Future Internet, Volume 10, Issue 10 (October 2018) – 11 articles

Forest fires are a fatal threat to environmental degradation. Wireless sensor networks (WSNs) are regarded as a promising candidate for forest fire monitoring and detection since they enable real-time monitoring and early detection of fire threats in an efficient way. However, compared to conventional surveillance systems, WSNs operate under a set of unique resource constraints, including limitations with respect to transmission range, energy supply and computational capability. Considering that long transmission distance is inevitable in harsh geographical features such as woodland and shrubland, energy-efficient designs of WSNs are crucial for effective forest fire monitoring and detection systems. In this paper, we propose a novel framework that harnesses the benefits of WSNs for forest fire monitoring and detection. The framework employs random deployment, clustered hierarchy network architecture and environmentally aware protocols. The goal is to accurately detect a fire threat as early as possible while maintaining a reasonable energy consumption level. ns-2-based simulation validates that the proposed framework outperforms the conventional schemes in terms of detection delay and energy consumption. Full article

Over the last few decades, semi-autonomous machine’s technology started to promote awareness towards the importance of human–robot interaction (HRI) for improving daily activities. More affordable social robots are being commercially released and in order to implement viable applications of HRI, a combination human-computer interaction and user experience methodologies could play a pivotal role in assessing new scenarios and evaluating new investigations. However, literature shows that it is still challenging to reach an optimal user experience with robotic companions. The aim of the study was to determine the chance to enhance the user experience with a semi-autonomous social robot, using user experience and human–computer interaction methodologies. In this study, a social robotic companion has been developed and prototyped in order to be adopted in a specific public environment such as a company workspace. The challenges emerged from this peculiar environment triggered the need for a more productive and comfortable office for the employees, and, at the same time, the usability, acceptance and likeability of the robotic companion have been evaluated. The results emphasize that, since HRI is highly interdisciplinary, the benefits of combining approaches from other fields could positively benefit from a meaningful social interaction with the users. Full article

The emerging Industrial Internet of Things (IIoT) will not only leverage new and potentially disruptive business models but will also change the way software applications will be analyzed and designed. Agility is a need in a systematic service engineering as well as a co-design of requirements and architectural artefacts. Functional and non-functional requirements of IT users (in smart manufacturing mostly from the disciplines of mechanical engineering and electrical engineering) need to be mapped to the capabilities and interaction patterns of emerging IIoT service platforms, not to forget the corresponding information models. The capabilities of such platforms are usually described, structured, and formalized by software architects and software engineers. However, their technical descriptions are far away from the thinking and the thematic terms of end-users. This complicates the transition from requirements analysis to system design, and hence the re-use of existing and the design of future platform capabilities. Current software engineering methodologies do not systematically cover these interlinked and two-sided aspects. The article describes in a comprehensive manner how to close this gap with the help of a service-oriented analysis and design methodology entitled SERVUS (also mentioned in ISO 19119 Annex D) and a corresponding Web-based Platform Engineering Information System (PEIS). Full article

The marine main diesel engine rotational speed automatic control plays a significant role in determining the optimal main diesel engine speed under impacting on navigation environment conditions. In this article, the application of fuzzy logic control theory for main diesel engine speed control has been associated with Particle Swarm Optimization (PSO). Firstly, the controller is designed according to fuzzy logic control theory. Secondly, the fuzzy logic controller will be optimized by Particle Swarm Optimization (PSO) in order to obtain the optimal adjustment of the membership functions only. Finally, the fuzzy logic controller has been completely innovated by Particle Swarm Optimization algorithm. The study results will be represented under digital simulation form, as well as comparison between traditional fuzzy logic controller with fuzzy logic control–particle swarm optimization speed controller being obtained. Full article

Due to the exponential growth of the data and its services, visiting multiple webs/apps by a user raises three issues—(1) consumption of extra bytes; (2) time killing process of surfing inside the webs/apps; (3) tedious task of remembering address of webs/apps with their credentials. The data mashup is a set of techniques and user-friendly approaches which not only resolves above issues but also allows ordinary user to fetch required data from multiple disparate data sources and to create the integrated view in his defined digital place. In this paper, we have proposed an extension of existing REST protocol called Structured Data REST (SDRest) protocol and user-friendly novel approach which allows even ordinary users to develop end to end data mashup, using the innovative concept of Structured Data Mashup Box (SDMB) and One Time Configuration (OTC)-Any Time Access (ATA) models. Our implementation shows that pre-mashup configuration can easily be performed by an ordinary user and an integrated user interface view of end user data mashup can be created without any technical knowledge or programming. We have also evaluated the proposed work by comparing it with some of the related works and found that the proposed work has developed user friendly configurable approach using the current state of the art techniques to involve not only the ordinary user but also the mashup service provider and the data service provider to develop public, private and hybrid data mashup. Full article

With the high popularity of the Internet, online trading has gradually replaced the traditional shopping model and extended to every corner of social life. However, online trading cannot avoid failures; thus, understanding how firms can best recover customers in online contexts to keep customer loyalty is very important. This study investigates the mechanisms by which customer participation types (physical, mental, and emotional) promote customers’ perceived justice and post-recovery satisfaction from a mental accounting perspective. Furthermore, the moderating effects of two modes of online apology speech acts (direct and indirect) on customer participation and perceived justice are investigated. A total of 1083 Chinese tourists who have purchased a Wi-Fi rental service in the past year were contacted according to the database provided by two travel agencies, and 329 stated having experienced an online recovery service and participated in the survey; 297 valid questionnaires were collected. Among them, 48.82% were males and 51.18% females. Most of the respondents were aged 20–35 years. By carrying out data analysis by partial least squares structural equation modeling (PLS-SEM) using SmartPLS, the results show that, first, only mental and physical participation can enhance perceived justice, while emotional participation does not influence perceived justice. Second, the positive influence of mental participation on perceived justice is most significant. Third, only when the service staff adopts the indirect mode to express an online apology, mental and physical participation can enhance perceived justice. Full article

Radio frequency identification (RFID) technology has widely been used in the last few years. Its applications focus on auto identification, tracking, and data capturing issues. However, RFID suffers from the main problem of tags collision when multiple tags simultaneously respond to the reader request. Many protocols were proposed to solve the collision problems with good identification efficiency and an acceptable time delay, such as the blocking anti-collision protocol (BA). Nevertheless, most of these protocols assumed that the RFID reader could decode the tag’s signal only when there was one tag responding to the reader request once each time. Hence, they ignored the phenomenon of the capture effect, which results in identifying the tag with the stronger signal as the multiple tags simultaneously respond. As a result, many tags will not be identified under the capture effect. Therefore, the purpose of this paper is to take the capture effect phenomenon into consideration in order to modify the blocking BA protocol to ensure a full read rate, i.e., identifying all the tags in the frame without losing any tag. Moreover, the modifications include distinguishing between collision and interference responses (for the period of staying tags) in the noisy environments, for the purpose of enhancing the efficiency of the identification. Finally, the simulation and analytical results show that our modifications and MBA protocol outperform the previous protocols in the same field, such as generalized query tree protocols (GQT1 and GQT2), general binary tree (GBT), and tweaked binary tree (TBT). Full article

Chinese event extraction uses word embedding to capture similarity, but suffers when handling previously unseen or rare words. From the test, we know that characters may provide some information that we cannot obtain in words, so we propose a novel architecture for combining word representations: character–word embedding based on attention and semantic features. By using an attention mechanism, our method is able to dynamically decide how much information to use from word or character level embedding. With the semantic feature, we can obtain some more information about a word from the sentence. We evaluate different methods on the CEC Corpus, and this method is found to improve performance. Full article

Mobile edge computing is a new communication paradigm, which stores content close to the end users, so as to reduce the backhaul delay and alleviate the traffic load of the backbone networks. Crowd participation is one of the most striking features of this technology, and it enables numerous interesting applications. The dynamics of crowd participation offer unprecedented opportunities for both content caching and data forwarding. In this paper, we investigate the influence of the dynamics of crowd participation, from the perspective of opportunistic caching and forwarding, and discuss how we can exploit such opportunities to allocate content and select relays efficiently. Some existing issues in this emerging research area are also discussed. Full article

Caching close to users in a radio access network (RAN) has been identified as a promising method to reduce a backhaul traffic load and minimize latency in 5G and beyond. In this paper, we investigate a novel community detection inspired by a proactive caching scheme for device-to-device (D2D) enabled networks. The proposed scheme builds on the idea that content generated/accessed by influential users is more probable to become popular and thus can be exploited for pro-caching. We use a Clustering Coefficient based Genetic Algorithm (CC-GA) for community detection to discover a group of cellular users present in close vicinity. We then use an Eigenvector Centrality measure to identify the influential users with respect to the community structure, and the content associated to it is then used for pro-active caching using D2D communications. The numerical results show that, compared to reactive caching, where historically popular content is cached, depending on cache size, load and number of requests, up to 30% more users can be satisfied using a proposed scheme while achieving significant reduction in backhaul traffic load. Full article

We present an occlusion-aware unsupervised neural network for jointly learning three low-level vision tasks from monocular videos: depth, optical flow, and camera motion. The system consists of three different predicting sub-networks simultaneously coupled by combined loss terms and is capable of computing each task independently on test samples. Geometric constraints extracted from scene geometry which have traditionally been used in bundle adjustment or pose-graph optimization are formed as various self-supervisory signals during our end-to-end learning approach. Different from prior works, our image reconstruction loss also takes account of optical flow. Moreover, we impose novel 3D flow consistency constraints over the predictions of all the three tasks. By explicitly modeling occlusion and taking utilization of both 2D and 3D geometry relationships, abundant geometric constraints are formed over estimated outputs, enabling the system to capture both low-level representations and high-level cues to infer thinner scene structures. Empirical evaluation on the KITTI dataset demonstrates the effectiveness and improvement of our approach: (1) monocular depth estimation outperforms state-of-the-art unsupervised methods and is comparable to stereo supervised ones; (2) optical flow prediction ranks top among prior works and even beats supervised and traditional ones especially in non-occluded regions; (3) pose estimation outperforms established SLAM systems under comparable input settings with a reasonable margin. Full article