3.4.12 DEBR: Distributed energy balance routing -- 3.4.13 Exponential and Sine Cost Function based Routing -- 3.5 Mobile sink routing (MSR -- Chapter 4 Approximation Algorithms for Clustering -- 4.1 Introduction -- 4.2 System model and problem formulation -- 4.3 Terminologies -- 4.4 Load balanced clustering algorithms -- 4.4.1 Algorithm for equal load -- 4.4.2 An illustration -- 4.4.3 Approximation algorithms for unequal load -- 4.4.4 Performances based on load balancing -- 4.4.5 Execution time -- 4.4.6 Heap based approximation algorithm -- 4.5 Chapter summary -- Chapter 5 Centralized Algorithms -- 5.1 Introduction -- 5.2 Load balancing algorithms -- 5.3 Energy e cient algorithm -- 5.4 Chapter Summary -- Chapter 6 Metaheuristic Approaches -- 6.1 Introduction -- 6.2 System model and terminologies -- 6.3 Genetic algorithms for clustering -- 6.3.1 An overview of genetic algorithm -- 6.3.2 GA based clustering -- 6.3.2.1 Problem formulation -- 6.3.2.2 Chromosome representation -- 6.3.2.3 Initial population -- 6.3.2.4 Fitness function -- 6.3.2.5 Selection -- 6.3.2.6 Crossover -- 6.3.2.7 Mutation -- 6.4 Di erential evolution for clustering and routing -- 6.4.1 An overview of di erential evolution -- 6.4.2 DE based clustering -- 6.4.2.1 Problem formulation -- 6.4.2.2 Initialization of the population vector -- 6.4.2.3 Derivation of fitness function -- 6.4.2.4 Mutation -- 6.4.2.5 Crossover -- 6.4.2.6 Local improvement -- 6.4.2.7 Selection -- 6.5 Particle swarm optimization based algorithms -- 6.5.1 An overview of particle swarm optimization -- 6.5.2 PSO based routing -- 6.5.2.1 LP Formulation for Routing Problem -- 6.5.2.2 Routing algorithm -- 6.5.3 PSO Based Clustering -- 6.5.3.1 NLP Formulation for clustering problem -- 6.5.3.2 Clustering algorithm -- 6.6 Chapter summary -- Chapter 7 Distributed Algorithms -- 7.1 Introduction