Sikkerhet i sensornettverk : hvordan innføring av sikkerhet i trådløse sensornettverk påvirker strømforbruket, funksjonaliteten og levetiden til sensornodene.

Abstract This master thesis presents the effect that security has on a sensor network. A wireless sensor network is composed of a magnitude of sensors that together form a sensor network through self- organizing and self- configuring capabilities. A sensor node is small unit that functions as a transformer which converts physical signals into electrical signals. The sensor unit itself is composed of a sensor, a processor, a transceiver and a power supply like a battery. This battery is a small sized battery, so it is important to optimize all the components and software, so that the sensor nodes consume as little power as possible. Introducing security in a system which has so many restrictions as a sensor network is very challenging. All these challenges rules out the use of asymmetric encryption techniques, which make symmetric encryption techniques more relevant for a sensor network. This report gives an overall description of the sensor network technology. We present security techniques that can be suitable for a sensor network. Additionally we look at the degree of security that we can obtain with the different security techniques. It is important that security is chosen carefully, so that the cost associated the particular security techniques don’t drastically degrade the lifetime of a sensor node. Then we are going to illustrate the problems that arise when security is to be introduced in a sensor network. Further we have studied the overhead associated with known symmetric techniques, and also how this overhead effects the packet growth, throughput, transmission time, and how this effects the power consumption. The power consumption has been calculated through several scenarios, where we investigated how the lifetime of the sensor nodes will degrade when different security overhead is introduced into the data packet, and also how the node density can effect the power consumption in the sensor network. We have also tested how a sensor network operates at different load, and we have calculated metrics like power consumption, end to end delay, and average throughput in the sensor network. All these results have been obtain through simulations with the use of NS-2 network simulator and Sensorsim, and the results shows that our suggestion for a security framework can be implemented in a sensor network

Toeplitz Inverse Covariance-Based Clustering of Multivariate Time Series Data

Subsequence clustering of multivariate time series is a useful tool for discovering repeated patterns in temporal data. Once these patterns have been discovered, seemingly complicated datasets can be interpreted as a temporal sequence of only a small number of states, or clusters. For example, raw sensor data from a fitness-tracking application can be expressed as a timeline of a select few actions (i.e., walking, sitting, running). However, discovering these patterns is challenging because it requires simultaneous segmentation and clustering of the time series. Furthermore, interpreting the resulting clusters is difficult, especially when the data is high-dimensional. Here we propose a new method of model-based clustering, which we call Toeplitz Inverse Covariance-based Clustering (TICC). Each cluster in the TICC method is defined by a correlation network, or Markov random field (MRF), characterizing the interdependencies between different observations in a typical subsequence of that cluster. Based on this graphical representation, TICC simultaneously segments and clusters the time series data. We solve the TICC problem through alternating minimization, using a variation of the expectation maximization (EM) algorithm. We derive closed-form solutions to efficiently solve the two resulting subproblems in a scalable way, through dynamic programming and the alternating direction method of multipliers (ADMM), respectively. We validate our approach by comparing TICC to several state-of-the-art baselines in a series of synthetic experiments, and we then demonstrate on an automobile sensor dataset how TICC can be used to learn interpretable clusters in real-world scenarios.Comment: This revised version fixes two small typos in the published versio

Middleware for Wireless Sensor Networks: An Outlook

In modern distributed computing, applications are rarely built directly atop operating system facilities, e.g., sockets. Higher-level middleware abstractions and systems are often employed to simplify the programmer’s chore or to achieve interoperability. In contrast, real-world wireless sensor network (WSN) applications are almost always developed by relying directly on the operating system. Why is this the case? Does it make sense to include a middleware layer in the design of WSNs? And, if so, is it the same kind of software system as in traditional distributed computing? What are the fundamental concepts, reasonable assumptions, and key criteria guiding its design? What are the main open research challenges, and the potential pitfalls? Most importantly, is it worth pursuing research in this field? This paper provides a (biased) answer to these and other research questions, preceded by a brief account on the state of the art in the field

A Simple Flood Forecasting Scheme Using Wireless Sensor Networks

This paper presents a forecasting model designed using WSNs (Wireless Sensor Networks) to predict flood in rivers using simple and fast calculations to provide real-time results and save the lives of people who may be affected by the flood. Our prediction model uses multiple variable robust linear regression which is easy to understand and simple and cost effective in implementation, is speed efficient, but has low resource utilization and yet provides real time predictions with reliable accuracy, thus having features which are desirable in any real world algorithm. Our prediction model is independent of the number of parameters, i.e. any number of parameters may be added or removed based on the on-site requirements. When the water level rises, we represent it using a polynomial whose nature is used to determine if the water level may exceed the flood line in the near future. We compare our work with a contemporary algorithm to demonstrate our improvements over it. Then we present our simulation results for the predicted water level compared to the actual water level.Comment: 16 pages, 4 figures, published in International Journal Of Ad-Hoc, Sensor And Ubiquitous Computing, February 2012; V. seal et al, 'A Simple Flood Forecasting Scheme Using Wireless Sensor Networks', IJASUC, Feb.201