arXiv subjects

Bechir Hamdaoui

Publications and source records attributed to Bechir Hamdaoui.

53 records · Page 3Linked to original sources

arXiv2018

An Efficient Technique for Protecting Location Privacy of Cooperative Spectrum Sensing Users

Cooperative spectrum sensing, despite its effectiveness in enabling dynamic spectrum access, suffers from location privacy threats, merely because secondary users (SUs)' sensing reports that need to be shared with a fusion center to make spectrum availability decisions are highly correlated to the users' locations. It is therefore important that cooperative spectrum sensing schemes be empowered with privacy-preserving capabilities so as to provide SUs with incentives for participating in the sensing task. In this paper, we propose an efficient privacy-preserving protocol that uses an additional architectural entity and makes use of various cryptographic mechanisms to preserve the location privacy of SUs while performing reliable and efficient spectrum sensing. We show that not only is our proposed scheme secure and more efficient than existing alternatives, but also achieves fault tolerance and is robust against sporadic network topological changes.

cs.NI↗

arXiv2018

LPOS: Location Privacy for Optimal Sensing in Cognitive Radio Networks

Cognitive Radio Networks (CRNs) enable opportunistic access to the licensed channel resources by allowing unlicensed users to exploit vacant channel opportunities. One effective technique through which unlicensed users, often referred to as Secondary Users (SUs), acquire whether a channel is vacant is cooperative spectrum sensing. Despite its effectiveness in enabling CRN access, cooperative sensing suffers from location privacy threats, merely because the sensing reports that need to be exchanged among the SUs to perform the sensing task are highly correlated to the SUs' locations. In this paper, we develop a new Location Privacy for Optimal Sensing (LPOS) scheme that preserves the location privacy of SUs while achieving optimal sensing performance through voting-based sensing. In addition, LPOS is the only alternative among existing CRN location privacy preserving schemes (to the best of our knowledge) that ensures high privacy, achieves fault tolerance, and is robust against the highly dynamic and wireless nature of CRNs.

cs.NI↗

arXiv2018

Location Privacy Preservation in Database-Driven Wireless Cognitive Networks Through Encrypted Probabilistic Data Structures

In this paper, we propose new location privacy preserving schemes for database-driven cognitive radio networks that protect secondary users' (SUs) location privacy while allowing them to learn spectrum availability in their vicinity. Our schemes harness probabilistic set membership data structures to exploit the structured nature of spectrum databases (DBs) and SUs' queries. This enables us to create a compact representation of DB that could be queried by SUs without having to share their location with DB, thus guaranteeing their location privacy. Our proposed schemes offer different cost-performance characteristics. Our first scheme relies on a simple yet powerful two-party protocol that achieves unconditional security with a plausible communication overhead by making DB send a compacted version of its content to SU which needs only to query this data structure to learn spectrum availability. Our second scheme achieves significantly lower communication and computation overhead for SUs, but requires an additional architectural entity which receives the compacted version of the database and fetches the spectrum availability information in lieu of SUs to alleviate the overhead on the latter. We show that our schemes are secure, and also demonstrate that they offer significant advantages over existing alternatives for various performance and/or security metrics.

cs.NI↗

arXiv2018

Exploiting wideband spectrum occupancy heterogeneity for weighted compressive spectrum sensing

Compressive sampling has shown great potential for making wideband spectrum sensing possible at sub-Nyquist sampling rates. As a result, there have recently been research efforts that aimed to develop techniques that leverage compressive sampling to enable compressed wideband spectrum sensing. These techniques consider homogeneous wideband spectrum where all bands are assumed to have similar PU traffic characteristics. In practice, however, wideband spectrum is not homogeneous, in that different spectrum bands could have different PU occupancy patterns. In fact, the nature of spectrum assignment, in which applications of similar types are often assigned bands within the same block, dictates that wideband spectrum is indeed heterogeneous, as different application types exhibit different behaviors. In this paper, we consider heterogeneous wideband spectrum, where we exploit this inherent, block-like structure of wideband spectrum to design efficient compressive spectrum sensing techniques that are well suited for heterogeneous wideband spectrum. We propose a weighted $\ell_1$-minimization sensing information recovery algorithm that achieves more stable recovery than that achieved by existing approaches while accounting for the variations of spectrum occupancy across both the time and frequency dimensions. Through intensive numerical simulations, we show that our approach achieves better performance when compared to the state-of-the-art approaches.

cs.NI↗

arXiv2018

Preserving the Location Privacy of Secondary Users in Cooperative Spectrum Sensing

Cooperative spectrum sensing, despite its effectiveness in enabling dynamic spectrum access, suffers from location privacy threats, merely because secondary users (SUs)' sensing reports that need to be shared with a fusion center to make spectrum availability decisions are highly correlated to the users' locations. It is therefore important that cooperative spectrum sensing schemes be empowered with privacy preserving capabilities so as to provide SUs with incentives for participating in the sensing task. In this paper, we propose privacy preserving protocols that make use of various cryptographic mechanisms to preserve the location privacy of SUs while performing reliable and efficient spectrum sensing. We also present cost-performance tradeoffs. The first consists on using an additional architectural entity at the benefit of incurring lower computation overhead by relying only on symmetric cryptography. The second consists on using an additional secure comparison protocol at the benefit of incurring lesser architectural cost by not requiring extra entities. Our schemes can also adapt to the case of a malicious fusion center as we discuss in this paper. We also show that not only are our proposed schemes secure and more efficient than existing alternatives, but also achieve fault tolerance and are robust against sporadic network topological changes.

cs.NI↗

arXiv2018

Location Privacy in Cognitive Radio Networks: A Survey

Cognitive radio networks (CRNs) have emerged as an essential technology to enable dynamic and opportunistic spectrum access which aims to exploit underutilized licensed channels to solve the spectrum scarcity problem. Despite the great benefits that CRNs offer in terms of their ability to improve spectrum utilization efficiency, they suffer from user location privacy issues. Knowing that their whereabouts may be exposed can discourage users from joining and participating in the CRNs, thereby potentially hindering the adoption and deployment of this technology in future generation networks. The location information leakage issue in the CRN context has recently started to gain attention from the research community due to its importance, and several research efforts have been made to tackle it. However, to the best of our knowledge, none of these works have tried to identify the vulnerabilities that are behind this issue or discuss the approaches that could be deployed to prevent it. In this paper, we try to fill this gap by providing a comprehensive survey that investigates the various location privacy risks and threats that may arise from the different components of this CRN technology, and explores the different privacy attacks and countermeasure solutions that have been proposed in the literature to cope with this location privacy issue. We also discuss some open research problems, related to this issue, that need to be overcome by the research community to take advantage of the benefits of this key CRN technology without having to sacrifice the users' privacy.

cs.NI↗

arXiv2018

Responsive Content-Centric Delivery in Large Urban Communication Networks: A LinkNYC Use-Case

Large urban communication networks such as smart cities are an ecosystem of devices and services cooperating to address multiple issues that greatly benefit end users, cities and the environment. LinkNYC is a first-of-its-kind urban communications network aiming to replace all payphones in the five boroughs of New York City (NYC) with kiosk-like structures providing free public Wi-Fi. We consolidate these networks with standalone edge cloud devices known as cloudlets and introduce geographically distributed content delivery cloudlets (CDCs) to store popular Internet content closer to end users; essential in environments with diverse and dynamic content interests. A content-centric and delivery framework is proposed leveraging NYC's population densities and CDCs for interest-based in-network caching. Analysis shows that although the adoption of multiple CDCs dramatically improves overall network performance, advanced caching policies are needed when considering increased content heterogeneity. Thus, we propose popularity-driven (pLFU) and cooperation-based (sLFU) caching policies at individual CDCs to account for user and content dynamics over time. The amalgamation of urban population densities, multiple CDC placements and smarter caching techniques help exploit the ultimate benefits of a content-centric urban communications network and dramatically improves overall network performance and responsiveness. Our proposed solutions are validated using LinkNYC as a use-case.

cs.NI↗

arXiv2018

Compressed Wideband Spectrum Sensing: Concept, Challenges and Enablers

Spectrum sensing research has mostly been focusing on narrowband access, and not until recently have researchers started looking at wideband spectrum. Broadly speaking, wideband spectrum sensing approaches can be categorized into two classes: Nyquist-rate and sub-Nyquist-rate sampling approaches. Nyquist-rate approaches have major practical issues that question their suitability for realtime applications; this is mainly because their high-rate sampling requirement calls for complex hardware and signal processing algorithms that incur significant delays. Sub-Nyquist-rate approaches, on the other hand, are more appealing due to their less stringent sampling-rate requirement. Although various concepts have been investigated to ensure sub-Nyquist rates, compressive sampling theory is definitely one concept that has attracted so much interest. This paper explains and illustrates how compressive sampling has been leveraged to improve wideband spectrum sensing by enabling spectrum occupancy recovery with sub-Nyquist sampling rates. The paper also introduces new ideas with great potential for further wideband spectrum sensing enhancements, and identifies key future research challenges and directions that remain to be investigated.

cs.IT↗

arXiv2017

A Particle Filtering Approach for Enabling Distributed and Scalable Sharing of DSA Network Resources

Handling the massive number of devices needed in numerous applications such as smart cities is a major challenge given the scarcity of spectrum resources. Dynamic spectrum access (DSA) is seen as a potential candidate to support the connectivity and spectrum access of these devices. We propose an efficient technique that relies on particle filtering to enable distributed resource allocation and sharing for large-scale dynamic spectrum access networks. More specifically, we take advantage of the high tracking capability of particle filtering to efficiently assign the available spectrum and power resources among cognitive users. Our proposed technique maximizes the per-user throughput while ensuring fairness among users, and it does so while accounting for the different users' quality of service requirements and the channel gains' variability. Through intensive simulations, we show that our proposed approach performs well by achieving high overall throughput while improving user's fairness under different objective functions. Furthermore, it achieves higher performance when compared to state-of-the-art techniques.

cs.NI↗

arXiv2017

Extracting and Exploiting Inherent Sparsity for Efficient IoT Support in 5G: Challenges and Potential Solutions

Besides enabling an enhanced mobile broadband, next generation of mobile networks (5G) are envisioned for the support of massive connectivity of heterogeneous Internet of Things (IoT)s. These IoTs are envisioned for a large number of use-cases including smart cities, environment monitoring, smart vehicles, etc. Unfortunately, most IoTs have very limited computing and storage capabilities and need cloud services. Hence, connecting these devices through 5G systems requires huge spectrum resources in addition to handling the massive connectivity and improved security. This article discusses the challenges facing the support of IoTs through 5G systems. The focus is devoted to discussing physical layer limitations in terms of spectrum resources and radio access channel connectivity. We show how sparsity can be exploited for addressing these challenges especially in terms of enabling wideband spectrum management and handling the connectivity by exploiting device-to-device communications and edge-cloud. Moreover, we identify major open problems and research directions that need to be explored towards enabling the support of massive heterogeneous IoTs through 5G systems.

cs.NI↗

arXiv2017

Optimizing Joint Data and Power Transfer in Energy Harvesting Multiuser Wireless Networks

Energy harvesting emerges as a potential solution for prolonging the lifetime of the energy-constrained mobile wireless devices. In this paper, we focus on Radio Frequency (RF) energy harvesting for multiuser multicarrier mobile wireless networks. Specifically, we propose joint data and energy transfer optimization frameworks for powering mobile wireless devices through RF energy harvesting. We introduce a power utility that captures the power consumption cost at the base station (BS) and the used power from the users' batteries, and determine optimal power resource allocations that meet data rate requirements of downlink and uplink communications. Two types of harvesting capabilities are considered at each user: harvesting only from dedicated RF signals and hybrid harvesting from both dedicated and ambient RF signals. The developed frameworks increase the end users' battery lifetime at the cost of a slight increase in the BS power consumption. Several evaluation studies are conducted in order to validate our proposed frameworks.

cs.NI↗

arXiv2017

Efficient Spectrum Availability Information Recovery for Wideband DSA Networks: A Weighted Compressive Sampling Approach

Compressive sampling has great potential for making wideband spectrum sensing possible at sub-Nyquist sampling rates. As a result, there have recently been research efforts that leverage compressive sampling to enable efficient wideband spectrum sensing. These efforts consider homogenous wideband spectrum, where all bands are assumed to have similar PU traffic characteristics. In practice, however, wideband spectrum is not homogeneous, in that different spectrum bands could present different PU occupancy patterns. In fact, the nature of spectrum assignment, in which applications of similar types are often assigned bands within the same block, dictates that wideband spectrum is indeed heterogeneous. In this paper, we consider heterogeneous wideband spectrum, and exploit its inherent, block-like structure to design efficient compressive spectrum sensing techniques that are well suited for heterogeneous wideband spectrum. We propose a weighted $\ell_1-$minimization sensing information recovery algorithm that achieves more stable recovery than that achieved by existing approaches while accounting for the variations of spectrum occupancy across both the time and frequency dimensions. In addition, we show that our proposed algorithm requires a lesser number of sensing measurements when compared to the state-of-the-art approaches.

cs.IT↗

arXiv2017

When the Hammer Meets the Nail: Multi-Server PIR for Database-Driven CRN with Location Privacy Assurance

We show that it is possible to achieve information theoretic location privacy for secondary users (SUs) in database-driven cognitive radio networks (CRNs) with an end-to-end delay less than a second, which is significantly better than that of the existing alternatives offering only a computational privacy. This is achieved based on a keen observation that, by the requirement of Federal Communications Commission (FCC), all certified spectrum databases synchronize their records. Hence, the same copy of spectrum database is available through multiple (distinct) providers. We harness the synergy between multi-server private information retrieval (PIR) and database- driven CRN architecture to offer an optimal level of privacy with high efficiency by exploiting this observation. We demonstrated, analytically and experimentally with deployments on actual cloud systems that, our adaptations of multi-server PIR outperform that of the (currently) fastest single-server PIR by a magnitude of times with information theoretic security, collusion resiliency, and fault-tolerance features. Our analysis indicates that multi-server PIR is an ideal cryptographic tool to provide location privacy in database-driven CRNs, in which the requirement of replicated databases is a natural part of the system architecture, and therefore SUs can enjoy all advantages of multi-server PIR without any additional architectural and deployment costs.

cs.NI↗

arXiv2016

FogMQ: A Message Broker System for Enabling Distributed, Internet-Scale IoT Applications over Heterogeneous Cloud Platforms

Excessive tail end-to-end latency occurs with conventional message brokers as a result of having massive numbers of geographically distributed devices communicate through a message broker. On the other hand, broker-less messaging systems, though ensure low latency, are highly dependent on the limitation of direct device-to-device (D2D) communication technologies, and cannot scale well as large numbers of resource-limited devices exchange messages. In this paper, we propose FogMQ, a cloud-based message broker system that overcomes the limitations of conventional systems by enabling autonomous discovery, self-deployment, and online migration of message brokers across heterogeneous cloud platforms. For each device, FogMQ provides a high capacity device cloning service that subscribes to device messages. The clones facilitate near-the-edge data analytics in resourceful cloud compute nodes. Clones in FogMQ apply Flock, an algorithm mimicking flocking-like behavior to allow clones to dynamically select and autonomously migrate to different heterogeneous cloud platforms in a distributed manner.

cs.NI↗

arXiv2016

Flocking Virtual Machines in Quest for Responsive IoT Cloud Services

We propose Flock; a simple and scalable protocol that enables live migration of Virtual Machines (VMs) across heterogeneous edge and conventional cloud platforms to improve the responsiveness of cloud services. Flock is designed with properties that are suitable for the use cases of the Internet of Things (IoT). We describe the properties of regularized latency measurements that Flock can use for asynchronous and autonomous migration decisions. Such decisions allow communicating VMs to follow a flocking-like behavior that consists of three simple rules: separation, alignment, and cohesion. Using game theory, we derive analytical bounds on Flock's Price of Anarchy (PoA), and prove that flocking VMs converge to a Nash Equilibrium while settling in the best possible cloud platforms. We verify the effectiveness of Flock through simulations and discuss how its generic objective can simply be tweaked to achieve other objectives, such as cloud load balancing and energy consumption minimization.

cs.NI↗

arXiv2015

A Simple Algorithm for Coloring m-Clique Holes

An m-clique hole is a sequence $ϕ=(Φ_1,Φ_2,\dots,Φ_m)$ of $m$ distinct cliques such that $|Φ_i| \leq m$ for all $i=1,2,\ldots,m$, and whose clique graph is a hole on $m$ vertices. That is, $ϕ$ is an m-clique hole if for all $i\neq j$, $i,j=1,2,\ldots,m$, $Φ_i \cap Φ_{j} \neq \emptyset$ if and only if $(j-1)~\mbox{mod}~m = (j+1)~\mbox{mod}~m = i~\mbox{mod}~m$. This paper derives a sufficient and necessary condition on m-colorability of m-clique holes, and proposes a coloring algorithm that colors m-clique holes with exactly m colors.

cs.DM↗

arXiv2015

Cloud-Assisted Remote Sensor Network Virtualization for Distributed Consensus Estimation

We develop cloud-assisted remote sensing techniques for enabling distributed consensus estimation of unknown parameters in a given geographic area. We first propose a distributed sensor network virtualization algorithm that searches for, selects, and coordinates Internet-accessible sensors to perform a sensing task in a specific region. The algorithm converges in linearithmic time for large-scale networks, and requires exchanging a number of messages that is at most linear in the number of sensors. Second, we design an uncoordinated, distributed algorithm that relies on the selected sensors to estimate a set of parameters without requiring synchronization among the sensors. Our simulation results show that the proposed algorithm, when compared to conventional ADMM (Alternating Direction Method of Multipliers), reduces communication overhead significantly without compromising the estimation error. In addition, the convergence time, though increases slightly, is still linear as in the case of conventional ADMM.

cs.NI↗