Robotic Wireless Sensor Networks

In this chapter, we present a literature survey of an emerging, cutting-edge, and multi-disciplinary field of research at the intersection of Robotics and Wireless Sensor Networks (WSN) which we refer to as Robotic Wireless Sensor Networks (RWSN). We define a RWSN as an autonomous networked multi-robot system that aims to achieve certain sensing goals while meeting and maintaining certain communication performance requirements, through cooperative control, learning and adaptation. While both of the component areas, i.e., Robotics and WSN, are very well-known and well-explored, there exist a whole set of new opportunities and research directions at the intersection of these two fields which are relatively or even completely unexplored. One such example would be the use of a set of robotic routers to set up a temporary communication path between a sender and a receiver that uses the controlled mobility to the advantage of packet routing. We find that there exist only a limited number of articles to be directly categorized as RWSN related works whereas there exist a range of articles in the robotics and the WSN literature that are also relevant to this new field of research. To connect the dots, we first identify the core problems and research trends related to RWSN such as connectivity, localization, routing, and robust flow of information. Next, we classify the existing research on RWSN as well as the relevant state-of-the-arts from robotics and WSN community according to the problems and trends identified in the first step. Lastly, we analyze what is missing in the existing literature, and identify topics that require more research attention in the future

Experiential Role of Artefacts in Cooperative Design

The role of material artefacts in supporting distributed and co-located work practices has been well acknowledged within the HCI and CSCW research. In this paper, we show that in addition to their ecological, coordinative and organizational support, artefacts also play an ‘experiential’ role. In this case, artefacts not only improve efficiency or have a purely functional role (e.g. allowing people to complete tasks quickly), but the presence and manifestations of these artefacts bring quality and richness to people’s performance and help in making better sense of their everyday lives. In a domain like industrial design, such artefacts play an important role for supporting creativity and innovation. Based on our prolonged ethnographic fieldwork on understanding cooperative design practices of industrial design students and researchers, we describe several experiential practices that are supported by mundane artefacts like sketches, drawings, physical models and explorative prototypes – used and developed in designers’ everyday work. Our main intention to carry out this kind of research is to develop technologies to support designers’ everyday practices. We believe that with the emergence of ubiquitous computing, there is a growing need to focus on personal, emotional and social side of people’s everyday experiences. By focusing on the experiential practices of designers, we can provide a holistic view in the design of new interactive technologies

Channel Selection for Network-assisted D2D Communication via No-Regret Bandit Learning with Calibrated Forecasting

We consider the distributed channel selection problem in the context of device-to-device (D2D) communication as an underlay to a cellular network. Underlaid D2D users communicate directly by utilizing the cellular spectrum but their decisions are not governed by any centralized controller. Selfish D2D users that compete for access to the resources construct a distributed system, where the transmission performance depends on channel availability and quality. This information, however, is difficult to acquire. Moreover, the adverse effects of D2D users on cellular transmissions should be minimized. In order to overcome these limitations, we propose a network-assisted distributed channel selection approach in which D2D users are only allowed to use vacant cellular channels. This scenario is modeled as a multi-player multi-armed bandit game with side information, for which a distributed algorithmic solution is proposed. The solution is a combination of no-regret learning and calibrated forecasting, and can be applied to a broad class of multi-player stochastic learning problems, in addition to the formulated channel selection problem. Analytically, it is established that this approach not only yields vanishing regret (in comparison to the global optimal solution), but also guarantees that the empirical joint frequencies of the game converge to the set of correlated equilibria.Comment: 31 pages (one column), 9 figure

Intellectual Capital Architectures and Bilateral Learning: A Framework For Human Resource Management

Both researchers and managers are increasingly interested in how firms can pursue bilateral learning; that is, simultaneously exploring new knowledge domains while exploiting current ones (cf., March, 1991). To address this issue, this paper introduces a framework of intellectual capital architectures that combine unique configurations of human, social, and organizational capital. These architectures support bilateral learning by helping to create supplementary alignment between human and social capital as well as complementary alignment between people-embodied knowledge (human and social capital) and organization-embodied knowledge (organizational capital). In order to establish the context for bilateral learning, the framework also identifies unique sets of HR practices that may influence the combinations of human, social, and organizational capital