| 1. |
- Ahlgren, Bengt, et al.
(author)
-
Internet of Things for Smart Cities : Interoperability and Open Data
- 2016
-
In: IEEE Internet Computing. - : IEEE Computer Society. - 1089-7801 .- 1941-0131. ; 20:6, s. 52-56
-
Journal article (peer-reviewed)abstract
- The Internet of Things (IoT) has become a promising technology for addressing societal challenges by connecting smart devices and leveraging Big Data analytics to create smart cities worldwide. As the IoT scales up, it's important to provide interoperability among different devices. Yet current simple standard protocols lack sufficient openness and interoperability. IoT for smart cities needs to guarantee the accessibility of open data and cloud services to allow industries and citizens to develop new services and applications. Here, the authors provide a case study of the GreenIoT platform in Uppsala, Sweden, to demonstrate the idea of interoperability and open data for smart cities.
|
|
| 2. |
- Borgh, Joakim, et al.
(author)
-
Employing attribute-based encryption in systems with resource constrained devices in an information-centric networking context
- 2017
-
In: 2017 Global Internet of Things Summit (GIoTS). - : IEEE. - 9781509058730 ; , s. 397-402
-
Conference paper (other academic/artistic)abstract
- Attribute-Based Encryption (ABE) is considered to be one of the most promising ways to be enforce access control in Information-Centric Networking (ICN). As the Internet of Things (IoT) is being considered as one of the primary use cases for ICN it raises the question of the compatibility between IoT and ABE. An important part of the IoT is the resource constrained devices, for them there is a challenge to perform the computationally expensive operations required for ABE. In this paper we consider ABE in sensor networks and discuss the strengths and weaknesses of a system solution where the ABE operations are performed on the sensors. To properly discuss these concerns we have implemented two ABE schemes, a Single-authority ABE (SA-CP-ABE) scheme and a Multi-authority ABE (MA-CP-ABE) scheme. Results regarding the execution time, RAM usage, data overhead and battery consumption of these implementations on a sensor are presented. We conclude that it is possible, already today, to perform ABE on sensors for smaller policies. The main limitation in deploying ABE in sensors is the RAM size of the sensors.
|
|
| 3. |
|
|
| 4. |
- Chen, Weili, et al.
(author)
-
Detecting Ponzi Schemes on Ethereum : Towards Healthier Blockchain Technology
- 2018
-
In: WWW '18. - New York, New York, USA : ACM Digital Library. - 9781450356398 ; , s. 1409-1418
-
Conference paper (peer-reviewed)abstract
- Blockchain technology becomes increasingly popular. It also attracts scams, for example, Ponzi scheme, a classic fraud, has been found making a notable amount of money on Blockchain, which has a very negative impact. To help dealing with this issue, this paper proposes an approach to detect Ponzi schemes on blockchain by using data mining and machine learning methods. By verifying smart contracts on Ethereum, we first extract features from user accounts and operation codes of the smart contracts and then build a classification model to detect latent Ponzi schemes implemented as smart contracts. The experimental results show that the proposed approach can achieve high accuracy for practical use. More importantly, the approach can be used to detect Ponzi schemes even at the moment of its creation. By using the proposed approach, we estimate that there are more than 400 Ponzi schemes running on Ethereum. Based on these results, we propose to build a uniform platform to evaluate and monitor every created smart contract for early warning of scams.
|
|
| 5. |
|
|
| 6. |
- Deng, Weipeng, et al.
(author)
-
Energy-Efficient Monitoring of Potential Side Effects from COVID-19 Vaccines
- 2022
-
In: Proceedings. - : IEEE. - 9781665454179 - 9781665454186 ; , s. 222-227
-
Conference paper (peer-reviewed)abstract
- COVID-19 has affected the world for almost two years causing lots of damages and losses of lives. With the development of sensing technology and digital health, research studies suggest to use wearable devices for monitoring COVID-19 symptoms or analyzing people’s behaviour change. As COVID-19 vaccines are getting widely available, their side effects have raised public concerns, though have not yet been thoroughly studied due to the short deployment time. As far as we know, this work is the first study to use wearable devices and mobile app to collect physiological data to explore potential side effects to human bodies from COVID-19 vaccinations. We designed and developed a mobile sensing system, which can monitor changes of physiological indicators through wearable devices, collect self-reported data from the users and proposed a green data transmission mechanism which can reduce the communication overheads. Pilot study has been conducted to evaluate the feasibility of our system. Preliminary results show that increased resting heart rate (RHR) and changes on average heart rate (HR) are observed in some participants after COVID-19 vaccinations. This study opens up the opportunity to collect larger amount of data and further investigate potential side effects from COVID-19 vaccinations.
|
|
| 7. |
- Ekberg, Pontus, et al.
(author)
-
A distributed swarm-intelligent localization for sensor networks with mobile nodes
- 2011
-
In: Proc. 7th International Wireless Communications and Mobile Computing Conference. - Piscataway, NJ : IEEE. - 9781424495399 ; , s. 83-88
-
Conference paper (peer-reviewed)abstract
- We present a novel distributed localization algorithm, called Swarm-Intelligent Localization (SIL), for computing the physical locations of nodes in wireless sensor networks. The algorithm assumes that only a small fraction of the nodes have a priori knowledge of their positions, and that noisy distance measurements are available between all neighboring nodes. The algorithm has no explicit global state and it can handle nodes that are both mobile and that can arrive in the network at any time. SIL works in two different phases, a coarse phase where nodes compute rough positions for themselves based on information about remote anchors, and a fine phase where nodes iteratively refine their positions from the coarse phase by collaborating with their neighbors. The average computational complexity per node running SIL is very low, namely constant in the network size and linear in the connectivity of the network. We evaluate the algorithm through extensive simulations. The results indicate that SIL is able to compute accurate positions for the majority of nodes in a wide range of network topologies and settings, and that it can handle difficulties such as large distance measurement errors and low network connectivity.
|
|
| 8. |
- Elsts, Atis, et al.
(author)
-
A Case for Node-Local Runtime Parameter Adaptation in Wireless Sensor Networks
- 2014
-
In: Proc. 10th Swedish National Computer Networking Workshop.
-
Conference paper (other academic/artistic)abstract
- The challenges posed to wireless sensor networks by the environments they are deployed cannot always be predicted beforehand. Therefore, adaptive behavior at the run-time may be required to achieve good reliability and energy-efficiency. We present a node-local runtime adaptation algorithm that adapts the over-the-air message encoding based on presence of weak links and external interference in the immediate neighborhood of the node. Evaluation with a network simulator shows that this algorithm leads to significant network-wide reduction of radio duty cycle under specific radio transmission failure models.
|
|
| 9. |
- Fang, Jing, et al.
(author)
-
Depression Prevalence in Postgraduate Students and Its Association With Gait Abnormality
- 2019
-
In: IEEE Access. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 2169-3536. ; 7, s. 174425-174437
-
Journal article (peer-reviewed)abstract
- In recent years, an increasing number of university students are found to be at high risk of depression. Through a large scale depression screening, this paper finds that around 6.5% of the university postgraduate students in China experience depression. We then investigate whether the gait patterns of these individuals have already changed as depression is suggested to associate with gait abnormality. Significant differences are found in several spatiotemporal, kinematic and postural gait parameters such as walking speed, stride length, head movement, vertical head posture, arm swing, and body sway, between the depressed and non-depressed groups. Applying these features to classifiers with different machine learning algorithms, we examine whether natural gait analysis may serve as a convenient and objective tool to assist in depression recognition. The results show that when using a random forest classifier, the two groups can be classified automatically with a maximum accuracy of 91.58%. Furthermore, a reasonable accuracy can already be achieved by using parameters from the upper body alone, indicating that upper body postures and movements can effectively contribute to depression analysis.
|
|
| 10. |
|
|
