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.

Data gathering is a fundamental operation in wireless sensor networks. For the online data gathering problem, we consider the key issues of balancing the load on the nodes to achieve longer network lifetime, and that of balancing the load on the network links to achieve greater reliability in the network. We model the given network as a shortest-distance DAG D, which defines a set of parent nodes for each node that determine the minimum-hops paths from the node to a sink. Data gathering in D is accomplished using a dynamic routing approach, where each node selects a parent using a parent selection function σ to forward the sensed data, which generates a dynamic forest (D, σ) in the network. We investigate a dynamic state-based routing approach where σ is defined using the current state of the network. Based on our earlier work [1], we propose two dynamic state-based routing algorithms -MPE Routing and WPE Routing, that aim to load-balance the nodes as well as the edges of D in order to achieve longer network lifetime as well as greater disjointness. We evaluate the performance of our algorithms with respect to the three goodness measures -network lifetime, nodal load-balancing and disjointness, and compare it with two benchmark algorithms as well as existing data gathering schemes. Our simulation results show that our algorithms perform consistently better with respect to our goodness measures than the benchmark algorithms and other approaches.

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Much of the research in the area of sensor networks is focused on homogeneous networks. Of late, there has been a steadily increasing amount of work on heterogeneous networks, mainly due to their flexibility and better fit into potential applications. In this paper, we present a heuristic, developed using a reverse engineering approach, that can be used to build efficient heterogeneous ad-hoc sensor networks based on a generic network model. A genetic algorithm is used to generate a set of heterogeneous sensor networks optimized for short paths and congestion. A thorough analysis of the optimal network set is done to extract rules and a heuristic is developed to embody these rules. The heuristic is then used to produce high-performance networks without genetic algorithms. We present simulation results and analysis of the heuristic networks and compare their performance with optimal heterogeneous networks as well as homogeneous networks.

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