Some have argued that the dichotomy between high-performance operation and low resource utilization is false – an artifact that will soon succumb to Moore’s Law and careful engineering. If such claims prove to be true, then the traditional 8/16- vs. 32-bit power-performance tradeoffs become irrelevant, at least for some low-power embedded systems. We explore the veracity of this thesis using the 32-bit ARM Cortex-M3 microprocessor and find quite substantial progress but not deliverance. The Cortex-M3, compared to 8/16-bit microcontrollers, reduces latency and energy consumption for computationally intensive tasks as well as achieves near parity on code density. However, it still incurs a ~2× overhead in power draw for “traditional” sense-store-send-sleepapplications. These results suggest that while 32-bit processors are not yet ready for applications with very tight power requirements, they are poised for adoption everywhere else. Moore’s Law may yet prevail. Ko et al, Low Power or High Performance? A Tradeoff Whose Time Has Come (and Nearly Gone), LNCS 2012
Design of current sensor network platforms has favored low power operation at the cost of communication throughput or range, which severely limits support for real-time monitoring applications with high throughput requirements. This letter presents the design of the versatile Opal platform that couples a Cortex M3 MCU with two IEEE 802.15.4 radios for supporting sensing applications with high transfer rates without sacrificing communication range. We present experiments that evaluate Opal's throughput and range when operating with one or two radios, and we compare these results with an Iris-based node and TelosB nodes. We introduce the spatial energy cost metric that measures the energy to transfer one bit of information in a unit area for comparing the performance of the platforms. The results show that Opal operating with dual radios increases the throughput compared to single radio platforms with the same data-rate by a factor of 3.7, without sacrificing communication range. Opal operating with one radio can deliver a 460% increase in throughput over other single radio nodes at reduced range. We also analyze the implications of Opal's design for multihop communication, showing that the dual radio architecture removes the bandwidth bottleneck in multihop communications that is inherent to single radio platforms. Jurdak et al, Opal: A Multiradio Platform for High Throughput Wireless Sensor Networks, Embedded Letters 2011.
Radio connectivity in wireless sensor networks is highly intermittent due to unpredictable and time-varying noise and interference patterns in the environment. Because link qualities are not predictable prior to deployment, current deterministic solutions to unreliable links, such as increasing network density or transmission power, require overprovisioning of network resources and do not always improve reliability. We propose a new dual-radio network architecture to improve communication reliability in wireless sensor networks. Specifically, we show that radio transceivers operating at well-separated frequencies and spatially separated antennas offer robust communication, high link diversity, and better interference mitigation. We derive the optimal parameters for the dual-transceiver setup from frequency and space diversity in theory. We observe that frequency diversity holds the most benefits as long as the antennas are sufficiently separated to prevent coupling. Our experiments on an indoor/outdoor testbed confirm the theoretical predictions and show that radio diversity can significantly improve end-to-end delivery rates and network stability at only a small increase in energy cost over a single radio. Simulation experiments further validate the improvements in multiple topology configurations, but also reveal that the benefits of radio diversity are coupled to the number of available routing paths to the destination. Kusy et al, Radio diversity for reliable communication in sensor networks, TOSN, 2014
The high popularity of wireless sensor networks has led to novel applications with diverse, and sometimes demanding, data communication requirements, for example, streaming camera images in surveillance applications. In response bulk-data transfer protocols were proposed that provide low latency and high throughput communication over multiple hops. However, due to typical hardware platforms only providing a single radio, which implies that forwarding nodes need to serialize send and receive actions, the maximum end-to-end throughput is limited to 1/2 the radio capacity. To bridge this performance gap we present Fast Forward, a connection-oriented multi-hop bulk-data transfer protocol optimized for dual-radio platforms, data packets are sent across a path of alternating radio and frequency channels to exploit parallel transfers and avoid intra-path interference. We implemented Fast Forward in TinyOS to run on the Opal platform equipped with two IEEE 802.15.4 radios. In this paper we show that, with some minor tweaking of the original protocol stack to streamline internal access to the SPI bus, Fast Forward is capable of operating both radios in parallel so packets can be forwarded at full speed. We have evaluated Fast Forward on a 12-node testbed in an office environment. The sustained throughput peaks around 23.7 kBps, or 76 % of the radio capacity while the best single-radio protocol flattens out at 19 %. When introducing artificial packet loss the built-in link-level acknowledgements ensure that Fast Forward manages to deliver packets with high yield (close to 100 %) at the sink across 11 hops. Ekbatanifard et al, FastForward: High-Throughput Dual-Radio Streaming, MASS'13.
Low power listening (LPL) has been widely adopted to save energy in wireless sensor networks. However, LPL is ineffective in adapting to dynamic networks with asymmetric traffic patterns, as it sets a network-wide check interval. As a result, nodes with low data traffic waste significant energy resources doing idle listening. This problem is particularly exacerbated in multi-radio networks where majority of data comes through the most reliable radio and the duty cycles of other radios could be reduced. We address this issue in AutoSync, a protocol that combines synchronous LPL with automatic selection of check intervals to reduce energy consumption in both single and multi-radio networks. We first present the justification for AutoSync's design, and we then discuss our implementation of AutoSync in TinyOS. We compare AutoSync against existing protocols in both simulations and empirical experiments. Results show that AutoSync attains a substantial increase in the operational lifetime and mean power consumption over existing protocols in single radio networks and even more in dual radio networks. Hansen et al, AutoSync: Automatic duty-cycle control for synchronous low-power listening, SECON 2012.
We study the problem of data collection from a large scale network of mobile sensors, in application scenarios that favor a highly asymmetric solution, with heavily duty-cycled sensor nodes communicating with a network of powered base stations. Individual nodes move freely in the environment, resulting in low-quality radio links and hot-spot arrival patterns with the available data exceeding the radio link capacity. We developed a novel scheduling algorithm, κ-Fair Scheduling Optimization Model (κ-FSOM), that maximizes the amount of collected data under the constraints of radio link quality and energy, while ensuring a fair access to the radio channel. We show the problem is NP-complete and propose a heuristic to approximate the optimal scheduling solution in polynomial time. We use empirical link quality data to evaluate the κ-FSOM heuristic in a realistic setting and compare its performance to other heuristics. We show that κ-FSOM heuristic achieves high data reception rates, under different fairness and node lifetime constraints. Li et al, κ-FSOM: Fair Link Scheduling Optimization for Energy-Aware Data Collection in Mobile Sensor Networks, EWSN, 2014
Development of wireless sensor network applications remains a challenge, due to lack of visibility into the global network state. Debugging instrumentation using printf-like instructions affects the execution timing and non-intrusive approaches, such as JTAG, have not been used beyond a single node due to their high cost. This paper presents Minerva, a testbed architecture for distributed debugging of wireless sensor networks. At the core of our architecture is a flexible debug board installed at each node. The board design is driven by cost-efficiency of the testbed instrumentation and provides access to the onchip debug port of the sensor node’s processor. We focus on three main debugging modalities: (i) non-intrusive networkwide tracing of the internal state of individual nodes; (ii) synchronous stopping of the whole network on a breakpoint; and (iii) distributed assertion checking. We demonstrate the debugging capabilities of Minerva in use-cases based on well-known sensor network protocols in a 20-nodes indoor testbed. Our results indicate that Minerva provides non-intrusive, network-wide debugging of sensor network applications at a low cost. Sommer et al, Minerva: distributed tracing and debugging in wireless sensor networks, SenSys'13
Location sensing provides endless opportunities for a wide range of applications in GPS-obstructed environments, where, typically, there is a need for a higher degree of accuracy. In this article, we focus on robust range estimation, an important prerequisite for fine-grained localization. Motivated by the promise of acoustic in delivering high ranging accuracy, we present the design, implementation, and evaluation of acoustic (both ultrasound and audible) ranging systems. We distill the limitations of acoustic ranging and present efficient signal designs and detection algorithms to overcome the challenges of coverage, range, accuracy/resolution, tolerance to Doppler's effect, and audible intensity. We evaluate our proposed techniques experimentally on TWEET, a low-power platform purpose-built for acoustic ranging applications. Our experiments demonstrate an operational range of 20m (outdoor) and an average accuracy ≈2cm in the ultrasound domain. Finally, we present the design of an audible-range acoustic tracking service that encompasses the benefits of a near-inaudible acoustic broadband chirp and approximately two times increase in Doppler tolerance to achieve better performance. Misra et al, Acoustical ranging techniques in embedded wireless sensor networked devices, TOSN 2013.
Embedded devices that sense the environment regularly observe new contexts and situations, yet their program logic for dealing with new contexts is typically static based on what programmers know at compile time. By allowing these devices to evolve their program logic in response to new contexts, we can ensure a high degree of versatility and adaptation in-situ without human involvement. We have demonstrated this concept for learning on mote devices and for personalisation of smart phones, where we implemented the first genetic programming framework on Android phones and allowed co-located smartphone to share their logic for quicker learning through the island model. More information can be found through the relevant publications:
P. Valencia, A. Haak, A. Cotillon, R. Jurdak, “Genetic Programming for Smart Phone Personalisation,” Applied Soft Computing,, 25 (2014) 86–96, DOI: 10.1016/j.asoc.2014.08.058, September, 2014.
A. Cotillon, P. Valencia, and R. Jurdak, “Android Genetic Programming Framework,” In proceedings of the 15th European Genetic Programming Conference (EuroGP), pages 13-24, Malaga Spain, April, 2012.
P. Valencia, R. Jurdak, and P. Lindsay. “Fitness Importance for Online Evolution,” In proceedings of the Late Breaking Workshop of the ACM Genetic and Evolutionary Computation Conference (GECCO), Portland Oregon, July 2010.
P. Valencia, P. Lindsay, and R. Jurdak. “Distributed Genetic Evolution in WSN,” In Proceedings of the 9th International Conference on Information Processing in Sensor Networks (IPSN), Stockholm Sweden, April, 2010.
This work was recently described in the following conversation article:
What if intelligent machines could learn from each other, The Conversation, August, 2016.