Search

A reliable recognition of human activities using IoT devices (e.g., on-body wearable sensors) enables various applications, such as fitness tracking, bad habit detecting, healthcare support, and eldercare support. However, inaccurate results may cause an adverse effect on users or even an unpredictable accident. In order to improve the accuracy in the daily life activities classification, in this article, we propose countable and uncountable activities to better facilitate the understanding of the nature of daily life activities. We design global and local features and their integrated feature set for classifying countable and uncountable activities. The key idea is to examine human daily life activities from different perspectives and attempt to give a comprehensive description of the characteristics of each activity through leveraging the global and local features. By using only one simple accelerometer, our approach is evaluated to be able to recognize daily life activities with higher accuracy than the state of the art, based on one self-collected and another public available data set.

The emerging paradigm of the Social Internet of Things (SIoT) has transformed the traditional notion of the Internet of Things (IoT) into a social network of billions of interconnected smart objects by integrating social networking facets into the same. In SIoT, objects can establish social relationships in an autonomous manner and interact with the other objects in the network based on their social behaviour. A fundamental problem that needs attention is establishing of these relationships in a reliable and trusted way, i.e., establishing trustworthy relationships and building trust amongst objects. In addition, it is also indispensable to ascertain and predict an object's behaviour in the SIoT network over a period of time. Accordingly, in this paper, we have proposed an efficient time-aware machine learning-driven trust evaluation model to address this particular issue. The envisaged model deliberates social relationships in terms of friendship and community-interest, and further takes into consideration the working relationships and cooperativeness (object-object interactions) as trust parameters to quantify the trustworthiness of an object. Subsequently, in contrast to the traditional weighted sum heuristics, a machine learning-driven aggregation scheme is delineated to synthesize these trust parameters to ascertain a single trust score. The experimental results demonstrate that the proposed model can efficiently segregates the trustworthy and untrustworthy objects within a network, and further provides the insight on how the trust of an object varies with time along with depicting the effect of each trust parameter on a trust score.

Today, with the fast changing technology and life-style, online social networks (OSNs) play an important role in everybody’s life. Online social networking has made a drastic change in the way we pursue our social life. At the same time, various security problems like fake profiles, online impersonation, socialbots, clickjacking, identity clone attacks, publish spam messages, etc have also grown. Moreover, the concept of applying a computational intelligence (CI) approaches in social network analysis is feasible and sound. Therefore, the papers of this special issue address variety of security and privacy issues in online social networks including, social network modelling and security issues, information revelation and privacy, mining and analyzing social data, social networking data analysis tools and services and security of other aspects of online social networks.

Recently, Integrated computing (IC) provides a promising solution to the industry for building the Internet of Things (IoT) systems and make innovation at a rapid pace. The new era of IC with reference to IoT for Industrial applications has three main components: 1) intelligent devices; 2) intelligent system of systems; and 3) end-to-end analytics. This Special Issue is integrating computational intelligence (CI) paradigms, advanced data analytics optimization opportunities to bring more compute to the IoT. CI paradigms are more appropriate for handling uncertainty and complexity of solving real work problems compared to traditional statistical approaches and tools presently being utilized. In fact, recent literatures have addressed the inherent power of fusion of CI approaches. Moreover, it can provide the effective solutions for machine understanding of data (structured/semi structured), optimization problems, specifically, dealing with incomplete or inconsistent information, with limited computational capability related to IoT.

Adaptability is a significant property which enables software systems to continuously provide the required functionality and achieve optimal performance. The recognised importance of adaptability makes its evaluation an essential task. However, the various adaptability dimensions and implementation mechanisms make adaptive strategies difficult to evaluate. In service oriented computing, several frameworks that extend the WS-BPEL, the de facto standard in composing distributed business applications, focus on enabling the adaptability of processes. We aim to evaluate the adaptability of processes specified from the extended-BPEL frameworks. In this paper, we propose metrics to measure the adaptability of an AO4BPEL process. The metrics is grounded in the perspective that a process is capable of dynamically adapting to changes in business requirements. This opens potential future work on evaluating the adaptability of processes specified from various aspect-oriented WS-BPEL frameworks., 7 pages conference paper submitted to ACSW 2017

© 2018 IEEE. Social recommendation incorporates the social information of the user, such as friend relationship and trust relationship into the traditional recommendation system. From this point of view, social recommendation expands the function of the traditional recommendation to some extent. However, the existing social recommendation methods mostly focus on the general social relation between users and neglects the refinement of group information based on individual interest and trust. To this end, this paper proposes a novel social recommendation model based on the collective intelligence awareness driven by individual interest and trust. Experiments on two real-world datasets demonstrate that the proposed social recommendation method based on group information and individual feature outperforms the three baseline methods on the two evaluation metrics MAE and RMSE.

In sustainable computing, Intelligent Decision Support Systems (IDSS) has been adopted for prediction, optimization and decision making challenges under variable number constraints based on un-structured data. The traditional systems are lack of efficiency, limited computational ability, inadequate and impreciseness nature of handling sustainable problems. Despite, Computational Intelligence (CI) paradigms have used for high computational power of intelligence system to integrate, analyze and share large volume of un-structured data in a real time, using diverse analytical techniques to discover sustainable information suitable for better decision making . In addition, CI has the ability to handle complex data using sophisticated mathematical models, analytical techniques. This chapter provides a brief overview of computational intelligence (CI) paradigms and its noteworthy character in intelligent decision support and analytics of sustainable computing problems. The objective of this chapter is to study and analyze the effect of CI for overall advancement of emerging sustainable computing technologies.

The articles in this special section aim at presenting the latest developments, trends, and solutions of Big Data analytics on the web.

Web services and Service-Oriented Computing (SOC) have become thriving areas of academic research, joint university/industry research projects, and novel IT products on the market. SOC is the computing paradigm that uses Web services as building blocks for the engineering of composite, distributed applications out of the reusable application logic encapsulated by Web services. Web services could be considered the best-known and most standardized technology in use today for distributed computing over the Internet. Web Services Foundations is the first installment of a two-book collection covering the state-of-the-art of both theoretical and practical aspects of Web services and SOC research. This book specifically focuses on the foundations of Web services and SOC and covers - among others - Web service composition, non-functional aspects of Web services, Web service selection and recommendation, and assisted Web service composition. The editors collect advanced topics in the second book of the collection, Advanced Web Services, (Springer, 2013). Both books together comprise approximately 1400 pages and are the result of an enormous community effort that involved more than 100 authors, comprising the worlds leading experts in this field.

We are surrounded by inanimate things that have inherent information. Unfortunately, the vast majority of applications built to use this information are built in ad-hoc manner, introducing issues with maintainability, share-ability and reusability. We discuss the architecture of Ambient Space Manager (ASM), a system to explore and control things within a context-aware ambient space. We define the context variables of such things to be the Capability, Location, Operations and QoS. Here we also elaborate the Capability context based on the atomic capabilities of things that enable them to offer contextual services.

Domains (unavailable categories): Internet of Things, Web of Things; International audience; The Web of Things (WoT) relies on Web standards to enable interoperability between physical objects (things) and build applications using them. While most approaches (Cyber-Physical Systems, Internet of Things) require complex domain-driven software design that combines different disciplines such as electronics, networks and computing, we believe that generic solutions are needed to support WoT applications across the variety of things and application domains. To this end, we propose the notion of avatar as a Web-compliant software extension of a thing. Avatars achieve interoperability between things using semantic technologies and expose high-level functionalities as RESTful resources. They can collaborate with other avatars and form standard-compliant WoT applications that match end-users’ needs. We detail the notion of avatar and describe how avatar-based WoT infrastructures can improve the quality of Web of Things applications. We show how their architecture allows avatars to embed advances in different areas, and focus on contributions at different levels: tolerance to network disconnection, contextual adaptation and multi-agent negotiation.