KEYWORDS: Mobile communications, Data communications, Telecommunications, Neural networks, Binary data, Stochastic processes, Receivers, Probability theory, Systems modeling
This paper determines optimal strategies for transmitting messages in a mobile ad-hoc network (MANET) in a communications-limited and lossy communications environment. When an agent generates or receives a message it must decide to which neighbors, and how many times, that message is to be passed. The opposing goals are to (1) propagate all messages throughout the MANET quickly and (2) to minimize the total number of messages sent. We compare two optimized decision strategies for the agents, reinforcement learning (RL) and game theory (GT) methods. For the RL framework, each node in the MANET acts as a reinforcement learning agent who must learn optimal decisions for when to send messages to whom. For the GT framework, we create a game tree where the nodes encompass message knowledge and connectivity information, and the decision branches represent sending messages to neighbors. We solve the game using a Monte-Carlo Tree Search (MCTS) variation to determine the probability that a message is sent to a neighbor. Performance is assessed in terms of the total number of messages sent, and the length of time for a given percentage of messages to reach a given percentage of nodes. Experiments with MANETs of varying size and connectivity are considered, and the RL and GT performance and training speed are compared. The decision strategies are domain agnostic and may be applied to ground, air, surface, sub-surface, or satellite networks.
Autonomous platforms are becoming ubiquitous in society, including UAVs, Roombas, and self-driving cars. With the increase in prevalence of autonomous platforms comes an increase in the threat of attacks against these platforms. These attacks can range from direct hacking to remotely take control of the platforms themselves [1], to attacks involving manipulation or deception such as spoofing or fooling sensor inputs [2, 3]. Ensuring autonomous systems are robust and resilient (R2) against these attacks will become an important challenge to overcome if they are to be trusted and widely adopted. This paper addresses the need to quantitatively define robustness and resilience against manipulation and deceptive attacks which are inherently harder to detect. We define a set of robust estimation metrics that are mathematically rigorous, can be applied to multiple algorithm use cases, and are easy to interpret. Since many of these functions are processed over time, the primary focus will be on process-based metrics. These metrics can be adapted over time by responding and reconfiguring at system runtime. This paper will: 1) provide background information on previous work in this area, including adversarial machine learning, robotics control, and engineering design. 2) Present the metrics and explain how to address our unique problem. 3) Apply these metrics to three different autonomy applications: target tracking, autonomous control, and automatic target recognition. 4) Discuss some additional caveats and potential areas for future work.
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