## 5 Comparison of Different Topologies
### Topology Comparison Based on Scalability and Self-Organization  
WSN should be scalable to varying sensor density, and should maintain performance that is independent of the number of nodes or gracefully degrade the performance depending on the number of surviving nodes. In addition, WSN will presumably be required to self-configure into connected networks, and will require different or at least adaptive protocols. For example, by allowing the algorithms and protocols to trade off accuracy and latency with energy dissipation, WSN can be scalable and flexible to the application requirements that might change over the WSN lifetime. Self-organization helps in maximizing the network lifetime. Nevertheless, self-organization should be kept in perspective with energy cost and speed. Sometimes letting a WSN to kill its nodes may be more energy efficient than trying to revive it [82]. Self-configuration time involves fault identification, fault localization and fault recovery phases. The self-configuration time for tree topology can be quite high, while that for cluster and chain-oriented topology is less compared with the time required for tree-based topology. In the chain-based and clustered hierarchical approaches, the chain leader and the CH respectively can initiate the localized reconfiguration of the chains and clusters. In flat topology, high number of sensor nodes increases load on the base station, which results in increased power consumption and complexity. In addition, as node density increases, the increase in collisions greatly degrades performance. Further, not all nodes have enough transmission range or the line-of-sight communication with the base station. It is difficult to scale flat WSN to more than a few nodes. For tree-based topologies, self-configuration and scalability are limited up to a certain number of depths of the tree. After that, WSN designers should carefully plan the transmission and duty cycle scheduling of the sensor nodes to avoid aforementioned negative effects of dense deployment. Therefore, in practice, tree-based topology works well for medium sized networks, but has scalability limitations that degrade performance for larger or densely deployed WSN. The scalability and self-organization issues of chain-based topology primarily depend on the number of chains in the network. Chain-oriented topology with a single chain (PEGASIS) has the same limitations that the tree-based topologies have. Multiple chain-based topology and clustered hierarchical topologies improve the scalability of the flat networks by assigning leaders and/or cluster heads to manage the local neighbourhood of sensor nodes. For example, LEACH and COSEN use localized coordination to enable scalability and robustness for dynamic networks [56,57]. In addition, the adaptive self-organizing capabilities of multi-chain and clustered hierarchical WSNs allow the periodic reformation of hierarchical chain/clusters of sensor nodes, in the event of environmental or topological changes when sensor nodes fail or new sensor nodes are added to improve connectivity and coverage. Besides, for scalability, the addressing structures of WSN are likely to be quite different; for example, geographic, data-centric, or address-free structures. Distributed and/or probabilistic assignments of addresses are only unique in a two-hop neighbourhood. For example, address-free architecture [81], which leverages the spatial and temporal locality of WSN to assign probabilistically unique identifiers for each new transaction, must only scale with the transaction density of WSN, while a statistically assigned global address space must scale with the total number of nodes in the WSN. Hierarchical tree and hierarchical clustered architectures are inherently amenable to a scalable addressing structure where the nodes are addressed based on their position in the hierarchy. For example, a node z that is a member of level-1 cluster y and level-2 cluster x could have an address \"x.y.z\" [83].