Showing papers by "Wai-Choong Wong published in 2016"

Geo-Spatial Location Spoofing Detection for Internet of Things

Abstract: We develop a new location spoofing detection algorithm for geo-spatial tagging and location-based services in the Internet of Things (IoT), called enhanced location spoofing detection using audibility (ELSA), which can be implemented at the backend server without modifying existing legacy IoT systems. ELSA is based on a statistical decision theory framework and uses two-way time-of-arrival (TW-TOA) information between the user’s device and the anchors. In addition to the TW-TOA information, ELSA exploits the implicit audibility information (or outage information) to improve detection rates of location spoofing attacks. Given TW-TOA and audibility information, we derive the decision rule for the verification of the device’s location, based on the generalized likelihood ratio test. We develop a practical threat model for delay measurements’ spoofing scenarios, and investigate in detail the performance of ELSA in terms of detection and false alarm rates. Our extensive simulation results on both synthetic and real-world datasets demonstrate the superior performance of ELSA compared to conventional non-audibility-aware approaches.

An Analysis Framework for Interuser Interference in IEEE 802.15.6 Body Sensor Networks: A Stochastic Geometry Approach

Abstract: Interuser interference occurs when multiple body sensor networks (BSNs) are transmitting simultaneously in close proximity to each other. Interference analysis in BSNs is challenging due to the hybrid medium access control (MAC) and the specific channel characteristics of BSNs. This paper presents a stochastic geometry analysis framework for interuser interference in IEEE 802.15.6 BSNs. An extended Matern point process is proposed to model the complex spatial distribution of the interfering BSNs caused by the hybrid MAC defined in IEEE 802.15.6. We employ a stochastic geometry approach to evaluate the performance of BSNs, considering the specific channel characteristics of BSNs near the human body. Performance metrics are derived in terms of outage probability and spatial throughput in the presence of interuser interference. We conduct performance evaluation through extensive simulations and show that the simulation results fit well with the analytic results. Insights are provided on the determination of the interference detection range, the BSN density, and the design of MAC for BSNs.

A Stackelberg Game Model for Overlay D2D Transmission With Heterogeneous Rate Requirements

Abstract: This paper studies the performance of overlay device-to-device (D2D) communication links via carrier-sense multiple access (CSMA) protocols. We assume that the D2D links have heterogeneous rate requirements and different willingness to pay, and each of them acts nonaltruistically to achieve its target rate while maximizing its own payoff. Spatial reuse is allowed if the links are not interfering with each other. A noncooperative game model is used to address the resource allocation among the D2D links, at the same time leveraging on the ideal CSMA network (ICN) model to address the physical channel access issue. We propose a Stackelberg game in which the base station (BS) in the cellular network acts as a Stackelberg leader to regulate the individual payoff by modifying the unit service price so that the total D2D throughput is maximized. The problem is shown to be quasi-convex and can be solved by a sequence of equivalent convex optimization problems. The pricing strategies are designed so that the network always operates within the feasible throughput region. The results are verified by simulations.

Pedestrian crowd level estimation by Head detection using bio-inspired retina model

Abstract: A system to estimate pedestrian crowd levels is proposed. It uses the Parvo channel output of the bio-inspired retina model for improved sensitivity to head patterns in low illumination. Head features are learned from the parvo output. Several features are explored, namely, Aggregate Channel Features (ACF), Integral Channel Features (ICF) and the Histogram of Oriented Gradients (HOG). ACF was selected based on the experimental results. The system is capable of detecting heads in most of the possible head poses, in low illumination situations also. A probabilistic temporal selection approach is proposed to improve the accuracy of the head detection and thereby improving the crowd level estimation accuracy. The system is tested with several standard research data sets such as the IIT Head pose and IHDP Head pose data sets. It exhibited superior performance with average precision and recall scores of 0.99 and 0.97 respectively. The system is currently used to estimate crowd levels in public locations like canteen seating areas.

A QoE-aware resource distribution framework incentivizing context sharing and moderate competition

Abstract: We contend that context information of Internet clients can help to efficiently manage a variety of underlying resources for different Internet services and systems. We therefore propose a resource distribution framework that provides quality of experience (QoE) aware service differentiation, which means that starving clients are prioritized in resource allocation to enhance the corresponding end-user's QoE. The framework also actively motivates each Internet client to consistently provide its actual context information and to adopt moderate competition policies, given that all clients are selfish but rational in nature. We analyze the Internet client's behavior by formulating a non-cooperative game and prove that the framework guides all clients (game players) towards a unique Nash equilibrium. Furthermore, we prove that the distribution results computed by the framework maximize a social welfare function. Throughout this paper, we demonstrate the motivation, operation and performance of the framework by presenting a Web system example, which leverages on the advanced context information deduced by a context-aware system.

Geo-spatial Location Spoofing Detection for Internet of Things

Abstract: We develop a new location spoofing detection algorithm for geo-spatial tagging and location-based services in the Internet of Things (IoT), called Enhanced Location Spoofing Detection using Audibility (ELSA) which can be implemented at the backend server without modifying existing legacy IoT systems. ELSA is based on a statistical decision theory framework and uses two-way time-of-arrival (TW-TOA) information between the user's device and the anchors. In addition to the TW-TOA information, ELSA exploits the implicit available audibility information to improve detection rates of location spoofing attacks. Given TW-TOA and audibility information, we derive the decision rule for the verification of the device's location, based on the generalized likelihood ratio test. We develop a practical threat model for delay measurements spoofing scenarios, and investigate in detail the performance of ELSA in terms of detection and false alarm rates. Our extensive simulation results on both synthetic and real-world datasets demonstrate the superior performance of ELSA compared to conventional non-audibility-aware approaches.

Efficient and Scalable Distributed Autonomous Spatial Aloha Networks via Local Leader Election

Abstract: This paper uses a spatial Aloha model to describe a distributed autonomous wireless network in which a group of transmit-receive pairs (users) shares a common collision channel via slotted-Aloha-like random access. The objective of this study is to develop an intelligent algorithm to be embedded into the transceivers so that all users know how to self-tune their medium access probability (MAP) to achieve overall Pareto optimality in terms of network throughput under spatial reuse while maintaining network stability. While the optimal solution requires each user to have complete information about the network, our proposed algorithm only requires users to have local information. The fundamental of our algorithm is that the users will first self-organize into a number of non-overlapping neighborhoods, and the user with the maximum node degree in each neighborhood is elected as the local leader (LL). Each LL then adjusts its MAP according to a parameter R which indicates the radio intensity level in its neighboring region, whereas the remaining users in the neighborhood simply follow the same MAP value. We show that by ensuring R less than or equal to 2 at the LLs, the stability of the entire network can be assured even when each user only has partial network information. For practical implementation, we propose each LL to use R=2 as the constant reference signal to its built-in proportional and integral controller. The settings of the control parameters are discussed and we validate through simulations that the proposed method is able to achieve close-to-Pareto-front throughput.

MAC Protocol for CRSN

Abstract: Cognitive Radio (CR) technology has gained popularity in Wireless Sensor Networks (WSN) because of scarcity caused by the increase in number of wireless devices and service, and it provides spectrumefficient communication for the resource constrained WSNs. However, appropriate protocols have to be devised to satisfy the requirements of both WSNs and CRs and to enjoy the benefits of cognition in sensor networks. In this chapter, the authors review the existing schemes for wired, wireless, and cognitive radio networks. In addition, they propose a novel energy-efficient and spectrum-aware Medium Access Control (MAC) protocol for the cognitive radio sensor network. The authors design a spectrum-aware asynchronous duty cycle approach that caters to the requirements of both the domains. The performance of the proposed MAC is evaluated via simulations. Performance evaluations are also compared with MCMAC, a multi-channel MAC for WSNs. The comparative results show that the proposed scheme outperforms the multi-channel scheme for WSN.

Efficient and Scalable Distributed Autonomous Spatial Aloha Networks via Local Leader Election

Abstract: This paper uses a spatial Aloha model to describe a distributed autonomous wireless network in which a group of transmit-receive pairs (users) shares a common collision channel via slotted-Aloha-like random access. The objective of this study is to develop an intelligent algorithm to be embedded into the transceivers so that all users know how to self-tune their medium access probability (MAP) to achieve overall Pareto optimality in terms of network throughput under spatial reuse while maintaining network stability. While the optimal solution requires each user to have complete information about the network, our proposed algorithm only requires users to have local information. The fundamental of our algorithm is that the users will first self-organize into a number of non-overlapping neighborhoods, and the user with the maximum node degree in each neighborhood is elected as the local leader (LL). Each LL then adjusts its MAP according to a parameter R which indicates the radio intensity level in its neighboring region, whereas the remaining users in the neighborhood simply follow the same MAP value. We show that by ensuring R less than or equal to 2 at the LLs, the stability of the entire network can be assured even when each user only has partial network information. For practical implementation, we propose each LL to use R=2 as the constant reference signal to its built-in proportional and integral controller. The settings of the control parameters are discussed and we validate through simulations that the proposed method is able to achieve close-to-Pareto-front throughput.

Intrusion detection and prevention in wireless sensor networks

Abstract: Due to the inherent features and weaknesses of Wireless Sensor Networks (WSNs) and the specific threats that target them, there have been many specially designed security protocols and mechanisms, such as Intrusion Detection Systems (IDSs), where correctness and energy efficiency are of vital importance. This chapter surveys recently proposed works on Intrusion Detection Systems in WSNs, and presents a comprehensive classification of various IDS approaches according to their employed detection techniques. The three main categories explored in this chapter are anomaly detection, misuse detection, and specification-based detection protocols. We give a description of existing security attacks in WSNs and the corresponding proposed IDS protocols to tackle those attacks. We analyze the works with respect to network architecture and highlight certain critical shortcomings that IDSs have. In addition, we define future research tracks for IDSs in wireless sensor networks. Though a few restricted survey works on this topic have already been done, we feel that there is a great need of performing a detailed and comprehensive study on the various facets so that the IDS in WSN could be analyzed from all the ‘need-to-know’ angles, thereby benefiting both generalists and specialists.