Preface to the Princeton Landmarks in Biology Edition vii Preface xi Symbols Used xiii 1. The Importance of Islands 3 2. Area and Number of Speicies 8 3. Further Explanations of the Area-Diversity Pattern 19 4. The Strategy of Colonization 68 5. Invasibility and the Variable Niche 94 6. Stepping Stones and Biotic Exchange 123 7. Evolutionary Changes Following Colonization 145 8. Prospect 181 Glossary 185 References 193 Index 201

The Theory of Island Biogeography

The distributed nature and dynamic topology of Wireless Sensor Networks (WSNs) introduces very special requirements in routing protocols that should be met. The most important feature of a routing protocol, in order to be efficient for WSNs, is the energy consumption and the extension of the network's lifetime. During the recent years, many energy efficient routing protocols have been proposed for WSNs. In this paper, energy efficient routing protocols are classified into four main schemes: Network Structure, Communication Model, Topology Based and Reliable Routing. The routing protocols belonging to the first category can be further classified as flat or hierarchical. The routing protocols belonging to the second category can be further classified as Query-based or Coherent and non-coherent-based or Negotiation-based. The routing protocols belonging to the third category can be further classified as Location-based or Mobile Agent-based. The routing protocols belonging to the fourth category can be further classified as QoS-based or Multipath-based. Then, an analytical survey on energy efficient routing protocols for WSNs is provided. In this paper, the classification initially proposed by Al-Karaki, is expanded, in order to enhance all the proposed papers since 2004 and to better describe which issues/operations in each protocol illustrate/enhance the energy-efficiency issues.

Energy-Efficient Routing Protocols in Wireless Sensor Networks: A Survey

Wireless sensor network (WSN) brings a new paradigm of real-time embedded systems with limited computation, communication, memory, and energy resources that are being used for huge range of applications where the traditional infrastructure-based network is mostly infeasible. The sensor nodes are densely deployed in a hostile environment to monitor, detect, and analyze the physical phenomenon and consume considerable amount of energy while transmitting the information. It is impractical and sometimes impossible to replace the battery and to maintain longer network life time. So, there is a limitation on the lifetime of the battery power and energy conservation is a challenging issue. Appropriate cluster head (CH) election is one such issue, which can reduce the energy consumption dramatically. Low energy adaptive clustering hierarchy (LEACH) is the most famous hierarchical routing protocol, where the CH is elected in rotation basis based on a probabilistic threshold value and only CHs are allowed to send the information to the base station (BS). But in this approach, a super-CH (SCH) is elected among the CHs who can only send the information to the mobile BS by choosing suitable fuzzy descriptors, such as remaining battery power, mobility of BS, and centrality of the clusters. Fuzzy inference engine (Mamdani’s rule) is used to elect the chance to be the SCH. The results have been derived from NS-2 simulator and show that the proposed protocol performs better than the LEACH protocol in terms of the first node dies, half node alive, better stability, and better lifetime.

A Fuzzy Logic-Based Clustering Algorithm for WSN to Extend the Network Lifetime

Clustering Objectives in Wireless Sensor Networks: A Survey and Research Direction Analysis

Lifetime enhancement has always been a crucial issue as most of the wireless sensor networks (WSNs) operate in unattended environment where human access and monitoring are practically infeasible Clustering is one of the most powerful techniques that can arrange the system operation in associated manner to attend the network scalability, minimize energy consumption, and achieve prolonged network lifetime To conquer this issue, current researchers have triggered the proposition of many numerous clustering algorithms However, most of the proposed algorithms overburden the cluster head (CH) during cluster formation To overcome this problem, many researchers have come up with the idea of fuzzy logic (FL), which is applied in WSN for decision making These algorithms focus on the efficiency of CH, which could be adoptive, flexible, and intelligent enough to distribute the load among the sensor nodes that can enhance the network lifetime But unfortunately, most of the algorithms use type-1 FL (T1FL) model In this paper, we propose a clustering algorithm on the basis of interval type-2 FL model, expecting to handle uncertain level decision better than T1FL model

Energy Efficient Clustering Algorithm for Multi-Hop Wireless Sensor Network Using Type-2 Fuzzy Logic

Wireless sensor networks are widely used in monitoring and managing environmental factors like air quality, humidity, temperature, and pressure. The recent works show that clustering is an effective technique for increasing energy efficiency, traffic load balancing, prolonging the lifetime of the network and scalability of the sensor network. In this paper, a new energy-efficient clustering technique has been proposed based on a genetic algorithm with the newly defined objective function. The proposed clustering method modifies the steady-state phase of the LEACH protocol in a heterogeneous environment. The proposed objective function considers three main clustering parameters such as compactness, separation, and number of cluster heads for optimization. The simulation result shows that the proposed protocol is more effective in improving the performance of wireless sensor networks as compared to other state-of-the-art methods, namely SEP, IHCR, and ERP.

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EEWC: energy-efficient weighted clustering method based on genetic algorithm for HWSNs

Gravitational search algorithm is a popular adaptive search algorithm among nature-inspired algorithms and has been successfully used for optimizing many real-world problems. Gravitational search algorithm uses the law of Newton gravity for finding the optimal solution. The performance of gravitational search algorithm is controlled by exploration and exploitation capabilities and Kbest is one of its parameters that controls this trade-off. In this paper, a novel chaotic Kbest gravitational search algorithm has been proposed that uses the chaotic model in Kbest to balance the exploration and exploitation non-linearly. The proposed algorithm shows better convergence rate at later iterations with high precision and does not trap into local optima. The experimental results validate that the proposed algorithm outperforms.

Chaotic Kbest gravitational search algorithm (CKGSA)

Gravitational search algorithm is a nature-inspired algorithm based on the mathematical modelling of the Newton’s law of gravity and motion. In a decade, researchers have presented many variants of gravitational search algorithm by modifying its parameters to efficiently solve complex optimization problems. This paper conducts a comparative analysis among ten variants of gravitational search algorithm which modify three parameters, namely Kbest, velocity, and position. Experiments are conducted on two sets of benchmark categories, namely standard functions and CEC2015 functions, including problems belonging to different categories such as unimodal, multimodal, and unconstrained optimization functions. The performance comparison is evaluated and statistically validated in terms of mean fitness value and convergence graph. In experiments, IGSA has achieved better precision with balanced trade-off between exploration and exploitation. Moreover, triple negative breast cancer dataset has been considered to analysis the performance of GSA variants for the nuclei segmentation. The variants performance has been analysed in terms of both qualitative and quantitive with aggregated Jaccard index as performance measure. Experiments affirm that IGSA-based method has outperformed other methods.

Gravitational search algorithm: a comprehensive analysis of recent variants

An exponential growth of histopathological digital images over the Internet requires an efficient method for organizing them properly for better retrieval and analysis process. For the same, an automatic histopathological image classification system can be useful. Moreover, such classification system may also be used to identify the inflamed and healthy tissues from tissue image datasets. However, complex background structures and heterogeneity among histopathological tissue images make it a complicated process. Therefore, this paper introduces an innovative method for categorization of histopathological images using an enhanced bag-of-feature framework. To obtain the optimal visual words in bag-of-features, a new spiral biogeography-based optimization algorithm has been proposed which introduces a spiral search and random search in the mutation operator to generate the suitability index variables. The efficacy of the spiral biogeography-based optimization algorithm has been tested on CEC 2017 benchmarks problems. Moreover, the applicability of the proposed classification method has been observed on two histopathological image datasets, Blue Histology image dataset and ADL Histopathological image dataset. The efficacy of the spiral biogeography-based optimization algorithm based bag-of-features method has been analyzed and compared with other state-of-the-art methods with respect to average accuracy, recall, precision, and F1-measure parameters.

Histopathological image classification using enhanced bag-of-feature with spiral biogeography-based optimization

Data clustering is one of the important tool in data analysis which partitions the dataset into different groups based on similarity and dissimilarity measures. Clustering is still a NP-hard problem for large dataset due to the presence of irrelevant, overlapping, missing and unknown features which leads to converge it into local optima. Therefore, this paper introduces a novel hybrid meta-heuristic data clustering approach which is based on K-means and biogeography-based optimization (BBO). The proposed method uses K-means to initialize the population of BBO. The simulation has been done on eleven dataset. Experimental and statistical results validate that proposed method outperforms the existing methods.

Raju Pal

Papers

EEWC: energy-efficient weighted clustering method based on genetic algorithm for HWSNs

Chaotic Kbest gravitational search algorithm (CKGSA)

Gravitational search algorithm: a comprehensive analysis of recent variants

Histopathological image classification using enhanced bag-of-feature with spiral biogeography-based optimization

Data clustering using enhanced biogeography-based optimization