Janne Riihijarvi

Bio: Janne Riihijarvi is an academic researcher from RWTH Aachen University. The author has contributed to research in topics: Wireless network & Cognitive radio. The author has an hindex of 37, co-authored 208 publications receiving 4464 citations. Previous affiliations of Janne Riihijarvi include University of Oulu.

Performance study of IEEE 802.15.4 using measurements and simulations

Abstract: IEEE 802.15.4 was developed to meet the needs for simple, low-power and low-cost wireless communication. In the past couple of years it has become a popular technology for wireless sensor networks. It operates primarily in the 2.4 GHz ISM band, which makes the technology easily applicable and worldwide available. However, IEEE 802.15.4 is potentially vulnerable to interference by other wireless technologies working in this band such as IEEE 802.11 and Bluetooth. This paper gives a short overview of the IEEE 802.15.4 and carefully analyzes the properties and performance of IEEE 802.15.4 through measurement of the RSSI, PER and run lengths distribution using real off-the-shelf hardware. Furthermore we present simulation results from the evaluation of the IEEE 802.15.4 MAC protocol. Finally, we address the coexistence between IEEE 802.11 and IEEE 802.15.4 and measure the impact these two wireless technologies have on each other when operating concurrently and in range

A survey on resource discovery mechanisms, peer-to-peer and service discovery frameworks

Abstract: Service and resource discovery has become an integral part of modern networked systems. In this survey we give an overview of the existing solutions for service and resource discovery for a wide variety of network types. We cover techniques used in existing systems, as well as recent developments from the research front. We also provide taxonomy for discovery systems and architectures, and review the various algorithms and search methods applicable for such systems. Peer-to-peer overlays are discussed in detail and solutions for non-IP-based networks are also included in the review. We also specifically comment on issues related to wireless networks, and give an overview of the various issues and complications that should be considered in future work in this domain.

Full length article: Empirical time and frequency domain models of spectrum use

Abstract: Dynamic spectrum access (DSA) has been proposed as a solution to the spectrum scarcity problem. However, the models for spectrum use, that are commonly used in DSA research, are either limited in scope or have not been validated against real-life measurement data. In this paper we introduce a flexible spectrum use model based on extensive measurement results that can be configured to represent various wireless systems. We show that spectrum use is clustered in the frequency domain and should be modelled in the time domain using geometric or lognormal distributions. In the latter case the probability of missed detection is significantly higher due to the heavy-tailed behaviour of the lognormal distribution. The listed model parameters enable accurate modelling of primary user spectrum use in time and frequency domain for future DSA studies. Additionally, they also provide a more empirical basis to develop regulatory or business models.

TV White Space in Europe

Abstract: In this paper, we study the availability of TV white spaces in Europe. Specifically, we focus on the 470-790 MHz UHF band, which will predominantly remain in use for TV broadcasting after the analog-to-digital switch-over and the assignment of the 800 MHz band to licensed services have been completed. The expected number of unused, available TV channels in any location of the 11 countries we studied is 56 percent when we adopt the statistical channel model of the ITU-R. Similarly, a person residing in these countries can expect to enjoy 49 percent unused TV channels. If, in addition, restrictions apply to the use of adjacent TV channels, these numbers reduce to 25 and 18 percent, respectively. These figures are significantly smaller than those recently reported for the United States. We also study how these results change when we use the Longley-Rice irregular terrain model instead. We show that while the overall expected availability of white spaces is essentially the same, the local variability of the available spectrum shows significant changes. This underlines the importance of using appropriate system models before making far-reaching conclusions.

Performance evaluation of IEEE 1609 WAVE and IEEE 802.11p for vehicular communications

Abstract: In this paper we study the performance of the IEEE 1609 WAVE and IEEE 802.11p trial standards for vehicular communications. We have implemented key components of these standards in a simulation environment also supporting realistic vehicular mobility simulation. We study both the overall capacity of vehicular networks utilizing the said standards, as well as delay performance, which is an extremely important performance metric especially for safety applications. Our results show that the traffic prioritization schemes selected for the standards work well, and even in the presence of multi-channel operation implemented by the IEEE 1609.4 the delay of control messages of highest priority remains on the order of tens of milliseconds. Thus even with high densities of vehicles these standards should yield a stable platform a variety of vehicular applications can be built on.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Random graphs

Abstract: We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

A Survey of Information-Centric Networking Research

Abstract: The current Internet architecture was founded upon a host-centric communication model, which was appropriate for coping with the needs of the early Internet users. Internet usage has evolved however, with most users mainly interested in accessing (vast amounts of) information, irrespective of its physical location. This paradigm shift in the usage model of the Internet, along with the pressing needs for, among others, better security and mobility support, has led researchers into considering a radical change to the Internet architecture. In this direction, we have witnessed many research efforts investigating Information-Centric Networking (ICN) as a foundation upon which the Future Internet can be built. Our main aims in this survey are: (a) to identify the core functionalities of ICN architectures, (b) to describe the key ICN proposals in a tutorial manner, highlighting the similarities and differences among them with respect to those core functionalities, and (c) to identify the key weaknesses of ICN proposals and to outline the main unresolved research challenges in this area of networking research.

Probability and Random Processes

Abstract: tions. Bootstrap has found many applications in engineering field, including artificial neural networks, biomedical engineering, environmental engineering, image processing, and radar and sonar signal processing. Basic concepts of the bootstrap are summarized in each section as a step-by-step algorithm for ease of implementation. Most of the applications are taken from the signal processing literature. The principles of the bootstrap are introduced in Chapter 2. Both the nonparametric and parametric bootstrap procedures are explained. Babu and Singh (1984) have demonstrated that in general, these two procedures behave similarly for pivotal (Studentized) statistics. The fact that the bootstrap is not the solution for all of the problems has been known to statistics community for a long time; however, this fact is rarely touched on in the manuscripts meant for practitioners. It was first observed by Babu (1984) that the bootstrap does not work in the infinite variance case. Bootstrap Techniques for Signal Processing explains the limitations of bootstrap method with an example. I especially liked the presentation style. The basic results are stated without proofs; however, the application of each result is presented as a simple step-by-step process, easy for nonstatisticians to follow. The bootstrap procedures, such as moving block bootstrap for dependent data, along with applications to autoregressive models and for estimation of power spectral density, are also presented in Chapter 2. Signal detection in the presence of noise is generally formulated as a testing of hypothesis problem. Chapter 3 introduces principles of bootstrap hypothesis testing. The topics are introduced with interesting real life examples. Flow charts, typical in engineering literature, are used to aid explanations of the bootstrap hypothesis testing procedures. The bootstrap leads to second-order correction due to pivoting; this improvement in the results due to pivoting is also explained. In the second part of Chapter 3, signal processing is treated as a regression problem. The performance of the bootstrap for matched filters as well as constant false-alarm rate matched filters is also illustrated. Chapters 2 and 3 focus on estimation problems. Chapter 4 introduces bootstrap methods used in model selection. Due to the inherent structure of the subject matter, this chapter may be difficult for nonstatisticians to follow. Chapter 5 is the most impressive chapter in the book, especially from the standpoint of statisticians. It provides real data bootstrap applications to illustrate the theory covered in the earlier chapters. These include applications to optimal sensor placement for knock detection and land-mine detection. The authors also provide a MATLAB toolbox comprising frequently used routines. Overall, this is a very useful handbook for engineers, especially those working in signal processing.