Wenlong Shen

Secure key establishment for Device-to-Device communications

With the rapid growth of smartphone and tablet users, Device-to-Device (D2D) communications have become an attractive solution for enhancing the performance of traditional cellular networks. However, relevant security issues involved in D2D communications have not been addressed yet. In this paper, we investigate the security requirements and challenges for D2D communications, and present a secure and efficient key agreement protocol, which enables two mobile devices to establish a shared secret key for D2D communications without prior knowledge. Our approach is based on the Diffie-Hellman key agreement protocol and commitment schemes. Compared to previous work, our proposed protocol introduces less communication and computation overhead. We present the design details and security analysis of the proposed protocol. We also integrate our proposed protocol into the existing Wi-Fi Direct protocol, and implement it using Android smartphones.

An energy efficient routing protocol for device-to-device based multihop smartphone networks

Device-to-device (D2D) communication is the need of the hour in the domain of next generation wireless networking and in the rapidly evolving smartphone network world. D2D technology facilitates mobile users to communicate with each other directly, bypassing the cellular base stations. As a popular D2D technique, WiFi-Direct is also a budding new technology that has the ability to set up wireless communications between a group of smartphones. While single-hop D2D based networks have been promising and energy efficient, multi-hop D2D based networks, though demanded in some emerging applications, are not well studied. In this paper, we elaborate the concept of multihop smartphone networks based on WiFi-Direct and propose an energy efficient cluster-based routing protocol, QGRP, to address the energy issue of increasing importance due to high energy costs of smartphones. Simulations demonstrate that QGRP can save significant amounts of energy compared to the cases without QGRP.

AMCloud: Toward a Secure Autonomic Mobile Ad Hoc Cloud Computing System

Cloud computing is a revolutionary paradigm to deliver computing resources, ranging from data storage/processing to software, as a service over the network, with the benefits of efficient resource utilization and improved manageability. The current popular cloud computing models encompass a cluster of expensive and dedicated machines to provide cloud computing services, incurring significant investment in capital outlay and ongoing costs. A more cost effective solution would be to exploit the capabilities of an ad hoc cloud which consists of a cloud of distributed and dynamically untapped local resources. The ad hoc cloud can be further classified into static and mobile clouds: an ad hoc static cloud harnesses the underutilized computing resources of general purpose machines, whereas an ad hoc mobile cloud harnesses the idle computing resources of mobile devices. However, the dynamic and distributed characteristics of ad hoc cloud introduce challenges in system management. In this article, we propose a generic em autonomic mobile cloud (AMCloud) management framework for automatic and efficient service/resource management of ad hoc cloud in both static and mobile modes. We then discuss in detail the possible security and privacy issues in ad hoc cloud computing. A general security architecture is developed to facilitate the study of prevention and defense approaches toward a secure autonomic cloud system. This article is expected to be useful for exploring future research activities to achieve an autonomic and secure ad hoc cloud computing system.

Development of Mobile Ad-hoc Networks over Wi-Fi Direct with off-the-shelf Android phones

The proliferation of smart phones enables ubiquitous Mobile Ad-hoc Networks (MANETs) where mobile devices communicate with peers over a wireless channel in an ad hoc mode. In this paper, we introduce a novel method to achieve multi-hop communication among open-source, non-rooted Android devices using Wi-Fi Direct Technology, also known as Wi-Fi Peer-to-Peer (P2P). Then we implement a proactive routing protocol in an MANET using multiple off-the-shelf smart phones to enable efficient message delivery over a multi-hop MANET.

Secure device-to-device communications over WiFi direct

D2D communications facilitate proximal devices to directly communicate with each other, bypassing cellular base stations or access points, and bring many benefits such as improvement in both spectral efficiency and energy efficiency. Among existing D2D enabling techniques, the recently released WiFi Direct is one promising protocol that offers high data rate D2D communications in local areas. However, WiFi Direct is susceptible to security threats due to the open access of wireless channels and lack of security infrastructures. In this article, we identify several attacks that challenge WiFi-Direct-based D2D communications. Since pairwise key establishment lies in the area of securing D2D communications, we introduce a short authentication-string-based key agreement protocol and analyze its security performance. We also integrate the SAS-based key agreement protocol into the existing WiFi Direct protocol, and implement it using Android smartphones.

Distributed Scheduling and Delay-Aware Routing in Multihop MR-MC Wireless Networks

In multiradio multichannel (MR-MC) networks with significantly expanded network resource space, many existing scheduling/routing algorithms rely on a link-based network model and apply different heuristics in algorithm design to achieve/approximate throughput optimality. In this paper, using a tuple-based multidimensional conflict graph model, we establish a cross-layer framework, which facilitates systematically studying distributed scheduling and routing in multihop multipath MR-MC networks. In this framework, each tuple link is installed with a routing controller, which feeds controlled amounts of data to the tuple-link output queues for scheduling and transmission. We rigorously prove that, under a set of certain conditions, the network is queue stable in the mean sense under the distributed maximal scheduling policy. Based on Lyapunov optimization, we further propose a distributed delay-aware multipath routing method, which aims at minimizing the end-to-end delay of each commodity flow. Extensive simulation results demonstrate that the proposed joint scheduling/routing algorithm outperforms existing link-based single-path and multipath algorithms and tuple-based cross-layer control algorithm.

A Distributed Secure Outsourcing Scheme for Solving Linear Algebraic Equations in Ad Hoc Clouds

The emerging ad hoc clouds form a new cloud computing paradigm by leveraging untapped local computation and storage resources. An important application of ad hoc clouds is to outsource computational intensive problems to nearby cloud agents. Specifically, for the problem of solving a linear algebraic equation (LAE), an outsourcing client assigns each cloud agent a subproblem, and then all involved agents apply a consensus-based algorithm to obtain the correct solution of the LAE in an iterative and distributed manner. However, such a distributed collaboration paradigm suffers from cyber security threats that undermine the confidentiality of the outsourced problem and the integrity of the returned results. In this paper, we identify a number of such security threats in this process, and propose a secure outsourcing scheme which not only preserves the privacy of the LAE parameters and the final solution from the participating agents, but also guarantees the correctness of the final solution. We prove that the proposed scheme has low computation complexity at each agent, and is robust against the identified security attacks. Numerical and simulation results are presented to demonstrate the effectiveness of the proposed method.

A Mobile Cloud Computing Middleware for Low Latency Offloading of Big Data

Recent years have witnessed an explosive growth of mobile applications. Thanks to improved network connectivity, it becomes a promising enabling solution to offload computation-intensive applications to the resource abundant public cloud to further augment the capacity of resource-constrained devices. As mobile applications usually have QoS requirements, it is critical to provide low latency services to the mobile users while maintain low leasing cost of cloud resources. However, the resources offered by cloud vendors are usually charged based on a time quanta while the offloading demand for heavy-lifting computation may occur infrequently on mobile devices. This mismatch would demotivate users to resort to public cloud for computation offloading. In this paper, we design a computation offloading middleware which bridges the aforementioned gap between cloud vendors and mobile clients, providing offloading service to multiple users with low cost and delay. The proposed middleware has two key components: Task Scheduler and Instance Manager. The Task Scheduler dispatches the received offloading tasks to execute in the instances reserved by the Instance Manager. Based on the arrival pattern of offloading tasks, the Instance Manager dynamically changes the number of instances to ensure certain service grade of mobile users. Our proposed mechanisms are validated through numerical results. It is shown that a lower average delay can be achieved through proposed scheduling heuristic, and the number of reserved instances well adapts to the offloading demands.

Real-time misbehavior detection in IEEE 802.11e based WLANs

The Enhanced Distributed Channel Access (EDCA) specification in the IEEE 802.11e standard supports heterogeneous backoff parameters and arbitration inter-frame space (AIFS), which makes a selfish node easy to manipulate these parameters and misbehave. In this case, the network-wide fairness cannot be achieved any longer. Many existing misbehavior detectors, primarily designed for legacy IEEE 802.11 networks, become inapplicable in such a heterogeneous network configuration. In this paper, we propose a novel real-time hybrid-share (HS) misbehavior detector for IEEE 802.11e based wireless local area networks (WLANs). The detector keeps updating its state based on every successful transmission and makes detection decisions by comparing its state with a threshold. We develop mathematical analysis of the detector performance in terms of both false positive rate and average detection rate. Numerical results show that the proposed detector can effectively detect both contention window based and AIFS based misbehavior with only a short detection window.

DAFEE: A Decomposed Approach for energy efficient networking in multi-radio multi-channel wireless networks

As wireless networks are gaining increasing popularity, the network energy efficiency has become a critical issue. In this paper, we focus on energy-efficient networking in a generic multi-radio multi-channel (MR-MC) wireless network where transmission scheduling, transmit power control, radio and channel assignment are coupled together in a multi-dimensional resource space, thus requiring joint optimization and low complexity algorithms. We propose a novel Decomposed Approach For energy-efficient (DAFEE) networking in MR-MC networks, with the objective to minimize network energy consumption while guaranteeing a certain level of performance. In particular, we leverage a multi-dimensional tuple-link based model and a concept of resource allocation pattern to transform the complex optimization problem into a linear programming (LP) problem. The LP problem however has a very large solution space due to the exponentially many possible resource allocation patterns. We then exploit delay column generation and distributed learning techniques to decompose the problem and solve it with an iterative process. Furthermore, we propose a sub-optimal algorithm to speed up the iteration with constant-bounded performance. Simulation results are presented to demonstrate the effectiveness of the proposed algorithm.