We present the Intelligent Robotic IoT System (IRIS), a modular, portable, scalable, and open-source testbed for robotic wireless network research. There are two key features that separate IRIS from most of the state-of-the-art multi-robot testbeds. (1)Portability: IRIS does not require a costly static global positioning system such as a VICON system nor time-intensive vision-based SLAM for its operation. Designed with an inexpensive Time Difference of Arrival (TDoA)localization system with centimeter level accuracy, the IRIS testbed can be deployed in an arbitrary uncontrolled environment in a matter of minutes. (2)Programmable Wireless Communication Stack: IRIS comes with a modular programmable low-power IEEE 802.15.4 radio and IPv6 network stack on each node. For the ease of administrative control and communication, we also developed a lightweight publish-subscribe overlay protocol called ROMANO that is used for bootstrapping the robots (also referred to as the IRISbots), collecting statistics, and direct control of individual robots, if needed. We detail the modular architecture of the IRIS testbed design along with the system implementation details and localization performance statistics.

In this paper we present new results on the problem of finding the best channel sensing order for multi-channel Dynamic Spectrum Access (DSA) networks. We start with the general assumption that all Secondary Users (SUs) cooperatively sense each Primary User (PU) channel at one time. Then, the SU sensing results are reported to a DSA base station that schedules SU transmissions in order to maximize DSA network throughput. We then assume that PU traffic parameters are not perfectly known to DSA network and change over time, and propose a novel PU channel sensing order scheme based on the quality of PU traffic estimation. We adopt a maximum likelihood estimator to estimate the traffic statistics of PU channels and derive the Cramer-Rao (CR) bounds for the PU traffic estimation performance. Based on the CR bound and its Gaussian approximation, we analyze the impact of the estimation error on the DSA network throughput by computing a new metric called sensing order confidence, i.e., the probability that the best selected sensing order is not affected by PU traffic estimation errors. Finally, we formulate a convex optimization problem to determine the minimum number of PU channel state samples required for estimating PU traffic parameters after determining a certain constraint on the sensing order confidence metric to achieve the best sensing order.