## 4 Survey of Existing Works
### Ant Colony Optimization
#### ACO in Data Aggregation  
A centralized approach to data gathering and communication for WSNs is presented in [94]. The method clearly partitions the work for the BS and sensor nodes according to their different functions and capabilities. A near-optimal chain named AntChain is achieved by using an ACO method running in the BS. The algorithm significantly simplifies the work of sensor nodes. It lowers the communication and computation workload. The authors claim that the AntChain algorithm out-performs LEACH and PEGASIS by eight times and two times, respectively.  
In 2006, [PERSON] _et al._ introduced an ant aggregation algorithm for optimal data aggregation in WSNs [95]. They observed that aggregation energy efficiency depends on the number of sources. The results of simulation reveal that optimal aggregation saves energy up to 45% and 20%, respectively, for moderate numbers of source nodes and large numbers of source nodes.  
Another scheme for solving the maximum lifetime data gathering problem in distributed intelligent robot networks (DIRNs), as a kind of wireless sensor and actuator network (WSAN), supporting multimedia traffic is developed in [96]. The previous methods used for multimedia traffic provided ineffective exploitation of network resources. With this scheme network lifetime is maximized by jointly optimizing data aggregation variables based on ACO algorithm using bionic swarm intelligence. Furthermore, experimental results demonstrate that the proposed methods attain significant improvements (24% better) in system lifetime compared to other conventional methods such as Minimum Energy Gathering Algorithm (MEGA).  
As we have mentioned several times before, that one of the biggest problems of WSN is energy. A few solutions exist to the problem such as LEACH and PEGASIS protocols. While LEACH selects CHs in random manner, the PEGASIS protocol forms a chain of all the nodes in the network, each node taking rounds in transmitting to the BS. In [97] authors discussed about an energy efficient protocol which can enhance the performance of LEACH and PEGASIS. As the nodes are deployed randomly in the area and the BS is located at a distance from them, it is clear that the nodes would actually dissipate energy during their transmission to the BS. The inter-nodal distance also plays a role in unequal energy dissipation of the nodes. This energy difference keeps on increasing resulting in poor network performance. In this scheme, authors claim to nullify the differences occurring due to these above mentioned causes. ACO is used for chain construction instead of the greedy algorithm to enhance the network performance. Extensive simulations have been carried out which showed significant improvement over other schemes.  
A new cluster formation technique named energy-efficient data gathering algorithm (EDGA) is discussed in [98], which integrates the advantages of hierarchical routing, chain, and multi-hop routing. A node, according to the degree of support from neighbors and its residual energy, makes its decision to compete for becoming a CH independently. Later, the CH adapts ACO to schedule access sequence of nodes (chain). Simulation results show that EDGA provides lower energy consumption and longer network lifetime than that of conventional algorithms. [PERSON] _et al._ have designed three dynamic ACO based algorithms: SinkDistComb, ResidualEnergy, and SinkAggreDist with improved heuristic rules and node selection rules integrated with in-network data aggregation [99]. They refined the heuristic function and the aggregation node selection method to maximize energy efficiency and to extend network lifetime. Two proposed algorithms are shown to yield longer maximum lifetime and another algorithm is shown to have improved scalability than the conventional algorithms.