Supporting Service Differentiation with Enhancements of the IEEE 802.11 MAC Protocol: Models and Analysis

As one of the fastest growing wireless access technologies, Wireless LANs must evolve to support adequate degrees of service differentiation. Unfortunately, current WLAN standards like IEEE 802.11 Distributed Coordination Function (DCF) lack this ability. Work is in progress to define an enhanced version capable of supporting QoS for multimedia traffic at the MAC layer. In this paper, we aim at gaining insight into three mechanisms to differentiate among traffic categories, i.e., differentiating the minimum contention window size, the Inter-Frame Spacing (IFS) and the length of the packet payload according to the priority of different traffic categories. We propose an analysis model to compute the throughput and packet transmission delays. In additions, we derive approximations to get simpler but more meaningful relationships among different parameters. Comparisons with discrete-event simulation results show that a very good accuracy of performance evaluation can be achieved by using the proposed analysis model

The Reputation, Opinion, Credibility and Quality (ROCQ) Scheme

An implicit assumption of trust in the participants is at the basis of most Peer-to-Peer (P2P) networks. However, in practice, not all participants are benign or cooperative. Identifying such peers is critical to the smooth and effective functioning of a P2P network. In this paper, we present the ROCQ mechanism, a reputation-based trust management system that computes the trustworthiness of peers on the basis of transaction-based feedback. The ROCQ model combines four parameters: Reputation (R) or a peer's global trust rating, Opinion (O) formed by a peer's first-hand interactions, Credibility (C) of a reporting peer and Quality (Q) or the confidence a reporting peer puts on the judgement it provides. We then present a distributed implementation of our scheme over FreePastry, a structured P2P network. Experimental results considering different models for malicious behavior indicate the contexts in which the ROCQ scheme performs better than existing schemes

Traffic Engineering in G-MPLS networks with QoS guarantees

In this paper a new Traffic Engineering (TE) scheme to efficiently route sub-wavelength requests with different QoS requirements is proposed for G-MPLS networks. In most previous studies on TE based on dynamic traffic grooming, the objectives were to minimize the rejection probability by respecting the constraints of the optical node architecture, but without considering service differentiation. In practice, some high-priority (HP) connections can instead be characterized by specific constraints on the maximum tolerable end-to-end delay and packet-loss ratio. The proposed solution consists of a distributed two-stage scheme: each time a new request arrives, an on-line dynamic grooming scheme finds a route which fulfills the QoS requirements. If a HP request is blocked at the ingress router, a preemption algorithm is executed locally in order to create room for this traffic. The proposed preemption mechanism minimizes the network disruption, both in term of number of rerouted low-priority connections and new set-up lightpaths, and the signaling complexity. Extensive simulation experiments are performed to demonstrate the efficiency of our scheme

Statistical Learning Theory for Location Fingerprinting in Wireless LANs

In this paper, techniques and algorithms developed in the framework of statistical learning theory are analyzed and applied to the problem of determining the location of a wireless device by measuring the signal strengths from a set of access points (location fingerprinting). Statistical Learning Theory provides a rich theoretical basis for the development of models starting from a set of examples. Signal strength measurement is part of the normal operating mode of wireless equipment, in particular Wi-Fi, so that no custom hardware is required. The proposed techniques, based on the Support Vector Machine paradigm, have been implemented and compared, on the same data set, with other approaches considered in the literature. Tests performed in a real-world environment show that results are comparable, with the advantage of a low algorithmic complexity in the normal operating phase. Moreover, the algorithm is particularly suitable for classification, where it outperforms the other techniques

Do not be afraid of local minima: affine shaker and particle swarm

Stochastic local search techniques are powerful and flexible methods to optimize difficult functions. While each method is characterized by search trajectories produced through a randomized selection of the next step, a notable difference is caused by the interaction of different searchers, as exemplified by the Particle Swarm methods. In this paper we evaluate two extreme approaches, Particle Swarm Optimization, with interaction between the individual "cognitive" component and the "social" knowledge, and Repeated Affine Shaker, without any interaction between searchers but with an aggressive capability of scouting out local minima. The results, unexpected to the authors, show that Affine Shaker provides remarkably efficient and effective results when compared with PSO, while the advantage of Particle Swarm is visible only for functions with a very regular structure of the local minima leading to the global optimum and only for specific experimental conditions

"May I borrow Your Filter?" Exchanging Filters to Combat Spam in a Community

Leveraging social networks in computer systems can be effective in dealing with a number of trust and security issues. Spam is one such issue where the "wisdom of crowds" can be harnessed by mining the collective knowledge of ordinary individuals. In this paper, we present a mechanism through which members of a virtual community can exchange information to combat spam. Previous attempts at collaborative spam filtering have concentrated on digest-based indexing techniques to share digests or fingerprints of emails that are known to be spam. We take a different approach and allow users to share their spam filters instead, thus dramatically reducing the amount of traffic generated in the network. The resultant diversity in the filters and cooperation in a community allows it to respond to spam in an autonomic fashion. As a test case for exchanging filters we use the popular SpamAssassin spam filtering software and show that exchanging spam filters provides an alternative method to improve spam filtering performance

Location-aware computing: a neural network model for determining location in wireless LANs

The strengths of the RF signals arriving from more access points in a wireless LANs are related to the position of the mobile terminal and can be used to derive the location of the user. In a heterogeneous environment, e.g. inside a building or in a variegated urban geometry, the received power is a very complex function of the distance, the geometry, the materials. The complexity of the inverse problem (to derive the position from the signals) and the lack of complete information, motivate to consider flexible models based on a network of functions (neural networks). Specifying the value of the free parameters of the model requires a supervised learning strategy that starts from a set of labeled examples to construct a model that will then generalize in an appropriate manner when confronted with new data, not present in the training set. The advantage of the method is that it does not require ad-hoc infrastructure in addition to the wireless LAN, while the flexible modeling and learning capabilities of neural networks achieve lower errors in determining the position, are amenable to incremental improvements, and do not require the detailed knowledge of the access point locations and of the building characteristics. A user needs only a map of the working space and a small number of identified locations to train a system, as evidenced by the experimental results presented