Sahin Cem Geyik

Conditional shortest path routing in delay tolerant networks

Delay tolerant networks are characterized by the sporadic connectivity between their nodes and therefore the lack of stable end-to-end paths from source to destination. Since the future node connections are mostly unknown in these networks, opportunistic forwarding is used to deliver messages. However, making effective forwarding decisions using only the network characteristics (i.e. average intermeeting time between nodes) extracted from contact history is a challenging problem. Based on the observations about human mobility traces and the findings of previous work, we introduce a new metric called conditional intermeeting time, which computes the average intermeeting time between two nodes relative to a meeting with a third node using only the local knowledge of the past contacts. We then look at the effects of the proposed metric on the shortest path based routing designed for delay tolerant networks. We propose Conditional Shortest Path Routing (CSPR) protocol that routes the messages over conditional shortest paths in which the cost of links between nodes is defined by conditional intermeeting times rather than the conventional intermeeting times. Through trace-driven simulations, we demonstrate that CSPR achieves higher delivery rate and lower end-to-end delay compared to the shortest path based routing protocols that use the conventional intermeeting time as the link metric.

Robust Dynamic Service Composition in Sensor Networks

Service modeling and service composition are software architecture paradigms that have been used extensively in web services where there is an abundance of resources. They mainly capture the idea that advanced functionality can be realized by combining a set of primitive services provided by the system. Many efforts in web services domain focused on detecting the initial composition, which is then followed for the rest of service operation. In sensor networks, however, communication among nodes is error-prone and unreliable, while sensor nodes have constrained resources. This dynamic environment requires a continuous adaptation of the composition of a complex service. In this paper, we first propose a graph-based formulation for modeling sensor services that maps to the operational model of sensor networks and is amenable to analysis. Based on this model, we formulate the process of sensor service composition as a cost-optimization problem and show that it is NP-complete. Two heuristic methods are proposed to solve the composition problem: the top-down and the bottom-up approaches. We discuss centralized and distributed implementations of these methods. Finally, using ns-2 simulations, we evaluate the performance and overhead of our proposed methods.

Efficient routing in delay tolerant networks with correlated node mobility

In a delay tolerant network (DTN), nodes are connected intermittently and the future node connections are mostly unknown. Since in these networks, a fully connected path from source to destination is unlikely to exist, message delivery relies on opportunistic routing. However, effective forwarding based on a limited knowledge of contact behavior of nodes is challenging. Most of the previous studies looked at only the pairwise node relations to decide routing. In contrast, in this paper, we analyze the correlation between the meetings of each node with other nodes and focus on the utilization of this correlation for efficient routing of messages. We introduce a new metric called conditional intermeeting time, which computes the average intermeeting time between two nodes relative to a meeting with a third node using only the local knowledge of the past contacts. Then, we show how we can utilize the proposed metric on the existing DTN routing protocols to improve their performance. For shortest-path based routing protocols in DTNs, we propose to route messages over conditional shortest paths in which the link cost between nodes are defined by conditional intermeeting times. Moreover, for metric-based forwarding protocols, we propose to use conditional intermeeting time as an additional delivery metric while making forwarding decisions of messages. Our trace-driven simulations on three different datasets show that the modified algorithms perform better than the original ones.

Dynamic Composition of Services in Sensor Networks

Service modeling and composition is a fundamental method for offering advanced functionality by combining a set of primitive services provided by the system. Unlike in the case of web services for which there is an abundance of reliable resources, in sensor networks, the resources are constrained and communication among nodes is error-prone and unreliable. Such a dynamic environment requires a continuous adaptation of the composition of services. In this paper, we first propose a graph-based model of sensor services that maps to the operational model of sensor networks and is amenable to analysis. Based on this model, we formulate the process of sensor service composition as a cost-optimization problem, which is NP-complete. We then propose two heuristic methods for its solution, the top-down and the bottom-up, and discuss their centralized and distributed implementations. Using simulations, we evaluate their performance.

Event Recognition in Sensor Networks by Means of Grammatical Inference

Modern military and civilian surveillance applica- tions should provide end users with the high level representation of events observed by sensors rather than with the raw data measurements. Hence, there is a need for a system that can infer higher level meaning from collected sensor data. We demonstrate that probabilistic context free grammars (PCFGs) can be used as a basis for such a system. To recognize events from raw sensor network measurements, we use a PCFG inference method based on Stolcke(1994) and Chen(1996). We present a fast algorithm for deriving a concise probabilistic context free grammar from the given observational data. The algorithm uses an evaluation metric based on Bayesian formula for maximizing grammar a posteriori probability given the training data. We also present a real-world scenario of monitoring a parking lot and the simulation based on this scenario. We described the use of PCFGs to recognize events in the results of such a simulation. We finally demonstrate the deployment details of such an event recognition system. its construction. Second, we are using a novel method for fast calculation of the metric that takes advantage of the properties of the chunk and merge operations, originally introduced by Stolcke. Please note that what we are presenting in this paper is actually a supervised learning method because the definitions of the events to be recognized are initially given to the learning algorithm. Then, the recognition phase takes place during which new data are classified into events using the grammar created in the learning stage. The rest of the paper is organized as follows. We start with a basic introduction to PCFGs. The next section describes the previous work on PCFG inference methods and applications of PCFGs to event detection in WSNs. Then, we define our method for grammar inference and analyze its complexity. Finally, we present a real world scenario of parking lot monitoring and its simulations, as well as deployment details of such a system on a sensor network.

Utilizing correlated node mobility for efficient DTN routing

In a delay tolerant network (DTN), nodes are connected intermittently and the future node connections are mostly not known. Therefore, effective forwarding based on limited knowledge of contact behavior of nodes is challenging. Most of the previous studies assumed that mobility of a node is independent from mobility of other nodes and looked at only the pairwise node relations to decide routing. In contrast, in this paper, we analyze the temporal correlation between the meetings of each node with other nodes and utilize this correlation for efficient routing. We introduce a new metric called conditional intermeeting time (CIT), which computes the average intermeeting time between two nodes relative to a meeting with a third node. Then, we modify existing DTN routing protocols using the proposed metric to improve their performance. Extensive simulations based on real and synthetic DTN traces show that the modified algorithms perform better than the original ones.

Policy-Aware Service Composition in Sensor Networks

Sensor applications are typically composed of a number of functional components that run distributedly on the nodes of a sensor network, communicating and interacting with one another. Service composition is emerging as a viable approach towards the automatic synthesis of such sensor applications. However, for service composition to be practical, it has to comply with policies that define security and management constraints on the use of these service components and the interconnections amongst them. Prior research efforts have primarily focused on efficient evaluation of security policies during the composition process, which is not sufficient when generic network management constraints need to be expressed and evaluated. In this work, we propose a policy model and evaluation approach that enables us to define and check attribute-based policies, for controlling the sensor service composition process. Attribute-based policies are generic and allows us to express a wider spectrum of constraints than currently possible. Using this model and based on a previously-proposed sensor service composition algorithm, we introduce a policy evaluation method that allows for efficient checking of policy constraints. We further present a novel implementation of the proposed approach in the IBM Sensor Fabric, a middleware framework that simplifies the development of distributed, sensor network services. We also present preliminary performance evaluation results using our prototype.

PCFG Based Synthetic Mobility Trace Generation

This paper introduces a novel method of generating mobility traces based on Probabilistic Context Free Grammars (PCFGs). A PCFG is a generalization of a context free grammar in which each production rule is augmented with a probability with which this production is applied during sentence generation. A concise PCFG can be inferred from the given real world trace collected from the actual mobile node behaviors. The resulting grammar can be used to generate sequences of arbitrary length mimicking the mobile node behavior. This is important when new protocol designs for mobile networks are tested by simulation. In the paper, we describe the methods developed to construct such grammars from training data mobility history). We also discuss how to generate the synthetic data with an already constructed grammar. We present the experimental results on two real data sets, measuring similarity of the actual traces with the synthetic ones. We compare our grammar based method to a 2-level Markov Model based trace generation method. The results demonstrate that the grammar based approach works as an excellent compression method for the actual data. On many metrics, the synthetic data generated from the PCFG match the training data much better than the one generated by the Markov Model.

Biologically Inspired Self Selective Routing with Preferred Path Selection

This paper presents a biologically inspired routing protocol called Self Selective Routing with preferred path selection (SSRP). Its operation resembles the behavior of a biological ant that finds a food source by following the strongest pheromone scent left by scout ants at each fork of a path. Likewise, at each hop of a multi-hop path, a packet using the Self Selective Routing (SSR) protocol moves to the node with the shortest hop distance to the destination. Each intermediate node on a route to the destination uses a transmission back-off delay to select a path to follow for each packet of a flow. Neither an ant nor a packet knows in advance the route that each will follow as it is decided at each step. Therefore, when a route becomes severed by a failure, they can dynamically and locally adjust their routing to traverse the shortest surviving path. Preferred path selection reduces transmission delay by essentially removing back-off delay for the node that carried the previous packet of the same flow. The results reported here for both simulation and execution of a MicaZ mote implementation, show that this is an efficient and fault-tolerant protocol with small transmission delay, high reliability and high delivery rate.

Grammatical Inference for Modeling Mobility Patterns in Networks

Modeling of the mobility patterns arising in computer networks requires a compact and faithful representation of the mobility data collected from observations and measurements of the relevant network applications. This data can range from the information on the mobility of the agents that are being monitored by a wireless network to mobility information of nodes in mobile network applications. In this paper, we examine the use of probabilistic context-free grammars as the modeling framework for such data. We present a fast algorithm for deriving a concise probabilistic context-free grammar from the given training data. The algorithm uses an evaluation metric based on Bayesian formula for maximizing grammar a posteriori probability given the training data. We describe the application of this algorithm in two mobility modeling domains: 1) recognizing mobility patterns of monitored agents in different event data sets collected by sensor networks, and 2) modeling and generating node movements in mobile networks. We also discuss the models performance in simulations utilizing both synthetic and real-world mobility traces.