One of the main characteristics in cognitive radios is situation awareness. By classifying the modulation schemes used in surrounding transmissions, a secondary user (SU) can identify the existing users in the system and adjust his/her transmission parameters accordingly. In this paper, we propose a multi-task learning (MTL) approach to recognize the modulation scheme used among a specific set of analog and digital modulations. This approach uses a deep convolutional neural network (CNN) to extract the necessary features in order to classify the different modulation schemes. The MTL is used to separately train the modulation classes that normally cause a considerable confusion and therefore improve the overall classification accuracy. Our results on the RadioML dataset show that the suggested architecture achieves higher overall classification accuracy compared to the recently proposed Convolutional, Long Short Term Memory (LSTM), Deep Neural Network (CLDNN). Our classification accuracy of 86.97% at 18 dB SNR outperforms the state-of-the-art with 5% relative improvement.

Indoor localization of mobile nodes is receiving great interest due to the recent advances in mobile devices and the increasing number of location-based services. Fingerprinting based on Wifi received signal strength (RSS) is widely used for indoor localization due to its simplicity and low hardware requirements. However, its positioning accuracy is significantly affected by random fluctuations of RSS values caused by fading and multi-path phenomena. This paper presents a convolutional neural network (CNN) based approach for indoor localization using RSS time-series from wireless local area network (WLAN) access points. Applying CNN on a time-series of RSS readings is expected to reduce the noise and randomness present in separate RSS values and hence improve the localization accuracy. The proposed model is implemented and evaluated on a multi-building and multi-floor dataset, UJIIndoorLoc dataset. The proposed approach provides 100% accuracy for building prediction, 100% accuracy for floor prediction and the mean error in coordinates estimation is 2.77 m.

One of the main challenges in cognitive radios is spectrum sensing. Cooperative spectrum sensing scheme among mobile users can be used to determine the usage profile of wide spectrum bands in a large geographical region. In a large mobile crowdsensing environment, the key step is to assign the sensing task among mobile users to maximize the spectrum sensing performance while reducing the cost incurred by the mobile users during the sensing process. In this paper, we propose two genetic algorithm-based approaches to solve the NP-hard problem of spectrum sensing task assignment among mobile users. The first algorithm uses a centralized genetic algorithm scheme to maximize the spectrum sensing utility function. The second algorithm uses an island genetic algorithm to assign the sensing task among mobile users in a distributive way. Simulation results show that both algorithms achieve comparable spectrum utility measure to the one obtained by running recently proposed particle swarm optimization and greedy approximation algorithms while reducing the running time of the algorithm by a significant factor. In addition, the island algorithm massively outperforms both algorithms in the running time by running the algorithm independently at each sensing location and exchanging the necessary information for the overlapping locations, removing the bottleneck of having a central spectrum profiling unit to assign the sensing tasks among mobile users.

Software-defined networking (SDN) abstracts and centralizes the network control functions in a software entity that runs on a server, known as SDN controller. The controller needs to respond to its controlled elements in a strictly timely manner, and the controller placement has a prominent effect on its response time. Originally, all SDN architectures assumed a physical wired connection between the SDN controller and its controlled elements, and the controller placement problem (CPP) has been only studied under such wired settings. Recently, novel SDN architectures have been proposed in which a direct wireless connection is assumed between the controller and its controlled elements. In this paper, we consider the 'wireless CPP,' when the link between the controller and the controlled element is wireless. Specifically, our contributions are as follows. First, we propose two joint controller placement and assignment formulations, assuming wired links between the controllers and their controlled elements; the first formulation considers an average response time constraint, whereas the second one considers a per-link response time constraint. Then, using chance- constrained stochastic programming (CCSP), we extend our formulation to the case when the links between the controllers and their controlled elements are wireless. Finally, we evaluate our joint placement and assignment schemes under various system parameters. Our results demonstrate the advantage of our joint scheme, in terms of reducing the required number of controllers, compared to a recent sequential assignment and placement scheme in the literature. They also show the ability of our CCSP-based scheme in probabilistically satisfying the controllers response time constraints.

Due to the tremendous and rapid growth of data traffic (e.g. video content), femtocaching has been introduced in 5G networks to bring the data closer to the clients using small, low-power cellular base stations with storage capacity called femtocaches (FCs), hence offloading some traffic off the macrocell base station (MBS). The process of content delivery can efficiently benefit from FCs by optimizing the process of files distribution among the clients using network coding, hence, offloading bandwidth off the MBS and saving costs. In this paper, we propose a genetic algorithm-based approach to minimize the number of orthogonal MBS channels used to distribute files among the clients. Simulation results show that our proposed algorithm almost achieves the optimal solutions found by an exhaustive brute-force search in about 98% of the runs on average. These results significantly make use of the FCs and save the MBS' bandwidth.

Due to the increasing demand for higher data rates and the congestion in communication systems, new research is focusing on the cooperation between the two most successful communication systems, LTE and WiFi. The overall performance of a WiFi system degrades with increasing the number of served users due to collisions. We propose in this paper a novel scheme for LTE and WiFi coexistence, where an LTE femto Base Station cooperates with a WiFi Access Point to maximize both of their profits. Our proposed scheme has the advantage of relieving a congested WiFi system. Thus, this creates a time gap for the LTE system to transmit its data. In addition, we investigate the capability of WiFi and LTE systems to work simultaneously under a certain maximum interference limit. We have formulated a multi- objective optimization problem for maximizing the rate of the WiFi system and the capacity of the LTE system. We developed an algorithm based on particle swarm optimization to determine the appropriate time ratios for WiFi and LTE transmission, the transmitting power of LTE under WiFi transmission, and the number of WiFi nodes to be transferred to LTE system. Simulation results confirm the capability for LTE to transmit besides WiFi without affecting its transmission rate.

Energy harvesting has been gaining a lot of attention in the past decade due to its ability to provide a -virtually-endless energy supply. Nodes in a Wireless Powered Communication Network (WPCN) depend, totally or partially, on the energy harvested from the Central Node (CN) which has a constant power supply. This work addresses a solution to the problem of lack of fairness in the distribution of energy broadcast to nodes from the CN. The solution presented here depends on cooperation between nodes, in which nodes that have harvested more energy can help other nodes with their transmission to achieve fairness. The main objective is to achieve a maximized common throughput by selecting the best relay node assuming Amplify-and-Forward relaying. An optimization problem is formulated to allocate time and energy resources for nodes' transmissions and relaying. The formulated optimization problem is proved to be convex, which allows for efficient solution calculation. Simulation results show the improved performance of our proposed cooperation and relay selection algorithms as compared to the no-cooperation scenario.

Regression testing is the way to ensure the current version of the program is up and running. Continuous testing throughout the development cycle leads to detecting bugs as early as possible; however, it imposes huge overhead if the entire test suite has to be run. In this paper, powered by the goal of early detection of bugs, a regression test selection technique is proposed. The proposed technique uses a cache to keep the potentially error prone files and link them with test cases in a dynamic way that evolves with the evolution of code and test cases. Rather than running the entire test suite, only test cases that are linked with the most error prone files are selected and run on a regular frequent basis. Our cache outperforms previous related cache technique in terms of cache hit rate on 5 Apache projects. Based on the modified cache, the test selection module of the proposed technique can achieve a fault detection rate that exceeded 80% in all projects.

In cognitive radio networks, cognitive users are allowed to use the licensed portions of spectrum, as long as no harmful interference affects the primary (licensed) users. Towards achieving this goal, spectrum sensing is an essential process to determine spectrum access opportunities for the cognitive users. In wide spectrum applications, it is recommended to distribute the sensing task of the required bands among the cognitive nodes to reduce the sensing process overhead. This paper presents a fuzzy inference based scheme for distributing the sensing task among cognitive nodes. This scheme is proven, via simulations, to reduce the overall probability of sensing error compared to two other cooperative sensing mechanisms.

Global warming, environmental pollution, and fuel shortage are currently major worldwide challenges. Ecorouting is one of several tools that attempt to address this challenge by minimizing network-wide vehicle fuel consumption and emission levels. Eco-routing systems select the most environmentally friendly route. The subpopulation feedback eco-routing (SPF-ECO) algorithm that is implemented in the INTEGRATION software can produce a reduction in fuel consumption levels by approximately 17%. However, in some cases, due to delayed updates or the lack for updates, its performance degrades. In this paper, we propose the ant colony based eco-routing technique (ACO-ECO), which is a novel feedback eco-routing and cost updating algorithm to overcome these shortcomings. In the ACO-ECO algorithm, real-time performance measures on various roadway links are shared. Vehicles build their minimum path routes using the latest real-time information to minimize their fuel consumption and emission levels. ACO-ECO is also able to capture randomness in route selection, pheromone updating, and pheromone evaporation. The results show that the ACO-ECO algorithm and SPF-ECO have similar performances in normal cases. However, in the case of link blocking, the ACO-ECO algorithm reduces the network-wide fuel consumption and CO2 emission levels in the range of 2.3% to 6.0%. It also reduces the average trip time by approximately 3.6% to 14.0%.

Communication and networking research introduces new protocols and standards with an increasing number of researchers relying on real experiments rather than simulations to evaluate the performance of their new protocols. A number of testbeds are currently available for this purpose and a growing number of users are requesting access to those testbeds. This motivates the need for better utilization of the testbeds by allowing concurrent experimentations. In this work, we introduce a novel mapping algorithm that aims to maximize wireless testbed utilization using frequency slicing of the spectrum resources. The mapper employs genetic algorithm to find the best combination of requests that can be served concurrently, after getting all possible mappings of each request via an induced sub-graph isomorphism stage. The proposed mapper is tested on grid testbeds and randomly generated topologies. The solution of our mapper is compared to the optimal one, obtained through a brute-force search, and was able to serve the same number of requests in 82.96% of testing scenarios. Furthermore, we show the effect of the careful design of testbed topology on enhancing the testbed utilization by applying our mapper on a carefully positioned 8-nodes testbed. In addition, our proposed approach for testbed slicing and requests mapping has shown an improved performance in terms of total served requests, about five folds, compared to the simple allocation policy with no slicing.

In this paper, we present a new carpooling recommendation system whose main objective is to find the best carpool matchings and recommend individuals to join their friends during trips or travels. The proposed recommendation system utilizes user's mobility history and user social network information to find carpool matchings. The proposed system employs a probabilistic model based on continuous time Markov chain to model user's mobility and to predict user future movements. Moreover, it uses two similarity measures (interest based similarity and friendship based similarity) to find the similarities between users. The interest based similarity uses a weighted bipartite graph between users and places, where the edges are weighted by the term frequency-inverse document frequency. The friendship based similarity uses the common friends as a similarity measure. The proposed system is evaluated using the number of carpool matchings that can be found, this number gives an indication of the reduction that can be made in vehicular traffic congestion, pollutant emissions and energy consumption.

One of the challenges of the design of Software Defined Radios (SDR) is to maintain a high level of reconfigurability without sacrificing data rates. In this paper, we consider the USRP N210, which is an SDR kit made by Ettus Research. It consists of an FPGA connected to an RF front-end. The USRP is operated by a host computer where most of the processing is done while the FPGA is used mainly to control the RF front-end, manage communication with the host, and convert sample rates. The maximal rate supported by the USRP hardware can not be practically achieved due to the bottleneck in the data transfer between the host and USRP and the limited computational ability of the computer. To deal with this problem, we implement a modulator and demodulator in the FPGA of the USRP. The proposed system is capable of processing data at the maximum sample rate supported by the hardware. This is accomplished by transferring only raw data between host and USRP, reducing transfer rate by up to 64 times; thus, bypassing the host transfer bottleneck. Additionally, offloading some of the processing to the FPGA makes communication at the maximum rate achievable with an off the shelf computer. An evaluation of the performance of the suggested system from the communication perspective is performed. We also showcase the system's ability to work at the maximum sample rate supported by the USRP.

The paper introduces a Vehicular Network Integrated Simulator (VNetIntSim) that integrates transportation modelling with Vehicular Ad Hoc Network (VANET) modelling. Specifically, VNetIntSim integrates the OPNET software, a communication network simulator, and the INTEGRATION software, a microscopic traffic simulation software. The INTEGRATION software simulates the movement of travellers and vehicles, while the OPNET software models the data exchange through the communication system. Information is exchanged between the two simulators as needed. As a proof of concept, the VNetIntSim is used to quantify the impact of mobility parameters (traffic stream speed and density) on the communication system performance, and more specifically on the data routing (packet drops and route discovery time).

This paper presents an analytical and simulation study of the efficiency of using cooperative communication for Dynamic Spectrum Access (DSA) in secondary infrastructure networks. The licensed and unlicensed network transmissions are modeled using a Discrete Time Markov Chain (DTMC). The secondary network spectrum occupancy and throughput are evaluated analytically and by simulation. A COoperative and COgnitive MAC protocol (CO2MAC) is developed to facilitate cooperative transmissions in secondary infrastructure networks. The results show significant performance enhancement for cooperative transmission over direct transmission, especially in crowded spectrum, where it achieves up to 80% enhancement in throughput compared to direct transmission.

Achieving timing-closure has become one of the hardest tasks in logic synthesis due to the required stringent timing constraints in very large circuit designs. In this paper, we propose a novel synthesis paradigm to achieve timing-closure called Timing-Aware CUt Enumeration (TACUE). In TACUE, optimization is conducted through three aspects: (1) a new divide-and-conquer strategy is proposed that generates multiple sub-cuts on the critical parts of the circuit; (2) two cut enumeration strategies are proposed; (3) an efficient parallel synthesis framework is offered to reduce computation time. Experiments on large and difficult industrial benchmarks show the promise of the proposed method.

In cognitive radio networks, dynamic spectrum access allows an unlicensed (secondary) user to use the frequency bands that are statically allocated to licensed (primary) users under condition of causing no harm to the primary transmission. For the process of the dynamic spectrum access to succeed, spectrum sensing becomes of great importance for the secondary user (SU) to capture the under-utilized frequency bands. This process must be as fast as possible to enable efficient spectrum utilization by the SU. In order to decrease the sensing time especially for wide band spectrum while keeping acceptable sensing accuracy, it is recommended to distribute this sensing task across the SUs in a way that leads to reduction of the probability of misdetection and the probability of false alarm. This work presents a channel assignment scheme for cooperative spectrum sensing in cognitive radio networks. Based on the individual - statistically calculated- probability of error of each SU over all channels, the algorithm assigns the best k adjacent channels for each SU to decrease the probability of misdetection and the probability of false alarm. It is shown by MATLAB simulations that the presented scheme is able to reach acceptable sensing probability of error in terms of reduced sensing delay when compared to full spectrum sensing where all nodes cooperate to sense all channels (no assignment takes place). Additionally, It is shown that the overall energy consumption of the cognitive radio network is reduced resulting in better network lifetime.

In this paper, PNCR, a routing framework based on Physical Network Coding is proposed. PNCR enables shared utilization of the channel during the broadcast and multiple access phases of transmissions. These additional transmission opportunities can be observed as performance gains in terms of less packet delays and better network throughput. In this paper, we discuss the details of our proposed framework to utilize the virtues presented by Physical Network Coding. Existing work on Physical Network Coding is theoretic and does not present itself as a tool for multi-hop network deployment except for limited network topologies. Our work in this paper is an effort to utilize such techniques for networks of arbitrary structure. We evaluate the performance of our proposed framework using simulations and show that it achieves better throughput and less delivery delay per packet than conventional wireless routing solutions without Physical Network Coding.

Currently, BDD packages such as CUDD depend on chained hash tables. Although they are efficient in terms of memory usage, they exhibit poor cache performance due to dynamic allocation and indirections of data. Moreover, they are less appealing for concurrent environments as they need thread-safe garbage collectors. Furthermore, to take advantage of the benefits from multi-core platforms, it is best to re-engineer the underlying algorithms, such as whether traditional depth-first search (DFS) construction, breadth-first search (BFS) construction, or a hybrid BFS with DFS would be best. In this paper, we introduce a novel BDD package friendly to multicore platforms that builds on a number of heuristics. Firstly, we re-structure the Unique Table (UT) using a concurrency-friendly Hopscotch hashing to improve caching performance. Secondly, we re-engineer the BFS Queues with hopscotch hashing. Thirdly, we propose a novel technique to utilize BFS Queues to simultaneously work as a Computed Table (CT). Finally, we propose a novel incremental Mark-Sweep Garbage Collector (GC). We report results for both BFS and hybrid BFS-DFS construction methods. With these techniques, even with a single-threaded BDD, we were able to achieve a speedup of up to 8× compared to a conventional single-threaded CUDD package. When two-threads are launched, another 1.5× speedup is obtained.

Geo-social information can be utilized for user benefits in many applications. Social interaction in vehicular ad hoc networks (VANETs) is an important source for this type of information. In this paper, we first propose and describe a general architecture of the social VANET system (S-VANET) that supports social interaction through vehicular networks. Then, we present a new carpooling recommendation system that works as S-VANET application. The main objective is to recommend individuals to join their friends during trips or travels. The proposed recommendation system uses check-in history and home location to model users, and utilizes Fast Fourier transform to represent user check-ins and find the similarity between users. The system uses hierarchical clustering with weighted center of mass method to estimate the user home location.

The effectiveness of Satisfiability (SAT)-based Bounded Model Checking (BMC) critically relies on the deductive power of the BMC instance. Although implication relationships have been used to help SAT solver to make more deductions, too many such implications would result in a large number of clauses and potentially degrade the underlying SAT solver performance. In this paper, we have formulated clause selection as a set-cover problem. Secondly, we have proposed a novel greedy strategy for optimal selection of static logic clauses. This technique maximizes the number of literals that can be deduced by the SAT solver during the Boolean Constraint Propagation (BCP) operation. Our strategy improves BMC by 1.74x against the case where all extended implications were added to the BMC instance. Compared with the original BMC without any implications, up to 55.32x speedup can be achieved.

The effectiveness of SAT-based Bounded Model Checking (BMC) critically relies on the deductive power of the BMC instance. Although implication relationships have been used to help SAT solver to make more deductions, frequently an excessive number of implications has been used. Too many such implications can result in a large number of clauses that could potentially degrade the underlying SAT solver performance. In this paper, we first propose a framework for a parallel deduction engine to reduce implication learning time. Secondly, we propose a novel set-cover technique for optimal selection of constraint clauses. This technique depends on maximizing the number of literals that can be deduced by the SAT solver during the BCP (Boolean Constraint Propagation) operation. Our parallel deduction engine can achieve a 5.7× speedup on a 36-core machine. In addition, by selecting only those critical implications, our strategy improves BMC by another 1.74× against the case where all extended implications were added to the BMC instance. Compared with the original BMC without any implication clauses, up to 55.32× speedup can be achieved.

In Cognitive Radio Networks, secondary users are affected by the the activity of primary users on licensed channels. To solve this problem, most routing protocols select the most stable links/channels, or use the minimum delay route and switch to other channels when primary users become active. However, these approaches may lead to competition between the secondary users on the most preferred links/channels which may cause congestion and affect the quality of the transmissions. In this paper, a multi-metric routing protocol with service differentiation is proposed. The main idea is to use different routing metrics for different traffic types to select routes that satisfy the specific requirements of each traffic type and to make load balancing across the network. Simulation results show that, compared to other related approaches, our protocol provides better end-to-end delay and increases the aggregate throughput. It also achieves load balancing across the network.

Current routing protocols for cognitive radio networks are severely affected by the frequent activity of primary users. Nodes that are in the interference range of an appearing primary user are not allowed to transmit, and therefore existing routes which utilize such nodes are obliged to undergo a route maintenance phase. This naturally provides other routes to the destination that may incur extra delay or increase packet queuing overhead. In this work, a novel route maintenance protocol is proposed that allows existing routes to endure the event of primary user presence by forming cooperative links between neighboring nodes and nulling out transmission at the primary receiver using cooperative beamforming. Our proposed protocol can be used in conjunction with any of the existing routing protocols, thus achieving modularity. Extensive simulations are done which prove that our proposed protocol outperforms existing route maintenance techniques in terms of end-to-end delay and loss ratio, with minimal incurred overhead.

Significant progress has been made in the past few years on Dynamic Spectrum Access (DSA) wireless networks which seek to use RF spectrum more efficiently and dynamically. For example, many measurements of current spectrum utilization are available and theoretical analyses and computational simulations of DSA networks abound. In sharp contrast, few network systems, particularly those with a decentralized structure, have been built even at a small scale to investigate the performance, behavior, and dynamics of DSA networks. Our contribution is designing a decentralized and asynchronous DSA network and building a prototype based on software radio technologies, signal detection and classification methods, distributed cooperative spectrum sensing systems, and mobile ad-hoc network (MANET) protocols. This paper details the network''s design and implementation as well as its enabling technologies. Through systematic experiments, we identify several factors influencing performance for decentralized DSA networks.

In this paper, we present the results of two simulation sets studying the effect of non-cooperation on the cognitive network performance. Simulations are conducted in the dynamic spectrum access ad-hoc cognitive network environment using the joint channel allocation-power control problem. The first simulations set studies the effect of mixing non-cognitive and cognitive nodes on the overall network performance while the second simulations set studies the effect of different selfish behaviors on the overall network performance. Simulation results and their analysis are provided.

This paper describes an architecture for a node in a cognitive network that employs distributed learning and reasoning. We present the architecture and describe a method of distributed reasoning using an island genetic algorithm. We then formulate a channel allocation problem that is unique to the use of cognitive radio networks for dynamic spectrum access. We provide simulation results for the island genetic algorithm as applied to our channel allocation problem.