Search

In this paper, we study the impact of computational complexity on the throughput limits of the fast Fourier transform (FFT) algorithm for orthogonal frequency division multiplexing (OFDM) waveforms. Based on the spectro-computational complexity (SC) analysis, we verify that the complexity of an $N$ -point FFT grows faster than the number of bits in the OFDM symbol. Thus, we show that FFT nullifies the OFDM throughput on $N$ unless the $N$ -point discrete Fourier transform (DFT) problem verifies as $\Omega (N)$ , which remains a “fascinating” open question in theoretical computer science. Also, because FFT demands $N$ to be a power of two $2^{i}$ ( $i>0$ ), the spectrum widening leads to an exponential complexity on $i$ , i.e. $O(2^{i}i)$ . To overcome these limitations, we consider the alternative frequency-time transform formulation of vector OFDM (V-OFDM), in which an $N$ -point FFT is replaced by $N/L$ ( $L > 0$ ) smaller $L$ -point FFTs to mitigate the cyclic prefix overhead of OFDM. Building on that, we replace FFT by the straightforward DFT algorithm to release the V-OFDM parameters from growing as powers of two and to benefit from flexible numerology (e.g., $L=3$ , $N=156$ ). Besides, by setting $L$ to $\Theta (1)$ , the resulting solution can run linearly on $N$ (rather than exponentially on $i$ ) while sustaining a non null throughput as $N$ grows.

Private data is transmitted and stored online every second. Therefore, security and privacy assurances should be provided at all times. However, that is not always the case. Private information is often unwillingly collected, sold, or exposed, depriving data owners of their rightful privacy. In this article, various privacy threats, concepts, regulations, and personal data types are analyzed. An overview of Privacy Enhancing Technologies (PETs) and a survey of anonymization mechanisms, privacy tools, models, and metrics are presented together with an analysis of respective characteristics and capabilities. Moreover, this article analyses the applicability of the reviewed privacy mechanisms on today's Cloud Services and identifies the current research challenges to achieve higher privacy levels in the Cloud.

The Cloud-Fog-Internet of Things continuum combines different paradigms to provide connectivity and ubiquity for end-users, while also granting low latency and low jitter to cope with different challenges, including the requirements of latency-sensitive applications, such as virtual/augmented reality and online gaming. This constitutes a complex and dynamic environment with heterogeneous resources that need to be managed or orchestrated, in order to accomplish application requirements for low latency. Common orchestration solutions make placement decisions based only on the resources of the underlying network and the application resource requests; however, using the profiles of applications to make placement decisions has the potential to enhance the final performance perceived by the end-users. This paper proposes the use of application profiles according to their popularity to guide their placement. To corroborate the effectiveness of the use of the profiles, two placement mechanisms are presented, one based on Genetic Algorithm and the other inspired on graph partitions. Simulation results show that it is possible to reduce the latency and jitter of applications via a service placement guided by the profiles. The mechanism based on graph partitions showed better results for all scenarios, followed closely by the Genetic Algorithm in the scenarios with lower load.

In this work, we present an optimal mapper for OFDM with index modulation (OFDM-IM). By optimal we mean the mapper achieves the lowest possible asymptotic computational complexity (CC) when the spectral efficiency (SE) gain over OFDM maximizes. We propose the spectro-computational (SC) analysis to capture the trade-off between CC and SE and to demonstrate that an N-subcarrier OFDM-IM mapper must run in exact Θ(N) time complexity. We show that an OFDM-IM mapper running faster than such complexity cannot reach the maximal SE whereas one running slower nullifies the mapping throughput for arbitrarily large N. We demonstrate our theoretical findings by implementing an open-source library that supports all DSP steps to map/demap an N-subcarrier complex frequency-domain OFDM-IM symbol. Our implementation supports different index selector algorithms and is the first to enable the SE maximization while preserving the same time and space asymptotic complexities of the classic OFDM mapper.

The Internet of Things paradigm enables a new set of smart end-user applications. The Cloud-Fog-Mist-Internet of Things infrastructure provides communication, compute, and storage support for these applications. However, this complex, heterogeneous, and distributed landscape requires orchestration and management mechanisms in order to guarantee their proper functioning. One particular factor to manage is the capacity to provide service resilience even in the presence of failures in components of the substrate infrastructure. This research proposes a set of mechanisms to formalize, orchestrate, and embed a batch of service requests for chained Virtual Functions to fulfill the specific requirements of applications while enhancing their availability and ultimately their resilience. In detail, this work introduces a formal grammar to describe customized Service Chains, allowing the definition of replicas for different Virtual Functions, and an Integer Linear Programming model for Virtual Function embedding that prioritizes the use of nodes with higher availability. Additionally, an alternative heuristic is presented to handle more complex scenarios by taking advantage of the multi-tier scenario comprising the Cloud-Fog-Mist-Internet of Things. Simulation results for the embedding mechanisms show that it is possible to increase the resilience of chained Virtual Functions, while balancing the load of the infrastructure nodes.

It is an established fact that the security of Industrial Automation and Control Systems (IACS) strongly depends on the robustness of the underlying supervisory control and data acquisition (SCADA) network protocols (among other factors). This becomes especially evident when considering the extent to which certain protocols, designed with poor or nonexistent security mechanisms, have led to a considerable number of past incident reports affecting critical infrastructures and essential services. Considering the current situation, it is rather obvious why the proper auditing and analysis of SCADA protocols are considered as key when it comes to design and/or protect IACS infrastructures. However, while the security of some protocols, such as Modbus or DNP3, has already been extensively analyzed, the same cannot be said for other protocols and technologies being used in the same domain that have not received the same amount of attention. In this paper, we provide a comprehensive security analysis of the PCOM SCADA protocol, including a dissection of PCOM, a demonstration of several attacks scenarios on PCOM-based systems, and also an analysis of possible mitigation strategies against these potential attacks. Moreover, this paper also describes a number of open-source tools that we developed for further analysis and research of PCOM security aspects, including a PCOM Wireshark dissector, a Nmap NSE PCOM scan, multiple Metasploit PCOM modules, a set of Snort PCOM rules, and several network traffic datasets containing multiple samples of different types of PCOM operations.

Abstract Recent developments in telecommunications have allowed drawing new paradigms, including the Internet of Everything, to provide services by the interconnection of different physical devices enabling the exchange of data to enrich and automate people’s daily activities; and Fog computing, which is an extension of the well-known Cloud computing, bringing tasks to the edge of the network exploiting characteristics such as lower latency, mobility support, and location awareness. Combining these paradigms opens a new set of possibilities for innovative services and applications; however, it also brings a new complex scenario that must be efficiently managed to properly fulfill the needs of the users. In this scenario, the Fog Orchestrator component is the key to coordinate the services in the middle of Cloud computing and Internet of Everything. In this paper, key challenges in the development of the Fog Orchestrator to support the Internet of Everything are identified, including how they affect the tasks that a Fog service Orchestrator should perform. Furthermore, different service Orchestrator architectures for the Fog are explored and analyzed in order to identify how the previously listed challenges are being tackled. Finally, a discussion about the open challenges, technological directions, and future of the research on this subject is presented.

Culprits' identification by the means of suspicious pattern detection techniques from mobile device data is one of the most important aims of the mobile forensic data analysis. When criminal activities are related to entirely automated procedures such as malware propagation, predicting the corresponding behavior is a rather achievable task. However, when human behavior is involved, such as in cases of traditional crimes, prediction and detection become more compelling. This paper introduces a combined criminal profiling and suspicious pattern detection methodology for two criminal activities with moderate to the heavy involvement of mobile devices, cyberbullying and low-level drug dealing. Neural and Neurofuzzy techniques are applied on a hybrid original and simulated dataset. The respective performance results are measured and presented, the optimal technique is selected, and the scenarios are re-run on an actual dataset for additional testing and verification.

IEEE 802.16 is yet a very recent technology and released hardware does frequently only support standards partially. The same applies to public available simulation tools, in particular for NS-2. As the latter is the de-facto standard in science and as we use it for our research in the context of the WEIRD project, we evaluate the IEEE 802.16 support for NS-2. We present several general but also specific issues, which areimportant in order to carry out reliable research based on thesetools. In particular, we show in much detail where modulesdeviate significantly and even fail totally.