Min-Heap Data Structure

The Min-Heap implementation provides an efficient data structure for managing a dynamically changing list of elements while maintaining the ability to quickly extract the minimum value. It’s a tree-based data structure that satisfies the heap property and supports common operations such as insertion, removal and popping the minimum element from the Min-Heap

This section explains the motivation behind the implementation, its internal structure, API usage, and example scenarios for embedded systems and real-time environments.

Heap Structure 

The heap is maintained as a complete binary tree stored in an array. Each node satisfies the min-heap property:

The value of each node is less than or equal to the values of its children.

This property ensures that the minimum element is always at the root (index 0).

Index:      0   1   2   3   4   5   6
Values:    [1,  3,  5,  8,  9, 10, 12]

               1
             /   \
           3       5
          / \     / \
         8   9  10  12

For any node at index i, its children are at indices:

Left child: $2 * i + 1$
Right child: $2 * i + 2$

Its parent is at index:

Parent: $(i - 1) / 2$

Use Cases 

MinHeap is well suited for:

Implementing priority queues
Sorting data incrementally
Resource arbitration (e.g., lowest-cost resource selection)
Scheduling in cooperative multitasking systems
Managing timeouts and delay queues
Priority-based sensor or data sampling

In RTOS environments like Zephyr, this heap can be used in kernel-level or application-level modules where minimal latency to obtain the highest priority resource is needed.