With the maturation of wireless charging technology, Wireless Rechargeable Sensor Networks (WRSNs) has become a promising solution for prolong network lifetimes. Recently studies propose to employ a mobile charger (MC) to simultaneously charge multiple sensors within the same charging range, such that the charging performance can be improved. In this paper, we aim to jointly optimize the number of dead sensors and the energy usage effectiveness in such multi-node charging scenarios. We achieve this by introducing the partial charging mechanism, meaning that instead of following the conventional way that each sensor gets fully charged in one time step, our work allows MC to fully charge a sensor by multiple times. We show that the partial charging mechanism causes minimizing the number of dead sensors and maximizing the energy usage effectiveness to conflict with each other. We formulate this problem and develop a multi-node temporal spatial partial-charging algorithm (MTSPC) to solve it. The optimality of MTSPC is proved, and extensive simulations are carried out to demonstrate the effectiveness of MTSPC.

An Effective Multi-node Charging Scheme for Wireless Rechargeable Sensor Networks

In recent years, applications increasingly adopt security primitives designed with better countermeasures against side channel attacks. A concrete example is Libgcrypt's implementation of ECDH encryption with Curve25519. The implementation employs the Montgomery ladder scalar-by-point multiplication, uses the unified, branchless Montgomery double-and-add formula and implements a constant-time argument swap within the ladder. However, Libgcrypt's field arithmetic operations are not implemented in a constant-time side-channel-resistant fashion. Based on the secure design of Curve25519, users of the curve are advised that there is no need to perform validation of input points. In this work we demonstrate that when this recommendation is followed, the mathematical structure of Curve25519 facilitates the exploitation of side-channel weaknesses. We demonstrate the effect of this vulnerability on three software applications---encrypted git, email and messaging---that use Libgcrypt. In each case, we show how to craft malicious OpenPGP files that use the Curve25519 point of order 4 as a chosen ciphertext to the ECDH encryption scheme. We find that the resulting interactions of the point at infinity, order-2, and order-4 elements in the Montgomery ladder scalar-by-point multiplication routine create side channel leakage that allows us to recover the private key in as few as 11 attempts to access such malicious files.

/pdf/may-the-fourth-be-with-you-a-microarchitectural-side-channel-3gm193yifn.pdf

May the Fourth Be With You: A Microarchitectural Side Channel Attack on Several Real-World Applications of Curve25519.

Fault injection in early microarchitecture-level simulation CPU models and beam experiments on the final physical CPU chip are two established methodologies to access the soft error reliability of a microprocessor at different stages of its design flow. Beam experiments, on one hand, estimate the devices expected soft error rate in realistic physical conditions by exposing it to accelerated particles fluxes. Fault injection in microarchitectural models of the processor, on the other hand, provides deep insights on faults propagation through the entire system stack, including the operating system. Combining beam experiments and fault injection data can deliver deep insights about the devices expected reliability when deployed in the field. However, it is yet largely unclear if the fault injection error rates can be compared to those reported by beam experiments and how this comparison can lead to informed soft error protection decisions in early stages of the system design.

Demystifying Soft Error Assessment Strategies on ARM CPUs: Microarchitectural Fault Injection vs. Neutron Beam Experiments

Collaboration of batteryless nodes is essential to their success in replacing traditional battery-based systems. Energy-harvesting sensor nodes experience spatio-temporal fluctuations of energy availability. These fluctuations become especially critical when sensor nodes do not have sufficient energy storage to compensate for them. Understanding the challenges and opportunities of operating groups of batteryless sensor nodes requires to record and reproduce spatio-temporal characteristics of real energy environments. We thus present Shepherd, a testbed for the batteryless IoT. Shepherd allows to record synchronized energy traces with a resolution of 3 μA and 50μV at a rate of 100 kHz, and to faithfully replay these traces to any number of sensor nodes to study their behavior. We release Shepherd as an open-source tool for the community, facilitating research into time synchronization, wireless networking, and other distributed algorithms for batteryless systems.

Shepherd: a portable testbed for the batteryless IoT

Distributed algorithms based on proximity for self-organizing fog computing systems

The broad landscape of new technologies currently being explored makes the current times very exciting for computer systems research. The community is actively researching an extensive set of topics, ranging from the small (e.g., energy-independent embedded devices) to the large (e.g., brain-scale deep learning), simultaneously addressing technology discontinuities (End of Moore's Law and EnergyWall), new challenges in security and privacy, and the rise of artificial intelligence (AI).

While industry is applying some of these technologies, its efforts are necessarily focused on only a few areas, and on relatively short-term horizons. This offers academic researchers the opportunity to attack the problems with a broader and longer-term view. Further, in recent times, the computer systems community has started to pay increasing attention to non-performance measures, such as security, complexity, and power. To make progress in this multi-objective world, the composition of research teams needs to change. Teams have to become inter-disciplinary, enabling the flow of ideas across computing fields.

While many research directions are interesting, this report outlines a few high-priority areas where inter-disciplinary research is likely to have a high payoff:

a) Developing the components for a usable planet-scale Internet of Things (IoT), with provably energy-efficient devices. This report envisions a highly-available, geographically distributed, heterogeneous large-scale IoT system with the same efficiency, maintainability, and usability as today's data centers. This planet-scale IoT will be populated by many computationally-sophisticated IoT devices that are ultra-low power and operate energy-independently.

b) Rethinking the hardware-software security contract in the age of AI. In light of the recent security vulnerabilities, this report argues for building hardware abstractions that communicate security guarantees, and for allowing software to communicate its security and privacy requirements to the hardware. Further, security and privacy mechanisms should be integrated into the disruptive emerging technologies that support AI.

c) Making AI a truly dependable technology that is usable by all the citizens in all settings. As AI frameworks automate an increasing number of critical operations, this report argues for end-to-end dependable AI, where both the hardware and the software are understood and verified. Further, AI needs to turn from a centralized tool into a capability easily usable by all the citizens in all settings to meet an ever expanding range of needs.

d) Developing solutions to tackle extreme complexity, possibly based on formal methods. This report argues for the need to tame the explosion of system complexity and heterogeneity by creating new abstractions and complexity-management solutions. Such solutions need to be accessible to domain experts. An important step towards this goal is to scale out and extend formal methods for the real world.

This report also describes other, related research challenges.

Inter-Disciplinary Research Challenges in Computer Systems for the 2020s

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Inter-Disciplinary Research Challenges in Computer Systems for the 2020s