## 1 Introduction  
The recent advances in miniaturization and low-power design have led to the development of small-sizes sensor devices that can be deployed in very large sensor networks [1]. These networks can operate unattended for years. Large-scale sensor networks will be based on wireless communication by using radio wave, inductive or capacitive coupling, light or even acoustic waves for communication [2]. Each sensor device constitutes an autonomous computing device. Individual sensors have limited computational processing and electrical power. Complex processing and the usage of a large amount of memory are not feasible. When a large amount of sensor devices are interconnected they constitute a massively distributed system [3]. Sensors are often densely deployed in an ad hoc manner and most sensors do not have a predetermined location. Furthermore, a centralized control in these networks becomes impractical. In order to save energy, these networks have sensors that typically run with low duty cycles. Hence the amount of processing of individual nodes and the message exchange between devices are kept to a minimum. Due to these characteristics conventional methods in network design such as a unique addressing, centralized routing schemes, centralized clock distribution etc. fail to meet the constraints of massively distributed systems. Computer scientists and engineers have often looked into nature for inspiration in search for solutions to complex problems. In biological systems there are many cases where complexity is handled by individuals with limited capabilities. By using simple rules for the behavior and the interaction among individuals a global optimum can be achieved on a large, system-level scale. Sensor networks as well as biological systems need to adapt to the varying environmental circumstances including the ability to self-organize, scale efficiently and to provide robust and resilient operation for the long-term survival of the system. These characteristics constitute the basis for the development of different approaches and algorithms at different network layer for efficient, robust and resilient communication and information networks [4, 5]. By looking carefully into nature, one can observe that the dynamics of many biological systems and the laws governing them are based on a surprisingly small number of simple rules. These rules yield collaborative and effective mechanisms for resource management, task allocation, social differentiation, synchronization without the need for any central controlling element. From biological systems we find motivation for derivation of different approaches and algorithms in systems design. Bioinspired (or natural) computing [6] represents a class of algorithms focusing on the efficient computing in applications such as optimization processes and pattern recognition. A lot of research effort is put into determining the candidates among which these algorithms can bring significant advantages over conventional methods. The research and development of bioinspired sensor systems with the goals of mimicking animal senses, e.g., to perform olfactory sensing [7] or tasting [8] has caught the attention of a large number of scientists and engineers. Both the electronic tongue and the electronic nose sensor system generate an output pattern that represents a synthesis of all the components in the sample. A few surveys of bioinspired networking and communication protocols and algorithms have been published recently [4, 5]. These survey reviews the current bioinspired thinking in networking. The most well-studied areas of bioinspired network engineering are swarm intelligence [9]. In particular, the ant colony optimization (ACO) and the particle swarms optimization (PSO) algorithms have been applied to networking. Natural time synchronization, artificial immune system (AIS) [10] and intercellular information exchange [11] are methods often found as well.