Beam-steering techniques using directional antennas are expected to play an important role in wireless network capacity expansion through ubiquitous small-cell deployment. However, integrating directional antennas into the existing wireless PHY and MAC stack of small cells has been challenging due to the added protocol overhead and lack of a robust antenna beam selection technique that can adapt well to environmental changes. This paper presents the design, implementation, and evaluation of LinkPursuit, a novel learning protocol for distributed antenna state selection in directional small-cell networks. LinkPursuit relies on reconfigurable antennas and a synchronous TimeDivision Multiple Access (TDMA) MAC to achieve simultaneous directional transmission and reception. Further, the system employs a practical antenna selection protocol based on the well known adaptive pursuit algorithm from the reinforcement learning literature. We implement a realtime prototype of LinkPursuit on the WARP platform and conduct extensive experiments to evaluate its performance. The empirical results show that appropriate use of directionality in LinkPursuit can result in higher network sum rates than omnidirectional transmission under various degrees of cross-link interference.

With the increasing programmability and fast-paced dynamics of modern wireless systems, it has become more difficult to gain timely insights into wireless network operations. In this demonstration1 we present WiART, an augmented reality framework to help visualize and interact with wireless network activities in real time. WiART collects real-time radio and network statistics from participating network devices and depicts them on users' mobile devices in an intuitive way, leveraging the virtual information overlay of augmented reality. Specifically in our current implementation, WiART takes inputs from a cognitive radio link controlling beam-steerable reconfigurable antennas and annotates on a live mobile screen the active pre-measured radiation patterns. In the reverse flow, WiART enables users to select desired antenna radiation patterns directly in the mobile app and observe their effects on link performance in real time. These capabilities add an unprecedented level of instant visualization and interaction with wireless activities and provides valuable insights into the dynamics of a reconfigurable antenna-based cognitive radio network.

Efficiency and flexibility are among the key requirements for Wireless Sensor and Actuator Networks (WSAN) of Internet of Things (IoT) era. In this work we present and demonstrate a novel WSAN and IoT platform. The new nodes are constructed by stacking together the different hardware modules encapsulating power sources, processing units, wired and wireless transceivers, sensors and actuators, or sets of those. Once a node is built, its processing unit can automatically identify all the connected hardware modules, obtain required software modules and tune node's operation accounting for its structure, available resources and active applications.

In the paper we present and demonstrate the modular prototyping platform designed for trialing the Internet of Things (IoT) applications. The new devices are constructed by stacking together the various hardware modules encapsulating power sources, processing units, wired and wireless transceivers, sensors and actuators, or sets of those. The main processing unit automatically identifies all the attached modules and adjusts own operation accordingly. The demo will showcase how the platform can be used for building up multi-radio technology enabled wireless devices which will automatically form a heterogeneous wireless sensor and actuator network (WSAN). The possible use case scenarios and the ongoing research activities around the platform will be highlighted as well.