# Intelligent Video Surveillance Platform for Wireless Multimedia Sensor Networks
## 2 Related Work  
In recent years, there have been studies on congestion control, reliable transmission, and congestion mitigation in WMSNs [17; 18; 19], but few of them really consider timing of realistic hardware constraints. Transmission control protocol (TCP) provides famous rate control scheme [20; 21]. TCP applies an additive increase and multiplicative decrease algorithm (AIMD). When the media rate approaches the bottleneck, the encoding rate also demonstrates sawtooth performance. The algorithm, which constantly generates sawtooth behavior, is not suitable for multimedia communication [22]. This rate variation in TCP results in poor video quality [23]. Numerous equation-based rate control schemes have also been investigated [24]. TCP friendly rate control (TFRC) is one of them [25]. TFRC is an equation-based congestion control algorithm, which uses throughput of TCP Reno. However, TFRC needs feedback on the basis of per-packet, which generates decrease or increase in the media rate (sawtooth) in very limited duration [26]. However, in WMSNs, priority should be given to delay sensitive data instead of delay tolerant flows. For these reasons, in this paper, we do not consider TCP fairness for our scheme. Existing work in congestion control can be divided into two sections. In the first section, we describe protocols with centralized congestion control scheme. In the centralized congestion control schemes, all the actions related to avoiding or controlling congestion are undertaken by the sink node.In a typical centralized congestion control scheme, the sink periodically collects data from the sensor node, detects the possibility of congestion, and accordingly sends messages to the involved sensor to overcome congestion. Rate-controlled reliable transport protocol (RCRT) is one example of a centralized congestion control scheme [27]. In RCRT, all functions including rate allocation, rate adaptation, and congestion detection are applied at sink node. RCRT uses negative acknowledgement (NACK) based technique to implement end-to-end explicit loss recovery mechanism. However, RCRT has very slow convergence when the network has extremely unstable RTTs. Quasi-static centralized rate allocation for sensor networks (QCRA) is a centralized rate allocation scheme that assigns a fair and efficient rate to each node given the link loss rate information, topology, and routing tree information [28]. This scheme determines the traffic levels in the vicinity of a node by computing a TDMA schedule among all neighbors of that node. The node with the highest traffic level determines the fair rate allocation. They also derive rate adaptation parameter by observing the behavior of the network during an epoch. Event-to-sink reliable transport protocol (ESRT) distinguishes network into five sections [29]. Rate allocation is centrally determined in ESRT. In ESRT, sending rate is regulated in such a way that packets reach at a sink node without generating congestion. Due to disadvantages of centralized methods, transient congestion is difficult to manage in ESRT. Centralized congestion control routing protocol based on multi-metrics (CCRPM) [30] combines the residual energy of a node, buffer occupancy rate, wireless link quality, and the current number of sub-nodes for the candidate parent to reduce the probability of network congestion in the process of network construction. In addition, it adopts a centralized way of determining whether the sub-nodes of the congested node need to be switched based on the traffic analysis when network congestion occurs. In the second section, we describe protocols with distributed congestion control mechanism. Congestion detection and avoidance (CODA) uses congestion mitigation technique [31]. The source needs to maintain its data rate using feedback from sink for persistent congestion. For transient congestion, every sensor node observes buffer occupancy level and channel utilization to detect congestion.