## 2 Related Work
This algorithm partitions the network area into sector zones, whose centers consist of a sensor node that lies within the sink's radius zone. The sink zone can be obtained by the method presented in the SNOID algorithm in [23]. The contribution in the DSG-MIP algorithm is to establish an angle gap to partition the network into sectors. The angle gap is determined by the nodes density described in [16]. A particular angle gap threshold is used to determine the sector's size. After several iterations, the isolated source nodes may be arranged to a new sector (with new itinerary). Here, instead of adding these isolated source nodes to a new sector, it can be inserted into existing sectors by expanding the angle with the consideration of the metric of shortest distance to existing itineraries. However, this solution increases the delay of the MA when the isolated source nodes are located far from the existing itineraries. Additionally, determining the optimal gap threshold in the DSG-MIP algorithm is still regarded as a challenging issue. Similarly, a genetic algorithm (GA)-based MIP was proposed in [17] to also determine the optimal number of MAs in MIP. The aim was to deal with MIP as a single problem instead of utilizing the four-parts in the outlined in CL-MIP [16] and DSG-MIP [20] algorithms. In GA-MIP, a GA is used to determine the number of MAs and their assigned source nodes by two-level coding method. The coding represents a gene that contains source-ordering-code (sequence array) and source grouping code (group array). A source-ordering-code is an array that includes segments, where each segment has a number of source nodes to be visited by a particular MA. A source grouping code is an array of numbers, with each number specifying the number of source nodes of each segment in the source-ordering-code. The two basic operations of GA (crossover and mutation) are used in each iteration, and a fitness function is adapted to select the better genes to survive. GA-MIP has better performance than other previous MIP algorithms in terms of delay and energy consumption, but it is a greedy approach which produces a substantially suboptimal MIP solution and high computation complexity. In most of the previously proposed MIP algorithms, the geographic information of the sensor nodes is the main parameter used to determine the optimal number of MAs and their itineraries. Recently, a greatest information in the greater memory-based MIP (GIGM-MIP) algorithm was proposed in [19]. This algorithm not only considers the geographic information, but also takes into account the data size in each partition to formulate the optimal number of MAs and their itineraries. In GIGM-MIP, k-means algorithm is used to partition the network into K clusters (partitions). After partitioning the network, GIGM-MIP calculates the data size of the source nodes in each partition. This data size will then determine how many MAs would be assigned to that partition such that each partition may have more than one MA. However, GIGM-MIP algorithm has two main drawbacks. The first one is that with k-means partitioning algorithm, the number of the partitions has to be manually identified by the user. This means that the partitioning of the network is not accurately obtained. The second limitation of the GIGM-MIP algorithm is that the distribution of more than one MA to a single partition would increase the number of MA's hops. Moreover, if the partition has more than one MA, each MA has to carry the processing code (data aggregation code) to its target source nodes. This limitation has caused an increase in energy and time consumption.