In elastic optical networks, digital coherent transceivers modify their symbol rate, modulation format, and forward error correction to best serve the network demands. In a nonlinear elastic optical network, these parameters are inherently coupled with the routing algorithm. We propose to use congestion aware routing in a nonlinear elastic optical network and demonstrate its efficacy for the NSFNET reference network. The network is sequentially loaded with 100 GbE demands until a demand becomes blocked, this procedure being repeated multiple times to estimate the network blocking probability (NBP).
Three routing algorithms are considered:
1) Shortest path routing;
2) Simple congestion aware algorithm;
3) Weighted congestion aware routing algorithm with flex grids.
For NBP = 1%
using a grid, congestion aware routing doubles the network capacity compared
with the shortest path routing. The congestion aware
routing algorithms investigated resulted in longer average paths, with 5% of
all routes exceeding the maximum shortest path in order to increase the overall
network capacity. When congestion aware
routing is combined with a resolution flex grid, a fivefold increase in network
capacity is afforded.
Sensor networks are composed of small sensing devices that have the capability to take various measurements of their environment such as temperature, sound, light etc. These devices are equipped with a processor and wireless communication antenna and are powered with a battery. Upon deployment in a field, they form an ad hoc network and communicate with each other and with data processing centers. The routing protocol in such networks has an important effect on congestion, especially with increasing sizes of the deployments. Congestion becomes worse when a particular area is generating most of the data. This may occur in some deployments when sensors in one area of interest are requested to gather and transmit data at a higher rate than others.
We believe that all data generated in a sensor network may not be equally important; some may have a low priority while others have a higher priority and hence differentiated service must be provided to these data. In such a scenario, routing dynamics can lead to congestion on specific paths. Since congestion is a self-compounding problem, these paths are usually close to each other which lead to an entire zone in the network facing congestion. We refer to this zone as the congestion zone or conzone.
Congestion can adversely affect the network in two ways.
First, it can lead to indiscriminate dropping of data, i.e. some packets of high priority might be dropped while others of less priority are delivered. This happens because sensor nodes are very simple devices and do not have the capability to differentiate packets (i.e. they do not have multiple queues for different priority levels). Second, congestion can cause an increase in energy consumption as links become saturated. This can lead to depletion of the limited energy available in the sensor nodes in the congested area.
In this paper, we examine data delivery issues in the presence of congestion in wireless sensor networks. We propose the use of data prioritization and a simple priority aware routing protocol, Congestion Aware Routing (CAR). CAR does not use multiple priority queues, a QoS aware MAC layer or specialized scheduling algorithms. The first step in this protocol is to dynamically discover the conzone. The second step is to enforce differentiated routing; high priority packets are routed in the conzone. Low priority packets generated outside the conzone stay outside while those generated within the conzone are routed out. In effect, conzone nodes are dedicated to serving high priority data which will enable them to provide better service and lengthen their lifetime.
Our extensive simulations show that CAR leads to a significant increase in the successful packet delivery ratio of high priority data to the sink, and a clear decrease in the average delay to CAR also provides low jitter which makes it able to support real-time multimedia applications. It also reduces the energy consumed in the nodes that lie on the conzone which leads to an increase in connectivity lifetime. We now consider the network formation process. Once the sink node discovers its surrounding neighbors, it broadcasts a “Build Mesh” message asking all nodes in the network to organize as a mesh. In that message the sink provides its ID and zero as its depth. Once a neighboring node hears this message it will check if it has already joined the routing network (i.e. if it knows its depth); if not then it sets its depth to one plus the depth in the message received and sets the source of the message as a parent.
Each node then rebroadcasts the Build Mesh message, with its own ID and depth to its neighbors. If a node is already a member of the network, then it will check the depth in the message, and if that depth is less than its own, then the source of the message is added as a parent. In that case, the message is not rebroadcast. In this fashion, the Build Mesh message is sent down the network until all nodes become part of this routing structure. Similar to TAG, the Build Mesh message can be periodically broadcast to maintain the topology and adapt to changes caused by the failure, addition or mobility of nodes.
1.3 SCOPE OF THE PROJECT:
Design goals of the congestion aware routing (CAR) protocol for sensor networks are to provide high priority data with better service quality compared to other routing schemes. These include higher delivery ratios, lower delays and lower jitter to support real-time data. We also aim at decreasing energy consumption which will lengthen the lifetime of the network. To achieve these goals, CAR divides the network into two regions; the congestion zone (conzone) and the remaining part of the network. While high priority data is routed through the conzone, low priority data is routed using the other nodes. Low priority data that originates outside the conzone is routed exclusively on off-conzone nodes using regular routing protocols such as low priority data that originate inside the conzone are efficiently routed out of the conzone.