Aylin Kantarci

Time Synchronization Based on Slow-Flooding in Wireless Sensor Networks

The accurate and efficient operation of many applications and protocols in wireless sensor networks require synchronized notion of time. To achieve network-wide time synchronization, a common strategy is to flood current time information of a reference node into the network, which is utilized by the de facto time-synchronization protocol Flooding Time-Synchronization Protocol (FTSP). In FTSP, the propagation speed of the flood is slow because each node waits for a given period of time to propagate its time information about the reference node. It has been shown that slow-flooding decreases the synchronization accuracy and scalability of FTSP drastically. Alternatively, rapid-flooding approach is proposed in the literature, which allows nodes to propagate time information as quickly as possible. However, rapid flooding is difficult and has several drawbacks in wireless sensor networks. In this paper, our aim is to reduce the undesired effect of slow-flooding on the synchronization accuracy without changing the propagation speed of the flood. Within this context, we realize that the smaller the difference between the speeds of the clocks, the smaller the undesired effect of waiting times on the synchronization accuracy. In the light of this realization, our main contribution is to show that the synchronization accuracy and scalability of slow-flooding can drastically be improved by employing a clock speed agreement algorithm among the sensor nodes. We present an evaluation of this strategy on a testbed setup including 20 MICAz sensor nodes. Our theoretical findings and experimental results show that employing a clock speed agreement algorithm among the sensor nodes drastically improves the synchronization accuracy and scalability of slow-flooding.

External Gradient Time Synchronization in Wireless Sensor Networks

Synchronization to an external time source such as Coordinated Universal Time (UTC), i.e., external synchronization, while preserving tight synchronization among neighboring sensor nodes may be crucial for applications such as determining the speed of a moving object in wireless sensor networks. However, existing time synchronization protocols in the literature, which can be used for external synchronization, poorly synchronize neighboring nodes. On the other hand, the only protocol that aims at optimizing the synchronization error between neighboring nodes is lack of a mechanism which synchronizes sensor nodes to a reference node, and hence, it cannot provide external synchronization. Therefore, there is a lack in the literature of a time synchronization protocol, which can be used by applications demanding both external synchronization and tight synchronization among neighboring nodes. In this paper, we answer the question of whether it is possible for sensor nodes to synchronize to a reference node while they optimize the clock skew between their neighboring nodes at the same time. Within this context, we present a novel time synchronization protocol, namely External Gradient Time Synchronization Protocol (EGSync). In EGSync, each sensor node synchronizes to a reference node by using time information flooded by this node, as well as synchronizes to its neighboring nodes by employing the agreement algorithm. We implemented EGSync on the MICAz platform using TinyOS and evaluated it on a testbed setup including 20 sensor nodes. We present the experimental results on our testbed and the simulation results for networks with larger diameters and densities.

Tracking Fast Moving Targets in Wireless Sensor Networks

Abstract We propose a dynamic distributed algorithm for tracking objects that move fast in a sensor network. In the earlier efforts in tracking moving targets, the current leader node at time t predicts the location only for time t + 1 and if the target moves in high speed, it can pass by a group of nodes very fast without being detected. Therefore, as the target increases its speed, the probability of missing that target also increases. In this study, we propose a target tracking system that predicts future k locations of the target and awakens the corresponding leader nodes so that the nodes along the trajectory self organize to form the clusters to collect data related to the target in advance and thus reduce the target misses. The algorithm first provides detection of the target and forms a cluster with the neighboring nodes around it. After the selection of the cluster leader, the coordinates of the target is estimated using localization methods and cooperation between the cluster nodes under the control of the leader node. The coordinates and the speed of the target are then used to estimate its trajectory. This information in turn provides the location of the nodes along the estimated trajectory which can be awaken, hence providing tracking of the moving object. We describe the algorithm, analyze its efficiency and show by simulations that it performs well to track very fast moving objects with speeds much higher than reported in literature.

Performance evaluation of cluster-based target tracking protocols for wireless sensor networks

Target tracking is an important application type for wireless sensor networks (WSN). Recently, various approaches [1–11] are proposed to maintain the accurate tracking of the targets as well as low energy consumption. Clustering is a fundamental technique to manage the scarce network resources [12–19]. The message complexity of an application can be significantly decreased when it is redesigned on top of a clustered network. Clustering has provided an efficient infrastructure in many existing studies [1–8]. The clusters can be constructed before the target enters the region which is called the static method [1–4] or clusters are created by using received signal strength (RSS) from target which is called the dynamic method [5–8]. In this paper we provide simulations of static and dynamic clustering algorithms against various mobility models and target speeds. The mobility models that we applied are Random Waypoint Model, Random Direct Model and Gauss Markov Model. We provide metrics to measure the tracking performance of both approaches. We show that the dynamic clustering is favorable in terms of tracking accuracy whereas the energy consumption of static clustering is significantly smaller. We also show that the target moving with Gauss Markov Model can be tracked more accurately than the other models.

Robust Quality Adaptation for Internet Video Streaming

Internet video streaming is a widely popular application however, in many cases, congestion control facilities are not well integrated into such applications. In order to be fair to other users that do not stream video, rate adaptation should be performed to respond to congestion. On the other hand, the effect of rate adaptation on the viewer should be minimized and this extra mechanism should not overload the client and the server. In this paper, we develop a heuristic approach for unicast congestion control. The primary feature of our approach is the two level adaptation algorithm that utilizes packet loss rate as well as receiver buffer data to maintain satisfactory buffer levels at the receiver. This is particularly important if receiver has limited buffer such as in mobile devices. When there is no congestion, to maintain best buffer levels, fine grain adjustments are carried out at the packet level. Depending on the level of congestion and receiver buffer level, rate shaping that involves frame discard and finally rate adaptation by switching to a different pre-encoded video stream are carried out. Additive increase multiplicative decrease policy is maintained to respond to congestion in a TCP- friendly manner. The algorithm is implemented and performance results show that it has adaptation ability that is suitable for both local area and wide area networks.

Drift estimation using pairwise slope with minimum variance in wireless sensor networks

Time synchronization is mandatory for applications and services in wireless sensor networks which demand common notion of time. If synchronization to stable time sources such as Coordinated Universal Time (UTC) is required, employing the method of flooding in order to provide time synchronization becomes crucial. In flooding based time synchronization protocols, current time information of a reference node is periodically flooded into the network. Sensor nodes collect the time information of the reference node and perform least-squares regression in order to estimate the reference time. However, least-squares regression exhibits a poor performance since sensor nodes far away from the reference node collect the time information with large deviations. Due to this fact, the slopes of their least-squares line exhibit large errors and instabilities. As a consequence, the reference time estimates of these nodes also exhibit large errors.This paper proposes a new slope estimation strategy for linear regression to be used by flooding based time synchronization protocols. The proposed method, namely Pairwise Slope With Minimum Variance (PSMV), calculates the slope of the estimated regression line by considering the pairwise slope between the earliest and the most recently collected data points. The PSMV slope is less affected by the large errors on the received data, i.e. it is more stable, and it is more computationally efficient when compared to the slope of the least-squares line. We incorporated PSMV into two flooding based time synchronization protocols, namely Flooding Time Synchronization Protocol (FTSP) and PulseSync. Experimental results collected from a testbed setup including 20 sensor nodes show that PSMV strategy improves the performance of FTSP by a factor of 4 and preserves the performance of PulseSync in terms of synchronization error with 40% less CPU overhead for linear regression. Our simulations show that these results also hold for networks with larger diameters and densities.

Design and implementation of a distributed teleradiaography system: DIPACS

Medical imaging informatics has origins spanning back over two or more decades. Currently, sharing of radiological images for diagnosis, collaborative and administrative purposes is one of the challenging issues in both medicine and computer science. In this study, we designed and implemented a distributed PACS system, namely DIPACS, for small and medium scale medical networks. DIPACS forms a virtual organization by combining the storage of health centers and providing transparent access to images. In this study, we introduce the DIPACS architecture together with the implementation details.

A dynamic lookahead tree based tracking algorithm for wireless sensor networks using particle filtering technique

Display Omitted The proposed system tracks very fast moving targets accurately.The proposed system tracks linear and nonlinear moving targets with less miss ratios.The proposed system includes dynamic and adaptive clustering mechanism.Less miss ratios, message transmissions and energy consumptions are obtained.Simple calculations used in the system result in less computational overhead. In this study, five different algorithms are provided for tracking targets that move very fast in wireless sensor networks. The first algorithm is static and clusters are formed initially at the time of network deployment. In the second algorithm, clusters that have members at one hop distance from the cluster head are provided dynamically. In the third algorithm, clustered trees where members of a cluster may be more than one hop distance from the cluster head are provided dynamically. In the fourth, algorithm lookahead trees are formed along the predicted trajectory of the target dynamically. Linear, Kalman and particle filtering techniques are used to predict the targets next state. The algorithms are compared for linear and nonlinear motions of the target against tracking accuracy, energy consumption and missing ratio parameters. Simulation results show that, for all cases, better performance results are obtained in the dynamic lookahead tree based tracking approach.

Bandwidth-effective streaming of educational medical videos

Real-time delivery of medical videos requires high level of quality of service that shows no tolerance to loss and delay. In this study, how content-adaptive streaming can aid to maintain high-quality streaming sessions is investigated. The proposed strategy allocates a specific amount of bandwidth to streaming sessions and takes advantage of the general structure of instructional medical videos to decrease bandwidth consumption below this limit. The main contribution of the proposed mechanism is that it eliminates the need for rate adaptation which is common in traditional streaming by employing a proper bandwidth management scheme that enables constant bandwidth consumption below the allocated capacity. A streaming system based on the proposed mechanism has been implemented and its performance has been tested via emulation. Experimental results indicate that content-adaptive streaming successfully eliminates the need of rate adaptation during the delivery of critical data and hence preserves viewers’ satisfaction.

Streaming of scalable h.264 videos over the Internet

To investigate the benefits of scalable codecs in the case of rate adaptation problem, a streaming system for scalable H.264 videos has been implemented. The system considers congestion level in the network and buffer status at the client during adaptation process. The rate adaptation algorithm is content adaptive. It selects an appropriate substream from the video file by taking into account the motion dynamics of video. The performance of the system has been tested under congestion-free and congestion scenarios. The performance results indicate that the system reacts to congestion properly and can be used for Internet video streaming where losses occur unpredictably.