# Joint Balanced Routing and Energy Harvesting Strategy for Maximizing Network Lifetime in WSNs
## 1 Introduction  
Wireless sensor networks (WSNs) have caused a lot of attention among scholars due to the growth of the Internet-of-Things (IoT) [1], and have achieved a wide success in daily life and industrial applications, such as smart grid [2], safety monitoring [3], and risk prevention [4]. The lifecycle of sensor nodes generally depends on the supplied battery power and numerous possible deployment methods to avoid unnecessary power depletion, including data processing, power control [5], energy efficient routing [6], etc. Long-term monitoring service is a key purpose for designing WSNs, but it is notably influenced by the network energy imbalance [7]. The challenge study issue of maximizing network lifecycle is preventing the energy imbalance, the condition happens when some nodes areexhausted, while others have plenty of residual energy, causing hotspot nodes in data transmission networks [8; 9]. Directly sending packets with long distances from sensors to the data sink is unattractive, the routing protocols are usually designed in a multi-hop fashion, which can be classified into two cases: flat routing [10] and hierarchical routing [11]. In the flat routing case, routing paths from the sending sensors to sink are computed according to the minimum energy cost. The energy expense can be reduced by using shorter distance communication. In the hierarchical routing case, the network topology is constructed by clusters, and each cluster head (CH) is able to fuse packets with data correlations; hence, it is helpful for removing redundant packets, and decreases the total amount of packets, thus improves operation lifecycle. Inventing a particular routing algorithm for energy efficient packet transmission is one main research direction. To consider the transmission costs and remaining battery power of sensors in [12], the authors formulate the network lifecycle maximum problem to a linear optimization problem and select the optimal shortest paths with the lowest cost, and near optimal lifecycle can be reached. To consider routing optimization in scenarios with obstacles in [13], a geographical routing is presented for the shortest path selection by using the Dijkstra algorithm. Each sensor is able to determine routing path according to location information of itself and the adjacent sensors, thus it is appropriate for the large scale WSNs. Another direction for balancing energy consumption is the modification on the typical flat and hierarchical routing schemes. To eliminate the unbalance power consumption of CH, the author in [14] studied how to determine the optimal cluster size for a decentralized hierarchical network. In addition, a General Self-Organized Tree-Based Energy-Balance (GSTEB) routing protocol is proposed in [15] to dynamically update the tree routing topology for the real-time situation and nodes can cooperate with each other via beacons to select the next node by their own routing decisions. Although all CHs produce an equal number of collected packets in [16], which leads the energy imbalance problem around the sink connection area (SCA), while causing underutilized energy for all the other sensors. Consequently, the research challenge issues in developing energy balance routing protocol are to equalize the power consumption among hotspots and achieve better power utilization of other sensors to improve the overall network lifecycle [17; 18; 19]. Another favorable technology to improve the lifecycle of WSNs is contributed by the recent energy harvesting models [20; 21; 22; 23]. Each sensor captures the energy from the surrounding environment with energy harvesting abilities, including solar radiations or vibrations, and collects the energy into its own rechargeable battery. Therefore, it has a high probability to achieve the immortal WSNs when the energy harvest amount in each node is greater than the energy consumption amount for packet transmission. However, the major limitation of the method is the intermittent energy capture, which can make the network performance degraded.