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Certificate validation in public key infrastructure (PKI) is a vital phase of establishing secure connections on any network. There has been a great deal of speculation on how to efficiently validate digital certificates in PKI on which the security of network communications rests. Developing such a system is challenging because digital certificates need to be quickly and securely validated for a large number of clients in a short period of time at a low cost. On the other hand, our analysis on the TLS handshakes of the Alexa Top 1 Million domains dataset indicates that the current popular certificate validation systems cannot deliver certificate validation information to the clients in a timely fashion and suffer from high overhead at the client side, making them susceptible to a number of attacks. Motivated by these observations, we present SecureGuard, a certificate validation system that can effectively handle certificate validation during TLS handshakes. Our system utilizes Internet service providers (ISPs) as the primary entity for certificate validation exploiting the fact that any Internet access request must pass through the ISP proxy-cache servers. We provide an extensive evaluation on SecureGuard and illustrate its efficiency. Moreover, we introduce a quantitative analysis method that can investigate the costs incurred by our system and other certificate validation approaches under the same evaluation scenarios. Our implementation results demonstrate that SecureGuard is able to validate the digital certificates within a short period of time, in a secure manner, with less network overhead. [ABSTRACT FROM AUTHOR]

Lane-level navigation has received a lot of attention in recent years. It has played a great role in assisting route planning, as well as navigating automated vehicles. Aside from sticking to the planned route, abnormal traffic situations which result in blocking lanes could impact lane switching decisions. Unfortunately, there is currently no navigation system that can sense and track a vehicle's lane position and to advise the driver of lane switching decisions. Google Maps stores a priori the number of lanes and their directions at each highway exit and provides this information to drivers when navigating. However, even with this information, some drivers may not be able to make an informed decision regarding when and where to make a correct lane switch. This motivated us to develop a mechanism for the detection and tracking of real-time lane changes. In this paper, we propose a GPS-aiding system that can sense and track a vehicle's lane position. The system leverages smart phones’ computing capability, rear cameras, and inertial motion sensors. With little extra computational overhead, the system applies computer vision techniques to achieve lane-level positioning. We also design a machine learning-based algorithm to detect and track lane switching. We conduct a series of experiments, analyze our system in real-world environments, and achieve very promising results. We believe our system can be a great asset to current smart phone navigation systems. [ABSTRACT FROM AUTHOR]

This paper considers the shortest link scheduling problem in wireless networks under the signal-to-interference-plus-noise ratio (SINR) model. We propose an $O({}\log({}l_{\max}/l_{\min}{}){})-approximation algorithm called shortest link scheduling with power control (SLSPC) with oblivious power assignment and an O(\log1+\varphi(l\max/l\min)-approximation algorithm called shortest link scheduling with uniform or mean power assignment (SLSUM) with uniform or mean power control, where \varphi > 0 is a constant serving as a regulatory factor for slight transmit power adjustment, and where l\max and l\min denote the lengths of the longest and shortest links, respectively. We conduct a rigorous theoretical performance analysis to analyze the feasibility and approximation factors of the proposed algorithms. We also carry out an extensive comparison-based simulation study, whose results indicate that the performances of SLSPC and SLSUM are superior over the state of the art as the set of the so-called “black and gray” links, which are difficult to schedule and should be sequentially scheduled, is completely removed by adjusting the transmit power appropriately via $\varphi$. Our numerical analysis demonstrates that the approximation ratios of our algorithms are tighter than the best known ratios. [ABSTRACT FROM PUBLISHER]

Most existing studies on cognitive-radio networks assume that cognitive users (CUs) can switch to any available channel, regardless of the frequency gap between a target channel and the current channel. However, due to hardware limitations, CUs can actually jump only so far from where the operating frequency of their current channel is. This paper studies the performance of cognitive-radio networks while considering realistic channel handoff agility, where CUs can only switch to their neighboring channels. We use a continuous-time Markov process to derive and analyze the forced termination and blocking probabilities of CUs. Using these derived probabilities, we then study and analyze the impact of limited spectrum handoff agility on cognitive spectrum access efficiency. We show that accounting for realistic spectrum handoff agility reduces performance of cognitive-radio networks in terms of spectrum access capability and efficiency. [ABSTRACT FROM PUBLISHER]

With the prosperity of vehicular networks and intelligent transport systems, vast amount of data can be easily collected by vehicular devices from their users and widely spread in the vehicular networks for the purpose of solving large-scale machine learning problems. Hence how to preserve the data privacy of users during the learning process has become a public concern. To address this concern, under the celebrated framework of differential privacy (DP), we present in this paper a decentralized parallel stochastic gradient descent (D-PSGD) algorithm, called DP2-SGD, which can offer protection for privacy of users in vehicular networks. With thorough analysis we show that DP2-SGD satisfies (ε, δ)-DP while the learning efficiency is the same as D-PSGD without privacy preservation. We also propose a refined algorithm called EC-SGD by introducing an error-compensate strategy. Extensive experiments show that EC-SGD can further improve the convergence efficiency over DP2-SGD in reality. [ABSTRACT FROM AUTHOR]

Location-based services (LBSs) are becoming an increasingly important component in our social and business life. All existing LBS providers support the nearest place searching via a single point of interest (POI) query. That is, in one query, a user is allowed to search for only one type of service. However, in real life, people usually need to search multiple different types of services and hope that their locations are as close as possible for convenience. For example, one user would like to search for a restaurant with a KTV nearby. To support this application scenario, we propose a novel LBS termed “ $N$ -in-One,” which is the first scheme to extend the function of single-POI LBS to multiple-POI LBS such that a single query can be employed to request multiple POIs that are geographically close. Providing “ $N$ -in-One” is challenging because: 1) serving a “ $N$ -in-One” query is not equivalent to serving $N$ queries independently due to the distance correlation among the $N$ POIs; and 2) the cask effect is getting worse in the service area mode of “ $N$ -in-one” as most of the returned results may be rendered useless when some hot POIs are blocked. To overcome these challenges, we propose several algorithms using computational geometry techniques to identify the best $K$ POIs that are geographically close and the service area (denoted by a given-sized rectangle) that can cover as many the best $Q$ clusters as possible while reducing the cask effect in the service area mode. Extensive simulations based on both synthetic and real world data demonstrate the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we propose a green spectrum and infrastructure leasing framework for sharing unused TV spectrum, in which the ownership of the network infrastructure and spectrum are decoupled, and each can be leased as a service on demand. An incumbent TV spectrum owner (TSO) can lease a share of the infrastructure from a network infrastructure owner (NIO) with a pay-per-use model, to provide new services to its end users. On the other hand, a TSO can rent a portion of its unused spectrum to the NIO for revenue such that the NIO can obtain access to the spectrum to serve its customers. This proposed framework achieves green communications through both resource sharing and energy saving. Our paper aims to analyze the interplay between TSO and NIO, and create a win–win situation to provide sufficient incentives for them to cooperate on this green communications. A multistage Stackelberg game is formulated, where TSO and NIO sequentially determine the total bandwidth supply for sharing, the infrastructure leasing price, the amount of infrastructure to lease, the spectrum leasing price, and the amount of spectrum to lease to maximize their utilities. The best strategies that will be taken by the TSO and NIO under various deployment scenarios are analyzed. Evaluation results show that the proposed green spectrum and infrastructure leasing framework is a promising scheme under which both TSO and NIO can benefit, and their utilities can be maximized in terms of user data throughput and revenue/payment, which provides sufficient incentives for them to cooperate. [ABSTRACT FROM PUBLISHER]

In this paper, we consider the problem of cooperative spectrum sensing scheduling (C3S) in a cognitive radio network (CRN) when there exist multiple primary channels. Our work focuses on a scenario in which each secondary user (SU) has the freedom to decide whether to participate in cooperative spectrum sensing; if not, the SU becomes a free rider. Such a mechanism can conserve the energy for spectrum sensing at a risk of sacrificing the spectrum sensing performance. To overcome this problem, we address the following two questions: “Which action (contributing to spectrum sensing or not) should be taken?” and “which channel should be sensed?” We model our framework as an evolutionary game in which each SU makes its decision based on its utility history and takes an action more frequently if it brings a relatively higher utility. We also develop a coalition formation algorithm based on the channel status, where each SU always chooses the coalition that brings the most information regarding the status of the corresponding channel. The simulation results demonstrate that the proposed scheme can guarantee the detection probability at a low false alarm rate. The results also indicate that our algorithm can satisfy different requirements by carefully tuning the system parameters. [ABSTRACT FROM PUBLISHER]

Radio-frequency identification (RFID) systems on roads (RSR) is a recently developed framework that focuses on enhancing transportation safety. In RSR, large-scale passive RFID tags with road-related information are deployed on road surfaces or roadside units. A vehicle with an onboard RFID reader can acquire road information via reading from these RFID tags. As a result, it is critical to update the tags with the latest road information (particularly emergent alerts) in a timely manner in RSR. In this paper, we design a novel cluster-based information diffusion scheme that aims to quickly and accurately update the contents of the RFID tags. To the best of our knowledge, our work is the first to focus on the problem of updating RFID tags in RSR. Our proposed scheme organizes RFID tags into clusters. The emergent information and update status of these clustered tags can be read by vehicles passing by. These vehicles can exchange such information with each other via vehicle-to-vehicle (or vehicle-to-infrastructure) (V2V or V2I) communications. After synthesizing the received information, these vehicles can update the nearby tags and spread the information accordingly. Our extensive simulation results demonstrate that our proposed information diffusion scheme can effectively update the tags within a certain time constraint under various scenarios. [ABSTRACT FROM AUTHOR]

The benefits of cognitive radio networking (CRN) have been well recognized with the emerging wireless applications in recent years. While many existing works assume that the secondary transmissions are negative interferences to the primary users (PUs), in this paper, we take secondary users (SUs) as positive potential cooperators for the PUs. In particular, we consider the problem of cooperative relay selection, in which the PUs actively select appropriate SUs as relay nodes to enhance their transmission performance. The most critical challenge for such a problem is how to select a relay efficiently. Due to the potentially large number of SUs, it is infeasible for a PU to first scan all the SUs and then pick the best. Basically, the PU transmitter intends to observe the SUs sequentially. After observing an SU, the PU needs to make a decision regarding whether to terminate its observation and use the current SU as its relay or to skip it and observe the next SU. We address this problem by using the optimal stopping theory and derive the optimal stopping rule. We also discuss the optimal observation order of the SUs and analyze the collision probability. To evaluate the performance of our proposed scheme, we compare our optimal stopping policy with the random selection policy through simulation study, and the results demonstrate the superiority of our policy. Extensive simulation study is conducted to investigate the impact of different parameters on the system performance, and the results indicate that our algorithm can satisfy different system requirements by carefully tuning the corresponding system parameters. [ABSTRACT FROM PUBLISHER]