Sensing as a Service Model for Smart Cities Supported by Internet of Things (1307.8198v1)

Abstract: The world population is growing at a rapid pace. Towns and cities are accommodating half of the world's population thereby creating tremendous pressure on every aspect of urban living. Cities are known to have large concentration of resources and facilities. Such environments attract people from rural areas. However, unprecedented attraction has now become an overwhelming issue for city governance and politics. The enormous pressure towards efficient city management has triggered various Smart City initiatives by both government and private sector businesses to invest in ICT to find sustainable solutions to the growing issues. The Internet of Things (IoT) has also gained significant attention over the past decade. IoT envisions to connect billions of sensors to the Internet and expects to use them for efficient and effective resource management in Smart Cities. Today infrastructure, platforms, and software applications are offered as services using cloud technologies. In this paper, we explore the concept of sensing as a service and how it fits with the Internet of Things. Our objective is to investigate the concept of sensing as a service model in technological, economical, and social perspectives and identify the major open challenges and issues.

• The paper introduces a novel Sensing as a Service model that integrates IoT infrastructure across sensor owners, publishers, extended service providers, and data consumers.
• It details a layered architectural approach and real-world applications, including waste management and smart agriculture, to optimize urban operations.
• The model enhances cost efficiency and scalability through cloud computing and real-time data processing, offering transformative potential for smart city development.

Sensing as a Service Model for Smart Cities Supported by Internet of Things

The paper "Sensing as a Service Model for Smart Cities Supported by Internet of Things," authored by Charith Perera, Arkady Zaslavsky, Peter Christen, and Dimitrios Georgakopoulos, explores the confluence of Internet of Things (IoT) and Smart Cities (SC) to propose a novel paradigm: Sensing as a Service (SaaS). This model leverages IoT's envisioned infrastructure to address urban challenges through efficient resource management.

Overview of Technological Integration

IoT and SC, though originating from distinct backgrounds, share a vision of leveraging ICT to manage urban resources effectively. The paper underscores the prediction from the European Commission asserting that by 2020, IoT-enabled devices would range between 50 to 100 billion, emphasizing the scope for integrating sensing technologies into urban life. The key proposition here aligns IoT towards facilitating Smart Cities via a robust, cloud-backed SaaS framework.

Sensing as a Service Model

The paper meticulously outlines the conceptual architecture of SaaS in four layers:

1. Sensors and Sensor Owners: These include sensors embedded in personal items, public infrastructure, and commercially deployed sensors. Owners retain control, deciding on data publication and terms.
2. Sensor Publishers (SP): These entities manage sensor data registration, mediation, and distribution.
3. Extended Service Providers (ESP): They add value by processing data from multiple publishers, tailoring it to consumer needs.
4. Sensor Data Consumers: Consumers, such as businesses and governments, utilize processed data for various applications.

Figures within the paper illustrate these interactions and depict a real-world scenario involving smart home appliances to explicate the model's operational dynamics.

Practical Implications and Use Cases

The paper introduces several application domains within SC that benefit from SaaS, including:

• Waste Management: Leveraging sensor networks in urban areas to optimize waste collection and processing, illustrated by a hypothetical use case where sensor data aids in scheduling efficient garbage collection, thereby reducing municipal costs.
• Smart Agriculture: Highlighted by the Phenonet project where sensor networks in agricultural research fields provide critical data, demonstrating the potential for collaborative research across borders and domains.
• Environmental Management: Utilizing existing sensor deployments for multi-purpose applications, such as climate monitoring and urban planning.

Advantages and Commitments

The authors articulate a robust case for the advantages SaaS offers:

• Cost Efficiency: Reduces sensor deployment and data acquisition costs through shared infrastructure and economies of scale.
• Innovation Promotion: Empowers new startups and fosters novel uses for sensor data, spurring advancements in AI and machine learning applications.
• Scalability and Flexibility: Built on cloud computing foundations, SaaS is scalable and offers flexible pay-as-you-go models.
• Real-time Data Utilization: Facilitates critical real-time decision-making for urban management and strategic planning.

Challenges and Future Directions

Despite its benefits, the SaaS model faces significant challenges:

• Technological: Ensuring interoperability, energy efficiency, and robust middleware capable of handling billions of data streams.
• Economical: Lowering entry barriers for startups, ensuring long-term sustainability, and creating fair licensing and business practices.
• Social: Building trust and social acceptance, safeguarding privacy, and establishing legal frameworks to handle data security ethically.

Conclusion

In conclusion, the paper posits that the Sensing as a Service model holds transformative potential for Smart Cities, converging technological, economical, and social dimensions to offer a sustainable, scalable solution to urban management. As the IoT landscape evolves, addressing the outlined challenges will be crucial to realizing the full promise of this model, fostering enhanced collaboration, and yielding unprecedented opportunities for innovation across urban ecosystems.