Transiently-powered devices are a class of small devices powered by energy harvesting. Because such devices are subject to frequent power outages, many recent works propose to checkpoint data residing in volatile RAM into non-volatile RAM. In this article, we propose a new incremental checkpointing mechanism supported by a common hardware component, namely a Memory Protection Unit (MPU). This mechanism leverages the hardware interrupts of the MPU: volatile RAM is read-only on boot and is progressively unlocked as soon as protection violations occur. The MPU interrupt handler is designed to flag the corresponding volatile RAM blocks as dirty, i.e., modified. When a power outage is foreseen to be imminent, the software simply has to copy the dirty blocks from volatile RAM into the non-volatile RAM to ensure application progress over power outages. We validate our approach analytically and in cycle-accurate simulation, and we show that the proposed solution can be easily implemented on real hardware.

In a near future, energy harvesting is expected to replace batteries in ultra-low-power embedded systems. Research prototypes of such systems have recently been proposed. As the power harvested in the environment is very low, such systems need to cope with frequent power outages. They are referred to as transiently-powered systems (TPS). In order to execute non-trivial applications, TPS need to retain information between power losses. To achieve this goal, emerging non-volatile memory (NVM) technologies are a key enabler: they provide a lightweight solution to retain, between power outages, the state of an application and of its peripheral devices. These include sensors, serial interface or radio devices for instance. Existing works have described various checkpointing mechanisms to adapt embedded applications to TPS but the use of peripherals was not yet handled. in these works. This paper proposes a solution for embedded applications using any peripheral device to run despite transient power. We follow a kernel-oriented approach resulting in minimal impact on the programming model of the application. We implement the new concepts in our lightweight kernel called Sytare, running on an MSP430FR5739 micro-controller and we analyze the cost of the proposed solution.

Reducing energy consumption is a key challenge to the realization of the Internet of Things. While emerging memory technologies may offer power reduction, they come with major drawbacks such as high latency or limited endurance. As a result, system designers tend to juxtapose several memory technologies on the same chip. This paper studies the interactions between dynamic memory allocation and architectural choices regarding this heterogeneity. We provide cycle accurate simulations of embedded platforms with various memory technologies and we show that different dynamic allocation strategies have a major impact on performance. We demonstrate that interesting performance gains can be achieved even for a low fraction of heap objects in fast memory, but only with a clever data placement strategy between memory banks.

GPS is a commonly used and convenient technology for determining absolute position in outdoor environments, but its high power consumption leads to rapid battery depletion in mobile devices. An obvious solution is to duty cycle the GPS module, which prolongs the device lifetime at the cost of increased position uncertainty while the GPS is off. This article addresses the trade-off between energy consumption and localization performance in a mobile sensor network application. The focus is on augmenting GPS location with more energy-efficient location sensors to bound position estimate uncertainty while GPS is off. Empirical GPS and radio contact data from a large-scale animal tracking deployment is used to model node mobility, radio performance, and GPS. Because GPS takes a considerable, and variable, time after powering up before it delivers a good position measurement, we model the GPS behavior through empirical measurements of two GPS modules. These models are then used to explore duty cycling strategies for maintaining position uncertainty within specified bounds. We then explore the benefits of using short-range radio contact logging alongside GPS as an energy-inexpensive means of lowering uncertainty while the GPS is off, and we propose strategies that use RSSI ranging and GPS back-offs to further reduce energy consumption. Results show that our combined strategies can cut node energy consumption by one third while still meeting application-specific positioning criteria.

International audience

This paper addresses the tradeoff between energy consumption and localization performance in a mobile sensor network application. The focus is on augmenting GPS location with more energy-efficient location sensors to bound position estimate uncertainty in order to prolong node lifetime. We use empirical GPS and radio contact data from a large-scale animal tracking deployment to model node mobility, GPS and radio performance. These models are used to explore duty cycling strategies for maintaining position uncertainty within specified bounds. We then explore the benefits of using short-range radio contact logging alongside GPS as an energy-inexpensive means of lowering uncertainty while the GPS is off, and we propose a versatile contact logging strategy that relies on RSSI ranging and GPS lock back-offs for reducing the node energy consumption relative to GPS duty cycling. Results show that our strategy can cut the node energy consumption by half while meeting application-specific positioning criteria.

In this paper we address the problem of dynamic memory management in real-time Java embedded systems. Our work aims at suppressing the need for garbage collection in order to avoid unpredictable pause times. For that we use a simple static analysis algorithm coupled with region-based memory management as presented in [15]. To overcome the well-known limitations of region inference, we propose in this paper to involve the developer in the analysis process by providing feedback on programming constructs likely to produce memory leaks. Experiments show that for most programming patterns, our system behaves as efficiently as a garbage collector in terms of memory consumption. Our analysis tool is furthermore able to provide useful feedback to the programmer to pinpoint problematic constructs.