Data Mining - Concepts and Techniques.

1. Introduction and Overview. 2. Detecting Influential Observations and Outliers. 3. Detecting and Assessing Collinearity. 4. Applications and Remedies. 5. Research Issues and Directions for Extensions. Bibliography. Author Index. Subject Index.

Regression Diagnostics: Identifying Influential Data and Sources of Collinearity

In this paper we seek to improve our understanding of human mobility in terms of social structures, and to use these structures in the design of forwarding algorithms for Pocket Switched Networks (PSNs) Taking human mobility traces from the real world, we discover that human interaction is heterogeneous both in terms of hubs (popular individuals) and groups or communities We propose a social based forwarding algorithm, BUBBLE, which is shown empirically to improve the forwarding efficiency significantly compared to oblivious forwarding schemes and to PROPHET algorithm We also show how this algorithm can be implemented in a distributed way, which demonstrates that it is applicable in the decentralised environment of PSNs

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Bubble rap: social-based forwarding in delay tolerant networks

The increasing penetration of smart devices with networking capability form novel networks Such networks, also referred as pocket switched networks (PSNs), are intermittently connected and represent a paradigm shift of forwarding data in an ad hoc manner The social structure and interaction of users of such devices dictate the performance of routing protocols in PSNs To that end, social information is an essential metric for designing forwarding algorithms for such types of networks Previous methods relied on building and updating routing tables to cope with dynamic network conditions On the downside, it has been shown that such approaches end up being cost ineffective due to the partial capture of the transient network behavior A more promising approach would be to capture the intrinsic characteristics of such networks and utilize them in the design of routing algorithms In this paper, we exploit two social and structural metrics, namely centrality and community, using real human mobility traces The contributions of this paper are two-fold First, we design and evaluate BUBBLE, a novel social-based forwarding algorithm, that utilizes the aforementioned metrics to enhance delivery performance Second, we empirically show that BUBBLE can substantially improve forwarding performance compared to a number of previously proposed algorithms including the benchmarking history-based PROPHET algorithm, and social-based forwarding SimBet algorithm

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BUBBLE Rap: Social-Based Forwarding in Delay-Tolerant Networks

The Transmission Control Protocol.

Delay-tolerant networks (DTNs) have the potential to interconnect devices in regions that current networking technology cannot reach. To realize the DTN vision, routes must be found over multiple unreliable, intermittently-connected hops. In this paper we present a practical routing protocol that uses only observed information about the network. We designed a metric that estimates the average waiting time for each potential next hop. This learned topology information is distributed using a link-state routing protocol, where the link-state packets are "flooded" using epidemic routing. The routing is recomputed each time connections are established, allowing messages to take advantage of unpredictable contacts. A message is forwarded if the topology suggests that the connected node is "closer" to the destination than the current node. We demonstrate through simulation that our protocol provides performance similar to that of schemes that have global knowledge of the network topology, yet without requiring that knowledge. Further, it requires significantly less resources than the alternative, epidemic routing, suggesting that our approach scales better with the number of messages in the network. This performance is achieved with minimal protocol overhead for networks of approximately 100 nodes.

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Practical Routing in Delay-Tolerant Networks

Delay-tolerant networks (DTNs) have the potential to connect devices and areas of the world that are under-served by current networks. A critical challenge for DTNs is determining routes through the network without ever having an end-to-end connection, or even knowing which "routers" will be connected at any given time. Prior approaches have focused either on epidemic message replication or on knowledge of the connectivity schedule. The epidemic approach of replicating messages to all nodes is expensive and does not appear to scale well with increasing load. It can, however, operate without any prior network configuration. The alternatives, by requiring a priori connectivity knowledge, appear infeasible for a self-configuring network.In this paper we present a practical routing protocol that only uses observed information about the network. We designed a metric that estimates how long a message will have to wait before it can be transferred to the next hop. The topology is distributed using a link-state routing protocol, where the link-state packets are "flooded" using epidemic routing. The routing is recomputed when connections are established. Messages are exchanged if the topology suggests that a connected node is "closer" than the current node.We demonstrate through simulation that our protocol provides performance similar to that of schemes that have global knowledge of the network topology, yet without requiring that knowledge. Further, it requires a significantly smaller quantity of buffer, suggesting that our approach will scale with the number of messages in the network, where replication approaches may not.

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Practical routing in delay-tolerant networks

In this tutorial wireless mesh networks (WMNs) is discussed, which has the routing protocols of actual capacity. The multi-radio systems, technical problems are also described in this issue, such that the WMN have significant congestion control and fairness problems.

Wireless mesh networks

Multi-hop wireless networks have the potential to dramatically reduce the cost of deploying communication infrastructure. However, the nature of this technology limits the capacity of radio links. Thus, it is important to utilize them as efficiently as possible. In this paper, we investigate load balancing across multiple paths as a possible mechanism to improve performance in multi-hop wireless networks. Given the inherent interference of multi-hop transmissions in a single radio channel, it is generally assumed that single-channel multipath routing cannot provide any benefits, but in fact would have detrimental effects on resource efficiency. However, a careful investigation of the issue reveals that under certain theoretic conditions, significant gains are possible. In fact, we show throughput improvements of 80-100% in some scenarios. We present a novel interference metric to assess the quality of a set of disjoint paths. We further present a heuristic path selection algorithm to find appropriate routing paths in structured networks, which is a first step towards the application of our basic results in realistic scenarios.

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Multipath load balancing in multi-hop wireless networks

Correlations among management metrics in software systems allow errors to be detected and their cause localized. Prior research shows that linear models can capture many of these correlations. However, our research shows that several factors may prevent linear models from accurately describing correlations, even if the underlying relationship is linear. Two common phenomena we have observed are relationships that evolve, typically with time, and heterogeneous variance of the correlated metrics. Two-variable linear models proposed thus far fail to capture these phenomena, and thus fail to describe system dynamics correctly. Often, these phenomena are caused by a missing variable. However, searching for three-variable correlations is O(n3) for n metrics, which is costly for systems with many metrics. In this paper we address the above challenges by improving on two-variable Ordinary Least Squares regression models. We validate our models using a realistic Java-Enterprise-Edition application. Using fault-injection experiments we show that our improved models capture system behavior accurately. We detect errors within 8 sample periods on average from the injection of the fault, which is less than half the time required by the current linear-model approach.

Paul A. S. Ward

Papers

Practical Routing in Delay-Tolerant Networks

Practical routing in delay-tolerant networks

Wireless mesh networks

Multipath load balancing in multi-hop wireless networks

System monitoring with metric-correlation models: problems and solutions