Data Mining - Concepts and Techniques.

Data Mining Practical Machine Learning Tools and Techniques

Topology control in a sensor network balances load on sensor nodes and increases network scalability and lifetime. Clustering sensor nodes is an effective topology control approach. We propose a novel distributed clustering approach for long-lived ad hoc sensor networks. Our proposed approach does not make any assumptions about the presence of infrastructure or about node capabilities, other than the availability of multiple power levels in sensor nodes. We present a protocol, HEED (Hybrid Energy-Efficient Distributed clustering), that periodically selects cluster heads according to a hybrid of the node residual energy and a secondary parameter, such as node proximity to its neighbors or node degree. HEED terminates in O(1) iterations, incurs low message overhead, and achieves fairly uniform cluster head distribution across the network. We prove that, with appropriate bounds on node density and intracluster and intercluster transmission ranges, HEED can asymptotically almost surely guarantee connectivity of clustered networks. Simulation results demonstrate that our proposed approach is effective in prolonging the network lifetime and supporting scalable data aggregation.

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HEED: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks

http://cpham.perso.univ-pau.fr/ENSEIGNEMENT/PAU-UPPA/RESA-M2/Clustering-abasi.pdf

A survey on clustering algorithms for wireless sensor networks

Topology Control (TC) is one of the most important techniques used in wireless ad hoc and sensor networks to reduce energy consumption (which is essential to extend the network operational time) and radio interference (with a positive effect on the network traffic carrying capacity). The goal of this technique is to control the topology of the graph representing the communication links between network nodes with the purpose of maintaining some global graph property (e.g., connectivity), while reducing energy consumption and/or interference that are strictly related to the nodes' transmitting range. In this article, we state several problems related to topology control in wireless ad hoc and sensor networks, and we survey state-of-the-art solutions which have been proposed to tackle them. We also outline several directions for further research which we hope will motivate researchers to undertake additional studies in this field.

/pdf/topology-control-in-wireless-ad-hoc-and-sensor-networks-3e17u396zt.pdf

Topology Control in Wireless Ad Hoc and Sensor Networks

We study the buffering requirements for zero cell loss for TCP over ABR. We show that the maximum buffers required at the switch is proportional to the maximum round trip time (RTT) of all VCs through the link. The number of round-trips depends upon the the switch algorithm used. With our ERICA [erica-final] switch algorithm, we find that the buffering required is independent of the number of TCP sources. We substantiate our arguments with simulation results.

Buffer Requirements For TCP/IP Over ABR

The Transmission Control Protocol (TCP) is a reliable conne ction-oriented stream protocol in the Internet Protocol suite. A TCP connection is like a virtual circuit be tw en two computers, conceptually very much like a telephone connection. To maintain this virtual circu it, TCP at each end needs to store information on the current status of the connection, e.g., the last byte sen t. TCP is called connection-oriented because it starts with a 3-way handshake, and because it maintains this tate information for each connection. TCP is called a stream protocol because it works with units of bytes .

/pdf/tcp-congestion-control-overview-and-survey-of-ongoing-5gd4n5unzd.pdf

TCP Congestion Control: Overview and Survey Of Ongoing Research

With the deployment of large-scale sensor-based decision support systems, quality assurance of decision-making becomes a must. This underscores the requirement of assessing trustworthiness of sensor data and the owners of this data. Provenance-based trust evaluation frameworks use data provenance along with data values to compute the trustworthiness of each data item. However, in a sizeable multi-hop network, provenance information requires a large and variable number of bits in each packet, which, in turn, results in high energy dissipation with extended period of radio communication, making trust systems unusable. We propose an energy-efficient provenance transmission and construction scheme, which we refer to as Probabilistic Provenance Flow (PPF). To the best of our knowledge, ours is the first approach to adapt the Probabilistic Packet Marking (PPM) approach of IP traceback for sensor networks. We propose two bit-efficient complementary provenance encoding and construction methods, and combine them to deal with topological changes in the network. Our TOSSIM simulations demonstrate that PPF requires at least 33% fewer packets and consumes 30% less energy than PPM-based approaches of IP traceback to construct provenance, yet still provides high accuracy in trust score calculation.

Energy-efficient provenance transmission in large-scale wireless sensor networks

Many network services such as voice, video, and collaborative applications require an informed view of network characteristics for effective operation. Sharing a network measurement service across multiple applications can significantly reduce measurement overhead, and remove the burden of performing network measurements from individual applications. To be effectively shared, a network measurement service must provide a variety of measurements to applications on-demand, including end-to-end available bandwidth, delay, and loss. We propose such a service, with a focus on quantifying and bounding the impact of active measurements on the network resources being measured. Resource bounds are necessary for wide-scale deployment, since not all users of the service can be trusted. The service informs applications of how service bounds affect their measurements. We introduce methods to characterize the behavior of active measurements for use in admission control and scheduling decisions. We evaluate our methods in experiments under realistic scenarios on the Emulab testbed.

Resource management in an active measurement service

Network monitoring capabilities are critical for both network operators and networked applications. In the context of an experimental test facility, network measurement is important for researchers experimenting with new network architectures and applications, as well as operators of the test facility itself. The Global Environment for Network Innovations (GENI) is a sophisticated test facility comprised of multiple “control frameworks.” In this paper, we describe the design and implementation of S3 Monitor, a scalable and extensible monitoring service for GENI. A key feature of S3 Monitor is a flexible design that allows easy “plug in” of new network measurement tools. We discuss our deployment experiences with S3 Monitor on GENI, and give experimental results to quantify the performance and system footprint of S3 Monitor. We find that the S3 Monitor service is light-weight and scales well as the number of paths to be monitored increases.

/pdf/design-and-evaluation-of-the-s-3-monitor-network-measurement-36xao0872a.pdf

Sonia Fahmy

Papers

Buffer Requirements For TCP/IP Over ABR

TCP Congestion Control: Overview and Survey Of Ongoing Research

Energy-efficient provenance transmission in large-scale wireless sensor networks

Resource management in an active measurement service

Design and evaluation of the S 3 monitor network measurement service on GENI