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Edge computing is one of the revolutionary technologies that enable high-performance and low-latency modern applications, such as smart cities, connected vehicles, etc. Yet its adoption has been limited by factors including high cost of edge resources, heterogeneous and fluctuating demands, and lack of reliability. In this paper, we study resource provisioning in edge computing, taking into account these different factors. First, based on observations from real demand traces, we propose a time-varying stochastic model to capture the time-dependent and uncertain demand and network dynamics in an edge network. We then apply a novel robustness model that accounts for both expected and worst-case performance of a service. Based on these models, we formulate edge provisioning as a multi-stage stochastic optimization problem. The problem is NP-hard even in the deterministic case. Leveraging the multi-stage structure, we apply nested Benders decomposition to solve the problem. We also describe several efficiency enhancement techniques, including a novel technique for quickly solving the large number of decomposed subproblems. Finally, we present results from real dataset-based simulations, which demonstrate the advantages of the proposed models, algorithm and techniques.

The last decade has witnessed research advances and wide deployment of Internet-of-things (IoT) in smart homes and connected industry. However, the recent spate of cyber attacks exploiting the vulnerabilities and insufficient security management of IoT devices have created serious challenges for securing IoT devices and applications. As a first step towards understanding and mitigating diverse security threats of IoT devices, this paper develops a measurement framework to automatically collect network traffic of IoT devices in edge networks, and build multidimensional behavioral profiles of these devices which characterize who, when, what, and why on the behavioral patterns of IoT devices based on continuously collected traffic data. To the best of our knowledge, this paper is the first effort to shed light on the IP-spatial, temporal, and cloud service patterns of IoT devices in edge networks, and to explore these multidimensional behavioral fingerprints for IoT device classification, anomaly traffic detection, and network security monitoring for millions of vulnerable and resource-constrained IoT devices on the Internet.

Asserting whether two devices are in close proximity is very important to many smartphone assisted security systems. For example, the smartphone based two-factor authentication usually requires the smartphone to stay in close proximity to the other device during authentication. However, relay attacks pose a serious threat to existing approaches for proximity assertions. In this paper, we present a novel near field assertion system that restricts the distance between the two devices to the scale of several centimeters. Our system explores acoustic communications and can prevent relay attacks. The generated assertion is a confidential binary sequence known only to the two devices. Our system is fully automated and light-weight, as demonstrated by extensive evaluations on a prototype.

In this paper, we propose a secure cloud-assisted location based reminder system. The proposed system is secure and responsive. Our system outsources the location testing task --- testing whether the current location is near a reminder location --- to the cloud server such that the device synchronization is not necessary in the system. This feature makes the proposed system more responsive, especially when the reminder message is of large size, e.g., audio, images. Above all, the proposed system protects a user's location privacy and the confidentiality of the reminder message. The system is designed in a way that the cloud server can perform location testing for a user but cannot learn about her current location, reminder locations, and reminder messages. We prove the security of the proposed system and demonstrate its efficiency using simulations on a Motorola Droid smartphone.

Mobile phone sensing is a new paradigm which takes advantage of the pervasive smartphones to collect and analyze data beyond the scale of what was previously possible. In a mobile phone sensing system, the platform recruits smartphone users to provide sensing service. Existing mobile phone sensing applications and systems lack good incentive mechanisms that can attract more user participation. To address this issue, we design incentive mechanisms for mobile phone sensing. We consider two system models: the platform-centric model where the platform provides a reward shared by participating users, and the user-centric model where users have more control over the payment they will receive. For the platform-centric model, we design an incentive mechanism using a Stackelberg game, where the platform is the leader while the users are the followers. We show how to compute the unique Stackelberg Equilibrium, at which the utility of the platform is maximized, and none of the users can improve its utility by unilaterally deviating from its current strategy. For the user-centric model, we design an auction-based incentive mechanism, which is computationally efficient, individually rational, profitable, and truthful. Through extensive simulations, we evaluate the performance and validate the theoretical properties of our incentive mechanisms.

Recent success of online social networks (OSNs) motivates the study of security issues in OSNs. A fundamental but challenging security issue in OSNs is to authenticate a friend's real identity. A solution to this issue will benefit a number of OSN security protocols. Existing solutions require users securely obtain some secret information from their friends before authentication takes place, which is not always possible in OSNs. In this paper, we propose a new authenticated key exchange protocol based on the exclusive secrets shared between friends. It provides identity authentication and key exchange in a plain setting, i.e., users do not need to securely exchange or distribute any information beforehand. The protocol is designed to work with low-entropy input information, because human beings are not good at dealing with a large amount of information. Another advantage of our protocol is its tolerance of input errors considering human error is always a possibility. We prove the security of the protocol in the universal composability (UC) framework and demonstrate its efficiency.

In this paper, we construct a system which can hide users' identity when they visit untrusted third party storage sites. We also define a fine-grained access control policy for the data owner to freely define who can access the record. That is to say, the data owner divide his friends into several groups and issues them corresponding credentials for accessing his data. However, he can adds a friend at any time in a revocation list (RL) so that that friend could not access the data owner's data any more even if he has credentials. We theoretically prove the security of our protocols, and evaluate the performance of our protocols through simulations.

On one hand, cooperative communication has been gaining more and more popularity since it has great potential to increase the capacity of wireless networks. On the other hand, the applications of cooperative communication technology are rarely seen in reality, even in some scenarios where the demands for bandwidth-hungry applications have pushed the system designers to develop innovative network solutions. A main obstacle lying between the potential capability of channel capacity improvement and the wide adoption of cooperative communication is the lack of incentives for the participating wireless nodes to serve as relay nodes. Hence, in this paper, we design TASC, an auction scheme for the cooperative communications, where wireless node can trade relay services. TASC makes an important contribution of maintaining truthfulness while fulfilling other design objectives. We show analytically that TASC is truthful and has polynomial time complexity. Extensive experiments show that TASC can achieve multiple economic properties without significant performance degradation compared with pure relay assignment algorithms.

In this paper, we study the joint spectrum allocation and scheduling problems with the objectives of maximizing through-put and achieving certain fairness in Dynamic Spectrum Access (DSA) wireless networks. A novel Multi-Channel Contention Graph (MCCG) is proposed to characterize the impact of interference under the protocol interference model. Based on MCCG, we present an optimal scheme to compute maximum throughput solutions. As simply maximizing throughput may result in a severe bias on resource allocation, we take fairness into consideration by presenting optimal schemes to compute fair solutions based on a simplified max-min fairness model and the well-known proportional fairness model. Fast and effective heuristics are also proposed to provide high throughput and fair solutions. Numerical results show that compared with the optimal schemes, our heuristic schemes produce very close performance and our proportional fair schemes achieve a good tradeoff between throughput and fairness. In addition, we extend our research to the physical interference model.

In this paper, we propose a new kind of simulated annealing algorithm called two-level simulated annealing for solving certain class of hard combinatorial optimization problems. This two-level simulated annealing algorithm is less likely to get stuck at a non-global minimizer than conventional simulated annealing algorithms. We also propose a parallel version of our two-level simulated annealing algorithm and discuss its efficiency. This new technique is then applied to the Molecular Conformation problem in 3 dimensional Euclidean space and implemented on the Thinking Machines CM-5. With the full Lennard-Jones potential function, we were able to get satisfactory results for clusters with as many as 100,000 atoms. A peak rate of over 0.8 giga flop per second in 64-bit operations was sustained on a partition with 512 processing elements. To the best of our knowledge, ground states of Lennard-Jones clusters of as large as these have never been reported before.