Sounds Shadowing Agents Generating Audible Features from Emergent Behaviors

Abstract

Wireless sensor networks have become incredibly popular due to the Internet of Things' (IoT) rapid development. IoT routing is the basis for the efficient operation of the perception-layer network. As a popular type of machine learning, reinforcement learning techniques have gained significant attention due to their successful application in the field of network communication. In the traditional Routing Protocol for lowpower and Lossy Networks (RPL) protocol, to solve the fairness of control message transmission between IoT terminals, a fair broadcast suppression mechanism, or Drizzle algorithm, is usually used, but the Drizzle algorithm cannot allocate priority. Moreover, the Drizzle algorithm keeps changing its redundant constant k value but never converges to the optimal value of k. To address this problem, this paper uses a combination based on reinforcement learning (RL) and trickle timer. This paper proposes an RL Intelligent Adaptive Trickle-Timer Algorithm (RLATT) for routing optimization of the IoT awareness layer. RLATT has triple-optimized the trickle timer algorithm. To verify the algorithm's effectiveness, the simulation is carried out on Contiki operating system and compared with the standard trickling timer and Drizzle algorithm. Experiments show that the proposed algorithm performs better in terms of packet delivery ratio (PDR), power consumption, network convergence time, and total control cost ratio.

Advances in Computer Entertainment Technology

“…Data mapping includes pattern recognition and feature extraction from swarm behaviors, applying a model based design of indirection that prolongs temporal dynamics, discussed is section 4.2. Details of system configuration are reported in [46].…”

“…Emergence of these features is uncertain and influenced by a performer, therefor AD4 durations required to induce phase transitions may be prolonged beyond the values indicated in Table 1. [46,47]. Attributes and their transformations can be organized by duration range, combining the duration required to generate a pattern and the duration required to recognize the pattern, such as a performer responding to influence a pattern.…”

Advancing Performability in Playable Media: A Simulation-based Interface as a Dynamic Score

“…Alternately a single sound source may be controlled by extracting data from behaviours of multiple agents, to enable independent scalability of sound sources and agents. Physics-based methods for this approach are discussed in [17,18] and statistical feature extraction [20] is discussed in this paper.…”

“…Separate statistics are provided as sources of sound control data: 1) measured across the entire swarm, 2) measured by species regardless of cluster, and 3) measured by cluster. Table 2 presents the extracted data available for sound control, discussed in detail in [20].…”

“…The extent of preconfigured control data and the resulting interactive responsiveness is a determining factor in listeners' engagement. In [19] we provide detailed discussion of sound generation with swarm data, and in [20] we introduce the concept of Procedural Sound Design Pattern, which is a unique set of preconfigured control data that creates an audible framework for interactive sound control data.…”