The explosive growth of medical data has dramatically increased the demand for computing power, resulting in insufficient spectrum resources and communication overload. Hospitals need to invest much money to expand computing resources. Various diseases require varying degrees of multi-sensor and continuous monitoring. Take venous thromboembolism (VTE) patients in the intensive care unit (ICU) as an example, enlargement of the right heart, widening of the pulmonary artery, and abnormal results of myocardial enzyme examination maybe lead to sudden death within a short time in the ICU inpatient ward. Steady and dynamic health monitoring is essential. Patients’ immediate risk perception can significantly improve medical efficiency and reduce adverse consequences. How to provide a more efficient and secure full-time monitoring scheme, dynamically adjust the workload, and allocate computing tasks and requests reasonably is a practical problem to be solved urgently. First, this paper defines a task similarity to measure the similarity between different task packages and determine the priority of tasks to avoid forwarding highly similar task packages and reduce energy consumption. Second, the edge gateway caching mechanism with a self-attention mechanism is constructed, which changes the centralized scheduling mode of traditional cloud computing, devolves the coordination function to the edge, and divides the network into multiple local sub-networks. The central node of the sub-network determines the scheduling scheme. The experimental results show that the system can ensure the quality of service and use the edge’s limited computing resources, effectively shield the inefficient data transmission requirements, reduce the use cost and medical quality, and has a specific theoretical and practical value.
The harsh environment of the battlefield challenges the delay and reliability of the cloud computing system composed of soldier terminals and BeiDou satellites. Based on this, this paper focuses on common problems in computational crowdsourcing under multi-agent and proposes a task distribution strategy optimization model based on battlefield edge computing. The process introduces the concept of flow pressure to solve these issues, load balancing and cascading congestion. Flow pressure means multiple servers can communicate and partially offload tasks that exceed the computational load to other servers. The computational overflow problem can be solved by task offloading based on flow pressure. Several different mainstream task allocation strategies are compared through experiments to demonstrate the model’s performance. The experimental results show that the model has lower latency and failure rate and reasonable computational resource occupation, which has a particular theoretical value and reference significance.
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