Wireless Sensor nodes used in remote monitoring applications typically transmit data in a timely manner, often optimising for the Age of Information (AoI). However, this approach focuses solely on keeping updates at the sink fresh without considering the actual value of each update. This method is inefficient for sensor networks, which have limited resources. Transmitting data indiscriminately without evaluating its significance not only increases energy consumption but also raises storage requirements. To address this challenge, we propose a reinforcement learning-based scheduling strategy that prioritises sensor transmissions based on the Age of Incorrect Information (AoII) using an edge mining technique. This ensures that the most valuable updates are received while reducing energy consumption. We frame the scheduling problem as a Restless Multi-Armed Bandit (RMAB) and introduce a Whittle Index-based Q-learning (WIQL) policy to dynamically select the most informative sensors. Additionally, we employ an edge mining technique, where raw sensor data is processed locally before transmission, enhancing state estimation at the sink. Experimental results demonstrate that WIQL achieves near-optimal performance while significantly reducing the number of transmitted packets by up to $70 \%$. This reinforcement learning-based approach provides a scalable and adaptive solution for efficient data scheduling in resource-constrained WSNs.

The rise ofNewSpaceprovides a platform for small and medium businesses to commercially launch and operate satellites in space. In contrast to traditional satellites,NewSpaceprovides the opportunity for delivering computing platforms in space. However, computational resources within space are usually expensive and satellites may not be able to compute all computational tasks locally. Computation offloading (CO), a popular practice in Edge/Fog computing, could prove effective in saving energy and time in this resource-limited space ecosystem. However, CO alters the threat and risk profile of the system. In this article, we analyze security issues in space systems and propose a security-aware algorithm for CO. Our method is based on the reinforcement learning technique, deep deterministic policy gradient (DDPG). We show, using Monte-Carlo simulations, that our algorithm is effective under a variety of environment and network conditions and provide novel insights into the challenge of optimized location of computation.

Mobile Cloud Computing or Fog computing refers to offloading computationally intensive algorithms from a mobile device to the cloud or an intermediate cloud in order to save resources e.g., time and energy in the mobile device. This paper proposes new solutions for situations when the cloud or fog is not available. First, the sensor network is modelled using a network of queues, then a linear programming technique is used to make scheduling decisions. Various centralized and distributed algorithms are then proposed, which improves overall system performance. Extensive simulations show slightly higher energy usage in comparison to the baseline non-offloading case, however, the job completion rate is significantly improved, the efficiency score metric shows the extra energy usage is justified. The algorithms have been simulated in various environments including high and low bandwidth, partial connectivity, and different rate of information exchanges to study the pros and cons of the proposed algorithms.