## 4 Lifetime Improvement Mechanisms in Routing
### Bio-Inspired Mechanisms
According to this protocol, the information is initially sent to different neighboring nodes by the source in the form of individual packages. This process is going on by each node till multiple paths are built eventually. Thus, a series of information about optimal paths will be received by the sink node. Some simulated ants as agents are used to obtain efficient routing while this operation is being performed. Since the sensor nodes lose their energy when they are in communication, the remaining energy of the nodes should also be taken into consideration as well as the lengths of the paths. Therefore, the average network lifetime would be increased by selecting nodes which have more energy in a routing task. Additionally, to achieve guaranteed delivery, acknowledgement signals are used in this scheme. When an acknowledgement is not received by a source node for a data package, it resends that package to a different path. To achieve lower energy consumption, shorter paths and transmitting data on them is chosen frequently [49]. One of the advantages of this algorithm is that it employs data aggregation to decrease the amount of messages to be transmitted and consequently conserves energy. However, it can negatively affect the accuracy of data due to the loss of details [50]. Another benefit of this ACO scheme is storing the identities of ants like sequence numbers in the node's memories, instead of saving them in the ants' memories. Pheromone values are also kept in the nodes' memory. Although it reduces the size of packets during transmission, the nodes' memory might be occupied by large lists of ants' identities and pheromone values. Simulation results [49] show that in comparison with the previously discussed EEABR protocol, the residual energy of the proposed ACO algorithm is grown by as much as 10%. The _Many-to-One-Improved Adaptive Routing (MO-IAR)_ protocol [51] for WSNs based on ant colony optimization and swarm intelligence (forward ants and backward ants) is another suggested ant- based algorithm to lessen the collisions with the help of a lightweight congestion control algorithm. To find the shortest and best route within a multi-hop manner in a WSN, this algorithm uses two strategies. Here each node knows its position and position of its destination. Each forward ant uses the ant-routing algorithm [52] to find the best next-hop neighboring node. Using probabilistic theory, the eligible node should be closer to the source and also closest to the sink. The binary exponential back off algorithm is used by the subsequent nodes for computing their channel access time. The shortest paths might merge or cross over at any intermediate node according to the convergence nature of the many-to-one routing scheme. Experimental results [51] indicate that MO-IAR experiences about 230% and 110% lower collision and end-to-end delays, respectively than the Flooded Piggybacked Ant Routing (FP) [53] algorithm. The reason is that the protocol employs the shortest paths accompanied by a lightweight congestion control technique to avoid collisions. The _Online-Battery Aware Geographic Routing (OBGR)_ algorithm which was presented in [54] is another location-based routing protocol that maximizes the nodes' residual energy while it guarantees some QoS metrics such as end-to-end delay and network reliability. By calculating the remaining battery capacity of nodes and using this information in data forwarding decisions the network lifetime could be extended. Actually, a node with low battery capacity can turn off itself and stop its routing and sensing processes. In this state, it follows the battery recovery scheme in which the battery can improve its power level in its idle states. In the proposed algorithm, the authors used the Ant Colony Optimization (ACO) mechanism.