In recent years, the significant growth in the Internet of Things (IoT) technology has brought a lot of attention to information and communication industry. Various IoT paradigms like the Internet of Vehicle Things (IoVT) and the Internet of Health Things (IoHT) create massive volumes of data every day which consume a lot of bandwidth and storage. However, to process such large volumes of data, the existing cloud computing platforms offer limited resources due to their distance from IoT devices. Consequently, cloud-computing systems produce intolerable latency problems for latency-sensitive real-time applications. Therefore, a new paradigm called fog computing makes use of computing nodes in the form of mobile devices, which utilize and process the real-time IoT devices data in orders of milliseconds. This paper proposes workload-aware efficient resource allocation and load balancing in the fog-computing environment for the IoHT. The proposed algorithmic framework consists of the following components: task sequencing, dynamic resource allocation, and load balancing. We consider electrocardiography (ECG) sensors for patient's critical tasks to achieve maximum load balancing among fog nodes and to measure the performance of end-to-end delay, energy, network consumption and average throughput. The proposed algorithm has been evaluated using the iFogSim tool, and results with the existing approach have been conducted. The experimental results exhibit that the proposed technique achieves a 45% decrease in delay, 37% reduction in energy consumption, and 25% decrease in network bandwidth consumption compared to the existing studies.

Fog computing; internet of health things; resource management