Biblio

This paper describes a novel distributed mobility management (DMM) scheme for the "named-object" information centric network (ICN) architecture in which the routers forward data based on unique identifiers which are dynamically mapped to the current network addresses of a device. The work proposes and evaluates two specific handover schemes namely, hard handoff with rebinding and soft handoff with multihoming intended to provide seamless data transfer with improved throughput during handovers. The evaluation of the proposed handover schemes using system simulation along with proof-of-concept implementation in ORBIT testbed is described. The proposed handoff and scheduling throughput gains are 12.5% and 44% respectively over multiple interfaces when compared to traditional IP network with equal share split scheme. The handover performance with respect to RTT and throughput demonstrate the benefits of clean slate network architecture for beyond 5G networks.

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.

In light of the problem for garbage cleaning in small water area, an intelligent miniature water surface garbage cleaning robot with unmanned driving and convenient operation is designed. Based on STC12C5A60S2 as the main controller in the design, power module, transmission module and cleaning module are controlled together to realize the function of cleaning and transporting garbage, intelligent remote control of miniature water surface garbage cleaning robot is realized by the WiFi module. Then the prototype is developed and tested, which will verify the rationality of the design. Compared with the traditional manual driving water surface cleaning devices, the designed robot realizes the intelligent control of unmanned driving, and achieves the purpose of saving human resources and reducing labor intensity, and the system operates security and stability, which has certain practical value.

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.

Most two-factor authentication (2FA) implementations rely on the user possessing and interacting with a secondary device (e.g. mobile phone) which has contributed to the lack of widespread uptake. We present a 2FA system, called Wi-Sign that does not rely on a secondary device for establishing the second factor. The user is required to sign at a designated place on the primary device with his finger following a successful first step of authentication (i.e. username + password). Wi-Sign captures the unique perturbations in the WiFi signals incurred due to the hand motion while signing and uses these to establish the second factor. Wi-Sign detects these perturbations by measuring the fine-grained Channel State Information (CSI) of the ambient WiFi signals at the device from which log-in attempt is being made. The logic is that, the user's hand geometry and the way he moves his hand while signing cause unique perturbations in CSI time-series. After filtering noise from the CSI data, principal component analysis is employed for compressing the CSI data. For segmentation of sign related perturbations, Wi-Sign utilizes the thresholding approach based on the variance of the first-order difference of the selected principal component. Finally, the authentication decision is made by feeding scrupulously selected features to a One-Class SVM classifier. We implement Wi-Sign using commodity off-the-shelf 802.11n devices and evaluate its performance by recruiting 14 volunteers. Our evaluation shows that Wi-Sign can on average achieve 79% TPR. Moreover, Wi-Sign can detect attacks with an average TNR of 86%.

This research was an experimental analysis of the Intrusion Detection Systems(IDS) with Honey Pot conducting through a study of using Honey Pot in tricking, delaying or deviating the intruder to attack new media broadcasting server for IPTV system. Denial of Service(DoS) over wire network and wireless network consisted of three types of attacks: TCP Flood, UDP Flood and ICMP Flood by Honey Pot, where the Honeyd would be used. In this simulation, a computer or a server in the network map needed to be secured by the inactivity firewalls or other security tools for the intrusion of the detection systems and Honey Pot. The network intrusion detection system used in this experiment was SNORT (www.snort.org) developed in the form of the Open Source operating system-Linux. The results showed that, from every experiment, the internal attacks had shown more threat than the external attacks. In addition, attacks occurred through LAN network posted 50% more disturb than attacks occurred on WIFI. Also, the external attacks through LAN posted 95% more attacks than through WIFI. However, the number of attacks presented by TCP, UDP and ICMP were insignificant. This result has supported the assumption that Honey Pot was able to help detecting the intrusion. In average, 16% of the attacks was detected by Honey Pot in every experiment.

Nowadays, robots are widely ubiquitous and integral part in our daily lives, which can be seen almost everywhere in industry, hospitals, military, etc. To provide remote access and control, usually robots are connected to local network or to the Internet through WiFi or Ethernet. As such, it is of great importance and of a critical mission to maintain the safety and the security access of such robots. Security threats may result in completely preventing the access and control of the robot. The consequences of this may be catastrophic and may cause an immediate physical damage to the robot. This paper aims to present a security risk assessment of the well-known PeopleBot; a mobile robot platform from Adept MobileRobots Company. Initially, we thoroughly examined security threats related to remote accessing the PeopleBot robot. We conducted an impact-oriented analysis approach on the wireless communication medium; the main method considered to remotely access the PeopleBot robot. Numerous experiments using SSH and server-client applications were conducted, and they demonstrated that certain attacks result in denying remote access service to the PeopleBot robot. Consequently and dangerously the robot becomes unavailable. Finally, we suggested one possible mitigation and provided useful conclusions to raise awareness of possible security threats on the robotic systems; especially when the robots are involved in critical missions or applications.

Person tracking is crucial in any automatic person surveillance systems. In this problem, person localization and re-identification (Re-ID) are both simultaneously processed to show separated trajectories for each individual. In this paper, we propose to use mixture of WiFi and camera systems for person tracking in indoor surveillance regions covered by WiFi signals and disjointed camera FOVs (Field of View). A fusion method is proposed to combine the position observations achieved from each single system of WiFi or camera. The combination is done based on an optimal assignment between the position observations and predicted states from camera and WiFi systems. The correction step of Kalman filter is then applied for each tracker to give out state estimations of locations. The fusion method allows tracking by identification in non-overlapping cameras, with clear identity information taken from WiFi adapter. The experiments on a multi-model dataset show outperforming tracking results of the proposed fusion method in comparison with vision-based only method.

Ubiquitous WiFi infrastructure and smart phones offer a great opportunity to study physical activities. In this paper, we present MobiCamp, a large-scale testbed for studying mobility-related activities of residents on a campus. MobiCamp consists of \textasciitilde2,700 APs, \textasciitilde95,000 smart phones, and an App with \textasciitilde2,300 opt-in volunteer users. More specifically, we capture how mobile users interact with different types of buildings, with other users, and with classroom courses, etc. To achieve this goal, we first obtain a relatively complete coverage of the users' mobility traces by utilizing four types of information from SNMP and by relaxing the location granularity to roughly at the room level. Then the popular App provides user attributes (grade, gender, etc.) and fine-grained behavior information (phone usages, course timetables, etc.) of the sampled population. These detailed mobile data is then correlated with the mobility traces from the SNMP to estimate the entire campus population's physical activities. We use two applications to show the power of MobiCamp.

The Internet of Things (IoT) represents a diverse technology and usage with unprecedented business opportunities and risks. The Internet of Things is changing the dynamics of security industry & reshaping it. It allows data to be transferred seamlessly among physical devices to the Internet. The growth of number of intelligent devices will create a network rich with information that allows supply chains to assemble and communicate in new ways. The technology research firm Gartner predicts that there will be 26 billion installed units on the Internet of Things (IoT) by 2020[1]. This paper explains the concept of Internet of Things (IoT), its characteristics, explain security challenges, technology adoption trends & suggests a reference architecture for E-commerce enterprise.