The Trickle Algorithm has enjoyed much popularity and widespread use as a basic network primitive ensuring lowcost data consistency in lossy networks. Trickle is shaped by the so-called short-listen problem, hence the imposition of a listenonly period. Such a period allows Trickle to robustly address the short-listen problem at the expense of increased latency. In this letter, we introduce a simple yet powerful optimization to Trickle that can dramatically decrease Trickle's latency with virtually no additional overhead to its scalability and robustness. Extensive simulation and testbed experiments are reported here, yielding greater than a factor of 10 decrease in propagation time.

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Enabling automatic, efficient and scalable discovery of the resources provided by constrained low-power sensor and actuator networks is an important element to empower the transformation towards the Internet of Things (IoT). To this end, many centralized and distributed resource discovery approaches have been investigated. Clearly, each approach has its own motivations, advantages and drawbacks. In this article, we present a hybrid centralized/distributed resource discovery solution aiming to get the most out of both approaches. The proposed architecture employs the well-known Constrained Application Protocol (CoAP) and features a number of interesting discovery characteristics including scalability, time and cost efficiency, and adaptability. Using such a solution, network nodes can automatically and rapidly detect the presence of Resource Directories (RDs), via a proactive RD discovery mechanism, and perform discovery tasks through them. Nodes may, alternatively, fall back automatically to efficient fully-distributed discovery operations achieved through Trickle-enabled, CoAP-based technics. The effectiveness of the proposed architecture has been demonstrated by formal analysis and experimental evaluations on dedicated IoT platforms.

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This paper presents BoostSole; a smart insole based system for automatic human gait recognition. It consists of a smart instrumented insole connected to the cloud via the patient's smartphone using low-power wireless communication. First, the design of BoostSole is introduced with discussions of sensors choice, placement, calibration, and data communication. Next, an adaptive multi-boost classification algorithm is deployed to accurately identify different gait patterns. The algorithm is fast and lightweight and can be implemented in ordinary smartphones with a small footprint in terms of computational requirements, energy consumption, and communication usage. Raw and ondevice classified data can be securely uploaded to a distant cloud server for continuous monitoring and analysis. Indeed, they can be visualized and exploited by doctors to identify/correct walking habits and assess the risks of chronic pain associated with an abnormal walk. The system has been evaluated on a dataset containing three gait patterns, namely: shuffle walk; toe walking; and normal gait. Obtained results are promising with more than 97% classification accuracy accompanied by low response time and computational demands.