## 3 Applying Bioinspired Networking
### Routing  
Among the many challenges encountered by sensor networks, routing issues are one of the problems that significantly block the scalability, robustness and energy-efficiency of the network. The design of routing protocols for large-scale sensor network is very difficult due to the limited available energy, the limited memory capacity and a frequent changes in network topology.  
The objective of every routing algorithm is to direct traffic from sources to destinations and to maximize network performance while minimizing the cost of transmission. Routing algorithms can be broadly classified as static or dynamic. In static routers the path taken by a packet is determined on the basis of the source and destination, without regard to the current network state. This path is usually chosen as the shortest one according to some cost criterion. Adaptive (or dynamic) routers are, in principle, more attractive, because they try to adapt the routing policy to the varying traffic conditions. However, oscillations in selected paths might occur, circular routes can be generated, and large fluctuations in performances might result. Another problem with dynamic algorithms applied to, e.g., sensor networks arises when changes in the network topology occur too frequently to allow routing updates to propagate through the entire network. In the absence of global identification and unique addressing schemes of individual nodes, the network data-centric routing based on probabilistic techniques becomes attractive.  
\begin{table}
\begin{tabular}{l|l|l} \hline Biological principle & Application fields in networking & References \\ \hline Swarm intelligence & Routing in computer networks, optimal node deployment, node localization, and network clustering & [12; 13; 14; 15; 17; 19; 29; 30; 31; 32; 33; 34] \\ Firefly synchronization & Robust and distributed clock synchronization, & [21; 22; 35; 36] \\ Artificial immune system & Misbehavior detection, intrusion detection systems, and node and rate selection & [23; 25; 26; 26; 37] \\ Intercellular communication & Coordination and control in distributed systems, network clustering, and protection (security and privacy) & [11; 38; 39] \\ \hline \end{tabular}
\end{table}
Table 1: Applications of bioinspired principles in networking.  
[PERSON], [PERSON], and [PERSON] [12] applied ACO algorithms to the classical traveling salesman problem. The main characteristics of the model were positive feedback, distributed computation, and the use of a constructive heuristics. It was demonstrated how synergy can arise from a number of cooperative ants collaborating in search compared to the same number of ants, each one acting independently from the others.  
[PERSON] _et al._[14] provided a survey of swarm intelligence applied to network routing. The study bases on the attracting principles such as scalability, robustness (fault tolerance), adaptation, speed, modularity, autonomy, and parallelism from the field of swarm intelligence.  
[PERSON] _et al._[13] proposed an improved routing protocol based on the ACO algorithm for wireless sensor networks. The authors report on simulation results that show a reduced total cost of routing in a wireless sensor networks, an improved scalability, and higher efficiency. [PERSON] _et al._[15] demonstrated an ACO-based routing protocol for wireless mesh networks. The protocol performance was compared to common ad hoc routing protocols: AODV and OLSR. The ACO-based routing protocol presented low overhead, high bandwidth utilization ratio, acceptable throughput and delay.  
The above mentioned applications all represent data-centric routing schemes based on bioinspired principles. They are founded on the basis that the collective behavior of a great number of nodes can result in a scalable, robust, and energy-efficient routing network infrastructure.