You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: A survey of transport protocols for wireless sensor networks 
Chapter Title:  Protocols for Congestion Control 
1. Abstract of Cited Paper (BIB001):  Network congestion occurs when offered traffic load exceeds available capacity at any point in a network. In wireless sensor networks, congestion causes overall channel quality to degrade and loss rates to rise, leads to buffer drops and increased delays (as in wired networks), and tends to be grossly unfair toward nodes whose data has to traverse a larger number of radio hops. Congestion control in wired networks is usually done using end-to-end and network-layer mechanisms acting in concert. However, this approach does not solve the problem in wireless networks because concurrent radio transmissions on different "links" interact with and affect each other, and because radio channel quality shows high variability over multiple time-scales. We examine three techniques that span different layers of the traditional protocol stack: hop-by-hop flow control, rate limiting source traffic when transit traffic is present, and a prioritized medium access control (MAC) protocol. We implement these techniques and present experimental results from a 55-node in-building wireless sensor network. We demonstrate that the combination of these techniques, Fusion, can improve network efficiency by a factor of three under realistic workloads. 
2. Abstract of Cited Paper (BIB002):  In this paper we propose a distributed and scalable algorithm that eliminates congestion within a sensor network, and that ensures the fair delivery of packets to a central node, or base station. We say that fairness is achieved when equal number of packets are received from each node. Since in general we have many sensors transmitting data to the base station, we consider the scenario where we have many-to-one multihop routing, noting that it can easily be extended to unicast or many-to-many routing. Such routing structures often result in the sensors closer to the base station experiencing congestion, which inevitably cause packets originating from sensors further away from the base station to have a higher probability of being dropped. Our algorithm exists in the transport layer of the traditional network stack model, and is designed to work with any MAC protocol in the data-link layer with minor modifications. Our solution is scalable, each sensor mote requires state proportional to the number of its neighbors. Finally, we demonstrate the effectiveness of our solution with both simulations and actual implementation in UC Berkeley's sensor motes. 
3. Abstract of Cited Paper (BIB003):  There is a critical need for new thinking regarding overload traffic management in sensor networks. It has now become clear that experimental sensor networks (e.g., mote networks) and their applications commonly experience periods of persistent congestion and high packet loss, and in some cases even congestion collapse. This significantly impacts application fidelity measured at the physical sinks, even under light to moderate traffic loads, and is a direct product of the funneling effect; that is, the many-to-one multi-hop traffic pattern that characterizes sensor network communications. Existing congestion control schemes are effective at mitigating congestion through rate control and packet drop mechanisms, but do so at the cost of significantly reducing application fidelity measured at the sinks. To address this problem we propose to exploit the availability of a small number of all wireless, multi-radio virtual sinks that can be randomly distributed or selectively placed across the sensor field. Virtual sinks are capable of siphoning off data events from regions of the sensor field that are beginning to show signs of high traffic load. In this paper, we present the design, implementation, and evaluation of Siphon, a set of fully distributed algorithms that support virtual sink discovery and selection, congestion detection, and traffic redirection in sensor networks. Siphon is based on a Stargate implementation of virtual sinks that uses a separate longer-range radio network (based on IEEE 802.11) to siphon events to one or more physical sinks, and a short-range mote radio to interact with the sensor field at siphon points. Results from analysis, simulation and an experimental 48 Mica2 mote testbed show that virtual sinks can scale mote networks by effectively managing growing traffic demands while minimizing the impact on application fidelity. 
4. Abstract of Cited Paper (BIB004):  In wireless sensor networks (WSNs), congestion occurs, for example, when nodes are densely distributed, and/or the application produces high flow rate near the sink due to the convergent nature of upstream traffic. Congestion may cause packet loss, which in turn lowers throughput and wastes energy. Therefore congestion in WSNs needs to be controlled for high energy-efficiency, to prolong system lifetime, improve fairness, and improve quality of service (QoS) in terms of throughput (or link utilization) and packet loss ratio along with the packet delay. This paper proposes a node priority-based congestion control protocol (PCCP) for wireless sensor networks. In PCCP, node priority index is introduced to reflect the importance of each node. PCCP uses packet inter-arrival time along with packet service time to measure a parameter defined as congestion degree and furthermore imposes hop-by-hop control based on the measured congestion degree as well as the node priority index. PCCP controls congestion faster and more energy-efficiency than other known techniques.

Literature Review:
Several congestion control protocols have been proposed for upstream convergent traffic in WSNs. They differ in congestion detection, congestion notification, or rate-adjustment mechanisms (Table 1) . Among them, Fusion BIB002 and CODA detect congestion based on queue length at intermediate nodes, while Congestion Control and Fairness (CCF) BIB003 infers congestion based on packet service time. Priority-based Congestion Control Protocol (PCCP) BIB005 calculates a congestion degree as the ratio of packet-interarrival time and packet-service time. Siphon BIB004 uses the same approach as in CODA to infer congestion; in addition, this approach can detect congestion based on the perceived application fidelity at the sink. CODA uses explicit congestion notification, while others BIB002 BIB003 BIB005 use implicit congestion notification. In Adaptive Rate Control (ARC) BIB001 , there is no congestion detection or notification; congestion control works as follows: an intermediate node increases its sending rate by a constant α if it overhears successful packet forwarding by its parent node. Otherwise, the intermediate node multiplies its sending rate by a factor β, I Figure 1 . Existing WSNs' transport protocols.