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Abstract-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 multi-hop communication, showing that the dual radio architecture removes the bandwidth bottleneck in multi-hop communications that is inherent to single radio platforms. I. INTRODUCTIONWireless Sensor Network (WSN) applications have evolved beyond the vision of smart dust and are now also being deployed to gather acoustic and visual data with a high demand for communication throughput. Equipment and deployment costs have proven to be a limiting factor for high spatial density deployments [1], highlighting the benefits of longer range communication. Sensor network users have also realized the higher-than-expected node cost and are moving towards deployments with more widely spaced nodes at the expense of data granularity.Energy-efficiency has so far been a dominant design target in WSN platforms, due to the limited battery capacity imposed by the device form factor. However, recent advances in energy harvesting, such as solar, have shown networks that can operate for years [1]. While energy remains a key consideration, the focus on energy-efficiency has so far sidelined other design considerations in WSNs, such as communication throughput and range.This letter introduces the Opal platform as a high throughput sensing module that delivers comparable energy efficiency to existing platforms. Opal includes two onboard 802.15.4 radios operating in the 900 MHz and 2.4 GHz bands to provide communication diversity [2] and an aggregate transfer rate of 3 Mbps. It embeds a 96 MHz Cortex SAM3U processor with dynamic core frequency scaling, a feature that can be used to fine-tune processing speed with the higher communication rates while minimizing energy consumption.

Energy management is a critical concern in wireless sensornets. Despite its importance, sensor network operating systems today provide minimal energy management support, requiring applications to explicitly manage system power states. To address this problem, we present ICEM, a device driver architecture that enables simple, energy efficient wireless sensornet applications. The key insight behind ICEM is that the most valuable information an application can give the OS for energy management is its concurrency. Using ICEM, a low-rate sensing application requires only a single line of energy management code and has an efficiency within 1.6% of a hand-tuned implementation. ICEM's effectiveness questions the assumption that sensornet applications must be responsible for all power management and sensornets cannot have a standardized OS with a simple API.

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We believe datacenters can benefit from more focus on per-node efficiency, performance, and predictability, versus the more common focus so far on scalability to a large number of nodes. Improving per-node efficiency decreases costs and fault recovery because fewer nodes are required for the same amount of work. We believe that the use of complex, general-purpose operating systems is a key contributing factor to these inefficiencies.Traditional operating system abstractions are ill-suited for high performance and parallel applications, especially on large-scale SMP and many-core architectures. We propose four key ideas that help to overcome these limitations. These ideas are built on a philosophy of exposing as much information to applications as possible and giving them the tools necessary to take advantage of that information to run more efficiently. In short, high-performance applications need to be able to peer through layers of virtualization in the software stack to optimize their behavior. We explore abstractions based on these ideas and discuss how we build them in the context of a new operating system called Akaros.