Understanding the impact of security choices on battery life

Device security and energy efficiency are two vital considerations for IoT developers, but the impact of security features on battery life is often underappreciated.

Given the increased deployment of IoT devices across critical infrastructure, military applications, and highly sensitive information networks, ensuring data security is of critical importance. With increasingly sophisticated security features available for device software and hardware, there’s a knock-on effect on the energy requirements.

For developers of these devices, understanding the impact of different security features on energy consumption is a crucial part of designing a scalable and resilient application.

Securing IoT devices

IoT devices often handle sensitive data or control systems that simply can’t tolerate any breaches. Security mechanisms such as encryption, authentication, network handshakes, key rotation, and over-the-air (OTA) updates are an essential part of device security, but each comes with an energy cost.

Consider three common security features:

• Encryption: Protecting data at rest and in motion uses CPU cycles to encrypt and decrypt bytes. Stronger algorithms (e.g., AES-256) consume more energy than lighter ones (e.g., AES-128), and public-key operations (e.g., ECC, RSA) are heavier still.
• Frequent Handshakes and Authentication: Protocols like TLS or DTLS establish secure sessions through multi-step handshakes. Repeating these often (especially over intermittent or low-power networks) increases radio and processor use.
• OTA Updates with Validation: Secure firmware updates involve downloading, signature checking, and sometimes rollback logic. Without energy-aware scheduling, this can drain battery life disproportionately.

The challenge for developers is not whether to implement security, but which features to choose, and how to deploy them in a batteryaware manner.

Quantifying the Trade-off

If you think of device energy as a budget, security features are line items alongside sensing, communication, and processing.

To manage this ‘budget’, developers should:

• Measure Baseline Consumption: Establish how much energy your device uses without security: sensing cycles, idle sleep current, and periodic data transmissions.
• Profile Security Operations: Benchmark how much energy each security operation consumes. For example, a TLS handshake might cost tens to hundreds of milliseconds of active radio and CPU time, and OTA updates will keep the radio active for extended periods.
• Model Scenarios Across Use Cases: A temperature sensor that sends an encrypted reading once per hour will have a very different energy profile from a camera that streams authenticated video continuously. Understand how application patterns such as frequency, payload size, and connectivity quality, drive consumption.

By plotting these considerations against the capacity of a battery, you can gain crucial insight into lifetime expectations. This is particularly important for devices with high-energy security demands.

There are strategies that developers can adopt to help manage the energy requirements of a device, from choosing the right algorithms (using lightweight cryptographic suites when appropriate) to batching communications to reduce transmission cycles. That doesn’t mean compromising security, but simply matching protection to how critical the data is and how regularly it needs to be relayed.

Edge processing and security

Emerging trends like edge processing add another dimension to battery selection, and you can read more about AI ‘at the edge’ and what it means for power consumption in our recent blog.

Rather than sending raw data to the cloud, edge AI devices can process it locally. This reduces the energy needed for communication (which is often the biggest consumer) and improves responsiveness.

But it also presents an extra layer of complexity for securing devices and the data they handle.

From a security perspective, this means fewer transmissions and thus fewer opportunities for interception. The downside is that, if devices are not optimized for local processing, there can be an increase in CPU activity and a significant drain on power.

The key for developers is balance. Using edge AI accelerators or dedicated microengines can lower the energy-per-operation compared to general-purpose cores. In many cases, the net energy saving from reduced radio use can outweigh the extra processing cost, but unlocking that benefit requires a full understanding of your device’s energy profile, and making sure you have the right battery solution for the task.

Striking the right balance

Security and energy efficiency don’t have to be opposing forces in IoT design. With careful measurement, algorithm choices, and protocol strategies, developers can build devices that are both secure and long-life. Thinking in terms of an energy budget, where every security feature has a cost and a payoff, leads to smarter architectures and better products.