349 research outputs found
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LEE: Light‐Weight Energy‐Efficient encryption algorithm for sensor networks
Data confidentiality in wireless sensor networks is mainly achieved by RC5 and Skipjack encryption algorithms. However, both algorithms have their weaknesses, for example RC5 supports variable-bit rotations, which are computationally expensive operations and Skipjack uses a key length of 80-bits, which is subject to brute force attack. In this paper we introduce a light-weight energy- fficient encryption-algorithm (LEE) for tiny embedded devices, such as sensor network nodes. We present experimental results of LEE under real sensor nodes operating in TinyOS. We also discuss the secrecy of our algorithm by presenting a security analysis of various tests and cryptanalytic attacks
Quality of Information in Mobile Crowdsensing: Survey and Research Challenges
Smartphones have become the most pervasive devices in people's lives, and are
clearly transforming the way we live and perceive technology. Today's
smartphones benefit from almost ubiquitous Internet connectivity and come
equipped with a plethora of inexpensive yet powerful embedded sensors, such as
accelerometer, gyroscope, microphone, and camera. This unique combination has
enabled revolutionary applications based on the mobile crowdsensing paradigm,
such as real-time road traffic monitoring, air and noise pollution, crime
control, and wildlife monitoring, just to name a few. Differently from prior
sensing paradigms, humans are now the primary actors of the sensing process,
since they become fundamental in retrieving reliable and up-to-date information
about the event being monitored. As humans may behave unreliably or
maliciously, assessing and guaranteeing Quality of Information (QoI) becomes
more important than ever. In this paper, we provide a new framework for
defining and enforcing the QoI in mobile crowdsensing, and analyze in depth the
current state-of-the-art on the topic. We also outline novel research
challenges, along with possible directions of future work.Comment: To appear in ACM Transactions on Sensor Networks (TOSN
Mobile-Beacon Assisted Sensor Localization with Dynamic Beacon Mobility Scheduling
International audienceIn mobile-beacon assisted sensor localization, beacon mobility scheduling aims to determine the best beacon trajectory so that each sensor receives sufficient beacon signals with minimum delay. We propose a novel DeteRministic bEAcon Mobility Scheduling (DREAMS) algorithm, without requiring any prior knowledge of the sensory field. In this algorithm, beacon trajectory is defined as the track of depth-first traversal (DFT) of the network graph, thus deterministic. The mobile beacon performs DFT under the instruction of nearby sensors on the fly. It moves from sensor to sensor in an intelligent heuristic manner according to RSS (Received Signal Strength)-based distance measurements. We prove that DREAMS guarantees full localization (every sensor is localized) when the measurements are noise-free. Then we suggest to apply node elimination and topology control (Local Minimum Spanning Tree) to shorten beacon tour and reduce delay. Through simulation we show that DREAMS guarantees full localization even with noisy distance measurements. We evaluate its performance on localization delay and communication overhead in comparison with a previously proposed static path based scheduling method
Reserved Parking Validation
A common situation that we can testify every day: fossil fuel cars occupying electric cars
charge only places, and handy capped reserved places, occupied with cars without the
proper authorization.
This is something that plagues our society, where the values and moral are forgotten, and
our duties and rights are lost in the day-to-day life. There are more and more cars moving,
every day, to the city center, where the lack of available parking, together with the lack
of proper public transportation creates a chaotic situation. Also, the large proliferation of
electric cars, that is not accompanied by a proportional availability of electric chargers,
raises issues, where these cars’ drivers are not allowed to charge their vehicles, most of
the times, because they are being used as abusive parking.
This dissertation has the goal to identify and propose a universal solution, with low implementation
and maintenance costs, that allows a fast and unambiguous validation of
authorization of a user, for parking in a reserved parking space
Awareness of wireless sensor network potential in healthcare industry: A second UTAUT study
This study concentrates on investigating the degree of awareness, future adoption and uptake of wireless sensor networks (WSNs) (in particular Motes) in the Health Monitoring arena via the use of our second Web-based survey. The Unified Theory of Acceptance and Use of Technology (UTAUT) has been applied to determine how viable this technology will be for health monitoring in healthcare institutions and patients' homes. Results from our study show positive support for the acceptance of the technology yet reveal some real concerns about the issues of security, privacy, ethics and safety
Solving Complex Data-Streaming Problems by Applying Economic-Based Principles to Mobile and Wireless Resource Constraint Networks
The applications that employ mobile networks depend on the continuous input of reliable data collected by sensing devices. A common application is in military systems, where as an example, drones that are sent on a mission can communicate with each other, exchange sensed data, and autonomously make decisions. Although the mobility of nodes enhances the network coverage, connectivity, and scalability, it introduces pressing issues in data reliability compounded by restrictions in sensor energy resources, as well as limitations in available memory, and computational capacity.
This dissertation investigates the issues that mobile networks encounter in providing reliable data. Our research goal is to develop a diverse set of novel data handling solutions for mobile sensor systems providing reliable data by considering the dynamic trajectory behavior relationships among nodes, and the constraints inherent to mobile nodes. We study the applicability of economic models, which are simplified versions of real-world situations that let us observe and make predictions about economic behavior, to our domain. First, we develop a data cleaning method by introducing the notion of “beta,” a measure that quantifies the risk associated with trusting the accuracy of the data provided by a node based on trajectory behavior similarity. Next, we study the reconstruction of highly incomplete data streams. Our method determines the level of trust in data accuracy by assigning variable “weights” considering the quality and the origin of data. Thirdly, we design a behavior-based data reduction and trend prediction technique using Japanese candlesticks. This method reduces the dataset to 5% of its original size while preserving the behavioral patterns. Finally, we develop a data cleaning distribution method for energy-harvesting networks. Based on the Leontief Input-Output model, this method increases the data that is run through cleaning and the network uptime
Towards cost-sensitive adaptation: when is it worth updating your predictive model?
Our digital universe is rapidly expanding,more and more daily activities are digitally recorded, data arrives in streams, it needs to be analyzed in real time and may evolve over time. In the last decade many adaptive learning algorithms and prediction systems, which can automatically update themselves with the new incoming data, have been developed. The majority of those algorithms focus on improving the predictive performance and assume that model update is always desired as soon as possible and as frequently as possible. In this study we consider potential model update as an investment decision, which, as in the financial markets, should be taken only if a certain return on investment is expected. We introduce and motivate a new research problem for data streams ? cost-sensitive adaptation. We propose a reference framework for analyzing adaptation strategies in terms of costs and benefits. Our framework allows to characterize and decompose the costs of model updates, and to asses and interpret the gains in performance due to model adaptation for a given learning algorithm on a given prediction task. Our proof-of-concept experiment demonstrates how the framework can aid in analyzing and managing adaptation decisions in the chemical industry
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