“WBC over DVB-H” testbed design, development and results

The wireless billboard channels (WBCs) are integral part of the ubiquitous consumer wireless world (UCWW)—a wireless next generation network proposal. The WBCs are used by the service providers to broadcast advertisements of their (wireless) services to the mobile terminals so that the mobile users may discover and associate with the “best” services following the user-driven “always best connected and best served” paradigm. A three-layer system architecture of WBCs established over the digital video broadcasting-handheld (DVB-H) standard is presented. The design and development of a corresponding “WBC over DVB-H” experimental testbed are described. Various results obtained from the testbed are presented and explained

Designing an ultra-low-power RTU for use in NB-IoT water applications

Abstract. This paper presents the design and realization of a ultra-low-power and low-cost remote transfer unit (RTU), working as an outdoor gateway for collecting hydrographic data, such as rainfall, water flow rate, water quality, etc. Based on the Narrow Band Internet of Things (NB-IoT) standard, it facilitates the communication between the sensors and the information center (server). The unit consists of an Advanced RISC Machine (ARM) microcontroller unit (MCU), a NB-IoT module, and a power supply module. The RTU was experimentally tested and its use successfully demonstrate

Performance analysis of “WBC over DVB-H” link layer

This paper presents two novel smart cross-layer error-control coding schemes for improving the error protection of service advertisements that are broadcast to mobile terminals on wireless billboard channels (WBCs) established over a digital video broadcast-handheld (DVB-H) infrastructure. These are the smart section erasure (SSE) and smart transport stream erasure (STSE) schemes, which are jointly executed by the link layer and service layer cross-layer algorithms. The new schemes are analysed and compared to existing schemes. The solution enables the “WBC over DVB-H” systemto operate with good flexibility, and in amore reliable way and with greater throughput efficiency than the standard IP datacasting supported in DVB-H

An IoT-based smart electric heating control system: design and implementation

This paper presents the design and realization of an IoT-based smart electric heating control system for homes, offices, schools, community centres, and the like. The architecture proposed provides a gateway to the IoT cloud for the control system through a Data Transfer Unit (DTU) which sends the sensor data to an IoT centre via a TCP server over a GPRS/Wi- Fi wireless interface and receives energy telecommands for the controllers, which thus switch off, on or adjust electric heating operation. The hardware and software descriptions set out here are from a small pilot system which was successfully designed and implemented

Designing a cloud tier for the IoT platform EMULSION

This paper presents some design aspects of the cloud tier of a generic multi-service cloud-based IoT operational platform EMULSION, which is developed as a non-expensive IoT platform primarily to serve the needs of small and medium business enterprises (SMEs). The EMULSION is a representative of the new horizontal type, next-generation, IoT platforms that come as a replacement of the existing vertical type platforms. The architectural design and main characteristics of the platform are presented and its multi-tiered structure is explained with particular attention paid to the cloud tier. This proposed cloud tier, with a Data Management Platform (DMP) based on a three-layer Lambda architecture, achieves improved high throughput and low latency. This is done through the inclusion of two distributed ‘publish-subscribe’ Kafka-based modules which are designed for data processing, and for data subscribing and message storage, respectively. Initial trials have begun with two pilot platform-demonstration IoT systems, utilizing this EMULSION platform. These are shortly to be presented in separate research papers

Weighted Item ranking for pairwise matrix factorization

Recommendation systems employed on the Internet aim to serve users by recommending items which will likely be of interest to them. The recommendation problem could be cast as either a rating estimation problem which aims to predict as accurately as possible for a user the rating values of items which are yet unrated by that user, or as a ranking problem which aims to find the top-k ranked items that would be of most interest to a user, which s/he has not ranked yet. In contexts where explicit item ratings of other users may not be available, the ranking prediction could be more important than the rating prediction. Most of the existing ranking-based prediction approaches consider items as having equal weights which is not always the case. Different weights of items could be regarded as a reflection of items’ importance, or desirability, to users. In this paper, we propose to integrate variable item weights with a ranking-based matrix factorization model, where learning is driven by Bayesian Personalized Ranking (BPR). Two ranking-based models utilizing different-weight learning methods are proposed and the performance of both models is confirmed as being better than the standard BPR method

Advances in quality and performance assessment for future wireless communication services

no abstract availabl

A cloud-based X73 ubiquitous mobile healthcare system: design and implementation

Based on the user-centric paradigm for next generation networks, this paper describes a ubiquitous mobile healthcare (uHealth) system based on the ISO/IEEE 11073 personal health data (PHD) standards (X73) and cloud computing techniques. A number of design issues associated with the system implementation are outlined. The system includes a middleware on the user side, providing a plug-and-play environment for heterogeneous wireless sensors and mobile terminals utilizing different communication protocols and a distributed "big data" processing subsystem in the cloud. The design and implementation of this system are envisaged as an efficient solution for the next generation of uHealth systems

A cloud-based car parking middleware for IoT-based smart cities: design and implementation

This paper presents the generic concept of using cloud-based intelligent car parking services in smart cities as an important application of the Internet of Things (IoT) paradigm. This type of services will become an integral part of a generic IoT operational platform for smart cities due to its pure business-oriented features. A high-level view of the proposed middleware is outlined and the corresponding operational platform is illustrated. To demonstrate the provision of car parking services, based on the proposed middleware, a cloud-based intelligent car parking system for use within a university campus is described along with details of its design, implementation, and operation. A number of software solutions, including Kafka/Storm/Hbase clusters, OSGi web applications with distributed NoSQL, a rule engine, and mobile applications, are proposed to provide ‘best’ car parking service experience to mobile users, following the Always Best Connected and best Served (ABC&S) paradigm

FeatureMF: an item feature enriched matrix factorization model for item recommendation

Matrix Factorization (MF) is one of the most successful Collaborative Filtering (CF) techniques used in recommender systems due to its effectiveness and ability to deal with very large user-item rating matrix. However, when the rating matrix sparseness increases its performance deteriorates. Expanding MF to include side-information of users and items has been shown by many researchers both to improve general recommendation performance and to help alleviate the data-sparsity and cold-start issues in CF. In regard to item feature side-information, most schemes incorporate this information through a two stage process: intermediate results (e.g., on item similarity) are first computed based on item attributes; these are then combined with MF. In this paper, focussing on item side-information, we propose a model that directly incorporates item features into the MF framework in a single step process. The model, which we name FeatureMF, does this by projecting every available attribute datum in each of the item features into the same latent factor space with users and items, thereby in effect enriching item representation in MF. Results are presented of comparative performance experiments of the model against three state-of-the-art item information enriched models, as well as against four reference benchmark models, using two public real-world datasets, Douban and Yelp, with four training:test ratio scenarios each. It is shown to yield the best recommendation performance over all these models across all contexts including data-sparsity situations, in particular, achieving over 0.9% to over 6.5% MAE recommendation performance improvement over the next best model, HERec. FeatureMF is also found to alleviate cold start and to scale well, almost linearly, in regard to computational time, as a function of dataset size