Evidence-Aware Mobile Computational Offloading

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Energy-efficient Communications in Cloud, Mobile Cloud and Fog Computing

This thesis studies the problem of energy efficiency of communications in distributed computing paradigms, including cloud computing, mobile cloud computing and fog/edge computing. Distributed computing paradigms have significantly changed the way of doing business. With cloud computing, companies and end users can access the vast majority services online through a virtualized environment in a pay-as-you-go basis. %Three are the main services typically consumed by cloud users are Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS). Mobile cloud and fog/edge computing are the natural extension of the cloud computing paradigm for mobile and Internet of Things (IoT) devices. Based on offloading, the process of outsourcing computing tasks from mobile devices to the cloud, mobile cloud and fog/edge computing paradigms have become popular techniques to augment the capabilities of the mobile devices and to reduce their battery drain. Being equipped with a number of sensors, the proliferation of mobile and IoT devices has given rise to a new cloud-based paradigm for collecting data, which is called mobile crowdsensing as for proper operation it requires a large number of participants. A plethora of communication technologies is applicable to distributing computing paradigms. For example, cloud data centers typically implement wired technologies while mobile cloud and fog/edge environments exploit wireless technologies such as 3G/4G, WiFi and Bluetooth. Communication technologies directly impact the performance and the energy drain of the system. This Ph.D. thesis analyzes from a global perspective the efficiency in using energy of communications systems in distributed computing paradigms. In particular, the following contributions are proposed: - A new framework of performance metrics for communication systems of cloud computing data centers. The proposed framework allows a fine-grain analysis and comparison of communication systems, processes, and protocols, defining their influence on the performance of cloud applications. - A novel model for the problem of computation offloading, which describes the workflow of mobile applications through a new Directed Acyclic Graph (DAG) technique. This methodology is suitable for IoT devices working in fog computing environments and was used to design an Android application, called TreeGlass, which performs recognition of trees using Google Glass. TreeGlass is evaluated experimentally in different offloading scenarios by measuring battery drain and time of execution as key performance indicators. - In mobile crowdsensing systems, novel performance metrics and a new framework for data acquisition, which exploits a new policy for user recruitment. Performance of the framework are validated through CrowdSenSim, which is a new simulator designed for mobile crowdsensing activities in large scale urban scenarios

Vehicle as a Service (VaaS): Leverage Vehicles to Build Service Networks and Capabilities for Smart Cities

Smart cities demand resources for rich immersive sensing, ubiquitous communications, powerful computing, large storage, and high intelligence (SCCSI) to support various kinds of applications, such as public safety, connected and autonomous driving, smart and connected health, and smart living. At the same time, it is widely recognized that vehicles such as autonomous cars, equipped with significantly powerful SCCSI capabilities, will become ubiquitous in future smart cities. By observing the convergence of these two trends, this article advocates the use of vehicles to build a cost-effective service network, called the Vehicle as a Service (VaaS) paradigm, where vehicles empowered with SCCSI capability form a web of mobile servers and communicators to provide SCCSI services in smart cities. Towards this direction, we first examine the potential use cases in smart cities and possible upgrades required for the transition from traditional vehicular ad hoc networks (VANETs) to VaaS. Then, we will introduce the system architecture of the VaaS paradigm and discuss how it can provide SCCSI services in future smart cities, respectively. At last, we identify the open problems of this paradigm and future research directions, including architectural design, service provisioning, incentive design, and security & privacy. We expect that this paper paves the way towards developing a cost-effective and sustainable approach for building smart cities.Comment: 32 pages, 11 figure

Trustworthy Edge Machine Learning: A Survey

The convergence of Edge Computing (EC) and Machine Learning (ML), known as Edge Machine Learning (EML), has become a highly regarded research area by utilizing distributed network resources to perform joint training and inference in a cooperative manner. However, EML faces various challenges due to resource constraints, heterogeneous network environments, and diverse service requirements of different applications, which together affect the trustworthiness of EML in the eyes of its stakeholders. This survey provides a comprehensive summary of definitions, attributes, frameworks, techniques, and solutions for trustworthy EML. Specifically, we first emphasize the importance of trustworthy EML within the context of Sixth-Generation (6G) networks. We then discuss the necessity of trustworthiness from the perspective of challenges encountered during deployment and real-world application scenarios. Subsequently, we provide a preliminary definition of trustworthy EML and explore its key attributes. Following this, we introduce fundamental frameworks and enabling technologies for trustworthy EML systems, and provide an in-depth literature review of the latest solutions to enhance trustworthiness of EML. Finally, we discuss corresponding research challenges and open issues.Comment: 27 pages, 7 figures, 10 table

Auction-Based Efficient Online Incentive Mechanism Designs in Wireless Networks

Recently, wide use of mobile devices and applications, such as YouTube and Twitter, has facilitated every aspect of our daily lives. Meanwhile, it has also posed great challenges to enable resource-demanding users to successfully access networks. Thus, in order to enlarge network capacity and fully make use of vacant resources, new communication architectures emerge, such as D2D communications, edge computing, and crowdsourcing, all of which ask for involvement of end mobile users in assisting transmission, computation, or network management. However, end mobile users are not always willing to actively provide such sharing services if no reimbursements are provided as they need to consume their own computation and communication resources. Besides, since mobile users are not always stationary, they can opt-in and opt-out the network for their own convenience. Thus, an important practical characteristic of wireless networks, i.e., the mobility of mobile users cannot be ignored, which means that the demands of mobile users span over a period of time. As one of promising solutions, the online incentive mechanism design has been introduced in wireless networks in order to motivate the participation of more mobile users under a dynamic environment. In this thesis, with the analyses of each stakeholder's economic payoffs in wireless networks, the auction-based online incentive mechanisms are proposed to achieve resource allocations, participant selections, and payment determinations in two wireless networks, i.e., Crowdsensing and mobile edge computing. In particular, i) an online incentive mechanism is designed to guarantee Quality of Information of each arriving task in mobile crowdsensing networks, followed by an enhanced online strategy which could further improves the competitive ratio; ii) an online incentive mechanism jointly considering communication and computation resource allocations in collaborative edge computing networks is proposed based on the primal-dual theory; iii) to deal with the nonlinear issue in edge computing networks, an nonlinear online incentive mechanism under energy budget constraints of mobile users is designed based on the Maximal-in-Distributional Range framework; and iv) inspired by the recent development of deep learning techniques, a deep incentive mechanism with the budget balance of each mobile user is proposed to maximize the net revenue of service providers by leveraging the multi-task machine learning model. Both theoretical analyses and numerical results demonstrate the effectiveness of the designed mechanisms

Structure and topology of transcriptional regulatory networks and their applications in bio-inspired networking

Biological networks carry out vital functions necessary for sustenance despite environmental adversities. Transcriptional Regulatory Network (TRN) is one such biological network that is formed due to the interaction between proteins, called Transcription Factors (TFs), and segments of DNA, called genes. TRNs are known to exhibit functional robustness in the face of perturbation or mutation: a property that is proven to be a result of its underlying network topology. In this thesis, we first propose a three-tier topological characterization of TRN to analyze the interplay between the significant graph-theoretic properties of TRNs such as scale-free out-degree distribution, low graph density, small world property and the abundance of subgraphs called motifs. Specifically, we pinpoint the role of a certain three-node motif, called Feed Forward Loop (FFL) motif in topological robustness as well as information spread in TRNs. With the understanding of the TRN topology, we explore its potential use in design of fault-tolerant communication topologies. To this end, we first propose an edge rewiring mechanism that remedies the vulnerability of TRNs to the failure of well-connected nodes, called hubs, while preserving its other significant graph-theoretic properties. We apply the rewired TRN topologies in the design of wireless sensor networks that are less vulnerable to targeted node failure. Similarly, we apply the TRN topology to address the issues of robustness and energy-efficiency in the following networking paradigms: robust yet energy-efficient delay tolerant network for post disaster scenarios, energy-efficient data-collection framework for smart city applications and a data transfer framework deployed over a fog computing platform for collaborative sensing --Abstract, page iii

Applications

Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications

Applications

Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications