Screening interacting factors in a wireless network testbed using locating arrays

Wireless systems exhibit a wide range of configurable parameters (factors), each with a number of values (levels), that may influence performance. Exhaustively analyzing all factor interactions is typically not feasible in experimental systems due to the large design space. We propose a method for determining which factors play a significant role in wireless network performance with multiple performance metrics (response variables). Such screening can be used to reduce the set of factors in subsequent experimental testing, whether for modelling or optimization. Our method accounts for pairwise interactions between the factors when deciding significance, because interactions play a significant role in real-world systems. We utilize locating arrays to design the experiment because they guarantee that each pairwise interaction impacts a distinct set of tests. We formulate the analysis as a problem in compressive sensing that we solve using a variation of orthogonal matching pursuit, together with statistical methods to determine which factors are significant. We evaluate the method using data collected from the w-iLab.t Zwijnaarde wireless network testbed and construct a new experiment based on the first analysis to validate the results. We find that the analysis exhibits robustness to noise and to missing data

Multi-objective optimization of cognitive radio networks

New generation networks, based on Cognitive Radio technology, allow dynamic allocation of the spectrum, alleviating spectrum scarcity. These networks also have a resilient potential for dynamic operation for energy saving. In this paper, we present a novel wireless network optimization algorithm for cognitive radio networks based on a cloud sharing-decision mechanism. Three Key Performance Indicators (KPIs) were optimized: spectrum usage, power consumption, and exposure. For a realistic suburban scenario in Ghent city, Belgium, we determine the optimal trade-off between the KPIs. Compared to a traditional Cognitive Radio network design, our optimization algorithm for the cloud-based architecture reduced the network power consumption by 27.5%, the average global exposure by 34.3%, and spectrum usage by 34.5% at the same time. Even for the worst-case optimization (worst achieved result of a single KPI), our solution performs better than the traditional architecture by 4.8% in terms of network power consumption, 7.3% in terms of spectrum usage, and 4.3% in terms of global exposure

An Experimental Approach for Developing RFID Ready Receiving and Shipping

Abstract: Radio Frequency Identification (RFID) and related technologies have been touted to allow exponential improvements in supply chain logistics and management. However, many industrial users have indicated that these technologies have not provided the anticipated benefits. The two complimentary strategies required to address the RFID reliability are: to improve the reliability of RFID technology and to design the supply chain infrastructure that enables RFID. The focus of this paper is on designing the supply chain infrastructure to enable RFID by developing guidelines for “RFID Ready Facilities”. These guidelines were developed based on a set of experiments conducted in the RFID supply chain laboratory. These guidelines were developed by using Design of Experiments (DOE) to determine the operational and facility factors that impact RFID reliability. The three different packaging strategies were tested on packages, boxes and their various combinations. The main factors considered in the experiments were the following among many others: Package Orientation (PO), Tag Placement (TP), Package Placement (PP), Reader Location (RL), Box Orientation (BO), Tag Placement on Box (TPB) and Tag Placement on Package (TPP). Based on the DOE results, general guidelines were developed for RFID packaging

Assessing the effectiveness of managed lane strategies for the rapid deployment of cooperative adaptive cruise control technology

Connected and Automated Vehicle (C/AV) technologies are fast expanding in the transportation and automotive markets. One of the highly researched examples of C/AV technologies is the Cooperative Adaptive Cruise Control (CACC) system, which exploits various vehicular sensors and vehicle-to-vehicle communication to automate vehicular longitudinal control. The operational strategies and network-level impacts of CACC have not been thoroughly discussed, especially in near-term deployment scenarios where Market Penetration Rate (MPR) is relatively low. Therefore, this study aims to assess CACC\u27s impacts with a combination of managed lane strategies to provide insights for CACC deployment. The proposed simulation framework incorporates 1) the Enhanced Intelligent Driver Model; 2) Nakagami-based radio propagation model; and 3) a multi-objective optimization (MOOP)-based CACC control algorithm. The operational impacts of CACC are assessed under four managed lane strategies (i.e., mixed traffic (UML), HOV (High Occupancy Vehicle)-CACC lane (MML), CACC dedicated lane (DL), and CACC dedicated lane with access control (DLA)). Simulation results show that the introduction of CACC, even with 10% MPR, is able to improve the network throughput by 7% in the absence of any managed lane strategies. The segment travel times for both CACC and non-CACC vehicles are reduced. The break-even point for implementing dedicated CACC lane is 30% MPR, below which the priority usage of the current HOV lane for CACC traffic is found to be more appropriate. It is also observed that DLA strategy is able to consistently increase the percentage of platooned CACC vehicles as MPR grows. The percentage of CACC vehicles within a platoon reaches 52% and 46% for DL and DLA, respectively. When it comes to the impact of vehicle-to-vehicle (V2V), it is found that DLA strategy provides more consistent transmission density in terms of median and variance when MPR reaches 20% or above. Moreover, the performance of the MOOP-based cooperative driving is examined. With average 75% likelihood of obtaining a feasible solution, the MOOP outperforms its counterpart which aims to minimize the headway objective solely. In UML, MML, and DL strategy, the proposed control algorithm achieves a balance spread among four objectives for each CACC vehicle. In the DLA strategy, however, the probability of obtaining feasible solution falls to 60% due to increasing size of platoon owing to DLA that constraints the feasible region by introduction more dimensions in the search space. In summary, UML or MML is the preferred managed lane strategy for improving traffic performance when MPR is less than 30%. When MRP reaches 30% or above, DL and DLA could improve the CACC performance by facilitating platoon formation. If available, priority access to an existing HOV lane can be adopted to encourage adaptation of CACC when CACC technology becomes publically available

Intrusion detection system for IoT networks for detection of DDoS attacks

PhD ThesisIn this thesis, a novel Intrusion Detection System (IDS) based on the hybridization of the Deep Learning (DL) technique and the Multi-objective Optimization method for the detection of Distributed Denial of Service (DDoS) attacks in Internet of Things (IoT) networks is proposed. IoT networks consist of different devices with unique hardware and software configurations communicating over different communication protocols, which produce huge multidimensional data that make IoT networks susceptible to cyber-attacks. The network IDS is a vital tool for protecting networks against threats and malicious attacks. Existing systems face significant challenges due to the continuous emergence of new and more sophisticated cyber threats that are not recognized by them, and therefore advanced IDS is required. This thesis focusses especially on the DDoS attack that is one of the cyber-attacks that has affected many IoT networks in recent times and had resulted in substantial devastating losses. A thorough literature review is conducted on DDoS attacks in the context of IoT networks, IDSs available especially for the IoT networks and the scope and applicability of DL methodology for the detection of cyber-attacks. This thesis includes three main contributions for 1) developing a feature selection algorithm for an IoT network fulfilling six important objectives, 2) designing four DL models for the detection of DDoS attacks and 3) proposing a novel IDS for IoT networks. In the proposed work, for developing advanced IDS, a Jumping Gene adapted NSGA-II multi-objective optimization algorithm for reducing the dimensionality of massive IoT data and Deep Learning model consisting of a Convolutional Neural Network (CNN) combined with Long Short-Term Memory (LSTM) for classification are employed. The experimentation is conducted using a High-Performance Computer (HPC) on the latest CISIDS2017 datasets for DDoS attacks and achieved an accuracy of 99.03 % with a 5-fold reduction in training time. The proposed method is compared with machine learning (ML) algorithms and other state-of-the-art methods, which confirms that the proposed method outperforms other approaches.Government of Indi

Topology Control Multi-Objective Optimisation in Wireless Sensor Networks: Connectivity-Based Range Assignment and Node Deployment

The distinguishing characteristic that sets topology control apart from other methods, whose motivation is to achieve effects of energy minimisation and an increased network capacity, is its network-wide perspective. In other words, local choices made at the node-level always have the goal in mind of achieving a certain global, network-wide property, while not excluding the possibility for consideration of more localised factors. As such, our approach is marked by being a centralised computation of the available location-based data and its reduction to a set of non-homogeneous transmitting range assignments, which elicit a certain network-wide property constituted as a whole, namely, strong connectedness and/or biconnectedness. As a means to effect, we propose a variety of GA which by design is multi-morphic, where dependent upon model parameters that can be dynamically set by the user, the algorithm, acting accordingly upon either single or multiple objective functions in response. In either case, leveraging the unique faculty of GAs for finding multiple optimal solutions in a single pass. Wherefore it is up to the designer to select the singular solution which best meets requirements. By means of simulation, we endeavour to establish its relative performance against an optimisation typifying a standard topology control technique in the literature in terms of the proportion of time the network exhibited the property of strong connectedness. As to which, an analysis of the results indicates that such is highly sensitive to factors of: the effective maximum transmitting range, node density, and mobility scenario under observation. We derive an estimate of the optimal constitution thereof taking into account the specific conditions within the domain of application in that of a WSN, thereby concluding that only GA optimising for the biconnected components in a network achieves the stated objective of a sustained connected status throughout the duration.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format

Cross-layer RaCM design for vertically integrated wireless networks

Includes bibliographical references (p. 70-74).Wireless local and metropolitan area network (WLAN/WMAN) technologies, more specifically IEEE 802.11 (or wireless fidelity, WiFi) and IEEE 802.16 (or wireless interoperability for microwave access, WiMAX), are well-suited to enterprise networking since wireless offers the advantages of rapid deployment in places that are difficult to wire. However, these networking standards are relatively young with respect to their traditional mature high-speed low-latency fixed-line networking counterparts. It is more challenging for the network provider to supply the necessary quality of service (QoS) to support the variety of existing multimedia services over wireless technology. Wireless communication is also unreliable in nature, making the provisioning of agreed QoS even more challenging. Considering the advantages and disadvantages, wireless networks prove well-suited to connecting rural areas to the Internet or as a networking solution for areas that are difficult to wire. The focus of this study specifically pertains to IEEE 802.16 and the part it plays in an IEEE vertically integrated wireless Internet (WIN): IEEE 802.16 is a wireless broadband backhaul technology, capable of connecting local area networks (LANs), wireless or fixed-line, to the Internet via a high-speed fixed-line link

New Swarm-Based Metaheuristics for Resource Allocation and Schwduling Problems

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingeniería Informática. Fecha de lectura : 10-07-2017Esta tesis tiene embargado el acceso al texto completo hasta el 10-01-201