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SummaryUnderwater wireless optical communication has been studied and proposed as a potential technology for various underwater applications including monitoring and surveillance using wireless sensor networks. In this context, we present a statistical model using an exhaustive Monte Carlo approach for an underwater wireless optical communication link between sensor nodes in a data mulling environment. This work provides insights into the science of photon transport underwater, its utilization in a communication system, evaluation, and optimization of underwater communication. We characterize the temporal channel behavior taking into account multiple scattering of photons involving the constraints of detector aperture diameter, field‐of‐view (FOV), and link distance for an aligned point‐to‐point link. We analyze the designed model in terms of total received power and bit error rate for clear ocean waters. Our simulation results show that the detector aperture and FOV has an impact towards a successful communication in the distance from 5 to 20 m. In addition, results show that the FOV is an affecting factor only in the presence of larger aperture sizes of the detector.

Mobile edge computing is capable of providing high data processing capabilities while ensuring low latency constraints of low power wireless networks, such as the industrial internet of things. However, optimally placing edge servers (providing storage and computation services to user equipment) is still a challenge. To optimally place mobile edge servers in a wireless network, such that network latency is minimized and load balancing is performed on edge servers, we propose a multi-agent reinforcement learning (RL) solution to solve a formulated mobile edge server placement problem. The RL agents are designed to learn the dynamics of the environment and adapt a joint action policy resulting in the minimization of network latency and balancing the load on edge servers. To ensure that the action policy adapted by RL agents maximized the overall network performance indicators, we propose the sharing of information, such as the latency experienced from each server and the load of each server to other RL agents in the network. Experiment results are obtained to analyze the effectiveness of the proposed solution. Although the sharing of information makes the proposed solution obtain a network-wide maximation of overall network performance at the same time it makes it susceptible to different kinds of security attacks. To further investigate the security issues arising from the proposed solution, we provide a detailed analysis of the types of security attacks possible and their countermeasures.

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