## 1 Introduction
ME-AODV uses multiple paths in a round robin fashion to evenly distribute energy consumption over the entire network. The Neighbor-aware Adaptive Load Balancing algorithm [18] uses the information of parent and child nodes along with a probability factor to balance the traffic and prolong network lifetime. All nodes send traffic load information to the gateway which subsequently calculates and broadcasts the determined probability factor used by the whole network. In order to provide a reliable routing service for data-intensive medical applications, we propose Load-Balanced Multipath Routing (LBMR). When designing LBMR, we prefer a non-cluster architecture because cluster heads potentially cause more bottleneck candidates. In data-intensive medical monitoring, reliable data transmission becomes more critical than saving energy. Therefore, LBMR takes traffic load as the cost function and adaptively updates load information with neighbors to calculate the least busy routes. Besides, LBMR uses multipath routing to provide more reliability. The main comparison with LBMR is Ad Hoc On-demand Multipath Distance Vector (AOMDV) [19]. AOMDV is a multipath solution based on AODV that can transmit data through equivalent paths with the same hop-counts to the gateway. The main purpose of the AOMDV multipath method is to enhance network reliability and avoid failure points. Obviously, the characteristics of the AOMDV multipath method have potential to improve load balancing for AODV. Unfortunately, AOMDV's equivalent paths must be thoroughly disjoint and cannot share nodes on their distinct paths. This limits the number of available alternatives. Besides, AOMDV does not consider traffic load when selecting the sending path and cannot maximize load-balancing effect. In addition, AODVand AOMDV are designed for mobile _ad hoc_ networks so their routing designs are not optimized for data-intensive services. To develop a better multipath solution to support data-intensive medical monitoring services, LBMR has a reliable layering architecture, where the routing computation is done locally among neighbors. This distributed layer architecture can avoid another bottleneck problem at cluster heads, such as the coordinator node in a Zigbee network. Besides, LBMR utilizes traffic load as the cost function and solves imbalanced load problems more directly than AOMDV. Furthermore, LBMR can enhance network reliability by providing multiple next-hop options and guarantees the shortest paths are selected. LBMR consists of three main designs: Layer Routing Construction (LRC), Load Estimation Algorithm (LEA), and Route Maintenance (RM) mechanism. In LRC, the data gateway is the top level and we define that nodes _closer_ to the gateway are in the _upper_ layers and nodes _farther_ from the gateway are in the _lower_ layers. Each sensor node may play both lower-layer and upper-layer node roles depending on the relative distance to the gateway compared with their neighbors. Each lower-layer node only needs to know the _local_ information of next-hop nodes in the immediate upper layer, and the path from source to the gateway is constructed hop-by-hop. This structured and inductive two-layer relationship establishes the reliable routing service. Furthermore, LEA allows each lower-layer node to calculate which upper-layer node has the least traffic load. Consequently, the upper-layer node with less traffic is selected as the next-hop to the gateway. Through the cooperation of LRC and LEA, local load-balancing optimization is accomplished. In the LBMR routing table, multiple paths are recorded and this allows more fault-tolerance once some next hop fails. Furthermore, another assurance of delivery quality is RM. Once any node fails to operate normally, RM allows lower-layer nodes to recalculate their best upper-layer nodes toward the gateway without broadcasting route error messages. Therefore, LBMR can quickly adapt sensor nodes to dynamic flow changes and malfunctioning links.