**2. Preliminary concepts**

74 Wireless Sensor Networks – Technology and Protocols

calculating and adjusting of observed reliability.

transmission delay in some other applications.

**1.5. Preview of our work** 

Regarding the trade-offs, (Boulis, et. al, 2003) proposed an energy-accuracy tradeoffs algorithm for periodic data-aggregation which is a threshold-based scheme where the sensors compare their fused estimations to a threshold to make a decision of regarding transmission. Energylatency tradeoffs algorithm (Yu at. el., 2004) is proposed for minimizing the overall energy consumption of the networks within a specific latency constraint where data aggregation is performed only after a node successfully collects data from all its children and its own local generated data. ADA (Adaptive Data Aggregation) (Chen et. al., 2008) is an adaptive data aggregation (ADA) for clustered wireless sensor networks. In ADA, sensed data are aggregated on two levels; one is aggregated at sensor nodes controlled by the reporting frequency (temporal reliability) of nodes; another is aggregated at cluster heads controlled by the aggregation ratio (spatial reliability). The reliability of observed data that is decided by the number of arrival data at sink node is compared with the reliability of desired data, which is decided by the application. According to comparison, nine characteristic regions and nine states are defined in which the eight states must change into the desired state through the

Most of the previously mentioned works focus on energy saving and aggregate as much data as possible. As a result, they prolong the transmission delay. Many works aimed to achieve energy-delay trade off, however they still have shortcomings for example (Yu at, el., 2004) has long waiting time at nodes with less event data while the constant latency makes the networks very inflexible in (Galluccio L. & Palazzo S., 2009). A desired energy-delay tradeoff is achieved in (Ye Z. et al., 2008); however the algorithm ignored the issue of data accuracy. Energy-delay-accuracy tradeoffs in (Mirian F. & Sabaei M.) and (Chen et al., 2008) adapt to a situation that could be described by the following question: 'what is the average temperature of this area at this hour?' The algorithms did not consider delay and accuracy among nodes and data, which may lead to large data deviation as well as

In this paper, at first, we show the analyses of transmission delay, energy consumption and data accuracy of non-aggregation, full aggregation and partial data aggregation with Markovian chain. The analytical results show that non-aggregation consumes much energy and full aggregation causes long transmission delay; but the proposed partial aggregation can trade off total delay, energy consumption, and data accuracy between non-aggregation and full aggregation. Then we intensively discuss the tradeoffs among energy consumption, transmission delay and data accuracy with a Trade Off Index (TOI). We discuss the TOI under the different conditions of accuracy dominant, energy dominant, and delay dominant. By comparing the TOI value among non-aggregation, full aggregation and partial aggregation in different data generation rates, we obtain the best TOI. The results show that with small data generation rate, non-aggregation is the best TOI; with moderate data generation rate, the partial aggregation is the best TOI while the data generation rate is large, the full aggregation is the best TOI. At last a multi view multi robot sensor network is discussed and a User

Dependent Multi-view Video Transmission (UDMVT) scheme is introduced.

In this section, we will introduce network topology, network parameters and the definitions of network parameters, which will be helpful in understanding our work clearly.
