A Survey on UAV-enabled Edge Computing: Resource Management Perspective

Edge computing facilitates low-latency services at the network's edge by distributing computation, communication, and storage resources within the geographic proximity of mobile and Internet-of-Things (IoT) devices. The recent advancement in Unmanned Aerial Vehicles (UAVs) technologies has opened new opportunities for edge computing in military operations, disaster response, or remote areas where traditional terrestrial networks are limited or unavailable. In such environments, UAVs can be deployed as aerial edge servers or relays to facilitate edge computing services. This form of computing is also known as UAV-enabled Edge Computing (UEC), which offers several unique benefits such as mobility, line-of-sight, flexibility, computational capability, and cost-efficiency. However, the resources on UAVs, edge servers, and IoT devices are typically very limited in the context of UEC. Efficient resource management is, therefore, a critical research challenge in UEC. In this article, we present a survey on the existing research in UEC from the resource management perspective. We identify a conceptual architecture, different types of collaborations, wireless communication models, research directions, key techniques and performance indicators for resource management in UEC. We also present a taxonomy of resource management in UEC. Finally, we identify and discuss some open research challenges that can stimulate future research directions for resource management in UEC.Comment: 36 pages, Accepted to ACM CSU

Long-term IaaS Cloud Service Selection

There are two primary subscription models for IaaS cloud services: a) pay-as-you and b) reservation. Reservation-based subscriptions are typically offered for a long-term period such as 1 to 3 years. Long-term subscriptions are typically cost-efficient than short-term subscriptions for consumers who need services for a long-term period. Large organizations such as airline companies, banks, and research institutes tend to utilize IaaS services on a long-term basis for economic reasons. The performance of IaaS services is a key criterion to consider when selecting a service for a long-term. Selecting a service that may exhibit poor performance in the future may cause a significant loss of revenue for a business organization. Most IaaS providers, however, are reluctant to provide detailed information about their long-term service performance. This research aims at developing a long-term IaaS cloud service selection framework where IaaS providers reveal limited performance information about their services. First, we propose a novel framework to find the closest match of IaaS cloud service according to a consumer's long-term QoS requirements. The proposed framework leverages free short-term trials to discover the unknown QoS performance information. A temporal skyline-based filtering method is proposed to select candidate services for short-term trials. A novel cooperative long-term QoS prediction approach is introduced that utilizes past trial experiences of similar consumers using a workload replay technique. We propose a new trial workload generation model that estimates a provider's long-term performance in the absence of past trial experiences. The confidence of the prediction is measured based on the trial experience of the consumer. Next, we propose a new long-term IaaS cloud service selection framework that utilizes a consumer's trial experience and the performance fingerprints of IaaS cloud services for the long-term selection. We design a novel equivalence partitioning-based trial strategy to discover the unknown QoS performance variability of IaaS cloud services. A trial experience transformation method is proposed to estimate the long-term performance of an IaaS cloud service. Next, we introduce a signature-based IaaS cloud service selection framework that leverages a new significance-based trial scheme and a signature technique to discover a service's long-term performance. Next, we propose a novel event-based change detection approach to manage changes in IaaS performance signatures. A new anomaly-based event detection technique is proposed to detect changes in long-term IaaS performance behavior over time. We then propose an IaaS performance noise model to identify noise and actual changes in IaaS performance accurately. A novel categorical signature-based approach is proposed to detect the long-term performance changes using the proposed performance noise model. Finally, we introduce a signature change detection framework that leverages a sliding window-based approach and a Signal-to-Noise ratio-based approach to detect long-term changes in IaaS performance signatures. We have conducted a set of experiments based on real-world datasets to evaluate the proposed frameworks. The proposed long-term selection framework achieved almost 92% ranking accuracy. The signature-based IaaS cloud service selection framework achieved 96% ranking accuracy. The proposed changed detection frameworks achieved up to 90% change detection accuracy

A User-Centric Knowledge Creation Model in a Web of Object-Enabled Internet of Things Environment

User-centric service features in a Web of Object-enabled Internet of Things environment can be provided by using a semantic ontology that classifies and integrates objects on the World Wide Web as well as shares and merges context-aware information and accumulated knowledge. The semantic ontology is applied on a Web of Object platform to virtualize the real world physical devices and information to form virtual objects that represent the features and capabilities of devices in the virtual world. Detailed information and functionalities of multiple virtual objects are combined with service rules to form composite virtual objects that offer context-aware knowledge-based services, where context awareness plays an important role in enabling automatic modification of the system to reconfigure the services based on the context. Converting the raw data into meaningful information and connecting the information to form the knowledge and storing and reusing the objects in the knowledge base can both be expressed by semantic ontology. In this paper, a knowledge creation model that synchronizes a service logistic model and a virtual world knowledge model on a Web of Object platform has been proposed. To realize the context-aware knowledge-based service creation and execution, a conceptual semantic ontology model has been developed and a prototype has been implemented for a use case scenario of emergency service

A User-Centric Knowledge Creation Model in a Web of Object-Enabled Internet of Things Environment

User-centric service features in a Web of Object-enabled Internet of Things environment can be provided by using a semantic ontology that classifies and integrates objects on the World Wide Web as well as shares and merges context-aware information and accumulated knowledge. The semantic ontology is applied on a Web of Object platform to virtualize the real world physical devices and information to form virtual objects that represent the features and capabilities of devices in the virtual world. Detailed information and functionalities of multiple virtual objects are combined with service rules to form composite virtual objects that offer context-aware knowledge-based services, where context awareness plays an important role in enabling automatic modification of the system to reconfigure the services based on the context. Converting the raw data into meaningful information and connecting the information to form the knowledge and storing and reusing the objects in the knowledge base can both be expressed by semantic ontology. In this paper, a knowledge creation model that synchronizes a service logistic model and a virtual world knowledge model on a Web of Object platform has been proposed. To realize the context-aware knowledge-based service creation and execution, a conceptual semantic ontology model has been developed and a prototype has been implemented for a use case scenario of emergency service

Building IoT Services for Aging in Place Using Standard-Based IoT Platforms and Heterogeneous IoT Products

An aging population and human longevity is a global trend. Many developed countries are struggling with the yearly increasing healthcare cost that dominantly affects their economy. At the same time, people living with old adults suffering from a progressive brain disorder such as Alzheimer’s disease are enduring even more stress and depression than those patients while caring for them. Accordingly, seniors’ ability to live independently and comfortably in their current home for as long as possible has been crucial to reduce the societal cost for caregiving and thus give family members peace of mind, called ‘aging in place’ (AIP). In this paper we present a way of building AIP services using standard-based IoT platforms and heterogeneous IoT products. An AIP service platform is designed and created by combining previous standard-based IoT platforms in a collaborative way. A service composition tool is also created that allows people to create AIP services in an efficient way. To show practical usability of our proposed system, we choose a service scenario for medication compliance and implement a prototype service which could give old adults medication reminder appropriately at the right time (i.e., when it is time to need to take pills) through light and speaker at home but also wrist band and smartphone even outside the home