Source-linked AI summary
Sensing as a Service Model for Smart Cities Supported by Internet of Things
Charith Perera, Arkady Zaslavsky, Peter Christen, Dimitrios Georgakopoulos
TL;DR
Rapid urbanization is increasing pressure on city management, while IoT and Smart City initiatives seek ICT-based resource solutions. The paper explores sensing as a service across technological, economic, and social perspectives, concluding that it can support efficient resource use while leaving substantial challenges to address.
Problem
The paper addresses how sensing as a service can fit the IoT paradigm and support Smart City resource management amid growing urban pressures.
Method
The paper provides an overview of the sensing as a service model and examines it through technological, economic, and social perspectives.
Results
The model is presented as sustainable, scalable, and powerful, enabling efficient resource use, shared sensor data, and applications in areas such as waste management and scientific research.
Takeaways & Limitations
Sensing as a service can create value for participating parties and support efficient and effective decision-making in IoT-based environments.
Takeaways & Limitations
Realizing the model requires unresolved technological, economic, and social challenges, including trust, security, privacy, and social acceptance.
Abstract
from arXiv · showhide
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.
1. INTRODUCTION
IoT and Smart Cities emerged from different origins but are converging around ICT-enabled urban management. The paper situates sensing as a service within this convergence and identifies technological, economic, and social challenges.
- IoT is primarily driven by technological advances, whereas Smart Cities originated to address problems in modern urban life.
- Smart City challenges include waste, traffic, energy, water, education, unemployment, health, and crime management.
- Smart Cities use ICT to pursue efficient and effective responses to urban challenges across smart economy, people, governance, mobility, environment, and living.
- The paper presents sensing as a service as a model connected to the converging Smart City and IoT agendas.
- The paper organizes its discussion around the model, scenarios, benefits, and open technological, economic, and social challenges.
2. THE TRENDS: EVERYTHING AS A SERVICE
Everything as a service extends cloud computing’s resource-sharing model to services consumed over the Internet. Its appeal comes from flexible, usage-based access and benefits such as scalability and reduced maintenance.
- Everything as a service is a cloud-computing category in which resources are offered as services to geographically distributed consumers.
- Cloud computing’s major service models are infrastructure-as-a-service, platform-as-a-service, and software-as-a-service.
- The pay-as-you-go model charges consumers only for the resources they use instead of requiring predefined resource purchases.
- Cloud service models provide business agility, scalability, elasticity, reliability, green initiatives, and reduced maintenance work.
- Smart City initiatives apply ICT investments to urban problems, alongside the growth of cloud computing and everything-as-a-service models.
3. SENSING AS A SERVICE MODEL
The sensing as a service model uses IoT infrastructure to expose sensor resources through layered interactions among owners, publishers, extended service providers, and consumers. Ownership, permissions, policies, and value-added services shape access to sensor data.
- The model consists of four conceptual layers: sensors and owners, sensor publishers, extended service providers, and sensor data consumers.
- Sensors and Sensor Owners Layer: Sensors measure physical phenomena and can send data to the cloud, while ownership may change over time.
- Sensors and Sensor Owners Layer: Sensors are classified by ownership into personal and household, private organizations and places, public organizations and places, and commercial data providers.
- Sensor Publishers Layer: Sensor publishers detect available sensors, obtain owner permission, publish sensor information, and broker offers between owners and consumers.
- Extended Service Providers Layer: Extended service providers add intelligence by selecting appropriate sensors for consumers’ high-level requirements and providing value-added services.
- Sensor Data Consumers Layer: Consumers register with a valid digital certificate and express data interests through constraints without directly communicating with sensors or owners.
4. THE FUTURE: A SCENARIO
A smart-home scenario illustrates sensing as a service through a refrigerator, its owner, a sensor publisher, data-consuming companies, and an extended service provider. The interactions combine permission, offers, transactions, and data analysis.
- The smart-home scenario uses a connected refrigerator to illustrate interactions among the model’s parties.
- The refrigerator detects household Wi-Fi, reports its available sensors to a publisher, and prompts the owner about publishing them.
- DairyIceCream offers either a 3% product discount or a monthly fee of $2 for access to refrigerator sensors.
- The owner accepts the discount, while EasySensing matches the owner’s expectations with consumer requirements and enables the transaction.
- ProductiveAnalytics acts as an extended service provider for GoldenCheese by handling transactions and data analysis through partner publishers.
5. SENSING AS A SERVICE IN ACTION
The sensing as a service model is illustrated across waste management, smart agriculture, and environmental management through shared sensor infrastructure, data, and processing. These scenarios show how sharing can reduce costs, support research across domains and borders, and enable reuse of existing sensors.
- Three use cases—waste management, smart agriculture, and environmental management—demonstrate distinct aspects of the sensing as a service model.The scenarios share common characteristics while also presenting domain-specific features.
- Waste management: In waste management, multiple interested groups share sensor infrastructure, collectively bear costs, and retrieve real-time data for their own objectives.The model supports sensors deployed in locations such as garbage cans and trucks, with direct or indirect communication to the cloud.
- Waste management: Shared waste-management data supports optimized garbage collection, recycling-process planning, and health-and-safety monitoring, creating a synergy effect and supporting IoT infrastructure sustainability.A city council can use the data to save garbage-truck fuel costs, while other groups optimize processing and monitoring.
- Smart agriculture: In smart agriculture, sensing as a service supports more efficient scientific research by enabling sensor networks and shared data to generate opportunities across research domains.The Phenonet project uses sensor nodes to monitor plant growth and performance in experimental crops.
- Smart agriculture: Researchers can share sensor resources across borders to study pest control, soil conditions, and phenomena unavailable in their own countries.This extends agricultural research beyond the resources and phenomena accessible to individual researchers.
- Environmental management: Environmental management can reuse sensors deployed for other purposes, allowing organizations to acquire relevant data without deploying sensors themselves.The model also supports different processing workflows, including prediction, visualization, and simulation, over shared data.
6. ADVANTAGES AND BENEFITS
The sensing as a service model provides shared, cloud-based access to sensor data, enabling broader sensing, lower acquisition costs, real-time decision support, innovation, and privacy control across Smart City applications.
- Cloud-based sensing as a service provides scalable resources, and consumers pay only for the sensor data they use.Sharing, participatory sensing, and reuse reduce data-acquisition costs.
- Participatory sensing distributes workload among participants, enabling rapid sensor deployment across wider geographical areas and diverse phenomena.
- Sharing lets multiple parties access already-deployed sensors by paying the sensor owner instead of independently deploying equipment.The model also provides incentives for users to adopt IoT-enabled devices and helps offset their additional sensing and communication costs.
- Shared and collaborative deployment reduces data-acquisition costs, stimulates further sensor deployment, and can make previously impossible data collection feasible.
- Sensor-generated data can reduce survey latency and inaccuracies while lowering acquisition costs, although collected data should be anonymized because of privacy concerns.
- Accessible sensor data supports Smart City applications, including energy-grid management through analysis and prediction of consumption patterns, trends, and needs.
- Real-time data from multiple domains facilitates decisions, while archived data supports longer-term policy choices such as evaluating tram-service investment.
- The model can create reciprocal benefits: sensor owners receive value, while businesses obtain real-time consumer-behaviour data and may avoid manual surveys and market analyses.
7. OPEN CHALLENGES
The paper identifies open sensing-as-a-service challenges across technological, economical, and social categories, with each challenge defining a direction for future research.
- Open challenges and issues are grouped into technological, economical, and social categories, with some challenges spanning multiple categories.The identified challenges are presented as research directions for future work in sensing as a service.
8. CONCLUSIONS
The paper presents sensing as a service as a comprehensive model for Smart Cities in the IoT paradigm, emphasizing efficient resource use and benefits for participating parties. It also identifies technological, economical, and social challenges that must be addressed, especially trust, security, privacy, and usability.
- Sensing as a service is examined for Smart Cities in the Internet of Things paradigm from technological, economical, and social perspectives.
- The model can use limited resources efficiently to accommodate large numbers of consumers and create benefits for all participating parties.
- Trust and social acceptance are vital to adoption, requiring long-term change management, greater awareness, and privacy protection and security protocols.
- Security and privacy must be implemented across technology, government and business policies, and legal terms and conditions.
- Usability, accessibility, safety, and legal terms remain among the open issues for realizing the sensing as a service vision.