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

Coverage Protocols for Wireless Sensor Networks: Review and Future Directions

The coverage problem in wireless sensor networks (WSNs) can be generally defined as a measure of how effectively a network field is monitored by its sensor nodes. This problem has attracted a lot of interest over the years and as a result, many coverage protocols were proposed. In this survey, we first propose a taxonomy for classifying coverage protocols in WSNs. Then, we classify the coverage protocols into three categories (i.e. coverage aware deployment protocols, sleep scheduling protocols for flat networks, and cluster-based sleep scheduling protocols) based on the network stage where the coverage is optimized. For each category, relevant protocols are thoroughly reviewed and classified based on the adopted coverage techniques. Finally, we discuss open issues (and recommend future directions to resolve them) associated with the design of realistic coverage protocols. Issues such as realistic sensing models, realistic energy consumption models, realistic connectivity models and sensor localization are covered

Bibliometric Analysis of Firefly Algorithm Applications in the Field of Wireless Sensor Networks

Wireless Sensor Network is a network of wireless sensor nodes that are capable of sensing information from their surroundings and transmit the sensed information to data collection point known as a base station. Applications of wireless sensor networks are large in number and forest fire detection, landslide monitoring, etc. are few applications to note. The research challenges in wireless sensor networks is the transmission of data from the sensor node to the base station in an energy-efficient manner and network life prolongation. Cluster-based routing techniques are extensively adopted to address this research challenge. Researchers have used different metaheuristic and soft computing techniques for designing such energy-efficient routing techniques. In the literature, a lot of survey article on cluster-based routing methods are available, but there is no bibliometric analysis conducted so far. Hence in this research article, bibliometric study with the focus on the firefly algorithm and its applications in wireless sensor network is undertaken. The purpose of this article is to explore the nature of research conducted concerning to authors, the connection between keywords, the importance of journals and scope for further research in soft computing based clustered routing methods. A detailed bibliometric analysis is carried out by collecting the details of published articles from the Scopus database. In this article, the collected data is articulated in terms of yearly document statistics, key affiliations of authors, contributing geographical locations, subject area statistics, author-keyword mapping, and many more essential aspects of bibliometric analysis. The conducted study helped in understanding that there is a vast scope for the research community to perform research work concerning firefly algorithm applications in the field of wireless sensor networks

Anchor-Free Localization Method for Mobile Targets in Coal Mine Wireless Sensor Networks

Severe natural conditions and complex terrain make it difficult to apply precise localization in underground mines. In this paper, an anchor-free localization method for mobile targets is proposed based on non-metric multi-dimensional scaling (Multi-dimensional Scaling: MDS) and rank sequence. Firstly, a coal mine wireless sensor network is constructed in underground mines based on the ZigBee technology. Then a non-metric MDS algorithm is imported to estimate the reference nodes’ location. Finally, an improved sequence-based localization algorithm is presented to complete precise localization for mobile targets. The proposed method is tested through simulations with 100 nodes, outdoor experiments with 15 ZigBee physical nodes, and the experiments in the mine gas explosion laboratory with 12 ZigBee nodes. Experimental results show that our method has better localization accuracy and is more robust in underground mines

EECLA: A Novel Clustering Model for Improvement of Localization and Energy Efficient Routing Protocols in Vehicle Tracking Using Wireless Sensor Networks

Due to increase of usage of wireless sensor networks (WSN) for various purposes leads to a required technology in the present world. Many applications are running with the concepts of WSN now, among that vehicle tracking is one which became prominent in security purposes. In our previous works we proposed an algorithm called EECAL (Energy Efficient Clustering Algorithm and Localization) to improve accuracy and performed well. But are not focused more on continuous tracking of a vehicle in better aspects. In this paper we proposed and refined the same algorithm as per the requirement. Detection and tracking of a vehicle when they are in larges areas is an issue. We mainly focused on proximity graphs and spatial interpolation techniques for getting exact boundaries. Other aspect of our work is to reduce consumption of energy which increases the life time of the network. Performance of system when in active state is another issue can be fixed by setting of peer nodes in communication. We made an attempt to compare our results with the existed works and felt much better our work. For handling localization, we used genetic algorithm which handled good of residual energy, fitness of the network in various aspects. At end we performed a simulation task that proved proposed algorithms performed well and experimental analysis gave us faith by getting less localization error factor

Applied (Meta)-Heuristic in Intelligent Systems

Engineering and business problems are becoming increasingly difficult to solve due to the new economics triggered by big data, artificial intelligence, and the internet of things. Exact algorithms and heuristics are insufficient for solving such large and unstructured problems; instead, metaheuristic algorithms have emerged as the prevailing methods. A generic metaheuristic framework guides the course of search trajectories beyond local optimality, thus overcoming the limitations of traditional computation methods. The application of modern metaheuristics ranges from unmanned aerial and ground surface vehicles, unmanned factories, resource-constrained production, and humanoids to green logistics, renewable energy, circular economy, agricultural technology, environmental protection, finance technology, and the entertainment industry. This Special Issue presents high-quality papers proposing modern metaheuristics in intelligent systems

Initial Phase Proximity for Reachback Firefly Synchronicity in WSNs: Node Clustering

Synchronicity is one of the essential basic services to support the main duties of Wireless Sensor Networks (WSNs). Synchronicity is the ability to arrange simultaneously collective actions in WSNs. A high-rate data sampling to analyze the seismic structure and volcanic monitoring is the important applications requiring a synchronicity. However, most of the existing synchronicity algorithm is still executed in a flat network, so that it requires a long time to achieve a synchronous condition. To increase the convergence rate, the synchronicity can be executed concurrently through a clustering scheme approach. In this work, the such scheme is called as the Node Clustering based on Initial Phase Proximity for Reachback Firefly Synchronicity (NCIPP-RFS). The NCIPP-RFS solves in three steps: (1) constructing the node clustering, (2) intra-cluster synchronicity, and (3) inter-cluster synchronicity. The NCIPP-RFS method is based on the firefly-inspired algorithm. The fireflies are a species in the natural system, which are able to manage their flashing for synchronicity in a distributed manner. The NCIPP-RFS was implemented in NS-3 and evaluated and compared with Reachback Firefly Algorithm (RFA). The simulation results show a significant increase in the convergence rate. The NCIPP-RFS can reach a convergence time shorter than the RFA. In addition, the NCIPP-RFS was compared in the various numbers of clusters, where the least number of clusters can reach the fastest convergence rate. Finally, it can also contribute significantly to the increase of the convergence rate if the number of nodes is greater than or equal to 50 nodes

EERP: Intelligent Cluster based Energy Enhanced Routing Protocol Design over Wireless Sensor Network Environment

Wireless Sensor Network (WSN)) and the associated technologies are growing day-by-day in a drastic level. The Wireless Sensor Network medium has a distributed communication logic, in which it is interconnected with set of wireless sensor nodes and a unique basestation. A basestation stays in a constant place to provide a support to the transceivers for achieving a successful communication between source and destination entities. This kind of wireless communication mediums highly depends on the basestation to acquire the transaction needs as well as the basestation acts as a gateway between transmitter and receiver units. The cluster based wireless communication models are introduced to provide a flaw free communication between entities on WSN region with handling of wireless sensor nodes in the form of cluster. In literature several cluster enabled wireless communication models are designed, but all are strucked up with improper node placements and associated energy level mismatching. These issues raise cost efficient problems in Wireless Sensor Network environment. SO, that a new energy efficient routing protocol with an effective communication strategy is required to solve such issues in past. This paper introduced a new routing protocol with high efficient data transmission norms, in which it is called as Energy Enhanced Routing Protocol (eeRP). The proposed approach of eeRP associates the powerful clustering logic in this scheme to provide a fault free communication model to the WSN environment. By using this approach the standardized routing model is constructed with respect to the sensor nodes and basestation. The most important part of cluster based wireless communication model is the handling of Cluster-Head (CH), in which it needs to be elected based on certain communication principles such as the estimation of distance, position of other nodes in the cluster region, basestation positioning and the node capability. These constraints are essential to analyze the Cluster-Head to improve the pathway estimation process. The proposed approach of eeRP utilizes the powerful CH election algorithm called Firefly to provide an intellectual cluster head election process. The performance level of the proposed approach eeRP is estimated based on the efficiency of throughput, path selection efficiency, reduced energy consumption ratio and the network lifetime improvement. The experimental results assure these metrics in resulting section with graphical proofs

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 parallel implementation on a multi-core architecture of a dynamic programming algorithm applied in cognitive radio ad hoc networks

Spectral resources allocation is a major problem in cognitive radio ad hoc networks and currently most of the research papers use meta-heuristics to solve it. On the other side, the term parallelism refers to techniques to make programs faster by performing several computations in parallel. Parallelism would be very interesting to increase the performance of real-time systems, especially for the cognitive radio ad hoc networks that interest us in this work. In this paper, we present a parallel implementation on a multi-core architecture of dynamic programming algorithm applied in cognitive radio ad hoc networks. Our simulations approve the desired results, showing significant gain in terms of execution time. The main objective is to allow a cognitive engine to use an exact method and to have better results compared to the use of meta-heuristics