Biblio

Mesh networks based on the wireless local area network (WLAN) technology, as specified by the standards amendment IEEE 802.11s, provide for a flexible and low-cost interconnection of devices and embedded systems for various use cases. To assess the real-world performance of WLAN mesh networks and potential optimization strategies, suitable testbeds and measurement tools are required. Designed for highly automated transport-layer throughput and latency measurements, the software FLExible Network Tester (Flent) is a promising candidate. However, so far Flent does not integrate information specific to IEEE 802.11s networks, such as peer link status data or mesh routing metrics. Consequently, we propose Flent extensions that allow to additionally capture IEEE 802.11s information as part of the automated performance tests. For the functional validation of our extensions, we conduct Flent measurements in a mesh mobility scenario using the network emulation framework Mininet-WiFi.

In the context of a rapidly changing and increasingly complex (industrial) production landscape, securing the (communication) infrastructure is becoming an ever more important but also more challenging task - accompanied by the application of radio communication. A worthwhile and promising approach to overcome the arising attack vectors, and to keep private networks private, are Physical Layer Security (PhySec) implementations. The paper focuses on the transfer of the IEEE802.11 (WLAN) PhySec - Secret Key Generation (SKG) algorithms to Next Generation Mobile Networks (NGMNs), as they are the driving forces and key enabler of future industrial networks. Based on a real world Long Term Evolution (LTE) testbed, improvements of the SKG algorithms are validated. The paper presents and evaluates significant improvements in the establishment of channel profiles, whereby especially the Bit Disagreement Rate (BDR) can be improved substantially. The combination of the Discrete Cosine Transformation (DCT) and the supervised Machine Learning (ML) algorithm - Linear Regression (LR) - provides outstanding results, which can be used beyond the SKG application. The evaluation also emphasizes the appropriateness of PhySec for securing private networks.

The recent analysis indicates more than 250,000 people in the United States of America (USA) die every year because of medical errors. World Health Organisation (WHO) reports states that 2.6 million deaths occur due to medical and its prescription errors. Many of the errors related to the wrong drug/dosage administration by caregivers to patients due to indecipherable handwritings, drug interactions, confusing drug names, etc. The espousal of Mobile-based speech recognition applications will eliminate the errors. This allows physicians to narrate the prescription instead of writing. The application can be accessed through smartphones and can be used easily by everyone. An application program interface has been created for handling requests. Natural language processing is used to read text, interpret and determine the important words for generating prescriptions. The patient data is stored and used according to the Health Insurance Portability and Accountability Act of 1996 (HIPAA) guidelines. The SMS4-BSK encryption scheme is used to provide the data transmission securely over Wireless LAN.

Over the past decade, distributed CSMA, which forms the basis for WiFi, has been deployed ubiquitously to provide seamless and high-speed mobile internet access. However, distributed CSMA might not be ideal for future IoT/M2M applications, where the density of connected devices/sensors/controllers is expected to be orders of magnitude higher than that in present wireless networks. In such high-density networks, the overhead associated with completely distributed MAC protocols will become a bottleneck. Moreover, IoT communications are likely to have strict QoS requirements, for which the `best-effort' scheduling by present WiFi networks may be unsuitable. This calls for a clean-slate redesign of the wireless MAC taking into account the requirements for future IoT/M2M networks. In this paper, we propose a reservation-based (for minimal overhead) wireless MAC designed specifically with IoT/M2M applications in mind.

Device-Free Localization and Tracking (DFLT) acts as a key component for the contactless awareness applications such as elderly care and home security. However, the random phase errors in WiFi signal and weak target echoes submerged in background clutter signals are mainly obstacles for current DFLT systems. In this paper, we propose the design and implementation of MuTrack, a multiparameter based DFLT system using commodity WiFi devices with a single link. Firstly, we select an antenna with maximum reliability index as the reference antenna for signal sanitization in which the conjugate operation removes the random phase errors. Secondly, we design a multi-dimensional parameters estimator and then refine path parameters by optimizing the complete data of path components. Finally, the Hungarian Kalman Filter based tracking method is proposed to derive accurate locations from low-resolution parameter estimates. We extensively validate the proposed system in typical indoor environment and these experimental results show that MuTrack can achieve high tracking accuracy with the mean error of 0.82 m using only a single link.

Packet loss detection and prevention is full-size module of MANET protection systems. In trust based approach routing choices are managed with the aid of an unbiased have faith table. Traditional trust-based techniques unsuccessful to notice the essential underlying reasons of a malicious events. AODV is an approachable routing set of guidelines i.e.it finds a supply to an endpoint only on request. LDoS cyber-attacks ship assault statistics packets after period to time in a brief time period. The community multifractal ought to be episodic when LDoS cyber-attacks are hurled unpredictably. Real time programs in MANET necessitate certain QoS advantages, such as marginal end-to-end facts packet interval and unobjectionable records forfeiture. Identification of malevolent machine, information security and impenetrable direction advent in a cell system is a key tasks in any wi-fi network. However, gaining the trust of a node is very challenging, and by what capability it be able to get performed is quiet ambiguous. This paper propose a modern methodology to detect and stop the LDoS attack and preserve innocent from wicked nodes. In this paper an approach which will improve the safety in community by identifying the malicious nodes using improved quality grained packet evaluation method. The approach also multiplied the routing protection using proposed algorithm The structure also accomplish covered direction-finding to defend Adhoc community against malicious node. Experimentally conclusion factor out that device is fine fabulous for confident and more advantageous facts communication.

Bluetooth technologies have widespread applications in personal area networks, device-to-device communications and forming ad hoc networks. Studying Bluetooth devices security is a challenging task as they lack support for monitor mode available with other wireless networks (e.g. 802.11 WiFi). In addition, the frequency-hoping spread spectrum technique used in its operation necessitates special hardware and software to study its operation. This investigation examines methods for analyzing Bluetooth devices' security and presents a proof-of-concept DoS attack on the Link Manager Protocol (LMP) layer using the InternalBlue framework. Through this study, we demonstrate a method to study Bluetooth device security using existing tools without requiring specialized hardware. Consequently, the methods proposed in the paper can be used to study Bluetooth security in many applications.

Wireless networking opens up many opportunities to facilitate miniaturized robots in collaborative tasks, while the openness of wireless medium exposes robots to the threats of Sybil attackers, who can break the fundamental trust assumption in robotic collaboration by forging a large number of fictitious robots. Recent advances advocate the adoption of bulky multi-antenna systems to passively obtain fine-grained physical layer signatures, rendering them unaffordable to miniaturized robots. To overcome this conundrum, this paper presents ScatterID, a lightweight system that attaches featherlight and batteryless backscatter tags to single-antenna robots to defend against Sybil attacks. Instead of passively "observing" signatures, ScatterID actively "manipulates" multipath propagation by using backscatter tags to intentionally create rich multipath features obtainable to a single-antenna robot. These features are used to construct a distinct profile to detect the real signal source, even when the attacker is mobile and power-scaling. We implement ScatterID on the iRobot Create platform and evaluate it in typical indoor and outdoor environments. The experimental results show that our system achieves a high AUROC of 0.988 and an overall accuracy of 96.4% for identity verification.

Attacks by Jamming on wireless communication network can provoke Denial of Services. According to the communication system which is affected, the consequences can be more or less critical. In this paper, we propose to develop an algorithm which could be implemented at the reception stage of a communication terminal in order to detect the presence of jamming signals. The work is performed on Wi-Fi communication signals and demonstrates the necessity to have a specific signal processing at the reception stage to be able to detect the presence of jamming signals.

We have proposed the Media Access Control method based on the Synchronization Phenomena of coupled oscillators (SP-MAC) to improve a total throughput of wireless terminals connected to a Access Point. SP-MAC can avoid the collision of data frames that occur by applying Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) based on IEEE 802.11 in Wireless local area networks (WLAN). Furthermore, a new throughput guarantee control method based on SP-MAC has been proposed. This method enable each terminal not only to avoid the collision of frames but also to obtain the requested throughput by adjusting the parameters of SP-MAC. In this paper, we propose a new throughput control method that realizes the fairness among groups of terminals that use the different TCP versions, by taking the advantage of our method that is able to change acquired throughput by adjusting parameters. Moreover, we confirm the effectiveness of the proposed method by the simulation evaluation.

Location privacy is one of most frequently discussed terms in the mobile devices security breaches and data leaks. With the expected growth of the number of IoT devices, which is 20 billions by 2020., location privacy issues will be further brought to focus. In this paper we give an overview of location privacy implications in wireless networks, mainly focusing on user's Preferred Network List (list of previously used WiFi Access Points) contained within WiFi Probe Request packets. We will showcase the existing work and suggest interesting topics for future work. A chronological overview of sensitive location data we collected on a musical festival in years 2014, 2015, 2017 and 2018 is provided. We conclude that using passive WiFi monitoring scans produces different results through years, with a significant increase in the usage of a more secure Broadcast Probe Request packets and MAC address randomizations by the smartphone operating systems.

The goal of this work was to identify and try to solve some of the vulnerabilities present in the AR Drone 2.0 by Parrot. The approach was to identify how the system worked, find and analyze vulnerabilities and flaws in the system as a whole and in the software, and find solutions to those problems. Analyzing the results of some tests showed that the system has an open WiFi network and the communication between the controller and the drone are unencrypted. Analyzing the Linux operating system that the drone uses, we see that "Pairing Mode" is the only way the system protects itself from unauthorized control. This is a feature that can be easily bypassed. Port scans reveal that the system has all the ports for its services open and exposed. This makes it susceptible to attacks like DoS and takeover. This research also focuses on some of the software vulnerabilities, such as Busybox that the drone runs. Lastly, this paper discuses some of the possible methods that can be used to secure the drone. These methods include securing the messages via SSH Tunnel, closing unused ports, and re-implementing the software used by the drone and the controller.

With the popularity of smart devices and the widespread use of the Wi-Fi-based indoor localization, edge computing is becoming the mainstream paradigm of processing massive sensing data to acquire indoor localization service. However, these data which were conveyed to train the localization model unintentionally contain some sensitive information of users/devices, and were released without any protection may cause serious privacy leakage. To solve this issue, we propose a lightweight differential privacy-preserving mechanism for the edge computing environment. We extend ε-differential privacy theory to a mature machine learning localization technology to achieve privacy protection while training the localization model. Experimental results on multiple real-world datasets show that, compared with the original localization technology without privacy-preserving, our proposed scheme can achieve high accuracy of indoor localization while providing differential privacy guarantee. Through regulating the value of ε, the data quality loss of our method can be controlled up to 8.9% and the time consumption can be almost negligible. Therefore, our scheme can be efficiently applied in the edge networks and provides some guidance on indoor localization privacy protection in the edge computing.

Modern Internet of Things networks are often proprietary, although based on open standards, or are built on the basis of conventional Wi-Fi network, which does not allow the use of energy-saving modes and limits the range of solutions used. The paper is devoted to the study and comparison of two solutions based on Wi-Fi and Bluetooth with the functions of a self-organizing network and switching between transmission channels. The power consumption in relation to specific actions and volumes of transmitted data is investigated; a conclusion is drawn on the conditions for the application of a particular technology.

With the current significant penetration of mobile devices (i.e. smartphones and tablets) and the tremendous increase in the number of the corresponding mobile applications, they have become an indispensable part of our lives. Nowadays, there is a significant growth in the number of sensitive applications such as personal health applications, personal financial applications, home monitoring applications, etc. In addition, with the significant growth of Internet-of-Things (IoT) devices, smartphones and the corresponding applications are widely considered as the Internet gateways for these devices. Mobile devices mostly use wireless LANs (WLANs) (i.e., WiFi networks) as the prominent network interface to the Internet. However, due to the broadcast nature of WiFi links, wireless traffics are exposed to any eavesdropping adversary within the WLAN. Despite WiFi encryption, studies show that application usage information could be inferred from the encrypted wireless traffic. The leakage of this sensitive information is very serious issue that will significantly impact users' privacy and security. In addressing this privacy concern, we design and develop a lightweight programmable privacy framework, called PrivacyGuard. PrivacyGuard is inspired by the vision of pushing the Software Defined Network (SDN)-like paradigm all the way to wireless network edge, is designed to support of adopting privacy preserving policies to protect the wireless communication of the sensitive applications. In this paper, we demonstrate and evaluate a prototype of PrivacyGuard framework on Android devices showing the flexibility and efficiency of the framework.

GPS signals, the main origin of navigation, are not functional in indoor environments. Therefore, Wi-Fi access points have started to be increasingly used for localization and tracking inside the buildings by relying on fingerprint-based approach. However, with these types of approaches, several concerns regarding the privacy of the users have arisen. Malicious individuals can determine a clients daily habits and activities by simply analyzing their wireless signals. While there are already efforts to incorporate privacy to the existing fingerprint-based approaches, they are limited to the characteristics of the homo-morphic cryptographic schemes they employed. In this paper, we propose to enhance the performance of these approaches by exploiting another homomorphic algorithm, namely DGK, with its unique encrypted sorting capability and thus pushing most of the computations to the server side. We developed an Android app and tested our system within a Columbia University dormitory. Compared to existing systems, the results indicated that more power savings can be achieved at the client side and DGK can be a viable option with more powerful server computation capabilities.

In this paper, we outline a novel, forward error correction-based information hiding technique for adaptive rate wireless communication systems. Specifically, we propose leveraging the functionality of wireless local area network modulation and coding schemes (MCS) and link adaptation mechanisms to significantly increase covert channel throughput. After describing our generalized information hiding model, we detail implementation of this technique within the IEEE 802.11ad, directional multi-Gigabit standard. Simulation results demonstrate the potential of the proposed techniques to develop reliable, high-throughput covert channels under multiple MCS rates and embedding techniques. Covert channel performance is evaluated in terms of the observed packet error ratio of the underlying communication system as well as the bit error ratio of the hidden data.

Nowadays, some workplaces have adopted the policy of BYOD (bring your own device) that permits employees to bring personally owned devices, and to use those devices to access company information and applications. Especially, small devices like smartphones are widely used due to the greater mobility and connectivity. A majority of organizations provide the wireless local area network which is necessary for small devices and business data transmission. The resources access through Wi-Fi network of the organization needs intense restriction. WPA2 Enterprise with 802.1X standard is typically introduced to handle user authentication on the network using the EAP framework. However, credentials management for all users is a hassle for administrators. Strong authentication provides higher security whereas the difficulty of deployment is still open issues. This research proposes the utility of Near Field Communication to securely transmit certificate data that rely on the hybrid cryptosystem. The approach supports enterprise Wi-Fi hotspot authentication based on WPA2-802.1X model with the EAP-TLS method. It also applies multi-factor authentication for enhancing the security of networks and users. The security analysis and experiment on establishing connection time were conducted to evaluate the presented approach.

Mobile Ad-hoc Networks (MANETs) have gained popularity both in research and in industrial fields. This is due to their ad hoc nature, easy deployment thanks to the lack of fixed infrastructure, self-organization of its components, dynamic topologies and the absence of any central authority for routing. However, MANETs suffer from several vulnerabilities such as battery power, limited memory space, and physical protection of network nodes. In addition, MANETs are sensitive to various attacks that threaten network security like Blackhole attack in its different implementation (single and multiple). In this article, we present the simulation results of single and multiple Blackhole attack in AODV and OLSR protocols on using NS-3.27 simulator. In this simulation, we took into consideration the density of the network described by the number of nodes included in the network, the speed of the nodes, the mobility model and even we chose the IEEE 802.11ac protocol for the pbysicallayer, in order to have a simulation, which deals with more general and more real scenarios. To be able to evaluate the impact of the attack on the network, the Packet delivery rate, Routing overhead, Throughput and Average End to End delay have been chosen as metrics for performance evaluation.

The traditional security system requires a lot of manpower, and the wireless security system has been developed to reduce costs. However, for wireless systems, stability and reliability are important system indicators. In order to effectively improve these two indicators, we have imported butterfly network coding algorithm into the wireless sensing network. Because this algorithm enables each node to play multiple roles, such as routing, encoding, decoding, sending and receiving, it can also improve the throughput of network transmission, and effectively improve the stability and reliability of the wireless security system. This paper used the Wi-Fi module to implement the butterfly network coding algorithm, and is actually installed in the building. The basis for transmission and reception of all nodes in the network is received signal strength indication (RSSI). On the other hand, this is an IoT system for security monitoring.

Securing multi-robot teams against malicious activity is crucial as these systems accelerate towards widespread societal integration. This emerging class of ``physical networks'' requires research into new methods of security that exploit their physical nature. This paper derives a theoretical framework for securing multi-agent consensus against the Sybil attack by using the physical properties of wireless transmissions. Our frame-work uses information extracted from the wireless channels to design a switching signal that stochastically excludes potentially untrustworthy transmissions from the consensus. Intuitively, this amounts to selectively ignoring incoming communications from untrustworthy agents, allowing for consensus to the true average to be recovered with high probability if initiated after a certain observation time T0 that we derive. This work is different from previous work in that it allows for arbitrary malicious node values and is insensitive to the initial topology of the network so long as a connected topology over legitimate nodes in the network is feasible. We show that our algorithm will recover consensus and the true graph over the system of legitimate agents with an error rate that vanishes exponentially with time.

The dynamic nature of the wireless channel poses a challenge to services requiring seamless and uniform network quality of service (QoS). Self-healing, a promising approach under the self-organizing networks (SON) paradigm, and has been shown to deal with unexpected network faults in cellular networks. In this paper, we use simple machine learning (ML) algorithms inspired by SON developments in cellular networks. Evaluation results show that the proposed approach identifies the faulty APs. Our proposed approach improves throughput by 63.6% and reduces packet loss rate by 16.6% compared with standard 802.11.

Smart Grid is a major element of the Smart City concept that enables two-way communication of energy data between electric utilities and their consumers. These communication technologies are going through sharp modernization to meet future demand growth and to achieve reliability, security, and efficiency of the electric grid. In this paper, we implement an IPv6 based two-way communication system between the transformer agent (TA), installed at local electric transformer and various customer agents (CAs), connected to customer's smart meter. Various homes share their energy usage with the TA which in turn sends the utility's recommendations to the CAs. Raspberry Pi is used as hardware for all the CAs and the TA. We implement a self-healing mesh network between all nodes using OpenLab IEEE 802.15.4 chips and Routing Protocol for Low-Power and Lossy Networks (RPL), and the data is secured by RSA/AES keys. Several tests have been conducted in real environments, inside and outside of Carleton University, to test the performance of this communication network in various obstacle settings. In this paper, we highlight the details behind the implementation of this IPv6-based smart grid communication system, the related challenges, and the proposed solutions.

Smartphone has become the tool which is used daily in modern human life. Some activities in human life, according to the usage of the smartphone can be related to the information which has a high privilege and needs a privacy. It causes the owners of the smartphone needs a system which can protect their privacy. Unfortunately, the secure the system, the unease of the usage. Hence, the system which has an invulnerable environment but also gives the ease of use is very needful. The aspect which is related to the ease of use is an authentication mechanism. Sometimes, this aspect correspondence to the effectiveness and the efficiency. This study is going to analyze the application related to this aspect which is a lock screen application. This lock screen application uses the context data based on the environment condition around the user. The context data used are GPS location and Mac Address of Wi-Fi. The system is going to detect the context and is going to determine if the smartphone needs to run the authentication mechanism or to bypass it based on the analysis of the context data. Hopefully, the smartphone application which is developed still can provide mobility and usability features, and also can protect the user privacy even though it is located in the environment which its context data is unknown.

Location determination in the indoor areas as well as in open areas is important for many applications. But location determination in the indoor areas is a very difficult process compared to open areas. The Global Positioning System (GPS) signals used for position detection is not effective in the indoor areas. Wi-Fi signals are a widely used method for localization detection in the indoor area. In the indoor areas, localization can be used for many different purposes, such as intelligent home systems, locations of people, locations of products in the depot. In this study, it was tried to determine localization for with the classification method for 4 different areas by using Wi-Fi signal values obtained from different routers for indoor location determination. Linear discriminant analysis (LDA) classification was used for classification. In the test using 10k fold cross-validation, 97.2% accuracy value was calculated.