Data Mining Practical Machine Learning Tools and Techniques

Indoor localization has recently witnessed an increase in interest, due to the potential wide range of services it can provide by leveraging Internet of Things (IoT), and ubiquitous connectivity. Different techniques, wireless technologies and mechanisms have been proposed in the literature to provide indoor localization services in order to improve the services provided to the users. However, there is a lack of an up-to-date survey paper that incorporates some of the recently proposed accurate and reliable localization systems. In this paper, we aim to provide a detailed survey of different indoor localization techniques, such as angle of arrival (AoA), time of flight (ToF), return time of flight (RTOF), and received signal strength (RSS); based on technologies, such as WiFi, radio frequency identification device (RFID), ultra wideband (UWB), Bluetooth, and systems that have been proposed in the literature. This paper primarily discusses localization and positioning of human users and their devices. We highlight the strengths of the existing systems proposed in the literature. In contrast with the existing surveys, we also evaluate different systems from the perspective of energy efficiency, availability, cost, reception range, latency, scalability, and tracking accuracy. Rather than comparing the technologies or techniques, we compare the localization systems and summarize their working principle. We also discuss remaining challenges to accurate indoor localization.

A Survey of Indoor Localization Systems and Technologies

The spatial features of emitted wireless signals are the basis of location distinction and determination for wireless indoor localization. Available in mainstream wireless signal measurements, the Received Signal Strength Indicator (RSSI) has been adopted in vast indoor localization systems. However, it suffers from dramatic performance degradation in complex situations due to multipath fading and temporal dynamics. Break-through techniques resort to finer-grained wireless channel measurement than RSSI. Different from RSSI, the PHY layer power feature, channel response, is able to discriminate multipath characteristics, and thus holds the potential for the convergence of accurate and pervasive indoor localization. Channel State Information (CSI, reflecting channel response in 802.11 a/g/n) has attracted many research efforts and some pioneer works have demonstrated submeter or even centimeter-level accuracy. In this article, we survey this new trend of channel response in localization. The differences between CSI and RSSI are highlighted with respect to network layering, time resolution, frequency resolution, stability, and accessibility. Furthermore, we investigate a large body of recent works and classify them overall into three categories according to how to use CSI. For each category, we emphasize the basic principles and address future directions of research in this new and largely open area.

From RSSI to CSI: Indoor Localization via Channel Response, A survey on indoor localization using PHY-layer information

In recent years, with the rapid development of current Internet and mobile communication technologies, the infrastructure, devices and resources in networking systems are becoming more complex and heterogeneous. In order to efficiently organize, manage, maintain and optimize networking systems, more intelligence needs to be deployed. However, due to the inherently distributed feature of traditional networks, machine learning techniques are hard to be applied and deployed to control and operate networks. Software defined networking (SDN) brings us new chances to provide intelligence inside the networks. The capabilities of SDN (e.g., logically centralized control, global view of the network, software-based traffic analysis, and dynamic updating of forwarding rules) make it easier to apply machine learning techniques. In this paper, we provide a comprehensive survey on the literature involving machine learning algorithms applied to SDN. First, the related works and background knowledge are introduced. Then, we present an overview of machine learning algorithms. In addition, we review how machine learning algorithms are applied in the realm of SDN, from the perspective of traffic classification, routing optimization, quality of service/quality of experience prediction, resource management and security. Finally, challenges and broader perspectives are discussed.

A Survey of Machine Learning Techniques Applied to Software Defined Networking (SDN): Research Issues and Challenges

In the era of smart cities, there are a plethora of applications where the localization of indoor environments is important, from monitoring and tracking in smart buildings to proximity marketing and advertising in shopping malls. The success of these applications is based on the development of a cost-efficient and robust real-time system capable of accurately localizing objects. In most outdoor localization systems, global positioning system (GPS) is used due to its ease of implementation and accuracy up to five meters. However, due to the limited space that comes with performing localization of indoor environments and the large number of obstacles found indoors, GPS is not a suitable option. Hence, accurately and efficiently locating objects is a major challenge in indoor environments. Recent advancements in the Internet of Things (IoT) along with novel wireless technologies can alleviate the problem. Small-size and cost-efficient IoT devices which use wireless protocols can provide an attractive solution. In this paper, we compare four wireless technologies for indoor localization: Wi-Fi (IEEE 802.11n-2009 at the 2.4 GHz band), Bluetooth low energy, Zigbee, and long-range wide-area network. These technologies are compared in terms of localization accuracy and power consumption when IoT devices are used. The received signal strength indicator (RSSI) values from each modality were used and trilateration was performed for localization. The RSSI data set is available online. The experimental results can be used as an indicator in the selection of a wireless technology for an indoor localization system following application requirements.

RSSI-Based Indoor Localization With the Internet of Things

Micro-location is the process of locating any entity with high accuracy (possibly in centimeters), while geofencing is the process of creating a virtual fence around a so-called Point of Interest (PoI). In this paper, we present an insight into various micro-location enabling technologies and services. We also discuss how these can accelerate the incorporation of Internet of Things (IoT) in smart buildings. We argue that micro-location based location-aware solutions can play a significant role in facilitating the tenants of an IoT equipped smart building. Also, such advanced technologies will enable the smart building control system through minimal actions performed by the tenants. We also highlight the existing and envisioned services to be provided by using micro-location enabling technologies. We describe the challenges and propose some potential solutions such that micro-location enabling technologies and services are thoroughly integrated with IoT equipped smart building.

Micro-location for Internet of Things equipped Smart Buildings

Microlocation is the process of locating any entity with a very high accuracy (possibly in centimeters), whereas geofencing is the process of creating a virtual fence around a point of interest (PoI). In this paper, we present an insight into various microlocation enabling technologies, techniques, and services. We also discuss how they can accelerate the incorporation of Internet of Things (IoT) in smart buildings. We argue that micro-location-based location aware solutions can play a significant role in facilitating the tenants of an IoT-equipped smart building. Also, such advanced technologies will enable the smart building control system through minimal actions performed by the tenants. We also highlight the existing and envisioned services to be provided by using microlocation enabling technologies. We describe the challenges and propose some potential solutions, such that microlocation enabling technologies and services are thoroughly integrated with IoT-equipped smart building.

Microlocation for Internet-of-Things-Equipped Smart Buildings

An experimental setup of 32 honeypots reported 17M login attempts originating from 112 different countries and over 6000 distinct source IP addresses. Due to decoupled control and data plane, Software Defined Networks (SDN) can handle these increasing number of attacks by blocking those network connections at the switch level. However, the challenge lies in defining the set of rules on the SDN controller to block malicious network connections. Historical network attack data can be used to automatically identify and block the malicious connections. There are a few existing open-source software tools to monitor and limit the number of login attempts per source IP address one-by-one. However, these solutions cannot efficiently act against a chain of attacks that comprises multiple IP addresses used by each attacker. In this paper, we propose using machine learning algorithms, trained on historical network attack data, to identify the potential malicious connections and potential attack destinations. We use four widely-known machine learning algorithms: C4.5, Bayesian Network (BayesNet), Decision Table (DT), and Naive-Bayes to predict the host that will be attacked based on the historical data. Experimental results show that average prediction accuracy of 91.68% is attained using Bayesian Networks.

Predicting network attack patterns in SDN using machine learning approach

Indoor localization has recently witnessed an increase in interest, due to the potential wide range of services it can provide by leveraging Internet of Things (IoT), and ubiquitous connectivity. Different techniques, wireless technologies and mechanisms have been proposed in the literature to provide indoor localization services in order to improve the services provided to the users. However, there is a lack of an up-to-date survey paper that incorporates some of the recently proposed accurate and reliable localization systems. In this paper, we aim to provide a detailed survey of different indoor localization techniques such as Angle of Arrival (AoA), Time of Flight (ToF), Return Time of Flight (RTOF), Received Signal Strength (RSS); based on technologies such as WiFi, Radio Frequency Identification Device (RFID), Ultra Wideband (UWB), Bluetooth and systems that have been proposed in the literature. The paper primarily discusses localization and positioning of human users and their devices. We highlight the strengths of the existing systems proposed in the literature. In contrast with the existing surveys, we also evaluate different systems from the perspective of energy efficiency, availability, cost, reception range, latency, scalability and tracking accuracy. Rather than comparing the technologies or techniques, we compare the localization systems and summarize their working principle. We also discuss remaining challenges to accurate indoor localization.

Faheem Zafari

Papers

A Survey of Indoor Localization Systems and Technologies

Micro-location for Internet of Things equipped Smart Buildings

Microlocation for Internet-of-Things-Equipped Smart Buildings

Predicting network attack patterns in SDN using machine learning approach

A Survey of Indoor Localization Systems and Technologies