In this work, we propose that packets travelling across a wireless sensor network (WSN) can be seen as the active agents that make up a complex system, just like a bird flock or a fish school, for instance. From this perspective, the tools and models that have been developed to study this kind of systems have been applied. This is in order to create a distributed congestion control based on a set of simple rules programmed at the nodes of the WSN. Our results show that it is possible to adapt the carried traffic to the network capacity, even under stressing conditions. Also, the network performance shows a smooth degradation when the traffic goes beyond a threshold which is settled by the proposed self-organized control. In contrast, without any control, the network collapses before this threshold. The use of the proposed solution provides an effective strategy to address some of the common problems found in WSN deployment by providing a fair packet delivery. In addition, the network congestion is mitigated using adaptive traffic mechanisms based on a satisfaction parameter assessed by each packet which has impact on the global satisfaction of the traffic carried by the WSN.

We study the problem of path computation in Cayley Graphs (CG) from an approach of word processing in groups. This approach consists in encoding the topological structure of CG in an automaton called Diff , then techniques of word processing are applied for computing the shortest paths. We present algorithms for computing the K-shortest paths, the shortest disjoint paths and the shortest path avoiding a set of nodes and edges. For any CG with diameter D, the time complexity of the proposed algorithms is O(KD|Diff |), where |Diff | denotes the size of Diff . We show that our proposal outperforms the state of art of topology-agnostic algorithms for disjoint shortest paths and stays competitive with respect to proposals for specific families of CG. Therefore, the proposed algorithms set a base in the design of adaptive and low-complexity routing schemes for networks whose interconnections are defined by CG.

Scientific research often involves collaboration among researchers, and coauthorship networks are a common means of exploring these collaborations. However, traditional coauthorship networks represent coauthorship relations using simple links, i.e., pairwise interactions, which fail to capture the strength of scientific collaborations in either small or large groups. In this study, we propose a novel methodology to address this issue, which involves using a multilayer network model that captures the strength of coauthorship relations and employs a convergence index to identify the collaboration order in which these properties converge. We apply this methodology to investigate the collaborative behavior of researchers in the context of the three main public universities in Mexico over the last decade, using Scopus data as the primary source of information. Our study reveals that community structure emerges in low-order collaborations, and higher-order collaborations lead to increased clustering and centrality measures. Our methodology provides a comprehensive and insightful way of analyzing scientific collaborations and sheds light on the dynamics of scientific collaboration, providing a valuable tool for future studies. Our proposed model and convergence index can be applied to other scientific domains to better capture the strength of collaborations among researchers.

Data center networks (DCNs) connect hundreds and thousands of computers and, as a result of the exponential growth in their number of nodes, the design of scalable (compact) routing schemes plays a pivotal role in the optimal operation of the DCN. Traditional trends in the design of DCN architectures have led to solutions, where routing schemes and network topologies are interdependent, i.e., specialized routing schemes. Unlike these, we propose a routing scheme that is compact and generic, i.e., independent of the DCN topology, the word-metric-based greedy routing. In this scheme, each node is assigned to a coordinate (or label) in the word-metric space (WMS) of an algebraic group and then nodes forward packets to the closest neighbor to the destination in this WMS. We evaluate our scheme and compare it with other routing schemes in several topologies. We prove that the memory space requirements in nodes and the forwarding decision time grow sub-linearly (with respect to n, the number of nodes) in all of these topologies. The scheme finds the shortest paths in topologies based on Cayley graphs and trees (e.g. Fat tree), while in the rest of topologies, the length of any path is stretched by a factor that grows logarithmically (with respect to n). Moreover, the simulation results show that many of the paths remain far below this upper bound.