Provably good task assignment on heterogeneous multiprocessor platforms for a restricted case but with a stronger adversary

Consider the problem of scheduling a set of implicit-deadline sporadic tasks to meet all deadlines on a heterogeneous multiprocessor platform. We consider a restricted case where the maximum utilization of any task on any processor in the system is no greater than one. We use an algorithm proposed in [1] (we refer to it as LP-EE) from state-of-the-art for assigning tasks to heterogeneous multiprocessor platform and (re-)prove its performance guarantee for this restricted case but for a stronger adversary. We show that if a task set can be scheduled to meet deadlines on a heterogeneous multiprocessor platform by an optimal task assignment scheme that allows task migrations then LP-EE meets deadlines as well with no migrations if given processors twice as fast.

Static-priority scheduling over wireless networks with multiple broadcast domains

We propose a wireless medium access control (MAC) protocol that provides static-priority scheduling of messages in a guaranteed collision-free manner. Our protocol supports multiple broadcast domains, resolves the wireless hidden terminal problem and allows for parallel transmissions across a mesh network. Arbitration of messages is achieved without the notion of a master coordinating node, global clock synchronization or out-of-band signaling. The protocol relies on bit-dominance similar to what is used in the CAN bus except that in order to operate on a wireless physical layer, nodes are not required to receive incoming bits while transmitting. The use of bit-dominance efficiently allows for a much larger number of priorities than would be possible using existing wireless solutions. A MAC protocol with these properties enables schedulability analysis of sporadic message streams in wireless multihop networks.

Nutritional composition of low protein and phenylalanine-restricted dishes prepared for phenylketonuric patients

Nutritional management is essential for Phenylketonuria (PKU) treatment, consisting in a semi-synthetic and low phenylalanine (Phe) diet, which includes strictly controlled amounts of low protein natural foods (essentially fruits and vegetables) supplemented with Phe-free protein substitutes and dietetic low-protein products. PKU diet has to be carefully planned, providing the best ingredient combinations, so that patients can achieve good metabolic control and an adequate nutritional status. Hereupon, it is mandatory to know the detailed composition of natural and/or cooked foodstuffs prepared specifically for these patients. We intended to evaluate sixteen dishes specifically prepared for PKU patients, regarding the nutritional composition, Phe and tyrosine (Tyr) contents, fatty acids profile, and vitamins E and B12 amounts. The nutritional composition of the cooked samples was 15.5–92.0 g/100 g, for moisture; 0.7–3.2 g/100 g, for protein; 0.1–25.0 g/100 g, for total fat; and 5.0–62.0 g/100 g, for total carbohydrates. Fatty acids profile and vitamin E amount reflected the type of fat used. All samples were poor in vitamin B12 (0.3–0.8 μg/100 g). Boiled rice presented the highest Phe content: 50.3 mg/g of protein. These data allow a more accurate calculation of the diet portions to be ingested by the patients according to their individual tolerance.

Feature Extraction in Densely Sensed Environments: Extensions to Multiple Broadcast Domains

The vision of the Internet of Things (IoT) includes large and dense deployment of interconnected smart sensing and monitoring devices. This vast deployment necessitates collection and processing of large volume of measurement data. However, collecting all the measured data from individual devices on such a scale may be impractical and time consuming. Moreover, processing these measurements requires complex algorithms to extract useful information. Thus, it becomes imperative to devise distributed information processing mechanisms that identify application-specific features in a timely manner and with a low overhead. In this article, we present a feature extraction mechanism for dense networks that takes advantage of dominance-based medium access control (MAC) protocols to (i) efficiently obtain global extrema of the sensed quantities, (ii) extract local extrema, and (iii) detect the boundaries of events, by using simple transforms that nodes employ on their local data. We extend our results for a large dense network with multiple broadcast domains (MBD). We discuss and compare two approaches for addressing the challenges with MBD and we show through extensive evaluations that our proposed distributed MBD approach is fast and efficient at retrieving the most valuable measurements, independent of the number sensor nodes in the network.