## 3 The Proposed Scheme
### Discussion  
In order to concentrate our attention on designing an energy efficient routing protocol, we made some assumptions:  
Figure 4: A void cell. 1. All sensor nodes are stationary after deployment. 2. Each sensor node is aware of its own geographic location based on GPS or some other technique [24, 25]. 3. Each senor node is also aware of its residual energy. 4. The sensor nodes are homogeneous and wireless channels are bidirectional. Like most of the applications in WSNs, all sensor nodes are stationary after deployment in CBDAS. As for the second assumption, being location aware, each sensor node can determine to which cell it belongs, so that sensor nodes can be clustered into cells. As a result, the grid infrastructure is constructed in the field. Besides, sensor nodes are energy aware so they can compete for the election of the cell head in their own cell. For simplicity, the sensor nodes are homogeneous, and wireless channels are bidirectional in terms of general definition. The scenario of our application is that each sensor node equipped with GPS is stationary after deployment. Initially, each sensor node determines which cell it is located in using Equation (1), and as a result the grid infrastructure is constructed. Each sensor node is aware of its residual energy to compete for being the head of its cell. So is the cell head to be designated as the cycle leader. Our clustering approach is static (not dynamic) so the GPS can be turned off after the grid infrastructure is constructed. Sadly, a sensor node is useless if its geographic location is not available, even though the other functions are normal. Some issues of the assumptions are discussed in the related literature [18, 19, 26, 27, 28]. In [26, 27], the authors proposed geographic routing strategies using the partial position information. In [18, 19], the authors proposed data dissemination schemes with mobile sinks. In [28], [PERSON] _et al_. proposed deployment and topology control schemes in heterogeneous WSNs. In spite of the fact the abovementioned issues are important, those issues are not our key issue. In this paper, we focus on a load balancing strategy to improve the chain-based data aggregation for WSNs. We could have constructed a linear chain with two ends like other approaches [8, 11, 15]. However, we generate a cyclic chain instead. We use a simple token passing approach initiated by the cycle leader to start the data transmission. In a given round, the cycle leader passes each of both neighboring cell heads a token in the opposite direction. When both tokens meet, the cyclic chain will be cut with two ends having almost the same hops to the cycle leader. Both end nodes subsequently transmit the gathered data simultaneously so that the data transmission will be more efficient. This benefits real time data transmission a lot. As a chain constructed by using the greedy algorithm, the neighbor distances will increase gradually. Besides, the gathered data will get bigger and bigger after aggregating. According to the first order radio model [5], as stated later in Section 4, the transmission consumption for a bigger message over a longer distance will greatly increase. As a result, this causes a portion of the nodes in the network to dissipate energy so extremely that the network lifetime shortens. However, the neighbor distances are approximately equal on a cyclic chain. The aggregated data is divided into two portions transmitted along two opposite paths. The energy load is evenly distributed so that the network lifetime is extended. In general, the chain-based routing (such as PEGASIS) suffers with its long distance and the cluster-based routing (such as LEACH) suffers the hot-spot effect for head (leader) nodes. The grid-based routing is a kind of the cluster-based routing. In this paper, we focused on the load balancing strategy to improve the chain-based data aggregation for WSNs. The aim of this paper is to provide another one solution for energy efficient WSNs.