This paper proposes that the flocking behavior of birds can guide the design of a robust, scalable and self-adaptive congestion control protocol in the context of wireless sensor networks (WSNs). The proposed approach adopts a swarm intelligence paradigm inspired by the collective behavior of bird flocks. The main idea is to 'guide' packets (birds) to form flocks and flow towards the sink (global attractor), whilst trying to avoid congestion regions (obstacles). The direction of motion of a packet flock is influenced by repulsion and attraction forces between packets, as well as the field of view and the artificial magnetic field in the direction of the artificial magnetic pole (sink). The proposed approach is simple to implement at the individual node, involving minimal information exchange. In addition, it displays global self-* properties and emergent behavior, achieved collectively without explicitly programming these properties into individual packets. Performance evaluations show the effectiveness of the proposed Flock-based Congestion Control (Flock-CC) mechanism in dynamically balancing the offered load by effectively exploiting available network resources and moving packets to the sink. Furthermore, Flock-CC provides graceful performance degradation in terms of packet delivery ratio, packet loss, delay and energy tax under low, high and extreme traffic loads. In addition, the proposed approach achieves robustness against failing nodes, scalability in different network sizes and outperforms typical conventional approaches.

The Winograd Schema Challenge, the task of resolving pronouns in certain carefully-structured sentences, has received considerable interest in the past few years as an alternative to the Turing Test. Systems developed to tackle this challenge have typically been evaluated on a small set of hand-crafted collections of sentences, since the development of new sentences by individuals is itself a rather challenging task, requiring care and creativity. In this paper we approach the problem of developing Winograd schemas via the introduction of WinoFlexi, a flexible online crowdsourcing system. Our empirical evaluation of the system's performance suggests that WinoFlexi allows crowdworkers to develop Winograd schemas of quality similar to that of most typical existing collections.

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