https://www.sciencedirect.com/science/article/am/pii/S1084804521002551

Recent advances in energy management for Green-IoT: An up-to-date and comprehensive survey

In this paper we focus on knowledge extraction from large-scale wireless networks through stream processing. We present the primary methods for sampling, data collection, and monitoring of wireless networks and we characterize knowledge extraction as a machine learning problem on big data stream processing. We show the main trends in big data stream processing frameworks. Additionally, we explore the data preprocessing, feature engineering, and the machine learning algorithms applied to the scenario of wireless network analytics. We address challenges and present research projects in wireless network monitoring and stream processing. Finally, future perspectives, such as deep learning and reinforcement learning in stream processing, are anticipated.

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A survey on data analysis on large-Scale wireless networks: online stream processing, trends, and challenges

Today’s communication is mainly done over wireless networks, with IEEE 802.11 (Wi-Fi) at the forefront. There are billions of devices and millions of access points (APs), but only very few non-overlapping channels. As a result, the performance of Wi-Fi devices is severely degraded, because perfect channel allocation - with every AP alone in its channel - is close to impossible. Even in situations where all networks are under centralised control, existing approaches quickly tend to be either unscalable or suboptimal. By focusing on a subset of problems, identifying Wireless Local Area Networks (WLANs) that severely interfere with each other, performance can be improved even in such a complex situation. We tackle this problem through machine learning and coin it Bad Neighbour Detection (BND). Based on this output alongside monitoring data about the networks’ activity, we then propose a channel allocation that optimises performance and as a side effect, stabilises networks that we do not control. We evaluate our approach in a field trial and show that we significantly improve the experience for users, eliminating virtually all interference-related issues.

A Machine Learning Approach for IEEE 802.11 Channel Allocation

Two evolutionary algorithms are proposed in this paper to handle with access point channel assignment in Wireless Local Area Network. The objective considered is to minimize the maximum level of interference experienced by the users. Two deterministic heuristics, commonly employed in the considered problem, are used as benchmark. The paper is focused on the IEEE 802.11ac standard, which operates exclusively in the 5 GHz band. This standard provides a bigger number of non-overlapped channels and higher throughput. Tests in three different scenarios and configurations, using channel width of 20, 40 and 80 MHz, are performed.

Using evolutionary algorithms for channel assignment in 802.11 networks

The sharing of the wireless spectrum is a major concern of network administrators. Access points in the same network interfere with each other, degrading the aggregate performance of stations. Moreover, wireless networks usually coexist with others applications that share the same spectrum and negatively impact the packet transmission. To overcome these issues, we propose the channel allocation algorithm designed for central controllers of infra-structured IEEE 802.11 networks. Our algorithm reduces the interference in controlled access points through the dynamic choice of their operating channels and, unlike other proposals, was designed to operate in a network composed of low cost devices from different brands, and open source software. Furthermore, we also consider the interference caused by unmanaged networks, adjusting the settings of the managed access points according to the wireless environment. The proposal was implemented and evaluated in an open testbed, and the results show that our controller efficiently manages the spectrum with low cost equipment and a low complexity algorithm.

Centralized channel allocation algorithm for IEEE 802.11 networks

SCIFI is an open source software wireless controller. Large scale wireless networks that use low cost (SOHO) Access Points are hard to install and run due to the complexity of configuring and monitoring many APs with a distributed interface, coupled with the difficulty of creating a good network configuration with no help from the system. On the other hand, hardware controllers and their compatible APs are too expensive, proprietary and ultimately tie the buyer to a single vendor. SCIFI allows the use of inexpensive hardware to create large scale wireless installations. This paper describes the SCIFI algorithms, its interface, the current deployment at UFF and the future work planned for SCIFI.

SCIFI – A Software-Based Controller for Efficient Wireless Networks

Energy efficiency is paramount for IoT. One of the culprits for energy consumption in a typical sensor or actuator -- core components of IoT -- is the radio. Thus, duty cycling (i.e., turning the radio off when it is not needed) becomes essential. While wireless communication standards commonly include provisions for duty cycling, they are usually based on synchronization, which may be expensive in terms of hardware, software, or control communication overhead. Asynchronous methods have been extensively studied in the context of wireless sensor networks and thus are valuable tools for green IoT deployments. In this article, we provide a review of existing methods, from seminal works to recent proposals. We categorize and compare them qualitatively and quantitatively. Finally, we discuss recent trends in the area and suggest topics for future research.

Asynchronous Radio Duty Cycling for Green IoT: State of the Art and Future Perspectives

The rapid growth of wireless networking technologies, along with the emergence of several new devices that offer or need Internet connection and an ever-increasing demand for wide-band access, especially away from the urban centers, aggravates the problem of the frequency spectrum exhaustion for telecommunications services. The need for more efficient use of the spectrum increases the demand for solutions such as the improvement and deployment of radios with cognitive ability. In this context, this work seeks to contribute to solve the so-called multi-channel rendezvous problem, presenting an asynchronous, distributed and robust mechanism to promote rendezvous and communication opportunities between two or more cognitive radios. The proposed solution employs frequency hopping with new sequences based on combinatorial design theory. Our evaluation indicates that our proposal is able to improve the expected time to rendezvous up to 131% for the same control overhead with respect to existing approaches.

BiRD—A Novel Bi-Dimensional Design to Multi-Channel Continuous Rendezvous in Cognitive Networks

Association instability is a common phenomenon in dense networks. The decision of whether or not to perform a handoff between access points in an infrastructured IEEE 802.11 network is taken exclusively by the wireless client stations. Even without mobility, static client devices may decide to migrate to another access point with the goal of improving performance. However, the criteria used to perform handoffs are not defined by the IEEE 802.11 standard and, thus, are dependent on specific vendor implementations. In this paper, we use data from a real large scale network and run experiments to demonstrate that such implementations are commonly deficient, resulting in high levels of association instability in dense environments. By analyzing the implementation used by the most common devices, we were able to conclude that this instability, known as the “ping-pong effect”, results from the direct usage of RSSI samples which are highly variable. Finally, we analyze the behavior of RSSI in indoor environments showing that its time series presents multimodal distribution. We argue that the findings presented in this study can help develop more stable handoff algorithms for dense wireless networks.

Helga Dolorico Balbi

Papers

Centralized channel allocation algorithm for IEEE 802.11 networks

SCIFI – A Software-Based Controller for Efficient Wireless Networks

Asynchronous Radio Duty Cycling for Green IoT: State of the Art and Future Perspectives

BiRD—A Novel Bi-Dimensional Design to Multi-Channel Continuous Rendezvous in Cognitive Networks

A Case Study of Association Instability in Dense IEEE 802.11 Networks