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Localization is one form of cooperative spectrum sensing that lets multiple sensors work together to estimate the location of a target transmitter. However, the requisite exchange of spectrum measurements leads to exposure of the physical loca- tion of participating sensors. Furthermore, in some cases, a com- promised participant can reveal the sensitive characteristics of all participants. Accordingly, a lack of sufficient guarantees about data handling discourages such devices from working together. In this paper, we provide the missing data protections by processing spectrum measurements within attestable containers or enclaves. Enclaves provide runtime memory integrity and confidentiality using hardware extensions and have been used to secure various applications [1]–[8]. We use these enclave features as building blocks for new privacy-preserving particle filter protocols that minimize disruption of the spectrum sensing ecosystem. We then instantiate this enclave using ARM TrustZone and Intel SGX, and we show that enclave-based particle filter protocols incur minimal overhead (adding 16 milliseconds of processing to the measurement processing function when using SGX versus unprotected computation) and can be deployed on resource-constrained platforms that support TrustZone (incurring only a 1.01x increase in processing time when doubling particle count from 10,000 to 20,000), whereas cryptographically-based approaches suffer from multiple orders of magnitude higher costs. We effectively deploy enclaves in a distributed environment, dramatically improving current data handling techniques. To our best knowledge, this is the first work to demonstrate privacy-preserving localization in a multi-party environment with reasonable overhead. 

We report results of an experiment in applying deep Q-learning for dynamic spectrum sharing (DSS) in the Alleys of Austin scenario from the DARPA Spectrum Collaboration Challenge. This scenario mimics mobile operations in an urban environment by up to five squads (teams) of soldiers. Each team operates its own wireless network. We consider teamwise– distributed DSS, where there is no central agent to coordinate spectrum usage across teams, but spectrum usage within each team is coordinated by a single member of that team. The spatial distributions of the soldiers creates opportunities for spatial reuse by certain subsets of the teams, and our experiment is set up to evaluate whether the deep Q-learning algorithm can discover and take advantage of these opportunities. The results show that deep Q-learning is able to take advantage of spatial reuse and that doing so results in better performance than a fair-share, disjoint spectrum allocation among the teams. 

A dynamic spectrum sharing problem with a mixed collaborative/competitive objective and partial information about peers’ performances that arises from the DARPA Spectrum Collaboration Challenge is considered. Because of the very high complexity of the problem and the enormous size of the state space, it is broken down into the subproblems of channel selection, flow admission control, and transmission schedule assignment. The channel selection problem is the focus of this paper. A reinforcement learning algorithm based on a reduced state is developed to select channels, and a neural network is used as a function approximator to fill in missing values in the resulting input-action matrix. The performance is compared with that obtained by a hand-tuned expert system. 

An algorithm to identify the bottleneck nodes linking two component networks in a simple network of networks (NoN) configuration is proposed. The proposed bottleneck identification algorithm is based on applying a support vector machine on clustered packet delay measurements. This algorithm has the advantage that it requires almost no information about the topology of the underlying NoN. Simulation results show that this algorithm can provide very good detection performance when the component networks of the NoN are not too small in size, or when the connectivity between nodes within the component networks is not too sparse. 

We consider the problem of jammer placement to partition a wireless network, where the network nodes and jammers are located in the real plane. In previous research, we found optimal and suboptimal jammer placements by reducing the search space for the jammers to the locations of the network nodes. In this paper, we develop techniques to find optimal jammer placements over all possible jammer placements in the real plane. Our approach finds a set of candidate jammer locations (CJLs) such that a jammer-placement solution using the CJLs achieves the minimum possible cardinality among all possible jammer placements in the real plane. The CJLs can be used directly with the optimal and fast, suboptimal algorithms for jammer placement from our previous work. 

Cooperative spectrum sensing is often necessary in cognitive radios systems to localize a transmitter by fusing the measurements from multiple sensing radios. However, revealing spectrum sensing information also generally leaks information about the location of the radio that made those measurements. We propose a protocol for performing cooperative spectrum sensing while preserving the privacy of the sensing radios. In this protocol, radios fuse sensing information through a distributed particle filter based on a tree structure. All sensing information is encrypted using public-key cryptography, and one of the radios serves as an anonymizer, whose role is to break the connection between the sensing radios and the public keys they use. We consider a semi-honest (honest-but-curious) adversary model in which there is at most a single adversary that is internal to the sensing network and complies with the specified protocol but wishes to determine information about the other participants. Under this scenario, an adversary may learn the sensing information of some of the radios, but it does not have any way to tie that information to a particular radio’s identity. We test the performance of our proposed distributed, tree-based particle filter using physical measurements of FM broadcast stations.