15,123 research outputs found
Energy and Information Management of Electric Vehicular Network: A Survey
The connected vehicle paradigm empowers vehicles with the capability to
communicate with neighboring vehicles and infrastructure, shifting the role of
vehicles from a transportation tool to an intelligent service platform.
Meanwhile, the transportation electrification pushes forward the electric
vehicle (EV) commercialization to reduce the greenhouse gas emission by
petroleum combustion. The unstoppable trends of connected vehicle and EVs
transform the traditional vehicular system to an electric vehicular network
(EVN), a clean, mobile, and safe system. However, due to the mobility and
heterogeneity of the EVN, improper management of the network could result in
charging overload and data congestion. Thus, energy and information management
of the EVN should be carefully studied. In this paper, we provide a
comprehensive survey on the deployment and management of EVN considering all
three aspects of energy flow, data communication, and computation. We first
introduce the management framework of EVN. Then, research works on the EV
aggregator (AG) deployment are reviewed to provide energy and information
infrastructure for the EVN. Based on the deployed AGs, we present the research
work review on EV scheduling that includes both charging and vehicle-to-grid
(V2G) scheduling. Moreover, related works on information communication and
computing are surveyed under each scenario. Finally, we discuss open research
issues in the EVN
Differential Privacy Techniques for Cyber Physical Systems: A Survey
Modern cyber physical systems (CPSs) has widely being used in our daily lives
because of development of information and communication technologies (ICT).With
the provision of CPSs, the security and privacy threats associated to these
systems are also increasing. Passive attacks are being used by intruders to get
access to private information of CPSs. In order to make CPSs data more secure,
certain privacy preservation strategies such as encryption, and k-anonymity
have been presented in the past. However, with the advances in CPSs
architecture, these techniques also needs certain modifications. Meanwhile,
differential privacy emerged as an efficient technique to protect CPSs data
privacy. In this paper, we present a comprehensive survey of differential
privacy techniques for CPSs. In particular, we survey the application and
implementation of differential privacy in four major applications of CPSs named
as energy systems, transportation systems, healthcare and medical systems, and
industrial Internet of things (IIoT). Furthermore, we present open issues,
challenges, and future research direction for differential privacy techniques
for CPSs. This survey can serve as basis for the development of modern
differential privacy techniques to address various problems and data privacy
scenarios of CPSs.Comment: 46 pages, 12 figure
Enabling Cognitive Smart Cities Using Big Data and Machine Learning: Approaches and Challenges
The development of smart cities and their fast-paced deployment is resulting
in the generation of large quantities of data at unprecedented rates.
Unfortunately, most of the generated data is wasted without extracting
potentially useful information and knowledge because of the lack of established
mechanisms and standards that benefit from the availability of such data.
Moreover, the high dynamical nature of smart cities calls for new generation of
machine learning approaches that are flexible and adaptable to cope with the
dynamicity of data to perform analytics and learn from real-time data. In this
article, we shed the light on the challenge of under utilizing the big data
generated by smart cities from a machine learning perspective. Especially, we
present the phenomenon of wasting unlabeled data. We argue that
semi-supervision is a must for smart city to address this challenge. We also
propose a three-level learning framework for smart cities that matches the
hierarchical nature of big data generated by smart cities with a goal of
providing different levels of knowledge abstractions. The proposed framework is
scalable to meet the needs of smart city services. Fundamentally, the framework
benefits from semi-supervised deep reinforcement learning where a small amount
of data that has users' feedback serves as labeled data while a larger amount
is without such users' feedback serves as unlabeled data. This paper also
explores how deep reinforcement learning and its shift toward semi-supervision
can handle the cognitive side of smart city services and improve their
performance by providing several use cases spanning the different domains of
smart cities. We also highlight several challenges as well as promising future
research directions for incorporating machine learning and high-level
intelligence into smart city services.Comment: 7 pages, 5 figures and 1 table. Final version is published in IEEE
Communications Magazin
Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges
The widespread popularity of smart meters enables an immense amount of
fine-grained electricity consumption data to be collected. Meanwhile, the
deregulation of the power industry, particularly on the delivery side, has
continuously been moving forward worldwide. How to employ massive smart meter
data to promote and enhance the efficiency and sustainability of the power grid
is a pressing issue. To date, substantial works have been conducted on smart
meter data analytics. To provide a comprehensive overview of the current
research and to identify challenges for future research, this paper conducts an
application-oriented review of smart meter data analytics. Following the three
stages of analytics, namely, descriptive, predictive and prescriptive
analytics, we identify the key application areas as load analysis, load
forecasting, and load management. We also review the techniques and
methodologies adopted or developed to address each application. In addition, we
also discuss some research trends, such as big data issues, novel machine
learning technologies, new business models, the transition of energy systems,
and data privacy and security.Comment: IEEE Transactions on Smart Grid, 201
Artificial Impostors for Location Privacy Preservation
The progress of location-based services has led to serious concerns on
location privacy leakage. For effective and efficient location privacy
preservation (LPP), existing methods are still not fully competent. They are
often vulnerable under the identification attack with side information, or hard
to be implemented due to the high computational complexity. In this paper, we
pursue the high protection efficacy and low computational complexity
simultaneously. We propose a scalable LPP method based on the paradigm of
counterfeiting locations. To make fake locations extremely plausible, we forge
them through synthesizing artificial impostors (AIs). The AIs refer to the
synthesized traces which have similar semantic features to the actual traces,
and do not contain any target location. Two dedicated techniques are devised:
the sampling-based synthesis method and population-level semantic model. They
play significant roles in two critical steps of synthesizing AIs. We conduct
experiments on real datasets in two cities (Shanghai, China and Asturias,
Spain) to validate the high efficacy and scalability of the proposed method. In
these two datasets, the experimental results show that our method achieves the
preservation efficacy of and , and its run time of building
the generators is only and seconds, respectively. This study
would give the research community new insights into improving the practicality
of the state-of-the-art LPP paradigm via counterfeiting locations
Privacy in Deep Learning: A Survey
The ever-growing advances of deep learning in many areas including vision,
recommendation systems, natural language processing, etc., have led to the
adoption of Deep Neural Networks (DNNs) in production systems. The availability
of large datasets and high computational power are the main contributors to
these advances. The datasets are usually crowdsourced and may contain sensitive
information. This poses serious privacy concerns as this data can be misused or
leaked through various vulnerabilities. Even if the cloud provider and the
communication link is trusted, there are still threats of inference attacks
where an attacker could speculate properties of the data used for training, or
find the underlying model architecture and parameters. In this survey, we
review the privacy concerns brought by deep learning, and the mitigating
techniques introduced to tackle these issues. We also show that there is a gap
in the literature regarding test-time inference privacy, and propose possible
future research directions
Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence
Along with the rapid developments in communication technologies and the surge
in the use of mobile devices, a brand-new computation paradigm, Edge Computing,
is surging in popularity. Meanwhile, Artificial Intelligence (AI) applications
are thriving with the breakthroughs in deep learning and the many improvements
in hardware architectures. Billions of data bytes, generated at the network
edge, put massive demands on data processing and structural optimization. Thus,
there exists a strong demand to integrate Edge Computing and AI, which gives
birth to Edge Intelligence. In this paper, we divide Edge Intelligence into AI
for edge (Intelligence-enabled Edge Computing) and AI on edge (Artificial
Intelligence on Edge). The former focuses on providing more optimal solutions
to key problems in Edge Computing with the help of popular and effective AI
technologies while the latter studies how to carry out the entire process of
building AI models, i.e., model training and inference, on the edge. This paper
provides insights into this new inter-disciplinary field from a broader
perspective. It discusses the core concepts and the research road-map, which
should provide the necessary background for potential future research
initiatives in Edge Intelligence.Comment: 13 pages, 3 figure
Towards Efficient Scheduling of Federated Mobile Devices under Computational and Statistical Heterogeneity
Originated from distributed learning, federated learning enables
privacy-preserved collaboration on a new abstracted level by sharing the model
parameters only. While the current research mainly focuses on optimizing
learning algorithms and minimizing communication overhead left by distributed
learning, there is still a considerable gap when it comes to the real
implementation on mobile devices. In this paper, we start with an empirical
experiment to demonstrate computation heterogeneity is a more pronounced
bottleneck than communication on the current generation of battery-powered
mobile devices, and the existing methods are haunted by mobile stragglers.
Further, non-identically distributed data across the mobile users makes the
selection of participants critical to the accuracy and convergence. To tackle
the computational and statistical heterogeneity, we utilize data as a tuning
knob and propose two efficient polynomial-time algorithms to schedule different
workloads on various mobile devices, when data is identically or
non-identically distributed. For identically distributed data, we combine
partitioning and linear bottleneck assignment to achieve near-optimal training
time without accuracy loss. For non-identically distributed data, we convert it
into an average cost minimization problem and propose a greedy algorithm to
find a reasonable balance between computation time and accuracy. We also
establish an offline profiler to quantify the runtime behavior of different
devices, which serves as the input to the scheduling algorithms. We conduct
extensive experiments on a mobile testbed with two datasets and up to 20
devices. Compared with the common benchmarks, the proposed algorithms achieve
2-100x speedup epoch-wise, 2-7% accuracy gain and boost the convergence rate by
more than 100% on CIFAR10
Security for 4G and 5G Cellular Networks: A Survey of Existing Authentication and Privacy-preserving Schemes
This paper presents a comprehensive survey of existing authentication and
privacy-preserving schemes for 4G and 5G cellular networks. We start by
providing an overview of existing surveys that deal with 4G and 5G
communications, applications, standardization, and security. Then, we give a
classification of threat models in 4G and 5G cellular networks in four
categories, including, attacks against privacy, attacks against integrity,
attacks against availability, and attacks against authentication. We also
provide a classification of countermeasures into three types of categories,
including, cryptography methods, humans factors, and intrusion detection
methods. The countermeasures and informal and formal security analysis
techniques used by the authentication and privacy preserving schemes are
summarized in form of tables. Based on the categorization of the authentication
and privacy models, we classify these schemes in seven types, including,
handover authentication with privacy, mutual authentication with privacy, RFID
authentication with privacy, deniable authentication with privacy,
authentication with mutual anonymity, authentication and key agreement with
privacy, and three-factor authentication with privacy. In addition, we provide
a taxonomy and comparison of authentication and privacy-preserving schemes for
4G and 5G cellular networks in form of tables. Based on the current survey,
several recommendations for further research are discussed at the end of this
paper.Comment: 24 pages, 14 figure
SecGrid: A Secure and Efficient SGX-enabled Smart Grid System with Rich Functionalities
Smart grid adopts two-way communication and rich functionalities to gain a
positive impact on the sustainability and efficiency of power usage, but on the
other hand, also poses serious challenges to customers' privacy. Existing
solutions in smart grid usually use cryptographic tools, such as homomorphic
encryption, to protect individual privacy, which, however, can only support
limited and simple functionalities. Moreover, the resource-constrained smart
meters need to perform heavy asymmetric cryptography in these solutions, which
is not applied to smart grid. In this paper, we present a practical and secure
SGX-enabled smart grid system, named SecGrid. Our system leverage trusted
hardware SGX to ensure that grid utilities can efficiently execute rich
functionalities on customers' private data, while guaranteeing their privacy.
With the designed security protocols, the SecGrid only require the smart meters
to perform AES encryption. Security analysis shows that SecGrid can thwart
various attacks from malicious adversaries. Experimental results show that
SecGrid is much faster than the existing privacy-preserving schemes in smart
grid
- …