You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: Energy optimization in wireless sensor network through natural science computing: A survey 
Chapter Title:  The Nonhuman living components for WSN application 
1. Abstract of Cited Paper (BIB001):  Characteristics of mobile ad hoc networks (MANETs) such as lack of central coordination, mobility of hosts, and limited availability of resources make quality of service (QoS) provisioning very challenging. Limited resource availability such as battery power and insecure medium is one of the major QoS issues to be dealt with. In this paper, we have proposed a new secure power-aware ant routing algorithm (SPA-ARA) for mobile ad hoc networks that is inspired from ant colony optimization (ACO) algorithms which are a swarm intelligent technique. In this algorithm, we have introduced a new metric, next-hop availability, which is a combination of two metrics. It maximizes path availability and minimizes travel time of packets, and therefore it offers a good balance between selection of fast paths and a better use of network resources. The protocol also incorporates a trust model which helps in detection of unauthorized and compromised nodes in MANETs. 
2. Abstract of Cited Paper (BIB002):  High efficient and energy-aware routing is an important issue for the design of resource constrained environments like Wireless Sensor Networks (WSNs). Many protocols have been developed for WSN that try to overcome the constraints that characterized this type of networks. Termite based routing protocols can add a significant contribution to assist in the maximization of the network lifetime without performance degradation. But this is only possible by means of an adaptable and balanced algorithm that takes into account the main constraints of WSN. This paper presents a biological inspired self-organized routing protocol for WSN which is based on termite colony optimization metaheuristic termed Termite-hill. The main objective of the proposed algorithm is to efficiently relay all the traffic destined for the sink, and also balance the network energy. The results of our extensive experiments on Routing Modeling Application Simulation Environment (RMASE) demonstrated that with sink mobility, our proposed routing algorithm was able to balance the network traffic load and prolong the network lifetime without performance degradation. 
3. Abstract of Cited Paper (BIB003):  SUMMARY ::: ::: Coalition is an essential mechanism in the multi-agent systems in the research of task-oriented area. Self-interested agents coordinate their behaviors in a coalition to pursue a common goal and obtain payoffs. We propose the clustering-based coalition formation and self-adjustment mechanisms for tasks in the wireless sensor network. Before coalition formation, the management center clusters attributes of sensors to reduce the scale of searching space during coalition formation. And then an improved MAX–MIN ant colony optimization algorithm is adopted to resolve the problem of coalition formation. If a coalition member fails to fulfill a task, it can sponsor a negotiation with some noncoalition nodes to execute coalition self-repairing autonomously. The stimulus-response mechanism of wasp colony is introduced to determine the probability of response to the task invitation to avoid consuming extra energy. Simulation results show that our model efficiently reduces energy consumption and network traffic, decreases the number of dead nodes, and prolongs the lifetime of the networks. Copyright © 2012 John Wiley & Sons, Ltd.

Literature Review:
A group of insects or other small organisms especially when in motion intelligently exchange the information about paths and directions. A group of bees with a queen bee migrate to establish the new colony intelligently. There is several real life problems can be optimized intelligently using the behavioural study based on organisms like social insects. The Nature Science inspiration from nonhuman living functions for wireless sensor network applications are integrated in Table 2 . The Swarm is combinebehaviour of social insects group in nature, such as ant colonies, honey bees and bird flocks   . These agents interacts together with certain behavioural patterns to cooperative and achieve tasks necessary for their survival. They have relatively limited capabilities on their own as individual. Due to the amazing grouping efficiency of the natural swarm, biologists and natural scientists inspired to have a study of these behaviours of social insects give efficient solution to practical problems. In the late-80s, computer scientists proposed the scientific insights of these natural swarm systems to the field of Artificial Intelligence. The Swarm Intelligence was first introduced by G Beni and J Wang in their proposal of global optimization framework, a set of algorithms for controlling robotic swarm in 1989  . The interaction among swarm as individuals can be either direct or indirect. The different societies of swarm are shown in Figure 4 . The direct interactions To balance the network traffic load and prolong the network lifetime without performance degradation. are through visual or audio contact, such as the waggle dance of honey bees. Indirect interaction occurs when one individual changes the environment and the other individuals respond to the new environment, such as the pheromone trails of ants that they deposit on their way to search for food sources. This indirect type of interaction is referred to as stigmergy, which essentially means communication through the environment BIB001 . Energy Optimization in Wireless Sensor Network 391 Figure 3 The Intelligent solution using Natural Science components The existing problems in communication networks can be efficiently solved using the Swarm intelligence. The solution to the problems like distributed system for interacting autonomous agents, performance optimization, robustness, self-organized control and decentralized cooperation can be possible for applications in wireless sensor networks. The other applications like indirect interactions, division of tasks and distributed task allocation in network operating system can effectively works. The study of swarm intelligence is providing insights that can help researchers to manage complex systems, for number of the applications in real life. The ants are considered as grand masters in search and exploitation. Asingle ant or bee isn't smart for the solutions, but their colonies are smart. The mechanism of an ant can be inherited for solution as cooperation and division of labour, adaptive task allocation, work stimulation by cultivation and the use pheromones the chemical substance. Ant colony optimization algorithm The conceptual model in  considers pheromone update rule, probabilistic transition rule and heuristic desirability. Probabilistic transition rule determine the probability of choosing an edge i.e. an ant's path by considering the existing pheromone level and its heuristic desirability. Pheromone updating rule specifies how the concentration of pheromone on the edges are modified by both depositing and evaporating procedure. Besides giving solution to search possible paths, using pheromone level and desirability is also considered for the inverse of the edge length, by which the ants choose a shorter path. The techniques  used by antcolonies have been studied to model distributed and fault-tolerant systems for solving problems. This area of studies of ant's locomotion is called biomimetics which led to, search engines that make use of foraging trails for fault-tolerant storage and networking algorithms. The methods proposed in BIB002 maximize the path availability and minimize travel time of packets using ant colony optimization algorithms of Swarm intelligence techniques. It offers a good balance between selection of fast paths and a better use of network resources. Another example of swarm species are honey bees. When a honey bee swarm comes from a hive they fly to limited distance at start. Some few meters from the hive, they gather in a tree or on a branch. There, they group around the queen and select some 20-50 scout bees to send for finding a suitable new nest location. The scout bees are the most experienced searcher in the group. An individual scout returning to the group and broadcast a location she found. She uses the waggle dance to indicate direction and distance to others in the group to convince other members to check out the location she found. The others may take off, check out the proposed site and promote the site further upon their return. Several different sites may be promoted by different members at first. After several hours and sometimes days, slowly a favourite location comes from this decision making process. When all members agree on a final location the whole group takes off and flies to it. This collective decision making process is remarkably successful in identifying the most suitable new nest site and keeping the swarm intact. The logic inspired from the honeybee swarm is modelled in  . The proposed method is based on natural bees' communication. The system confirms the proof of work efficiently in either a homogenous or a densely populated network. The protocol is compared with DIR routing protocol for finding shortest path. Both algorithms use the notion of angles to choose the shortest path by selecting the smallest angle in each hop toward the final destination. Another approach in  uses the Queen-bee algorithm to create energy efficient clusters in wireless sensor networks. The author justifies that the clustering by the QB algorithm decreases the energy consumption with regard to the other existing algorithms and increases the lifetime of the network. The behavior of social insects that live in colonies, such as ants, bees, wasps and termites, has always been a source of fascination for naturalists and artists. Individual insects seem to do their own thing without any central control, yet the colony as a whole behaves in a highly coordinated manner. The combine effort of such behaviors can solve highly complex problems. The stimulus-response mechanism of wasp colony is introduced in BIB004 to determine the probability of response to the task invitation to avoid consuming extra energy. Simulation results show that our model efficiently reduces energy consumption and network traffic, decreases the number of dead nodes, and prolongs the lifetime of the networks. The Termites are another swarm species works with three types of agents: reproductive, soldiers and worker based system. In reproduction phase male reproductive king and the female reproductive queen are the primary source of pheromones which is useful in colon integration, and these are thought to be spread through shared feeding in termite colonies. The soldier's agent classifies as forward soldiers and backward soldiers depending upon the direction in which they travel. The third agent undertakes the labors of foraging. The workers feed the other members of the colony with substances derived from the digestion of plant material, either from the mouth or anus. The proposal BIB003 explores termite colony optimization Meta heuristic termed Termite-hill. The proposed algorithm efficiently relay all the traffics destined to the target, and also balance the network energy and increase the network lifetime without performance degradation.