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Upgrading to smart street lighting comes with many considerations, Nicola Brittain looks at a few of the most important.

A city’s ambience and security is hugely influenced by the street lighting in place. But there are financial considerations too - street lamps make up over 40% of a city’s budget and 15% of the world’s energy demand. Smart and future proof solutions are therefore essential if planners are to keep costs low while considering safety, wellbeing, investment and sustainability targets.

A smart street lamp is primarily composed of a lamp, a light controller, (LC) sensors, a communication module, and a power supply. It may also include charging ports, a Wi-Fi hotspot and other auxiliary devices.

The LC processes and transmits data from integrated IoT sensors, receives commands from a central control, manages the lamp’s brightness by regulating power supply, and monitors the luminaire’s condition and electrical parameters.

Smart street lighting uses low-cost LED bulbs which offer savings of around 70% compared with traditional discharge lamps and sensors. Making them part of an IoT system can reduce consumption by a further 30%, according to a recent whitepaper from smart city experts Schreder. The environmental case is irrefutable if smart street lighting is deployed properly, but designers and technologists will have some major decisions to make for this to be the case.

Scalability and redundancy

Scalability will be a key concern for technologists looking to future proof their smart city. As a representative from IoT specialist Silicon Labs explained the best way of ensuring this is by adopting an open-source standard such as the Wi-SUN FAN mesh networking solution configured around an open-source protocol.

The mesh structure means that each device in a network can speak to its neighbours, allowing messages to travel a long distance – hopping between each node in the network. When deployed with sufficient density, such networks will have multiple paths back from the end-device to the backhaul network.. If the usual route is blocked, the end-device should have plenty of other pathways available, thereby providing redundancy. Such a system gets stronger as the size and density of the network increases.

Open-source standards enable multi-vendor interoperability meaning cities are not locked into a single vendor when expanding.

Security

Another critical consideration for smart street lighting designers is security. Any device plugged into the municipal network can potentially expose the public infrastructure to hackers. Silicon Labs uses Secure Vault firmware meaning that their wireless system on a chip (SoC) solutions have a PSA Certified Level 3 certification. Wi-SUN FAN includes an integrated public key infrastructure to allow only authenticated nodes to join the network.

Sensors and remote monitoring

Smart street lights can be equipped with sensors and communication modules that allow remote monitoring and control. This means that their brightness and timing can be altered based on real-time data to reduce unnecessary energy usage. They can also be equipped with motion sensors to detect activity in the vicinity, automatically brightening when pedestrians or vehicles are nearby and dimming during quiet periods. This saves energy and enhances safety by ensuring well-lit areas for citizens.

Weather conditions

Web APIs in conjunction with installed sensors on street-light systems can gather real-time information across large geographic areas and be adjusted according to weather conditions like cloud cover or fog and alter light levels accordingly.

Other factors such as traffic densities and road changes or priorities can be added as plug ins too. As a part of a wider IoT ecosystem, smart street lights can connect with traffic lights or surveillance cameras to share data with planners and emergency services. They can also be used to create Wi-Fi hotspots or electric vehicle charging points leading to better resource allocation.

Managing smart lights

Models used to operate responsive smart street lights include artificial intelligence techniques such as decision trees, support vector machines, and deep learning models which “adeptly capture the non-linear characteristics of traffic patterns without needing huge amounts of high-quality data or significant computational resources for training” according to one expert.

Well planned city lighting can lead to increased investment, improved wellbeing and significant cost savings, but planners and designers will need to keep abreast of the rapidly changing technology landscape if they are to ensure their cities are future proofed for the benefit of all.

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Autor(en)/Author(s): Nicola Brittain

Quelle/Source: Engineer Live, 04.04.2024

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