Biblio

This paper investigates the secrecy performance of full-duplex relay mode in underlay cognitive radio networks using decode-and-forward relay selection. The analytical results prove that full-duplex mode can guarantee security under critical conditions such as the bad residual self-interference and the presence of hi-tech eavesdropper. The secrecy outage probability is derived based on the statistical characteristics of channels in this considered system. The system is examined under five circumferences: 1) Different values of primary network's desired outage probability; 2) Different values of primary transmitter's transmit power; 3) Applying of multiple relays selection; 4) Systems undergo path-loss during the transmission process; 5) Systems undergo self-interference in relays. Simulation results are presented to verify the analysis.

We address secure resource allocation for an OFDMA cooperative cognitive radio network (CRN) with energy harvesting (EH) capability. In the network, one primary user (PU) cooperates with several untrusted secondary users (SUs) with one SU transmitter and several SU receivers, where the SU transmitter and all SU receivers may overhear the PU transmitter's information while all SU receivers may eavesdrop on each other's signals. We consider the scenario when SUs are wireless devices with small physical sizes; therefore to improve system performance we suppose that SUs are equipped with co-located orthogonally dual-polarized antennas (ODPAs). With ODPAs, on one hand, the SU transmitter can first harvest energy from radio frequency (RF) signals emitted by the PU transmitter, and then utilize the harvested energy to simultaneously serve the PU and all SU receivers. On the other hand, by exploiting polarization-based signal processing techniques, both the PU's and SUs' physical-layer security can be enhanced. In particular, to ensure the PU's communication security, the PU receiver also sends jamming signals to degrade the reception performance of SUs, and meanwhile the jamming signals can also become new sources of energy powering the SU transmitter. For the considered scenario, we investigate the joint allocation of subcarriers, powers, and power splitting ratios to maximize the total secrecy rate of all SUs while ensuring the PU's minimum secrecy rate requirement. Finally, we evaluate the performance of our resource allocation scheme through numerical analyses.

The exponential growth in the number of mobile devices, combined with the rapid demand for wireless services, has steadily stressed the wireless spectrum, calling for new techniques to improve spectrum utilization. A geo-location database has been proposed as a viable solution for wireless users to determine spectrum availability in cognitive radio networks. The protocol used by secondary users (SU) to request spectral availability for a specific location, time and duration, may reveal confidential information about these users. In this paper, we focus on SUs' location privacy in database-enabled wireless networks and propose a framework to address this threat. The basic tenet of the framework is obfuscation, whereby channel requests for valid locations are interwoven with requests for fake locations. Traffic redirection is also used to deliberately confuse potential query monitors from inferring users' location information. Within this framework, we propose two privacy-preserving schemes. The Master Device Enabled Location Privacy Preserving scheme utilizes trusted master devices to prevent leaking information of SUs' locations to attackers. The Crowd Sourced Location Privacy Preserving scheme builds a guided tour of randomly selected volunteers to deliver users channel availability queries and ensure location privacy. Security analysis and computational and communication overhead of these schemes are discussed.

Reliable detection of intrusion is the basis of safety in cognitive radio networks (CRNs). So far, few scholars applied intrusion detection systems (IDSs) to combat intrusion against CRNs. In order to improve the performance of intrusion detection in CRNs, a distributed intrusion detection scheme has been proposed. In this paper, a method base on Dempster-Shafer's (D-S) evidence theory to detect intrusion in CRNs is put forward, in which the detection data and credibility of different local IDS Agent is combined by D-S in the cooperative detection center, so that different local detection decisions are taken into consideration in the final decision. The effectiveness of the proposed scheme is verified by simulation, and the results reflect a noticeable performance improvement between the proposed scheme and the traditional method.

Primary user emulation (PUE) attack is one of the main threats affecting cognitive radio (CR) networks. The PUE can forge the same signal as the real primary user (PU) in order to use the licensed channel and cause deny of service (DoS). Therefore, it is important to locate the position of the PUE in order to stop and avoid any further attack. Several techniques have been proposed for localization, including the received signal strength indication RSSI, Triangulation, and Physical Network Layer Coding. However, the area surrounding the real PU is always affected by uncertainty. This uncertainty can be described as a lost (cost) function and conditional probability to be taken into consideration while proclaiming if a PU/PUE is the real PU or not. In this paper, we proposed a combination of a Bayesian model and trilateration technique. In the first part a trilateration technique is used to have a good approximation of the PUE position making use of the RSSI between the anchor nodes and the PU/PUE. In the second part, a Bayesian decision theory is used to claim the legitimacy of the PU based on the lost function and the conditional probability to help to determine the existence of the PUE attacker in the uncertainty area.

Recently, some papers that apply a multi-armed bandit algorithm for channel selection in a cognitive radio system have been reported. In those papers, channel selection based on Upper Confidence Bound (UCB) algorithm has been proposed. However, in those selection, secondary users are not allowed to transmit data over same channels at the same time. Moreover, they do not take security of wireless communication into account. In this paper, we propose secure channel selection methods based on UCB algorithm, taking secrecy capacity into account. In our model, secondary users can share same channel by using transmit time control or transmit power control. Our proposed methods lead to be secure against an eavesdropper compared to conventional channel selections based on only estimated channel availability. By computer simulation, we evaluate average system secrecy capacity. As a result, we show that our proposed channel selections improve average system secrecy capacity compared to conventional channel selection.

Cognitive radio networks (CRNs) have a great potential in supporting time-critical data delivery among the Internet of Things (IoT) devices and for emerging applications such as smart cities. However, the unique characteristics of different technologies and shared radio operating environment can significantly impact network availability. Hence, in this paper, we study the channel assignment problem in time-critical IoT-based CRNs under proactive jamming attacks. Specifically, we propose a probabilistic spectrum assignment algorithm that aims at minimizing the packet invalidity ratio of each cognitive radio (CR) transmission subject to delay constrains. We exploit the statistical information of licensed users' activities, fading conditions, and jamming attacks over idle channels. Simulation results indicate that network performance can be significantly improved by using a security- availability- and quality-aware channel assignment that provides communicating CR pair with the most secured channel of the lowest invalidity ratio.

Cognitive radio networks (CRNs) enable secondary users (SU) to make use of licensed spectrum without interfering with the signal generated by primary users (PUs). To avoid such interference, the SU is required to sense the medium for a period of time and eventually use it only if the band is perceived to be idle. In this context, the encryption process is carried out for the SU requests prior to their transmission whilst the strength of the security in CRNs is directly proportional to the length of the encryption key. If a request of a PU on arrival finds an SU request being either encrypted or transmitted, then the SU is preempted from service. However, excessive sensing time for the detection of free spectrum by SUs as well as extended periods of the CRN being at an insecure state have an adverse impact on network performance. To this end, a generalized stochastic Petri net (GSPN) is proposed in order to investigate sensing vs. security vs. performance trade-offs, leading to an efficient use of the spectrum band. Typical numerical simulation experiments are carried out, based on the application of the Mobius Petri Net Package and associated interpretations are made.

Cognitive radio network (CRN) is regarded as an emerging technology for better spectrum efficiency where unlicensed secondary users (SUs) sense RF spectrum to find idle channels and access them opportunistically without causing any harmful interference to licensed primary users (PUs). However, RF spectrum sensing and sharing along with reconfigurable capabilities of SUs bring severe security vulnerabilities in the network. In this paper, we analyze physical-layer security (secrecy rates) of SUs in CRN in the presence of eavesdroppers, jammers and PU emulators (PUEs) where SUs compete not only with jammers and eavesdroppers who are trying to reduce SU's secrecy rates but also against PUEs who are trying to compel the SUs from their current channel by imitating the behavior of PUs. In addition, a legitimate SU competes with other SUs with a sharing attitude for dynamic spectrum access to gain a high secrecy rate, however, the malicious users (i.e., attackers) attempt to abuse the channels egotistically. The main contribution of this work is the design of a game theoretic approach to maximize utilities (that is proportional to secrecy rates) of SUs in the presence of eavesdroppers, jammers and PUEs. Furthermore, SUs use signal energy and cyclostationary feature detection along with location verification technique to detect PUEs. As the proposed approach is generic and considers different attackers, it can be particularized to a situation with eavesdroppers only, jammers only or PUEs only while evaluating physical-layer security of SUs in CRN. We evaluate the performance of the proposed approach using results obtained from simulations. The results show that the proposed approach outperforms other existing methods.

This paper investigates physical layer security of non-orthogonal multiple access (NOMA) in cognitive radio (CR) networks. The techniques of NOMA and CR have improved the spectrum efficiency greatly in the traditional networks. Because of the difference in principles of spectrum improving, NOMA and CR can be combined together, i.e. CR NOMA network, and have great potential to improving the spectrum efficiency. However the physical layer security in CR NOMA network is different from any single network of NOMA or CR. We will study the physical layer security in underlay CR NOMA network. Firstly, the wiretap network model is constructed according to the technical characteristics of NOMA and CR. In addition, new exact and asymptotic expressions of the security outage probability are derived and been confirmed by simulation. Ultimately, we have studied the effect of some critical factors on security outage probability after simulation.

Continuing progress and integration levels in silicon technologies make possible complete end-user systems consisting of extremely high number of cores on a single chip targeting either embedded or high-performance computing. However, without new paradigms of energy- and thermally-efficient designs, producing information and communication systems capable of meeting the computing, storage and communication demands of the emerging applications will be unlikely. The broad topic of power and thermal management of massive multicore chips is actively being pursued by a number of researchers worldwide, from a variety of different perspectives, ranging from workload modeling to efficient on-chip network infrastructure design to resource allocation. Successful solutions will likely adopt and encompass elements from all or at least several levels of abstraction. Starting from these ideas, we consider a holistic approach in establishing the Power-Thermal-Performance (PTP) trade-offs of massive multicore processors by considering three inter-related but varying angles, viz., on-chip traffic modeling, novel Networks-on-Chip (NoC) architecture and resource allocation/mapping On-line workload (mathematical modeling, analysis and prediction) learning is fundamental for endowing the many-core platforms with self-optimizing capabilities [2][3]. This built-in intelligence capability of many-cores calls for monitoring the interactions between the set of running applications and the architectural (core and uncore) components, the online construction of mathematical models for the observed workloads, and determining the best resource allocation decisions given the limited amount of information about user-to-application-to-system dynamics. However, workload modeling is not a trivial task. Centralized approaches for analyzing and mining workloads can easily run into scalability issues with increasing number of monitored processing elements and uncore (routers and interface queues) components since it can either lead to significant traffic and energy overhead or require dedicated system infrastructure. In contrast, learning the most compact mathematical representation of the workload can be done in a distributed manner (within the proximity of the observation /sensing) as long as the mathematical techniques are flexible and exploit the mathematical characteristics of the workloads (degree of periodicity, degree of fractal and temporal scaling) [3]. As one can notice, this strategy does not postulate a-priori the mathematical expressions (e.g., a specific order of the autoregressive moving average (ARMA) model). Instead, the periodicity and fractality of the observed computation (e.g., instructions per cycles, last level cache misses, branch prediction successes and failures, TLB access/misses) and communication (request-reply latency, queues utilization, memory queuing delay) metrics dictate the number of coefficients, the linearity or nonlinearity of the dynamical state equations and the noise terms (e.g., Gaussian distributed) [3]. In other words, dedicated minimal logic can be allocated to interact with the local sensor to analyze the incoming workload at run-time, determine the required number of parameters and their values as a function of their characteristics and communicate only the workload model parameters to a hierarchical optimization module (autonomous control architecture). For instance, capturing the fractal characteristics of the core and uncore workloads led to the development of more efficient power management strategy [1] than those based on PID or model predictive control. In order to develop a compact and accurate mathematical framework for analyzing and modeling the incoming workload, we describe a general probabilistic approach that models the statistics of the increments in the magnitude of a stochastic process (associated with a specific workload metric) and the intervals of time (inter-event times) between successive changes in the stochastic process [3]. We show that the statistics of these two components of the stochastic process allows us to derive state equations and capture either short-range or long-range memory properties. To test the benefits of this new workload modeling approach, we describe its integration into a multi-fractal optimal control framework for solving the power management for a 64-core NoC-based manycore platform and contrast it with a mono-fractal and non-fractal schemes [3]. A scalable, low power, and high-bandwidth on-chip communication infrastructure is essential to sustain the predicted growth in the number of embedded cores in a single die. New interconnection fabrics are key for continued performance improvements and energy reduction of manycore chips, and an efficient and robust NoC architecture is one of the key steps towards achieving that goal. An NoC architecture that incorporates emerging interconnect paradigms will be an enabler for low-power, high-bandwidth manycore chips. Innovative interconnect paradigms based on optical technologies, RF/wireless methods, carbon nanotubes, or 3D integration are promising alternatives that may indeed overcome obstacles that impede continued advances of the manycore paradigm. These innovations will open new opportunities for research in NoC designs with emerging interconnect infrastructures. In this regard, wireless NoC (WiNoC) is a promising direction to design energy efficient multicore architectures. WiNoC not only helps in improving the energy efficiency and performance, it also opens up opportunities for implementing power management strategies. WiNoCs enable implementation of the two most popular power management mechanisms, viz., dynamic voltage and frequency scaling (DVFS) and voltage frequency island (VFI). The wireless links in the WiNoC establish one-hop shortcuts between the distant nodes and facilitate energy savings in data exchange [3]. The wireless shortcuts attract a significant amount of the overall traffic within the network. The amount of traffic detoured is substantial and the low power wireless links enable energy savings. However, the overall energy dissipation within the network is still dominated by the data traversing the wireline links. Hence, by incorporating DVFS on these wireline links we can save more energy. Moreover, by incorporating suitable congestion aware routing with DVFS, we can avoid thermal hotspots in the system [4]. It should be noted that for large system size the hardware overhead in terms of on-chip voltage regulators and synchronizers is much more in DVFS than in VFI. WiNoC-enabled VFI designs mitigate some of the full-system performance degradation inherent in VFI-partitioned multicore designs, and it also help in eliminating it entirely for certain applications [5]. The VFI-partitioned designs used in conjunction with a novel NoC architecture like WiNoC can achieve significant energy savings while minimizing the impact on the achievable performance. On-chip power density and temperature trends are continuously increasing due to high integration density of nano-scale transistors and failure of Dennard Scaling as a result of diminishing voltage scaling. Hence, all computing is temperature-constrained computing and therefore, employing thermal management techniques that keep chip temperatures within safe limits along with meeting the constraints of spatial/temporal thermal gradients and avoid wear-out effects [8] is key. We introduced the novel concept of Dark Silicon Patterning, i.e. spatio-temporal control of power states of different cores [9] Sophisticated patterning and thread-to-core mapping decisions are made considering the knowledge of process variations and lateral heat dissipation of power-gated cores in order to enhance the performance of multi-threaded workloads through dynamic core count scaling (DCCS). This is enabled by a lightweight online prediction of chip's thermal profile for a given patterning candidate. We also present an enhanced temperature-aware resource management technique that, besides active and dark states of cores, also exploit various grey states (i.e., using different voltage-frequency levels) in order to achieve a high performance for mixed ILP-TLP workloads under peak temperature constraints. High ILP applications benefit from high V-f and boosting levels, while high TLP applications benefit from As the scaling trends move from multi-core to many-core processors, the centralized solutions become infeasible, and thereby require distributed techniques. In [6], we proposed an agent-based distributed temperature-aware resource management technique called TAPE. It assigns a so-called agent to each core, a software or hardware entity that acts on behalf of the core. Following the principles of economic theory, these agents negotiate with each other to trade their power budgets in order to fulfil the performance requirements of their tasks, while keep the TPeak≤Tcritical. In case of thermal violations, task migration or V-f throttling is triggered, and a penalty is applied to the trading process to improve the decision making.

New Internet of Things (IoT) technologies such as Long Range (LoRa) are emerging which enable power efficient wireless communication over very long distances. Devices typically communicate directly to a sink node which removes the need of constructing and maintaining a complex multi-hop network. Given the fact that a wide area is covered and that all devices communicate directly to a few sink nodes a large number of nodes have to share the communication medium. LoRa provides for this reason a range of communication options (centre frequency, spreading factor, bandwidth, coding rates) from which a transmitter can choose. Many combination settings are orthogonal and provide simultaneous collision free communications. Nevertheless, there is a limit regarding the number of transmitters a LoRa system can support. In this paper we investigate the capacity limits of LoRa networks. Using experiments we develop models describing LoRa communication behaviour. We use these models to parameterise a LoRa simulation to study scalability. Our experiments show that a typical smart city deployment can support 120 nodes per 3.8 ha, which is not sufficient for future IoT deployments. LoRa networks can scale quite well, however, if they use dynamic communication parameter selection and/or multiple sinks.

Working in ISM band becomes overcrowded, shared unlicensed spectrum band, leads to a reduction in the quality of communication. This makes increase in packet loss caused by collisions and results in the necessity of packets retransmissions. In wireless sensor networks a large amount of energy of sensor nodes will be wasted during retransmissions. Cognitive radio is the technology makes it possible for sensor nodes to make use of licensed bands. In this paper a routing technique for cognitive radio wireless sensor networks is presented, that is based on a cross-layer design that jointly considers route and spectrum selection. This method has two main phases: next hop selection and channel selection. The routing is done hop-by-hop with local information and decisions, which are more compatible with sensor networks. Primary user action and prevention from interfering with them is considered in all spectrum decisions. It uniformly distributes frequency channels between neighboring nodes, which lead to a local reduction in collision probability. This clearly affects energy consumption in all sensor nodes. The route selection is energy-aware and a learning based technique is used to reduce the packet delay with respect to hop-count. The imitation reveals that by applying cognitive radio technology to WSNs and selecting a proper channel, we can consciously decrease collision probability. This saves energy of sensor nodes and improves the network lifetime.

Wireless Sensor Networks (WSNs) are becoming more and more popular to support a wide range of Internet of Things (IoT) applications. Time-Slotted Channel Hopping (TSCH) is a technique to enable ultra reliable and ultra low-power wireless multi-hop networks. TSCH consist of a channel hopping scheme for sending link-layer frames in different time slots and frequencies in order to efficiently combat external interference and multi-path fading. The keystone of TSCH is the scheduling algorithm, which determines for every node at which opportunity (a combination of time slots and channels) it is allowed to send. However, current scheduling algorithms are not suited for dense deployments and have important scalability limitations. In this paper, we investigate TSCH's scheduling performance in dense deployments and show how the scheduling can be improved for such environments. We performed an extensive analysis of the scalability for different scheduling approaches showing the performance drops as the number of nodes increases. Moreover, we propose a novel textlessutextgreaterDetextless/utextgreatercentralized textlessutextgreaterBrtextless/utextgreateroadcast-based textlessutextgreaterStextless/utextgreatercheduling algorithm called DeBraS, based on selective broadcasting to inform nodes about each other's schedule. Through extensive simulations, we show that DeBraS is highly more scalable than centralized solutions and that it outperforms the current decentralized 6Tisch algorithms in up to 88.5% in terms of throughput for large network sizes.

In this paper we discuss several improvements to the security and reliability of a classic Bluetooth network (piconet) that can arise from the fact of being able to transmit the same frame with two frequencies on each slot, instead of the actual standard, that uses only one frequency. Furthermore, we build upon this possibility and we show that piconet participants can explore many strategies to increase the security of their communications by confounding eavesdroppers, such as multiple hopping sequences, random selection of a hopping sequence on each transmission slot and variable frame encryption per hopping sequence. Finally, all this can be decided independently by any piconet participant without having to agree in real time on some type of service with other participants of the same piconet.

Heterogeneous cognitive wireless networks (HeCoNets)) are consisted of macrocells that are overlaid by small cells (e.g, femtocells, picocells). These small cells operate over the cognitive radio paradigm. In this paper, we consider a cooperative model in the uplink of HetCoNets, that includes picocell and famtocells networks that are using unlicensed channels from the macrocesll network. In our cooperative model, cognitive picocell users' equipments (CPUEs) and cognitive femtocell users (CFUEs) get incentives from cooperating with each other to improve the unlicensed channels usage and mitigate inter-tier and intra-tier interference while maximizing sum-rate of users in the HetCoNet. We apply a coalition game approach in which CPUEs and CFUEs are considered as players of the game. We have intensively simulated the proposed idea in matlab. Our simulation results show the effectiveness of our proposed compared with non-cooperative model.

The cooperative spectrum leasing process between the primary user (PU) and the secondary user (SU) in a cognitive radio network under the overlay approach and the decode and forward (DF) cooperative protocol is studied. Considering the Quality of Service (QoS) provisioning of both users, which participate in a three-phase leasing process, we investigate the maximization of PU's effective capacity subject to an average energy constraint for the SU under a heuristic power and time allocation mechanism. The aforementioned proposed scheme treats with the basic concepts of the convex optimization theory and outperforms a baseline allocation mechanism which is proven by the simulations. Finally, important remarks for the PU's and the SU's performance are extracted for different system parameters.

Spectrum sensing (signal detection) under low signal to noise ratio is a fundamental problem in cognitive radio networks. In this paper, we have analyzed maximum eigenvalue detection (MED) and energy detection (ED) techniques known as semi-blind spectrum sensing techniques. Simulations are performed by using independent and identically distributed (iid) signals to verify the results. Maximum eigenvalue detection algorithm exploits correlation in received signal samples and hence, performs same as energy detection algorithm under high signal to noise ratio. Energy detection performs well under low signal to noise ratio for iid signals and its performance reaches maximum eigenvalue detection under high signal to noise ratio. Both algorithms don't need any prior knowledge of primary user signal for detection and hence can be used in various applications.

Cognitive radio (CR) has emerged as a promising technology to increase the utilization of spectrum resource. A pivotal challenge in CR lies on secondary users' (SU) finding each other on the frequency band, i.e., the spectrum locating. In this demo, we implement two kinds of multi-channel rendezvous technology to solve the problem of spectrum locating: (i) the common control channel (CCC) based rendezvous scheme, which is simple and effective when a control channel is always available; and (ii) the channel-hopping (CH) based blind rendezvous, which could also obtain guaranteed rendezvous on all commonly available channels of pairwise SUs in a short time without a CCC. Furthermore, the cognitive nodes in the demonstration could adjust their communication channels autonomously according to the dynamic spectrum environment for continuous data transmission.

Consensus algorithms provide strategies to solve problems in a distributed system with the added constraint that data can only be shared between adjacent computing nodes. We find these algorithms in applications for wireless and sensor networks, spectrum sensing for cognitive radio, even for some IoT services. However, consensus-based applications are not resilient to compromised nodes sending falsified data to their neighbors, i.e. they can be the target of Byzantine attacks. Several solutions have been proposed in the literature inspired from reputation based systems, outlier detection or model-based fault detection techniques in process control. We have reviewed some of these solutions, and propose two mitigation techniques to protect the consensus-based Network Intrusion Detection System in [1]. We analyze several implementation issues such as computational overhead, fine tuning of the solution parameters, impacts on the convergence of the consensus phase, accuracy of the intrusion detection system.

As smart grid becomes more popular and emergent, the need for reliable communication technology becomes crucial to ensure the proper and efficient operation of the grid. Therefore, cognitive radio has been recently utilized to provide a scalable and reliable communication infrastructure for smart grid. However, accurate spectrum sensing is the core of this infrastructure. In this paper, we propose an architecture, utilizing Role-Based Delegation to manage spectrum sensing within the cognitive-radio-based communication infrastructure for smart grid and ensure its reliability and security.