## 2 Related Works
There are other location based approaches that attempt to achieve localised routing operations in ad-hoc networks, however, most of these approaches focus either on end-to-end routing issues or only on the dissemination of a message to a predetermined area [5]. The route(s) for a sensor node to reply with data can be selected based on various criteria, for example, using a small hopcount in [21-23], minimum energy cost of data delivery in [17], adopting reliable routes from multiple candidates to avoid route failure in [3], or using an alternative path obtained from neighbouring nodes to improve security [24]. The reverse-tree-based data collection is simple to construct and imposes little additional overhead, as it is built on the basis of query dissemination. In contrast, a data collection tree with a root at the sink causes most of the collected data to be aggregated at the sink, and nodes near the sink experience heavier traffic than nodes remote from the sink, draining their energy faster and leading to network faults even though there are many nodes with substantial residual energy. A tour based data collection approaches is proposed in [25]. The aim of this approach is to save energy consumption in data collection and query propagation. The sink builds a source route as a tour for data collection, and the source route tour guides the delivery of query together with data collection from sensors. In the data collection from small sensors, this approach is good to reduce energy. However, in case of data collection from a large number of nodes, the source route is a high cost of overhead for each sensor to handle. Further, the efficiency of source route is low and complex [26]. Mobile sink based data collection has been studied in recent years. The support of mobile sinks in sensor networks increases the flexibility of user interaction with sensor networks. The mobile sink can be a user's mobile phone. It can be applied in case a user is in the sensor networks. The mobile sink alleviates the energy overuse at a certain place as that in the sensor networks with static sink [27, 28]. On the other hand, the mobility of mobile sink introduces the routing complexity. Using mobile sink to fetch sensing data was considered in [29]. When sensing data is required, the mobile sink will go to the place near the sensor node, and direct collect sensing data by one-hop communication. This requires the high intelligent sink which can move automatically. This approach might be applied to the intelligent robot network. Recent researches have tried to analyze the phenomenon of uneven energy consumption in sensor network [30-32]. One attempt to alleviate the problem of energy uneven consumption by using specialized node deployment is proposed in [30]. More nodes are deployed near the sink node to share the energy consumption. The demerit of this approach is that the specialized deployment also causes deployment redundancy. This paper discusses the problems in on-demand information retrieval. The disadvantages of conventional on-demand information retrieval are summarized as follows: first, query messages are often broadcast throughout the network in a data-centric manner, without distinguishing the corresponding individual sensor node ID. These on-demand data queries generate an excessive amount of redundant network traffic and energy consumption in large sensor networks. Second, sensor nodes reply to queries by sending data back towards the sink node via various paths (such as the reversed paths obtained from query broadcasting), and most of the data are accumulated at a sink node. This causes high and unbalanced energy consumption, since nodes near the sink node are likely to consume more energy than nodes remote from the sink node.