Lab

Sensors, Clouds, and Services Laboratory

About the lab

The SCS Lab focuses on disciplinary research areas of Internet of Things (IoT), cloud, and service computing. The lab mission is to develop a world class creative environment that focuses on the design and development of innovative sensors, IoT, cloud technologies and applications, delivered as services.

Featured projects (6)

Project
Our objective is to crowdsource wireless energy from IoT devices to create a green IoT environment. Energy could be harvested using renewable energy sources, i.e., the kinetic movement of IoT users or their body heat. For example, a smart shoe may harvest energy from the physical activity of its user. The harvested energy could be used to charge the nearby IoT devices wirelessly. This research project aims to propose a service model for crowdsourced energy. The project also aims to propose a novel framework to compose crowdsourced energy services in a dynamic IoT environment.
Project
The objective is to develop a novel IoT service recommendation framework for multi-resident smart homes. Our motivation is to enhance the convenience and efficiency of the residents by recommending appropriate IoT services. However, different residents may have different preferences for using an IoT service in a home. For example, one resident may prefer the light to be “on” while watching TV, and another resident may prefer it to be “off” while watching TV. Hence, an IoT service conflict is raised. The project aims to build a novel conflict taxonomy that categorizes different types of conflicts. The taxonomy provides the baseline to design a conflict detection framework considering the functional and non-functional properties of IoT services. The project also aims to develop an adaptive service recommendation model which resolves all the conflicts and suggest available services to the residents to make their lives convenient and comfortable.
Project
Drones are a new type of IoT devices that offer cost-effective and fast delivery services. The intrinsic and extrinsic features of drones such as battery and payload capacities and highly uncertain dynamic service environment limit the potential use of drones for delivery purposes. We leverage the service paradigm to address the key challenges in delivery by drones. The functional and non-functional properties of drones are abstracted as Drone-as-a-Service (DaaS). Our objective is to propose a novel DaaS composition framework focusing on constraint-aware and situation-aware service selection and composition techniques for making practical drone deliveries. The outcome of this research is an efficient drone service selection and composition infrastructure to create new opportunities for interruptive, innovative, faster, and cheaper delivery solutions.
Project
Drone swarms are teams of autonomous unmanned aerial vehicles that act as a collective entity to perform a certain task. The objective is to compose Swarm-based Drone-as-a-Service (SDaaS) services for delivery purposes. The composed service will consist of a swarm of drones working together to deliver services from one point to another in a skyway network. We take into consideration the highly constrained environment surrounding the delivery. This includes intrinsic constraints, e.g. different rates of power consumption and window constrained arrival times, and extrinsic constraints, e.g. weather conditions. We consider several quality of service metrics to select the best SDaaS. The outcome of this research is an efficient infrastructure that shall create new opportunities in smart cities.
Project
Energy sharing service, also known as Energy-as-a-Service (EaaS), is defined as transferring wireless energy among IoT devices using the service paradigm. An energy provider is a thing that can share energy. An energy consumer is a thing that requires energy. Consumers and providers are owned by users. Energy may be harvested through wearables e.g. smart textile or smart shoes. The project focuses on the selection and composition of energy service requests from the provider’s perspective.

Featured research (68)

We introduce the concept of adaptive trust in crowdsourced IoT services. It is a customized fine-grained trust tailored for specific IoT consumers. Usage patterns of IoT consumers are exploited to provide an accurate trust value for service providers. A novel adaptive trust management framework is proposed to assess the dynamic trust of IoT services. The framework leverages a novel detection algorithm to obtain trust indicators that are likely to influence the trust level of a specific IoT service type. Detected trust indicators are then used to build service-to-indicator model to evaluate a service's trust at each indicator. Similarly, a usage-to-indicator model is built to obtain the importance of each trust indicator for a particular usage scenario. The per-indicator trust and the importance of each trust indicator are utilized to obtain an overall value of a given service for a specific consumer. We conduct a set of experiments on a real dataset to show the effectiveness of the proposed framework.
We propose a novel generic reputation bootstrapping framework for composite services. Multiple reputation-related indicators are considered in a layer-based framework to implicitly reflect the reputation of the component services. The importance of an indicator on the future performance of a component service is learned using a modified Random Forest algorithm. We propose a topology-aware Forest Deep Neural Network (fDNN) to find the correlations between the reputation of a composite service and reputation indicators of component services. The trained fDNN model predicts the reputation of a new composite service with the confidence value. Experimental results with real-world dataset prove the efficiency of the proposed approach.
Drone swarms are required for the simultaneous delivery of multiple packages. We demonstrate a multi-stop drone swarm-based delivery in a smart city. We leverage formation flying to conserve energy and increase the flight range of a drone swarm. An adaptive formation is presented in which a swarm adjusts to extrinsic constraints and changes the formation pattern in-flight. We utilize the existing building rooftops in a city and build a line-of-sight skyway network to safely operate the swarms. We use a heuristic-based A* algorithm to route a drone swarm in a skyway network.
Wireless energy sharing is a novel convenient alternative to charge IoT devices. In this demo paper, we present a peer-to-peer wireless energy sharing platform. The platform enables users to exchange energy wirelessly with nearby IoT devices. The energy sharing platform allows IoT users to send and receive energy wirelessly. The platform consists of (i) a mobile application that monitors and synchronizes the energy transfer among two IoT devices and (ii) and a backend to register energy providers and consumers and store their energy transfer transactions. The eveloped framework allows the collection of a real wireless energy sharing dataset. A set of preliminary experiments has been conducted on the collected dataset to analyze and demonstrate the behavior of the current wireless energy sharing technology.
We propose a novel conflict resolution framework for IoT services in multi-resident smart homes. The proposed framework employs a preference extraction model based on a temporal proximity strategy. We design a preference aggregation model using a matrix factorization-based approach (i.e., singular value decomposition). The concepts of current resident item matrix and ideal resident item matrix are introduced as key criteria to cater to the conflict resolution framework. Finally, a set of experiments on real-world datasets are conducted to show the effectiveness of the proposed approach.

Lab head

Athman Bouguettaya
Department
  • School of Computer Science
About Athman Bouguettaya
  • Athman Bouguettaya currently works at the School of omputer Science, University of Sydney. Athman does research in service computing applying his research to cloud computing and IoT. His current project is 'Sensor Cloud Services.'.

Members (8)

Belal Alsinglawi
  • Postdoctoral Fellow
Balsam Alkouz
  • The University of Sydney
Dipankar Chaki
  • The University of Sydney
Babar Shahzaad
  • The University of Sydney
Abdallah Lakhdari
  • The University of Sydney
Amani Abusafia
  • The University of Sydney
Muhammad Umair Mujahid
  • The University of Sydney
Sameera Jayaratna
  • Swinburne University of Technology
Abdelkarim Erradi
Abdelkarim Erradi
  • Not confirmed yet
Azadeh Ghari Neiat
Azadeh Ghari Neiat
  • Not confirmed yet
Sajib Mistry
Sajib Mistry
  • Not confirmed yet

Alumni (8)

Azadeh Ghari Neiat
  • The University of Sydney
Sajib Mistry
  • The University of Sydney
Sheik Mohammad Mostakim Fattah
  • The University of Sydney
Mohammed Bahutair
  • The University of Sydney