eSCIFI: An Energy Saving Mechanism for WLANs Based on Machine Learning

As wireless local area networks grow in size to provide access to users, power consumption becomes an important issue. Power savings in a large-scale Wi-Fi network, with low impact to user service, is undoubtedly desired. In this work, we propose and evaluate the eSCIFI energy saving mechanism for Wireless Local Area Networks (WLANs). eSCIFI is an energy saving mechanism that uses machine learning algorithms as occupancy demand estimators. The eSCIFI mechanism is designed to cope with a broader range of WLANs, which includes Wi-Fi networks such as the Fluminense Federal University (UFF) SCIFI network. The eSCIFI can cope with WLANs that cannot acquire data in a real time manner and/or possess a limited CPU power. The eSCIFI design also includes two clustering algorithms, named cSCIFI and cSCIFI+, that help to guarantee the network’s coverage. eSCIFI uses those network clusters and machine learning predictions as input features to an energy state decision algorithm that then decides which Access Points (AP) can be switched off during the day. To evaluate eSCIFI performance, we conducted several trace-driven simulations comparing the eSCIFI mechanism using both clustering algorithms with other energy saving mechanisms found in the literature using the UFF SCIFI network traces. The results showed that eSCIFI mechanism using the cSCIFI+ clustering algorithm achieves the best performance and that it can save up to 64.32% of the UFF SCIFI network energy without affecting the user coverage.

A User Prioritisation Algorithm for Horizontal Handover in Dense WLANs

This paper proposes an algorithm that enhances horizontal handover (HO) in dense wireless local area networks (WLANs), which is implemented in a software-defined wireless networking (SDWN)-based architecture. The algorithm considers the concept of user prioritisation, classifying the WLAN stations (STAs) into two categories representing high and low priorities respectively, and always attempts to guarantee the best quality of experience (QoE) to the high priority users. The architecture that implements the algorithm leverages the flexibility, programmability, and centralised nature of SDWN to efficiently manage the HO process. Moreover, the paper presents a performance evaluation campaign that demonstrates significant achievements against a state-of-the-art solution in terms of the provided QoE, throughput and delay. Finally, we discuss the importance of considering user prioritisation in a HO algorithm for dense WLANs.

Improving Spatial Reuse of Wireless LAN Uplink Using BSS Color and Proximity Information

With the density of wireless networks increasing rapidly, one of the major goals in next-generation wireless LANs (Local Area Networks) is to support a very dense network with a large number of closely deployed APs (Access Points) and crowded users. However, the CSMA (Carrier-Sense Multiple Access)-based medium access control of current wireless network systems suffers from significantly degraded performance when the network becomes dense. Recent WLAN (Wireless Local Area Networks) standards include measures for increasing spatial reuse such as BSS (Basic Service Set) coloring, but the schemes based on BSS coloring such as OBSS/PD (Overlapping BSS/Preamble Detection) have limitations in improving spatial reuse. In this paper, we propose a spatial reuse method for uplink which can utilize BSS color and proximity information to improve the efficiency of carrier sensing and thus spatial reuse. Specifically, through the BSS color and the proximity information, a node receiving a preamble can figure out how far the receiver of the ongoing traffic is located. This information is used to determine whether the node should aggressively start transmitting or defer its transmission to protect the ongoing transmission. Simulation results show that the proposed method outperforms existing methods in terms of throughput and fairness.

UWB Dual-Band-Notched Lanky-Leaf-Shaped Antenna with Loaded Half-Square-Like Slots for Communication System

A novel printed compact single-layer dual-band-notched antenna for the use of ultra-wide band (UWB) is proposed in this paper, and one lanky-leaf-like structure with a coplanar waveguide (CPW) feed is designed as the radiated element for a large operating bandwidth. To realize the dual-band-notched characteristics of microwave access (WiMAX) and wireless local area networks (WLAN), two half-square-like slots are etched on the metallic surface. The fabricated prototype of this proposed antenna has a compact size of 27 × 32 mm2 and operates at 2.8 GHz to 10 GHz, excepting for rejection bands at 3.06–3.83 GHz and 5.05–5.96 GHz. Nearly omnidirectional radiation patterns are obtained in the working band. Furthermore, one conformal design on cylinder and transfer characteristics are made to validate its potential application. These findings indicate that this antenna can be taken as a promising option for use in the UWB communication field.

Towards Enabling Multihop Wireless Local Area Networks for Disaster Communications

xCalamities such as earthquakes and tsunami affect communication services by devastating the communication network and electrical infrastructure. Multihop relay networks can be deployed to restore the communication environment quickly in catastrophe-stricken areas. However, performance in terms of throughput is affected by deploying the relay networks. In wireless local area networks (WLANs), the primary purpose of multiband transmission employing multihop relay networks is to increase the throughput and reduce the latency. In the future, wireless networks are believed to carry high throughput, more data rates, and less latency by expanding bandwidth-demanding applications. Simultaneous multiband transmission in WLAN systems is considered to increase the coverage area without power escalation. Due to the inherent characteristics of different bands and channel conditions, transmission rates tend to be different. The impact of such conditions may cater to the disproportional distribution of data among bands, causing some of the bands to be overwhelmed, which incurs buffer overflow and packet loss. In contrast, the channel capacity of some of the bands remains underutilized. In this paper, we consider the channel conditions and transmission rates of each band on either side of the relay to address the problems mentioned above. Furthermore, this paper proposes a load distribution-based end-to-end traffic scheduling technique to improve system performance. The simulation results demonstrate the effectiveness of our proposed method with maximizing throughput and minimizing end-to-end delay.

Optimal Access Point Power Management for Green IEEE 802.11 Networks

In this paper, we present an approach and an algorithm aimed at minimising the energy consumption of enterprise Wireless Local Area Networks (WLANs) during periods of low user activity. We act on two network management aspects: powering off some Access Points (APs), and choosing the level of transmission power of each AP. An efficient technique to allocate the user terminals to the various APs is the key to achieving this goal. The approach has been formulated as an integer programming problem with nonlinear constraints, which comes from a general but accurate characterisation of the WLAN. This general problem formulation has two implications: the formulation is widely applicable, but the nonlinearity makes it NP-hard. To solve this problem to optimality, we devised an exact algorithm based on a customised version of Benders’ decomposition method. The computational results proved the ability to obtain remarkable power savings. In addition, the good performance of our algorithm in terms of solving times paves the way for its future deployment in real WLANs.

802.11 wireless simulation and anomaly detection using HMM and UBM

Despite the growing popularity of 802.11 wireless networks, users often suffer from connectivity problems and performance issues due to unstable radio conditions and dynamic user behavior, among other reasons. Anomaly detection and distinction are in the thick of major challenges that network managers encounter. The difficulty of monitoring broad and complex Wireless Local Area Networks, that often requires heavy instrumentation of the user devices, makes anomaly detection analysis even harder. In this paper we exploit 802.11 access point usage data and propose an anomaly detection technique based on Hidden Markov Model (HMM) and Universal Background Model (UBM) on data that is inexpensive to obtain. We then generate a number of network anomalous scenarios in OMNeT++/INET network simulator and compare the detection outcomes with those in baseline approaches—RawData and Principal Component Analysis. The experimental results show the superiority of HMM and HMM-UBM models in detection precision and sensitivity.