Waltenegus Dargie

A Survey on Mobility and Mobility-Aware MAC Protocols in Wireless Sensor Networks

In wireless sensor networks nodes can be static or mobile, depending on the application requirements. Dealing with mobility can pose some formidable challenges in protocol design, particularly, at the link layer. These difficulties require mobility adaptation algorithms to localize mobile nodes and predict the quality of link that can be established with them. This paper surveys the current state-of-art in handling mobility. It first describes existing mobility models and patterns; and analyzes the challenges caused by mobility at the link layer. It then provides a comparative study of several mobility-aware MAC protocols.

Dynamic Power Management in Wireless Sensor Networks: State-of-the-Art

In the last few years, interest in wireless sensor networks has increased considerably. These networks can be useful for a large number of applications, including habitat monitoring, structural health monitoring, pipeline monitoring, transportation, precision agriculture, supply chain management, and many more. Typically, a wireless sensor network consists of a large number of simple nodes which operate with exhaustible batteries, unattended. Manual replacement or recharging the batteries is not an easy or desirable task. Hence, how energy is utilized by the various hardware subsystems of individual nodes directly affects the scope and usefulness of the entire network. This paper provides a comprehensive assessment of state-of-the-art of dynamic power management (DPM) in wireless sensor networks. It investigates aspects of power dissipation in a node and analyses the strength and limitations of selective switching, dynamic frequency, and voltage scaling.

Power Consumption Estimation Models for Processors, Virtual Machines, and Servers

The power consumption of presently available Internet servers and data centers is not proportional to the work they accomplish. The scientific community is attempting to address this problem in a number of ways, for example, by employing dynamic voltage and frequency scaling, selectively switching off idle or underutilized servers, and employing energy-aware task scheduling. Central to these approaches is the accurate estimation of the power consumption of the various subsystems of a server, particularly, the processor. We distinguish between power consumption measurement techniques and power consumption estimation models. The techniques refer to the art of instrumenting a system to measure its actual power consumption whereas the estimation models deal with indirect evidences (such as information pertaining to CPU utilization or events captured by hardware performance counters) to reason about the power consumption of a system under consideration. The paper provides a comprehensive survey of existing or proposed approaches to estimate the power consumption of single-core as well as multicore processors, virtual machines, and an entire server.

Evaluation of the reliability of RSSI for indoor localization

In wireless sensor networks, nodes can be static or mobile, depending on the application requirements. Dealing with mobility can pose some formidable challenges in protocol design, particularly, at the link and network layers. These difficulties require mobility adaption algorithms to efficiently localize mobile nodes and predict the quality of link that can be established with these nodes. An off the shelf development platform that uses Radio Signal Strength Indication (RSSI) is mostly selected as the sensor localization method, especially in the indoor environment. Despite this, not much research work has been done to practically demonstrate the reliability of RSSI for indoor localization. Therefore, in this paper, we aim to calibrate and map RSSI to distance by doing a series of experiments. The result shows that the RSSI technology gives an unacceptable high error and thus is not reliable for the indoor sensor localization.

Energy-Efficient Routing in Linear Wireless Sensor Networks

Wireless sensor networks are used for structure monitoring and border surveillance. Typical applications, such as sensors embedded in the outer surface of a pipeline or mounted along the supporting structure of a bridge, feature a linear sensor arrangement. Economical power use of sensor nodes is essential for long-lasting operation. In this paper, we present MERR (minimum energy relay routing), a novel approach to energy-efficient data routing to a single control center in a linear sensor topology. Based on an optimal transmission distance, relay paths are established that aim for minimizing the total power consumption. We study MERR by both stochastic analysis and simulation, comparing it to other possible approaches and a theoretically optimal protocol. We find that MERR consumes 80% less power than conventional approaches and performs close to the theoretical optimum for practicable sensor networks.

Does Live Migration of Virtual Machines Cost Energy

Live migration, the process of moving a virtual machine (VM) interruption-free between physical hosts is a core concept in modern data centers. Power management strategies use live migration to consolidate services in a cluster environment and to switch off underutilized machines to save power. However, most migration models do not consider the energy cost of migration. This paper experimentally investigates the factors that affect the power consumption and the duration of virtual machine migration. We use the KVM platform for our experiment and show that a live migration entails an energy overhead and the size of this overhead varies with the size of the virtual machine and the available network bandwidth.

Localized power-aware routing in linear wireless sensor networks

Energy-efficency is a key concern when designing protocols for wireless sensor networks (WSN). This is of particular importance in commercial applications where demonstrable return on investment is a crucial factor. One such commercial application that motivated this work is telemetry and control for freight railroad trains. Since a railroad train has a global linear structure by nature, we consider in this paper linear WSNs as sensor networks having, roughly, a linear topology. Aiming at such networks, we introduce two routing schemes that efficiently utilize energy: Minimum Energy Relay Routing (MERR) and Adaptive MERR (AMERR). We derive a theoretical lower bound on the optimal power consumption of routing in a linear WSN, where we assume a Poisson model for the distribution of nodes along a linear path. We evaluate the efficiency of our protocols with respect to the theoretical optimal lower bound and with respect to other well-known protocols. AMERR achieves optimal performance for practical deployment settings, while MERR rapidly approaches optimal performance as sensors are more densely deployed. Compared to other protocols, we show that MERR and AMERR are less complex and have better scalability. We also postulate how both protocols might be generalized to a two-dimensional WSN.

Modelling the energy cost of a fully operational wireless sensor network

Several applications have been proposed for wireless sensor networks, including habitat monitoring, structural health monitoring, pipeline monitoring, precision agriculture, active volcano monitoring, and many more. The energy consumption of these applications is a critical feasibility metric that defines the scope and usefulness of wireless sensor networks. This paper provides a comprehensive energy model for a fully functional wireless sensor network. While the model uses toxic gas detection in oil refineries as an example application, it can easily be generalized. The model provides a sufficient insight about the energy demand of the existing or proposed communication protocols.

Analysis of Time and Frequency Domain Features of Accelerometer Measurements

This paper addresses the signal processing aspect of wireless sensor networks. It analyzes several time and frequency domain features of measurements that are taken from 3D accelerometer sensors. The measurements represent various types of movements related to humans and cars. The aim is to obtain quantitative as well as qualitative comparisons concerning the expression power of these features in the presence of various sources of uncertainties (calibration, placement of sensors, and time synchronization). For the qualitative analysis, we define fuzzy sets and fuzzy membership functions for all the features. Particular attention is given to the analysis of the existence of correlation between measurements of different sensor nodes. We will demonstrate that correlation coefficients of both time and frequency domain features exhibit high degrees of uncertainties. On the other hand, short time Fourier transformations (STFT) of all types of movements prove to be agnostic of various forms of measurement and calibration errors. I. INTRODUCTION

Energy Model for H2S Monitoring Wireless Sensor Network

Several applications have been proposed for wireless sensor networks. These include habitat monitoring, structural health monitoring, pipeline (gas, water, and oil) monitoring, precision agriculture, active volcano monitoring, and many more. To demonstrate the feasibility of the proposals, researchers have developed prototypes and deployed them into real-world environments. Even though each prototype was developed for a specific sensing task, interestingly most of the networks share several characteristics in common. Some of these are: the need for time synchronisation, high sampling rate of short duration, multi-hop routing, periodical sampling and sleeping, and medium access control. Whereas there are a plethora of existing and proposed protocols to address these issues, each prototype chooses to address the issues in a proprietary manner. The lack of reuse practice poses a generalisation problem. In this paper we motivate toxic gas detection during oil exploration and refinery and demonstrate how existing or proposed protocols can be employed to establish a fully functional network. Moreover, we provide a comprehensive energy model to evaluate the feasibility of employing wireless sensor network for the monitoring task.