A simple expression for the maximum data rate that can be reliably communicated (i.e., capacity) through a narrowband, resonant antenna with a Lorentzian frequency response is derived. Both transmit and receive cases are considered. In general, resonant antennas handle different data rates on receive than transmit because receive antennas filter out some of the external noise, but they also add their own loss related noise. It is also shown how adding loss, an impedance mismatch, or a matching network can increase the channel capacity by widening the resonant bandwidth at the expense of a reduced signal strength. A baseband simulation of communication between a narrowband transmitter and wideband receiver employing single-carrier quadrature amplitude modulation (QAM) is developed. The simulated data rates closely approach the derived theoretical limit. Finally, the simulation is experimentally validated by transmitting a quadrature phase shift keyed (QPSK) modulated signal through a single-resonant narrowband filter.

This paper presents an energy-autonomous wireless communication system for ultra-small Internet-of-Things (IoT) platforms. In the proposed system, all necessary components, including the battery, energy-harvesting solar cells, and the RF antenna, are fully integrated within a millimeter-scale form factor. Designing an energy-optimized wireless communication system for such a miniaturized platform is challenging because of unique system constraints imposed by the ultra-small system dimension. The proposed system targets orders of magnitude improvement in wireless communication energy efficiency through a comprehensive system-level analysis that jointly optimizes various system parameters, such as node dimension, modulation scheme, synchronization protocol, RF/analog/digital circuit specifications, carrier frequency, and a miniaturized 3-D antenna. We propose a new protocol and modulation schemes that are specifically designed for energy-scarce ultra-small IoT nodes. These new schemes exploit abundant signal processing resources on gateway devices to simplify design for energy-scarce ultra-small sensor nodes. The proposed dynamic link adaptation guarantees that the ultra-small IoT node always operates in the most energy efficient mode for a given operating scenario. The outcome is a truly energy-optimized wireless communication system to enable various classes of new applications, such as implanted smart-dust devices.

Modern enterprise data centers take advantage of virtual machine consolidation to allocate virtual machines to virtualized servers to increase energy efficiency. One key problem is to minimize the number of virtualized servers required while maintaining service quality. A promising approach is to exploit recurring load patterns exhibited by enterprise VMs for increased allocation efficiency. This paper shows that bin packing heuristics can deliver the same allocation quality as integer linear programs if calculation time is constrained. There were no significant differences between vector bin packing heuristics in simulations based on CPU load profiles obtained from enterprise data centers. We further show that consolidating in clusters of a few hundred virtual machines is sufficient as solution quality does not improve with larger clusters.

This paper introduces privacy and accountability techniques for crowd-powered systems. We focus on email task management: tasks are an implicit part of every inbox, but the overwhelming volume of incoming email can bury important requests. We present EmailValet, an email client that recruits remote assistants from an expert crowdsourcing marketplace. By annotating each email with its implicit tasks, EmailValet's assistants create a task list that is automatically populated from emails in the user's inbox. The system is an example of a valet approach to crowdsourcing, which aims for parsimony and transparency in access con-trol for the crowd. To maintain privacy, users specify rules that define a sliding-window subset of their inbox that they are willing to share with assistants. To support accountability, EmailValet displays the actions that the assistant has taken on each email. In a weeklong field study, participants completed twice as many of their email-based tasks when they had access to crowdsourced assistants, and they became increasingly comfortable sharing their inbox with assistants over time.