Abhay Mangalore, Software Engineering Manager at Arlo Inc — Innovation in IoT, Edge AI Challenges, AI in Home Security, Future of Wireless Communication, Secure Embedded Systems, and Career Advice – AI Time Journal

The rapid evolution of IoT, Edge AI, and wireless communication is redefining how we approach security, connectivity, and intelligence. Abhay Mangalore, Software Engineering Manager at Arlo Inc., brings deep expertise in these domains, driving innovation in smart security solutions. In this interview, Abhay discusses the challenges and opportunities in Edge AI deployment, the future of AI-powered home security, and the role of cybersecurity in IoT. He also shares valuable career insights for aspiring engineers. Read on to explore his perspectives on the technologies shaping the next generation of secure, intelligent devices.

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You have spent nearly two decades engineering groundbreaking solutions in embedded systems, IoT, and wireless communications. What was a pivotal moment in your career when you realized you were pushing the boundaries of innovation?

Throughout my career, I have been fortunate to work at the intersection of embedded systems, IoT, and wireless communications, constantly pushing the boundaries of what’s possible. A pivotal moment in my career was when I worked on developing AI-powered security cameras that combined embedded intelligence, IoT connectivity, and real-time video processing. We were pushing the boundaries of innovation—designing cameras that could differentiate between people, animals, and vehicles, reducing false alerts while ensuring a seamless user experience.

One of the most satisfying aspects was seeing end users truly appreciate and integrate these cameras into their daily lives. Knowing that the technology we built was actively protecting homes and businesses, giving people peace of mind, and making security more accessible was incredibly rewarding. It reinforced my passion for creating intelligent, user-centric solutions that go beyond just engineering excellence—they genuinely enhance everyday life.

As a Software Engineering Manager at Arlo Inc., you work at the intersection of security, connectivity, and intelligence. How do you approach balancing performance, power efficiency, and security in the evolving IoT landscape?

In the evolving IoT landscape, balancing performance, power efficiency, and security is a continuous challenge that requires a system-level approach rather than isolated optimizations. At Arlo Inc., where we develop cutting-edge security cameras, I focus on three core strategies to achieve this balance:

1. Edge AI for Real-Time Performance & Power Efficiency
   Traditional cloud-based processing introduces latency and power constraints. To address this, we leverage Edge AI, enabling on-device intelligence for real-time video analytics, such as object detection and person recognition. By processing data locally, we reduce cloud dependency, lower bandwidth usage, and improve power efficiency—a critical factor for battery-powered devices.
2. Adaptive Wireless & Energy Management
   Connectivity is a major power consumer in IoT devices. We implement dynamic power scaling and adaptive wireless protocols (e.g., Wi-Fi 6, BLE, and Thread) to optimize transmission power based on environmental conditions. This ensures that devices stay connected without unnecessary energy drain.
3. Security-First Architecture
   With growing cyber threats, IoT security is non-negotiable. We take a multi-layered security approach by embedding secure boot, hardware root of trust, and end-to-end encryption in our devices. We also comply with other security global standards like to ensure privacy and data protection while maintaining device integrity.

Ultimately, the key to balancing these factors is cross-functional collaboration—working closely with hardware, firmware, mobile apps and cloud teams to ensure that optimizations in one area don’t compromise another. By integrating AI-driven efficiency, intelligent connectivity, and proactive security, we ensure our IoT products deliver best-in-class performance while remaining energy-efficient and secure.

Edge AI is rapidly transforming industries, from smart security systems to autonomous vehicles. What do you see as the most significant challenges in deploying AI at the edge, and how is Arlo tackling these challenges?

Edge AI is reshaping industries by enabling real-time, intelligent decision-making without relying on cloud infrastructure. However, deploying AI at the edge presents three key challenges:

1. Compute Constraints vs. AI Complexity
   Edge devices have limited processing power, memory, and energy compared to cloud servers. Running deep learning models efficiently on such constrained hardware requires aggressive model optimization techniques like quantization, pruning, and knowledge distillation. To tackle this, we can implement lightweight neural networks optimized for the low-power SoCs (System-on-Chip) to ensure high-performance AI inference with minimal power draw.
2. Security and Privacy Risks
   Processing sensitive data on edge devices raises security and privacy concerns. Unlike cloud environments, edge devices are more vulnerable to physical attacks and firmware tampering. To mitigate this, we can adopt secure boot, hardware root of trust, and encrypted AI models to prevent adversarial attacks. Moreover, the usage of radar-based activity zones reduces reliance on video data, addressing privacy regulations like GDPR.
3. Scalability and Continuous Learning
   AI models need to evolve with new data, but updating models on edge devices at scale is challenging due to network constraints and device variability. This can be tackled by implementing federated learning, where devices collaboratively train models without sharing raw data, enhancing privacy while keeping AI models up to date. Additionally, over-the-air (OTA) updates enable us to push AI enhancements seamlessly across millions of devices.

By addressing these challenges through hardware-software co-optimization, robust security architectures, and scalable AI updates, we are pioneering the next generation of smart, autonomous security systems powered by Edge AI.

In your experience with video streaming security cameras, how have advancements in computer vision and AI changed the way we think about home security and surveillance?

Advancements in computer vision and AI have fundamentally transformed home security and surveillance, shifting from passive monitoring to proactive intelligence. Traditionally, security cameras relied on motion detection, often leading to excessive false alerts from environmental factors like shadows, trees, or pets. Today, AI-driven computer vision has redefined security in three key ways:

1. Smart Object and Activity Recognition
   Modern AI models can differentiate between people, vehicles, animals, and packages, reducing false alerts and ensuring users receive only relevant notifications. Cameras utilize deep learning-based object detection, improving situational awareness while reducing unnecessary cloud processing.
2. Edge AI for Real-Time Decision Making
   Instead of relying solely on cloud servers, AI models now run directly on cameras, enabling instantaneous threat detection. For instance, real-time anomaly detection can distinguish between normal household movement and suspicious behavior, helping homeowners prevent break-ins rather than just record them.
3. Privacy-Focused AI Innovations
   AI is also reshaping how we balance security with privacy. Features like automated privacy zones—enabled by mmWave radar and AI-driven motion tracking—ensure that cameras focus only on relevant areas, addressing global privacy regulations like GDPR. Additionally, on-device processing minimizes data transmission, reducing cybersecurity risks.

These advancements mean that security cameras are no longer just recording devices but intelligent guardians that predict, alert, and adapt to security needs in real time. With continuous improvements in computer vision, AI efficiency, and privacy-centric technologies, home security is becoming more intelligent, responsive, and user-friendly than ever before.

With automation and AI redefining business landscapes, what trends do you predict will have the most profound impact on IoT and wireless communication in the next five years?

Over the next five years, automation and AI will drive unprecedented advancements in IoT and wireless communication, shaping how devices connect, process data, and make intelligent decisions. The most profound trends will include:

1. Edge AI and Federated Learning for Smarter IoT
   AI models are shifting from centralized cloud computing to on-device intelligence, enabling real-time decision-making with lower latency and better privacy. Federated learning will play a key role, allowing IoT devices to learn from localized data without transmitting sensitive information to the cloud. This will be especially crucial in smart security systems, healthcare monitoring, and industrial automation.
2. mmWave and 6G for Ultra-Reliable, Low-Latency Communication (URLLC)
   As IoT applications demand higher bandwidth and ultra-low latency, mmWave technology and 6G will enable real-time AI applications, such as autonomous drones, robotic surveillance, and smart cities. These advancements will support massive machine-type communications (mMTC), allowing billions of IoT devices to seamlessly connect.
3. Energy-Efficient AIoT for Sustainable Tech
   With growing concerns over energy consumption, the next wave of IoT devices will leverage AI-driven power management, energy-harvesting sensors, and adaptive wireless protocols like Wi-Fi 6 and BLE 5.3. These innovations will extend device lifespans and reduce operational costs, making AIoT more sustainable.
4. AI-Driven Security for Zero-Trust IoT Networks
   With the rapid expansion of IoT, cybersecurity threats are increasing. AI-driven security models will enhance real-time anomaly detection, automated threat mitigation, and blockchain-based authentication to establish zero-trust IoT networks. This will be crucial for smart homes, connected healthcare, and industrial IoTecosystems.
5. Privacy-Centric IoT with On-Device Processing
   As privacy regulations tighten (e.g., GDPR, CCPA), IoT devices will increasingly adopt on-device AI processing, encrypted data storage, and user-controlled access. Technologies like homomorphic encryption and differential privacy will ensure that IoT systems remain intelligent yet privacy-compliant.

The next five years will see AI, advanced wireless technologies, and security innovations converging to create autonomous, energy-efficient, and privacy-aware IoT ecosystems. Companies that adapt to these trends early will lead the next wave of AIoT transformation.

Many experts debate between cloud-based AI processing versus edge-based intelligence. What are your insights on when and where each approach is most effective, particularly in security applications?

The debate between cloud-based AI and edge-based intelligence is not about which is better, but rather when and where each is most effective—especially in security applications, where latency, privacy, and computational efficiency are critical.

1.     Edge AI: Best for Real-Time, Privacy-Sensitive Applications

Edge-based intelligence is most effective when:

• Low latency is critical → Real-time security applications, such as intruder detection, facial recognition, or anomaly detection, require instantaneous responses. Processing AI models directly on the device eliminates cloud latency.
• Privacy concerns exist → With regulations like GDPR and CCPA, reducing data transmission protects user privacy. On-device AI ensures that sensitive video or audio data isn’t unnecessarily uploaded to the cloud.
• Bandwidth is limited → Security cameras operating in remote locations or on battery power benefit from AI inference at the edge, reducing bandwidth usage and extending device life.

2.     Cloud AI: Best for Large-Scale Analytics and Continuous Learning

Cloud-based AI is most effective when:

• Deep learning requires high compute power → Training and refining AI models demand massive computational resources. Security companies use cloud AI for continuous model improvements based on aggregated data.
• Cross-device intelligence is needed → Cloud AI enables multi-camera integration for behavioral pattern analysis across a property or citywide surveillance network.
• Long-term storage and advanced analytics → AI-driven forensic analysis, such as searching for specific objects or tracking movements over days/weeks, benefits from cloud processing power.

The future of security AI lies in a hybrid approach, where edge devices handle real-time decisions while the cloud provides deeper learning, scalability, and long-term analytics. Innovations like federated learning will further enhance security by enabling on-device model updates without raw data leaving the device, striking the perfect balance between efficiency, security, and intelligence.

Security vulnerabilities in IoT devices remain a growing concern. How do you approach designing secure embedded systems that can withstand evolving cybersecurity threats?

Security in IoT devices is a moving target, with cyber threats constantly evolving. Designing secure embedded systems requires a multi-layered security approach that integrates hardware, software, and network-level protections. We should always prioritize security across the entire device lifecycle by focusing on:

1.     Hardware-Rooted Security