Active Optical Networks vs. Passive Optical Networks for Next-Gen ITS

Mainstream network models for ITS have traditionally utilized switch/router Ethernet technologies to manage communications between the core and the edge. “Active Optical Networks (AON)”, as the naming indicates, deploy “active” network management devices within the system architecture to facilitate network management capabilities.

Recent years have seen technologies typically utilized by communications providers, proposed for use in support of next-gen ITS. One of those architectures utilizes multiplexing and “passive” fiber optic splitters to maximize the use of fiber optic infrastructure. Passive Optical Networks (PON), deploy the same amount of equipment, in addition to “passive” optical splitters in order to facilitate multiplexing of data at the head-end and distribution of data in the field.

The following provides a comparison of the advantages and disadvantages of “actively switched” and “passively split” fiber optic communications technologies.

ACTIVE OPTICAL NETWORK (AON)

AON’s utilize intelligent switching and routing to direct and manage data flow within a network.  The emergence of the Ethernet architecture, protocols and topologies in the transportation and the Intelligent Transportation systems (ITS) arena occurred around 1999, and is rooted in traditional, switched Ethernet networks that date back over 35 years.

Advantages

• Active Optical Networks have been successfully utilized for Transportation and ITS networks for more than 10 years, and currently represent the industry standard.
• AON’s provide a framework and architecture that enables physical route redundancies, which provides protection against fiber cuts and enables optimized data management strategies.
• Adding new devices to AON networks is simple “plug and play”, with little-to-no network reconfiguration required.
• AON’s provide the ability to deploy equipment and protocols that are standardized within the telecommunications industry.
• AON hardware is ubiquitous, with plenty of hardware vendors available, thus minimizing specific hardware/software/vendor dependencies.
• An AON ring architecture allows for simplified network reconfiguration, implementation of additional “head-ends”, or the relocation of head-end by simply connecting to the nearest point on the nearest ring.
• AON’s are fully bi-directional with equal upstream and downstream transmission rates.

Disadvantages

• Edge switches cost more than PON cabinet devices Optical Network Terminals (ONTs).
• Fully redundant core switch with optics cost more than the fully redundant PON’s OLT’s.
• Requires the installation of additional conduit and cable runs to achieve physical redundancies.

PASSIVE OPTICAL NETWORK (PON)

PON’s are a relatively new technology that has emerged from the telecoms and cable industry, most notably as a result of the surging demand for consumer-driven, high-bandwidth applications for the residential market.  Due to the required data payloads, including voice, video and data, the communications providers realized a need for maximizing the use of each and every single fiber deployed in their network.  Gigabit PON or GPON is an asymmetrical, “shared” bandwidth configuration with a maximum downstream bandwidth of 2.4Gbps and an upstream total bandwidth of 1.2Gbps per fiber.

Advantages

• Edge devices (cabinet transceivers or ONTs) are less expensive than typical Ethernet edge switches.
• PON’s utilize multiplexing (mux) technologies that enable multiple channels (typically 32 or 64) to utilize a single fiber.
• Due to the multiplexing architecture, the PON allows for the mux of 32 individual channels per single fiber, enabling communications to 32 different devices over the single mux’d fiber.

Disadvantages

• Due to the Point-to-Point architecture of PONs, the technology is unable to feasibly implement physical network redundancies for communications routes.
• If a splitter goes offline(splitter failure or cabinet hit), or a break in a cable occurs between the splitter and the head-end OLT (TMC), then all devices downstream from the break and the splitter will go offline until repairs can be rendered. Splitters represent a significant Single Point of Failure (SPOF) – which network design typically tries to minimize.
• Due to the multiplexing nature of the data transmission, faults are difficult to identify/isolate/remedy.
• The nature of PONs requires that fiber be installed in a manner that does not provide as much flexibility for future network configurations. All fiber backbone radiates from a single point,
• The PON architecture does not allow for the implementation of typical Ethernet network management features/protocols associated with layer 3 (OSI) networking protocols.
• Passive Optical LAN architecture is not recognized by any of the North American or international standards bodies as being a standards-based architecture for the enterprise environment.
• PON’s asymmetrical framework (designed for delivering video/voice to the home) results in an architecture that provides more downstream bandwidth (2.4Gbps) than upstream bandwidth (1.2Gbps) – generally opposite of the needs related to a typical ITS installation, and thus an inefficient use of the technology. The potential downstream bandwidth (2.4 Gbps) will probably never be realized, nor required, where the upstream limitation of 1.2Gbps could potentially yield a constraint in some future installations.
• Because a defined group of ONT’s share an upstream path, there is a potential for the collision of data. To correct for this, the OLT allocates specific time slots for each ONT to transmit Its data back to the OLT.  This works well with Telecommunications/cable company networks where a majority of the data payload demand is downstream.  However, in an ITS environment, the opposite is true as the upstream demand is exponentially greater and more critical.
• No Power Over Ethernet (PoE) capabilities via ONT.

Sources and Resources

Passive Optical Networks

http://en.m.wikipedia.org/wiki/Passive_optical_network

 

Building Next-Gen ITS for “Big Data” Value

A lot has been written about “big data” lately.  The rapid growth of varying data sources coupled with the enhanced density in data sources is establishing  a huge resource for transportation operators.  The rapid proliferation of data sources from new devices such as smartphones and other newly connected devices, in conjunction with the advancement of technologies for data collection and management have manifested a sizeable inflection point in the availability of data.  So what does this mean for ITS operators and the systems they currently manage?  What will be required to extract and leverage values associated with “big data”?

At First Glance

Federal regulations for performance measures and real-time monitoring associated with MAP-21 and 23 CFR 511 have implemented a framework for the increased need of new, refined data and information systems.  System enhancements will require improvements to existing networks and communications systems in order to optimize data and metadata flows between data sources and central applications. Robust central network equipment, including L3 switches, servers and storage will also be required.  Enhanced security measures  associated with new data sources and big data values will also need to be reviewed and attended to.  New central data warehouse infrastructure will also be required, including new database applications (such as Hadoop), that are capable of managing “big data” and the “Internet of Things” (IoT).

Deeper Dive

A closer look reveals additional layers of change required in order to begin abstracting value from the new data sources.  “Big data” will also require somewhat less obvious changes in the way transportation agencies currently do business.

Increased Data Management and Analytics Expertise –  The new data paradigm will require new staff skills, most notably, experience in data analytics (Quants).  Staff skills will not only require knowledge of the data available now or potentially available in the near term, but also understand transportation systems in order to apply the most beneficial data mining tactics available.  The new role must not only be aware of current data and information needs and values, but also be cognizant of what is capable, and potential hidden values currently unrealized or unknown by an operating agency.  The new role will also be an integral part of the development of embedded system features and be able to identify nuances in data meaning, as well as establish effective predictive analytics.

Policy and Digital Governance –  New data sources are also giving rise to discussion regarding privacy and liability.  Data sourced from private entities will always contend with privacy fears and concerns, at least for the near term, although recent analysis is showing a steady lessoning of those fears as “digital natives” begin to represent a greater percentage of the traveling public.  Data generated from sources outside of transportation agencies, but utilized by transportation agencies  for systems operations, can lead one to question who is responsible should data errors occur that might affect a system.

Networks and Communications – Data sources, formats and general data management practices will need extensive review of existing conditions. What values are attained from real-time, or near real-time collection from subsequent analytics, as well as determining what data is less time dependent.  Existing formats and protocols should also be included in the mapping exercise. For example, CV will require a mandatory upgrade of IP protocols from IPv4 to IPv6.  General planning regarding the utilization of “the cloud” need to be weighed for benefit-cost.  Third-party data brokers and other outsourcing alternatives such as cloud computing need to also be assessed.

Data Management and Analysis Tools – Operating entities also need to look at implementing data management tools (applications) that will assist in extracting value from large data sets.  These tools  should be integrated with core systems, and provide real-time metrics of collected data.  The tools should also provide the ability for “Cloud collaboration”, in order to process data stored by third parties, or general data stored in the cloud.

What to do

Transportation budgets are as tight as ever. How can operating agencies begin to make incremental steps towards the goal of realizing benefits associated with “big data”?  The first step is to begin now.  Start by mapping existing data sources to existing data management technologies, policies and processes, from end to end.  Also, widen your perspective and begin to look at possible benefits from a wide array of new data sources.  In addition, “open” it up, and benefit from the wisdom of the crowd.  New analytics skill sets should be considered a condition of certain new hires in the transportation and ITS planning departments.  A staff member should be designated for leading the way with decisions regarding “big data”, relationships with third party data brokers, cloud management, as well as be responsible for implementing an agile framework for next-gen data systems.

References and Resources
Developing a Data Management Program for Next-Gen ITS: A Primer for Mobility Managers
Big Data and Transport
TransDec: Big Data for Transportation
Update from the Data Liberation Front                                                                                           

Next-Gen ITS and Systems Life-Cycle Management

The ITS planner, designer and operator should always be cognizant of the life-cycle of the overall system and its integrated subsystems and components. Timing of next-gen ITS integration can be optimized, both fiscally as well as technically, by considering the wide spectrum of variables associated with life-cycle management.  The following graphic presents a general overview of a typical systems life-cycle:

Typical life-cycle management should also include evaluation of the maturity of next-gen ITS technologies and the systems required to support a new ITS solution.