Fuzzy-Based Clustering Solution for Hot Spot Problem in Wireless Sensor Networks

Transcription

1 Fuzzy-Based Clustering Solution for Hot Spot Problem in Wireless Sensor Networks Mahmoud Naghibzadeh Department of Computer Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran Hoda Taheri Young Researchers and Elite Club, Mashhad Branch, Islamic Azad University, Mashhad, Iran Peyman Neamatollahi Department of Computer Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran Abstract Clustering is an effective approach for organizing network nodes into hierarchical topology, aggregating sending data to the base station and prolonging the network lifetime. However, it may cause sudden death of nodes in some network regions, i.e., hot spots, due to heavy traffic load leading to disruption in network services. This problem is traditional for data collection scenarios in which Cluster Heads (CHs) are responsible of gathering and relaying the sensed data. To balance the workload over the nodes, the CH role must be rotated among all nodes and the cluster size should be determined such that uniformly distribute the energy consumption in network. In this paper, we propose a clustering algorithm that selects the nodes with highest remaining energy in each region as candidate CHs in order to pick the best nodes among them as final CHs. To consider the hot spot issue it employs fuzzy logic in order to adjust the cluster radius of CH nodes based on some local information (distance to base station and local density). Simulation results show that the proposed approach achieves an improvement in terms of network lifetime through mitigating the hot spot problem. IndexTerms Wireless sensor networks, unequal clustering, hot spot problem, fuzzy logic, network lifetime, energy-efficiency. I. INTRODUCTION The hundreds or even thousands of inexpensive powerconstrained sensor devices is required to organize a Wireless Sensor Network (WSN) which is designed for detecting and monitoring the physical parameters of sensing area through the use of radio frequencies to perform distributed sensing tasks [1, 2]. WSNs have attracted a lot of attention because their potential applications are highly varied such as traffic control, intrusion detection, object tracking, battlefield surveillance, environmental monitoring, health related applications and inventory management in factory [1]. As these sensor nodes are equipped with limited batteries and often scattered in non-rechargeable environments the energy resource of WSN should be managed wisely to prolong the lifetime of nodes. Although much attention has been paid to this major issue in signal processing techniques and low-power hardware design, energy efficient protocols must be applied at different networking layers. In addition to decreasing energy dissipation generally, to prolong the network lifetime it is very significant that energy consumption be distributed among all sensor nodes. Due to the characteristics of wireless channels in large-scale WSNs, it is more energy efficient to let CHs cooperate with each other in forwarding their data to the BS. Most of the previous researches in the literature [3-7] employ multi-hop for inter-cluster and single hop for intra-cluster communication. However, this many-to-one traffic pattern leads to the hot spot problem in which some nodes in particular areas drain their energy and die sooner than other nodes in the network, causing network partitions, and reducing sensing coverage. Although many proposed protocols in the literature in order to increase energy efficiency reduce energy consumption on forwarding paths, they do not necessarily prolong the lifecycle of network due to the unbalanced energy consumption [7-10]. In order to avoid this problem, some unequal clustering algorithms are proposed such as [3-6]. In these algorithms, the network is split into clusters with different sizes but approximately equal workload. The size of clusters in the vicinity of BS is smaller than that of clusters far from BS to save energy in intra-cluster communication in order to tolerate extra inter-cluster workload. Some previous clustering algorithms [3, 4, 11] that consider hot spot problem assumed uniform distribution of nodes in WSNs. However, in practical applications of WSNs, the nodes are usually randomly deployed. So, if the clustering algorithm does not consider the local density of nodes in the field, using unequal clustering strategy based on only nodes distance from BS may cause unbalanced energy consumption. Considering that the deployment of nodes follows random distribution, we propose an Unequal Clustering approach using Fuzzy logic (UCF). The goal of UCF is to prolong the lifecycle of network and mitigate the hot spot problem through evenly distributing the workload. Most of the existing unequal clustering approaches [3, 4, 6, 11] considered the residual energy of sensor nodes to choose final CHs after random selection of tentative CHs. The main contribution of the paper is that in addition to regarding the residual energy of nodes in CH election process directly, some local information of nodes is considered to act as a fuzzy descriptor in order to compute

2 cluster radius wisely during the clustering process. In other words, for this scenario, we calculate the radius of clusters based on location properties of nodes such as distance to BS and local density using Fuzzy Inference System (FIS), and take residual energy of nodes into account to elect more eligible CH nodes. Therefore, the cluster size of nodes decreases as its distance to the BS reduces. In this way, they can preserve some more energy for the inter-cluster relay traffic. The simulation results show that the proposed approach can generate more balanced clusters than previous distributed algorithms and enhance the network life cycle. The rest of this paper is organized as follows. Section II briefly introduces some preliminaries of the scheme including radio energy model and system assumptions. Then, a fuzzylogic-based clustering algorithm is proposed in Section III containing FIS description and proposed clustering algorithm. Section IV evaluates the performance of the algorithm using some experiments in simulation environment. The paper is concluded in Section V. II. PRELIMINARIES Here, we introduce characteristics of the radio energy model and explain system assumptions to be used in the proposed approach. A. Radio Energy Model In this paper, a simplified radio model shown in [8] is used to model the communication energy dissipation. The free space model (d 2 power loss) is used when the distance between the sender and receiver is less than a threshold value d 0 (i.e. d < d 0 ). Otherwise, the multipath fading channel model (d 4 power loss) is employed. Equation (1) represents the amount of energy spent for transmission of a k-bit packet over distance d, while the amount of energy consumed for receiving k-bit of data is given by equation (2). E Tx (k, d) = { ke elec + kε fs d 2 d < d 0 ke elec + kε md d 4 (1) d d 0 E Rx (k) = E Rx elec (k) = ke elec (2) E elec represents the transmitter and receiver circuits energy dissipation per bit. Also, ε fs and ε md are the energy consumption factor of amplification for the free space and multipath radio models, respectively. Although the amplifier energy, ε fs d 2 or ε md d 4, depends on the distance to the receiver and the acceptable bit-error rate, the electronics energy E elec depends on the digital coding, modulation, filtering, and spreading of the signal. B. System Assumptions This paper is essentially proposed for WSN applications including energy-constrained sensor nodes that are randomly distributed over a vast geographic area to continuously monitor the environment. In these multi-hop WSNs, the basic operation is to gather information by nodes and transmit collected data to the BS for further processing. Besides, we make some assumptions about the network model as follows: All sensor nodes and the BS are stationary after deployment phase. All sensor nodes have the same capabilities. So, the network is considered homogeneous. Nodes can use power control to adjust the amount of transmission power according to the distance of receiver nodes. The nodes are not equipped with GPS antenna so they are location unaware. Instead, approximate distance between nodes can be computed based on the received signal strength. The radio link is symmetric. It means energy consumption of data transmission is the same from node A to node B and vice versa. An ideal MAC layer and error-free communication links are assumed for simplicity because the focus is on energy efficiency in the network layer such as [5, 12]. III. THE APPROACH The operation of UCF is divided into rounds (similar to LEACH [8]) and clustering algorithm configures clusters at the beginning of every round. The task of being a CH is rotated among sensors in each round to distribute the energy consumption across the network. Furthermore, UCF generates unequal cluster sizes to mitigate the hot spot problem using FIS. In UCF, considering some local information (distance to BS and local density) each node computes its cluster radius using FIS. In this section, at first, the FIS is described and then the proposed unequal clustering algorithm is illustrated with pseudo code. A. Fuzzy Inference System The distribution of CHs can be controlled over the network such that less local connectivity density and further distance to the BS leads to larger cluster radius and on the contrary, more local density with closer distance to the BS results in smaller cluster radius. For a particular WSN, the maximum value of cluster radius (R 0) is a static parameter. Each sensor node can compute the competition (cluster) radius according to the multiplication of the value of this parameter with the output of FIS. The maximum distance to the BS is also a static parameter, as we proposed that the sensor nodes are stationary. Each sensor node can determine its relative position to the BS regarding maximum distance to the BS in the WSN. Sensor node S i (the sensor with ID number i) can calculate its relative distance to BS as follows: D to_bs = d(s i, BS) d min (4) d max d min d(s i, BS) denotes the distance between S i and the BS and d max and d min indicate the maximum and minimum distance to BS respectively. On the other hand, the node S i can calculate its local density with Equation (5): Den = Neighbor(S i) (5) N Neighbor(S i ) signifies the number of node S i 's neighbors and the network sensor node set S = {S 1, S 2,..., S N },

3 where S = N. In this paper, FIS is used for handling the uncertainties in calculating cluster radius of each node. As it is depicted in Fig. 1, the distance to BS and the local density of node are applied as two input variables for the FIS, and the only output parameter is the cluster range of a node, cluster radius. The high amount of cluster radius means that if the node can be elected as a CH it will handle a big cluster size. The illustrated fuzzy set in Fig. 1(a) describes the distance to BS input variable. The close, adequate, and far are linguistic variables for this fuzzy set. The close and far linguistic variables employ a trapezoidal membership function, while a triangular membership function is applied for adequate. The other fuzzy input variable is the local density of node. In Fig. 1(b), a fuzzy set is depicted that describes local density input variable. The linguistic variables of this fuzzy set are low, medium, and high. The only fuzzy output variable is the cluster radius of a node. (a) (b) (c) Fig. 1. Fuzzy set for input and output variables. (a) Distance to BS. (b) Local density. (c) Cluster radius. TABLE I. FUZZY RULES. The fuzzy set for the cluster radius output variable is shown in Fig. 1(c). The nine linguistic variables of cluster radius are very high, high, rather high, medium high, medium, medium low, rather low, low, and very low. To handle the uncertainty, the cluster radius computation is performed using predefined fuzzy if-then mapping rules. 9 fuzzy mapping rules are determined in Table I based on the two fuzzy input variables. To take advantage of this fuzzy variable in practice it has to be altered to a single crisp number. In our approach, defuzzification of the cluster radius is done by the Center of Area (CoA) method. B. Unequal Clustering Algorithm This section describes a distributed self-organization and balanced clustering algorithm for WSNs (UCF), which can handle stochastic distribution of sensor nodes. In this competitive algorithm, choosing the CHs is primarily based on the residual energy of nodes. In the continuance, we explain the clustering algorithm in detail using the pseudo code given in Algorithm 1. First, applied variables in the algorithm are denoted as follows. S CH and S candidate_ch are sets of all deterministic and candidate CHs in the neighborhood of node S i such that node S i had heard from them. Also, α (0 < α < 1) and β (β > 1) are the sensor parameters determined constantly by specific application of WSNs. Before the beginning of clustering operations (Lines 6-26), each node computes the probability of becoming a CH (CH prob ) depending on the residual energy of node as follows. CH prob α MAX (S i. RE S i. ME, p min) (6) In which, S i. ME is the maximum energy corresponding to a fully charged battery and S i. RE is the current energy of the node. Note that it is not permitted that the value of CH prob falls below α p min. This threshold limits the required time of clustering process. Assume R 0 is the maximum cluster radius, which is predefined, and the length of node s competition range (cluster radius) is achieved as multiplication of R 0 in output of FIS mentioned above. Different competition ranges are employed to generate unequal cluster sizes. If S i becomes a CH at the end

4 of the competition, there will not be another CH S j in S i s competition range. In the CH selection algorithm, every control message is scattered in the broadcast radius of R 0, which enables S i to hear all messages from nodes in its S CH and S candidate_ch. Therefore, node S j is an adjacent node of S i if S j is in S i s competition radius or S i is in S j s competition radius. During this process, a node can become a candidate, a CH, or a regular node joining another CH. While the status of node S i turns into regular or CH it gives up the competition. Note that all variables are defined locally for node S i. At first, a node with a higher level of energy has more chances (CH prob ) of becoming candidate_ch in competition with other nodes in its vicinity (see Lines 8 and 9). If a node becomes candidate_ch, it broadcasts its new status along with its identification number and cluster radius to the nodes in its range via Candidate_CH_msg. In the next iterations, if this node receives any CH_msg from a CH in its vicinity it turns into regular node and before leaving the competition, broadcasts a Quit_Election_msg in its range (see Lines 10-13). By receiving this message, the receiver node removes the node S i from candidate_ch list (i.e. S candidate_ch). On the contrary, if the node S i has not heard from any final CH in its vicinity and its CH prob reaches bigger than 1, its status turns to CH and leaves the competition while broadcasting a CH_msg to its neighbors in the cluster range (see Lines 14-16). The receiver candidate_ch node immediately gives up the competition, and informs all nodes in its vicinity by broadcasting a Quit_Election_msg. After selecting CHs, each regular node picks its closest CH from its S CH to join and informs it by sending a Join_CH_msg. The CH provides a TDMA schedule and sends it to its cluster nodes. After receiving the TDMA schedule by all cluster members, the setup phase is accomplished and the steady-state operation (data transmission) can be started. IV. PERFORMANCE EVALUATION To evaluate our proposed approach, UCF, the experimental results are demonstrated comparing UCF with three different clustering algorithms, namely LEACH, HEED [7], and CHEF [14]. As LEACH depends on only a probability model for CH selection its energy efficiency is not maximized. Besides, it employs single-hop routing during delivering data from CHs to the BS. HEED has an iterative CH selection algorithm which uses an initial probability for each node to become a tentative CH depends on its residual energy, and a communication cost for electing final CHs among them. In our simulations, we apply the node degree communication cost of HEED. In both HEED and CHEF protocols, CHs relay data to the BS via multi-hop routing. CHEF utilizes a fuzzy system with two fuzzy descriptors to fulfill CH election, which are residual energy of each node and local distance. They defined local distance as the total distance between the tentative CH and the nodes in its vicinity. In the following of this section, Figures 2 to 5 will illustrate the superiority of UCF over mentioned algorithms. Also, more details are described. A. Setup In this section, we describe the simulation setup of the experiments. Some assumptions of the environment and parameters are: Energy consumption considers only when a sensor transmits or receives data or performs data aggregation. We assumed a square network field of 100m 100m area. In the simulation experiments, 100 nodes are randomly distributed in the field. The BS is located at coordinate (50, 175). Also, we set R 0 to 44m. The values used in the simulation parameters are given in Table II. B. Results In this section, we evaluate the superiority of UCF over distributed clustering protocols LEACH, HEED and CHEF focusing on network lifetime. It is notable that in our simulations the nodes are scattered randomly over the network area. Fig. 2 shows this deployment of 100 sensor nodes in an area of 100m 100m.

5 TABLE II. PARAMETER SETTINGS higher value among other protocols. Nevertheless, in the case of LND, network is not applicable any more. Fig. 2. Deployment of nodes in the field. Fig. 3 depicts the cluster formation of mentioned protocols. Nodes in green color are dead nodes. Each node in blue color, live node, can be a member of a CH node represented by red color. A cluster is recognized by some regular nodes connected to a CH via a blue link. This figure belongs to round number 595 of network run. Clusters in UCF have different sizes, those closer to BS are smaller in size and the farther ones from BS are bigger in size. The reason is the CH selection algorithm which employs fuzzy descriptors (distance to BS and local density) in the FIS. It is worth mentioning that all nodes in UCF are alive up to this round while in other protocols some dead nodes are observable. All the clusters in HEED have the same size but they are not well organized in order to suit well the actual distribution. The CH selection in LEACH is done randomly so, the number of CHs is not determined and predictable. Besides, CHs can be located in one another s cluster range. On the other hand, because forwarding sensed data to the BS is done in a single hop fashion nodes far from the BS die sooner. Although CHEF benefits from multi-hop routing, the cluster structure in CHEF is in a way that it can easily generate orphan nodes. These nodes must send their data to the BS directly as they do not belong to any cluster. Fig. 4 illustrates the number of live nodes in different protocols. Better CH distribution in the area and load balancing are the main two feature of UCF, which results in longer network lifetime. Fig. 5 shows network lifetime in details using three definitions; FND (the round number in which the first node dies), HNA (the round number in which half of the nodes remain alive), and LND (the round number in which the last node in the network dies). UCF performs better than the other protocols in general. In both Figs 4 and 5 the LND factor of LEACH has the Fig. 3. Cluster formation in round number 595. Fig. 4. The number of alive nodes over the rounds.

6 Fig. 5. The network lifetime. V. CONCLUSION The proposed algorithm in this paper is designed for the WSNs having stationary sensor nodes and random distribution in the field. The main objective of our algorithm is to prolong the network lifetime through evenly distributing the workload and hence to avoids hot spot problem through constructing unequal clusters. To attain this purpose, we have mostly focused on choosing proper CHs from available sensor nodes and adjusting cluster size to mitigate the hot spot problem using fuzzy logic based on the distance of node to BS and local density distribution of node. UCF selects the CHs only by considering the residual energy of sensor nodes without the use of random function. The results of simulation show that UCF is more efficient than other well-known distributed protocols, such as CHEF, LEACH, and HEED. To find the optimal fuzzy set and to compare the contributed proposal with other clustering algorithms are left as a future work. [7] O. Younis and S. Fahmy, "HEED: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks," Mobile Computing, IEEE Transactions on, vol. 3, pp , [8] W. B. Heinzelman, A. P. Chandrakasan, and H. Balakrishnan, "An application-specific protocol architecture for wireless microsensor networks," Wireless Communications, IEEE Transactions on, vol. 1, pp , [9] P. Neamatollahi, H. Taheri, M. Naghibzadeh, and M. H. Yaghmaee, "DESC: Distributed Energy Efficient Scheme to Cluster Wireless Sensor Networks," in Wired/Wireless Internet Communications, ed: Springer, 2011, pp [10] H. Taheri, P. Neamatollahi, O. M. Younis, S. Naghibzadeh, and M. H. Yaghmaee, "An energy-aware distributed clustering protocol in wireless sensor networks using fuzzy logic," Ad Hoc Networks, vol. 10, pp , [11] C. Li, M. Ye, G. Chen, and J. Wu, "An energy-efficient unequal clustering mechanism for wireless sensor networks," in Mobile Adhoc and Sensor Systems Conference, IEEE International Conference on, 2005, pp. 8 pp [12] D. Wei, Y. Jin, S. Vural, K. Moessner, and R. Tafazolli, "An energy-efficient clustering solution for wireless sensor networks," Wireless Communications, IEEE Transactions on, vol. 10, pp , [13] J.-S. Lee and W.-L. Cheng, "Fuzzy-logic-based clustering approach for wireless sensor networks using energy predication," Sensors Journal, IEEE, vol. 12, pp , [14] J.-M. Kim, S.-H. Park, Y.-J. Han, and T.-M. Chung, "CHEF: sensor sensor election mechanism using fuzzy logic in wireless sensor networks," in Advanced communication technology, ICACT th international conference on, 2008, pp ACKNOWLEDGEMENT The authors would like to thank Research and Technology Affairs, Mashhad Branch, Islamic Azad University, for supporting our research. REFERENCES [1] G. Anastasi, M. Conti, M. Di Francesco, and A. Passarella, "Energy conservation in wireless sensor networks: A survey," Ad Hoc Networks, vol. 7, pp , [2] J. Yick, B. Mukherjee, and D. Ghosal, "Wireless sensor network survey," Computer networks, vol. 52, pp , [3] H. Bagci and A. Yazici, "An energy aware fuzzy unequal clustering algorithm for wireless sensor networks," in Fuzzy Systems (FUZZ), 2010 IEEE International Conference on, 2010, pp [4] G. Chen, C. Li, M. Ye, and J. Wu, "An unequal cluster-based routing protocol in wireless sensor networks," Wireless Networks, vol. 15, pp , [5] Y. Liao, H. Qi, and W. Li, "Load-Balanced Clustering Algorithm With Distributed Self-Organization for Wireless Sensor Networks," Sensors Journal, IEEE, vol. 13, pp , [6] S. Mao, C. Zhao, Z. Zhou, and Y. Ye, "An improved fuzzy unequal clustering algorithm for wireless sensor network," in Communications and Networking in China (CHINACOM), th International ICST Conference on, 2011, pp