Panagiotis G. Sarigiannidis

Adaptive wireless networks using learning automata

Wireless networks operate in environments with unknown and time-varying characteristics. The changing nature of many of these characteristics will significantly affect network performance. This fact has a profound impact on the design of efficient protocols for wireless networks and as a result adaptivity arises as one of the most important properties of these protocols. Learning automata are artificial intelligence tools that have been used in many areas where adaptivity to the characteristics of the wireless environment can result in a significant increase in network performance. This article reviews state of the art approaches in using learning automata to provide adaptivity to wireless networking.

Architectures and Bandwidth Allocation Schemes for Hybrid Wireless-Optical Networks

The combination of the most prestigious optical and wireless technologies for implementing a modern broadband integrated access network has been progressively gaining ground. By extending the network coverage in a cost-efficient way, hybrid wireless-optical networks are able to enclose a larger number of potential subscribers than standalone access architectures. Hence, they are capable of increasing revenue levels and facilitating commercial penetration to the telecom market. At the same time, hybrid wireless-optical networks pose an ambitious, alternative, and efficient solution to coping with new bandwidth-hungry user applications. Hybrid wireless-optical networks incorporate sophisticated modules, fabrics, and network entities to effectively provide adequate quality of service (QoS) provisioning. This survey endeavors to classify the main features of wireless-optical integration. We provide a comprehensive compilation of the latest architectures, integrated technologies, QoS features, and dynamic bandwidth allocation (DBA) schemes. In addition, new trends towards wireless-optical convergence are presented. Moreover, as the up-to-date hybrid network standards remain under development, since there is not yet an integrated standard for approving hybrid network access platforms, we accompany this survey with detailed challenges indicating potential avenues of future research.

Detecting Sybil attacks in wireless sensor networks using UWB ranging-based information

Development of a novel rule-based Sybil attack detection system for large-scale WSNs.Integration of UWB ranging-features with expert knowledge in the detection process.Development of a defense scheme against direct, simultaneous Sybil attacks.Derivation of a rigorous analytic framework to determine the system performance.Introduction of an accurate simulation environment to validate the detection analysis. Security is becoming a major concern for many mission-critical applications wireless sensor networks (WSNs) are envisaged to support. The inherently vulnerable characteristics of WSNs appoint them susceptible to various types of attacks. This work restrains its focus on how to defend against a particularly harmful form of attack, the Sybil attack. Sybil attacks can severely deteriorate the network performance and compromise the security by disrupting many networking protocols. This paper presents a rule-based anomaly detection system, called RADS, which monitors and timely detects Sybil attacks in large-scale WSNs. At its core, the proposed expert system relies on an ultra-wideband (UWB) ranging-based detection algorithm that operates in a distributed manner requiring no cooperation or information sharing between the sensor nodes in order to perform the anomaly detection tasks. The feasibility of the proposed approach is proven analytically, while the performance of RADS in exposing Sybil attacks is extensively assessed both mathematically and numerically. The obtained results demonstrate that RADS achieves high detection accuracy and low false alarm rate appointing it a promising ADS candidate for this class of wireless networks.

A High-Throughput Scheduling Technique, With Idle Timeslot Elimination Mechanism

A new media-access-control protocol is introduced in this paper. The authors consider a wavelength-division-multiplexing (WDM) network with star topology. A single-hop WDM system is considered, so that there is a full connectivity between every node-pair in just one hop. The protocol adopted is pretransmission coordination-based, so the protocol coordinates nodes before the actual transmission. The coordination is achieved with one demand (or traffic) matrix, which saves the predetermination of the timeslots each node transmits. Each transmission frame (or cycle) has two phases: the control phase and the data phase. In order to eliminate the possible delay added by the schedule computation between the two phases of each frame, they consider a traffic prediction scheme, which is based upon the hidden Markov chain model. The control phase functions as a learning period in which the predictor is trained. The training is based on the traffic of the network. During the data phase, each station transmits its packets based on the predicted reservations, which are the predictors output. In the same frame, the predictor computes the reservations for the next frame. They show that their protocol, although suffering from small packet delay loss, introduces a new method of computing the reservations of the demand matrix and brings some performance improvement in terms of channel utilization and results in higher network throughput, which is proven by extensive simulations

A Novel Adaptive Framework for Wireless Push Systems Based on Distributed Learning Automata

A novel adaptive scheme for wireless push systems is presented in this paper. In this wireless environment two entities play the most important role: the server side and the client side that is connected to the system. The server side is responsible to broadcast an item per transmission in order to satisfy the clients’ requests. The performance of the server side depends on item selections. Hence, the server broadcasts an item and the clients are satisfied if the transmitted item was the desired one. In this work, a set of learning automata try to estimate the client demands in a distributed manner. More specifically, an autonomous learning automaton is utilized on each client group, since the clients are gathered into groups based on their location. The output of each automaton is combined in order to produce a well-performed transmission schedule. Concurrently, a round robin phase is adopted, giving the opportunity to the non-popular items to be transmitted. In this manner, the various client demands are treated fairly. The introduced technique is compared with a centralized adaptive scheme and the results indicate that the proposed scheduling framework outperforms the centralized one, in terms of response time and fairness.

CS-POSA: A High Performance Scheduling Algorithm for WDM Star Networks

In this paper a new packet scheduling algorithm for WDM star networks is introduced. The protocol adopted is pre-transmission coordination-based and packet collisions have been eliminated due to predetermination of the timeslots each node transmits in a demand matrix. The requests of the transmitted packets are predicted through Markov chains in order to reduce the calculation time of the final scheduling matrix. This is accomplished by pipelining the schedule computation. The innovation that this algorithm introduces is to modify the service sequence of the node. The proposed algorithm is studied via extensive simulation results and it is proved that changing the sequence that nodes transmit, from the node with the largest number of requests to the node with the fewest requests, that there is an increase in the throughput of the network, with a minimum (almost zero) cost in mean time delay and in delay variance.

A Novel Adaptive Mapping Scheme for IEEE 802.16 Mobile Downlink Framing

IEEE 802.16 (WiMAX) constitutes one of the most promising broadband access technologies for high-capacity and high-distance wireless access networks, supporting user mobility. The contribution of this paper is twofold. Firstly, a novel mapping scheme for IEEE 802.16 Mobile standard is introduced, applying horizon mapping. Secondly, an efficient adaptive prediction-based scheme is devised, which is able to adjust the downlink sub-frame capacity, accordingly to the traffic load, since the standard allows the downlink-to-uplink subframe ratio to be changeable from 3:1 to 1:1. The novel adaptive horizon burst mapping (AHBM) scheme is evaluated by simulation experiments, which indicate that the proposed scheme operates effectively and efficiently, by reducing the number of unserved users, the number of unserved traffic requests, and the portion of wasted bandwidth.

Towards a QoS-Aware IEEE 802.16 downlink sub-frame mapping scheme

IEEE 802.16 (WiMAX) is considered as one of the most promising wireless access technologies supporting high-capacity and long-distance communications as well as user mobility. A problem that should be addressed in the context of multi-access communications is the efficient accommodation of traffic requests to the downlink subframe. The allocation operation in downlink sub-frame is not standardized, while only restrictions on the usage of downlink sub-frames bandwidth are defined. The most noticeable restriction is the rectangular restriction, requiring all downlink allocations to be mapped in a two-dimensions rectangular shaping. This study is a first step towards defining a QoS-aware mapping scheme, prioritizing traffic requests in accordance with the strict delay requirements they pose. The mapping scheme applies horizon scheduling, permitting bursts to be scheduled efficiently and in a simple way, following the horizons as pilots. The QoS-aware mapping scheme is evaluated by means of simulation experiments, which indicate that the proposed scheme operates effectively and efficiently, by reducing the number of unserviced users and traffic requests, and the portion of the dropped real-time traffic.

SRNET: a real-time, cross-based anomaly detection and visualization system for wireless sensor networks

Security concerns are a major deterrent in many applications wireless sensor networks are envisaged to support. To date, various security mechanisms have been proposed for these networks dealing with either Medium Access Control (MAC) layer or network layer security issues, or key management problems. Security visualization is the latest weapon that has been added in the arsenal of a security officer who is tasked with detecting network anomalies by analyzing large amounts of audit data. This paper proposes a novel security visualization system for analyzing and detecting complex patterns of sensor network attacks, called SRNET. Both selective forwarding and jamming attacks are identified through visualizing and analyzing network traffic data on multiple coordinated views, namely the multidimensional crossed view, the crossed view perspective, and the track area view. Through simulations, we demonstrate that SRNET is able to help detect and further identify the root cause of the aforementioned sensor network attacks.

A novel HMM-based learning framework for improving dynamic wireless push system performance

Abstract A new machine learning framework is introduced in this paper, based on the hidden Markov model (HMM), designed to provide scheduling in dynamic wireless push systems. In realistic wireless systems, the clients’ intentions change dynamically; hence a cognitive scheduling scheme is needed to estimate the desirability of the connected clients. The proposed scheduling scheme is enhanced with self-organized HMMs, supporting the network with an estimated expectation of the clients’ intentions, since the system’s environment characteristics alter dynamically and the base station (server side) has no a priori knowledge of such changes. Compared to the original pure scheme, the proposed machine learning framework succeeds in predicting the clients’ information desires and overcomes the limitation of the original static scheme, in terms of mean delay and system efficiency.