Mobile Edge Computing (MEC) enables computational task offloading from resource-constrained End Devices (EDs) to nearby edge servers, thereby reducing latency and energy consumption. This problem has been extensively studied in the literature, however, existing approaches often neglect realistic device constraints, particularly battery dynamics, and rely on limited and non-reproducible datasets. To address these gaps, this paper proposes a Battery-Dependent Partial Offloading Scheme (BDPOS) that integrates the current battery levels of EDs into the cost model, resulting in energy-aware and optimal offloading decisions. The proposed framework jointly optimizes three objectives: determining the optimal number of components, identifying task partitioning, and selecting offloading policies to identify the overall minimum-cost policy. To ensure reproducibility, BDPOS is further used to generate multiple synthetic datasets of varying sizes. A comprehensive comparative analysis of multiple AI models within a sequential Multi-Task Learning (MTL) framework is conducted on these datasets for intelligent offloading in MEC environments. The evaluated models include Deep Neural Networks (DNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), Gated Recurrent Unit (GRU), Bidirectional GRU (Bi-GRU), Minimal Gated Unit (MGU), and Temporal Convolutional Network (TCN). The datasets are pre-processed and optimized using Bayesian-based hyperparameter tuning before model training. The trained models are then evaluated using multiple performance metrics, the Wilcoxon signed-rank test, and computational cost analysis. Moreover, the energy consumption of the proposed algorithm is compared with existing schemes in the literature. Simulation results demonstrate that the proposed technique BDPOS, significantly reduces overall energy consumption compared to existing strategies, while the MTL-based GRU model achieves superior performance, attaining 87.96% accuracy in component optimization, a mean absolute error of 0.0780 for task partitioning, and 63.37% accuracy for offloading policy prediction. These findings highlight the importance of battery-aware modelling and provide actionable insights into the design of efficient, data-driven offloading strategies for next-generation MEC systems.

The growing demand for real-time computing applications on mobile devices is burdening their processing power and battery life. Mobile edge computing helps by allowing these tasks to be offloaded to nearby servers having more processing power. However, when it comes to multiple servers and tasks, choosing the optimal components for offloading becomes challenging. This is because we need to balance between reducing the amount of data transferred and keeping communication latency low. To address this problem, an energy-efficient parallel computation offloading mechanism through deep learning (EPCOD), is proposed. An algorithm using deep learning (DL) is developed and trained as a decision-making system. This system selects the best combination of application components taking into account various factors, such as energy consumption, network conditions, computational load , data transfer volume, and communication latency. A cost function that includes all these factors is developed to calculate the cost for each possible offloading policy combination. By analyzing a large dataset, we find the best policies. Additionally, we use a DL network to efficiently handle this computational task. Simulation results demonstrate that EPCOD effectively minimizes both latency and energy consumption, achieving a high accuracy of deep neural network of up to 73.5%.

Delayed delivery of packets hinders the performance of time-sensitive Internet of Things (IoT) applications and incurs increased power consumption. Stochastic routing schemes solve the problem of saving all participating nodes from getting their power drained out quickly. However, stochastic routing incurs the problem of delivery delays and reliable end-to-end delivery. This paper proposes a novel routing scheme, called $Q_{i j}$ routing, to solve these problems. The proposed $Q_{i j}$ routing scheme is a combination of a classic routing scheme, called Distance Vector Algorithm, with a novel re-definition of the cost of a link to find the best path from source to destination. $Q_{i j}$ takes into account the wireless link reliability of any connection between two nodes, and the transmission delay of IoT devices working together in a distributed network. With the presented mathematical model, a routing table is maintained that let an individual node in a network find the distinctly prominent reliable path among many routes from source to destination. The superior efficiency of $Q_{i j}$ routing scheme over eminent stochastic routing schemes is proven through simulation results in terms of reduced end-to-end expected delivery delay and increased expected delivery ratio.

Mobile edge computing (MEC) is used for compu-tationally complex applications by offloading it to the nearby edge server either partially or entirely. The problem arises of selecting whether the component is to be offloaded to the mobile edge server (MES) for execution, or it needs to be executed locally. Therefore, we propose a time-efficient decision offloading scheme (TEDOS) to derive a data set and train an artificial neural network (ANN) on the derived data set. TEDOS provide the smart decision on the optimal permutation of the divided components based on delay. We developed a mathematical model for delays in communication, execution and component queuing. We obtained a final delay for all possible permutations of component offloading policies. Our model obtained 91 % accurate results as compared to the existing schemes. The simulation result shows that our proposed model outperforms the state-of-the-art.

Punctual and reliable transmission of collected information is indispensable for many Internet of Things (IoT) applications. Such applications rely on IoT devices operating over wireless communication links which are intrinsically unreliable. Consequently to improve packet delivery success while reducing delivery delay is a challenging task for data transmission in the IoT. In this paper, we propose an improved distributed stochastic routing algorithm to increase packet delivery ratio and decrease delivery delay in IoT with unreliable communication links. We adopt the concept of absorbing Markov chain to model the network and evaluate the expected delivery ratio and expected delivery delay over multiple hops. Simulations are performed in order to evaluate the performance of the proposed algorithm in comparison with three existing decentralized routing algorithms.