Zone Based Energy Efficient Clustering Routing Protocol for Wireless Sensor Network

Transcription

1 IJEEE, Vol. 3, Issue 4 (Aug 2016) e-issn: p-issn: Zone Based Energy Efficient Clustering Routing Protocol for Wireless Sensor Network 1 Inderjit Singh, 2 Tripatjot Singh Panag Electronics and Communication Deptt., Baba Banda Singh Bahadur College of Engineering, Punjab, India 1 inder0523@yahoo.com, 2 tripatjot.singh@bbsbec.ac.in Abstract- Clustering preserves energy resources, offers robustness, lends support to scalability and saves communication bandwidth. Clustering is categorized into two classes, static and dynamic, depending on the frequency of clustering. Clustering can also be equal or unequal based on the size of clusters or number of nodes. In hierarchical routing protocols, during multi-hop communication hot-spots are created due to imbalanced energy consumption among the nodes. Unequal dynamic clustering is used to resolve this problem but does not guarantee connectivity and causes high overhead. To use as little overhead as possible, guarantee connectivity and alleviate the hot-spot problem, a zone based energy efficient clustering routing protocol has been proposed in this paper. The sensing area is partitioned into fixed number of equal static clusters. The clusters are allotted to two zones known as near and far zone. The zone towards the base station is the near zone and rest of the network space is the far zone. Dual cluster heads are used in the near zone for sharing the reception, aggregation and data forwarding tasks. Distance from the base station, residual energy and centrality factors elect the cluster heads. An energy efficient intercluster communication based on residual energy and distance to the base station is applied for data forwarding. The proposed protocol prevents formation of hot-spots, uses balanced energy consumption among the nodes and prolongs lifetime. Keywords- cluster, hot-spots, multi-hop, network lifetime I. INTRODUCTION A wireless sensor network (WSN) is formed by a collection of tiny sensor nodes possessing limited resources. Presently small-sized, inexpensive, low-power and radio frequency (RF) based sensor nodes are readily available. Each node is capable of performing on its own. WSNs improve reliability, extend the sensing range and are accurate compared to existing ad-hoc networks. It is infeasible to replace or recharge the battery of a sensor node post deployment. There is a requirement of data aggregation and energy-efficient hierarchical routing protocols to minimize the energy consumption of nodes. Base station (BS) is used to carry out periodic gathering of data and is often placed away from the sensing area [1]. Based on specific criteria, the nodes are grouped into clusters. Cluster head (CH) is that node among the nodes of a cluster which coordinates the tasks of rest of the cluster members (CMs). Either the nodes themselves elect the CH or the CH is pre-defined by the designer, based on suitable parameters. Clustering reduces energy consumption and post clustering the network becomes scalable. With the use of clustering, network activities are properly scheduled, collisions between CMs are avoided, resources are efficiently assigned and overhead for maintaining the topology is minimized. Aggregation performed by the CH reduces the number of packets to be relayed. In a cluster based sensor network, traffic uses intracluster and inter-cluster communication. Intra-cluster communication is performed within a cluster and is either single-hop or multi-hop. Inter-cluster communication assigns all the CHs the task of forwarding the data to the BS either directly or through other CHs. Single-hop communication becomes impractical for large area networks. Beyond a predefined threshold value, multi-hop communication becomes absolutely necessary to conserve the battery of a transmitting node. Thus, multi-hop communication turns out to be more energy-efficient and it also allows room for sufficient network scalability [2]. The hierarchical topology is disadvantageous due to uneven energy consumption between the CHs and CMs. Performing rotation of the CHs within a cluster balances the energy consumption between CHs and CMs. But while performing multi-hop inter-cluster communication, the problem of imbalanced energy consumption among the CHs persists. During single-hop communication, the CHs located at the periphery of the network dissipate more energy and as a result get exhausted quickly. Whereas during multi-hop communication, the CHs present in close proximity of the sink expend their energy quickly. The reason for this is attributed to heavy burden of relay traffic and the problem is termed as hot-spot problem in WSN [3]. This brings the data transmission to a halt; the network loses its sensing coverage and gets partitioned. All the nodes should be alive for sufficiently high period of time. The condition can only be achieved in the absence of hot-spots and when the nodes are consuming energy at a uniform rate. Many researchers have tried addressing the energy-hole problem in a clustered WSN. The unequal clustering is often adopted to balance the energy consumption among the CHs [4]. Small sized clusters near the BS save the energy of their CHs for relaying data during inter-cluster communication. But its use introduces several other problems in the network. The scheme being dynamic results in high overhead, International Journal of Electrical & Electronics Engineering 12

2 coverage and connectivity issues remain in the network. One way is to make the most out of the network design space by forming static clusters that do not require overhead for repeated cluster formation. If we have a powerful BS with unlimited computational and processing capabilities, abundant resources and physical location accessibility, it can easily be used for performing cluster formation and CH selection processes. In this paper, zone based energy efficient clustering routing protocol (ZBEEC) is proposed to prevent the hotspots and prolong the network lifetime. The static cluster formation uses a small overhead. ZBEEC divides the network into near and far zone leading to balanced network traffic across the two zones. Dual CHs approach is proposed for the near zone clusters. The CHs are optimally elected based on residual energy, centrality and distance from BS factors. Residual energy and distance to BS are used as the metrics for finding out the next-hop during inter-cluster communication. ZBEEC balances the energy consumption among all the nodes and prevents the nodes from dying prematurely. The rest of the paper is organized as follows: Section II reviews the related work, Section III presents the network model and problem formulation, Section IV describes the proposed protocol operation phases in detail, Section V discusses the simulation results and Section VI concludes the paper. II. RELATED WORK Firstly we present basic hierarchical routing protocols followed by protocols that involve network organization and finally unequal clustering based protocols are presented. Heinzelman [5] et al. proposed low energy adaptive hierarchical clustering protocol (LEACH). The protocol uses localized coordination, and induces scalability and robustness in dynamic networks. A node that did not act as a CH in the previous rounds is chosen as a CH and it does not participate for selection in the pre-defined number of upcoming rounds. A CH forwards fused data packets to the BS via single-hop communication making use of fixed spreading code. Energy criterion is not considered during CH election. The distribution of CHs is uneven and the position of the CH in a cluster is not taken into account. Heinzelman [6] et al. proposed centralized- LEACH protocol which is based on centralized CH selection mechanism. The remaining energy and location values are conveyed by each node to the BS. The nodes having residual energy greater than the average energy of the nodes are only considered during CH election. The amount of energy required by the CMs to transmit data to their designated CH is greatly reduced. It improves network lifetime over LEACH but the clustering overhead is still prominent. It is not guaranteed that the elected CH will possess the maximum energy. The issue is addressed by the LEACH- centralized efficient (LEACH -CE) protocol that selects CHs having the highest residual energy. The use of single-hop transmission again leads to non-uniform energy dissipation among the CHs. Prabhat [7] et al. developed an improved LEACH-CE protocol which divides the sensing area into near and far zones based on the threshold distance. The far zone area is further sectioned into number of sub-zones. Residual energy is the only parameter used for CHs election. Intermediate CHs of sub-zones are used to relay the data to the near zone CHs. The proposed protocol is tailor-made for large area WSNs. Honglei [8] et al. proposed a multi-hop supported version of LEACH. MH-LEACH chooses an efficient path from the CHs to the BS via intermediate relay CHs. It is well suited to large area networks that have BS deployed away from the target area. Each CH maintains the routing table information built using received signal strength information (RSSI). The greater value of RSSI points towards closer node proximity. The process of cluster formation and CH selection is distributive and is similar to LEACH. The CHs lying towards the BS have the onus of forwarding the relay traffic and lead to hot-spots. Zahoor [9] et al. proposed an area zonal rectangular (AZR) LEACH protocol which is based on static clustering scheme. The protocol creates equal sized rectangular clusters and forms zones using those clusters. The clusters closest to centrally placed BS act as advanced clusters and possess higher energy resources. The size of every zone is the same and the zones help to balance the network traffic across the entire sensor network. M. Venkateswarlu [10] et al. proposed a zone based routing protocol (ZBRP). The protocol considers both the clustering and network design space as factors to alleviate the hot-spot problem. The number of neighbours and distance to BS are used as the factors to elect the CHs. The maximum visibility ensures coverage and connectivity. The node from downstream that is closer to the BS, that has fewer earlier forwarded messages and that has greater residual energy is chosen as the next-hop during data relaying. M. Ye [11] et al. proposed an energy efficient unequal clustering scheme (EECS). The CH is chosen based on the remaining energy, distance to BS and distance to neighbor factors. The distance to neighbors ensures minimum intra-cluster communication cost and distance to BS minimizes the workload of the farther located CHs. The CHs communicate with BS via single-hop mode and dissipate energy non-uniformly. C.F Li [12] et al. proposed another energy efficient unequal clustering (EEUC) protocol. The competition range for the nodes is influenced by the distance from the BS and the election process takes into account the residual energy. The protocol supports multi-hop data transmission to the BS. The CHs choose relay nodes optimally based on the residual energy and distance from the BS. The cluster formation process being dynamic, expends significant overhead. Chen [13] et al. proposed an unequal cluster based routing protocol (UCR) which is also a self-organized algorithm. The proposed method elects CHs based on remaining energy values. The competition range increases as the distance from BS increases, the topology management is performed using EEUC and inter-cluster transmission elects the next-hop based on energy cost of links and residual energy of nodes. Jiguo Yu [14] et al. proposed an energy driven unequal clustering protocol (EDUC) which is based purely on single- International Journal of Electrical & Electronics Engineering 13

3 hop communication. The clusters located further away from the BS are of smaller sizes. The protocol strives to balance the energy consumption among the CHs. Energy driven CH rotation scheme is adopted to balance the energy consumption for that cluster. A node becomes a CH only once during the entire lifespan. Jiguo Yu [15] et al. proposed an energy aware distributed unequal clustering (EADUC) protocol. The ratio of the residual energy of the node and the average residual energy of its neighbors is used to elect the CH. The competition radius considers the residual energy as well as the distance to BS. The CHs adjacent to BS have smaller cluster sizes. The high energy nodes have more chances of getting elected as CHs and there are no isolate points in EADUC. III. NETWORK MODEL AND PROBLEM FORMULATION This section presents an overview of the network model used by the ZBEEC protocol. Then it describes the problems that the proposed protocol aims to resolve. A. Network Model The following network assumptions are made: BS is within reach of every node. BS possesses sufficient computational resources. Nodes are homogeneous having same capabilities. Both, the nodes and the BS are stationary. BS knows the location and ID of all the sensor nodes. The first order radio model is used for evaluation of energy consumption during transmission and reception of data. RF circuitry and amplifier circuitry are the two components of the transmitter that require energy. When a k-bit message is transmitted over a distance d, energy is utilized as per (1) and (2). The receiver requires energy for only operating the radio electronics as given by (3). E Tx (k,d) = E elec.k + E fs. d 2.k, d < d 0 (1) E Tx (k,d) = E elec.k + E mp. d 4.k, d > d 0 (2) E Rx (k) = E Rx-elec (k) = E elec.k (3) Here, E elec energy consumption per bit, E fs free space coefficient, E mp multi-path coefficient of the amplifier, k number of transmitted data bits, d actual transmission distance and d 0 threshold distance. For the transmission distance less than the threshold value, free space energy model is followed otherwise multi-path energy model is used. For ZBEEC, infinite compressibility model is considered for data aggregation in which a CH aggregates the data into a single packet of fixed length. B. Problem Formulation The basic protocols face problems when the BS is situated far away from the large area sensing field. The introduction of multi-hop communication for data transmission creates hotspots. The formation of unequal clusters removes hot-spots but leads to other issues such as non-uniform energy consumption between the CMs and CHs. Dynamic clustering results in high overhead, the coverage and connectivity are still not fully ensured and every round uses different number of clusters. To overcome these shortcomings, ZBEEC has been proposed which makes use of equal clustering and static cluster formation schemes. ZBEEC enhances stability period, uses very low overhead and suppress the occurrence of hotspots. IV. ZBEEC PROTOCOL DETAILS This paper proposes ZBEEC protocol that forms equal sized static clusters and divides the network into zones using these clusters, uses dual CHs for near zone clusters, implements optimum CHs selection procedure and follows energy-efficient multi-hop communication for data transmission. The protocol attempts to improve the network lifetime by balancing the energy consumption within a cluster and also across the clusters. Every round in ZBEEC has two phases. First one is the set-up phase that includes cluster formation, zoning and CH selection procedure. Second one is the steady state phase in which actual transmission of the data occurs via relay nodes. All the stages of the set-up phase are explained in detail followed by the steady state phase. A. Network Organization ZBEEC considers 100 sensor nodes that are homogeneous and are deployed randomly in 100 m x 100 m field. The deployment can be uniform as well as non-uniform. The BS is assumed to be placed at (150, 50) and (300, 50) coordinates. A powerful BS capable of forwarding the data from the CHs to the intended recipients is used. B. Formation of Equal Size Clusters using Static Clustering The total sensing area is partitioned into fixed number of equal sized rectangular clusters by the BS. We present the case of nine clusters formed by the BS to explain the protocol operation. ZBEEC offers reliable coverage and connectivity. Equal sized clusters ensure uniform energy consumption among the clusters and cluster formation is energy-efficient. C. Formation of Zones based on Clusters The clusters form two zones known as near and far zone. The zone near the BS is termed as near zone and rest of the field makes up the far zone. Near zone has three and far zone has six clusters. Far zone is further divided into two subzones. The zone based topology balances the traffic across the zones, lends support for multi-hop communication and prohibits formation of hot-spots. The near zone restricts the number of transmissions above the threshold distance. D. Cluster Head Selection Procedure ZBEEC scheme is fully centralized and CHs are allotted by the BS. The CH IDs are broadcasted by the BS and in the situation of being a match between a sensor node and CH IDs, that node becomes a CH. Otherwise, the node gets its time slot for data transmission. Each near zone cluster contains two CHs, one main cluster head (MCH) and one auxiliary cluster head (ACH). The average residual energy, Eavg, of all the neighbor nodes is given by (4) and their average d istance to BS, International Journal of Electrical & Electronics Engineering 14

4 Davg_bs, is given by (5). The residual energy threshold, T, of current node in (6), its centrality factor, Dc, in (7) and its distance to BS, D bst, in (8) are normalized values that constitute the weighed function for election of CHs according to (9). The node having the maximum threshold becomes a MCH or ACH depending on the Eavg, as shown in pseudo code in TABLE I. As long as Eavg stays beyond a fixed threshold for example 0.1 Joule, ACH is prioritized over MCH and otherwise MCH is given priority over ACH. Eavg = Davg _ bs = i1 Erem _ i i1 Dbs _ i (4) n 1 (5) T = Erem Eavg (6) Don _ i Dc= k* i1 n 1 (7) D bst = Dbs Davg _ bs (8) T CH = (()()) q1 * T q2 *1 / Dc q3 *1 / DbsT 3 (9) Where, n is the number of nodes, Erem_i is the residual energy of a node other than the current node, Don_i is the distance of the current node from ith other node, k is the scaling factor for normalization, Dbs_i is the distance to BS for the ith node and Dbs is the distance of the current node from BS. Nodes with high residual energy, lying towards the BS and having a good centrality value are favoured as CHs. A lower distance to BS preserves energy for data transmission to BS and a good centrality value lowers the transmission distance for CMs within a cluster. MCHs and ACHs are rotated in every round as per the formulated threshold. TABLE I. PSEUDO CODE FOR CLUSTER HEAD SELECTION Start for (all nodes of the near zone cluster) compute CH threshold T CH end for sort T CH in increasing order if ( Eavg > 0.1 ) ACH max ( T CH ) End else MCH max ( T CH ) 1 [ACH is given priority] MCH max ( T CH ) [MCH is given priority] ACH max ( T CH ) 1 end if Dual CHs scheme in the near zone maximizes the utilization of every node s energy and uses balanced energy consumption. E. Steady State Phase In ZBEEC, the distance from the BS and the remaining energy are used for selecting a relay node. ACH collects the data, performs aggregation and forwards it to the MCH. Aggregation gets rid of the uncorrelated noise and amplifies the useful signal. MCHs are responsible for efficient relaying of data packets of far zone CHs to the BS. Far sub-zone1 CHs transmit packets to MCHs either directly or via far sub-zone2 CHs, far sub-zone2 CHs forward packets to MCHs via singlehop and MCHs transmit/relay data directly to the BS as shown in Fig. 1. Fig. 1. Zone Based Energy Efficient Clustering Routing Protocol V. SIMULATION RESULTS The simulation of ZBEEC is carried out in MATLAB; simulation parameters, performance metrics along with the simulation results are presented in this section. A. Simulation Parameters Table II shows the parameters used for carrying out the ZBEEC simulation. TABLE II. SIMULATION PARAMETERS Parameters B. Performance Metrics The performance is evaluated based on following metrics: International Journal of Electrical & Electronics Engineering 15 Value Sensor field 100 x 100 Nodes 100 Initial energy of nodes E elec 0.5 J 50 nj/bit E fs 10 pj/bit/m 2 E amp pj/bit/m 4 E da D 0 Packet size 5 nj/bit 87.7 m 4000 bits

5 1) Stability Period (FND): It is the time between the start of network operation and when the first node is dead. 2) Last Node Dead (LND): It is the time in terms of rounds when the last alive node dies down. 3) Number of Alive Nodes: It is the total number of nodes which are alive in a given round. 4) Percentage Nodes Alive (PNA): It is the time in terms of rounds when ninety percent of nodes are alive. 5) Average Energy Consumption: It is the average energy expended by the network in a single round. 6) Half Node Dead (HND) : It is the time in terms of rounds when half of the total nodes are dead. C. Simulation Results Fig. 2 shows that the ZBEEC protocol outperforms the other three protocols in terms of number of alive nodes available in a given round. The average energy consumption of ZBEEC protocol is less than 0.05 Joule over the entire network lifetime. Even placing the sink at (300, 50) does not affect the duration of stability period. Network lifetime values for uniform and non-uniform deployment are comparable. Fig. 2. Number of alive nodes in each round ZBEEC improves stability period - FND value by 500%, 410%, 207%; HND value by 335%, 257%, 153% over LEACH, MH-LEACH and EADUC protocols respectively. ZBEEC enhances network lifetime PNA value by 467%, 287%, 134% and network lifetime - LND value by 235%, 226%, 206% compared to LEACH, MH-LEACH and EADUC protocols respectively as depicted by Table III. Value TABLE III. LEACH SIMULATION RESULTS COMPARISON MH- LEACH EADUC ZBEEC FND HND PNA LND VI. CONCLUSION It is concluded that in ZDEEC, the use of equal sized static rectangular clusters turns out to be energy-efficient due to low overhead and assured connectivity. The number of clusters and cluster heads remain the same throughout the simulation. The formation of zones and partitioning into subzones balances the network traffic across the entire network. The protocol uses balanced energy consumption between the cluster heads and member nodes. The use of dual cluster heads in near zone prevents formation of hot-spots. Cluster heads are optimally elected based on residual energy, centrality and distance to base station factors. Energy consumption is balanced between cluster heads across the clusters by using multi-hop transmission between the zones based on residual energy and distance to base station factors. The method prolongs stability period and raises the network lifetime of a WSN. REFERENCES [1] Xuxun Liu, A Survey on Clustering Routing Protocols in Wireless Sensor Networks, Sensors, ISSN , Vol. 12, ; doi: /s , [2] Nikolaos, A. Pantazis, A. Nikolidakis and D. Vergados, "Energy- Efficient Routing Protocols in Wireless Sensor Networks: A Survey, in IEEE Comm. Survey & Tutorials, Vol. 15, 2013, pp l. [3] S.S. Kanhere, N. Ahmed and S. Jha, The holes problem in wireless sensor networks: A survey, ACM SIGMOBILE Mobile Computing and Communications, pp. 9-18, April [4] G. Vennira, R.Manoharan, A Survey of Energy Efficient Unequal Clustering Algorithms for Wireless Sensor Networks, International Journal of Comp. Appl. ( ), Vol. 79 no. 1, October [5] W. R. Heinzelman, A. Chandrakasan, and H. Balakrishnan, Energy efficient communication protocol for wireless micro-sensor networks, in Proc. of the Hawaii International Conference on System Science, Hawaii, USA, 2000, pp [8] W. R. Heinzelman, A. Chandrakasan, and H. Balakrishnan, An application-specific protocol architecture for wireless micro sensor networks, IEEE Transactions on Wireless Communications, Vol. 1, no. 4, pp , [9] P. Kumar, J. P Singh, Deepak Kumar and M. P. Singh, Energy Efficient Multi-Hop Routing Based on Improved LEACH-CE for Wireless Sensor Network, / IEEE. [10] Muni Venkateswarlu, Adiyapatham and Chandrasekaran, Zone-Based Routing Protocol for Wireless Sensor Networks, Hindawi Publishing Corporation, International Scholarly Research Notices, Volume 2014, Article ID ,, 9 pages [11] M. Ye, C. Li, G. Chen, J. Wu, EECS: an energy efficient clustering scheme in wireless sensor networks, 24th IEEE International Performance, comp. and comm. conference IPCCC, 2005, pp [12] C. Li, M. Ye, G. Chen, J. Wu, An energy-efficient unequal clustering mechanism for wireless sensor network, IEEE International conference on mobile ad-hoc and sensor sys., pp , [13] G. Chen, C. Li, M. Ye, J.Wu, An unequal cluster-based routing protocol in wireless sensor networks, Wireless Networks, , DOI: /s , [14] J. Yu, Y. Qi, and G.Wang, An energy-driven unequal clustering protocol for heterogeneous wireless sensor networks, Journal of Control Theory and Applications, vol. 9, no. 1, pp , [15] J. Yu, Y. Qi, G. Wang, Q. Guo and X. Gu, An Energy-Aware Distributed Unequal Clustering Protocol for Wireless Sensor Networks, Hindawi Pub. Corp. International Jour. of Distributed Sensor Networks, Volume 2011, Article ID , 8 pages, doi: /2011/ International Journal of Electrical & Electronics Engineering 16