Pragmatic Low-Power Interoperability: ContikiMAC vs TinyOS LPL

Standardization has driven interoperability at multiple layers of the stack, such as the routing and application layers, standardization of radio duty cycling mechanisms have not yet reached the same maturity. In this work, we pitch the two de facto standard flavors of sender-initiated radio duty cycling mechanisms against each other: ContikiMAC and TinyOS LPL. Our aim is to explore pragmatic interoperability mechanisms at the radio duty cycling layer. This will lead to better understanding of interoperability problems moving forward, as radio duty cycling mechanisms get standardized. Our results show that the two flavors can be configured to operate together but that parameter configuration may severely hurt performance

Demo: An Interoperability Development and Performance Diagnosis Environment

Interoperability is key to widespread adoption of sensor network technology, but interoperable systems have traditionally been difficult to develop and test. We demonstrate an interoperable system development and performance diagnosis environment in which different systems, different software, and different hardware can be simulated in a single network configuration. This allows both development, verification, and performance diagnosis of interoperable systems. Estimating the performance is important since even when systems interoperate, the performance can be sub-optimal, as shown in our companion paper that has been conditionally accepted for SenSys 2011

EngageMon: Multi-modal engagement sensing for mobile games

Singapore National Research Foundatio

Adaptive Power Allocation and Splitting with Imperfect Channel Estimation in Energy Harvesting Based Self-Organizing Networks

As miniature-sized embedded computing platforms are ubiquitously deployed to our everyday environments, the issue of managing their power usage becomes important, especially when they are used in energy harvesting based self-organizing networks. One way to provide these devices with continuous power is to utilize RF-based energy transfer. Previous research in RF-based information and energy transfer builds up on the assumption that perfect channel estimation is easily achievable. However, as our preliminary experiments and many previous literature in wireless network systems show, making perfect estimations of the wireless channel is extremely challenging due to their quality fluctuations. To better reflect reality, in this work, we introduce an adaptive power allocation and splitting (APAS) scheme which takes imperfect channel estimations into consideration. Our evaluation results show that the proposed APAS scheme achieves near-optimal performances for transferring energy and data over a single RF transmission

Fast Monte-Carlo Approximation of the Attention Mechanism

We introduce Monte-Carlo Attention (MCA), a randomized approximation method for reducing the computational cost of self-attention mechanisms in Transformer architectures. MCA exploits the fact that the importance of each token in an input sequence vary with respect to their attention scores; thus, some degree of error can be tolerable when encoding tokens with low attention. Using approximate matrix multiplication, MCA applies different error bounds to encode input tokens such that those with low attention scores are computed with relaxed precision, whereas errors of salient elements are minimized. MCA can operate in parallel with other attention optimization schemes and does not require model modification. We study the theoretical error bounds and demonstrate that MCA reduces attention complexity (in FLOPS) for various Transformer models by up to 11 in GLUE benchmarks without compromising model accuracy. Source code and appendix: https://github.com/eis-lab/monte-carlo-attentio