Nature Inspired Range Based Wireless Sensor Node Localization Algorithms

Localization is one of the most important factors highly desirable for the performance of Wireless Sensor Network (WSN). Localization can be stated as the estimation of the location of the sensor nodes in sensor network. In the applications of WSN, the data gathered at sink node will be meaningless without localization information of the nodes. Due to size and complexity factors of the localization problem, it can be formulated as an optimization problem and thus can be approached with optimization algorithms. In this paper, the nature inspired algorithms are used and analyzed for an optimal estimation of the location of sensor nodes. The performance of the nature inspired algorithms viz. Flower pollination algorithm (FPA), Firefly algorithm (FA), Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) for localization in WSN is analyzed in terms of localization accuracy, number of localized nodes and computing time. The comparative analysis has shown that FPA is more proficient in determining the coordinates of nodes by minimizing the localization error as compared to FA, PSO and GWO

An Evaluation of Performance Enhancements to Particle Swarm Optimisation on Real-World Data

Swarm Computation is a relatively new optimisation paradigm. The basic premise is to model the collective behaviour of self-organised natural phenomena such as swarms, flocks and shoals, in order to solve optimisation problems. Particle Swarm Optimisation (PSO) is a type of swarm computation inspired by bird flocks or swarms of bees by modelling their collective social influence as they search for optimal solutions. In many real-world applications of PSO, the algorithm is used as a data pre-processor for a neural network or similar post processing system, and is often extensively modified to suit the application. The thesis introduces techniques that allow unmodified PSO to be applied successfully to a range of problems, specifically three extensions to the basic PSO algorithm: solving optimisation problems by training a hyperspatial matrix, using a hierarchy of swarms to coordinate optimisation on several data sets simultaneously, and dynamic neighbourhood selection in swarms. Rather than working directly with candidate solutions to an optimisation problem, the PSO algorithm is adapted to train a matrix of weights, to produce a solution to the problem from the inputs. The search space is abstracted from the problem data. A single PSO swarm optimises a single data set and has difficulties where the data set comprises disjoint parts (such as time series data for different days). To address this problem, we introduce a hierarchy of swarms, where each child swarm optimises one section of the data set whose gbest particle is a member of the swarm above in the hierarchy. The parent swarm(s) coordinate their children and encourage more exploration of the solution space. We show that hierarchical swarms of this type perform better than single swarm PSO optimisers on the disjoint data sets used. PSO relies on interaction between particles within a neighbourhood to find good solutions. In many PSO variants, possible interactions are arbitrary and fixed on initialisation. Our third contribution is a dynamic neighbourhood selection: particles can modify their neighbourhood, based on the success of the candidate neighbour particle. As PSO is intended to reflect the social interaction of agents, this change significantly increases the ability of the swarm to find optimal solutions. Applied to real-world medical and cosmological data, this modification is and shows improvements over standard PSO approaches with fixed neighbourhoods

Green Communication for Sixth-Generation Intent-Based Networks:An Architecture Based on Hybrid Computational Intelligence Algorithm

The sixth-generation (6G) is envisioned as a pivotal technology that will support the ubiquitous seamless connectivity of substantial networks. The main advantage of 6G technology is leveraging Artificial Intelligence (AI) techniques for handling its interoperable functions. The pairing of 6G networks and AI creates new needs for infrastructure, data preparation, and governance. Thus, Intent-Based Network (IBN) architecture is a key infrastructure for 6G technology. Usually, these networks are formed of several clusters for data gathering from various heterogeneities in devices. Therefore, an important problem is to find the minimum transmission power for each node in the network clusters. This paper presents hybridization between two Computational Intelligence (CI) algorithms called the Marine Predator Algorithm and the Generalized Normal Distribution Optimization (MPGND). The proposed algorithm is applied to save power consumption which is an important problem in sustainable green 6G-IBN. MPGND is compared with several recently proposed algorithms, including Augmented Grey Wolf Optimizer (AGWO), Sine Tree-Seed Algorithm (STSA), Archimedes Optimization Algorithm (AOA), and Student Psychology-Based Optimization (SPBO). The experimental results with the statistical analysis demonstrate the merits and highly competitive performance of the proposed algorithm

Cache-Aided Delivery Networks with Correlated Content in a Shared Cache Framework

Internet traffic is growing exponentially due to the penetration of powerful internet-connected devices and cutting-edge technologies. Additionally, the rise in internet usage has coincided with a shift in the nature of data traffic from voice-based to content-based usage, putting significant stress on delivery networks. Despite the infrastructural advancements in communication networks over the past few years, content delivery networks (CDNs) still face challenges in keeping up with the high delivery data rates and suffer from the imbalanced network load between off-peak hours and peak hours. In this regard, content caching has emerged as an efficient technique to combat the high delivery date rates and maintain a balanced network load while improving the quality of services (QoS) by storing some popular content close to the end users. Caching networks operate in two phases; the placement phase during off-peak hours before users reveal their demands and the delivery phase, which is accomplished when users’ demands are revealed to the server during peak hours. As the server is unaware of the demands during the placement phase, this phase must be designed carefully to minimize the delivery rate regardless of the requested content during peak hours. This dissertation studies cache-aided delivery networks with correlated content in a shared cache framework. A shared cache framework is beneficial in the current and next-generation wireless networks as it provides a local cache to all users within small base stations (SBSs), relieving strain on the backhaul. Furthermore, the library of a caching network could consist of content with a high degree of similarity in many practical applications; Therefore, exploiting the similarity among library content can also be leveraged to reduce the delivery rate in such networks. In this dissertation, we look at the proposed caching network from an information-theoretic perspective and formulate it as a distributed source coding problem with side information at the decoder. Then, the critical question arises as to what should be cached as side information to reduce the delivery rate of the network efficiently. To answer this question, we propose an automatic clustering scheme using artificial intelligence (AI)-based optimization techniques to identify the selected side information for the entire library. We comprehensively evaluate the performance of the general clustering framework in a separate chapter by considering different datasets and distance measures. The general clustering framework enables us to develop two novel clustering schemes as a part of the placement phase of the proposed caching networks under different settings throughout this study, considering both the similarity and popularity of the library content. Upon identifying the selected side information for such networks, the next question that should be answered is how we should place the side information into caches; And consequently, what is the delivery strategy for this placement scheme? We have furnished our answer to these questions by considering three different caching networks: first, a network in a single shared cache framework under lossy caching. Next is a network with multiple shared caches under uniform popularity, and finally, a network with multiple shared caches under non-uniform preferences. In such networks, we address the placement and delivery strategy to show the trade-off between the delivery rate and the memory size of the system. We calculate the peak and expected rates of the studied networks by considering the rate-distortion function and caching strategy. We also introduce the optimum library partitioning formulated to minimize the peak delivery rate in the system. The performance analysis and extensive simulations of the proposed solution confirm that our scheme provides a considerable boost in network efficiency compared to legacy caching schemes due to our problem formulation and the careful extraction of side information during the placement phase

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms

A GPS-Free Flocking Model for Aerial Mesh Deployments in Disaster-Recovery Scenarios

n/

Reliable cost-optimal deployment of wireless sensor networks

Wireless Sensor Networks (WSNs) technology is currently considered one of the key technologies for realizing the Internet of Things (IoT). Many of the important WSNs applications are critical in nature such that the failure of the WSN to carry out its required tasks can have serious detrimental effects. Consequently, guaranteeing that the WSN functions satisfactorily during its intended mission time, i.e. the WSN is reliable, is one of the fundamental requirements of the network deployment strategy. Achieving this requirement at a minimum deployment cost is particularly important for critical applications in which deployed SNs are equipped with expensive hardware. However, WSN reliability, defined in the traditional sense, especially in conjunction with minimizing the deployment cost, has not been considered as a deployment requirement in existing WSN deployment algorithms to the best of our knowledge. Addressing this major limitation is the central focus of this dissertation. We define the reliable cost-optimal WSN deployment as the one that has minimum deployment cost with a reliability level that meets or exceeds a minimum level specified by the targeted application. We coin the problem of finding such deployments, for a given set of application-specific parameters, the Minimum-Cost Reliability-Constrained Sensor Node Deployment Problem (MCRC-SDP). To accomplish the aim of the dissertation, we propose a novel WSN reliability metric which adopts a more accurate SN model than the model used in the existing metrics. The proposed reliability metric is used to formulate the MCRC-SDP as a constrained combinatorial optimization problem which we prove to be NP-Complete. Two heuristic WSN deployment optimization algorithms are then developed to find high quality solutions for the MCRC-SDP. Finally, we investigate the practical realization of the techniques that we developed as solutions of the MCRC-SDP. For this purpose, we discuss why existing WSN Topology Control Protocols (TCPs) are not suitable for managing such reliable cost-optimal deployments. Accordingly, we propose a practical TCP that is suitable for managing the sleep/active cycles of the redundant SNs in such deployments. Experimental results suggest that the proposed TCP\u27s overhead and network Time To Repair (TTR) are relatively low which demonstrates the applicability of our proposed deployment solution in practice

A fuzzified systematic adjustment of the robotic Darwinian PSO

The Darwinian Particle Swarm Optimization (DPSO) is an evolutionary algorithm that extends the Particle Swarm Optimization using natural selection to enhance the ability to escape from sub-optimal solutions. An extension of the DPSO to multi-robot applications has been recently proposed and denoted as Robotic Darwinian PSO (RDPSO), benefiting from the dynamical partitioning of the whole population of robots, hence decreasing the amount of required information exchange among robots. This paper further extends the previously proposed algorithm adapting the behavior of robots based on a set of context-based evaluation metrics. Those metrics are then used as inputs of a fuzzy system so as to systematically adjust the RDPSO parameters (i.e., outputs of the fuzzy system), thus improving its convergence rate, susceptibility to obstacles and communication constraints. The adapted RDPSO is evaluated in groups of physical robots, being further explored using larger populations of simulated mobile robots within a larger scenario