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The Smart Grid AMI is at the forefront of modernizing energy management for efficient energy distribution and maintaining grid reliability. However, the seamless flow of data within AMI introduces critical security challenges, necessitating innovative approaches to ensure secure data transmission. This paper addresses these challenges by proposing a Quantum Key Distribution (QKD) based on Multi-Objective Optimization (MOO) and Reinforcement Learning (RL) framework for secure data transmission within the Smart Grid AMI. Primarily, we formulate the routing problem as a MOO challenge with four primary objectives namely, energy efficiency, latency minimization, reliability, and security to capture the essence of efficient and secure data transmission. Further, we introduce an RL agent based on the Proximal Policy Optimization (PPO) algorithm for robust policy learning. The RL agent by exploring diverse routing actions and receiving rewards optimizes the routing decisions within the network environment based on the objectives specified in the multi-objective function (MOF). A novel MOF quantifies trade-offs between security and performance metrics, integrating QKD-based security metrics with traditional optimization objectives. This function guides the RL agent in making informed routing decisions. Through extensive simulations using the NS-2 simulator in a realistic Smart Grid AMI environment, the proposed approach obtained energy consumption of 750 J, latency of 55 ms, and security level of 96% and also revealed significant improvements compared to conventional techniques widely employed for secured communication in AMI networks. Overall, the simulation results exhibited that the proposed method showed outstanding performance by achieving energy efficiency of 0.95, latency reduction of 0.92, reliability improvement of 0.94, and security enhancement of 0.96. Overall, this integration offers a pioneering solution to address the evolving security challenges in the Smart Grid landscape and contributes to the advancement of secure data transmission in Smart Grid AMI networks, fostering a resilient and secure energy ecosystem. [ABSTRACT FROM AUTHOR]

In path-constrained multi-hop sensor networks (M-WSNs), maximizing data collection with minimal energy consumption is critical, especially for delay-sensitive applications. Because a mobile element (ME) moving at a constant speed along a constrained-path must receive data from sensor nodes (SNs) within a given time bound. This issue can be addressed by efficiently scheduling the SNs' data transmission. The shortest path data transmission (SPT) or its variations are a simple and energy-efficient technique for scheduling data transmission from SNs. Although this method considerably minimizes total energy spent, it does not enhance data collection efficiency due to uneven data forwarding load. To solve this issue, this paper proposes a novel efficient heuristic method. The proposed method first computes a set of discrete sub-paths on a given path based on the start and end distance points of nearby SNs, the speed of ME, and the given time delay. Then, for each sub-path and SN-communication model, a network flow graph is used to schedule and optimize SN data transmission to ME. Finally, a sub-path is chosen among those which return maximum data with minimum energy consumption. The network flow graphs are created by considering two distinct SN-communication models: (1) energy-unrestricted SN-model and (2) energy-restricted SN-model. Finally, the simulation reports demonstrate the proposed method's efficacy over baseline schemes in terms of data collected, energy usage efficiency, and success-delivery ratio. [ABSTRACT FROM AUTHOR]

A wireless sensor network (WSN) is made up of many sensor nodes with insufficient energy, storage, and processing capabilities. Data gathering and transmission to the base station are two of the main responsibilities of the sensor nodes (BS). As a result, the network lifespan becomes the key factor in the successful design of data collection strategies in WSN. In this study, we provide the Enriched energy optimized LEACH (EE-OLEACH) protocol for data transfer. Through a combination of efficient optimum clustering and an optimal route selection mechanism, it provides a means for energy-efficient routing in WSN. The Homogeneous Hunter-Wolf optimization (HHWO) is used for clustering, and a cluster head is chosen for each cluster to reduce energy loss among sensor nodes and maximize efficiency in their use of available resources. Nodes with the highest residual energy may receive the most energy-efficient routing. In order to send the data to BS, the nodes with the greatest residual energy are chosen. The pheromone-profound Ant optimization (PPAO) method was then used to reduce energy consumption throughout the path-selection process. It contributes to a higher packet delivery ratio while reducing power consumption. According to the experimental findings, the proposed EE-OLEACH performs better than the current Protocol in terms of packet delivery ratio, end-to-end latency, and energy usage. In this paper, we compare the performance of the existing hierarchical routing protocols under varying conditions (packet size, starting energy level, etc.) and demonstrate how the optimal CH selection based on a suggested algorithm improves both network lifetime and energy consumption. The Simulation results shows that the EE-OLEACH enhances energy efficiency by 30%, delay by 35%, node survived by 45%, network lifetime by 56%, packet delivery ratio by 47% and throughput by 38% when compared with other existing protocols. The results clearly show that the suggested EE-OLEACH extends the lifespan of the network and reduce the energy consumption. [ABSTRACT FROM AUTHOR]