Innovations in Memory Allocation for Resource-Constrained Embedded and IoT Devices
January 26, 2026
Blog
If you have worked with embedded systems and Internet of Things (IoT) technologies, you have likely grappled with their resource limitations. Regardless of how sophisticated they are or the environment in which they are deployed, they often face insufficient memory or poor memory management.
These problems may be common, but that doesn’t mean they are inevitable. By addressing the resource allocation issue at its source with innovative memory management strategies, you can prevent data loss and increase devices’ lifespans.
Memory Limitations in IoT Devices
IoT devices’ intrinsic constraints include limited bandwidth, insufficient computational power and heightened power consumption. These limitations are more pronounced in embedded systems, especially those in critical or time-sensitive sectors, such as aerospace and defense.
Memory fragmentation is among the most significant issues with traditional resource allocation in embedded software. As dynamic memory is allocated and deallocated through a program’s runtime, memory spaces can fragment. Over time, this can result in insufficient contiguous memory blocks, causing unexpected allocation failures or technical issues.
Memory leaks also pose a problem in resource-constrained systems, leading to unreliable behavior or crashes. Identifying and addressing them can be complicated and time-consuming. Finding an alternative to IoT is not a practical solution given its widespread adoption.
This technology is popular in industrial, commercial and consumer markets. For instance, experts expect over 402 million smartwatches to ship by 2027, increasing the penetration rate among consumers. Manufacturers, fleet owners and military bases also use internet-enabled devices. Therefore, professionals should focus on addressing resource allocation at the source.
Advances in Memory Allocation for the IoT
Embedded systems often use Linux-based operating systems, which use ptmalloc as the default memory allocator. It has memory management limitations, so experts have proposed substitutes such as tcmalloc, mimalloc and jemalloc. These general-purpose alternatives are overly complex and have excessive libraries.
Those flaws would be forgivable if they addressed the resource allocation issues, but they suffer from heavy memory consumption and gradual performance degradation. The prevalence of such problems has necessitated more efficient, intelligent solutions.
A Solution for Linux-Based Embedded Systems
A research group led by Dr. Hwajung Kim at the Seoul National University of Science and Technology has developed LWMalloc, a high-performance dynamic memory allocator purpose-built for resource-constrained environments. It is based on a lightweight data structure and utilizes dedicated small chunk pools that segregate small memory requests into fixed-sized pools for optimization.
Its deferred coalescing policy postpones redundant operations until allocation, reducing the execution overhead and enhancing efficiency. It accelerates execution time by 53 percent and has 23 percent lower memory usage. With a size of 20 kilobytes, consisting of 530 lines of code, it is considerably smaller than ptmalloc, which is 116 kilobytes with 4,838 lines of code.
A Solution for Resource-Constrained Environments
Existing resource allocation methods, such as multi-agent reinforcement learning and evolutionary algorithms, are inefficient at adapting to dynamic IoT networks due to their high cost and computational complexity. One research group proposes an intelligent alternative for IoT networks that uses machine learning and clustering techniques.
Initially, it groups IoT devices using the K-means algorithm based on factors like power consumption and bandwidth requirements. Then, a random forest model accurately predicts each cluster’s resource needs, enabling optimal allocation. Compared to existing methods, simulations demonstrate it improves prediction accuracy to around 95 percent, reduces energy consumption by 20 percent and decreases response time by 10 percent.
What This Means for Embedded Computing
Optimizing resource allocation and preventing memory fragmentation enhances system performance and reduces energy consumption, potentially extending the lifespans of IoT devices. These fundamental changes could accelerate IoT’s penetration rate, which is already high. Some estimates predict device volume will reach 30.9 billion by 2025.
The implications for embedded computing extend beyond these immediate improvements. Innovative memory allocation techniques enable low-power hardware to run more complex operations. The benefit is twofold — in addition to making implementation more accessible by reducing electricity costs related to power consumption, it opens the door for novel applications in time-sensitive sectors, like automotive manufacturing and aerospace engineering.
Industry professionals may not notice their day-to-day change, but they will experience fewer system crashes, unplanned breakdowns and odd behaviors, ultimately increasing uptime and streamlining operations.
The Prospect of More Memory Optimization
These research groups’ advanced techniques are just the beginning of innovations in memory allocation for resource-constrained environments. The timing couldn’t be better, as embedded and IoT devices are quickly becoming critical in various industries. Many similar improvements will emerge in time, further optimizing cutting-edge technologies.
Eleanor Hecks is a writer with 8+ years of experience contributing to publications like freeCodeCamp, Smashing Magazine, and Fast Company. You can find her work as Editor-in-Chief of Designerly Magazine, or keep up with her on LinkedIn.
