Enhancing shopping experiences in smart retailing

The retailing market has undergone a paradigm-shift in the last decades, departing from its traditional form of shopping in brick-and-mortar stores towards online shopping and the establishment of shopping malls. As a result, “small” independent retailers operating in urban environments have suffered a substantial reduction of their turnover. This situation could be presumably reversed if retailers were to establish business “alliances” targeting economies of scale and engage themselves in providing innovative digital services. The SMARTBUY ecosystem realizes the concept of a “distributed shopping mall”, which allows retailers to join forces and unite in a large commercial coalition that generates added value for both retailers and customers. Along this line, the SMARTBUY ecosystem offers several novel features: (i) inventory management of centralized products and services, (ii) geo-located marketing of products and services, (iii) location-based search for products offered by neighboring retailers, and (iv) personalized recommendations for purchasing products derived by an innovative recommendation system. SMARTBUY materializes a blended retailing paradigm which combines the benefits of online shopping with the attractiveness of traditional shopping in brick-and-mortar stores. This article provides an overview of the main architectural components and functional aspects of the SMARTBUY ecosystem. Then, it reports the main findings derived from a 12 months-long pilot execution of SMARTBUY across four European cities and discusses the key technology acceptance factors when deploying alike business alliances

A lightweight framework for prediction-based resource management in future wireless networks

The vast proliferation and widespread use of a variety of mobile devices in the heterogeneous networking environment necessitates the introduction of lightweight management mechanisms to ease the administration complexity and optimise the overall system performance. To this end, one key research problem is the design of novel functionalities in network nodes to enable their self-adaptation to varying operational conditions, e.g. their own resources saturation-and to the status of other neighbouring nodes, to assure stability and optimality in the resource management. In these terms, the introduction of advanced techniques for the load balancing of users' requests in order to avoid the resources saturation is a fundamental objective. The latter addresses both the local node level as well as the cluster level of neighbouring nodes. In this article, an appropriate model for the management of computational system resources is proposed, enhanced with prediction schemes. An algorithmic framework is introduced for the proactive load balancing of user decision-making requests, assuming reconfigurable and autonomous mobile devices. The latter is based on the proposed metric of user satisfaction; such metric is a function of the network response time for serving the decision-making requests. An analytical model has been proposed to compute the predicted values of the user satisfaction, extending the prediction models by Andreolini. Acting on top of the typical load-balancing actions for handling the current resources saturations, the goal of this framework is to avoid the full utilisation of system resources in the near future. Afterwards, the introduced prediction-based load-balancing framework has initially been evaluated in a test-single node system and then applied in a case study system. The obtained results show the gains of the presented framework in terms of the number of dropped user requests. The introduction of prediction schemes enables to minimise the number of dropped user requests for both classes of mobile devices. It should be noted that the prediction framework optimises the failure rates for the autonomous mobile devices. This outcome indicates that the introduction of intelligence in the mobile devices eases their proactive management. © 2012 Patouni et al

Enhanced buying experiences in smart cities: The SMARTBUY approach

The establishment of shopping malls and the growth of online shopping increasingly diminishes the turnover of “small”, independent retailers in urban environments. However, retailers could reverse this trend through complementing the offline experiences they already offer with online offerings and establishing business “alliances” to achieve economies of scale and enable the provision of innovative digital services. The EU-funded project SMARTBUY aims at realizing the concept of a “distributed shopping mall” ecosystem which allows retailers to band together in a large commercial coalition which generates added-value for its retailers-members and customers: centralized products and services inventory management; geo-located marketing of products/services; location-based search for products offered by nearby retailers; personalized recommendations for purchasing products based on innovative recommendation systems. In effect, SMARTBUY proposes a blended shopping paradigm, wherein the benefits of online shopping are combined with the appeal of traditional store shopping. The article provides an overview of the main outcomes and achievements of SMARTBUY. It also reports on conclusions drawn in the context of the project’s official pilot execution in four European cities