Autonomous Network System Research Articles

Autonomous Network System with Specialized and Integrated Multi-Sensor Technology for Dynamic Monitoring of Marine Pollution (SMARTPOL)

Autonomous Network System with Specialized and Integrated Multi-Sensor Technology for Dynamic Monitoring of Marine Pollution (SMARTPOL)

Multiagent Meta-Reinforcement Learning for Adaptive Multipath Routing Optimization.

In this article, we investigate the routing problem of packet networks through multiagent reinforcement learning (RL), which is a very challenging topic in distributed and autonomous networked systems. In specific, the routing problem is modeled as a networked multiagent partially observable Markov decision process (MDP). Since the MDP of a network node is not only affected by its neighboring nodes' policies but also the network traffic demand, it becomes a multitask learning problem. Inspired by recent success of RL and metalearning, we propose two novel model-free multiagent RL algorithms, named multiagent proximal policy optimization (MAPPO) and multiagent metaproximal policy optimization (meta-MAPPO), to optimize the network performances under fixed and time-varying traffic demand, respectively. A practicable distributed implementation framework is designed based on the separability of exploration and exploitation in training MAPPO. Compared with the existing routing optimization policies, our simulation results demonstrate the excellent performances of the proposed algorithms.

Semantics-Empowered Communication for Networked Intelligent Systems

Wireless connectivity has traditionally been regarded as an opaque data pipe carrying messages, whose context-dependent meaning and effectiveness have been ignored. Nevertheless, in emerging cyber-physical and autonomous networked systems, acquiring, processing, and sending excessive amounts of distributed real-time data, which ends up being stale or useless to the end user, will cause communication bottlenecks, increased latency, and safety issues. We envision a communication paradigm shift, which makes the semantics of information (i.e., the significance and usefulness of messages) the foundation of the communication process. This entails a goal-orient-ed unification of information generation, transmission, and reconstruction, by taking into account process dynamics, signal sparsity, data correlation, and semantic information attributes. We apply this structurally new, synergetic approach to a communication scenario where the destination is tasked with real-time source reconstruction for the purpose of remote actuation. Capitalizing on semantics-empowered sampling and communication policies, we show significant reduction in both reconstruction error and cost of actuation error, as well as in the number of uninformative samples generated.

BDCC: Backpressure routing and dynamic prioritization for congestion control in WMSNs

Rapid technological advances and innovations in the area of autonomous systems push the researchers towards autonomous networked systems with emphasis on Wireless Multimedia Sensor Networks (WMSNs). In WMSN event-driven applications, it is critical to report the detected events in the area, resulting in sudden bursts of traffic due to occurrence of spatially-correlated or multiple events, causing loss of data. Also, nodes have very limited power due to hardware constraints. Packet losses and retransmissions resulting from congestion, cost precious energy and shorten the lifetime of sensor nodes. Till now, in WMSNs, Congestion control techniques are based on detection of congestion and recovery, but they cannot eliminate or prevent the occurrence of congestion. Collision is a symptom of congestion in the wireless channel and can result in a time-variant channel capacity. The method in the proposed algorithm is that the routing algorithms do not precalculate the routes and the next step is chosen dynamically. Decisions are made based on the congestion degree on neighbor nodes; each node sees its own queue backlog and neighbor's queue backlog and chooses its own degree and route based on the queue backlogs obtained from its neighbors. If there is two or more data with the same condition in the backpressure routing, we use service differentiation to prioritize packets. The results obtained from simulation test done by NS-2 simulator indicate that the proposed model is more innovative and presents better performance in compare with CCF and PCCP protocols.