Predicting network connectivity for context-aware ... - CiteSeerX

Predicting network connectivity for context-aware pervasive systems with localized network availability Yves Vanrompay {Yves.Vanrompay,

Peter Rigole

Peter.Rigole, Yolande.Berbers}@cs.kuleuven.be Department of Computer Science Katholieke Universiteit Leuven Belgium

ABSTRACT In pervasive computing environments the availability of network connectivity is expected to evolve in time. We propose a reactive resource scheduling mechanism that relies on a pattern learning method. Patterns in the evolution of network availability in time are predicted with Markov chains. Our scheduling mechanism is able to predict future network connectivity in terms of probabilities and makes use of historical information. The goal is to achieve an efficient use of available resources. By knowing when network connectivity can be expected, energy consumption related to network discovery is lowered. We validated our approach by applying it to a real-life scenario and illustrate initial experimental results with a prototype application.

1.

Yolande Berbers

INTRODUCTION

In pervasive computing environments the availability of network connectivity evolves in time. Moreover, network availability and bandwidth are constrained. By recognizing the current context of the system and reasoning with historical information, network availability can be predicted. Our approach is designed to be deployed in environments with localized network availability. This means that at certain fixed locations network access points are available, whereas in other environments network access is not guaranteed. In a day to day job, it is likely that a person follows a particular path and comes from time to time at access points where there is network availability. The future evolution of network availability can be predicted by using patterns learned from historical information. Several approaches to pattern detection exist. Examples are techniques from machine learning such as case based reasoning, reinforcement learning and artificial neural networks. The approach adopted here is the use of Markov chains. There are several advantages in knowing when to expect network availability. Firstly, collaborative use of the net-

work can be achieved in an environment where a number of mobile devices acting as alter ego of persons are active. When two mobile devices are in each other’s reach and each device knows when it will have network connectivity, critical data could be transferred to the device which predicts to have network connectivity at the earliest moment in the future. By implementing this data hopping a fast transfer of data to a central server can be achieved. Secondly, the high energy consumption related to the continuous polling for network discovery can be avoided. Efficient polling by only doing network discovery at time intervals when network availability is expected, leads to a more efficient use of battery power. The ability to schedule activities like data synchronization that depend on network availability dynamically is of crucial importance for the optimal use of limited resources. Dynamic scheduling leads to the execution of an activity at the right moment. To schedule activities in a dynamic way, information about the current context must be acquired. Static information such as a map of the environment can be consulted. Information about the current location of the person can be obtained using location sensors. By only using the first and the second option, information about the current context can be obtained. However, to predict future evolution of the network connectivity, this information about current context is not enough on its own. Evolution of the network availability can be predicted with historical information and detecting patterns from history. This paper is organized as follows. In section 2 the Markov chain approach to network connectivity prediction is presented. Then, in section 3, an example case study is discussed. To validate the approach a prototype example has been build. Section 4 discusses the experimental results gathered from the example. Finally section 5 gives an overview of related work and section 6 concludes and discusses further research.

2. MARKOV CHAINS FOR NETWORK CONNECTIVITY PREDICTION The evolution in network connectivity is uncertain because a person can follow different paths in performing an activity. We use a probabilistic approach to model this uncertainty. Decisions are made based on the most probable evolution in time of network connectivity. Key context elements to

be taken into account are time, place and activity. The performed activity can depend on the time of the day. We assume that at a certain fixed place there is network connectivity. While performing activities a person walks around on the work floor visiting a number of places. The place where a person is located can be inferred from sensors (eg. RFID) or it can be deduced from the person’s current activities. Each activity has a path associated with it that can be recorded and used to learn patterns. So essentially we are doing path prediction. Based on this path, we predict the expected time interval during which network connectivity will be available. The assumptions we make for the approach taken here to work are the following: • There is a collection of places which a person with a PDA visits. These places are points in the path a person follows.

k-step transition probability matrix represents the probabilities of reaching each state after k transitions, given the current state of the system. The initial probability P r(Xn+1 = j|Xn = i) is 1/m where m is the number of places that can be reached from the current place i. P r(Xn+1 = j|Xn = i) could be updated by counting how often location j is reached from location i and dividing this number by the total amount of locations that were reached from location i. This means however that the past is as important as the present. In most environments the path the user usually takes while doing an activity will evolve through time. Consequently, the transition probability function should be updated in a way that recent transitions have more importance than those from the past. For that, the following exponential smoothing method can be used so that the past is weighted with exponentially decreasing weights:

• At certain fixed locations there are network access points. These locations are known in advance. • The person follows a path that can vary to a certain extent. The followed path depends on the performed activity. • There is a mechanism to infer on a regular basis where the person is located. This location-awareness mechanism does not depend on the current availability of the network. As an example in which these assumptions hold, imagine a service technician who walks around in a building to check machines. There is a most probable path that he follows depending on his activity. From the information about the location of each machine and the data he gives in on his PDA, the location of the technician can be inferred. If the area where there is network connectivity is known, the expected time interval in which an access point will be reached can be computed. In section 3, a case study taken from the medical field will be explored in detail. To do path prediction and thus predict future evolution of network availability, we use Markov chains [4]. A Markov chain describes at successive times the states of a system. Changes of state are called transitions. In our case, the states of the Markov chain correspond to locations a person visits while performing an activity. The transitions represent probabilities of going from one location to another. The series of states of the system has the Markov property. A series with the Markov property is such that the probability of reaching a state in the future, given the current and past states, is the same probability as that given only the current state. So past states give no information about future states. If the machine is in state x at time n, the probability that it moves to state y at time n + 1, depends only on the current state x and not on past states. The transition probability distribution can be represented as a matrix P, called a transition matrix, with the (i, j)th element of P equal to Pij = P r(Xn+1 = j|Xn = i) . This is a stochastic matrix and the k-step transition probability can be computed as the kth power of the transition matrix, P k . The

Pij = αxj + (1 − α)Pij0 Pij0 represents the old probability and xj is the value for the choice taken at location i with respect to location j. Xj is zero or one. If xj = 1 then location j was chosen after i, xj = 0 if not. Using this method, the sum of all outgoing probabilities remains 1, as needed for a transition probability matrix. The parameter α is a real number between 0 and 1 that controls how important recent observations are compared to history. If α is high, the present is far more important than history. In this setting, the system will adapt quickly to the behavior of the user. This can be necessary in a rapidly changing environment or when the system is deployed and starts to learn. In a rather static environment, α can be set low.

3.

CASE STUDY

In this section an example application from the domain of medical health care will be worked out in detail. Mobile solutions for patient care are currently beginning to emerge. However, full coverage of the building by Wifi is not possible in a medical environment. The case study is an example of such a mobile solution. The nurse in a hospital has a PDA on which she can consult information about the patient and by which she can modify or give in new data concerning a patient. Illustration 1 gives a representation of the environment nurses (represented by black bullets) are working in. There is a corridor with patient rooms with beds on both sides. The nurse’s room is located centrally and a Bluetooth access point is located there, represented by the red bullet, which has a certain range represented by the circle c. The data has to be synchronized to a central server for further processing by administrative and medical services. For the moment a nurse has to go to the nurse’s room where there is a computer and a cradle. By pushing a button the data is explicitly synchronized. The goal of the new system is to make data synchronization completely transparent for the nurse. The system has to determine autonomously the best time to synchronize. Because network connectivity must be available to do synchronization, the time interval during which it

Figure 1: Groundplan of medical workfloor is expected to have network connectivity must be predicted. First of all, static information can be used. At fixed times the nurse will be in the nurse’s room to prepare medicine or to report to colleagues at a shift change. During these time intervals it can be expected that there will be network connectivity. Secondly, a model of the environment is used. The model consists primarily of a file that associates patients with rooms and a field that tells us the location of the Bluetooth access point. Lastly, the evolution of network connectivity in time is predicted with the help of Markov chains. Key context elements in this case are time, activity and location. Depending on the time of the day certain activities are more likely to be performed than others. For example around noon there is a high probability that the lunch distribution activity takes place. The followed path is dependent on the activity. Therefore, each activity has its own associated Markov chain. Also the amount of time spent on each patient depends on the activity. The current activity can be inferred by looking at the time and at what data is viewed or modified. For example when the activity is parameter taking, parameters of the patient are given in in the PDA. By giving in parameters of a certain patient and by using a patient-room association file (which is statically given information) it can be determined at what location the nurse is for the moment. Then the most probable path and the expected time interval in which there will be network availability can be computed using Markov chains.

4.

EXPERIMENTATION AND EVALUATION

A simulator has been developed for the case described in the previous section. Illustration 2 shows a screenshot of the program. A model of the environment is used by reading a file that describes rooms, associations between patients and rooms, and the location of the Bluetooth access point (room 106). The path followed by the nurse can be simulated in two ways. In manual simulation, patients can be chosen from the patient list. The program then infers the room where the nurse is located. A second option is to do simulation automatically. In that case a file describing successive locations of the nurse is used. The current location of the nurse can be seen on the map of the rooms and is highlighted in blue. In the lower table the probabilities are

displayed to reach the location with Bluetooth connectivity for nine time intervals in the future. For simplicity the mean time the nurse needs to do her work with each patient is set to five minutes. As can be seen the current location of the nurse is room 104. The program has learned from past observations that after room 104 the next room that will be visited is room 105, and then room 106, were the Bluetooth access point is located. In the prediction table the probability that there will be network connectivity in the second next time interval indeed is highest from all future probabilities. For the moment only experimentation with artificial data has been carried out. We plan to obtain real-life data to test our approach in a more realistic setting.

5.

RELATED WORK

Markov chains and hidden Markov models are used in a wide range of applications. In the domains of speech recognition and the prediction of genome sequences in bioinformatics they have proven to be a fruitful approach. Recently, hidden Markov models have been used to learn movement patterns in a mobile network to perform GSM tracking[3]. Information related to the paths followed by mobile phones can be learned using hidden Markov models and the prediction method allows for the anticipation of resource allocation. This means dynamic scheduling takes place. Chinchilla, Lindsey and Papadopouli [2] use Markov chains to predict to which access point a wireless client will connect, given the last access point the client was connected to. The goal is to improve the performance of the wireless infrastructures by load balancing, admission control and resource reservation across access points. Mobility patterns of clients are learned using historical information. The system has been tested on a university campus with a wireless infrastructure and the next access point a wireless client connects to can be predicted with high accuracy according to experimental results. Papadopouli, Shen and Spanakis [7] present a methodology that shows how mobility patterns and associations between users and access points evolve not only in space, but also in time. Therefore wireless access patterns are characterized based on stochastic parameters such as visit duration. Liu, Bahl and Chlamtac [6] propose a

Figure 2: Screenshot of the simulator mobility modeling and trajectory prediction algorithm for wireless ATM networks. The location prediction algorithm makes use of semi-Markov processes. Chakraborty, Yau and Lui [1] evaluate several heuristics that, based on the movement history of a mobile client, estimate an optimal time for communication. Time is optimal when the least energy will be used. The goal is thus to minimize the energy consumption necessary for wireless communication. Statistical information about a client’s movement history is represented as heuristics based on Markov models.

6.

CONCLUSIONS AND FUTURE WORK

The use of Markov chains to do path prediction in environments with localized network connectivity is a promising way to achieve the goal of efficient use of available resources. The approach is conceptually simple and computationally inexpensive. The case taken from the health care domain shows that there are settings where the assumptions made in our research hold and where the proposed mechanism can effectively be used. However, to validate our approach, more experimentation is needed. The developed simulator can be used as the basis for future experimentations with real-life data. Also, the influence of the value of the parameter alpha on the learning process has to be evaluated.

7.

ACKNOWLEDGMENTS

This research is carried out in the context of the IWTTETRA project 50093 [5].

8.

REFERENCES

[1] S. Chakraborty, D. Yau, and J. Lui. On the effectiveness of movement prediction to reduce energy consumption in wireless communication, 2003. Extended Abstract in: Joint International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS), San Diego, CA, June 2003. [2] F. Chinchilla, M. Lindsey, and M. Papadopouli. Analysis of wireless information locality and association patterns in a campus, 2004. [3] Jean-Marc Francois, Guy Leduc, and Sylvain Martin. Learning movement patterns in mobile networks: A generic method. [4] http://en.wikipedia.org/wiki/Markov chain.

[5] http://ingenieur.kahosl.be/projecten/rabbit. [6] T. Liu, P. Bahl, and I. Chlamtac. Mobility modeling, location tracking, and trajectory prediction in wireless networks, 1998. [7] Maria Papadopouli, Haipeng Shen, and Manolis Spanakis. Characterizing the mobility and association patterns of wireless users in a campus.