Mitra Baratchi

Sensing Solutions for Collecting Spatio-Temporal Data for Wildlife Monitoring Applications: A Review

Movement ecology is a field which places movement as a basis for understanding animal behavior. To realize this concept, ecologists rely on data collection technologies providing spatio-temporal data in order to analyze movement. Recently, wireless sensor networks have offered new opportunities for data collection from remote places through multi-hop communication and collaborative capability of the nodes. Several technologies can be used in such networks for sensing purposes and for collecting spatio-temporal data from animals. In this paper, we investigate and review technological solutions which can be used for collecting data for wildlife monitoring. Our aim is to provide an overview of different sensing technologies used for wildlife monitoring and to review their capabilities in terms of data they provide for modeling movement behavior of animals.

On the use of mobility data for discovery and description of social ties

Ever-increasing emergence of location-aware ubiquitous devices has facilitated collection of time-stamped mobility data. This large volume of data not only provides trajectory information but also information about social interaction between individuals. Unlike trajectory representation and discovery, discovery of social ties and interactions hidden in mobility data has not yet been fully explored. To identify such interaction, social network analysis has been recently used. However, compared with data from emails, phone calls, and messages, which are commonly used for social network analysis, mobility data convey less information about interaction between entities. Therefore, identifying the type of tie between two entities using only mobility data is a great challenge. In this paper, we propose a method for measuring the strength and type of social ties between people only based on their spatio-temporal correlations. Using mutual information metric, we propose utilization of two types of measures for identifying the purpose of being in a certain location. Our experimental results using a location-aware sensing device show that our method can identify different social ties between various entities successfully.

Finding frequently visited paths: Dealing with the uncertainty of spatio-temporal mobility data

With the ever-increasing advancements in sensor technology and localization systems, large amounts of spatio-temporal data can be collected from moving objects equipped with wireless sensor nodes. Analysis of such data provides the opportunity of extracting useful information about movement behaviour and interaction between moving objects. Inherent characteristics of wireless sensor nodes cause the data collected by them to have low or irregular frequency and often be erroneous. Existence of different levels of uncertainty in these data makes the procedure of finding movement patterns difficult and ambiguous. In this paper, we propose a hierarchical approach to find the frequently visited paths using location data of people carrying a custom designed mobile wireless sensor node. We hierarchically cluster trajectories and find their resemblance at the finest level while dealing with the uncertainties. The performance evaluation results show that compared with previous schemes, our method performs better in presence of ambiguity and sources of data uncertainty.

An Efficient Geographical Addressing Scheme for the Internet

Geocast is a scheme which allows packets to be sent to a destination area instead of an address. This allows the addressing of any device in a specific region without further knowledge. In this paper we present an addressing mechanism that allows efficient referral to areas of arbitrary size. The binary representation of our addressing mechanism fits in an IPv6 address and can be used for route lookup with simple exclusive-or operations. We show that our addressing mechanism can be used to address areas accurately enough to be used as a mechanism to route packets close to their destination.

Towards decisive garments for heat stress risk detection

One of the numerous applications of wearable computers is providing safety in occupations where heat-related injuries are prevalent. Core temperature, as a parameter that cannot be measured by on-body sensors is a variable that is specifically interesting for realizing such applications. In the context of the design of a sensor-shirt that can be used by firefighters, in this paper we study the importance of different types of sensor measurements and their placement for estimating core temperature. We propose a model for inferring the dangerous states of core temperature. Our evaluation results show that our model can to a great extent estimate hazardous situations caused by heat accumulation.

Traffic-adaptive duty cycle adaptation in TR-MAC protocol for wireless sensor networks

The Medium Access Control (MAC) layer can influence the energy consumption of a wireless sensor network (WSN) to a significant level. TR-MAC is an energy-efficient preamble sampling based MAC protocol for low power WSNs suitable for low data rate and low duty cycle scenario. However, low data rate is not always maintained in wireless sensor networks which often have to deal with event-driven scenarios where a sudden event rapidly increases traffic load within the network. In this paper we propose a traffic-adaptive duty cycle adaptation mechanism in order to provide responsiveness to traffic rate variations for TR-MAC protocol. This mechanism increases throughput and decreases packet delay while maintaining energy-efficiency without any extra information exchange among the sensor nodes in the network.

Evaluation of Geocast Routing Trees on Random and Actual Networks

Efficient geocast routing schemes are needed to transmit messages to mobile networked devices in geographically scoped areas. To design an efficient geocast routing algorithm a comprehensive evaluation of different routing tree approaches is needed. In this paper, we present an analytical study addressing the efficiency of possible routing trees for geocast packets. We evaluate the Shortest Path Tree, Minimum Spanning Tree and a Steiner Heuristic based routing tree for geocast packet distribution on real world networks and random graphs. We compare the results to those for multicast routing for which such evaluations have been performed in the past. Our results show that due to the correlation of geographic distance and network distance in most wired networks, Shortest Path forwarding efficiency can come close to an ideal Steiner Tree. We also identify a correlation between the forwarding efficiency and network characteristics such as the node degree and betweenness. This information could be useful in deciding on a choice of routing method or even help with network design.

Chapter 14 - WiFi Tracking of Pedestrian Behavior

Abstract Tracking pedestrian behavior is receiving increasingly more attention. Various techniques have been used so far, yet tracking through WiFi seems to be the most popular one. This popularity comes from the ubiquity of modern smartphones, of which it is known that most have their WiFi enabled all the time. In this chapter we concentrate exclusively on how this WiFi tracking works, and explain its potentials and pitfalls. Special attention is given to the quality of data from WiFi scanning devices, and how this data can, and should be cleaned up before attempts at extracting information from sets of detected devices. As an illustration of the power of WiFi tracking, we also briefly discuss a few recent results from gathering WiFi data from a large event that attracted over 100,00 people spread across three days.

Inferring the social-connectedness of locations from mobility data

An often discriminating feature of a location is its social character or how well its visitors know each other. In this paper, we address the question of how we can infer the social contentedness of a location by observing the presence of mobile entities in it. We study a large number of mobility features that can be extracted from visits to a location. We use these features for predicting the social tie strengths of the device owners present in the location at a given moment in time, and output an aggregate score of social connectedness for that location. We evaluate this method by testing it on a real-world dataset. Using a synthetically modified version of this dataset, we further evaluate its robustness against factors that normally degrade the quality of such ubiquitously collected data (e.g. noise, sampling frequency). In each case, we found that the accuracy of the proposed method highly outperforms that of a state-of-the-art baseline methodology.

Spaceprint: a Mobility-based Fingerprinting Scheme for Spaces

In this paper, we address the problem of how automated situational awareness in a specific location can be achieved by characterizing the fingerprint of recurrent situations from ubiquitously generated mobility data. Without semantic input about the time and space (location) where situations take place, this turns out to be a fundamental challenging problem. Uncertainties in data also introduce technical challenges when data is generated in irregular time intervals, being mixed with noise, and errors. Purely relying on temporal patterns observable in mobility data, in this paper, we propose Spaceprint, a fully automated algorithm for finding the repetitive pattern of similar situations in spaces. We evaluate this technique by showing how the latent variables describing the actual identity of a space can be discovered from the extracted situation patterns.