# Stability-Aware Geographic Routing in Energy Harvesting Wireless Sensor Networks
## 2 Paper Overview  
### Related Work  
Several works on energy-harvesting routing protocols have been published. In [5; 6; 17], the authors maximized the workload that can be autonomously sustained by a network that can transmit a packet, through an edge, proportional to the maximum flow of the edge, which was calculated using an extension of the Ford-Fulkerson algorithm. The authors in [18] developed a model to characterize the performance of multi-hop radio networks using energy constraints and design-routing algorithms to optimally utilize available energy. In [19], the authors presented an energy management and routing method that maximized energy sustainability by modeling the energy buffer as a G/G/1(/N) queue. The authors in [7] proposed a novel route selection method that considered network energy wastage due to overcharging of finite-capacity batteries, based on dynamic source routing, which is an on-demand method. The disadvantage of on-demand routing is that each node requires the full routing path in the packets forwarded from the source to destination. In a dynamic source routing protocol, a node aggressively caches all overheard source routes to reduce the propagation scope of other nodes' flooded route requests [11]. When network nodes expand, the routing packets must establish routing paths. Geographic routing is an appropriate approach for large-scale WSNs because it allows nodes to be nearly stateless, and each node makes routing decisions based on the location information for itself, its neighbors, and the sink node. However, geographic routing cannot optimize the number of hops when a node has no neighbor closer to the sink node, which is known as the local minimum problem. When using a greedy forwarding method, packets are stuck at node A because no neighboring node can be found closer to the sink node (Figure 1). This study, despite adopting geographic routing for EH-WSNs, attempted to solve the local minimum problem in an energy-harvesting environment by incorporating residual energy, location information, and \(PRR\) in routing path selection. Various geographic routing protocols have been proposed. Greedy perimeter stateless routing (GPSR) is a fundamental geographic routing protocol [11] with high successful packet delivery rates, although it requires high extra costs to maintain planar graph information on sensor nodes. In [20], the authors used geographic routing and defined energy distance as a routing metric encoded in energy information. More specifically, the distance between a sender and a receiver is transformed to a weighted distance. A node with less energy along its path is further away, _i.e.,_ the energy distance. This concept finds the shortest distance path based on the minimum total energy distance, rather than physical distance. It does not take into account the influences of wireless link qualities or collisions on packet transmissions. The authors in [8] proposed an adaptive opportunistic routing (AOR) protocol based on the geographic routing method for multi-hop EH-WSNs. The protocol achieved high throughput using a region scheme that utilized network conditions and energy availability, although in cases of high data rates, this protocol may produce too many duplicate data packets over networks. The authors in [21] proposed a contention-based strategy of geographic forwarding for wireless sensor networks. Their first solution combines a splitting tree algorithm (random relay selection) with sectoral decision regions; this allows them to define circular sectors in order to increase the probability of finding a relay, and thus reducing the contention resolution interval. On the other hand, their second solution resorts to an auction-based relay selection and convex-lenses decision region. In this solution,  
Figure 1: An example of local minimum problem. the decision area is halved at every round, which allows the elimination of some candidate relays from the auction, and therefore the contention resolution interval is significantly reduced.