2024 RTP round - Developing Economic Models for Transient Edge Services

Status: Closed

Applications open: 7/07/2023
Applications close: 25/08/2023

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About this scholarship
Description/Applicant information

Crowdsourcing is one of the key technologies to deploy massive and pervasive Internet of Things (IoT) services in a cost-effective way. Humans are equipped with portable IoT devices. Smart wearables (e.g., phones and watch), smart cars and drones are examples of crowdsourced Edge service (CES) providers and consumers. For example, Apple AirTag is wearable tracking device that provides location services using the crowdsourced mesh networks of iPhone. Similarly, the underutilised resources of collocated mobile devices could be transformed into the on-demand femto cloud services at the edge. Wireless energy, WiFi hotspots, Environmental sensing are few examples of CES.
Humans, smart wearables and portable IoT devices are mobile and usually change their locations frequently. The two major aspects of CES are (1) economies of scale and (2) temporal sensitivity. Hence, the service provision and consumption behaviour in the CES market is inherently different from other service-based marketplace e.g., web and cloud. The services in the CES market may be realised as a collection of short duration provider-consumer couplings which are triggered under specific conditions like completion of certain events, temporal activities, or geo-location trajectories of the provider and consumer. We define such short duration of provider-consumer couplings without service level agreement (SLA) as transient Edge services. Transient service provisioning aims to provision services to end users (or consumers) over highly volatile service provider networks. The critical factor in such an environment is the mobility of the end user which, in turn, induces the notion of transience. Another unique property of the CES market is that providers' and consumers' roles are reversible over a period of time unlike other service markets. For example, an Airtag may act as a consumer of wireless charging services from nearby devices and a provider of location services to nearby devices at the same time. Although a majority of the services in the CES market are transient, the consumer usually requires a long-term service. For example, an Airtag while traveling from source A to destination B in a lost bag may compose location services provided in nearby phones to update its location periodically.
Our objective is to compose transient services from a market perspective, i.e., to enable equivalence of service provision and consumption with respect to total number of providers and consumers. To the best of our knowledge, existing long-term crowdsourced IoT service composition approaches are mainly from consumers' perspective, i.e., selecting the optimal set of services that satisfies a single consumer's long-term functional and non-functional requirements. Similarly, there exists long-term cloud service composition approaches from the provider's perspective, i.e., selecting the optimal set of consumers that maximise the provider's economic objective of profit. These approaches are not applicable from the market perspective, i.e., to reach equivalence state. As the number of providers and consumers are finite in the CES market, they may compete with each other to maximise their individual goals. The goal of the egalitarian composition is to minimise the trade-off between individual objectives and market equivalence. For example, in the non-egalitarian market, if a provider's goal is to provide service for one hour, composition from the provider's perspective may choose to provide service to only one consumer (rejecting other five consumers). The egalitarian approach will provide services to six consumers (10 minutes each) which improves the ratio of service consumption with respect to total number of consumers as well as maximising individual provider's goal. 

Although the notion of transient services is relatively new, there exists a body of literature that talks about ad-hoc service orchestration solutions in mobile edge and fog computing environments. Traditional non-functional service composition techniques are hardly applicable to the composition of crowdsourced edge services due to the mobile edge computing (MEC)’s unique characteristics and requirements, e.g., long-term service provision without a long-term commitment from the providers, task offloading or migration between different providers, and mobility of services. Existing short-term and long-term composition approaches are not applicable in CES due to the unique properties of transient services: a) provider and consumers may change their roles during the composition, and b) lack of service level agreements (SLAs) as providers move anytime.
The economic models of cloud services are not related to crowdsourced edge services as these microservices advertise the short-term, cost-effective, and better QoS (e.g., faster response) alternate solutions to Pay-as-you-Go cloud solutions without major QoSs, e.g., trust, reliability, and availability. We use the following analogy: if cloud services are the ‘taxis’ in the computing road, crowdsourced edge services are the ‘car-pooling/Uber’. Hence, a novel unified economic model is required to address the service composition based on long-term economic models of both consumers and providers in CES. The consumers’ economic model will help consumers to select crowdsourced Edge service providers with the purpose of cost saving and QoS satisfaction. On the other hand, the Edge service providers’ economic model will assist providers to select service requests with the aim of maximising their long-term economic benefit (e.g., maximising revenue or egalitarian, i.e., maximising serving the number of consumers). Such a unified model will characterise, model, and capture the inherent consumers’ and providers’ long-term goals and QoS requirements using a variety of techniques such as time-based, multi-dimensional statistical and graph theoretic techniques. Specific aims of the projects are 1) defining and specifying a unified economic model for CES consumers and providers and 2) Devising an Egalitarian composition framework and optimisation techniques. 

The key research questions that this project is going to explore are summarised as follows:
1) How do we formally represent the crowdsource edge services and requests that will create the foundation of composability models, and encapsulates the transient service behaviour?
2) What is the definition and specification of a unified economic model for CES consumers and providers?
3) How do we solve the egalitarian composition from the CES market’s perspective, i.e., i.e., providing continuous services to the maximum number of consumers, and minimising the consumers’ cost of service usages over a long-term period? 

This project offers several economic benefits, both for the participants in the crowd and for organisations leveraging the collected data. Here are some of the economic significances of economic models in crowdsourced edge and IoT services:
Cost Reduction: Crowdsourced edge IoT can significantly reduce costs for organisations compared to traditional data collection methods. Instead of investing in expensive infrastructure and devices, organisations can leverage existing edge devices owned by individuals or businesses within the crowd. This eliminates the need for upfront capital expenditure and ongoing maintenance costs associated with building and managing a dedicated IoT infrastructure.
Scalability: The crowd provides a scalable and flexible resource pool for data collection and processing. Organisations can tap into a vast network of distributed edge devices, enabling them to scale their data collection efforts as needed. This scalability allows for the collection of larger datasets, facilitating more accurate analytics, insights, and decision-making.
Geographic Coverage: By leveraging the crowd, organisations can achieve wider geographic coverage for their IoT data collection efforts. The distributed nature of edge devices means that data can be collected from various locations, including remote or hard-to-reach areas. This expanded coverage enhances the richness and diversity of the collected data, enabling organisations to gain insights from different regions and demographics.
Real-time Data Availability: Crowdsourced edge IoT facilitates real-time data availability, enabling organisations to access and analyse data as it is collected. This immediate access to data allows for faster decision-making, quicker response times, and the ability to identify trends or anomalies promptly. Real-time data availability is particularly valuable in time-sensitive applications such as environmental monitoring, logistics optimisation, and smart city initiatives.
Monetisation Opportunities: Organisations can incentivise participants within the crowd by offering rewards, financial compensation, or other benefits in exchange for their data contributions. 
Innovation and Collaboration: Crowdsourced edge IoT fosters innovation and collaboration by involving a diverse community of contributors. This collective intelligence and varied perspectives can lead to development of new applications, services, and solutions. Organisations can tap into this crowd-driven innovation to address complex challenges, uncover new business opportunities, and drive economic growth. 

Student type

• Future Students

Faculty

• Faculty of Science & Engineering
  • Science courses
  • Engineering courses

Course type

• Higher Degree by Research

Citizenship

• Australian Citizen
• Australian Permanent Resident
• New Zealand Citizen
• Permanent Humanitarian Visa

Scholarship base

• Merit Based

Value

The annual scholarship package (stipend and tuition fees) is approx. $60,000 - $70,000 p.a.

Successful HDR applicants for admission will receive a 100% fee offset for up to 4 years, stipend scholarships at the 2023 RTP rate valued at $32,250 p.a. for up to a maximum of 3 years, with a possible 6-month completion scholarship. Applicants are determined via a competitive selection process and will be notified of the scholarship outcome in November 2023. 

For detailed information, visit: Research Training Program (RTP) Scholarships | Curtin University, Perth, Australia.

Scholarship Details
Maximum number awarded

1

Eligible courses

All applicable HDR courses

Eligibility criteria

We are looking for a self-motivated PhD candidate with excellent organisation, problem-solving and project management skills. Candidates with strong quantitative skills, including familiarity with programming and data analysis are desired for this project. Must be eligible to enrol in PhD programs at Curtin.

Application process

If this project excites you, and your research skills and experience are a good fit for this specific project, you should contact the Project Lead (listed below in the enquires section) via the Expression of Interest (EOI) form. ahead of the closing date. Please note you should apply as soon as possible, as once a suitable candidate has been identified this opportunity will no longer be available to receive an EOI.

Enrolment Requirements

Eligible to enrol in a Higher Degree by Research Course at Curtin University by March 2024.

Recipients must complete their milestone 1 within 6 months of enrolment and remain enrolled on a full-time basis for the duration of the scholarship.

Enquiries

To enquire about this project opportunity that includes a scholarship application, contact the Project lead, Sajib Mistry via the EOI form above.

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