A reconfigurable platform for sensor networks is presented. This platform has features that allow easy reuse of the node in several applications avoiding redesigning the system from scratch. The node includes an FPGA which is the core of the reconfiguration capabilities of the node. Several hardware interfaces for sensor standar protocols like I2C or PWM have been developed and implemented in the FPGA. Remote reconfiguration is an important feature and sensor networks can take advantage of it in order to improve the global performance.

resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. This general definition shows the power that cloud computing makes available to the Internet of Things. The basic idea is to collect information, usually big amounts of data, in the point of interest (this so-called the edge, where information is generated) and upload it to be properly processed in the cloud, where ideally permanent and enough processing resources are available. This model may work perfectly with some approaches where the energy resource is not a limitation or the time required for any decision does not impose a very fast reaction. However, the cloud computing paradigm applied directly to IoT presents a set of drawbacks regarding latency, bandwidth and storage [10] because of the huge amount of data that have to be uploaded and processed. Due to these limitations, more layers have been proposed during the last years, getting closer to the source of data.

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Cyber-Physical Systems are experiencing a paradigm shift in which processing has been relocated to the distributed sensing layer and is no longer performed in a centralized manner. This approach, usually referred to as Edge Computing, demands the use of hardware platforms that are able to manage the steadily increasing requirements in computing performance, while keeping energy efficiency and the adaptability imposed by the interaction with the physical world. In this context, SRAM-based FPGAs and their inherent run-time reconfigurability, when coupled with smart power management strategies, are a suitable solution. However, they usually fail in user accessibility and ease of development. In this paper, an integrated framework to develop FPGA-based high-performance embedded systems for Edge Computing in Cyber-Physical Systems is presented. This framework provides a hardware-based processing architecture, an automated toolchain, and a runtime to transparently generate and manage reconfigurable systems from high-level system descriptions without additional user intervention. Moreover, it provides users with support for dynamically adapting the available computing resources to switch the working point of the architecture in a solution space defined by computing performance, energy consumption and fault tolerance. Results show that it is indeed possible to explore this solution space at run time and prove that the proposed framework is a competitive alternative to software-based edge computing platforms, being able to provide not only faster solutions, but also higher energy efficiency for computing-intensive algorithms with significant levels of data-level parallelism.

While for years traditional wireless sensor nodes have been based on ultra-low power microcontrollers with sufficient but limited computing power, the complexity and number of tasks of today’s applications are constantly increasing. Increasing the node duty cycle is not feasible in all cases, so in many cases more computing power is required. This extra computing power may be achieved by either more powerful microcontrollers, though more power consumption or, in general, any solution capable of accelerating task execution. At this point, the use of hardware based, and in particular FPGA solutions, might appear as a candidate technology, since though power use is higher compared with lower power devices, execution time is reduced, so energy could be reduced overall. In order to demonstrate this, an innovative WSN node architecture is proposed. This architecture is based on a high performance high capacity state-of-the-art FPGA, which combines the advantages of the intrinsic acceleration provided by the parallelism of hardware devices, the use of partial reconfiguration capabilities, as well as a careful power-aware management system, to show that energy savings for certain higher-end applications can be achieved. Finally, comprehensive tests have been done to validate the platform in terms of performance and power consumption, to proof that better energy efficiency compared to processor based solutions can be achieved, for instance, when encryption is imposed by the application requirements.

Abstract-in this paper, we present the design and implementation of a prototype system of Smart Parking Services based on Wireless Sensor Networks (WSNs) that allows vehicle drivers to effectively find the free parking places. The proposed scheme consists of wireless sensor networks, embedded web-server, central web-server and mobile phone application. In the system, low-cost wireless sensors networks modules are deployed into each parking slot equipped with one sensor node. The state of the parking slot is detected by sensor node and is reported periodically to embedded web-server via the deployed wireless sensor networks. This information is sent to central web-server using Wi-Fi networks in real-time, and also the vehicle driver can find vacant parking lots using standard mobile devices.

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Wireless sensor networks have been a big promise during the last few years, but a lack of real applications makes difficult the establishment of this technology. In this paper a real monitoring application in an instant coffee factory is presented. This application belongs to the group of environmental solutions based on wireless sensor networks, and it is focused on the impact of the instant coffee production processes in one of the largest instant coffee factories in Europe. The paper includes the entire application scenario, from the hardware of the WSN nodes to the software that will evaluate the impact and will close the loop.