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This paper proposes an Enhanced Edge-Linked Caching (EELC) scheme for Internet of Things (IoT) environments under Information-Centric Networking (ICN), employing an advanced use of Proximal Policy Optimization (PPO), a form of deep reinforcement learning, to inform the caching decisions of edge nodes. The rapid proliferation of IoT devices has led to significant challenges in managing content efficiently within networks, particularly in terms of scalability, latency, and energy consumption. Traditional IP-based architectures are inadequate in addressing these challenges, necessitating a shift towards a content-centric approach provided by ICN. By leveraging PPO, EELC dynamically adapts to changing IoT network conditions, optimizing caching strategies to enhance energy efficiency and improve network responsiveness. In our simulation, we verify the performance of EELC in comparison to the Edge-Linked Caching (ELC) and Leave Copy Everywhere (LCE) approaches under different cache sizes and Zipf-distributed content requests. EELC performs far better than ELC under energy efficiency, cache hit ratios, and server hit reduction in all tested scenarios. This indicates that EELC could be a potential approach for significantly improving network efficiency and responsiveness in IoT networks.

Wireless Sensor Networks (WSNs) play an instrumental role in monitoring and data collection across a plethora of domains. As these networks expand and get more intricate, the need for energy-efficient and smart sensing models becomes paramount. This paper introduces the energy-efficient collaborative sensing model using game theory for WSNs. This model is unique in its approach, amalgamating game theory and reinforced learning to ensure optimal energy consumption without compromising the quality of service. A cornerstone of this model is the introduction of the Selection Propensity Index (SPI), which helps in the decision-making process of choosing the right sensors for specific tasks. Moreover, the model employs a Distributed Anticipatory Time-slot selection Algorithm (DATA), an RL-based algorithm that facilitates collaborative communication. This aspect ensures that the sensors while communicating, select the time slots that would result in minimal energy expenditure and optimal data transmission. Simulations are performed over the proposed model and the results obtained showcase that the proposed model outperforms the existing similar methods in terms of energy efficiency, packet drop ratio, and throughout. The proposed model exhibits an enhancement in terms of network lifetime by 202%, throughput by 30%, and is faster by more than 60% compared to the existing methods running on full load, thereby providing an innovative approach to energy efficiency in sensor networks.