The Rise of Private Mobile Networks and Edge Computing
The integration of Private Mobile Networks (PMN) with edge intelligence is expected to play an instrumental role in realizing the next generation of industry applications. This combination, collectively termed Intelligent Private Networks (IPN), deployed within the scope of specific industries such as transport systems, can unlock several use cases and critical applications that, in turn, can address rising business demands.
As connectivity is now found in almost all facets of modern lives, the number of connected devices and subsequent processes have risen immensely. The advantages this connectivity brings into our lives are not only limited to convenience and globalization but also provide the basis for use cases and applications that substantially contribute towards the socioeconomic welfare of our society.
Due to the rise in capacity, security, and critical communication needs, major industries are now seeking to establish their own Private Mobile Networks (PMN). These PMN are private, wide-area, multiple-access wireless networks that are highly scalable, provide an adequate range, and consistent Quality of Service (QoS) in comparison to license-exempt solutions such as Wi-Fi.
The privatization of mobile networks was made possible with the advent of Long-Term Evolution (LTE) due to its simplicity and an all-IP nature. Such networks consist of an Evolved Packet Core (EPC) usually integrated with IP Multimedia System (IPMS) for handling voice calls and a typical Radio Access Network (RAN). Another key enabler for PMN is the virtualization of computing and network resources, where an entire core network and parts of the access network can be hosted in the cloud, while the cognitive radio at the edge of the network can make efficient use of unused spectrum to provide seamless end-to-end connectivity.
Improvements in the LTE technologies in terms of added intelligence and improved capacity, both in the core and access networks, have paved the way for 5G mobile networks. We perceive that these improvements, along with application-centric intelligence, can further shape PMN for individual industries.
Challenges in Next-Generation Railway Systems (NGRS)
Current railway systems are facing many challenges due to the deficiency of advanced 5G functions and capabilities to support various use cases, services, and scenarios. The 5G networking model use cases that support railway systems architecture are amazing speed, great service in a crowd, ubiquitous device communications, super real-time and reliable connections, and the best user experience. All of these use cases are challenges to railway networks, which, in turn, become a necessity due to high-rise data rates and capacity needs.
In this article, we present IPN for the Next-Generation Railway System (NGRS) and outline enabling technologies and frameworks that will be the key enablers in the scope of intelligent mobility.
Public Safety and Security
Public panic situations in the current underground train network can become severely adverse if prompt monitoring and response are not provided. Safety and security measures are highly essential for diffusing such situations in the interest of public safety. There are also concerns about crime and disorder that act as a barrier to passengers’ travel. An evolving focus on such issues is critical to reduce crime and improve passengers’ confidence.
Passenger Congestion and Traffic Management
Congestion due to traffic rise causes impatience, anger, and frustration within passengers, which, in turn, influence their travel behavior, causing the journeys to become slow, unpredictable, and stagnant. For transport businesses, any form of disruption or congestion in their services directly or indirectly affects their Capital Expenditure (CAPEX) and Operational Expenditure (OPEX) while causing harm not only to the business operations but also to the reputation of the company.
Passenger Mobility and Energy Efficiency
Identification of passenger movement in underground train networks is also a persisting challenge that contributes to high energy consumption and subsequent failure of effective optimization measures for mobile networks.
Reactive vs. Proactive Operations
Common transport systems are designed based on the principle of reaction for any action. While this has proven to work in many cases, with the current increase in quick response requirements, other modes of reactive operations are now being sought. There is a need to address these issues with proactiveness that can administer the dynamics of the transport, specifically in an underground train network where the conditions are rapidly changing.
Spectrum Scarcity and Mission-Critical Applications
Real-time, mission-critical applications and quick emergency response operations require the satisfaction of specific latency criteria without considering existing operational technology for train networks. However, the current wideband radio spectrum shared among various services pose challenges and obstacles in effectively serving the critical transport entity operations.
Cost and Sustainability
Existing technologies within underground train networks are extremely beneficial; however, they bring in deployment and sustainability-related costs. Several strategies and techniques have been developed and deployed for the planning and costing of densely populated networks and energy-efficient systems, but they fail to address many challenges.
Weather Resiliency
The impact of weather on the transport network is another main challenge that requires daily/hourly weather forecasting and understanding of the anticipated impact of change in the climate. The transport system must be resilient to extreme weather conditions and must have the capability of self-healing against climate adversities.
Unlocking the Next Generation of IoT with Intelligent Private Networks
To address the challenges faced by NGRS, the integration of Private Mobile Networks (PMN) and edge intelligence can play a crucial role. The Intelligent Private Network (IPN) architecture, which combines these two elements, can provide a range of innovative applications and use cases for the next-generation of railway systems.
User-Centric Intelligence
The Quality of Service (QoS) of any network is a key metric in evaluating its performance, as it correlates to the user’s level of satisfaction. In a similar pursuit, added intelligence in the network makes them more user-centric by including the user’s experience in the feedback loop. Beyond 5G (B5G) networks will be able to assess the users’ demands by running various distributed federated learning algorithms at the edge of the network. With the inference capability derived from federated learning, the edge nodes performing user sentiment analysis can subsequently inform the network of required changes.
Network-Centric Intelligence
The efficient utilization of network resources can make the network more sustainable in terms of lower Operational and Capital Expenditure (OPEX/CAPEX). With that in mind, self-organizing and self-optimizing algorithms deployed at the network edge can enable timely inference and quick decisions for effective radio resource management, user mobility management, network orchestration, and service provisioning.
Application-Centric Intelligence
In B5G networks, several applications are expected to be hosted at the edge of the network. This will require virtualization of application containers and related computational resources at the base station. Since applications have varying demands, the application intelligence employed, along with the deployment strategy, will vary from case to case. For example, the AI algorithms used in train automation will have a different deployment strategy compared to AI campaigns that elevate passenger travel experience.
Intelligent Private Network (IPN) Architecture
The presented IPN architecture aims to integrate edge intelligence and Private Mobile Networks (PMN) to address the challenges faced by NGRS. The IPN controls the functions of all connected links and devices by preprocessing through software-defined tools. The training of edge devices is a lightweight task that mainly relies on the edge servers performed by IPN in the initial setup. Once edge devices are trained, they take their intelligent decisions locally without IPN consultation on every step, which reduces the overhead time and cost.
The IPN also allows direct connectivity to local cloud resources and offloads major tasks to an on-premise server. The on-premise server then takes care of synchronization with the centralized cloud for reporting purposes. Under edge intelligence, edge caching is a simplistic method to effectively improve the performance of the IPN, where it administers the incoming distributed data coming toward the edge devices from end-users and its surrounding environment.
Furthermore, the IPN utilizes federated learning to train the AI models, which are then deployed on the edge devices. This approach ensures that the communication link to the IPN would only be used for backup, restore operation, or reporting purposes, as the edge devices take their own decisions locally.
IPN-Driven Use Cases for Next-Generation Railway Systems
The IPN architecture, with its integration of edge intelligence and PMN, can enable a wide range of use cases to address the challenges faced by NGRS. Here are some of the key use cases:
IT2T Communication and Intelligent Train Signaling
The wireless industry is developing processes and methods that will achieve sustainable solutions to support Infrastructure-to-Train (IT2T) communications. IT2T communications will be able to offer low-latency intelligent train networks, autonomous trains, fast adaptive management of congestion control, onboard Internet and infotainment services, broadcasting of real-time fault incidents, real-time fault detection and recovery, and teleoperated trains ensuring fast and safe control against hazards.
Contact-Tracing and Passenger Distancing
The novel Coronavirus 2019 (COVID-19) pandemic proved its viral infectious nature and consequently forced the entire world into a lockdown. For transport operators, such an outbreak placed an enormous challenge in continuing their services in a safe manner. An Intelligent Contact-Tracing technique, potentially hosted at the edge of the network integrated within the transport infrastructure, will become an essential need for transport operators to avoid further spread of such viral outbreaks.
Intelligent Drone Systems (IDS)
Encouraged by the promising 5G capabilities, we envisage surveying information capturing and live surveillance streaming through Intelligent Drone Systems (IDS). The functional application of the IDS will be a key enabler to address overcrowding situations in places such as train terminals. The other two functions for IDS can be the provisioning of communication services through network surveillance using low-bandwidth control signals and/or streaming live camera surveillance using high-bandwidth data signals.
Transport Hub Crowd Management (THCM)
Predicting and managing major transport hubs’ capacity in both business-as-usual (BAU) and exceptional circumstances, such as rush hour travel disruption and planned events, by using Machine Learning (ML) is one of the challenging aspects for Transport Hub Crowd Management (THCM). Sensors in the hubs would use a triangulation method to detect the location of passengers that would be echoed from their 5G handsets, called echolocation. Digital tools such as Real-Time Location Systems (RTLS) or Intelligent Positioning Systems (IPS) hosted at the network edge will be important for precise echolocation and gathering of real-time crowd information.
Intelligent Wireless Audio (IWA) and Mission-Critical Push-To-Talk (MCPTT)
Emergency services in the underground environment use wireless audio links through existing technologies that offer highly specialized RF communication solutions. An innovative edge intelligence agent employed within the IPN can address such needs with the use of Ultra-Reliable Low-Latency Communications (URLLC). Mission-Critical Push-To-Talk (MCPTT) empowered with Enhanced Mobile Broadband (eMBB) can be an essential focus in ITS when considering the underground train environment.
Intelligent Depot (ID) and Industry 4.0
AI-based industrial automation powered by IPN is making a tremendous revolution toward B5G technologies. In the field of supply chain, this transposes toward the edge intelligence of Logistics 4.0 that advances beyond process automation by using intelligent collaboration and interconnection between connected systems, providing transformative applications. Intelligent Depot (ID) in the train network is an important use case that will push communications to another level, where train depots will use automated coordination between objects and autonomous self-optimizing logistics.
Conclusion and Future Directions
The motivation of IPN comes from a combination of private mobile networks and capabilities of B5G networks in lieu of ITS and more specifically future railway systems. The idea of running several applications within a single network environment consolidates with IPNs scalability, security, flexibility, and coverage expansion. This will evolve the existing private mobile networks toward smart transport digital facilities with the use of Machine Learning (ML), Artificial Intelligence (AI), and cognitive analytics that is fully primed for existence at the edge, establishing ground toward B5G networks.
As we move toward a net-zero carbon footprint, research on energy-efficient Heterogeneous Networks (HetNets) will also be required to address the energy consumption of NGRS IPN. Other challenges that would need to be addressed in order to make IPN open up for general public use will be to enable neutral hosting, security measures through blockchain, and dynamic spectrum sharing among mobile network operators for adaptive capacity protocols.
The integration of edge intelligence and private mobile networks holds immense potential to unlock the next generation of IoT, particularly in the domain of intelligent transportation systems. By addressing the key challenges faced by NGRS, the IPN architecture can enable a wide range of innovative applications and use cases, paving the way for a more connected, efficient, and sustainable future in the railway industry.