". The artifacts can be found in the public TCD GitLab project percom2020-srmm 1 .

A variety of self-tracking applications and devices have been developed in recent years to support users in tracking their weight, calories eaten, physical activities, sleep and productivity. The availability of all this data from multiple streams provides a rich environment for experimentation that allows users to improve certain aspects of their lives such as losing weight, getting better sleep or being more productive. In this paper we propose a framework that guides users to define, track, analyse, improve and control goals for better personal productivity. We present the outcome of a single-subject case study that was implemented over one year based on the proposed framework for academic productivity. This pilot study demonstrates how longitudinal multistream self-tracking data can be leveraged to gain actionable insights into personal productivity.

In service-oriented architectures, collaborative filtering is a key technique for service recommendation based on QoS prediction. Matrix factorisation has emerged as one of the main approaches for collaborative filtering as it can handle sparse matrices and produces good prediction accuracy. However, this process is resource-intensive and training must take place in the cloud, which can lead to a number of issues for user privacy and being able to update the model with new QoS information. Due to the time-varying nature of QoS it is essential to update the QoS prediction model to ensure that it is using the most recent values to maintain prediction accuracy. The request time, which is the time for a middleware to submit a user's information and receive QoS metrics for a candidate services is also important due to the limited time during dynamic service adaptations to choose suitable replacement services. In this paper we propose a stacked autoencoder with dropout on a deep edge architecture and show how this can be used to reduce training and request time compared to traditional matrix factorisation algorithms, while maintaining predictive accuracy. To evaluate the accuracy of the algorithms we compare the actual and predicted QoS values using standard error metrics such as MAE and RMSE. In addition, we propose an alternative evaluation technique using the predictions as part of a service composition and measuring the impact that the predictions have on the response time and throughput of the final composition. This more clearly shows the direct impact that these algorithms will have in practice.

Many modern software systems and applications are built using heterogeneous services provided by a range of devices, from high-power devices located in the Cloud to potentially resource-constrained and/or mobile services from IoT devices at the edge of the network. The large growth in the number of these services has led to some functionally similar services. When selecting services, a critical criterion is Quality of Service (QoS), which includes factors such as response time, location and cost. As the value of dynamic QoS attributes vary with time, there is a need to accurately forecast future QoS values to identify if a service may be about to fail. In this paper, we propose using an LSTM-based neural network to forecast future QoS values. We evaluate the use of an LSTM network against the existing state of the art in experiments using an established web service dataset and a new dataset collected by deploying services on low power IoT devices, which we publicly release. This mixture of datasets covers the heterogeneity that would be expected in a typical IoT environment.

Smart cities provide software services to citizens that are likely to be deployed in large, dynamic, heterogeneous, and distributed environments. The discovery of these services needs to be efficient and pervasive, based on the specific context of the city, and the integration of diverse providers. We identify a trade-off between accuracy and performance in the discovery of services in this scenario. Existing research has proposed solutions that focus either on semantic methods to improve accuracy with performance negatively affected, or vice versa. Additionally, the composition of services from different sources has not been explored in smart cities and large scenarios. We propose to address the trade-off by extending both how service information is organised, and the service discovery process. Service organisation uses urban context to spread service descriptions to the right urban-places; the service discovery process uses this model to forward requests where they are more likely to be solved. We simulate our model as a network of gateways that covers Dublin city center and manages services information. Results show that our model solves more requests than previous work in a smart city environment. In addition, response time keeps acceptable even when there are 100 thousand services.

Internet of Things (IoT) applications can be built from a number of heterogeneous services provided by a range of devices, which are potentially resource constrained and/or mobile. As these services and applications continue to be more widespread, a key research question is how to predict user-side quality of service (QoS), to ensure the optimal selection, composition and adaptation of IoT services. The exponential growth in the number of these services means that it is not practical to invoke all candidate services to test their QoS, especially during runtime service adaptation. QoS can vary by time and location, which makes it difficult for service providers to give accurate estimates of how the service will perform for users located in changing network topologies. We propose IoTPredict, a novel neighbourhood-based prediction approach for the IoT, which uses an alternative similarity computation mechanism. Our collaborative approach requires no additional invocation of services, which is a key requirement for resource constrained devices in the IoT. We evaluate our algorithm on a QoS dataset and show that it achieves higher QoS prediction accuracy than other state of the art approaches.

This paper discusses the challenges in developing an IoT platform for registering, discovering and composing heterogeneous services from multiple provider types, viz., Wireless Sensor Networks (WSNs), Web Service Providers (WSPs), and Autonomous Service Providers (ASPs), without human intervention. The platform executes a service composition in a decentralised fashion, with a mechanism to detect service provider failure and fallback to previously discovered services to complete a service composition flow. We comment on technical and scientific challenges involved in managing these heterogeneous, autonomous, and resilient IoT services.

Recently, there have been a large number of proposals for IoT middleware solutions. In addition, a few recent studies have surveyed and qualitatively evaluated these IoT middleware proposals against functional and non-functional features. A quantitative evaluation is also needed to complement these existing qualitative studies and provide a more in-depth perspective of the state of the art. This paper presents a quantitative evaluation of 4 representative proposals: OpenIoT, CHOReOS, LinkSmart and UBIWARE. The evaluation results, based on a small real-life scenario, show that research is needed in the area of autonomous and scalable service registration, discovery and composition, heterogeneity, and interoperability of IoT middlewares.