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.

/pdf/heed-a-hybrid-energy-efficient-distributed-clustering-4c3cjgsfv9.pdf

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

Improving the performance of TCP over the ATM-UBR service

We study sleep/wake scheduling for low-duty cycle sensor networks. Our work explicitly considers the effect of synchronization error. We focus on a widely used synchronization scheme and show that its synchronization error is nonnegligible and using a conservative guard time is energy wasteful. We formulate an optimization problem that aims to set the capture probability threshold for messages from each individual node such that the expected energy consumption is minimized, and the collective quality of service (QoS) over the nodes is guaranteed. The problem is nonconvex. Nonetheless, we are able to obtain a solution with energy consumption that is provably at most 37% larger than the optimal solution. Simulations demonstrate the efficacy of our solution.

/pdf/optimal-sleep-wake-scheduling-for-time-synchronized-sensor-2pjtoqis8w.pdf

Optimal sleep/wake scheduling for time-synchronized sensor networks with QoS guarantees

There is a critical need for a common evaluation methodology for distributed denial-of-service (DDoS) defenses, to enable their independent evaluation and comparison. We describe our work on developing this methodology, which consists of: (i) a benchmark suite defining the elements necessary to recreate DDoS attack scenarios in a testbed setting, (ii) a set of performance metrics that express a defense system's effectiveness, cost, and security, and (iii) a specification of a testing methodology that provides guidelines on using benchmarks and summarizing and interpreting performance measures. We identify three basic elements of a test scenario: (i) the attack, (ii) the legitimate traffic, and (iii) the network topology including services and resources. The attack dimension defines the attack type and features, while the legitimate traffic dimension defines the mix of the background traffic that interacts with the attack and may experience a denial-of-service effect. The topology/resource dimension describes the limitations of the victim network that the attack targets or interacts with. It captures the physical topology, and the diversity and locations of important network services. We apply two approaches to develop relevant and comprehensive test scenarios for our benchmark suite: (1) we use a set of automated tools to harvest typical attack, legitimate traffic, and topology samples from the Internet, and (2) we study the effect that select features of the attack, legitimate traffic and topology/resources have on the attack impact and the defense effectiveness, and use this knowledge to automatically generate a comprehensive testing strategy for a given defense.

/pdf/benchmarks-for-ddos-defense-evaluation-429fgs2on0.pdf

Benchmarks for DDOS Defense Evaluation

To date, the measurement of user-perceived degradation of quality of service during denial of service (DoS) attacks remained an elusive goal. Current approaches mostly rely on lower level traffic measurements such as throughput, utilization, loss rate, and latency. They fail to monitor all traffic parameters that signal service degradation for diverse applications, and to map application quality-of-service (QoS) requirements into specific parameter thresholds. To objectively evaluate an attack's impact on network services, its severity and the effectiveness of a potential defense, we need precise, quantitative and comprehensive DoS impact metrics that are applicable to any test scenario.We propose a series of DoS impact metrics that measure the QoS experienced by end users during an attack. The proposed metrics consider QoS requirements for a range of applications and map them into measurable traffic parameters with acceptable thresholds. Service quality is derived by comparing measured parameter values with corresponding thresholds, and aggregated into a series of appropriate DoS impact metrics. We illustrate the proposed metrics using extensive live experiments, with a wide range of background traffic and attack variants. We successfully demonstrate that our metrics capture the DoS impact more precisely than the measures used in the past.

/pdf/towards-user-centric-metrics-for-denial-of-service-48790dn1cc.pdf

Sonia Fahmy

Papers

Improving the performance of TCP over the ATM-UBR service

Optimal sleep/wake scheduling for time-synchronized sensor networks with QoS guarantees

Benchmarks for DDOS Defense Evaluation

Towards user-centric metrics for denial-of-service measurement

Path-aware overlay multicast