354 research outputs found
The role of communication systems in smart grids: Architectures, technical solutions and research challenges
The purpose of this survey is to present a critical overview of smart grid concepts, with a special focus on the role that communication, networking and middleware technologies will have in the transformation of existing electric power systems into smart grids. First of all we elaborate on the key technological, economical and societal drivers for the development of smart grids. By adopting a data-centric perspective we present a conceptual model of communication systems for smart grids, and we identify functional components, technologies, network topologies and communication services that are needed to support smart grid communications. Then, we introduce the fundamental research challenges in this field including communication reliability and timeliness, QoS support, data management services, and autonomic behaviors. Finally, we discuss the main solutions proposed in the literature for each of them, and we identify possible future research directions
Gridchain: an investigation of privacy for the future local distribution grid
As part of building the smart grid, there is a massive deployment of so-called smart meters that aggregate information and communicate with the back-end office, apart from measuring properties of the local network. Detailed measurements and communication of, e.g., consumption allows for remote billing, but also in finding problems in the distribution of power and overall to provide data to be used to plan future upgrades of the network. From a security perspective, a massive deployment of such Internet of Things (IoT) components increases the risk that some may be compromised or that collected data are used for privacy-sensitive inference of the consumption of households. In this paper, we investigate the privacy concerns regarding detailed readings of smart meters for billing purposes. We present Gridchain, a solution where households can opt-in to hide their consumption patterns and thus make Non-Intrusive Load Monitoring (NILM) more challenging. Households form groups where they can trade real consumption among themselves to achieve reported consumption that would be resistant to NILM. Gridchain is built on a publish/subscribe model and uses a permissioned blockchain to record any trades, meaning that dishonest households can be discovered and punished if they steal from other households in the group or the electricity company in the end. We implement and release a proof of concept of Gridchain and use public datasets to allow reproducibility. Our results show that even if an attacker has access to the reported electricity consumption of any member of a Gridchain group, this reported consumption is significantly far from the actual consumption to allow for a detailed fingerprint of the household activities
Navigating the IoT landscape: Unraveling forensics, security issues, applications, research challenges, and future
Given the exponential expansion of the internet, the possibilities of
security attacks and cybercrimes have increased accordingly. However, poorly
implemented security mechanisms in the Internet of Things (IoT) devices make
them susceptible to cyberattacks, which can directly affect users. IoT
forensics is thus needed for investigating and mitigating such attacks. While
many works have examined IoT applications and challenges, only a few have
focused on both the forensic and security issues in IoT. Therefore, this paper
reviews forensic and security issues associated with IoT in different fields.
Future prospects and challenges in IoT research and development are also
highlighted. As demonstrated in the literature, most IoT devices are vulnerable
to attacks due to a lack of standardized security measures. Unauthorized users
could get access, compromise data, and even benefit from control of critical
infrastructure. To fulfil the security-conscious needs of consumers, IoT can be
used to develop a smart home system by designing a FLIP-based system that is
highly scalable and adaptable. Utilizing a blockchain-based authentication
mechanism with a multi-chain structure can provide additional security
protection between different trust domains. Deep learning can be utilized to
develop a network forensics framework with a high-performing system for
detecting and tracking cyberattack incidents. Moreover, researchers should
consider limiting the amount of data created and delivered when using big data
to develop IoT-based smart systems. The findings of this review will stimulate
academics to seek potential solutions for the identified issues, thereby
advancing the IoT field.Comment: 77 pages, 5 figures, 5 table
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Survey in Smart Grid and Smart Home Security: Issues, Challenges and Countermeasures
The electricity industry is now at the verge of a new era. An era that promises, through the evolution of the existing electrical grids to Smart Grids, more efficient and effective power management, better reliability, reduced production costs and more environmentally friendly energy generation. Numerous initiatives across the globe, led by both industry and academia, reflect the mounting interest around the enormous benefits but also the great risks introduced by this evolution. This paper focuses on issues related to the security of the Smart Grid and the Smart Home, which we present as an integral part of the Smart Grid. Based on several scenarios we aim to present some of the most representative threats to the Smart Home / Smart Grid environment. The threats detected are categorized according to specific security goals set for the Smart Home/Smart Grid environment and their impact on the overall system security is evaluated. A review of contemporary literature is then conducted with the aim of presenting promising security countermeasures with respect to the identified specific security goals for each presented scenario. An effort to shed light on open issues and future research directions concludes the paper
A survey of secure middleware for the Internet of Things
The rapid growth of small Internet connected devices, known as the Internet of Things (IoT), is creating a new set of challenges to create secure, private infrastructures. This paper reviews the current literature on the challenges and approaches to security and privacy in the Internet of Things, with a strong focus on how these aspects are handled in IoT middleware. We focus on IoT middleware because many systems are built from existing middleware and these inherit the underlying security properties of the middleware framework. The paper is composed of three main sections. Firstly, we propose a matrix of security and privacy threats for IoT. This matrix is used as the basis of a widespread literature review aimed at identifying requirements on IoT platforms and middleware. Secondly, we present a structured literature review of the available middleware and how security is handled in these middleware approaches. We utilise the requirements from the first phase to evaluate. Finally, we draw a set of conclusions and identify further work in this area
Challenges in Blockchain as a Solution for IoT Ecosystem Threats and Access Control: A Survey
The Internet of Things (IoT) is increasingly influencing and transforming
various aspects of our daily lives. Contrary to popular belief, it raises
security and privacy issues as it is used to collect data from consumers or
automated systems. Numerous articles are published that discuss issues like
centralised control systems and potential alternatives like integration with
blockchain. Although a few recent surveys focused on the challenges and
solutions facing the IoT ecosystem, most of them did not concentrate on the
threats, difficulties, or blockchain-based solutions. Additionally, none of
them focused on blockchain and IoT integration challenges and attacks. In the
context of the IoT ecosystem, overall security measures are very important to
understand the overall challenges. This article summarises difficulties that
have been outlined in numerous recent articles and articulates various attacks
and security challenges in a variety of approaches, including blockchain-based
solutions and so on. More clearly, this contribution consolidates threats,
access control issues, and remedies in brief. In addition, this research has
listed some attacks on public blockchain protocols with some real-life examples
that can guide researchers in taking preventive measures for IoT use cases.
Finally, a future research direction concludes the research gaps by analysing
contemporary research contributions
TorSH: Obfuscating consumer Internet-of-Things traffic with a collaborative smart-home router network
When consumers install Internet-connected smart devices in their homes, metadata arising from the communications between these devices and their cloud-based service providers enables adversaries privy to this traffic to profile users, even when adequate encryption is used. Internet service providers (ISPs) are one potential adversary privy to users’ incom- ing and outgoing Internet traffic and either currently use this insight to assemble and sell consumer advertising profiles or may in the future do so. With existing defenses against such profiling falling short of meeting user preferences and abilities, there is a need for a novel solution that empowers consumers to defend themselves against profiling by ISP-like actors and that is more in tune with their wishes. In this thesis, we present The Onion Router for Smart Homes (TorSH), a network of smart-home routers working collaboratively to defend smart-device traffic from analysis by ISP-like adversaries. We demonstrate that TorSH succeeds in deterring such profiling while preserving smart-device experiences and without encumbering latency-sensitive, non-smart-device experiences like web browsing
Algorithms design for improving homecare using Electrocardiogram (ECG) signals and Internet of Things (IoT)
Due to the fast growing of population, a lot of hospitals get crowded from the huge amount of
patients visits. Moreover, during COVID-19 a lot of patients prefer staying at home to minimize
the spread of the virus. The need for providing care to patients at home is essential. Internet
of Things (IoT) is widely known and used by different fields. IoT based homecare will help
in reducing the burden upon hospitals. IoT with homecare bring up several benefits such as
minimizing human exertions, economical savings and improved efficiency and effectiveness. One
of the important requirement on homecare system is the accuracy because those systems are
dealing with human health which is sensitive and need high amount of accuracy. Moreover,
those systems deal with huge amount of data due to the continues sensing that need to be
processed well to provide fast response regarding the diagnosis with minimum cost requirements.
Heart is one of the most important organ in the human body that requires high level of caring.
Monitoring heart status can diagnose disease from the early stage and find the best medication
plan by health experts. Continues monitoring and diagnosis of heart could exhaust caregivers
efforts. Having an IoT heart monitoring model at home is the solution to this problem. Electrocardiogram
(ECG) signals are used to track heart condition using waves and peaks. Accurate
and efficient IoT ECG monitoring at home can detect heart diseases and save human lives.
As a consequence, an IoT ECG homecare monitoring model is designed in this thesis for detecting
Cardiac Arrhythmia and diagnosing heart diseases. Two databases of ECG signals are used;
one online which is old and limited, and another huge, unique and special from real patients
in hospital. The raw ECG signal for each patient is passed through the implemented Low
Pass filter and Savitzky Golay filter signal processing techniques to remove the noise and any
external interference. The clear signal in this model is passed through feature extraction stage
to extract number of features based on some metrics and medical information along with feature extraction algorithm to find peaks and waves. Those features are saved in the local database to
apply classification on them. For the diagnosis purpose a classification stage is made using three
classification ways; threshold values, machine learning and deep learning to increase the accuracy.
Threshold values classification technique worked based on medical values and boarder lines. In
case any feature goes above or beyond these ranges, a warning message appeared with expected
heart disease. The second type of classification is by using machine learning to minimize the
human efforts. A Support Vector Machine (SVM) algorithm is proposed by running the algorithm
on the features extracted from both databases. The classification accuracy for online and hospital
databases was 91.67% and 94% respectively. Due to the non-linearity of the decision boundary, a
third way of classification using deep learning is presented. A full Multilayer Perceptron (MLP)
Neural Network is implemented to improve the accuracy and reduce the errors. The number of
errors reduced to 0.019 and 0.006 using online and hospital databases.
While using hospital database which is huge, there is a need for a technique to reduce the amount
of data. Furthermore, a novel adaptive amplitude threshold compression algorithm is proposed.
This algorithm is able to make diagnosis of heart disease from the reduced size using compressed
ECG signals with high level of accuracy and low cost. The extracted features from compressed
and original are similar with only slight differences of 1%, 2% and 3% with no effects on machine
learning and deep learning classification accuracy without the need for any reconstructions. The
throughput is improved by 43% with reduced storage space of 57% when using data compression.
Moreover, to achieve fast response, the amount of data should be reduced further to provide
fast data transmission. A compressive sensing based cardiac homecare system is presented.
It gives the channel between sender and receiver the ability to carry small amount of data.
Experiment results reveal that the proposed models are more accurate in the classification of
Cardiac Arrhythmia and in the diagnosis of heart diseases. The proposed models ensure fast
diagnosis and minimum cost requirements. Based on the experiments on classification accuracy,
number of errors and false alarms, the dictionary of the compressive sensing selected to be 900.
As a result, this thesis provided three different scenarios that achieved IoT homecare Cardiac
monitoring to assist in further research for designing homecare Cardiac monitoring systems. The experiment results reveal that those scenarios produced better results with high level of accuracy
in addition to minimizing data and cost requirements
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