2,455 research outputs found
Portability, compatibility and reuse of MAC protocols across different IoT radio platforms
To cope with the diversity of Internet of Things (loT) requirements, a large number of Medium Access Control (MAC) protocols have been proposed in scientific literature, many of which are designed for specific application domains. However, for most of these MAC protocols, no multi-platform software implementation is available. In fact, the path from conceptual MAC protocol proposed in theoretical papers, towards an actual working implementation is rife with pitfalls. (i) A first problem is the timing bugs, frequently encountered in MAC implementations. (ii) Furthermore, once implemented, many MAC protocols are strongly optimized for specific hardware, thereby limiting the potential of software reuse or modifications. (iii) Finally, in real-life conditions, the performance of the MAC protocol varies strongly depending on the actual underlying radio chip. As a result, the same MAC protocol implementation acts differently per platform, resulting in unpredictable/asymmetrical behavior when multiple platforms are combined in the same network. This paper describes in detail the challenges related to multi-platform MAC development, and experimentally quantifies how the above issues impact the MAC protocol performance when running MAC protocols on multiple radio chips. Finally, an overall methodology is proposed to avoid the previously mentioned cross-platform compatibility issues. (C) 2018 Elsevier B.V. All rights reserved
Context-awareness for mobile sensing: a survey and future directions
The evolution of smartphones together with increasing computational power have empowered developers to create innovative context-aware applications for recognizing user related social and cognitive activities in any situation and at any location. The existence and awareness of the context provides the capability of being conscious of physical environments or situations around mobile device users. This allows network services to respond proactively and intelligently based on such awareness. The key idea behind context-aware applications is to encourage users to collect, analyze and share local sensory knowledge in the purpose for a large scale community use by creating a smart network. The desired network is capable of making autonomous logical decisions to actuate environmental objects, and also assist individuals. However, many open challenges remain, which are mostly arisen due to the middleware services provided in mobile devices have limited resources in terms of power, memory and bandwidth. Thus, it becomes critically important to study how the drawbacks can be elaborated and resolved, and at the same time better understand the opportunities for the research community to contribute to the context-awareness. To this end, this paper surveys the literature over the period of 1991-2014 from the emerging concepts to applications of context-awareness in mobile platforms by providing up-to-date research and future research directions. Moreover, it points out the challenges faced in this regard and enlighten them by proposing possible solutions
Energy challenges for ICT
The energy consumption from the expanding use of information and communications technology (ICT) is unsustainable with present drivers, and it will impact heavily on the future climate change. However, ICT devices have the potential to contribute signi - cantly to the reduction of CO2 emission and enhance resource e ciency in other sectors, e.g., transportation (through intelligent transportation and advanced driver assistance systems and self-driving vehicles), heating (through smart building control), and manu- facturing (through digital automation based on smart autonomous sensors). To address the energy sustainability of ICT and capture the full potential of ICT in resource e - ciency, a multidisciplinary ICT-energy community needs to be brought together cover- ing devices, microarchitectures, ultra large-scale integration (ULSI), high-performance computing (HPC), energy harvesting, energy storage, system design, embedded sys- tems, e cient electronics, static analysis, and computation. In this chapter, we introduce challenges and opportunities in this emerging eld and a common framework to strive towards energy-sustainable ICT
Application and Energy-Aware Data Aggregation using Vector Synchronization in Distributed Battery-less IoT Networks
The battery-less Internet of Things (IoT) devices are a key element in the
sustainable green initiative for the next-generation wireless networks. These
battery-free devices use the ambient energy, harvested from the environment.
The energy harvesting environment is dynamic and causes intermittent task
execution. The harvested energy is stored in small capacitors and it is
challenging to assure the application task execution. The main goal is to
provide a mechanism to aggregate the sensor data and provide a sustainable
application support in the distributed battery-less IoT network. We model the
distributed IoT network system consisting of many battery-free IoT sensor
hardware modules and heterogeneous IoT applications that are being supported in
the device-edge-cloud continuum. The applications require sensor data from a
distributed set of battery-less hardware modules and there is provision of
joint control over the module actuators. We propose an application-aware task
and energy manager (ATEM) for the IoT devices and a vector-synchronization
based data aggregator (VSDA). The ATEM is supported by device-level federated
energy harvesting and system-level energy-aware heterogeneous application
management. In our proposed framework the data aggregator forecasts the
available power from the ambient energy harvester using long-short-term-memory
(LSTM) model and sets the device profile as well as the application task rates
accordingly. Our proposed scheme meets the heterogeneous application
requirements with negligible overhead; reduces the data loss and packet delay;
increases the hardware component availability; and makes the components
available sooner as compared to the state-of-the-art.Comment: 10 pages, 11 figure
Analysis of Power-aware Buffering Schemes in Wireless Sensor Networks
We study the power-aware buffering problem in battery-powered sensor
networks, focusing on the fixed-size and fixed-interval buffering schemes. The
main motivation is to address the yet poorly understood size variation-induced
effect on power-aware buffering schemes. Our theoretical analysis elucidates
the fundamental differences between the fixed-size and fixed-interval buffering
schemes in the presence of data size variation. It shows that data size
variation has detrimental effects on the power expenditure of the fixed-size
buffering in general, and reveals that the size variation induced effects can
be either mitigated by a positive skewness or promoted by a negative skewness
in size distribution. By contrast, the fixed-interval buffering scheme has an
obvious advantage of being eminently immune to the data-size variation. Hence
the fixed-interval buffering scheme is a risk-averse strategy for its
robustness in a variety of operational environments. In addition, based on the
fixed-interval buffering scheme, we establish the power consumption
relationship between child nodes and parent node in a static data collection
tree, and give an in-depth analysis of the impact of child bandwidth
distribution on parent's power consumption.
This study is of practical significance: it sheds new light on the
relationship among power consumption of buffering schemes, power parameters of
radio module and memory bank, data arrival rate and data size variation,
thereby providing well-informed guidance in determining an optimal buffer size
(interval) to maximize the operational lifespan of sensor networks
Hypnos: a Hardware and Software Toolkit for Energy-Aware Sensing in Low-Cost IoT Nodes
Through the Internet of Things, autonomous sensing
devices can be deployed to regularly capture environmental and
other sensor measurements for a variety of usage scenarios.
However, for the market segment of stand-alone, self-sustaining
small IoT nodes, long term deployment remains problematic
due to the energy-constrained nature of these devices, requiring
frequent maintenance. This article introduces Hypnos, an open
hardware and software toolkit that aims to balance energy intake
and usage through adaptive sensing rate for low-cost Internetconnected IoT nodes. We describe the hardware architecture of
the IoT node, an open hardware board based on the Arduino
Uno form-factor packing the energy measurement circuitry, and
the associated open source software library, that interfaces with
the sensing node’s microcontroller and provides access to the
low-level energy measurements. Hypnos comes equipped with a
built-in, configurable, modified sigmoid function to regulate duty
cycle frequency based on energy intake and usage, yet developers
may also plug in their custom duty/sleep balancing function.
An experiment was set up, whereby two identical boards ran
for two months: one with the Hypnos software framework and
built-in energy balancing function to regulate sensing rate and
the other with fixed sensing rate. The experiment showed that
Hypnos is able to successfully balance energy usage and sensing
frequency within configurable energy ranges. Hereby, it increases
reliability by avoiding complete shutdown, while at the same time
optimizing performance in terms of average amount of sensor
measurements
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