Congestion Aware Routing in Sensor Networks

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1 Congestion Aware Routing in Sensor Networks Raju Kumar, Hosam Rowaihy, Guohong Cao, Farooq Anjum, Aylin Yener and Thomas La Porta Department of Computer Science and Engineering The Pennsylvania State University University Park, PA Telcordia Technologies Department of Electrical Engineering The Pennsylvania State University University Park, PA Abstract All data generated in wireless sensor networks may not be alike; some data may be more important than others and hence may have different delivery requirements. As deployment sizes and data rates grow, congestion arises as a major problem in these networks. This congestion leads to indiscriminate dropping of data, i.e. data of high importance might be dropped while others of less importance are delivered. In this paper, we take a look at data delivery issues in the presence of congestion in wireless sensor networks. We propose the use of data prioritization and a priority aware routing protocol - Congestion Aware Routing (). dynamically discovers the congestion zone (conzone) and enforces differentiated routing based on conzone and data priority. While high priority packets are routed inside the conzone, low priority packets generated outside the conzone use off-conzone nodes only for routing and those generated within the conzone are routed out. In effect, conzone nodes are dedicated to serving high priority data, thereby providing better service. Our extensive simulations show that as compared to, increases the fraction of high priority data delivery, decreases delay and jitter for such delivery while using energy uniformly in the deployment. Since reduces the energy consumed in the nodes, it leads to an increase in connectivity lifetime. Moreover, we look at issues related to realtime audio/video traffic and conclude that can effectively handle such data. I. INTRODUCTION 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 leads 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 (). 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 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 compared to. 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. The rest of this paper is organized as follows. In section 2 we motivate our work with a real life scenario and provide an overview of the solution. In section 3, we discuss some of the related work. We then provide details of our scheme in section and discuss some optimizations that can be applied to in section 5. Simulation details and results are presented in section 6 and we conclude in Section 7.

2 Low Priority Sink Battlefront High Priority Sink High Priority Sink Critical Area Low Priority Sink Low Priority Data High Priority Data Fig.. All data generated in a deployment may not be alike. An edge of a sensor network deployment may generate high priority data at a high rate. Such flash flood of important data causes congestion in a part of the deployment which is made worse by the presence of regular data being routed in that area. Fig. 2. Presence of congestion with routing in a network subjected to high priority data rate = 3 pps and background low priority traffic rate =.5 pps. Thin lines represent low priority traffic while thick lines represent high priority traffic. All critical nodes send high priority data to the high priority sink while rest of the nodes send low priority data to nodes and 6. II. OVERVIEW In this section we provide an overview of the problem and our solution. We provide motivation of our work through a realistic scenario. We also present our main assumptions. A. Motivation Consider the scenario of a battlefield in which an army battalion is deployed. An attack is concentrated on one portion of the field that we call the battlefront or critical area. The commanders and the data processing center are in a safe place on the other side of the battlefield. Before the battle starts, sensors are deployed throughout the field and fill the area between the data processing centers and any possible critical area. In such a scenario, there might be several data processing centers to collect different types of information: one for temperature, one to measure the presence of any lethal chemical gases, one to process a video feed and so on. There might also be one data processing center dedicated for collecting sensitive data from the sensors that would help the commanders lead their troops. Such data is assigned a higher priority than other data such as periodic temperature reports. Similarly, different levels of officers (platoon, company, battalion level) at different parts of the network may rely on the sensor network to collect data. At one particular moment, if a platoon is in danger, all sensor data distend to the commanding officer (sink) in that platoon may be assigned higher priority than data distend to other parts of the network. Such applications require data prioritization. The high priority data should receive better service, such as higher delivery ratios and minimal delays. It should also experience low jitter, especially for real-time data. The low priority data, such as periodic temperature readings or measurements of environmental conditions away from the critical area, do not need any special service. In fact, some low priority messages may be dropped or significantly delayed without severe consequences. B. The Problem of Existing Solutions Because in this scenario nodes in the network send all high priority data to a single sink, tree-based routing is the most appropriate. In this routing scheme, a spanning tree is built with the high priority sink as its root. The setup of such a tree uses controlled flooding from the sink to all nodes in the network. Low priority data, on the other hand, do not need to follow the same routing scheme. This is true because there may be multiple low priority sinks and a node might send data to any of them. For example, temperature readings might be forwarded to one sink while the motion detection measurements go to another sink, and so on. It has been shown in Hull et al. [6] that tree based routing schemes suffer from congestion, especially if the number of messages generated in the leaves is high. This problem becomes worse when we have a mixture of high priority and low priority traffic traveling through the network. This is because low priority messages will cross the tree that is formed to route high priority data in order to reach their destinations. Therefore even when the rate of high priority data is relatively low, the background noise created by low priority traffic will create a congestion zone that spans the deployment from the critical area to the high priority sink. Nodes in this zone become overwhelmed and indiscriminately drop high and low priority messages. These nodes also consume more energy compared to other nodes in the network and hence die sooner. This will lead to only sub-optimal paths being available to route high priority data, or a total loss of connectivity from critical area to the sink even though other nodes outside the conzone might still be alive. This problem can also occur if a single routing scheme is used to route both types of traffic. Figure 2 shows the congestion zone that is formed when is used for routing all data. In the figure, thin lines represents low priority traffic while thick lines represents high priority traffic. Node is the high priority sink and nodes and 6 are the low priority sinks. Only critical area nodes (5, 6, 7) send high priority data

3 to sink while other nodes in the network send data to either of the low priority sinks. The congestion zone in this case spans the area between the critical area and the high priority sink. C. Solution Overview A naive solution to this problem would be to stop generating or forwarding any low priority data. This is clearly not an acceptable solution because although low priority data are not of high importance, some amount of low priority data may still be needed to maintain an overall view of the sensor field. Information from other areas is also useful to determine if the critical area has changed its location. On the other extreme, we can add sophisticated weighted fair queuing and active queue management schemes to provide high priority data with better service. This goes against the design philosophy of keeping sensor nodes simple. Our goal is to provide a solution that is very simple, yet effectively provides high priority data with better service while not entirely eliminating low priority data. By simple we mean that our solution should not require any extra resources and should be possible to implement even in basic sensor devices. The basic idea behind our solution is to create a barrier between low and high priority data. This barrier consists of the boundaries of the conzone. Low priority data is always delivered using nodes outside the conzone via regular routing protocols such as, while high priority data is always delivered using nodes inside the conzone using a specially built high priority routing network. Note that low priority data generated outside the conzone is not allowed into the conzone. To achieve this, first, a multipath high priority routing network is built. Next, the conzone is dynamically discovered. Finally, all low priority data generated within the conzone is routed to nodes that lie outside the conzone to be delivered using alternative routes. Effectively, conzone nodes become dedicated to serving high priority data which enables them to provide better service and lengthens their lifetime. The details of our solution are discussed in section. D. Assumptions In this paper, we make the following assumptions. There is one high priority sink which receives all high priority data, and a contiguous part of the edge of the network which generates high priority data (critical area); the rest of the network generates low priority messages. There are several low priority sinks in the network. Low priority traffic can be destined to any of the low priority sinks. We also assume that nodes are densely and uniformly deployed. All nodes in the network are assumed to know their location coordinates. Finally, although our scheme can support sensor node mobility, we do not look at any mobility issues in this paper. III. RELATED WORK Routing data in sensor networks has an important correlation with the data dissemination techniques being used. This data dissemination can be pure push, pure pull or a hybrid mechanism. Push approaches use techniques from ad hoc routing protocols such as [6] and DSR [9] and are efficient when the number of queries in the network is large but waste the network bandwidth when the query frequency is low. Pull based mechanisms are more efficient when the data generation rate is higher than the data query rate. A well known data dissemination scheme for sensor networks is directed diffusion [7] which takes a data centric approach. Queries in this approach seek data that is named by attribute-value by using interests. The interests propagate in one direction which will attract data to travel on the reverse path of interest. Data is also aggregated at the intermediate nodes. Limited dynamics in the network makes this scheme relatively stable and hence it has high energy efficiency. However, directed diffusion was designed and tested for a small number of sources and lower data rates compared to what we consider here. Other techniques to disseminate data in sensor networks include SPIN [5] and GRAB [2]. Hybrid schemes that use both push and pull mechanisms include approaches like combs and needles []. Congestion in sensor networks that use tree based routing schemes, as TAG [2], is discussed in Hull et al. [6]. The main symptoms of congestion in sensor networks are packet drops due to channel errors or buffer overflows and starvation of nodes due to traffic from nodes one hop away from the sink. To overcome this problem three techniques are used in [6] - hopby-hop flow control, source rate limiting and prioritized MAC. To detect congestion, a node uses queue size and channel sampling. Prioritized MAC is based on the observation that a packet sent by a parent can be more important than one sent by a child if it is used for signaling congestion. Though [6] takes an important step to address congestion in sensor networks, it treats all data equally. Our work shows that a better service can be provided to high priority data by routing low priority data sub-optimally. Because of the resource constraints of sensor nodes, energy consumption and lifetime of nodes in sensor networks has received great attention recently. Nodes in a sensor network can be in receiving, transmitting, idle or sleeping state. Prior works [], [8] have shown that energy consumption in all states other than sleeping is high and is roughly the same. Hence, nodes should sleep more to conserve energy. Several papers discuss increasing network lifetime by introducing different sleeping mechanisms. The problem of energy conservation has been addressed at the routing layer by works like GAF [9], and at MAC layers by S-MAC [2] and B-MAC [7]. However, these works consider maximizing the lifetime of all nodes in the network whereas in this work we consider maximizing the lifetime of a particular zone that we consider critical, in addition to the overall life of the network.

4 Low Priority Sink Sink Low Priority Sink High Priority Data Low Priority Data Conzone Boundary Fig. 3. High priority data is routed using the conzone nodes. Low priority data originating inside the conzone is routed out. Low priority data originating outside the congestion zone does not use conzone. QoS in sensor network has also received interest in works like [3] and [8]. While [3] uses coverage as the metric for QoS, [8] addresses the problem of the optimality of the number of sensors generating data in the network. SPEED [] provides soft real-time requirements using feedback control. Akkaya et al. [] propose an energy aware QoS routing protocol which can support the delivery of real-time data in the presence of non real-time data by using multiple queues in each node in a cluster based network. The division of data into non-real-time and real-time data is similar to our concept of low and high priority data but it does not consider the impact of congestion due to excessive data rates in a localized part of the network. We also avoid using queuing schemes in favor of a priority aware routing scheme. Multipath routing has been discussed in [5], [], [3] to provide load-balancing and fault-tolerance. While [5] proposes disjoint paths, [3] proposes braided multipaths and compares them with disjoint multipaths. Our scheme of multiple parents in the routing network leads to a hybrid of these schemes. IV. THE CONGESTION AWARE ROUTING () PROTOCOL As was stated earlier, the design goals of the congestion aware routing () 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, 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. Those low priority data that originate Critical Area Build Mesh - {src, depth, x, y} Area Covered - {src, depth, x, y, x 2, y 2 } Discover Conzone From Edge (D-Edge) - {src, depth} Discover Conzone From Sink (D-Sink) - {src, depth, x, y, x 2, y 2 } Destroy Conzone - {src, depth} Fig.. Control messages used in and their components. Build Mesh message is used to build the routing mesh with the high priority sink as root. Area Covered messages are used to discover edge nodes that a node connects to the sink via a shortest path. D-Edge messages are used to discover conzone from critical area to the sink while the D-Sink messages are used to discover the conzone from sink to the critical area. Destroy Conzone messages are used to destroy the conzone and restore regular routing in the deployment. inside the conzone are efficiently routed out of the conzone. Figure 3 shows an example of this differentiated routing. In the following, we discuss the details of ; we explain how builds the high priority routing network. Next, we discuss the conzone discovery and destruction mechanisms. Finally, we talk about routing low and high priority data inside the conzone. Figure shows the formats of the different control messages used in. A. High Priority Routing Mesh Formation After deployment of the sensor nodes, the high priority data collection center (the sink) initiates the process of building the high priority routing network that is used to deliver high priority data. This network covers all nodes because at the time of deployment, the sink will usually have no information on the whereabouts of the critical area nodes. Also, the critical area can change locations during the network lifetime and hence all nodes should be able to route high priority data. Because all high priority data will be destined to a single sink, the routing network is based on a minimum distance spanning tree rooted at the sink. As with TAG [2], this tree structure ensures that all nodes have shortest paths to the sink. However, instead of every node having a single parent in the tree as in other schemes, we allow nodes to have multiple parents, i.e. a node that has multiple neighbors with depths (distances in hops to the sink) less than its own considers them all as parents as shown in Figure 5. This allows for loadbalancing and provides multi-shortest-path routing making this routing network more resilient to failures. More importantly, this multi-parent feature allows sensor nodes to route data with different priority to different places. 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

5 High Priority Sink Level Nodes Level 2 Nodes Level 3 Nodes Fig. 5. In a dense deployment, multiple nodes can be parents of a node. Each parent lies on different shortest path routes to the sink. Such parents can be used to provide multi-path routing inside conzone and to enable efficient routing out of low priority data generated inside the conzone. 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 [2], 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. B. Dynamic Conzone Discovery and Destruction After building the high priority routing network, the next task is to dynamically discover the conzone. As mentioned earlier, the conzone is formed when one area is generating high priority data. We refer to this area as the critical area. Conzone discovery is dynamic because the critical area is usually not fixed and can change during the lifetime of the deployment. The conzone can be discovered and destroyed either from the edge nodes to the sink or vice-versa. If nodes on the edge of the network detect a high priority event, they initiate conzone discovery to the sink to provide better service for the imminent high priority data flood. At other times, the sink will have advance knowledge of an impending event and it can prepare for a flood of high priority data from the critical area by initiating the conzone discovery itself. Similarly, the conzone can be destroyed both by the critical area nodes or by the sink. Conzone discovery and destruction schemes are summarized in Figure 6. The algorithms allow nodes, in a distributed fashion, to determine if they are on a potentially congested path between the critical area and the high priority sink. If they are, they mark themselves as on-conzone. ) Conzone Discovery from edge: We first look at conzone discovery from the edge to the sink. In this case, critical area nodes detect an event that triggers discovery. A conzone must be then discovered from that neighborhood to the sink on which nodes will be dedicated to deliver high priority data. To do this, critical area nodes broadcast discover conzone from edge (D-Edge) messages. This message includes the ID of the source and its depth. The depth is included here, to ensure that nodes do not respond to messages heard from the other direction. When a node hears more than a threshold, α, of distinct D-Edge messages coming from its children, the node marks itself as on-conzone and propagates a single D-Edge message. In our scheme, α is a linear function of the neighborhood size (i.e. number of nodes within communication range) and the depth of the node in the routing network. For node x with depth d x and neighborhood size n x, we have α x = β dx d x n x Since the depth and neighborhood size are different for every node, α is different. Setting β correctly, for different depths, ensures that the conzone is of the appropriate width. If β is small such that nodes become on-conzone after the reception of a single D-Edge message, the conzone will be very wide which will degrade the service of low priority data. On the other hand, if β is large, more D-Edge messages are required to be received in order for a node to join conzone. This will make the conzone very narrow. In this scheme, α needs to be directly proportional to the neighborhood size because the larger the neighborhood size, the more D-Edge messages will be received. Depth must also be taken into account because if the threshold is the same for different depths, the conzone will become very narrow. This is because a node that receives several D-Edge messages will, for efficiency reasons, only rebroadcast a single message. So, the number of received D-Edge messages for nodes closer to the sink will be lower. An important goal of the conzone discovery algorithm is to split the parents and siblings in the high priority routing network into on-conzone and off-conzone parents and siblings respectively. This is important because this knowledge is used when routing data inside the conzone. Initially, all parents and siblings are marked as off-conzone. Because a node will forward D-Edge message only if it becomes on conzone, when a node hears such a broadcast from its parent or sibling, it marks that neighbor as on-conzone. 2) Discovery from sink: We have also considered conzone discovery from the sink. This will be useful if the sink knows of an event in advance and desires to pre-configure the conzone. After the high priority routing network formation, each node discovers the area it covers approximated by a rectangle. Every edge node then reports its coverage area to its parents using the Area Covered message. This message contains the ID of the node and the area it covers. The parent then combines the coverage area of its children and sends another Area Covered to its parents. This continues until the sink is reached. Hence, the rectangular area that a node knows is a compact representation of the edge nodes that it connects to the sink. To initiate conzone discovery the sink broadcasts a discover

6 Local variables: Set of off-conzone parents: P Off = {p, p 2,..., p n } Set of off-conzone siblings: S Off = {s, s 2,..., s m } Set of children: Children = {c, c 2,..., c k } Node s on-conzone status: On Conzone = False D-Edge threshold: α x = β dx d x n x Set of on-conzone parents: P On = {} Set of on-conzone siblings: S On = {} D-Edge messages received: D-Edge received = Conzone Discovery From Edge: if node x receives D-Edge from child c i then if On Conzone == False then if D-Edge received > α x then On Conzone = True if x is not sink then broadcast D-Edge with d x else D-Edge received ++ else if node x receives D-Edge from parent p j then P Off -= {p j } P On += {p j } else if node x receives D-Edge from sibling s l then S Off -= {s l } S On += {s l } Conzone Discovery From Sink: if node x receives D-Sink from parent p i then P Off -= {p i } P On += {p i } if On Conzone == False then if x has an edge child c j criticalarea then On Conzone = True if x is not edge node then broadcast D-Sink with depth x else if node x receives D-Edge from sibling s l then S Off -= {s l } S On += {s l } Fig. 6. Algorithm for conzone discovery and destruction in Routing Low Priority Data: if P Off {} then send data to any p P Off else if a sibling s S Off then send data to s else send data to the farthest parent p from dividing line Routing High Priority Data: if P On {} then send data to any p P On else if a sibling s S On then send data to s else send data to any p P On P Off Fig. 7. Routing Algorithm for for low and high priority data inside the conzone. While high priority data is routed via multiple shortest paths available, low priority data is routed out of the conzone efficiently. conzone from sink (D-Sink) message that is propagated down the network. This message contains the ID of the source and its depth, along with the x-y coordinates of diagonally opposite ends of the rectangular area that includes critical area nodes. When the D-Sink message arrives at a node with the coordinates of the rectangular region of the critical area, it will only mark itself as on-conzone if its coverage rectangle intersects with the received rectangle. It will then propagate the message down the network so that other nodes can be added to the conzone. As with to conzone discovery from the edge, conzone discovery from sink also splits the parents and siblings into on-conzone and off-conzone. Initially, all parents and siblings are marked as off-conzone. Because a node will forward D- Sink message only if it becomes on conzone, when a node hears such a broadcast from its parent or sibling, it marks that neighbor as on-conzone. 3) Destruction: Since the presence of a conzone leads to sub-optimal routing for low-priority data due to on-conzone nodes being dedicated to serve high priority data, the network needs a mechanism for destroying the conzone once it is of no use. The conzone is destroyed from the sink by the propagation of destroy conzone messages towards the critical area. Only an on-conzone node sends such a message and sends exactly one such message to avoid the problem of broadcast flood. Conzone destruction from the edge propagates the Destroy message in the other direction, i.e. from critical area nodes towards the sink. During conzone destruction, when a node hears a neighbor broadcasting a destroy conzone message, it marks that neighbor as off-conzone. Hence conzone destruction restores regular routing in the network.

7 High Priority Sink A X Fig. 8. Routing out low priority data based on the knowledge of the line connecting the high priority sink to the center of critical area. In the absence of off-conzone parents or siblings, low priority data is routed out to the parent farthest from the line. This scheme provides efficient routing out of low priority data. C. Differentiated Routing Once the conzone is discovered, our next task is to route high priority data on the conzone and route the low priority data off the conzone. Since the critical area is a part of the conzone, all high priority data will be generated inside the conzone. Routing of high priority data in this case is very simple; a node always forwards the data to one of its parents. This parent is chosen randomly from the parent list to balance the load between them. This continues until the sink is reached. If for some reason the links to all parents are broken, because of node failures for example, a node will forward the data to a sibling which is on the conzone. If that is impossible it will forward the data to any of its neighbors hoping that it can return to an on-conzone node. All low priority data generated inside the conzone must be routed out. There are two cases to consider. In the first, an on-conzone node that generates or receives low priority data has a parent or sibling that is off-conzone; in the second, it does not. In the first case, when an on-conzone node gets a low priority message it forwards it to an off-conzone parent, if there are any. Otherwise the low priority data is forwarded to an off-conzone sibling (which is a node with the same depth). If there are no parents or siblings that are off-conzone, we resort to the following method. After discovering the conzone, the sink sends a message through the conzone which contains the coordinates of a line that cuts the conzone in half. This line connects the sink to the center of the critical area. Using this information and its own coordinates, a node can determine on which half of the conzone it lies and hence route low priority data to the parent that is closest to the conzone boundary, i.e. farthest from the line. With the assumption of uniform deployment density, this ensures that all low priority data generated inside the conzone is routed out efficiently and along the shortest path. For example, let us say that in Figure 8 both nodes X and Y have some low priority data that needs to be forwarded. B Y Critical Area The conzone boundaries are represented with dotted lines and the dividing line is represented by a solid line. A dotted circle around both X and Y represents their receiving range. Within the circle, darker nodes represent the parents. Since X lies in the lower half of the conzone, it will route all low priority data to A which is the farthest parent from the dividing line. Node Y lies in the top half of the conzone, so it routes low priority data to B which is again the farthest parent from the dividing line. It is important to note here that low priority routing decisions inside the conzone are static. That is once a node decides to which neighbor it is going to forward low priority data, it uses the same parent for all low priority packets. Of course, if that parent fails or moves an alternative must be found using the same rules. In-conzone routing for both low and high priority data is summarized in Figure 7. V. OPTIMIZATIONS By discovering the required conzone and using differentiated routing we can free the conzone from most of the low priority traffic traveling through the network. This will help nodes on the conzone to provide better service to high priority data. In this section we look at two improvements that can help to eliminate any low priority traffic in the conzone. We also discuss the dynamic scheme. A. Eliminating Low Priority Traffic in Conzone In, low priority data is generated by all nodes including those on the conzone. Low priority data that is generated outside the conzone stays outside; however, while the low priority data generated inside the conzone is being routed out, it requires the conzone nodes to dedicate some of their resources. This will degrade the service for high priority data. To better serve high priority data, we disable any low priority message generation by on-conzone nodes. This reduces the amount of traffic that is routed inside the conzone which in effect reduces the delay seen by high priority data. We call this enhancement. B. Eliminating Low Priority Traffic Around the Critical Area In the previous improvement, we eliminated low priority traffic inside the conzone. However, because of the shared nature of the wireless channel, high priority messages can be dropped by the critical area nodes themselves due to collisions with other low priority data from neighboring nodes. This is especially true if the amount of low priority traffic surrounding the critical area is large. As a second improvement, we disable sending of low priority data in all nodes that are within the communication range of any critical area node. Since nodes know their neighbors and their status, once a node discovers that one of its neighbors is on the critical area, it disables generation and forwarding of any low priority data. In the rest of this paper, we call this enhancement +.

8 C. Dynamic can work in a dynamic mode. If the rate of low priority data is not affecting the service provided to high priority data then plain is used. This allows more low priority data to be delivered. As the rate of low priority increases and service provided to high priority data degrades, can be enabled to ease the congestion inside the conzone. The timing of this transition can be determined by a threshold. Once the delivery ratio of high priority data drops beneath that threshold, the sink will send a message to all conzone nodes asking them to stop transmitting any low priority data. In the same manner, + can be enabled to provide even better service. Of course, as we move from to or + we will lose more low priority data. VI. PERFORMANCE EVALUATIONS In this section, we discuss the results of our simulations. First, we describe our experiment setup, then, we discuss the different results in detail. A. Simulation Setup The simulations were conducted in NS-2 with a deployment area of size meters. In this area, 2 nodes are placed in a 5X8 grid with separation between neighboring nodes along both axes being meters. A grid is used for simplicity and is not a requirement of our solution. The TwoRayGround propagation model is used to determine the receiving thresholds for varying receiving ranges and 82. is used as the MAC layer operating at Mbps. The deployment that was used is shown in Figure 9. Three nodes -, and 6, are designated as sinks for all the data that is generated in the deployment. While nodes and 6 receive all the low priority data, node receives all the high priority data. Nodes 5, 6 and 7 form the critical area and send high priority data. The rest of the nodes in the network, other than the three sinks and three critical area nodes, send low priority data to either of the low priority sinks. Results were recorded when the system reached steady state. The implementation of is based on implementation available in NS-2. In the results below, we compare the performance of, and + against. We do not show comparisons with DSR because over large multi-hop networks, DSR fails to route any high priority data successfully. DSR is intended to work over networks with a small number of hops, as reported in [2], and does not perform very well in large sensor networks such as the ones we are considering here. We also considered Directed Diffusion [7] but determined that it is unable to route any high priority data successfully due to the large control overhead involved. As with DSR, Directed Diffusion is not intended for such applications. It was mainly designed to work in cases where the number of sinks and senders is small. Tree based routing schemes such as TAG [2] do not perform well in our experiments since they are Low Priority Sink High Priority Sink Low Priority Sink Fig. 9. Simulation scenario consisting of 2 nodes deployed in meters field. Nodes and 6 are the low priority sinks while node is the high priority sink. Nodes 5-7 are the critical area nodes and send high priority data. Rest of the nodes send low priority data. prone to congestion and are not able to handle high data rates as was shown in [6]. In our simulation, we build the conzone from the edge. Nodes are added to the conzone if they receive at least 2 D- Edge messages. So, the value of β for different depths were found using an α of 2. For example, with transmission range of 3 meters, the neighborhood size of a node distant from the edge is equal to 36. For a node at depth 3, β 3 is set to.8. For nodes with depth 2, β 2 is set to.27 and so on. B. Simulation Results We analyze two aspects of the -based schemes: feasibility and performance. For feasibility, we have analyzed delay to form the routing network and discuss conzone discovery and destruction delays. Our simulations show that these delays are all acceptable. As the range increases, the routing network formation delay stays under seconds. The delays for both conzone discovery and destruction tend to decrease as the number of hops from the edge of the network to the sink decreases. In our simulations, the conzone discovery and destruction delays were found to be less than second. Because this delay is small compared to the duration of a flood of high priority data in a realistic deployment, is a feasible solution that can quickly adjust to different events. Next, we first give a high level comparison of the protocol and the protocol, and then present detailed comparisons by varying the transmission range, the low priority data rate and the high priority data rate. ) A High Level Comparison of and Using Routing Views: Figures (a)-(d) depict the routing of low and high priority packets by,, and +. Lightly shaded edges denote low priority data while heavily shaded edges denote high priority data. The thickness of edges is directly proportional to the number of packets routed over them. A circle around a node denotes that the node has dropped high priority packets; the radius of the circle is directly proportional to the number of high priority packets dropped. Also, the larger nodes in -based schemes denote the nodes that belong to the conzone. Critical Area

9 (a) (b) (c) (d) + Fig.. Routing Views for Range = 3 meters, Low Priority Data Rate =.5 pps, High Priority Data Rate = 3 pps From Figure (a), we observe that routes both low and high priority data along the same paths. As a result, many high priority packets are dropped at the edge. Also, our simulations show that does not route all data along the shortest paths available in the network, which results in some data experiencing higher delay. The conzone can be observed in Figures (b)-(d). As shown in these figures, the set of shortest path routes from the critical area to the sink route all high priority data while the rest of the network routes low priority data. Low priority data generated inside the conzone for is effectively routed out using the minimum number of hops inside the conzone. For the routing views shown in Figures(a) and (b), our simulations results show that routes only 5.7% of high priority data successfully while delivers 96.2%. These figures also show locations of packet drops occurring in the deployment. Here, nodes which drop packets are marked by a surrounding circle whose radius is directly proportional to the number of high priority packets dropped by the node. To illustrate nodes dropping high priority packets clearly, Figure (a) represents the routing diagram for without the lines for low and high priority data for clarity. We observe that drops high priority packets mostly at the source. The other nodes that drops packets are almost all multiple hops away from the sink. s sub-optimal routing leads to high priority packets being routed along the edge of the network, and hence a considerable number of packets are also dropped at the edge. Though critical area nodes drop packets in as well, they drop considerably fewer than, as shown in Figure (b). The packets drops of are magnified by the presence of low priority traffic that cuts across the routes used to route high priority packets. Nodes that lie on the intersection of such low and high priority routes in face congestion and indiscriminately drop packets. For example, in Figure (a), manages to route some high priority data to nodes 26 and 2 which are and 2 hops aways from the sink, respectively, but these nodes fail to deliver the high priority data any further. This does not occur in because differentiated routing ensures that conzone nodes face little congestion due to low priority traffic. The reasons for and dropping high priority packets are analyzed in Figure (b). The most prominent reason for dropping packets is MAC callbacks. This occurs when the MAC layer fails to route a packet after several retransmission attempts. The only clear reason for this failure is congestion which makes it very difficult for a node to capture the channel to transmit data. Other reasons for packet drops include buffer overflow and no route found. Figures (c) and (d) show the routing views of

10 Number of High Priority packets dropped No Route MAC Callback Buffer Overflow (a) Drop View For (b) Comparison of Packet Drops for and. Fig.. High Priority Packet Drop Analysis for Range = 3 meters, Low Priority Rate =.5 pps and High Priority Rate = 3 pps. Fraction of High Priority data received Receiving Range [mt] Total Delay for High Priority Data [seconds] Receiving Range [mt] Standard Deviation [seconds] Receiving Range [mt] (a) High Priority Data Delivery Fraction (b) High Priority Data Delivery Delay (c) Standard Deviation of High Priority data delivery delay Path Length [number of hops] Receiving Range [mt] Energy used [joules] Receiving Range [mt] Fraction of Low Priority data received Receiving Range [mt] (d) High Priority data path length (e) Maximum energy used in a node (f) Low Priority Data Delivery Fraction Fig. 2. Results for varying Range with Low Priority Data Rate =.5 pps and High Priority Data Rate = 3 pps and +, where nodes on the conzone do not generate low priority data. Additionally, for +, all nodes within one hop of the critical area nodes do not generate low priority data. It can be seen from the figures that -based schemes effectively perform differentiated routing. Also, all high priority data uses multipath routing. This not only provides the shortest path, but also balances the load between on-conzone nodes. Moreover, it adds fault-tolerance to our routing scheme. If an on-conzone node faces congestion for a small period of time, this does not lead to dropping of all packets of a particular critical area source for that duration. Other packets of this specific critical area source that select other shortest path routes leading to the sink will still be routed successfully. 2) Varying the Transmission Range: In this experiment, we ran the simulations over transmission ranges of 9, 3, 7 and 2 meters. As the transmission range increases, the number of hops from the edge of the network to the sink decreases from 6 to 3. The low priority data rate of each node, other than the critical area nodes and the sinks, was set to.5

11 packets/sec (pps) while the high priority data rate of critical area nodes was set to 3 pps which corresponds to the data rate needed for full motion video. Figure 2(a) plots the fraction of high priority data delivered to the sink. As the transmission range increases, the network becomes more congested and more collisions occur. As a result, the performance of degrades severely and it routes less than % of high priority data successfully. On the other hand,, and +, all route a higher fraction of the data. At ranges larger than or equal to 3 meters, all these schemes route more than 9% of the data. We note that + routes more data than, which in turn routes more data than. As the transmission range increases, the increased congestion also has an impact on the performance of in terms of the average delay for the delivery of high priority data to the sink. As Figure 2(b) shows, as the range increases, delay for increases while delay for all -based schemes decreases. As mentioned in the previous subsection, this is due to the considerably more congestion that nodes in face compared to. Figure 2(c) shows the standard deviation for the delivery delays of high priority packets delivered to the sink. Standard deviation increases for as the range increases while it decreases steadily for -based schemes. This happens because as the delivery ratio in decreases, packet delays become non uniform, resulting in a larger standard deviation. Among -based schemes, and + provide smaller standard deviations than itself. The results for standard deviation show that the level of congestion within the conzone is stable. In our simulations, we often observed that routes high priority data sub-optimally, i.e. despite shorter paths being available, does not necessarily route data along such paths. As Figure 2(d) shows, -based schemes always find a shorter path on average. Routing along shortest paths has several implications including less congestion and less overall network energy usage. As a result, s use of longer paths to route high priority data reflect the sub-optimal performance of the protocol. Congestion has a debilitating impact on the network in terms of the energy used. Figure 2(e) shows the maximum energy used by any node in the deployment. This includes the energy used to route all possible traffic, not just high priority packets. The energy used by is more than that for. uses less energy and + uses the minimum energy among all the schemes. Also, as the range increases, the maximum energy used by a node using stays almost constant while it decreases for all -based schemes. This clearly indicates that congestion has a greater impact on while based schemes successfully subvert congestion and thereby its impacts. It also shows that -based schemes consume energy more uniformly. Our simulation results also show that the average energy consumed over all nodes in is less than that of. Although our focus is to provide better service to high priority data, low priority data can still be of importance and should not be totally neglected. Figure 2(f) shows the fraction of low priority data routed by the different schemes. We note that delivery ratio decreases sharply as the range increases. This is because tries to route low priority data through the conzone causing many of the low priority packets to be dropped. prevents low priority data from entering the conzone; this forces all such packets to find alternate routes. These routes are less congested, and hence more packets can be delivered over them. Note that and + deliver less low priority data compared to because they generate less data. These simulations show the effectiveness of -based schemes across varying ranges. 3) Varying Low Priority Data Rate: In this set of simulations, the low priority data rate is varied while the range is set to 3 meters and the high priority data rate of each critical area nodes is set to 3 pps. The purpose of this set of experiments is to test tolerance of in face of an increase in low priority data rate. As shown in Figure 3(a), as the low priority data rate increases, the fraction of high priority data packets routed by to the sink sharply falls to zero. This is expected, since in, low priority traffic is competing with high priority traffic. In contrast, though the fraction of data routed successfully to the sink by the -based schemes decreases, these schemes still route more than 6% of the data even when does not route any data at all. This shows that based schemes are successful in separating the two levels of priority in such a way that increasing low priority data rate does not have a large impact on the delivery ratio of high priority data. As low priority data rate increases, packets also face increased queuing delays at intermediate nodes in the network. Figure 3(b) shows that as the low priority data rate increases, the delays for and increase while they stay almost constant for and +. Standard deviation results, in Figure 3(c), show that -based schemes do a better job compared to. As with the previous experiment, Figure 3(d) shows that -based schemes always use shorter routes. Figure 3(e) shows the maximum energy used by a node. Since routes a very small fraction of high priority packets and a smaller fraction of low priority data packets, the maximum energy used by stays the same as the rate of low priority data varies. The energy consumed in the -based schemes increases as the low priority data rate becomes larger. In all cases it is lower than. Figure 3(f) shows that as the low priority data rate increases, the fraction of low priority data successfully routed by drops. For it decreases from around 9% to 85%. For and + this fraction stays almost constant. ) Varying High Priority Data Rate: In the final set of experiments, we varied the high priority data sending rate of critical area nodes and fixed the low priority data rate of

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