Mapping how digital twin technology can work with UC

How does digital twin technology work with UC?

Digital twin technology has three parts: data collection, model creation and simulation. UC data is collected from various sources to run simulations. Some top-notch digital twin models incorporate a fourth stage for advanced network operations. Let's examine each step.

1. Data collection

VoIP phones, network devices, routers, gateways, wearables, headsets, microphones, speakers, cameras, video conferencing gear and AI chatbots generate large volumes of data. Digital twins collect real-time enterprise data from UC hardware and software through advanced sensors and monitoring tools that comb through enterprise interaction, collaboration, shared files and meeting summaries.

2. Model creation

Using advanced AI and machine learning (ML) software, digital twinning creates virtual replicas of the enterprise's UC infrastructure. It uses the collected data to mimic the behavior of the actual UC system.

3. Simulation

Digital twins run simulations to create what-if scenarios. Such simulations engage real-time UC components and use data analytics for accurate results.

Use cases for digital twin technology in UC

Because of its ability to mimic the behavior of the actual UC system, digital twin technology can virtually generate a wide variety of actions, from training scenarios to reflecting how employees and vendors collaborate. The resulting data can be used to predict downtime and optimize overall performance. Other use cases include the following:

1. Scenario testing

This is a key component of digital twin technology across a wide range of industries. Digital twins create circumstances that mimic a fully operational contact center, such as a flood of customer queries, cyberattacks, smart offices and device failures.

2. Predictive maintenance

Digital twin technology can emulate AI and ML-based predictive maintenance strategies used to extend the lifespan of UC devices and reduce downtime. In addition, digital twinning can monitor call quality, network bandwidth, speed of data exchange, storage and various performance metrics to detect early signs of hardware failure or information loss. Alerts can notify UC engineers about the potential failure of VoIP phones, routers, switches, endpoints, conferencing systems, servers and even UC software.

3. Workflow automation

Digital twinning lets companies simulate on-premises or remote enterprise-level UC workflows before they are deployed. These simulations can include internal communications, chats with vendors or textual/vocal interaction between AI bots and customers.

Digital twins also replicate critical UC functions like multi-codec transcoding, auto-routing, call handling, voicemail, scheduling, collaborating CRM tools and call completion. To test UC infrastructure in remote set-ups, digital twins enhance smart collaboration through transcoding, communication management, meeting summarization, automated IT support and troubleshooting.

4. Contact centers

Enterprises can use digital twin technology prior to launching a new contact center or integrating capabilities with an existing deployment. The technology enables enterprises to replicate and verify how well incoming calls are handled and how quickly they are routed to the proper department. The digital twin model relies on AI and ML algorithms or other UC software to dictate how calls are distributed. Based on priority queues and internal data, the digital twin model escalates the incoming call to AI-powered virtual assistants.

Digital twinning can even mimic the behavior of a frustrated customer. It records and analyzes AI assistant responses through data analytics. Such data helps UC managers reprogram AI assistants to generate better responses in real-time contact centers.

5. Network optimization

Digital twin models simulate network performance issues, such as traffic loads, long waiting queues, voice quality, latency and disturbances. These scenarios let administrators more accurately troubleshoot network resource allocation, management, planning and scaling.

6. Third-party integration

UC interoperability is an ongoing challenge. Historically, single-vendor packages have performed well for enterprises. Digital twins virtually create a configuration where UC managers can test the functionality and performance of different tools. As a result, enterprises can test and optimize multivendor UC infrastructures before they are deployed.

7. IoT connectivity

Integrated UC and IoT deployments are gaining traction in an effort to gain real-time insights, improve manufacturing processes, optimize resources and boost productivity. API-based middleware integrates IoT devices and UC hardware to build modern smart offices. With digital twinning, organizations can build virtual models combining IoT infrastructure with UC workflows to enable communication between employees and IoT-enabled devices.

8. On-site security

Digital twins can boost on-premises security in enterprises, modeling IoT-driven networks equipped with smart sensors, cameras and biometrics that prohibit unauthorized persons from entering office facilities.

9. Cybersecurity

Digital twins simulate various UC-oriented cyberattacks, such as VoIP-specific threats like phishing and vishing, PBX hacking, caller ID spoofing, Zoombombing, DDoS, Session Initiation Protocol DoS, man-in-the-middle attacks, snooping and credential theft for unauthorized log-in attempts. Armed with this information, enterprises can deploy appropriate firewalls and cipher key defenses.

10. Quantum networking

Quantum networking and UC remain hypothetical, but digital twins can virtually mimic quantum networking protocols and applications. As such, models performing quantum key distribution, entanglement, superposition and the no-cloning theorem let organizations pit Alice and Bob against eavesdroppers in a UC infrastructure.

Challenges of digital twin technology for UC

Despite the benefits digital twins can offer to UC networking architects, implementation remains challenging. The challenges of real-world implementation include the following:

1. Lack of standardization

Unlike IoT and manufacturing, there are no standards defining how digital twins can be integrated within UC infrastructure. UC systems, especially legacy ones, operate on incompatible protocols, making integration difficult.

2. Time constraints

UC and digital twin technologies are heterogeneous. UC data is typically massive; as a result, unified digital twin set-up and deployment processes are time-consuming.

3. Cybersecurity concerns

Digital twins mirror an organization's actual UC infrastructure and use sensitive enterprise data for simulations. Digital twins mirror the organization's actual UC infrastructure. As a result, digital twin hijacking, integration vulnerabilities and data breaches can arise during the implementation.

4. Costs and scalability

Digital twins require hefty investments in software, hardware and technical expertise in both ML and UC engineering -- a rare combination. In addition, when considering how digital twins work with UC, they need constant maintenance for better results. Finally, digital twins consume time and money, limiting enterprise-level scalability. They cannot quickly adapt to new updates or increased data demands fueled by an expanding UC infrastructure.

Venus Kohli is an engineer turned technical content writer, having completed a degree in electronics and telecommunication at Mumbai University in 2019. Kohli writes for various tech and media companies on topics related to semiconductors, electronics, networking, programming, quantum physics and more.