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Virtual Multipath Attack and Defense for Location Distinction in Wireless Networks

In wireless networks, location distinction aims to detect location changes or facilitate authentication of wireless users. To achieve location distinction, recent research has focused on investigating the spatial uncorrelation property of wireless channels. Specifically, differences in wireless channel characteristics are used to distinguish locations or identify location changes. However, we discover a new attack against all existing location distinction approaches that are built on the spatial uncorrelation property of wireless channels. In such an attack, the adversary can easily hide her location changes or impersonate movements by injecting fake wireless channel characteristics into a target receiver. To defend against this attack, we propose a detection technique that utilizes an auxiliary receiver or antenna to identify these fake channel characteristics. We also discuss such attacks and corresponding defenses in OFDM systems. Experimental results on our USRP-based prototype show that the discovered attack can craft any desired channel characteristic with a successful probability of 95.0 percent to defeat spatial uncorrelation based location distinction schemes and our novel detection method achieves a detection rate higher than 91.2 percent while maintaining a very low false alarm rate.

Utility Maximization for Multimedia Data Dissemination in Large-Scale VANETs

With the increasing demand of media-rich entertainment and location-aware services from people on the road, how to disseminate the multimedia data in large-scale Vehicular Ad-Hoc Networks (VANETs) efficiently and reliably is a pressing issue. Due to the high mobility, large scale, and limited contact time between vehicles, it is quite challenging to support the multimedia data dissemination in VANETs. In this paper, we first utilize a hybrid framework to model the VANETs to address the mobility and scalability issues. Then, we formulate a utility-based maximization problem to find the best delivery strategy and select an optimal path for the multimedia data dissemination, where the utility function has taken the delivery delay, Quality of Services (QoS), and storage cost into consideration. With rigorous analysis, we obtain the closed-form of the expected utility of a path, and then obtain the optimal solution of the problem with the convex optimization theory. Finally, we conduct trace-driven simulations to evaluate the performance of the proposed algorithm with real traces collected by taxis in Shanghai. The simulation results demonstrate the rigorousness of our theoretical analysis, and the effectiveness of the proposed solution.

Traffic Decorrelation Techniques for Countering a Global Eavesdropper in WSNs

We address the problem of preventing the inference of contextual information in event-driven wireless sensor networks (WSNs). The problem is considered under a global eavesdropper who analyzes low-level RF transmission attributes, such as the number of transmitted packets, inter-packet times, and traffic directionality, to infer event location, its occurrence time, and the sink location. We devise a general traffic analysis method for inferring contextual information by correlating transmission times with eavesdropping locations. Our analysis shows that most existing countermeasures either fail to provide adequate protection, or incur high communication and delay overheads. To mitigate the impact of eavesdropping, we propose resource-efficient traffic normalization schemes. In comparison to the state-of-the-art, our methods reduce the communication overhead by more than 50 percent, and the end-to-end delay by more than 30 percent. To do so, we partition the WSN to minimum connected dominating sets that operate in a round-robin fashion. This allows us to reduce the number of traffic sources active at a given time, while providing routing paths to any node in the WSN. We further reduce packet delay by loosely coordinating packet relaying, without revealing the traffic directionality.

SEND: A Situation-Aware Emergency Navigation Algorithm with Sensor Networks

When emergencies happen, navigation services that guide people to exits while keeping them away from emergencies are critical in saving lives. To achieve timely emergency navigation, early and automatic detection of potential dangers, and quick response with safe paths to exits are the core requirements, both of which rely on continuous environment monitoring and reliable data transmission. Wireless sensor networks (WSNs) are a natural choice of the infrastructure to support emergency navigation services, given their relatively easy deployment and affordable costs, and the ability of ubiquitous sensing and communication. Although many efforts have been made to WSN-assisted emergency navigation, almost all existing works neglect to consider the hazard levels of emergencies and the evacuation capabilities of exits. Without considering such aspects, existing navigation approaches may fail to keep people farther away from emergencies of high hazard levels and would probably encounter congestions at exits with lower evacuation capabilities. In this paper, we propose SEND, a situation-aware emergency navigation algorithm, which takes the hazard levels of emergencies and the evacuation capabilities of exits into account and provides the mobile users the safest navigation paths accordingly. We formally model the situation-aware emergency navigation problem and establish a hazard potential field in the network, which is theoretically free of local minima. By guiding users following the descend gradient of the hazard potential field, SEND can thereby achieve guaranteed success of navigation and provide optimal safety. The effectiveness of SEND is validated by both experiments and extensive simulations in 2D and 3D scenarios.

Online Throughput Maximization for Energy Harvesting Communication Systems with Battery Overflow

Energy harvesting communication system enables energy to be dynamically harvested from natural resources and stored in capacitated batteries to be used for future data transmission. In such a system, the amount of future energy to harvest is uncertain and the battery capacity is limited. As a consequence, battery overflow and energy dropping may happen, causing energy underutilization. To maximize the data throughput by using the energy efficiently, a rate-adaptive transmission schedule must address the trade-off between a high-rate transmission which avoids energy overflow and a low-rate transmission which avoids energy shortage. In this paper, we study an online throughput maximization problem without knowing future information. To the best of our knowledge, this is the first work studying the fully-online transmission rate scheduling problem for battery-capacitated energy harvesting communication systems. We consider the problem under two models of the communication channel, a static channel model that assumes the channel status is stable, and a fading channel model that assumes the channel status varies. For the former, we develop an online algorithm that approximates the offline optimal solution within a constant factor for all possible inputs. For the latter, that the channel gains vary in range [hmin; hmax], we propose an online algorithm with a proven ⊖(log(hmax/ hmin))-competitive ratio. Our simulation results further validate the efficiency of the proposed online algorithms.

Near Optimal Data Gathering in Rechargeable Sensor Networks with a Mobile Sink

We study data gathering problem in Rechargeable Sensor Networks (RSNs) with a mobile sink, where rechargeable sensors are deployed into a region of interest to monitor the environment and a mobile sink travels along a pre-defined path to collect data from sensors periodically. In such RSNs, the optimal data gathering is challenging because the required energy consumption for data transmission changes with the movement of the mobile sink and the available energy is time-varying. In this paper, we formulate data gathering problem as a network utility maximization problem, which aims at maximizing the total amount of data collected by the mobile sink while maintaining the fairness of network. Since the instantaneous optimal data gathering scheme changes with time, in order to obtain the globally optimal solution, we first transform the primal problem into an approximate network utility maximization problem by shifting the energy consumption conservation and analyzing necessary conditions for the optimal solution. As a result, each sensor does not need to estimate the amount of harvested energy and the problem dimension is reduced. Then, we propose a Distributed Data Gathering Approach (DDGA), which can be operated distributively by sensors, to obtain the optimal data gathering scheme. Extensive simulations are performed to demonstrate the efficiency of the proposed algorithm.

MoZo: A Moving Zone Based Routing Protocol Using Pure V2V Communication in VANETs Sign In or Purchase

Vehicular Ad-hoc Networks (VANETs) are an emerging field, whereby vehicle-to-vehicle communications can enable many new applications such as safety and entertainment services. Most VANET applications are enabled by different routing protocols. The design of such routing protocols, however, is quite challenging due to the dynamic nature of nodes (vehicles) in VANETs. To exploit the unique characteristics of VANET nodes, we design a moving-zone based architecture in which vehicles collaborate with one another to form dynamic moving zones so as to facilitate information dissemination. We propose a novel approach that introduces moving object modeling and indexing techniques from the theory of large moving object databases into the design of VANET routing protocols. The results of extensive simulation studies carried out on real road maps demonstrate the superiority of our approach compared with both clustering and non-clustering based routing protocols.

GreenCoMP: Energy-Aware Cooperation for Green Cellular Networks

Switching off base stations (BSs) is an effective and efficient energy-saving solution for green cellular networks. The previous works focus mainly on when to switch off BSs without sacrificing the traffic demands of current active users, and then enlarge the coverage of the stay-on cells to cover as many users as possible. Based on this objective, both constant power and transmission power of each BS become the major energy consumption sources. However, the transmission powers of enlarged cells, which have not been taken into account in previous research, are not negligible as compared to other energy consumption sources. To tackle this problem, we observe that the transmission power of one specific BS could be reduced via cooperation among two or more BSs, which is typically used to improve the throughput or enhance the spectrum efficiency in wireless systems. The challenges come mainly from how to jointly consider which BSs to switch off and how to cooperate among active-mode BSs. In this paper, we design energy-aware cooperation strategies that ensure that our system is energy-saving while satisfying user demands. To cope with sleep-mode BSs and perform cooperation among active BSs, we formulate this problem as a binary integer programming problem, and prove it is NP-hard. Based on our formulation, we derive a performance lower bound for this problem via Lagrangian Relaxation with search enumeration. Furthermore, we propose two heuristic algorithms accounting for the properties of energy savings and the constraints of bandwidth resources. The simulation results show that our algorithms outperform pure power control mechanisms that do not consider the transmission power and pure cooperation without power control in terms of the total consumed energy. We also observe that larger cooperative size does not imply a better strategy under different scenarios. Compared to the total consumed energy given that all BSs are turned on, our algorithms can save up to 60 percent of energy. This demonstrates that our methods are indeed efficient energy-saving cooperation strategies for green cellular networks.

GDVAN: A New Greedy Behavior Attack Detection Algorithm for VANETs

Vehicular Ad hoc Networks (VANETs), whose main objective is to provide road safety and enhance the driving conditions, are exposed to several kinds of attacks such as Denial of Service (DoS) attacks which affect the availability of the underlying services for legitimate users. We focus especially on the greedy behavior which has been extensively addressed in the literature for Wireless LAN (WLAN) and for Mobile Ad hoc Networks (MANETs). However, this attack has been much less studied in the context of VANETs. This is mainly because the detection of a greedy behavior is much more difficult for high mobility networks such as VANETs. In this paper, we propose a new detection approach called GDVAN (Greedy Detection for VANETs) for greedy behavior attacks in VANETs. The process to conduct the proposed method mainly consists of two phases, which are namely the suspicion phase and the decision phase. The suspicion phase is based on the linear regression mathematical concept while decision phase is based on a fuzzy logic decision scheme. The proposed algorithm not only detects the existence of a greedy behavior but also establishes a list of the potentially compromised nodes using three newly defined metrics. In addition to being passive, one of the major advantages of our technique is that it can be executed by any node of the network and does not require any modification of the IEEE 802.11p standard. Moreover, the practical effectiveness and efficiency of the proposed approach are corroborated through simulations and experiments.

DIVERT: A Distributed Vehicular Traffic Re-Routing System for Congestion Avoidance

Centralized solutions for vehicular traffic re-routing to alleviate congestion suffer from two intrinsic problems: scalability, as the central server has to perform intensive computation and communication with the vehicles in real-time; and privacy, as the drivers have to share their location as well as the origins and destinations of their trips with the server. This article proposes DIVERT, a distributed vehicular re-routing system for congestion avoidance. DIVERT offloads a large part of the re-routing computation at the vehicles, and thus, the re-routing process becomes practical in real-time. To take collaborative re-routing decisions, the vehicles exchange messages over vehicular ad hoc networks. DIVERT is a hybrid system because it still uses a server and Internet communication to determine an accurate global view of the traffic. In addition, DIVERT balances the user privacy with the re-routing effectiveness. The simulation results demonstrate that, compared with a centralized system, the proposed hybrid system increases the user privacy by 92 percent on average. In terms of average travel time, DIVERT’s performance is slightly less than that of the centralized system, but it still achieves substantial gains compared to the no re-routing case. In addition, DIVERT reduces the CPU and network load on the server by 99.99 and 95 percent, respectively.

Design and Analysis of an Efficient Friend-to-Friend Content Dissemination System

Opportunistic communication, off-loading, and decentrlaized distribution have been proposed as a means of cost efficient disseminating content when users are geographically clustered into communities. Despite its promise, none of the proposed systems have not been widely adopted due to unbounded high content delivery latency, security, and privacy concerns. This paper, presents a novel hybrid content storage and distribution system addressing the trust and privacy concerns of users, lowering the cost of content distribution and storage, and shows how they can be combined uniquely to develop mobile social networking services. The system exploit the fact that users will trust their friends, and by replicating content on friends’ devices who are likely to consume that content it will be possible to disseminate it to other friends when connected to low cost networks. The paper provides a formal definition of this content replication problem, and show that it is NP hard. Then, it presents a community based greedy heuristic algorithm with novel dynamic centrality metrics that replicates the content on a minimum number of friends’ devices, to maximize availability. Then using both real world and synthetic datasets, the effectiveness of the proposed scheme is demonstrated. The practicality of the proposed system, is demonstrated through an implementation on Android smartphones.

Analysis of Multi-Hop Probabilistic Forwarding for Vehicular Safety Applications on Highways

Safety applications based on the dedicated short-range communication (DSRC) in vehicular networks have very strict performance requirements for safety messages (in terms of delay and packet delivery). However, there is a lack of systematic approach to achieve the performance requirements by leveraging the potential of multi-hop forwarding. This paper proposes a generic multi-hop probabilistic forwarding scheme that achieves these requirements for event-driven safety messages, is compatible with the 802.11 broadcasting protocol and inherits some of the best features of solutions proposed so far for vehicular safety applications. In addition, we develop a unified and comprehensive analytical model to evaluate the performance of the proposed scheme taking into account the effect of hidden terminals, vehicle densities, and the spatial distribution of the multiple forwarders, in a one-dimensional highway scenario. Our numerical experiments confirm the accuracy of the model and demonstrate that the proposed protocol can improve the packet delivery performance by up to 209 percent, while maintaining the delay well below the required threshold. Finally, the utility of the analytical model is demonstrated via an optimal design for the coefficients of a forwarding probability function in the proposed scheme.