Ville Kaseva

Energy-efficient neighbor discovery protocol for mobile wireless sensor networks

Low energy consumption is a critical design requirement for most wireless sensor network (WSN) applications. Due to minimal transmission power levels, time-varying environmental factors and mobility of nodes, network neighborhood changes frequently. In these conditions, the most critical issue for energy is to minimize the transactions and time consumed for neighbor discovery operations. In this paper, we present an energy-efficient neighbor discovery protocol targeted at synchronized low duty-cycle medium access control (MAC) schemes such as IEEE 802.15.4 and S-MAC. The protocol effectively reduces the need for costly network scans by proactively distributing node schedule information in MAC protocol beacons and by using this information for establishing new communication links. Energy consumption is further reduced by optimizing the beacon transmission rate. The protocol is validated by performance analysis and experimental measurements with physical WSN prototypes. Experimental results show that the protocol can reduce node energy consumption up to 80% at 1-3m/s node mobility.

A Wireless Sensor Network for RF-Based Indoor Localization

An RF-based indoor localization design targeted for wireless sensor networks (WSNs) is presented. The energy-efficiency of mobile location nodes is maximized by a localization medium access control (LocMAC) protocol. For location estimation, a location resolver algorithm is introduced. It enables localization with very scarce energy and processing resources, and the utilization of simple and low-cost radio transceiver HardWare (HW) without received signal strength indicator (RSSI) support. For achieving high energy-efficiency and minimizing resource usage, LocMAC is tightly cross-layer designed with the location resolver algorithm. The presented solution is fully calibration-free and can cope with coarse grained and unreliable ranging measurements. We analyze LocMAC power consumption and show that it outperforms current state-of-the-art WSN medium access control (MAC) protocols in location node energy-efficiency. The feasibility of the proposed localization scheme is validated by experimental measurements using real resource constrained WSN node prototypes. The prototype network reaches accuracies ranging from 1 m to 7 m.With one anchor node per a typical office room, the current room of the localized node is determined with 89.7% precision.

Positioning with coverage area estimates generated from location fingerprints

We present a method to estimate the coverage areas of transmitters, and a method to use a database of such coverage areas for personal positioning. The coverage areas are modelled as ellipsoids, and their location and shape parameters are computed from reception samples (fingerprints) using Bayesian estimation. The position is computed as a weighted average of the ellipsoid centers, with weights determined by the ellipsoid shape parameters. The methods are tested using a subset of a prototype wireless sensor network (TUTWSN) consisting of 30 nodes deployed indoors on one floor of a university building. The network nodes use low power commercial off-the-shelf components including a 2.4GHz radio.

WSN API: Application Programming Interface for Wireless Sensor Networks

In this paper, an application programming interface for wireless sensor networks (WSN API) is presented. The WSN API consists of a client-side API (gateway API) and a sensor-side API (node API). The WSN API conceals the complexities of WSN communication protocols and architectures, and provides a well-defined and easy-to-use way to collect data from sensors. Also, easy expandability for new sensor components and applications is provided. The WSN API is implemented practically in TUTWSN prototype platforms

Low-Power Wireless Sensor Networks

Wireless sensor network (WSN) is an ad-hoc network technology comprising even thousands of autonomic and self-organizing nodes that combine environmental sensing, data processing, and wireless networking. The applications for sensor networks range from home and industrial environments to military uses. Unlike the traditional computer networks, a WSN is application-oriented and deployed for a specific task. WSNs are data centric, which means that messages are not send to individual nodes but to geographical locations or regions based on the data content. A WSN node is typically battery powered and characterized by extremely small size and low cost. As a result, the processing power, memory, and energy resources of an individual sensor node are limited. However, the feasibility of a WSN lies on the collaboration between the nodes. A reference WSN node comprises a Micro-Controller Unit (MCU) having few Million Instructions Per Second (MIPS) processing speed, tens of kilobytes program memory, few kilobytes data memory. In addition, the node contains a short-range radio, and a set of sensors. Supply power is typically obtained with small batteries. Assuming a target lifetime of one year using AA-size batteries, the available power budget is around 1 mW. This book covers the low-power WSNs services ranging from hardware platforms and communication protocols to network deployment, and sensor data collection and actuation. The implications of resource constraints and expected performance in terms of throughput, reliability and latency are explained. As a case study, this book presents experiments with low-energy TUTWSN technology to illustrate the possibilities and limitations of WSN applications.

Range-free algorithm for energy-efficient indoor localization in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) form an attractive technology for ubiquitous indoor localization. The localized node lifetime is maximized by using energy-efficient radios and minimizing their active time. However, the most low-cost and low-power radios do not include Received Signal Strength Indicator (RSSI) functionality commonly used for RF-based localization. In this paper, we present a range-free localization algorithm for localized nodes with minimized radio communication and radios without RSSI. The low complexity of the algorithm enables implementation in resource-constrained hardware for in-network localization. We experimented the algorithm using a real WSN implementation. In room-level localization, the area was resolved correctly 96% of the time. The maximum point-based error was 8.70 m. The corresponding values for sub-room-level localization are 100% and 4.20 m. The prototype implementation consumed 1900 B of program memory. The data memory consumption varied from 18 B to 180 B, and the power consumption from 345 μW to 2.48 mW depending on the amount of localization data.

A wireless sensor network for hospital security: from user requirements to pilot deployment

Increasing amount of Wireless Sensor Network (WSN) applications require low network delays. However, current research on WSNs has mainly concentrated on optimizing energy-efficiency omitting low network delays. This paper presents a novel WSN design targeted at applications requiring low data transfer delays and high reliability. We present the whole design flow from user requirements to an actual pilot deployment in a real hospital unit. The WSN includes multihop low-delay data transfer and energy-efficient mobile nodes reaching lifetime of years with small batteries. The nodes communicate using a low-cost low-power 2.4 GHz radio. The network is used in a security application with which personnel can send alarms in threatening situations. Also, a multitude of sensor measurements and actuator control is possible with the WSN. A full-scale pilot deployment is extensively experimented for performance results. Currently, the pilot network is in use at the hospital.

Low-power Wireless Sensor Network Platforms

Wireless sensor network (WSN) is a technology comprising even thousands of autonomic and self-organizing nodes that combine environmental sensing, data processing, and wireless multihop ad-hoc networking. The features of WSNs enable monitoring, object tracking, and control functionality. The potential applications include environmental and condition monitoring, home automation, security and alarm systems, industrial monitoring and control, military reconnaissance and targeting, and interactive games. This chapter describes low-power WSN as a platform for signal processing by presenting the WSN services that can be used as building blocks for the applications. It explains the implications of resource constraints and expected performance in terms of throughput, reliability and latency.

HybridKernel: Preemptive kernel with event-driven extension for resource constrained wireless sensor networks

A low-power wireless sensor network (WSN) implements dynamic communication protocols and embedded sensing applications on resource constrained platform. WSNs utilize dozens of tasks, which have differentiated realtime requirements. This requires an efficient implementation with the use of a real-time operating system optimized for WSNs. Current WSN operating systems are based either on preemptive or event-driven kernels. Preemption provides accurate timings but requires large data memory footprint. Event-driven kernels have small footprint but do not support time as accurately. This paper presents a new HybridKernel for WSNs which combines the advantages of both kernels. It meets five key requirements without any major drawbacks: it halves footprint of preemptive kernels, it provides 2 µs timing accuracy, it minimizes energy consumption, and it can be easily configured and used between preemptive and event-driven parts through a coherent system call interface.

Sensor Data Collection

While some sensor networks can operate independently, e.g. by performing smart actuation based on measured sensor readings, in a typical use case sensor data is collected and utilized outside the WSN [16]. This necessitates data collection facilities and a connection between the sensor network and the rest of the world.