Biological systems present remarkable adaptation, reliability, and robustness in various environments, even under hostility. Most of them are controlled by the individuals in a distributed and self-organized way. These biological mechanisms provide useful resources for designing the dynamical and adaptive routing schemes of wireless mobile sensor networks, in which the individual nodes should ideally operate without central control. This paper investigates crucial biologically inspired mechanisms and the associated techniques for resolving routing in wireless sensor networks, including Ant-based and genetic approaches. Furthermore, the principal contributions of this paper are as follows. We present a mathematical theory of the biological computations in the context of sensor networks; we further present a generalized routing framework in sensor networks by diffusing different modes of biological computations using Ant-based and genetic approaches; finally, an overview of several emerging research directions are addressed within the new biologically computational framework.

Abstract Most research on wireless sensor networks has focused on homogeneous networks where all nodes have identical transmission ranges. However, heterogeneous networks, where nodes have different transmission ranges, are potentially much more efficient. In this chapter, we study how heterogeneous networks can be configured by distributed self-organization algorithms where each node selects its own transmission range based on local information. We define a specific performance function, and show empirically that self-organization based on local information produces networks that are close to optimal, and that including more information provides only marginal benefit. We also investigate whether the quality of networks configured by self-organization results from their generic connectivity distribution (as is argued for scale-free networks) or from their specific pattern of heterogeneous connectivity, finding the latter to be the case. The study confirms that heterogeneous networks outperform homogeneous ones, though with randomly deployed nodes, networks that seek homogeneous out-degree have an advantage over networks that simply use the same transmission range for all nodes. Finally, our simulation results show that highly optimized network configurations are as robust as non-optimized ones with respect to random node failure, but are much more susceptible to targeted attacks that preferentially remove nodes with the highest connectivity, confirming the trade-off between optimality and robustness postulated for optimized complex systems.

Load-balancing is a fundamental problem in wireless sensor networks (WSNs), essential for maximizing lifetime and throughput, and for minimizing delay. Although the WSN architecture usually imposes restrictions on the network topology, utilizing dynamic network structures allows for better load-balancing than could be achieved by any particular static structure. In this thesis, we propose a study of dynamic network structures and associated algorithms and protocols, to achieve optimal load-balancing in the network. In our current work, we consider the problem of dynamically routing the sensed data from source nodes to the sink nodes. The distance-DAG is a natural model for data gather in a WSN, since by defining parent nodes for each sensor node, the DAG not only represents the direction of data flow but also encapsulates the shortest paths (in minimum number of hops) to the sink. There are two approaches for routing in the distance-DAG — using dynamic paths where the source node forwards the data along one of the several different paths to the sink, and, using dynamic forests where a new spanning tree rooted at the sink is constructed periodically and used for data gather.