Powertrace: Network-level Power Profiling for Low-power Wireless Networks

Low-power wireless networks are quickly becoming a critical part of our everyday infrastructure. Power consumption is a critical concern, but power measurement and estimation is a challenge. We present Powertrace, which to the best of our knowledge is the first system for network-level power profiling of low-power wireless systems. Powertrace uses power state tracking to estimate system power consumption and a structure called energy capsules to attribute energy consumption to activities such as packet transmissions and receptions. With Powertrace, the power consumption of a system can be broken down into individual activities which allows us to answer questions such as “How much energy is spent forwarding packets for node X?”, “How much energy is spent on control traffic and how much on critical data?”, and “How much energy does application X account for?”. Experiments show that Powertrace is accurate to 94% of the energy consumption of a device. To demonstrate the usefulness of Powertrace, we use it to experimentally analyze the power behavior of the proposed IETF standard IPv6 RPL routing protocol and a sensor network data collection protocol. Through using Powertrace, we find the highest power consumers and are able to reduce the power consumption of data collection with 24%. It is our hope that Powertrace will help the community to make empirical energy evaluation a widely used tool in the low-power wireless research community toolbox

A sub-mW IoT-endnode for always-on visual monitoring and smart triggering

This work presents a fully-programmable Internet of Things (IoT) visual sensing node that targets sub-mW power consumption in always-on monitoring scenarios. The system features a spatial-contrast $128\mathrm{x}64$ binary pixel imager with focal-plane processing. The sensor, when working at its lowest power mode ($10\mu W$ at 10 fps), provides as output the number of changed pixels. Based on this information, a dedicated camera interface, implemented on a low-power FPGA, wakes up an ultra-low-power parallel processing unit to extract context-aware visual information. We evaluate the smart sensor on three always-on visual triggering application scenarios. Triggering accuracy comparable to RGB image sensors is achieved at nominal lighting conditions, while consuming an average power between $193\mu W$ and $277\mu W$, depending on context activity. The digital sub-system is extremely flexible, thanks to a fully-programmable digital signal processing engine, but still achieves 19x lower power consumption compared to MCU-based cameras with significantly lower on-board computing capabilities.Comment: 11 pages, 9 figures, submitteted to IEEE IoT Journa

Temporal and spatial combining for 5G mmWave small cells

This chapter proposes the combination of temporal processing through Rake combining based on direct sequence-spread spectrum (DS-SS), and multiple antenna beamforming or antenna spatial diversity as a possible physical layer access technique for fifth generation (5G) small cell base stations (SBS) operating in the millimetre wave (mmWave) frequencies. Unlike earlier works in the literature aimed at previous generation wireless, the use of the beamforming is presented as operating in the radio frequency (RF) domain, rather than the baseband domain, to minimise power expenditure as a more suitable method for 5G small cells. Some potential limitations associated with massive multiple input-multiple output (MIMO) for small cells are discussed relating to the likely limitation on available antennas and resultant beamwidth. Rather than relying, solely, on expensive and potentially power hungry massive MIMO (which in the case of a SBS for indoor use will be limited by a physically small form factor) the use of a limited number of antennas, complimented with Rake combining, or antenna diversity is given consideration for short distance indoor communications for both the SBS) and user equipment (UE). The proposal’s aim is twofold: to solve eroded path loss due to the effective antenna aperture reduction and to satisfy sensitivity to blockages and multipath dispersion in indoor, small coverage area base stations. Two candidate architectures are proposed. With higher data rates, more rigorous analysis of circuit power and its effect on energy efficiency (EE) is provided. A detailed investigation is provided into the likely design and signal processing requirements. Finally, the proposed architectures are compared to current fourth generation long term evolution (LTE) MIMO technologies for their anticipated power consumption and EE

Supporting Cyber-Physical Systems with Wireless Sensor Networks: An Outlook of Software and Services

Sensing, communication, computation and control technologies are the essential building blocks of a cyber-physical system (CPS). Wireless sensor networks (WSNs) are a way to support CPS as they provide fine-grained spatial-temporal sensing, communication and computation at a low premium of cost and power. In this article, we explore the fundamental concepts guiding the design and implementation of WSNs. We report the latest developments in WSN software and services for meeting existing requirements and newer demands; particularly in the areas of: operating system, simulator and emulator, programming abstraction, virtualization, IP-based communication and security, time and location, and network monitoring and management. We also reflect on the ongoing efforts in providing dependable assurances for WSN-driven CPS. Finally, we report on its applicability with a case-study on smart buildings

Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

Selective Jamming of LoRaWAN using Commodity Hardware

Long range, low power networks are rapidly gaining acceptance in the Internet of Things (IoT) due to their ability to economically support long-range sensing and control applications while providing multi-year battery life. LoRa is a key example of this new class of network and is being deployed at large scale in several countries worldwide. As these networks move out of the lab and into the real world, they expose a large cyber-physical attack surface. Securing these networks is therefore both critical and urgent. This paper highlights security issues in LoRa and LoRaWAN that arise due to the choice of a robust but slow modulation type in the protocol. We exploit these issues to develop a suite of practical attacks based around selective jamming. These attacks are conducted and evaluated using commodity hardware. The paper concludes by suggesting a range of countermeasures that can be used to mitigate the attacks.Comment: Mobiquitous 2017, November 7-10, 2017, Melbourne, VIC, Australi

Low Power, Low Delay: Opportunistic Routing meets Duty Cycling

Traditionally, routing in wireless sensor networks consists of two steps: First, the routing protocol selects a next hop, and, second, the MAC protocol waits for the intended destination to wake up and receive the data. This design makes it difficult to adapt to link dynamics and introduces delays while waiting for the next hop to wake up. In this paper we introduce ORW, a practical opportunistic routing scheme for wireless sensor networks. In a dutycycled setting, packets are addressed to sets of potential receivers and forwarded by the neighbor that wakes up first and successfully receives the packet. This reduces delay and energy consumption by utilizing all neighbors as potential forwarders. Furthermore, this increases resilience to wireless link dynamics by exploiting spatial diversity. Our results show that ORW reduces radio duty-cycles on average by 50% (up to 90% on individual nodes) and delays by 30% to 90% when compared to the state of the art

RIOT OS Paves the Way for Implementation of High-Performance MAC Protocols

Implementing new, high-performance MAC protocols requires real-time features, to be able to synchronize correctly between different unrelated devices. Such features are highly desirable for operating wireless sensor networks (WSN) that are designed to be part of the Internet of Things (IoT). Unfortunately, the operating systems commonly used in this domain cannot provide such features. On the other hand, "bare-metal" development sacrifices portability, as well as the mul-titasking abilities needed to develop the rich applications that are useful in the domain of the Internet of Things. We describe in this paper how we helped solving these issues by contributing to the development of a port of RIOT OS on the MSP430 microcontroller, an architecture widely used in IoT-enabled motes. RIOT OS offers rich and advanced real-time features, especially the simultaneous use of as many hardware timers as the underlying platform (microcontroller) can offer. We then demonstrate the effectiveness of these features by presenting a new implementation, on RIOT OS, of S-CoSenS, an efficient MAC protocol that uses very low processing power and energy.Comment: SCITEPRESS. SENSORNETS 2015, Feb 2015, Angers, France. http://www.scitepress.or

Thermal efficiency and emission analysis of advanced thermodynamic strategies in a multi-cylinder diesel engine utilizing valve-train flexibility

Stringent emission regulations and a growing demand for fossil fuel drive the development of new technologies for internal combustion engines. Diesel engines are thermally efficient but require complex aftertreatment systems to reduce tailpipe emissions of unburned hydrocarbons (UHC), particulate matter (PM), and nitrogen oxides (NOx). These challenges require research into advanced thermodynamic strategies to improve thermal efficiency, control emission formation and manage exhaust temperature for downstream aftertreatment. The optimal performance for different on-road conditions is analyzed using a fully flexible valve-train on a modern diesel engine. The experimental investigation focuses on thermal management during idling and high-way cruise conditions. In addition, simulation are used to explore the fuel efficiency of Miller cycling at elevated geometric compression ratios. ^ Thermal management of diesel engine aftertreatment is a significant challenge, particularly during cold start and extended idle operation. For instance, to be effective, NOx-mitigating selective catalytic reduction (SCR) systems require bed and gas inlet temperatures of at least 200°C, and diesel oxidation catalysts coupled with upstream fuel injection require inlet temperatures of at least 300°C in order to raise diesel particulate filter inlet temperatures to at least 500°C for active regeneration. However, during peak engine efficiency idle operation, the exhaust temperatures only reach 120 and 200°C for unloaded (800 rpm/ 0.26 bar BMEP) and loaded (800 rpm/ 2.5 bar BMEP) idle, respectively, for a typical modern-day diesel engine. For this and other engines like it, late injections or throttling (for instance via an over-closed variable geometry turbocharger) can be used to increase exhaust temperatures above 200°C (unloaded idle) and 300°C (loaded idle), but result in fuel consumption increases in excess of 100% and 67%, respectively. Fortunately, and as this thesis describes, cylinder deactivation can be used to increase exhaust temperatures above 300°C at the loaded idle condition without increasing fuel consumption. Further, at the unloaded idle condition, the combination of cylinder deactivation and flexible valve actuation on the activated cylinders allows 200°C exhaust temperatures without a fuel consumption penalty. At both operating conditions the primary benefits are realized by reducing the air flow through the engine, directly resulting in higher exhaust temperatures; and as good, or better, open cycle efficiencies compared with conventional 6 cylinder operation. In all cases, comparisons are made with strict limits on engine out NOx, unburned hydrocarbons, and particulate matter emissions. ^ Internal exhaust gas recirculation (iEGR), late intake valve closure (LIVC) and cylinder deactivation (CDA) were experimentally investigated as methods for fuel economy and thermal management at 1200 RPM and 7.58 bar brake mean effective pressure (BMEP), which corresponds to the highway cruise condition for over the road trucks. These strategies were compared with conventional operation on the basis of optimized fuel consumption, exhaust temperature, and exhaust power at three NOx targets. Physical constraints and emission limits were set to ensure realistic engine operation and emission regulations. The results show that conventional valve profiles lead to the best fuel economy, but iEGR, LIVC and CDA increase achievable exhaust temperature by 57-216 °C. iEGR increases exhaust temperatures by eliminating the heat rejection that occurs when using external EGR. Both LIVC and CDA increase combustion temperature by reducing the air to fuel ratio. ^ Advanced thermodynamic strategies such as the Miller cycle and Atkinson cycles have been realized on production spark ignition engine through variable valve timing. However, fewer efforts have been directed to compression ignition engines. Increases in geometric compression ratio typically lead to increased thermal efficiency, but the application is constrained by physical limits including peak cylinder pressure and turbine inlet temperature. An experimentally validated model was used to obtain the trade-off; between fuel economy and NOx emissions in order to thoroughly investigate Miller cycling at elevated geometric compression ratio. The results demonstrate the expected improvement in thermal efficiency, however, as expected, the maximum in-cylinder pressure and temperature violate the physical constraints at elevated power conditions. These challenges can be addressed through the use of Miller cycling via a reduced effective compression ratio through the modulation of intake valve closure. Miller cycling enables the engine operation with elevated geometric compression ratio at maximum power condition and further improves fuel economy by advancing combustion. The results present a 5% fuel economy improvement at operating conditions without EGR and equivalent fuel consumption when EGR is incorporated. Brake thermal efficiency (BTE) is improved by 0.1%-2% using Miller cycle at elevated GCR. Although EGR was able to achieve very low NOx emissions, fuel economy was sacrificed at medium load condition. Moreover peak cylinder pressure (PCP) and turbine inlet temperature (TIT) exceeded the upper limits at maximum power condition using EGR with elevated geometric compression ratio

Using Personal Environmental Comfort Systems to Mitigate the Impact of Occupancy Prediction Errors on HVAC Performance

Heating, Ventilation and Air Conditioning (HVAC) consumes a significant fraction of energy in commercial buildings. Hence, the use of optimization techniques to reduce HVAC energy consumption has been widely studied. Model predictive control (MPC) is one state of the art optimization technique for HVAC control which converts the control problem to a sequence of optimization problems, each over a finite time horizon. In a typical MPC, future system state is estimated from a model using predictions of model inputs, such as building occupancy and outside air temperature. Consequently, as prediction accuracy deteriorates, MPC performance--in terms of occupant comfort and building energy use--degrades. In this work, we use a custom-built building thermal simulator to systematically investigate the impact of occupancy prediction errors on occupant comfort and energy consumption. Our analysis shows that in our test building, as occupancy prediction error increases from 5\% to 20\% the performance of an MPC-based HVAC controller becomes worse than that of even a simple static schedule. However, when combined with a personal environmental control (PEC) system, HVAC controllers are considerably more robust to prediction errors. Thus, we quantify the effectiveness of PECs in mitigating the impact of forecast errors on MPC control for HVAC systems.Comment: 21 pages, 13 figure