There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

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.).

Machine learning

Wireless indoor positioning systems have become very popular in recent years. These systems have been successfully used in many applications such as asset tracking and inventory management. This paper provides an overview of the existing wireless indoor positioning solutions and attempts to classify different techniques and systems. Three typical location estimation schemes of triangulation, scene analysis, and proximity are analyzed. We also discuss location fingerprinting in detail since it is used in most current system or solutions. We then examine a set of properties by which location systems are evaluated, and apply this evaluation method to survey a number of existing systems. Comprehensive performance comparisons including accuracy, precision, complexity, scalability, robustness, and cost are presented.

Survey of Wireless Indoor Positioning Techniques and Systems

The PASCAL Visual Object Classes Challenge

An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

A node device (601) of a wireless sensor network comprises a receiver (651) for receiving transmissions from other nodes. A controller (641) selectively switches on said receiver (651) according to a timetable. The node device (601) maintains synchronization with and receives beacon transmissions from another node in said wireless sensor network. If a failure is observed in previously maintained synchronization, the controller (641) reads from memory previously received information about neighboring nodes with which said node device (601) did not yet maintain synchronization. The controller (641) utilizes such stored information to selectively switch on the receiver (651) to attempt receiving a beacon transmission from such a neighboring node.

Energy-efficient neighbor discovery for mobile wireless sensor networks

The IEEE 802.15.4 standard defines the medium access control and physical layer for low-rate, low-power wireless personal area networks (WPAN). As a number of WPAN applications require protected communications, the standard defines security procedures. Since the procedures typically consume most processing capacity in the limited 802.15.4 devices, efficient implementations are needed. As a solution, this paper presents a compact and energy-efficient hardware design, supporting all the security suites of the standard. Compared to typical WPAN processors, the presented FPGA prototype and the estimated ASIC implementation offer significantly higher performance and lower energy consumption. The FPGA throughput at the highest security level is 90 Mb/s and the energy consumption is 1/190 of an 8-bit microcontroller and 1/5 of an ARM9. The estimated energy consumption for the equivalent ASIC implementation is 1/10 of the FPGA prototype. In addition to 802.15.4, the hardware design supports all wireless technologies derived from the IEEE 802.11i security specification.

Efficient hardware implementation of security processing for IEEE 802.15.4 wireless networks

IEEE 1588 is a new standard for precise clock synchronization for networked measurement and control systems in LAN environment. This paper presents the design and implementation of an IEEE 1588 PC software prototype for Wireless LANs (WLAN). Accuracy is improved using two new developed methods for outbound latency estimation. In addition, an algorithm for adjusting the local clock is presented. The achieved accuracy is measured and compared between WLAN and fixed LAN environments. The results show that 2.8 μs average clock offset can be reached on WLAN, while wired Ethernet connection enables 2.5 μs.

Precision Time Protocol Prototype on Wireless LAN

This paper presents an energy-efficient multi-hop routing protocol for wireless sensor networks. The protocol uses cost metrics to create gradients from a source to a destination node. The cost metrics consist of energy, node load, delay, and link reliability information that provide a trade-off between performance and energy usage. A node can query routes from its neighbors, which allows efficient recovery from route losses. The protocol is the first cost-field based WSN routing protocol suitable for low processing and memory capacity nodes that is tested in a practical real-world environment. The protocol performance is evaluated on full scale prototype implementation consisting of 38 ultra-low power nodes in indoor environment. Compared to traditional flooding, the protocol requires only 25% of the bandwidth, while having smaller end-to-end delays.

Cost-Aware Dynamic Routing Protocol for Wireless Sensor Networks - Design and Prototype Experiments

When a mobile node changes its point of attachment in the network, it experiences a time when it cannot receive or send any packets. This time, called handover delay, might also cause packet loss, resulting in undesired quality-of-service degradation for various types of applications. This issue remains a problem, although "Mobility Support in IPv6" has just reached IETF (Internet Engineering Task Force) RFC status. Earlier, we proposed a fast handover method called flow-based fast handover for Mobile IPv6 (FFHMIPv6) to speed up the L3 handover process. FFHMIPv6 employs flow-based information and IPv6-in-IPv6 tunneling for the fast redirection of flows during MIPv6 handover. The paper compares the performance of the FFHMIPv6 method to other fundamental handover methods with Network Simulator 2 (ns-2). Both UDP and TCP based applications are studied.

Timo Hämäläinen

Papers

Energy-efficient neighbor discovery for mobile wireless sensor networks

Efficient hardware implementation of security processing for IEEE 802.15.4 wireless networks

Precision Time Protocol Prototype on Wireless LAN

Cost-Aware Dynamic Routing Protocol for Wireless Sensor Networks - Design and Prototype Experiments

Performance evaluation of the flow-based fast handover method for Mobile IPv6 network