## 2 Related Work
The mobile hash-table (MHT) [29] addresses this challenge by mapping a data item to a path through the environment. In contrast to existing DHT, MHT does not to maintain routing tables and thereby can be used in networks with highly dynamic topologies. Thus, in mobile environments it stores data items with low maintenance overhead on the moving nodes and allows the MHT to scale up to several ten thousands of nodes. In [30], the appropriateness of using DHT routing paths for service placement in an SBON was evaluated, when aiming to minimize network usage. SBONs are one approach to implementing large-scale stream processing systems. A fundamental consideration in an SBON is that of service placement, which determines the physical location of in-network processing services or operators, in such a way that network resources are used efficiently. Service placement consists of two components: node discovery, which selects a candidate set of nodes on which services might be placed, and node selection, which chooses the particular node to host a service. By viewing the placement problem as the composition of these two processes we can trade-off quality and efficiency between them. For this, two DHT-based algorithms for node discovery, which use either the union or intersection of DHT routing paths in the Stream-based overlay networks (SBON), was considered and compared their performance to other techniques. In [31] a GHT based service discovery protocol including the mechanism that constructs topology-aware overlay networks in wireless sensor network was proposed. It does not require a central lookup server and does not rely on multicast or flooding. A Similarity Search Algorithm (SSA) for efficiently processing similarity search queries was proposed in [32]. A data-centric storage structure based on the concept of Hilbert curve and DHT was presented and then an algorithm designed for efficiently probing the most similar data item for the sensor network was proposed. A dynamic geographic hash table for data-centric storage in wireless sensor networks was proposed in [33]. Unbalanced resource utilization problem was addressed by proposing a dynamic GHT solution that relies on two schemes--a temporal-based geographic hash table to achieve overall load balancing among sensor nodes over time and a location selection scheme based on node contribution potential to proactively adapt the system to network dynamics. An effective hotspot storage management schemes to solve the hotspot storage problem in GHT was proposed in [35]. The scheme included the cover-up and multi-threshold mechanisms. The cover-up mechanism can adjust to another storage node dynamically when a storage node is full, while the multi-threshold mechanism can spread the data into several storage nodes for load balancing of the sensor nodes. Increasing Ray Search (IRS), an energy efficient and scalable search protocol, and k-IRS, an enhanced variant of IRS based on the GHT was proposed in [36]. The priority of IRS is energy efficiency and sacrifices latency whereas k-IRS is configurable in terms of energy-latency trade-off and this flexibility makes it applicable to varied application scenarios. The basic principle of these protocols is to route the search packet along a set of trajectories called rays that maximizes the likelihood of discovering the target information by consuming least amount of energy. The classical protocols for service location in wired networks rely on a central server called a service directory. The central server advertises its information periodically. Users who want to use a service connect to the service directory in order to obtain a service description. In wired networks, the network topologies hardly change; therefore, users can access the service directory anytime. However, the central server cannot be used in a wireless network because the network topology in a wireless network frequently changes. The simplest form of service location is global flooding in the network; however, flooding does not scale well.