Edge Intelligence for Smart Cities entails the merger of edge computing and artificial intelligence to promote the development of smart cities. By harnessing the capabilities of edge intelligence, smart cities can effectively handle, process, and analyze massive data influx in real-time, right at the edge of the network where data production occurs. This enables swifter decision-making for managing various city infrastructures such as transportation, utilities, surveillance, and public services.

Specific attributes of edge intelligence include:

• Immediate Analysis: Edge intelligence conducts instant data analysis right at the source of data production. This on-the-spot processing precludes the need for data to travel back to a distant cloud or central data center for analysis.

• Reduced Latency: By processing real-time data locally, edge intelligence drastically cuts down the latency that’s typically involved while sending data to the cloud and back.

• Improved Security: As data doesn’t have to traverse through several networks to reach a central repository, the potential risk of security breaches and data losses considerably reduce.

• Efficiency in Bandwidth: Sending sizable data volumes over the network becomes unnecessary which significantly boosts network efficiency and reduces bandwidth usage.

• Autonomous Functionality: Edge intelligence allows devices to operate and make smart decisions autonomously without reliance on centralized systems.

Edge intelligence in smart cities is leveraged for its immediate data processing, enhanced security, reduced latency, efficiency in bandwidth consumption, and autonomous functionality.

An effective strategy to implement edge intelligence in a smart city involves a thorough understanding of the city's needs and the selection of a suitable solution. Conducting a cost-benefit evaluation and assessing vendors to ascertain optimal alignment with the city's requirements is vital. Consideration of hardware capabilities and integration challenges along with a cautious monitoring and evaluation of implementation is necessary for successful edge intelligence deployment. By adopting this approach, edge intelligence can be a transformational tool to achieve the objectives of a smart city.

Edge intelligence provides a multitude of benefits to smart cities, including:

• Reduced Latency: As data processing happens locally on edge devices, latency drastically reduces. This is especially crucial in scenarios where split-second decisions are needed, such as in traffic management systems, emergency response units, and real-time surveillance.

• Improved Security and Privacy: Local processing of data mitigates the risk of data breaches during transit. It further ensures privacy as sensitive data about cities and its residents isn't sent to remote servers or clouds.

• Bandwidth Savings: By minimizing the data that needs to traverse the network, edge intelligence aids in preserving bandwidth which is a valuable commodity in densely populated urban areas.

• Cost-Effective: Edge intelligence eliminates the need for data transmission to central cloud installations, thereby contributing to cost savings in data storage and transmission.

• Scalability: In smart cities, the data influx is ever-increasing. Edge intelligence allows for scalable data management as devices increase their processing capabilities without necessarily impacting the central infrastructure.

• Real-Time Decision Making: By leveraging machine learning algorithms and artificial intelligence at the edge, municipalities can make real-time, informed decisions for the efficient management of city infrastructures.

Despite significant benefits, edge intelligence also encompasses a few drawbacks:

• Hardware Limitations: Edge devices might face technical limitations in terms of computation, storage, and battery life, while running complex AI models.

• Security Risks: Although edge intelligence can reduce data breaches during transit, edge devices themselves can become potential targets for cyber-attacks.

• Integration Challenges: Successful implementation of edge intelligence requires seamless integration with the existing infrastructure, which can be challenging and resource-consuming.

• Limited Customization: Edge intelligence solutions are designed to cater to a broad spectrum of applications, which may not always meet the specific needs of a city.