Strategyproof auctions for balancing social welfare and fairness in secondary spectrum markets

Secondary spectrum access is emerging as a promising approach for mitigating the spectrum scarcity in wireless networks. Coordinated spectrum access for secondary users can be achieved using periodic spectrum auctions. Recent studies on such auction design mostly neglect the repeating nature of such auctions, and focus on greedily maximizing social welfare. Such auctions can cause subsets of users to experience starvation in the long run, reducing their incentive to continue participating in the auction. It is desirable to increase the diversity of users allocated spectrum in each auction round, so that a trade-off between social welfare and fairness is maintained. We study truthful mechanisms towards this objective, for both local and global fairness criteria. For local fairness, we introduce randomization into the auction design, such that each user is guaranteed a minimum probability of being assigned spectrum. Computing an optimal, interference-free spectrum allocation is NP-Hard; we present an approximate solution, and tailor a payment scheme to guarantee truthful bidding is a dominant strategy for all secondary users. For global fairness, we adopt the classic maxmin fairness criterion. We tailor another auction by applying linear programming techniques for striking the balance between social welfare and max-min fairness, and for finding feasible channel allocations. In particular, a pair of primal and dual linear programs are utilized to guide the probabilistic selection of feasible allocations towards a desired tradeoff in expectation. © 2011 IEEE.published_or_final_versionThe IEEE INFOCOM 2011, Shanghai, China, 10-15 April 2011. In Conference Proceedings, 2011, p. 3020-302

Resource Allocation and Pricing in Secondary Dynamic Spectrum Access Networks

The paradigm shift from static spectrum allocation to a dynamic one has opened many challenges that need to be addressed for the true vision of Dynamic Spectrum Access (DSA) to materialize. This dissertation proposes novel solutions that include: spectrum allocation, routing, and scheduling in DSA networks. First, we propose an auction-based spectrum allocation scheme in a multi-channel environment where secondary users (SUs) bid to buy channels from primary users (PUs) based on the signal to interference and noise ratio (SINR). The channels are allocated such that i) the SUs get their preferred channels, ii) channels are re-used, and iii) there is no interference. Then, we propose a double auction-based spectrum allocation technique by considering multiple bids from SUs and heterogeneity of channels. We use virtual grouping of conflict-free buyers to transform multi-unit bids to single-unit bids. For routing, we propose a market-based model where the PUs determine the optimal price based on the demand for bandwidth by the SUs. Routes are determined through a series of price evaluations between message senders and forwarders. Also, we consider auction-based routing for two cases where buyers can bid for only one channel or they could bid for a combination of non-substitutable channels. For a centralized DSA, we propose two scheduling algorithms-- the first one focuses on maximizing the throughput and the second one focuses on fairness. We extend the scheduling algorithms to multi-channel environment. Expected throughput for every channel is computed by modelling channel state transitions using a discrete-time Markov chain. The state transition probabilities are calculated which occur at the frame/slot boundaries. All proposed algorithms are validated using simulation experiments with different network settings and their performance are studied

Research on efficiency and privacy issues in wireless communication

Wireless spectrum is a limited resource that must be used efficiently. It is also a broadcast medium, hence, additional procedures are required to maintain communication over the wireless spectrum private. In this thesis, we investigate three key issues related to efficient use and privacy of wireless spectrum use. First, we propose GAVEL, a truthful short-term auction mechanism that enables efficient use of the wireless spectrum through the licensed shared access model. Second, we propose CPRecycle, an improved Orthogonal Frequency Division Multiplexing (OFDM) receiver that retrieves useful information from the cyclic prefix for interference mitigation thus improving spectral efficiency. Third and finally, we propose WiFi Glass, an attack vector on home WiFi networks to infer private information about home occupants. First we consider, spectrum auctions. Existing short-term spectrum auctions do not satisfy all the features required for a heterogeneous spectrum market. We discover that this is due to the underlying auction format, the sealed bid auction. We propose GAVEL, a truthful auction mechanism, that is based on the ascending bid auction format, that avoids the pitfalls of existing auction mechanisms that are based on the sealed bid auction format. Using extensive simulations we observe that GAVEL can achieve better performance than existing mechanisms. Second, we study the use of cyclic prefix in Orthogonal Frequency Division Multiplexing. The cyclic prefix does contain useful information in the presence of interference. We discover that while the signal of interest is redundant in the cyclic prefix, the interference component varies significantly. We use this insight to design CPRecycle, an improved OFDM receiver that is capable of using the information in the cyclic prefix to mitigate various types of interference. It improves spectral efficiency by decoding packets in the presence of interference. CPRecycle require changes to the OFDM receiver and can be deployed in most networks today. Finally, home WiFi networks are considered private when encryption is enabled using WPA2. However, experiments conducted in real homes, show that the wireless activity on the home network can be used to infer occupancy and activity states such as sleeping and watching television. With this insight, we propose WiFi Glass, an attack vector that can be used to infer occupancy and activity states (limited to three activity classes), using only the passively sniffed WiFi signal from the home environment. Evaluation with real data shows that in most of the cases, only about 15 minutes of sniffed WiFi signal is required to infer private information, highlighting the need for countermeasures

Vehicle as a Service (VaaS): Leverage Vehicles to Build Service Networks and Capabilities for Smart Cities

Smart cities demand resources for rich immersive sensing, ubiquitous communications, powerful computing, large storage, and high intelligence (SCCSI) to support various kinds of applications, such as public safety, connected and autonomous driving, smart and connected health, and smart living. At the same time, it is widely recognized that vehicles such as autonomous cars, equipped with significantly powerful SCCSI capabilities, will become ubiquitous in future smart cities. By observing the convergence of these two trends, this article advocates the use of vehicles to build a cost-effective service network, called the Vehicle as a Service (VaaS) paradigm, where vehicles empowered with SCCSI capability form a web of mobile servers and communicators to provide SCCSI services in smart cities. Towards this direction, we first examine the potential use cases in smart cities and possible upgrades required for the transition from traditional vehicular ad hoc networks (VANETs) to VaaS. Then, we will introduce the system architecture of the VaaS paradigm and discuss how it can provide SCCSI services in future smart cities, respectively. At last, we identify the open problems of this paradigm and future research directions, including architectural design, service provisioning, incentive design, and security & privacy. We expect that this paper paves the way towards developing a cost-effective and sustainable approach for building smart cities.Comment: 32 pages, 11 figure

Spectrum clouds: A session based spectrum trading system for multi-hop cognitive radio networks

Abstract—Spectrum trading creates more accessing opportu-nities for secondary users (SUs) and economically benefits the primary users (PUs). However, it is challenging to implement spectrum trading in multi-hop cognitive radio networks (CRNs) due to harsh cognitive radio (CR) requirements on SUs ’ devices and complex conflict and competition relationship among dif-ferent CR sessions. Unlike the per-user based spectrum trading designs in previous studies, in this paper, we propose a novel session based spectrum trading system, spectrum clouds, in multi-hop CRNs. In spectrum clouds, we introduce a new service provider, called secondary service provider (SSP), to harvest the available spectrum bands and facilitate the accessing of SUs without CR capability. The SSP also conducts spectrum trading among CR sessions w.r.t. their conflicts and competitions. Lever-aging a 3-dimensional (3-D) conflict graph, we mathematically describe the conflicts and competitions among the candidate sessions for spectrum trading. Given the rate requirements and bidding values of candidate trading sessions, we formulate the optimal spectrum trading into the SSP’s revenue maximization problem under multiple cross-layer constraints in multi-hop CRNs. In view of the NP-hardness of the problem, we have also developed heuristic algorithms to pursue feasible solutions. Through extensive simulations, we show that the solutions found by the proposed algorithms are close to the optimal one. I