Field-based Coordination with the Share Operator

Field-based coordination has been proposed as a model for coordinating collective adaptive systems, promoting a view of distributed computations as functions manipulating data structures spread over space and evolving over time, called computational fields. The field calculus is a formal foundation for field computations, providing specific constructs for evolution (time) and neighbor interaction (space), which are handled by separate operators (called rep and nbr, respectively). This approach, however, intrinsically limits the speed of information propagation that can be achieved by their combined use. In this paper, we propose a new field-based coordination operator called share, which captures the space-time nature of field computations in a single operator that declaratively achieves: (i) observation of neighbors' values; (ii) reduction to a single local value; and (iii) update and converse sharing to neighbors of a local variable. We show that for an important class of self-stabilising computations, share can replace all occurrences of rep and nbr constructs. In addition to conceptual economy, use of the share operator also allows many prior field calculus algorithms to be greatly accelerated, which we validate empirically with simulations of frequently used network propagation and collection algorithms

Effective collective summarisation of distributed data in mobile multi-agent systems

One of the key applications of physically-deployed multi-agent systems, such as mobile robots, drones, or personal agents in human mobility scenarios, is to promote a pervasive notion of distributed sensing achieved by strict agent cooperation. A quintessential operation of distributed sensing is data summarisation over a region of space, which finds many applications in variations of counting problems: Counting items, measuring space, averaging environmental values, and so on. A typical strategy to perform peer-to-peer data summarisation with local interactions is to progressively accumulate information towards one or more collector agents, though this typically exhibits several sources of fragility, especially in scenarios featuring high mobility. In this paper, we introduce a new multi-agent algorithm for dynamic summarisation of distributed data, called parametric weighted multi-path, based on a local strategy to break, send, and then re-combine sensed data across neighbours based on their estimated distance, ultimately resulting in the formation of multiple, dynamic and emergent paths of information flow towards collectors. By empirical evaluation via simulation in synthetic and realistic case studies, accounting for various sources of volatility, using different state-of-the-art distance estimations, and comparing to other existing implementations of aggregation algorithms, we show that parametric weighted multi-path is able to retain adequate accuracy even in high-variability scenarios where all other algorithms are significantly diverging from correct estimations