Deep Meta Q-Learning based Multi-Task Offloading in Edge-Cloud Systems

Resource-Constrained Edge Devices Can Not Efficiently Handle the Explosive Growth of Mobile Data and the Increasing Computational Demand of Modern-Day User Applications. Task Offloading Allows the Migration of Complex Tasks from User Devices to the Remote Edge-Cloud Servers Thereby Reducing their Computational Burden and Energy Consumption While Also Improving the Efficiency of Task Processing. However, Obtaining the Optimal Offloading Strategy in a Multi-Task Offloading Decision-Making Process is an NP-Hard Problem. Existing Deep Learning Techniques with Slow Learning Rates and Weak Adaptability Are Not Suitable for Dynamic Multi-User Scenarios. in This Article, We Propose a Novel Deep Meta-Reinforcement Learning-Based Approach to the Multi-Task Offloading Problem using a Combination of First-Order Meta-Learning and Deep Q-Learning Methods. We Establish the Meta-Generalization Bounds for the Proposed Algorithm and Demonstrate that It Can Reduce the Time and Energy Consumption of IoT Applications by Up to 15%. through Rigorous Simulations, We Show that Our Method Achieves Near-Optimal Offloading Solutions While Also Being Able to Adapt to Dynamic Edge-Cloud Environments

An Information-Theoretic Framework for Optimal Location Tracking in Multi-System 4G Wireless Networks

Abstract — An information-theoretic framework is developed for optimal location management in multi-system, fourth generation (��) wireless networks. The framework envisions that each individual sub-system operates fairly independently, and does not require public knowledge of individual sub-network topologies. To capture the variation in paging and location update costs in this heterogeneous environment, the location management problem is formulated in terms of a new concept of weighted entropy. The update process is based on the Lempel-Ziv compression algorithms, which are applied to a vector-valued sequence consisting of both the mobile’s movement pattern and its session activity state. Three different tracking strategies which differ in their degrees of centralized control and provide trade off between the location update and paging costs, are proposed and evaluated. While both the proposed centralized and distributed location management strategies are endowed with optimal update capability, the proposed selective location management heuristic also offers a practical trade off between update and paging costs. Simulation experiments demonstrate that our proposed schemes can result in more than � % savings in both update and paging costs, in comparison with the basic movement-based, multisystem location management strategy. These update strategies can be realized with only modest amounts of memory ( – � Kbytes) on the mobile

Quality and context-aware smart health care: Evaluating the cost-quality dynamics

Many emerging pervasive health-care applications require the determination of a variety of context attributes of an individual\u27s activities and medical parameters and her surrounding environment. Context is a high-level representation of an entity\u27s state, which captures activities, relationships, capabilities, etc. In practice, high-level context measures are often difficult to sense from a single data source and must instead be inferred using multiple sensors embedded in the environment. A key challenge in deploying context-driven health-care applications involves energy-efficient determination or inference of high-level context information from low-level sensor data streams. Because this abstraction has the potential to reduce the quality of the context information, it is also necessary to model the tradeoff between the cost of sensor data collection and the quality of the inferred context. This article describes a model of context inference in pervasive computing, the associated research challenges, and the significant practical impact of intelligent use of such context in pervasive health-care environments

Autoconfiguration, Registration and Mobility Management for Pervasive Computing

Special Issue of Pervasive Computing</p

Determining quality- and energy-aware multiple contexts in pervasive computing environments

Ministry of Education, Singapore under its Academic Research Funding Tier 2; Singapore National Research Foundation under International Research Centres in Singapore Funding Initiativ