Energy-Driven Adaptive Clustering Data Collection Protocol in Wireless Sensor Networks *

Transcription

1 Energy-Driven Adaptive Clustering Data Collection Protocol in Wireless Sensor Networks * Yongcai Wang, Qianchuan Zhao and Dazhong Zheng Department of Automation Tsinghua University Beijing, , P.R.China wangyongcai@mails.tsinghua.edu.cn Abstract - Wireless sensor networks consisting of a mass of inexpensive, low-power and multifunctional sensor nodes are deployed in military or citizen field, collecting information or monitoring environments. Gathering data in energy efficient way is critically important to prolong the lifetimes of the networks. In [3], data collection problem is studied in homogeneous network. An adaptive clustering protocol LEACH is presented and makes a breakthrough in this area. By localized data fusion and randomized cluster heads rotation, energy is averagely depleted among nodes and LEACH achieves 7-8 times improvement than three traditional protocols measuring the dieing times of nodes. In this paper, we propose EDAC (Energy-Driven Adaptive Clustering) protocol, which is an improvement over LEACH in heterogeneous networks. Unlike the homogeneous network, if we apply LEACH in heterogeneous networks, the averagely energy dissipation mechanism will result in early death of powerless nodes and can not use the advantage of the powerful nodes. In EDAC, we use energy-driven cluster heads rotation, which enables cluster heads change asynchronously and coordinate energy consuming closely based on the heterogeneous property of nodes energy. Time synchronization is relaxed and topology maintenance is localized by event triggering rotation. Also, the energy cost in topology maintenance can be kept small even when only little data to be transmitted in networks. Simulation results show that the lifetimes of nodes in EDAC can be times as those in LEACH in heterogeneous network, and that it also works as well as LEACH in homogeneous networks. Index Terms -Adaptive clustering, Data collection protocol, Energy-driven, Energy efficiency, Heterogeneous network. I. INTRODUCTION Recent fast development of wireless communication, sensor technique and MEMS enables the fast development of inexpensive, low-power and multifunctional micro-sensors. A mass of these micro-sensors coordinate by wireless communication to compose a wireless sensor network [1] [2]. Such networks can be deployed in military or citizen fields to collect data and monitor environments [10]. Nodes in network could be homogeneous (all nodes are same) or heterogeneous (nodes are different in function, energy-level, etc). They collect sound, temperature, light etc, and communicate via radio to finish data collection tasks coordinately. For the difficulty of the battery renewal and the long period demand of application, such as battle field monitoring and wild animal tracking, energy becomes one of the most precious resources in wireless sensor network. In [3], communication protocols and their energy consuming in homogenous networks are studied. Three traditional protocols, Direct, MTE (Minimum Transmission Energy) and Static Clustering are summarized, and an elegant solution, energy efficient adaptive clustering protocol LEACH is presented. With adaptive clustering and randomized cluster heads rotation, energy can dissipate averagely among nodes. Together with locally data fusion, LEACH achieves a factor of 7-8 improvement in network lifetime comparing with the Direct, MTE and Static Clustering. In LEACH, data collection problem is defined as in a round of communication, every nodes have a packet to send to the distant base. Cluster heads randomly generate at the beginning of every communication round. ALEP [4] and PEGASIS [5] follows the line of LEACH and propose more energy efficient solutions in homogeneous networks. ALEP takes the left energy of whole net into account and PEGASIS proposes a single chain structure for clustering. In LEACH, ALEP and PEGASIS, cluster based topology reduces the long range communication; data fusion saves energy by compressing data and the cluster heads rotation enables energy averagely depleting, so they prolong the lifetimes of nodes effectively. We apply LEACH in our study of heterogeneous network, and find that the essential idea of LEACH to coordinate energy consuming among nodes based on their energy characteristics can be further improved. How to extend it to heterogeneous scenario is studied in this paper. Heterogeneous network is also an important area in wireless sensor networks. With the fast developing of sensor technique, multifunctional, various kinds of sensors are developed. It is promising that different kinds of these micro sensors working coordinately to finish specific tasks [6] [7]. There are also some important applications have been investigated in heterogeneous networks, such as TinyDB [8] etc, also some studies in energy consuming [9]. Heterogeneity of sensors may exhibit in function, processing capability, energy level etc. But the heterogeneity of energy dominates new challenges for data gathering protocols. In heterogeneous * This work was supported by NSFC(Grant No , ), National Key Project of China, Fundamental Research Funds from Tsinghua University, Chinese Scholarship Council and Ministry of Education of China.

2 networks, the suitable protocol should conserve energy for the powerless nodes and exert the advantage of the powerful nodes to extend the lifetime of the whole network, and it should also keep performance in homogeneous network. In this paper, we present an Energy-Driven Adaptive Clustering protocol: EDAC. It is an improvement of LEACH in heterogeneous networks. In EDAC, the cluster header rotation is energy-driven. Nodes take the role of cluster heads asynchronously. When a node serves as cluster head, it manages the local cluster and performs data fusion to save energy and suffers the long range communication to the base station. It will work until its left energy falls lower than a threshold. Then, cluster head rotation happens. The node in scheduling list, which has more energy left in this cluster will take over the cluster head token. And then, local cluster topology is reconstructed by adaptive competition. Without time triggering, topology maintenance is less frequent and the time synchronization requirement is relaxed. We also consider the energy consumed in topology construction and maintenance, In EDAC this part of energy cost is shown to be small, even if only little data to be transmitted in network. Simulation results show that EDAC can effectively prolong the lifetimes of heterogeneous nodes to be times as in LEACH, and that it also works well in homogeneous networks. II. MODEL DESCRIPTION In LEACH, a data collection model is described as shown in fig 1. One hundred of homogeneous nodes are uniformly distributed in a 50m 50m square region. All the nodes collect local data and send them to the base station located at (25,- 100). This model is based on the military object tracking and hazards environment monitoring application background, where the base is often far from application area. Some assumptions are made that node can selected its transmission range and every node knows the positions of other nodes and itself. The selectable range assumption is closely based on the function of current sensor devices. For example, Mica2 mote provides totally 100 different transmission radiuses [11]. The assumption of the known of positions needs additional localization process at the setting up of the network [12]. Fig. 1 Data collection sensor network model, circle stands for sensors and base is located at (25,-100) In LEACH, initial energy of every node equals to E ini. We adopt this model as homogeneous model and make it more general by allowing the nodes to be heterogeneous. In this paper, we only consider one of the simplest such heterogeneous scenarios, that sensors are equipped with different battery power. For heterogeneous model, initial power of sensor nodes is randomly generated in the close set [, Emin E max ] Emin, where and is the lower bound and upper bound of initial power respectively. All the nodes obey same energy consuming model as described in [3].The energy consuming model is described as following. For transmission, when a node transmits k bits data to another node with distance d, the energy it consumed is: 2 E ( kd, ) = E k+ ε k d (1) E max Tx elec amp For receiving, when a node receives k bits data, the energy it consumed is: E ( k) = E k (2) Rx elec Where E nj/bit is the circuit energy cost when elec transmitting or receiving one bit of data, and d 2 energy loss model is used due to the channel attenuation and ε is the amp amplifier coefficient. For the easy of comparison, in this paper we use the same constant coefficient as in LEACH: ( E elec, amp, ) ε k =(50,100,2000). III. ENERGY COST ANALYSIS In this section, we will analyze the energy cost of data gathering from the sensor network to the distant base. We follow the experiment of LEACH in homogeneous network. Fig. 2 shows the result. By cluster header rotation and data fusion, LEACH achieves a factor of 7-8 improvement in network lifetime comparing to other three. To apply these protocols in heterogeneous network, Fig. 3 shows the result. Comparing Fig. 2 and Fig. 3, we can see that the places of Direct and MTE haven t changed. This is because of either in homogeneous or heterogeneous network, in Direct protocol it is always the nodes far from the base die quickly for the long range communication, and in MTE protocol it is always the nodes near the base die quickly for it suffers more packets forwarding. Static Clustering makes a big improve in heterogeneous network. This is because in the selection of static cluster header we use a simple heuristic. We choose the most powerful several nodes as the static cluster headers and apply data fusion. This enhanced static clustering uses the advantage of the powerful nodes and results in a better performance. There is other works [13] studying in static cluster header selection technique in heterogeneous network. When the network contains power unlimited nodes, it is not a bad choice. The heterogeneity lefts room for data collection protocols to improve. In heterogeneous network, LEACH can not achieve the performance as better as in homogeneous network. This is because that the time-driven cluster header rotation mechanism averagely distributes the energy consumption. The powerless nodes die quickly and powerful nodes haven t contributed their advantage.

3 For the fair of the comparison of different protocols, ignoring the energy consumed in topology construction and topology maintenance is not suitable. Because comparing with Direct approach, all clustering based protocols and routing table based protocols need consume additional energy in topology construction and topology maintenance. The goal of data collection protocol is to transmit data effectively and prolong the network s lifetime. It requires reducing the proportion of the additional energy cost as much as possible. So we give following definitions. requests. We name them command packets. This kind of packets is often much smaller than the data packets. In this paper, we set the size of command packets k, and k is equal to the one tenth of the size of data packets. k = k /10=200bits. Analyzing Direct, MTE and Static Clustering protocols, the efficiency of Direct is 100%; In MTE and Static Clustering, once the topology is constructed, no more additional energy cost needed during the data collection process, so as time passed, η approaches 100%. In LEACH, global topology maintenance is done at the beginning of every round. An idea to improve the efficiency of adaptive clustering protocols is to localize the topology construction, and avoid time-driven cluster headers rotation. EDAC protocol uses energy-driven mechanism, to coordinate energy consuming based on the heterogeneity of nodes. Cluster header rotation is asynchronous. Cluster topology maintenance only happens when local cluster header rotating and global topology reconstruction is avoided. In the next section, we discuss it in details. IV. EDAC PROTOCOL In this section, we will introduce EDAC protocol in detail. EDAC includes two phases: initializing phase and self organized data collection phase. Fig. 2 Performance comparison in homogeneous network Fig. 4 Flow of initializing phase Fig. 3 Performance comparison in heterogeneous network Definition 1: Additional energy cost, Effective energy cost and Efficiency In data collection process, the energy consumed in topology construction and topology maintenance is called additional energy cost (E a ); the energy consumed in data transmission and receiving is called effective energy cost (E e ); the sum of the additional energy cost and the effective energy cost is the total energy cost (E t ), and efficiency (η ) is defined as the proportion of the effective energy cost in the total energy cost. η = E / E = E /( E + E ) (3) e t e e a In topology construction and maintenance, another kind of packets is used to send commands and answers for the Fig. 5 Network topology of EDAC after initializing A. Initializing phase In initializing phase, a fix proportion of sensors randomly

4 select themselves as cluster headers as described in [3]. [3] and [6] have analyzed the cluster headers proportion problem both in homogeneous and heterogeneous network. They show that 5%-8% cluster headers can achieve good performance both in homogeneous and heterogeneous network in various parameter settings. We adopt these results and randomly select 5% cluster headers at the initializing phase. When a node is selected to be cluster header, it generates a cluster header token. Then, every selected cluster header advertises its token by CSMA/CA MAC protocol to all its neighbors. Non cluster header nodes receive these advertisements and compare their signal strength. It only keeps the token with the strongest signal in every comparison and randomly choose one when tie occurs. After advertisements, every non cluster header node recognizes the source of the token as its cluster header and broadcasts topology answer packets by CSMA/CA MAC protocol back to the cluster header. In the answer packets, node s positions and node s current energy level are included. The cluster headers receive these answer packets and set up a schedule list for its local cluster. In the schedule list, TDMA schedule [3] is created. Different from LEACH, we add an energy attribute into the schedule list. It is set up by recording the left energy data in the answer packets and is maintained by cluster header for the use of cluster header rotation. An energy threshold is calculated when a node is selected to be cluster header. It is also the basis for cluster header rotation. B. Self organized data collection phase After initializing phase, self organized data collection phase starts. Every node collects local data, and sends the packet to the cluster header in its allocated transmission time. Based on the received signal strength of the cluster header s advertisement and the symmetrical assumption of the radio channel, this transmission can use a minimal amount of energy. The radio of other nodes can be turned off until their allocated transmission time to save energy. In every data packet, node s current energy level is attached. The cluster header nodes keep their receiver on to collect data from its cluster and continuously update the energy table in the schedule list based on the received packets. When data from all the non cluster header nodes has been received, the cluster header runs a data fusion function to aggregate all the received data into one signal. Data fusion can be simple averaging or complex data processing methods. After data fusion, the cluster header sends the signal which stands for the information of this cluster to the base station located far away. For the cluster header need receive many packets and have to consume more power in long range transmission, it is the node that the energy used most quickly in this cluster. In order to extend the life of the network, cluster header rotation is necessary. EDAC use energy-driven demise scheme to rotate the cluster header. 1) Demise process: In [3], [4]and [5], the cluster header rotation is driven by time. Some nodes are randomly chosen to be cluster headers at the beginning of every time round. In this paper, we introduce energy-driven scheme. A threshold is calculated in the cluster header when it is selected to be a cluster header. Then in the data collection process, if the cluster header finds its left energy is less than the threshold, it sends demise request message to the top three nodes with maximum energy left in its schedule list. Choosing three nodes assures the reliability of demise process. When a non-cluster-head node receives the demise request message, it sends back a demise answer message. From the received demise answer messages, cluster header chooses the node with maximum energy left to be its inheritor and demise the cluster header token to this node. After the node received the cluster header token, it calculate the threshold, reconstructs the cluster topology and recreates the schedule list for the cluster, following the same way described in initializing phase. 2) Threshold function: Threshold function takes an important role in the demise process. It is calculated when a node is selected to be cluster header. When the cluster header s left energy has dropped below this threshold, demise process will start. In this paper, we choose a simple proportional function. threshold = P E c (4) Where P is a constant proportional parameter and E c is the energy left when the node selected to be cluster header. From (4) we can see that threshold is different for different nodes and different for the same node at different time. It is only calculated and kept by the current cluster headers. Its dynamic property insures that even if all the nodes have only little energy left, the protocol still works. Fig. 6 comparison of threshold in homogenous network Fig. 7 Comparison of threshold in heterogeneous network

5 V. EXPERIMENT RESULTS To evaluate the performance of EDAC, we examine its properties from different aspects. We simulate EDAC using MATLAB with the random network shown in Fig. 1. For homogenous network, we choose initial power of every nodes to be 0.25J, and for heterogeneous network we choose E min =0.05J and E max =1J. First we compare the effect of different thresholds. Fig. 6 shows the performance of EDAC in homogeneous network with the proportional parameter P increasing from 5% to 30%. Fig. 7 shows these results in heterogeneous network. From these two figures we can see a typical characteristic of EDAC. When threshold is small, nodes averagely die as time passed. When threshold is larger, the curve trends to be a right angle. Most of nodes last for a longer life but they die almost simultaneously in a short time interval. The reason for this phenomenon is that in EDAC when P is small, a node selected to be cluster header will consume most of their energy on its cluster header position. After rotation, it has little energy left and will die quickly thereafter. When P is larger, a cluster header can still reserve some energy for later use after it finishes its cluster header work. And since non cluster header consumes less energy than cluster header, the reserved energy can help it last for longer life. Nodes die almost simultaneously because after some times of rotations, all the nodes have little energy left. Rotation rules can not help to prolong the life of a net when all nodes with little power. Then we compare the performance of EDAC with other protocols in different networks. We choose P=0.25 in EDAC. Fig. 8 shows the comparison result in homogeneous network and Fig. 9 shows the result in heterogeneous network. From Fig. 8, we can see that EDAC is 7-8 factors better than Direct, MTE and Static Clustering. Comparing with LEACH, only less than twenty nodes have little shorter lifetimes than in LEACH among total 100 nodes. Fig. 9 shows these results in heterogeneous network. EDAC is 7-8 factors better than Direct and MTE. It is also better than enhanced Static Clustering. This is because that in EDAC we not only select powerful nodes as cluster header, we also make cluster headers rotation after their power drained under a level. Comparing with LEACH, the lifetimes of nodes in EDAC can be 1 to 2.5 times as that in LEACH, except the last several nodes. Recalling homogeneous network, there is same phenomenon. This is because of in LEACH, energy depletes averagely. The several nodes with much more initial power deplete energy the same way as other nodes, and they need not to do more contribution for their energy advantage as in EDAC. So these nodes can gain longer lives. For homogenous network, it is the same reason. Fig. 8 Performance comparison in homogeneous network Fig. 9 Performance comparison in heterogeneous network Considering for application, limited sensing range and limited communication range require wireless sensor networks to keep a certain density to keep the network operational. A few nodes alive in a large network can not make more contribution. The lifetime of a sensor network should be defined as the time when still enough nodes still alive to keep the net operational. TABLE I PERFORMANCE COMPARISON FOR DIFFERENT NETWORK SETTINGS Network setting Protocol 10% 20% 30% 40% 50% 60% 70% 80% 90% 100m*100m space, base located at (50,-200), homogeneous LEACH network, initial energy is 1J for every node EDAC m*100m square, base located at (50,-200), LEACH heterogeneous network, Emin=0.1J,Emax=2J EDAC m*200m square, 200nodes, base located at (100,-100), LEACH heterogeneous network, Emin=0.1J,Emax=1J EDAC The relationship between nodes density and connectivity and coverage is still hot topic in wireless sensor network [14]. It is out the scope of this paper, but we can compare EDAC and LEACH when different proportion of nodes dead. We do the comparison in different scale networks and with different initial conditions. Table1 summarizes the results. Table1 shows that for different scale network, different initial energy and different nodes number, Lifetimes of nodes in EDAC can be times long comparing with LEACH in heterogeneous network. And it also works well as LEACH in large scale

6 homogeneous networks. After these comparisons of lifetimes, we compare the efficiency of EDAC and LEACH. In this experiment, we decrease the size of the data packet from 2000 bits continuously to 200 bits and fix the size of command packet at 200 bits. We choose 50m*50m network, 100 nodes, E min =0.05J and E max =1J. Fig. 10 shows the results. We can see that with the decreasing of the size of data packet, the efficiency of LEACH decreased quickly and EDAC still keeps a high efficiency. This is because that small data packet means little data to transmit. But for time driven protocol, although only little energy has consumed, cluster headers are still rotated as time round coming. Energy-driven EDAC concerns the left energy in the nodes and it is suitable for heavy and light traffic networks. Fig. 10 Efficiency comparison for EDAC and LEACH VI. CONCLUSTION AND FUTURE WORK In this paper, we describe EDAC, an energy-driven adaptive clustering data collection protocol in wireless sensor networks. Its energy-driven cluster header s rotation mechanism relaxes the time synchronization request and enables the rotation process happens asynchronously. Also, the energy-aware rotation mechanism enables EDAC not only be suitable for homogeneous network, but also works better than LEACH etc in heterogeneous networks. We compare the performance of EDAC with other protocols in different parameter settings. The results show that EDAC is 7-8 factors better than Direct, MTE and 2 factors better than enhanced Static Clustering in heterogeneous network. It is as good as LEACH in homogeneous network and 1-2 factors better than LEACH in heterogeneous network. Moreover, in EDAC, topology maintenance only happens in a local area, avoiding global topology reconstruction. Cluster topology doesn t change until the cluster has lower power than the threshold. Without time triggering topology reconstruction, EDAC can keep a high efficiency even in light traffic network, when only little data to transmit. In order to verify our discussions about EDAC, we will use extended network simulator ns-2 for wireless communication [15] to simulate EDAC, LEACH, Direct, MTE and Static Clustering. This can help us to understand the advantage and disadvantage of these protocols more accurately. From our MATLAB results, we can expect that EDAC will have better performance than Direct, MTE and Static Clustering, and it will have a better performance than LEACH in heterogeneous network. Also, demise process is used in EDAC. It is suitable to inherit useful data between cluster headers when rotation happens. We can further develop the demise process and EDAC protocols for typical applications as objects detection, location and tracking. 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