Dynamic Packet Aggregation to Solve Performance Anomaly in 802.11 Wireless Networks

In the widely used 802.11 standard, the so called performance anomaly is a well known issue. Several works have tried to solve this problem by introducing mechanisms such as packet fragmentation, backoff adaptation, or packet aggregation during a fixed time interval. In this paper, we propose a novel approach solving the performance anomaly problem by packet aggregation using a dynamic time interval, which depends on the busy time of the wireless medium. Our solution differs from other proposition in the literature because of this dynamic time interval, which allows increasing fairness, reactivity, and in some cases efficiency. In this article, we emphasize the performance evaluation of our proposal

TOMAC-WSN: A new WSN efficient protocol for monitoring big distributed mechanical systems

International audienceThis paper addresses a wireless sensor network dedicated to monitor a large mechanical system. The chosen system for the scenario is a chairlift. In this case the wireless sensor network special feature is the mobility of nodes following an invariant path traveled repeatedly. A sensor node is put on each chair and a sink node is at ground at the upper end of the chairlift. A new protocol called TOMAC-WSN is designed in order to schedule frames transmission using token concept. This avoids collision at the medium access. The second concept used by TOMAC-WSN is frame aggregation. This new protocol has been modelled using Finite State Automata. An experimental implementation on Arduino boards shows the correct operation of the network. Network performance in terms of delivery time and packet loss rate is evaluated using simulation. The results show that the proposed TOMAC-WSN protocol delivers the appropriate quality of service for the monitoring of large physical systems

A software framework for alleviating the effects of MAC-aware jamming attacks in wireless access networks

The IEEE 802.11 protocol inherently provides the same long-term throughput to all the clients associated with a given access point (AP). In this paper, we first identify a clever, low-power jamming attack that can take advantage of this behavioral trait: the placement of a lowpower jammer in a way that it affects a single legitimate client can cause starvation to all the other clients. In other words, the total throughput provided by the corresponding AP is drastically degraded. To fight against this attack, we design FIJI, a cross-layer anti-jamming system that detects such intelligent jammers and mitigates their impact on network performance. FIJI looks for anomalies in the AP load distribution to efficiently perform jammer detection. It then makes decisions with regards to optimally shaping the traffic such that: (a) the clients that are not explicitly jammed are shielded from experiencing starvation and, (b) the jammed clients receive the maximum possible throughput under the given conditions. We implement FIJI in real hardware; we evaluate its efficacy through experiments on two wireless testbeds, under different traffic scenarios, network densities and jammer locations. We perform experiments both indoors and outdoors, and we consider both WLAN and mesh deployments. Our measurements suggest that FIJI detects such jammers in realtime and alleviates their impact by allocating the available bandwidth in a fair and efficient way. © Springer Science+Business Media

Dynamic Packet Aggregation to Solve Performance Anomaly in 802.11 Wireless Networks

In the widely used 802.11 standard, the so called performance anomaly is a well known issue. Several works have tried to solve this problem by introducing mechanisms such as packet fragmentation, backoff adaptation, or packet aggregation during a fixed time interval. In this paper, we propose a novel approach solving the performance anomaly problem by packet aggregation using a dynamic time interval, which depends on the busy time of the wireless medium. Our solution differs from other proposition in the literature because of this dynamic time interval, which allows increasing fairness, reactivity, and in some cases efficiency. In this article, we emphasize the performance evaluation of our proposal

An Adaptive Packet Aggregation Algorithm (AAM) for Wireless Networks

Packet aggregation algorithms are used to improve the throughput performance by combining a number of packets into a single transmission unit in order to reduce the overhead associated with each transmission within a packet-based communications network. However, the throughput improvement is also accompanied by a delay increase. The biggest drawback of a significant number of the proposed packet aggregation algorithms is that they tend to only optimize a single metric, i.e. either to maximize throughput or to minimize delay. They do not permit an optimal trade-off between maximizing throughput and minimizing delay. Therefore, these algorithms cannot achieve the optimal network performance for mixed traffic loads containing a number of different types of applications which may have very different network performance requirements. In this thesis an adaptive packet aggregation algorithm called the Adaptive Aggregation Mechanism (AAM) is proposed which achieves an aggregation trade-off in terms of realizing the largest average throughput with the smallest average delay compared to a number of other popular aggregation algorithms under saturation conditions in wireless networks. The AAM algorithm is the first packet aggregation algorithm that employs an adaptive selection window mechanism where the selection window size is adaptively adjusted in order to respond to the varying nature of both the packet size and packet rate. This algorithm is essentially a feedback control system incorporating a hybrid selection strategy for selecting the packets. Simulation results demonstrate that the proposed algorithm can (a) achieve a large number of sub-packets per aggregate packet for a given delay and (b) significantly improve the performance in terms of the aggregation trade-off for different traffic loads. Furthermore, the AAM algorithm is a robust algorithm as it can significantly improve the performance in terms of the average throughput in error-prone wireless networks