Mehrdad Khaledi

Dynamic Spectrum Sharing Auction With Time-Evolving Channel Qualities

Spectrum auction is considered a suitable approach to efficiently allocate spectrum among unlicensed users. In a typical spectrum auction, secondary users (SUs) bid to buy spectrum bands from a primary owner (PO) who acts as the auctioneer. Existing spectrum auctions assume that SUs have static and known values for the channels. However, in many real world settings, the SUs do not know the exact value of channel access at first, but they learn it and adapt it over time. In this paper, we study spectrum auctions in a dynamic setting where SUs can change their valuations based on their experiences with the channel quality. We propose ADAPTIVE, a dynAmic inDex Auction for sPectrum sharing with TIme-evolving ValuEs that maximizes the social welfare of the SUs. ADAPTIVE is based on multi-armed bandit models where for each user an allocation index is independently calculated in polynomial time. Then we generalize ADAPTIVE to Multi-ADAPTIVE that auctions multiple channels at each time. We provide a sufficient condition under which Multi-ADAPTIVE achieves the maximum social welfare. Both ADAPTIVE and Multi-ADAPTIVE have some desired economic properties that are formally proven in the analysis. Also, we provide a numerical performance comparison between our proposed mechanisms and the well known static auctions, namely the Vickrey second price auction and the VCG mechanism.

ADAPTIVE: A Dynamic Index Auction for Spectrum sharing with Time-evolving Values

Spectrum auction is considered a suitable approach to efficiently allocate spectrum among unlicensed users. In a typical spectrum auction, Secondary Users (SUs) bid to buy spectrum bands from a Primary Owner (PO) who acts as the auctioneer. Existing spectrum auctions assume that SUs have static and known values for the channels. However, in many real world settings, SUs do not know the exact value of channel access at first, but they learn it over time. In this paper, we study spectrum auctions in a dynamic setting where SUs can change their valuations based on their experiences with the channel. We propose ADAPTIVE, a dynAmic inDex Auction for sPectrum sharing with TIme-evolving ValuEs that maximizes the social welfare of the SUs. ADAPTIVE is based on multi-armed bandit models where for each user an allocation index is independently calculated in polynomial time. ADAPTIVE has some desired economic properties that are formally proven in the analysis. Also, we provide a numerical performance comparison between ADAPTIVE and the well known Vickrey second price auction as a representative of static auctions.

Fuzzy Mobility Analyzer: A Framework for Evaluating Mobility Models in Mobile Ad-Hoc Networks

Mobility is one of the most challenging issues in mobile ad-hoc networks and has a significant impact on performance of network protocols. Different kinds of mobility models have been proposed to represent the movement pattern of mobile nodes in mobile ad-hoc networks. These models attempt to capture various mobility characteristics existing in real movement of mobile nodes. In this paper, a new framework called Fuzzy Mobility Analyzer has been proposed to evaluate mobility models. At first, our framework categorizes mobility models based on their mobility characteristics into five classes; subsequently it uses mobility metrics to capture the mobility and graph connectivity characteristics of mobile nodes in each class; finally it specifies the similarity degree between mobility classes and real world movements of mobile nodes by using the fuzzy set theory. Experimental results on well known mobility models with real world mobility traces is presented to verify our claims.

Mobility Analyzer: A Framework for Analysis and Recognition of Mobility Traces in Mobile Ad-Hoc Networks

Mobility is one of the most challenging issues in mobile Ad-Hoc networks which has a significant impact on performance of network protocols. To cope with this issue, the protocol designers should be able to analyze the movement of mobile nodes in a particular wireless network. In this paper, a new framework called Mobility Analyzer has been introduced for analysis and recognition of mobility traces. At first, the Mobility Analyzer acquires some mobility traces collected by GPS or generated with mobility simulators; then it calculates some mobility metrics which represent the movement behavior of the mobile nodes; finally it attempts to classify mobility traces into particular mobility models using a simple supervised learning method. Simulation results show high efficiency of this framework to classify mobility traces into different mobility classes.

Profitable Task Allocation in Mobile Cloud Computing

We propose a game theoretic framework for task allocation in mobile cloud computing that corresponds to offloading of compute tasks to a group of nearby mobile devices. Specifically, in our framework, a distributor node holds a multidimensional auction for allocating the tasks of a job among nearby mobile nodes based on their computational capabilities and also the cost of computation at these nodes, with the goal of reducing the overall job completion time. Our proposed auction also has the desired incentive compatibility property that ensures that mobile devices truthfully reveal their capabilities and costs and that those devices benefit from the task allocation. To deal with node mobility, we perform multiple auctions over adaptive time intervals. We develop a heuristic approach to dynamically find the best time intervals between auctions to minimize unnecessary auctions and the accompanying overheads. We evaluate our framework and methods using both real world and synthetic mobility traces. Our evaluation results show that our game theoretic framework improves the job completion time by a factor of 2-5 in comparison to the time taken for executing the job locally, while minimizing the number of auctions and the accompanying overheads. Our approach is also profitable for the nearby nodes that execute the distributors tasks with these nodes receiving a compensation higher than their actual costs.

Simultaneous Power-Based Localization of Transmitters for Crowdsourced Spectrum Monitoring

The current mechanisms for locating spectrum offenders are time consuming, human-intensive, and expensive. In this paper, we propose a novel approach to locate spectrum offenders using crowdsourcing. In such a participatory sensing system, privacy and bandwidth concerns preclude distributed sensing devices from reporting raw signal samples to a central agency; instead, devices would be limited to measurements of received power. However, this limitation enables a smart attacker to evade localization by simultaneously transmitting from multiple infected devices. Existing localization methods are insufficient or incapable of locating multiple sources when the powers from each source cannot be separated at the receivers. In this paper, we first propose a simple and efficient method that simultaneously locates multiple transmitters using the received power measurements from the selected devices. Second, we build sampling approaches to select sensing devices required for localization. Next, we enhance our sampling to also take into account incentives for participation in crowdsourcing. We experimentally evaluate our localization framework under a variety of settings and find that we are able to localize multiple sources transmitting simultaneously with reasonably high accuracy in a timely manner.

Preserving Location Privacy in Radio Networks Using a Stackelberg Game Framework

Radio network information is leaked well beyond the perimeter in which the radio network is deployed. We investigate attacks where person location can be inferred using the radio characteristics of wireless links (e.g., the received signal strength). An attacker can deploy a network of receivers which measure the received signal strength of the radio signals transmitted by the legitimate wireless devices inside a perimeter, allowing the attacker to learn the locations of people moving in the vicinity of the devices inside the perimeter. In this paper, we develop the first solution to this location privacy problem where neither the attacker nodes nor the tracked moving object transmit any RF signals. We first model the radio network leakage attack using a Stackelberg game. Next, we define utility and cost functions related to the defender and attacker actions. Last, using our utility and cost functions, we find the optimal strategy for the defender by applying a greedy method. We evaluate our game theoretic framework using experiments and find that our approach significantly reduces the chance of an attacker determining the location of people inside a perimeter.