Manoel Camillo Penna

An Efficient Distributed Algorithm for Constructing Spanning Trees in Wireless Sensor Networks

Monitoring and data collection are the two main functions in wireless sensor networks (WSNs). Collected data are generally transmitted via multihop communication to a special node, called the sink. While in a typical WSN, nodes have a sink node as the final destination for the data traffic, in an ad hoc network, nodes need to communicate with each other. For this reason, routing protocols for ad hoc networks are inefficient for WSNs. Trees, on the other hand, are classic routing structures explicitly or implicitly used in WSNs. In this work, we implement and evaluate distributed algorithms for constructing routing trees in WSNs described in the literature. After identifying the drawbacks and advantages of these algorithms, we propose a new algorithm for constructing spanning trees in WSNs. The performance of the proposed algorithm and the quality of the constructed tree were evaluated in different network scenarios. The results showed that the proposed algorithm is a more efficient solution. Furthermore, the algorithm provides multiple routes to the sensor nodes to be used as mechanisms for fault tolerance and load balancing.

An XML model for SLA definition with key indicators

This work proposes a XML-based model for the specification of service level agreements (SLA). The model has XML elements to define a semantic to represent key performance indicators (KPI) and key quality indicators (KQI) and the relationship between them. Upper and lower thresholds are associated to the indicators in order to indicate warnings or errors conditions. The relationship between the indicators is expressed by reusable functions which are evoked by the XML-based model. An example of reusable function for calculating the KQI service availability based on KPI indicators is also presented in this paper.

Optimal DiffServ AC Design using Non-Linear Programming

Most DiffServ admission control (AC) algorithms rely on tuning parameters to help in the decision making. Tuning these parameters is a difficult task, especially when one considers the problem of assuring QoS guarantees to individual flows. This paper proposes a method for helping the design of DiffServ AC algorithms based on non-linear programming optimization. It enables to find the values for the AC parameters that permits to satisfy the QoS guarantees for individual VoIP flows, while minimizing a cost function that represents the performance goals of the service provider. This approach is used to compare the performance of some commonly used DiffServ AC techniques and also to design a novel AC algorithm based on queue estimates.

A Clustered SDN Architecture for Large Scale WSON

Software defined network (SDN) allows the rethinking of traditional approaches to network design and architecture. The distribution of the unified control-plane can be necessary in several SDN scenarios, particularly for large scale inter-domain optical networks. Distribution is necessary in inter-domain networks due to privacy issues, and can be necessary in large networks to improve scalability and management. This paper proposes a new architectural model in which network elements are grouped by proximity (in clusters) around distributed SDN controllers. The Open Flow protocol with wavelength switching extensions is used for intra-cluster control while inter-cluster coordination is performed by a new control application. The proposed model is applied to large-scale wavelength switched optical networks (WSON) and is validated by simulation. The results show that to increase the number of controllers is not justifiable if the only concern is the setup time performance. However, a multi-cluster approach is advantageous when light paths are created more frequently between nearby nodes. Also, the clustered SDN can be successfully used in a multi-administrative domain, because inter-domain light paths can be created while keeping the privacy of the network information within a cluster.

New models for long-term Internet traffic forecasting using artificial neural networks and flow based information

This paper investigates the use of ensembles of artificial neural networks in predicting long-term Internet traffic. It discusses a method for collecting traffic information based on flows, obtained with the NetFlow protocol, to build the time series. It also proposes four traffic forecasting models based on ensembles of TLFNs (Time-Lagged FeedFoward Networks), each one differing from the others by the way it reads the training data and by the number of artificial neural networks used in the forecasts. The proposed prediction models are confronted with the classic method of Holt-Winters, by comparing the mean absolute percentage error (MAPE) of the forecasts. It is concluded that the proposed models perform well, and can be considered a good option for planning network links that transport Internet traffic.

Modeling a multi-queue network node with a fuzzy predictor

Capacity planning of IP-based networks is a difficult task. Ideally, in order to estimate the maximum amount of traffic that can be carried by the network, without violating QoS requirements such as end-to-end delay and packet loss, it is necessary to determine the queue length distribution of the network nodes under different traffic conditions. When per-flow guarantees are required (e.g., VoIP traffic), it is also necessary to determine the impact of the queue behavior on the performance of individual flows. Analytical models for queue length distribution are available only for relatively simple traffic patterns. This paper proposes a generic method for building a fuzzy predictor for modeling the behavior of a DiffServ node with multiple queues. The method combines nonlinear programming (NLP) and simulation to build a fuzzy predictor capable of determining the performance of a DiffServ node subjected to both per-flow and aggregated performance guarantees. This approach does not require deriving an analytical model, and can be applied to any type of traffic. In this paper, we employ the fuzzy approach to model the behavior of a multi-queue node where (aggregated ON-OFF) VoIP traffic and (self-similar) data traffic compete for the network resources.

A new algorithm for dimensioning resilient optical networks for shared-mesh protection against multiple link failures

Abstract Failures of fiber links can result in major loss of data in high-speed optical communication networks. Survivability is of critical importance, making high levels of availability essential, given the increased level of infrastructure vulnerability to natural disasters, massive power failures, and malicious attacks. A typical approach to the design of resilient optical networks is through protection schemes that predetermine and reserve protection resources based on single and double link-failure scenarios. In this paper we propose a planning heuristic for WDM networks that computes the resource capacity required to transport the traffic demand and protect the optical connections while meeting availability requirements in scenarios of multiple link failures. The method is based on two algorithms, one for path-selection and the other for computing connection unavailability. The numerical results show that the method allows network topology to be exploited to significantly reduce connection unavailability.

A multi-agent approach to optimal channel assignment in WLANs

The performance of a wireless local area network depends on the channel assignments among neighboring access points (APs). The limited number of non-overlapping channels may lead to severe interference scenarios if no appropriated spectrum planning is employed. Additionally, in dense urban areas it is usual to find wireless networks scenarios with interfering APs belonging to different administrative domains. In such cases the use of centralized algorithms is not feasible and the already proposed distributed methods do not guarantee optimal channel assignment. In this paper, we formalize the channel allocation as a distributed constraint optimization problem and propose a new cooperative channel allocation strategy using the distributed pseudotree-optimization procedure (DPOP). The adjacent channel interference is analytically formulated for DPOP. The simulation results show that the proposed strategy always achieves the optimal solution and is scalable in terms of the number of exchanged messages.

A Tunable Fraud Detection System for Advanced Metering Infrastructure using Short-Lived Patterns

We propose a fraud detection systems (FDS) for advanced metering infrastructure based on anomaly detection on the energy consumption reports from smart meters. Fraud inspection is triggered when a discrepancy between the energy supplied by the grid and that reported by smart meters is detected. We use an innovative approach, where consumption reports registered shortly before and after the discrepancy detection are compared to detect a fraud. Our FDS introduces an important innovation by showing that it is possible to use only a small set of recent measures to define a consumption pattern. We call these patterns short-lived because they are expected to represent the behavior of a consumer for a short period, only enough to detect an ongoing fraud. This approach allows the FDS to account for natural changes in the consumption behavior of users and also helps to preserve their privacy. The FDS can be tuned, using an optimization procedure, by imposing constraints on the true or false alarm rates, or maximizing an objective function that represents the revenue of the utility.

A simple iterative positioning algorithm for client node localization in WLANs

The ability to determine in real-time the geographic location of client nodes is an important tool in wireless networks, allowing instantaneous mobile tracking, implementation of location-aware services and also efficient channel and power allocation planning. Among existing classical cooperative localization techniques for wireless networks, the maximum likelihood estimator (MLE) is theoretically the best. However, the gradient-based algorithms that are commonly used for maximum likelihood estimation are quite sensitive to the initial values and cannot achieve the theoretical optimal performance. In this paper, we propose a new iterative positioning algorithm based on received signal strength information that employs a location ordering strategy and a numerical nonlinear optimization method. The algorithm performance is evaluated through simulation for different network scenarios. A real wireless network scenario is also implemented in order to demonstrate the algorithm effectiveness. The proposed algorithm, while presenting a simplified implementation, can achieve better positioning estimates than the classical MLE approach based on the conjugated gradient.