Ozlem Durmaz Incel

Fast Data Collection in Tree-Based Wireless Sensor Networks

We investigate the following fundamental question-how fast can information be collected from a wireless sensor network organized as tree? To address this, we explore and evaluate a number of different techniques using realistic simulation models under the many-to-one communication paradigm known as convergecast. We first consider time scheduling on a single frequency channel with the aim of minimizing the number of time slots required (schedule length) to complete a convergecast. Next, we combine scheduling with transmission power control to mitigate the effects of interference, and show that while power control helps in reducing the schedule length under a single frequency, scheduling transmissions using multiple frequencies is more efficient. We give lower bounds on the schedule length when interference is completely eliminated, and propose algorithms that achieve these bounds. We also evaluate the performance of various channel assignment methods and find empirically that for moderate size networks of about 100 nodes, the use of multifrequency scheduling can suffice to eliminate most of the interference. Then, the data collection rate no longer remains limited by interference but by the topology of the routing tree. To this end, we construct degree-constrained spanning trees and capacitated minimal spanning trees, and show significant improvement in scheduling performance over different deployment densities. Lastly, we evaluate the impact of different interference and channel models on the schedule length.

MC-LMAC: A multi-channel MAC protocol for wireless sensor networks

In traditional wireless sensor network (WSN) applications, energy efficiency may be considered to be the most important concern whereas utilizing bandwidth and maximizing throughput are of secondary importance. However, recent applications, such as structural health monitoring, require high amounts of data to be collected at a faster rate. We present a multi-channel MAC protocol, MC-LMAC, designed with the objective of maximizing the throughput of WSNs by coordinating transmissions over multiple frequency channels. MC-LMAC takes advantage of interference and contention-free parallel transmissions on different channels. It is based on scheduled access which eases the coordination of nodes, dynamically switching their interfaces between channels and makes the protocol operate effectively with no collisions during peak traffic. Time is slotted and each node is assigned the control over a time slot to transmit on a particular channel. We analyze the performance of MC-LMAC with extensive simulations in Glomosim. MC-LMAC exhibits significant bandwidth utilization and high throughput while ensuring an energy-efficient operation. Moreover, MC-LMAC outperforms the contention-based multi-channel MMSN protocol, a cluster-based channel assignment method, and the single-channel CSMA in terms of data delivery ratio and throughput for high data rate, moderate-size networks of 100 nodes at different densities.

A survey of online activity recognition using mobile phones

Physical activity recognition using embedded sensors has enabled many context-aware applications in different areas, such as healthcare. Initially, one or more dedicated wearable sensors were used for such applications. However, recently, many researchers started using mobile phones for this purpose, since these ubiquitous devices are equipped with various sensors, ranging from accelerometers to magnetic field sensors. In most of the current studies, sensor data collected for activity recognition are analyzed offline using machine learning tools. However, there is now a trend towards implementing activity recognition systems on these devices in an online manner, since modern mobile phones have become more powerful in terms of available resources, such as CPU, memory and battery. The research on offline activity recognition has been reviewed in several earlier studies in detail. However, work done on online activity recognition is still in its infancy and is yet to be reviewed. In this paper, we review the studies done so far that implement activity recognition systems on mobile phones and use only their on-board sensors. We discuss various aspects of these studies. Moreover, we discuss their limitations and present various recommendations for future research.

Fusion of Smartphone Motion Sensors for Physical Activity Recognition

For physical activity recognition, smartphone sensors, such as an accelerometer and a gyroscope, are being utilized in many research studies. So far, particularly, the accelerometer has been extensively studied. In a few recent studies, a combination of a gyroscope, a magnetometer (in a supporting role) and an accelerometer (in a lead role) has been used with the aim to improve the recognition performance. How and when are various motion sensors, which are available on a smartphone, best used for better recognition performance, either individually or in combination? This is yet to be explored. In order to investigate this question, in this paper, we explore how these various motion sensors behave in different situations in the activity recognition process. For this purpose, we designed a data collection experiment where ten participants performed seven different activities carrying smart phones at different positions. Based on the analysis of this data set, we show that these sensors, except the magnetometer, are each capable of taking the lead roles individually, depending on the type of activity being recognized, the body position, the used data features and the classification method employed (personalized or generalized). We also show that their combination only improves the overall recognition performance when their individual performances are not very high, so that there is room for performance improvement. We have made our data set and our data collection application publicly available, thereby making our experiments reproducible.

Survey Paper: A survey on multi-channel communication in wireless sensor networks

The ability of newer generations of commercially available radios used on the sensor nodes to tune their operating frequency over different channels provides an opportunity to alleviate the effects of interference and consequently improve the network performance. In this paper we investigate the use of multi-channel communication to improve the capacity of wireless sensor networks. We provide an extensive survey in the field together with discussing the research challenges. Initially, we focus on the capacity issues in wireless sensor networks and identify the limiting factors on the capacity improvements. Then, we provide a brief survey on multi-channel communication in wireless ad hoc networks, which share similar characteristics with wireless sensor networks, and next focus on the multi-channel communication in wireless sensor networks. According to this survey, we observe that many of the protocols do not properly address the challenges of multi-channel communication, such as the channel overlaps. Related to these issues, we point out the possible future research directions in the field and list the properties of a well-designed multi-channel protocol for wireless sensor networks.

Enhancing the Data Collection Rate of Tree-Based Aggregation in Wireless Sensor Networks

What is the fastest rate at which we can collect a stream of aggregated data from a set of wireless sensors organized as a tree? We explore a hierarchy of techniques using realistic simulation models to address this question. We begin by considering TDMA scheduling on a single channel, reducing the original problem to minimizing the number of time slots needed to schedule each link of the aggregation tree. The second technique is to combine the scheduling with transmission power control to reduce the effects of interference. To better cope with interference, we then study the impact of utilizing multiple frequency channels by introducing a simple receiver-based frequency and time scheduling approach. We find that for networks of about a hundred nodes, the use of multi-frequency scheduling can suffice to eliminate most of the interference. The data collection rate then becomes limited not by interference, but by the maximum degree of the routing tree. Therefore we consider finally how the data collection rate can be further enhanced by the use of degree-constrained routing trees. Considering deployments at different densities, we show that these enhancements can improve the streaming aggregated data collection by as much as 10 times compared to the baseline of single-channel data collection over non-degree constrained routing trees. Addition to our primary conclusion, in the frequency scheduling domain we evaluate the impact of different interference models on the scheduling performance and give topology-specific bounds on time slot and frequency channel requirements.

Multi-channel scheduling algorithms for fast aggregated convergecast in sensor networks

Fast and periodic collection of aggregated data is of considerable interest for mission-critical and continuous monitoring applications in sensor networks. In the many-to-one communication paradigm known as convergecast, we consider scenarios where data packets are aggregated at each hop en route to a sink node along a tree-based routing topology and focus on maximizing the data collection rate at the sink by employing TDMA scheduling and multiple frequency channels. Our key result in the paper lies in proving that minimizing the schedule length for an arbitrary network in the presence of multiple frequencies is NP-hard, and in designing approximation algorithms with worst-case provable performance guarantees for geometric networks. In particular, we design a constant factor approximation for networks modeled as unit disk graphs (UDG) where every node has a uniform transmission range, and a O(Δ(T)log n) approximation for general disk graphs where nodes have different transmission ranges; n is the number of nodes in the network and Δ(T) is the maximum node degree on a given routing tree T. We also prove that a constant factor approximation is achievable on UDG even for unknown routing topologies so long as the maximum node degree in the tree is bounded by a constant. We also show that finding the minimum number of frequencies required to remove all the interfering links in an arbitrary network in NP-hard. We give an upper bound on the maximum number of such frequencies required and propose a polynomial time algorithm that minimizes the schedule length under this scenario. Finally, we evaluate our algorithms through simulations and show various trends in performance for different network parameters.

Complex Human Activity Recognition Using Smartphone and Wrist-Worn Motion Sensors.

The position of on-body motion sensors plays an important role in human activity recognition. Most often, mobile phone sensors at the trouser pocket or an equivalent position are used for this purpose. However, this position is not suitable for recognizing activities that involve hand gestures, such as smoking, eating, drinking coffee and giving a talk. To recognize such activities, wrist-worn motion sensors are used. However, these two positions are mainly used in isolation. To use richer context information, we evaluate three motion sensors (accelerometer, gyroscope and linear acceleration sensor) at both wrist and pocket positions. Using three classifiers, we show that the combination of these two positions outperforms the wrist position alone, mainly at smaller segmentation windows. Another problem is that less-repetitive activities, such as smoking, eating, giving a talk and drinking coffee, cannot be recognized easily at smaller segmentation windows unlike repetitive activities, like walking, jogging and biking. For this purpose, we evaluate the effect of seven window sizes (2–30 s) on thirteen activities and show how increasing window size affects these various activities in different ways. We also propose various optimizations to further improve the recognition of these activities. For reproducibility, we make our dataset publicly available.

User, device and orientation independent human activity recognition on mobile phones: challenges and a proposal

Smart phones equipped with a rich set of sensors are explored as alternative platforms for human activity recognition in the ubiquitous computing domain. However, there exist challenges that should be tackled before the successful acceptance of such systems by the masses. In this paper, we particularly focus on the challenges arising from the differences in user behavior and in the hardware. To investigate the impact of these factors on the recognition accuracy, we performed tests with 20 different users focusing on the recognition of basic locomotion activities using the accelerometer, gyroscope and magnetic field sensors. We investigated the effect of feature types, to represent the raw data, and the use of linear acceleration for user, device and orientation-independent activity recognition.

Scheduling Algorithms for Tree-Based Data Collection in Wireless Sensor Networks

Data collection is a fundamental operation in wireless sensor networks (WSN) where sensor nodes measure attributes about a phenomenon of interest and transmit their readings to a common base station. In this chapter, we survey contention-free time division multiple access (TDMA)-based scheduling protocols for such data collection applications over tree-based routing topologies. We classify the algorithms according to their common design objectives, identifying the following four as the most fundamental and most studied with respect to data collection in WSNs: (i) minimizing schedule length, (ii) minimizing latency, (iii) minimizing energy consumption, and (iv) maximizing fairness. We also describe the pros and cons of the underlying design constraints and assumptions and provide a taxonomy according to these metrics. Finally, we discuss some open problems together with future research directions.