A New Gradient-Based Routing Protocol in Wireless Sensor Networks Li Xia, Xi Chen, and Xiaohong Guan Department of Automation, Tsinghua University, Beijing, 100084, China
[email protected] http://www.sensornetwork.net
Abstract. A new gradient-based routing protocol is proposed in this paper. It takes into account the minimum hop count and remaining energy of each node while relaying data from source node to the sink. The optimal routes can be established autonomously with our protocol. A simple acknowledgement scheme, which can be implemented without extra overheads, is proposed. Our protocol also employs data aggregation to save transmission energy. To handle the frequent change of the topology of the network, one scheme for topology update is provided. At last, simulation results illustrate the effect of system parameters on the protocol performance.
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Introduction
Wireless sensor network is a new technology and it will have significant impacts on human’s future life [1∼3]. It consists of a large number of sensor nodes densely deployed in an area of interest. It has a wide range of applications such as military sensing, physical security, environment monitoring, traffic surveillance and etc. A system of networked sensors can detect and track threats and be used for weapon targeting and area denial. As sensor nodes are limited in power, computational capacities, and memory, sensor networks in general pose considerable technical problems in data processing, communication, and sensor management. One fundamental problem is communication protocol. Routing algorithm is one important research topic in wireless sensor network. After sensor nodes gathered the data of circumstance, such data need to be transmitted from the from source nodes to the sink node. Due to limited energy, source node usually cannot send the data to the sink directly. The data need to be relayed by medium sensor nodes. There may be many routes from the source to the sink. Routing is to find the right route. When designing a routing protocol, we need take into account node’s energy efficiency (or the lifetime of the sensor network), and possible change of network’s topology due to the failure of nodes or various other reasons. There are several routing protocols proposed in sensor networks. However none of them is always perfect for every situation. For example, LEACH is a good one in many situations [4]. In LEACH, the cluster head node can aggregate Z. Wu et al. (Eds.): ICESS 2004, LNCS 3605, pp. 318–325, 2005. c Springer-Verlag Berlin Heidelberg 2005
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the data of its cluster member nodes so that greatly reduce packet transmission in sensor network. However, when the sensor nodes measure different physical phenomenon, this scheme is no longer effective. Moreover, in LEACH algorithm the cluster head nodes directly communicate with the sink node. The head node will consume much more energy when it is far away from the sink node. And the head node may run out of its energy soon. As we know, the relationship between wireless communication energy consumption E and transmission distance d is: E∝d k , where k is usually 2∼4. Hence short distance multiple hop communication is preferable to long distance direct communication in senor network. In this paper, we propose a short distance multiple hop routing protocol. It is derived from the minimum hop count approach. In the traditional minimum hop count algorithm, hop count is the only metric, which measures the quality of route. In our protocol, we not only consider the hop count but also adopt the remaining energy of each node as the metrics of the Quality of Service (QoS) of the link. The goal is to prolong the network’s lifetime by optimizing the energy consumption. In the route setup stage, when one node receives the setup message, it waits for a short time Twait for messages with better metric, which may arrive during this period. When Twait expires, the node rebroadcasts the message with the best metrics in all messages it has received. By this way, the number of setup messages in the whole network can decrease greatly. According to the omni-direction property of radio signal, when one node relays a packet to its neighboring nodes, it can hear this packet if its neighbor node rebroadcasts this packet. It makes the node be sure that its neighbor node has received the packet. The rebroadcast packet also serves as an acknowledgement from the neighbor node. In our protocol, we adopt such simple acknowledgement scheme. Relay nodes are augmented with data aggregation function. When relaying packets, they can aggregate similar packets into one packet. Then send out the new aggregated packet. This scheme is helpful to save energy for data transmission. At last, by simulation, we explore the effect of system parameters on protocol performance and show that this routing protocol performs well comparing with some other traditional routing protocols.
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Description of the New Routing Protocol
In wireless sensor networks, since the energy of each sensor node is limited, energy conservation is of the most importance to prolong the lifetime of networks. The routing protocol we propose in this paper can optimize the transmission energy and equalize the energy consumption of all sensor nodes. It can prolong the operating lifetime of the network. Moreover, it has many other features to improve the routing performance. The schemes and algorithm are described one by one in the following subsections.
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2.1
Routing Establishment Algorithm
Routing establishment algorithm is the base of our routing protocol. It aims to establish the cost field and find a minimum cost path from the source node to the sink. The cost metrics include current transmission energy consumption, remaining energy and so on. One similar concept can be found in [5]. Initially, each sensor node sets its own cost metric R to ∞ but the sink node’s cost is 0. The sink broadcasts a message containing its own cost metric Rsink =0. This message is rebroadcast and updated throughout the network. Suppose node M rebroadcasts this message containing its own cost RM and node N receives this message. Node N compares its own cost metric RN with RM + CM,N , where CM,N is the transmission metric between node M and N . If RM + CM,N is smaller, node N sets its cost RN to RM + CM,N and records node M as its relay node in its relay list. Then node N rebroadcasts this message containing RN to its neighbor nodes. If RM + CM,N is larger than its own cost metric RN , node N just discards this message without any updates. This process will continue until the message propagates throughout the network. At last, the cost field of the network is established. Each node can find a minimum cost path back to the sink. This path is also called the gradient of the cost field. In the minimum hop count routing protocol, the cost metric is the hop count between two nodes. The communication metric CM,N is set to hop count between M and N . The source node will find the minimum hop count path to the sink. In our protocol, we use hop count and remaining energy of each node as the metrics. The cost CM,N is set to hop-count /EN , where hop-count is the hop count between node M and N , while EN is the remaining energy of node N . This metric can balance the energy consumption of the network. It will prolong the operating lifetime of network. Furthermore, we can use the QoS of the link as one of the metrics of the network. We will discuss this issue in another paper. Figure1 illustrates a simple example of the procedure of generating minimum cost gradient. The source node has three routes to reach the sink, route1: S–>D– >A, rout2: S–>B and route3: S–>E–>C. At the routing setup stage, the source node will receive three different setup messages. The cost metric of route1 is: 1/40+1/40=1/20. It is the smallest cost in these three routes. So the source node will choose route1 as its optimal route. It records node D as its previous relay node. After a period, the nodes in route1 may have low energy level. In this situation, route2 may be chosen as the good route. In order to improve the routes’ fault tolerance ability, we provide several alternative relay nodes for each node. For example, we provide 3 alternative relay nodes for each node. During the routing establishment stage, the second and third best metric nodes are also recorded as members of relay nodes set. In figure1, the relay nodes table of source node is {D, E, B}. The default relay node is D. If the default route is down, we can use the alternative route quickly. This scheme will save a lot of route rebuilding cost. It is also very easy to implement without extra payments.
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Fig. 1. The procedure of generating minimum cost gradient 2.2
Back-Off Waiting Scheme
When the route setup message propagates through the network, this message may be replicated and rebroadcast many times. Because of the impact of random delay of retransmission, one node is likely to receive and rebroadcast the setup message many times. If so, the node will consume much energy and the explosive setup messages will aggravate the congestion of the network. We use a back-off waiting scheme to alleviate this effect. When a node receives the setup message whose cost metric is smaller than its own metric, we update it but don’t rebroadcast it instantly. It will start a back-off timer and wait for a constant time Twait . During this period, if all the received setup messages’ metrics are not better than this one, it rebroadcast the message containing its own metric. If it receives the message whose metric is better than this one, it will update its metric and reset the back-off timer. This scheme can greatly reduce the number of setup messages in the network. If Twait is large enough, we can see that each node will only rebroadcast the setup message once. It can save a lot of communication energy. The relationship between the number of messages and the parameter Twait is illustrated by simulation in section 3. 2.3
Acknowledgement Scheme
It is well known that wireless communication is frequently influenced by various factors such as multi-path fading, interference and etc. The communication may fail sometimes. On the other hand, sensor nodes usually turn off to save energy. It is possible that the relay nodes are power off when the data packets pass them. So when the packets are relayed from source nodes to the sink, we need to consider the situation when the relay nodes fail to receive the packets. We propose a simple acknowledgement scheme to handle this problem. This scheme is easy to implement in the sensor networks. It uses the omni-directional radio signal property to acknowledge a received packet. When the relay node
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receives the packet and relays it, the sending node will also receive this relayed packet. If the sending node didn’t receive it, it means the packet is possibly lost because the relay node is power off or the radio channel is degraded. The sending node will then degrade this relay node’s metric and choose another alternative relay node to transfer the packet. This acknowledgement scheme can improve the success rate of packet transmission. It only brings a little extra payment caused by the sending node’s listening to the relayed packets. We can use this scheme to improve the routes’ QoS a lot with a small extra energy consumption. 2.4
Routing Update Scheme
In the sensor network, some nodes may be mobile and can move around in the area of interest. Moreover, in order to save energy, sensor nodes may turn off from time to time. Hence, the topology of the network may change frequently. On the other hand, the medium relay nodes may consume energy more quickly than other nodes. Their energy level drops much faster and, after a certain period, their energy level may become too low to take charge of relaying packets. We need a timely scheme to update the routes. If one sensor node gets ready for turning off, it needs to inform its neighbor nodes. If the neighbor node’s relay list includes this node, this node will be indicated as an inactive node in the list. When this node turns on, it also needs to announce this event so that its neighbor nodes can mark it as an active node in the relay node list. If a node does not know its relay node has turned off, when it sends packet to the relay node, it will not receive any acknowledgement. In this situation, it updates its relay node list and marks this node inactive until receiving the active announcement from the relay node. When a node moves to a new area, the node needs to update its relay node list. It sends out a request message to ask for its neighbor nodes’ cost metric and chooses the node with the smallest metric as its relay node and then updates its relay node list. Because relay nodes consume their energy more rapidly, the entire routes need to be updated periodically. Such update can be done by following the same procedure as the routing establishment scheme in section 2.1, i.e. the sink broadcasts the setup message and rebuilds the whole routes. 2.5
Data Aggregation
In the sensor network, when an event of interest occurs, the nearby sensor nodes can detect it and send the related information back to the sink. These data may be similar to each other. If we aggregate these packets, relay nodes can save a lot of transmission energy. Hence data aggregation is useful for the sensor network. In our routing protocol, we implement data aggregation function in the medium relay nodes. When the relay nodes receive data packets, it stores them
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and waits for other new packets’ coming. After a certain waiting time, the relay node aggregates these packets, which are newly received. Moreover, the medium nodes can utilize their computation resources to analyze the received packets, and only transfer useful information to the sink. This scheme can reduce the amount of data to be relayed in order to improve the energy efficiency and prolong the network’s lifetime.
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Simulation Experiments
In simulation experiments, we consider the area of interest as a square of 100 × 100 m2 , where 900 sensor nodes are randomly scattered in the area. The transmission range of each node is 10 meters. The sink node is at down-left corner (0, 0). When a node sends a message to its neighbor, there will be a transmission delay Tdelay , which is caused by radio channel interference or slow transmission data rate. We suppose the delay Tdelay is uniformly distributed in [0, 50ms]. The full battery energy of each node is assumed to be 10,000 units and sending one packet consumes 2 units of energy. In routing establishment stage, the sink sends out setup message and sensor nodes involve in relaying it to the network. By simulation, we study the relationship between the number of relayed setup messages and the back-off waiting time Twait . From Figure 2 we can see, when Twait is larger, the total message for setting up the network’s routes will be smaller. When Twait is larger than 40ms, the total number of setup message is about 900, i.e., when Twait is large enough, each node only rebroadcasts the setup message once. So the back-off waiting scheme is quite effective for saving the energy consumption when establishing the network’s routes. However, it delays the establishment of routes for a while. The size of such delay is basically proportional to Twait , as illustrated in Figure3.
Fig. 2. The relationship between the number of setup messages and Twait
Fig. 3. The relationship between routes establishment time and Twait
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In Figure 4, we compare the performance of our new algorithm with the original gradient-based routing algorithm on node exhausting rate. The initial battery energy of each node is a uniform random number between 0 and 10,000. The source packets are produced randomly in the whole network. If the nodes with little energy are still used as relay nodes, they will be exhausted soon. Our routing algorithm can prevent this situation from happening. Simulation results demonstrate that our new algorithm has much better performance than the original one. 160
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Conclusions
A new gradient-based routing algorithm is proposed in this paper. It optimizes the routes taking into account the remaining energy metric and hop count metric. Back-off waiting scheme is implemented to deal with the explosive message flooding problem in routing establishment stage. A simple and effective acknowledgement scheme is employed, too. Data aggregation in this routing algorithm helps to save energy and prolong the operating lifetime of the whole sensor network. With these features, our protocol performs better than the traditional one. It prevents long distance direct communication, which is quite energy consuming. When the categories of measured data are different, our protocol is more efficient than other data aggregation protocols, e.g. LEACH algorithm. Our protocol is robust and applicable to the network, whose topology may change periodically or randomly. Also, our protocol is easy for implementation. Our routing protocol can well cooperate with other applications in sensor network. For example, our protocol is good for the application of target tracking because of its adaptability to the changing environments. At last, simulation results illustrate the efficiency of our routing protocol.
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Acknowledgement This work is partially supported by National Outstanding Young Investigator Grant (6970025), regular NSFC grant (60074012, 60243001, 60274011), 863 High Tech Development Plan (2001AA413910, 2003AA142060) of China, National Key Project of China, Fundamental Research Funds from Tsinghua University, Chinese Scholarship Council and Ministry of Education of China.
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