Differential Evolution-based 3D Directional Wireless Sensor Network Deployment Optimization

Wireless sensor networks (WSNs) are applied more and more widely in real life. In actual scenarios, 3D directional wireless sensors (DWSs) are constantly employed, thus, research on the real-time deployment optimization problem of 3D directional wireless sensor networks (DWSNs) based on terrain big data has more practical significance. Based on this, we study the deployment optimization problem of DWSNs in the 3D terrain through comprehensive consideration of coverage, lifetime, connectivity of sensor nodes, connectivity of cluster headers and reliability of DWSNs. We propose a modified differential evolution (DE) algorithm by adopting CR-sort and polynomial-based mutation on the basis of the cooperative coevolutionary (CC) framework, and apply it to address deployment problem of 3D DWSNs. In addition, to reduce computation time, we realize implementation of message passing interface (MPI) parallelism. As is revealed by the experimentation results, the modified algorithm proposed in this paper achieves satisfying performance with respect to either optimization results or operation time

A Densely-Deployed, High Sampling Rate, Open-Source Air Pollution Monitoring WSN

Air quality, especially particulate matter, has recently attracted a lot of attention from governments, industry, and academia, motivating the use of denser air quality monitoring networks based on low-cost sensing strategies. However, low-cost sensors are frequently sensitive to aging, environmental conditions, and pollutant cross-sensitivities. These issues have been only partially addressed, limiting their usage. In this study, we develop a low-cost particulate matter monitoring system based on special-purpose acquisition boards, deployed for monitoring air quality on both stationary and mobile sensor platforms. We explore the influence of all model variables, the quality of different calibration strategies, the accuracy across different concentration ranges, and the usefulness of redundant sensors placed in each station. The collected sensor data amounts to about 50GB of data, gathered in six months during the winter season. Tests of statically immovable stations include an analysis of accuracy and sensors’ reliability made by comparing our results with more accurate and expensive standard β radiation sensors. Tests on mobile stations have been designed to analyze the reactivity of our system to unexpected and abrupt events. These experiments embrace traffic analysis, pollution investigation using different means of transport and pollution analysis during peculiar events. With respect to other approaches, our methodology has been proved to be extremely easy to calibrate, to offer a very high sample rate (one sample per second), and to be based on an open-source software architecture. Database and software are available as open source in [1]

Introductory Review of Swarm Intelligence Techniques

With the rapid upliftment of technology, there has emerged a dire need to fine-tune or optimize certain processes, software, models or structures, with utmost accuracy and efficiency. Optimization algorithms are preferred over other methods of optimization through experimentation or simulation, for their generic problem-solving abilities and promising efficacy with the least human intervention. In recent times, the inducement of natural phenomena into algorithm design has immensely triggered the efficiency of optimization process for even complex multi-dimensional, non-continuous, non-differentiable and noisy problem search spaces. This chapter deals with the Swarm intelligence (SI) based algorithms or Swarm Optimization Algorithms, which are a subset of the greater Nature Inspired Optimization Algorithms (NIOAs). Swarm intelligence involves the collective study of individuals and their mutual interactions leading to intelligent behavior of the swarm. The chapter presents various population-based SI algorithms, their fundamental structures along with their mathematical models.Comment: Submitted to Springe

AN IMPROVED PARTICLE SWARM OPTIMIZATION ALGORITHM FOR SPECTRUM ALLOCATION IN COGNITIVE RADIO NETWORKS

The seriousness of the spectrum scarcity has increased dramatically due to the rapid increase of wireless services. The key enabling technology that can be viewed as a novel approach for utilizing the spectrum more efficiently is known as Cognitive Radio. Therefore, assigning the spectrum opportunistically to the unlicensed users without interfering with the licensed users, concurrently with maximizing the spectrum utilization is addressed as a major challenge problem in cognitive radio networks. In this paper, an improved metaheuristic optimization algorithm has been proposed to solve this problem that contingent on a graph coloring model. The proposed approach is a hybrid algorithm composed of a Particle Swarm Optimization algorithm with Random Neighborhood Search. The key objective function is maximizing the spectrum utilization in the cognitive radio networks with the subjected constraints. MATLAB R2021a was used for conducting the simulation. The proposed hybrid algorithm improved the system utilization by 1.23% compared to Particle Swarm Optimization algorithm, 5.57% compared to Random Neighborhood Search, 7.9% compared to Color Sensitive Graph Coloring algorithm, and 27.33% compared to Greedy algorithm. Moreover, the system performance was evaluated with various deployment scenarios of the primary users, secondary users, and channels for investigating the impact of varying these parameters on the system performance

Location Privacy for Mobile Crowd Sensing through Population Mapping

Opportunistic sensing allows applications to “task” mobile devices to measure context in a target region. For example, one could leverage sensor-equipped vehicles to measure traffic or pollution levels on a particular street or users\u27 mobile phones to locate (Bluetooth-enabled) objects in their vicinity. In most proposed applications, context reports include the time and location of the event, putting the privacy of users at increased risk: even if identifying information has been removed from a report, the accompanying time and location can reveal sufficient information to de-anonymize the user whose device sent the report. We propose and evaluate a novel spatiotemporal blurring mechanism based on tessellation and clustering to protect users\u27 privacy against the system while reporting context. Our technique employs a notion of probabilistic k-anonymity; it allows users to perform local blurring of reports efficiently without an online anonymization server before the data are sent to the system. The proposed scheme can control the degree of certainty in location privacy and the quality of reports through a system parameter. We outline the architecture and security properties of our approach and evaluate our tessellation and clustering algorithm against real mobility traces

Differential Evolution in Wireless Communications: A Review

Differential Evolution (DE) is an evolutionary computational method inspired by the biological processes of evolution and mutation. DE has been applied in numerous scientific fields. The paper presents a literature review of DE and its application in wireless communication. The detailed history, characteristics, strengths, variants and weaknesses of DE were presented. Seven broad areas were identified as different domains of application of DE in wireless communications. It was observed that coverage area maximisation and energy consumption minimisation are the two major areas where DE is applied. Others areas are quality of service, updating mechanism where candidate positions learn from a large diversified search region, security and related field applications. Problems in wireless communications are often modelled as multiobjective optimisation which can easily be tackled by the use of DE or hybrid of DE with other algorithms. Different research areas can be explored and DE will continue to be utilized in this contex

A Novel Computer Vision-Based Framework For Supervised Classification Of Energy Outbreak Phenomena

Today, there is a need to implement a proper design of an adequate surveillance system that detects and categorizes explosion phenomena in order to identify the explosion risk to reduce its impact through mitigation and preparedness. This dissertation introduces state-of-the-art classification of explosion phenomena through pattern recognition techniques on color images. Consequently, we present a novel taxonomy for explosion phenomena. In particular, we demonstrate different aspects of volcanic eruptions and nuclear explosions of the proposed taxonomy that include scientific formation, real examples, existing monitoring methodologies, and their limitations. In addition, we propose a novel framework designed to categorize explosion phenomena against non-explosion phenomena. Moreover, a new dataset, Volcanic and Nuclear Explosions (VNEX), was collected. The totality of VNEX is 10, 654 samples, and it includes the following patterns: pyroclastic density currents, lava fountains, lava and tephra fallout, nuclear explosions, wildfires, fireworks, and sky clouds. In order to achieve high reliability in the proposed explosion classification framework, we propose to employ various feature extraction approaches. Thus, we calculated the intensity levels to extract the texture features. Moreover, we utilize the YCbCr color model to calculate the amplitude features. We also employ the Radix-2 Fast Fourier Transform to compute the frequency features. Furthermore, we use the uniform local binary patterns technique to compute the histogram features. Additionally, these discriminative features were combined into a single input vector that provides valuable insight of the images, and then fed into the following classification techniques: Euclidian distance, correlation, k-nearest neighbors, one-against-one multiclass support vector machines with different kernels, and the multilayer perceptron model. Evaluation results show the design of the proposed framework is effective and robust. Furthermore, a trade-off between the computation time and the classification rate was achieved

3D deployment optimization for Heterogeneous Wireless Directional Sensor Networks on Smart City

The development of smart cities and the emergence of 3D urban terrain data have introduced new requirements and issues to the research of wireless sensor network (WSN) 3D deployment. In this paper, we study the deployment issue of heterogeneous wireless directional sensor networks (HWDSNs) on 3D smart cities. Traditionally, studies about the deployment problem of WSNs show solicitude for omni-directional sensors on 2D plane or in 3D full space. As WSNs exist in complex 3D environments and directional sensors are emerging, the work of this paper will have more practical significance. Based on the 3D urban terrain data, we transform the deployment problem into a multiobjective optimization problem (MOP), in which objectives of Coverage, Connectivity Quality and Lifetime, as well as the Connectivity and Reliability constraints are simultaneously given close attention to. A graph-based 3D signal propagation model employing the line-of-sight (LOS) concept is used to calculate the signal path loss. The novel distributed parallel multiobjective evolutionary algorithms (MOEAs) are proposed. For verification, a real-world and an artificial urban terrains are utilized. Compared with other state-of-the-art MOEAs, the novel algorithms address the deployment problem more effectively and more efficiently in terms of optimizatio

A Tutorial on Clique Problems in Communications and Signal Processing

Since its first use by Euler on the problem of the seven bridges of K\"onigsberg, graph theory has shown excellent abilities in solving and unveiling the properties of multiple discrete optimization problems. The study of the structure of some integer programs reveals equivalence with graph theory problems making a large body of the literature readily available for solving and characterizing the complexity of these problems. This tutorial presents a framework for utilizing a particular graph theory problem, known as the clique problem, for solving communications and signal processing problems. In particular, the paper aims to illustrate the structural properties of integer programs that can be formulated as clique problems through multiple examples in communications and signal processing. To that end, the first part of the tutorial provides various optimal and heuristic solutions for the maximum clique, maximum weight clique, and $k$-clique problems. The tutorial, further, illustrates the use of the clique formulation through numerous contemporary examples in communications and signal processing, mainly in maximum access for non-orthogonal multiple access networks, throughput maximization using index and instantly decodable network coding, collision-free radio frequency identification networks, and resource allocation in cloud-radio access networks. Finally, the tutorial sheds light on the recent advances of such applications, and provides technical insights on ways of dealing with mixed discrete-continuous optimization problems

Try Living in the Real World: the importance of experimental radar systems and data collection trials

While simulations of increasingly high fidelity are an important tool in radar science, experimentation is still needed as a source of validation for simulation, to explore complex phenomena which cannot be accurately simulated and ultimately in turning theory and simulation into a real world system with real world applications. Experimental systems can range from laboratory based, installations on the ground with limited fields of view all the way up to flying demonstrators which may be prototypes for radar products. In this paper we will discuss the importance of experimentation in the development of radar science and radar products with examples of systems used by a sub-set of the members of the UK EMSIG