A Comprehensive Survey of Clustering Approaches in Wireless Sensor Networks Bhawna1,∗ , Tanu Pathak2 and Virender Ranga3 1,2 Department 3 Department
of Computer Engineering, PG Student, Banasthali Vidyapith, Tonk, Rajasthan 304 022, India. of Computer Engineering, Faculty of National Institute of Technology Kurukshetra, NIT Kurukshetra, Kurukshetra, Haryana 136 119, India. e-mail:
[email protected]@gmail.com,
[email protected]
Abstract. Presently, Wireless Sensor Networks (WSNs) are not only limited to military application but also used by general public for their number of applications areas like healthcare applications, home automation, habitat monitoring, medicine health monitoring, engineering applications etc. Since sensor nodes are battery operated and energy is the biggest constraint for wireless sensor capabilities. In this paper, we survey different approaches based on energy efficiency, security, network lifetime and formulate the problems of WSNs with the help of routing based protocols, game theory, genetic algorithm, swarm intelligence and security based approaches. Then, we present a comprehensive taxonomy of energy efficient clustering approaches, which are discussed in the depth. The basic challenges, open research issues and research gaps are briefly explored in this paper. Finally, we conclude our work insight for future research direction about energy conservation in WSNs. Keywords: theory.
Wireless sensor networks, Cluster head, Energy efficiency, Clustering, Genetic algorithm, Game
1. Introduction WSN consists of small embedded devices spread over a large geographical area for monitoring temperature, humidity, vibrations, seismic events, and so on. Typically, Wireless Sensor Networks formed by hundreds or thousands of nodes that can communicate with each other and pass their data from one to another. These nodes are grouped to form a cluster and each cluster has their own cluster head (CH). All sensor nodes send their data to CH, which in turns routes this data to base station known as clustering. The improvement in wireless technology and lower cost of sensors has helped the WSNs market growth. Some challenges that the researchers and developers faced during designing a WSN are: limited resources of energy, processing power, network lifetime and memory [1]. A number of energy-efficient routing protocols have been discussed with their pros and cons in literature to deal with energy dissipation, system cost, latency, security etc. Because choosing the wrong protocol may cause severe inefficiency and prevents the WSN to accomplish user need. In the recent years, Game Theory (GT) is widely used in WSNs. In cooperative games a group of players shows cooperative behavior and take decision as a whole group; rather than individual players, but in case of non-cooperative where nodes act selfishly to maximize their benefit. Genetic Algorithm (GA) is also used to improve network lifetime. Swarm Intelligence becomes increasing popular over the last decades because of its coordinated behavior; swarms also achieved its desired goals like scalability, robustness. Examples of Swarm Intelligence are ant colony optimization (ACO), particle swarm optimization (PSO), artificial bee colony (ABC) etc. Wireless sensor networks is continue to grow, so there is a need of effective security mechanism. With this in mind, we present a number of incentive detection mechanisms (IDM) which helps in energy conservation and also provides security against attacker. In this work, we demonstrate that clustering approaches based on different criteria and features used in WSNs. The main objective of this work is to provide a common framework for the state-art-of-study of this research area i.e. and compared the proposed approaches with different performance parameters. ∗ Corresponding author
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The remainder of this paper is organized as follows. Section 2 describes the motivation work. Section 3 discusses proposed methodology that presents a novel taxonomy. Section 4 provides a comparative study to understand various performance metrics to improve energy efficiency in the present scenarios. Finally, section 5 describes conclusion and future work. 2. Motivation A major cloud burst shook India on 16th June 2013. The cloud burst triggered massive floods at Kedarnath have left behind a trail of death and destruction in the entire valley stretching from temple town to Gaurikund and further downstream. Multiple agencies undertaking relief and rescue operations were performed. Due to foggy and rainy weather some difficulties arises. On 26th August 2013, an Indian National Police Agency report confirmed 23,592 deaths 46,285 injured and 63,823 peoples missing. Due to cloud burst and then flash floods caused by torrential rain it was found that quick and energy efficient network for communication is required for the rescue operation. WSN is a more suitable and cheaper network for such type of scenario. However, most challenging design issue in WSNs is energy conversation and we need to rely upon renewable resources of energy which can be used when all the traditional energy plants like electricity stations are unable to work due to the calamity. Hence, there is a need to develop some energy efficient clustering scheme to overcome this critical problem. 2.1 Challenges and open research issues In order to develop a platform which provides energy efficient clustering, many challenges and open research issues need to be faced and solved. Some of them are: • Energy efficiency: There are thousand numbers of nodes in WSNs where energy is not distributed uniformly among these nodes. Moreover, it is impossible to recharge or replace nodes in sensor network because it is time-consuming and complex in nature. • Resources constrained at each sensor node: Sensor have limited battery energy, a relatively low performance CPU and constrained memory size. Therefore, it is crucial to use the available resources in WSNs in very efficient way. • Robustness: Robustness of the network resources requires tolerance of sensor nodes that nodes may fail no battery power etc. • Security: Sensor nodes are deployed in diverse geographical environments i.e. they are more prone to attach from different nodes. Hence, security is also a key issue while designing WSNs. • Fault tolerance in WSNs: Most of the applications of WSNs are in harsh environment. Large number of nodes may cause extra energy consumption due to increase in communication overhead and more collisions. So, instead of applying some traditional fault tolerant approach used in WSNs, a real time restoration technique should be used in the network. 3. Proposed Methodology To the best of our knowledge there is no specific taxonomy for energy efficient mechanisms based upon different criteria for WSNs till date. Although there is some related work like Renita Machado et al. [41] which represents WSNs challenges for future enhancement. Figure 1 shows the novel taxonomy for energy efficient clustering approaches and is explained based on the state-of-the-art of literature review. A. Routing based protocols The paper [2] described an energy efficient clustering algorithm based on adjacent nodes and residual electric power which provides higher performance than LEACH, HEED and HIT. In this paper, the higher performance is achieved by considering the node having large remaining electric power and covers many adjacent nodes which not covered by other CHs. In [3] Nikolidakis et al. propose Equalized Cluster Head Election Routing Protocol (ECHERP), in which, they calculate the current and estimated future residual energy of the nodes along with the number of rounds. In this paper, we can see that cluster formation in network takes place by gathering the data from the BS. Now CH is selected by the BS using Gaussian elimination algorithm and join request is send from CH to nodes. BS creates the TDMA schedule and sends these nodes to the CH. The execution of protocol is finished when nodes in the network run out of energy. 24
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They found that the use of ECHERP protocol provides substantial energy efficiency and expand network lifetime. Moreover, the authors do not considered some more parameters to enhance ECHERP that’s why; it cannot perform well in heterogeneous environments. In [4–6], heterogeneous-aware protocols are developed by modifying the LEACH. As LEACH is a best hierarchical routing protocol but still it cannot outperforms well in a heterogeneous environments because in LEACH CH is selected randomly, not suitable for large area and CHs are not uniformly distributed. So, in this model author’s optimized LEACH protocol through genetic algorithm which extends the time interval before the death of the first node. In LEACH, we found that CH plays a significant role during data collection and data forwarding but if the CH die, then CH becomes useless therefore; to beat this problem LEACH-sub protocol is proposed which consists of CH, sub-CH, member nodes. If CH dies then sub CH takes the responsibilities of CH. The result shows that now it is more energy efficient, effectively prolong network lifetime provide better efficiency in terms of delay, packet drop and packet delivery ratio as compared to LEACH. R. Devika et al. [7] elaborate different routing types and routing protocol with their advantages and disadvantages. They classified routing techniques into three classes: flat, hierarchical and location based routing. In Flat routing protocol, all node play same role e.g. flooding, gossiping, SPIN. In hierarchical routing, cluster is created and a leader is assigned to each cluster which collect all the data and send it to the BS. This cluster-based routing reduces network traffic, energy consumption hence; increase the network lifetime also it is found to be best routing protocol. In location based routing, geographical location of nodes should be known before communication takes place and then forward the data. They also compared different routing protocols like Direct Diffusion, LEACH, TEEN, APTEEN, HEED, PEGASIS, MBCR, MMBCR, SPIN, EEHC etc. Authors finally, conclude that hierarchical routing is best routing because it has more scalability, consume less energy and robust in nature. Hybrid Distributed Energy Efficient Heterogeneous Clustering Protocol (HDEEHC) is given by Jun Wang et al. [8] which achieve longer network lifetime and in heterogeneous environments. HEED results are unsatisfactory in heterogeneous networks therefore; to overcome this problem HDEEHC protocol is discovered which holds the parameters of heterogeneity. It regularly selects cluster heads as per their residual energy, type of node in the election of CH and node degree. It consists of two rounds: cluster formation and data communication. During cluster formation nodes having high initial and residual energy will have more chances to become CHs than the low-energy nodes. In data communication all alive node periodically collect the data, send to CH and finally to the sink. Simulations result shows that HDEEHC achieved better performance in heterogeneous environments. Mourad Hadjila et al. [9] proposed another chain-based routing protocol for wireless sensor networks (WSNs) in which a main chain is constructed having same amount of power and includes leaders of each chain. The nearest node gathers the data from the neighbor nodes and sends to the sink. In the next transmission, the node that has the higher residual energy performs this task. Hence; this approach is effective rather than constructing multiple chains towards the sink because it decreases the energy consumption and maximize the lifetime of the WSNs. But it is not appropriate for heterogeneous environments Appropriate selection of CH in WSN is a challenging issue therefore; Sudakshina Dasgupta et al. [10] proposed a democratic cluster head election algorithm to select CH’s in each round which increase network lifetime and reduce communication overhead among the nodes in WSNs. The proposed scheme based on “single hop with clustering” architecture to transmit data to base station. To select CH an algorithm is proposed in which firstly initialization and cluster formation takes place. For CH selection distance and residual energy parameters are considered. Further voting procedure takes place and finally elected nodes become CH of that cluster. During steady state, nodes transmit their continuously to the BS. Cluster rotation takes place when the remaining energy of CH falls below the threshold value. Because of considering residual energy and distance parameter network lifetime improved tremendously. In [11] Jun Yue et al. proposed an Energy Efficient and Balanced Cluster-based Data Aggregation Algorithm (EEBCDA) in which CH transmit data to BS by one-hop communication. This algorithm consists of set-up and steady-phase. During set-up phase, CH selection and cluster formation takes place. The selection of CH is done as per maximum residual energy factor. During steady phase, data transmission takes place according to TDMA schedule. It is found that EEBCDA enhance network lifetime, energy efficiency. Balancing energy is the key issue so, Yan Gu et al. [12] proposed a layered clustering algorithm i.e. Energy Efficient Layered Clustering (EEHC) which is a hierarchical clustering protocol. Here fixed clustering is made and network model is used for energy calculation during packet transmission and reception. CH is selected as per residual energy of nodes. It is found that EEHC improves network lifetime and energy efficiency.
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Rogaia Mhemed et al. [13] propose Fuzzy Logic Cluster Formation Protocol (FLCFP) which is an extension of LEACH protocol. They considered three variables i.e. distance to CH, distance to BS and energy level. To extend the network lifetime these three variables are further divided into three levels i.e. low, medium, and high. In FLCFP non-CH also calculate chance value for each CH. The CH chance value determined as Crisp values are forwarded and determine the membership function called fuzzification. IF-THEN rules is used to determine new fuzzy output set and then aggregation rules applied to obtained outputs. The simulation result shows that first node death (FND) rate is improved; enhance network lifetime and uniformly consumption of energy takes place. As LEACH have some shortcomings like improper distribution of energy, randomly election of CH and small network lifetime. So, Qian Liao et al. [14] proposed energy balanced clustering algorithm named as L-LEACH which consider two parameters: node energy and position information to improve the LEACH algorithm. L-LEACH considers the distance between the normal node and the BS. After comparing this distance if node is close to BS then no CH will be selected otherwise choose the CH whose distance is smaller than the distance from the node to BS it will be regarded as CH. Then non-CH nodes choose the optimal CH according to cost function. The simulation result shows, that by using L-LEACH algorithm network lifetime effectively expand and provides better convergence. Saadi et al. [15], proposed an Energy Aware Scheme Clustering (EASM) in which every sensor nodes elect its CH independently as per their initial and residual energy. They considered three types of sensor nodes: super nodes, advanced nodes and normal nodes. We have found that in EASM new optimum probability threshold is introduced in which initial and residual energy parameters are considered. Now, probability is calculated for all three nodes. Node is eligible to be CH if it’s random number is less than threshold value, then node becomes the CH during the current round and broadcast message to other nodes. During advertisement non-CH nodes must keep their receivers on and hear the advertisement of all the CHs. After this phase, non-CH decides to which they will belongs for this round and that will be based on received signal strength of the advertisement. As per given TDMA non-CH can also communicate with its own CH. After receiving all data CH node compress the data into single signal and send aggregated data to BS. Finally, the authors proved that a good scheme and properly selection of CH leads to saves energy in better way and prolong network lifetime. Mariam Alnuaimi et al. [16] elaborate some clustering protocols and classify them based on the clustering technique formation, the way data is aggregated to the BS. LEACH is a hierarchical based clustering protocol in which CH is selected randomly and rotates this role randomly for uniformly distribution of energy. After electing CH, broadcast this message to non-CH and then non-CH choose the CH as per received signal strength. Now, CH compresses the information and this aggregated data to BS. Here authors proposed PEGASIS which is a chain based algorithm and it is advanced version of LEACH. It forms a chain from sensor nodes and only one node is selected from chain to transmit data to BS. Chain consists of those nodes which are very close to each other and make a path to the BS. In this way it avoids cluster formation and save energy. HEED and SEP both are weighted based clustering protocol. HEED is multi-hop clustering algorithm where CH selection is done by considering residual energy parameter. SEP is for heterogeneous WSN and consists of two types of nodes based on residual energy: advanced nodes and normal nodes. Advanced nodes have more energy than normal nodes. CH is chosen as per residual energy factor. They compare all the protocols and finally conclude that SEP has longest network lifetime among the others, because it is using advanced nodes. LEACH has shortest network lifetime because it randomly selects the CHs. HEED perform better than LEACH because it uses residual energy of each node to elect CH. In [17], Xiaowen Ma et al. presented a modified LEACH protocol because LEACH has some shortcomings: suitable only for small size network, not considered residual energy and distance parameter. But in modified LEACH, during CH election phase, CH will be rotated by computing the residual energy of nodes. Then select the node with maximal energy and can broadcast the information within its own clustering area. During data transmission phase distance parameter is used. For data transmissions select the nearby CH and receive the aggregated data. The simulation result shows that modified LEACH algorithm has better performance than LEACH. 3.1 B. Genetic algorithm based approaches Finally, another work where authors compared an intelligent energy-efficient hierarchical clustering protocol [18] which performed better than the traditional cluster based protocols. They improved the HCR protocol by using a Genetic Algorithm (GA). The GA end result identifies the best cluster heads for the network. Using the minimum distance strategy base station assigns member nodes to each cluster head and broadcasts the network details to the 26
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nodes. All the sensor nodes receive the details transmitted by the BS and clusters are configured. Then in data transfer phase, nodes sends messages to their cluster heads and then fitness function (F) is computed. After every round, the best fit chromosome is evaluated. Finally, they compared LEACH with modified HCR and simulation result signifies that HCR performed better than LEACH in wide area and increase network lifetime. Moreover, HCR do not deal with the cross layer optimization between queries and also with multi-hop communication between cluster heads. Multi-objective two-nested genetic algorithm (M2NGA) is given by Ali Peiravi et al. [19] in which they proposed the idea to optimize the energy consumption and delay simultaneously. For this Strength Pareto Evolutionary Algorithm (SPEA) method is used at the top level and another GA is used for efficient topology selection. This algorithm consists of two-level clustering of nodes. For generating initial population, a node is selected randomly. After proposing cluster schemes, their fitness values are evaluated using another GA. Finally, ranking selection method is used for selection of chromosomes. Then mutation and crossover takes place. The simulation result shows that M2NGA consume less energy per bit better perform with increased number of nodes. But in M2NGA multi hop intra cluster data transmission is considered, which is not possible in most heuristic clustering methods and also it cannot perform efficiently in dynamic network. Esmaeil saeedian et al. [20] proposes an algorithm i.e. Clustered WSN using Fuzzy logic and Genetic algorithm. In CFGA fuzzy interface engine is used for determining best node in each region and that node will act as a candidate for the CH. Network information is received by the base station which is further classified into balanced clusters. Now, Genetic Algorithm is applied at the sink to determine the optimal mode, number and location of cluster heads between cluster head candidates. Then single-point crossover and mutation takes place. After performing these operations BS select a chromosome with minimum difference in energy that node becomes a CH in the network. It is found that CFGA causes reduction rate of network’s energy in uniform manner and helps in achieving longer lifetime. Shu et al. [21] tried to reduce the energy consumption that’s why; they developed a new multi hop protocol which performs cluster management in energy efficient manner. Hence, a novel optimized algorithm clonal multi-hop routing algorithm (OCMRA) is developed which is based on clonal selection strategy and adapts network topology. In OCMRA the main idea for each node is to receive and transmit data to close neighbour and each have a turn being the leader for transmission to BS and this evenly distributes the load among the sensor nodes. Genetic Algorithm also plays a significant role in it. Firstly, during initialization phase randomly deploy the sensors in homogeneous space with same energy then clone the chromosome with best fitness value. Crossover and mutation takes place. Finally, data transmission takes place from sensors to CH and calculates energy consumption while gathering the data, each node receive data from one neighbour, fuses its own data and send to further on chain and do this until the death of all sensors in WSN. The simulation result shows that this algorithm enhance lifetime of network, improve energy efficiency and performance. C. Game theory based approaches Zheng Zeng-wei et al. [22] proposed an Adaptive Clustering Hierarchy Based on Game Theoretic Technique (ACHDT) routing algorithm for WSNs. In ACHDT individual sensor of algorithm are act as players for electing cluster-head nodes as per node energy payoff function. When BS broadcast the data packets to all nodes then after receiving all nodes save nodes position values in the cache and route this data packets along with remainder energy to the BS. Strategy followed by this algorithm is that nodes which have lower remainder energy are used quickly. The authors found that this technique uniformly distributes the energy load among sensor with prolonging network lifetime but this algorithm cannot work well in dynamic system. In [23], the authors presented Mixed Game Theoretic K-means using Game Theory argued that the problem regarding equipartition and compaction are optimized simultaneously by using non-cooperative game. It is an extension of GTK-means. This method uses advantage of mixed strategies and mixed Nash Equilibrium. In mixed strategy Nash equilibrium (NE) players choose independent strategies and lead to probability distribution of strategy vector. GTK-means provides three important steps: algorithm starts single iteration of K-means means act as several initialization of clusters and results equi-partition clustered. But if cluster centers are not equip-partitioned then a new game is formulated in order to make their size equal and game is calculated with pay-off calculation. Pure NE solution identified from set of pay-off matrix in order to determine final strategy set. The main advantage of MGTK is that it handles mixed strategy as well as pure ones. The paper [24], given by Babak Arisian and Kourosh Eshghi, presented a model called Pricing Payment Technique also known as Reputation Punishment Model which is used for generating an optimal path in WSN. This technique © Elsevier Publications 2014.
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considers reliable energy and traffic load parameters. All the nodes with utility are participated and helped in finding a path. This way reliability of the path is maximized. In this model, destination node act as credit to source node for getting each data packet successfully and source node also pay some credit to intermediate node for participating in data forwarding. If profit is found to be positive each node incentive to cooperative game and its location on path but if it is negative, then node is not consider in the game. The authors found that their technique enhance network lifetime, uniformly distribute network load and improve path reliability. Moreover, there is a need to optimize the algorithm to find the path with maximum utility function and also optimize the transmission cost. In [25], the authors analyzed the game theoretic clustering (GTC) is basically a distributed clustering algorithm, which minimize the energy consumption for the WSNs. GTC consists of two parts load balancing and cluster formation. Load balancing ensure equalization of energy consumption levels of CHs at different region. In cluster formation Win Stay Lost Shift strategy (WSLS) is used in which if most recent pay-off is high then same choice is repeated otherwise, the choice would be changed. It is found that GTC has main characteristics: cluster size and cooperation between CHs is determined adaptively, balance the energy consumption levels and extends the network lifetime. But the main problem of this algorithm is that there is only one CH in each region and this assumption is somehow unreasonable, and it also limits the applicability of GTC. In [26], authors presented a Trading Model in which they focus on energy saving through cooperation using game theory. This Trading Model is based on Cournot game that not only provide incentive to relay and achieve significant energy saving but also an incurred gain in throughput due to achieve higher data rates on short range links. In this model a strategy likes oligopolistic market. They also considered a scenario i.e. non-cooperative game in which there are two level of competition. Relay sell its goods and source nodes pays reasonable price and source node complete with each other for buying the goods from relay. After comparing energy consumption with cooperation and non- cooperation it is found that during cooperation most packets are sent with smaller energy per bit. The simulation result shows that cooperative game theory save energy in an efficient way and improve network lifetime. But the main problem of this model is that there is an assumption in which data bits are exchanged at virtual price and also need to extend this work to consider exchanging the credits too. Mark Felegyhazi and Jean-Pierre Hubaux [27] presented a game-theoretic model. This model gives an idea regarding cooperation in multi-domain sensor networks. Here author considered sensor networks which are spread in the same area, but are regulated by different authorities. System model and the cooperative game are used for checking cooperative packet forwarding in co-located sensor networks. For investigation purpose there are two scenarios i.e. common sink and separate sink. In this model, two routes are established for each sensor i.e. non-cooperative routing and cooperative routing. If a sensor loss its energy completely, then its domain is removed from the game and routes are recalculated. Their goal is to determine the best strategy for the authorities that control the sensor networks. It is found that cooperation of co-located sensor networks provides better result in terms of network lifetime. Moreover; it is necessary to investigate the effect of different lifetime and also take the effect of asynchronous wakeup of the sensors to enhance the results. M. J. Shamani et al. [28] presented an Adaptive Energy aware strategy in which they emphasized on cooperation between networks. Authors considered heterogeneous multi-domain sensor networks, in which routing is hierarchical and their topology are heterogeneous. Because of heterogeneous topology SEP is used as cluster head election mechanism. Based on situation nodes can choose any strategy like cooperative strategy or non-cooperative strategy or behavioral strategy. The simulation result shows that this strategy extends network lifetime. But this strategy not investigates packet forwarding in cooperative games and also routing in multi-domain wireless sensor networks. The paper [29] explained a cooperative game theoretic model for clustering in WSNs. Cooperative game focused on cost but not on benefits to overcome this problem shapely value is the solution. In this model, during election phase, each CH broadcasts an advertisement and rest of the SNs determined their cluster according to the received signal strength of the advertisement messages. In a cluster, a CH collects the data from all sensor nodes from all SNs in the same cluster through a pre-set TDMA schedule and sends this whole summery to the base station in each frame. Therefore; it helps in prolonging network lifetime, reduces transmission time and achieve higher energy efficiency. In paper [30], authors proposed game theory based routing approach (GBRA) to enforce the security in the network, and prolong its lifetime. In this model, while searching a routing path from an original sender to the destination, each hop of the routing path is supposed to be a game between a sender and its next hop relay node. Out of the selfish nature, each of the two players will try to maximize its own pay-off function. The pay-off function of a sender node 28
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is related to its opponent’s reputation, remaining energy, the distance between its opponent and the destination. The relay’s pay-off function is in proportion to the sender’s reputation. Based on the pay-off function, the sender will select the node that can maximize its pay-off from all the potential relay nodes. Then, provide route to forward the packets in the network since there may be malicious nodes in the networks and these nodes will drop the incoming packets deliberately. Hence; authors adopted a reputation-based mechanism to resist the selective forwarding attack and enforce the security of WSN. The main advantages are: prolong the lifetime of the networks and achieve a higher throughput even when there are malicious nodes in the networks. Debashis De et al. [31] proposed two new approaches of bubbling mechanism and border clustering to save energy and increase lifetime of WSN. In bubbling mechanism there are two clusters head (CHs) i.e. heavily loaded and lightly loaded. These both Ch works according to situation. In border cluster mechanism active nodes identify the entry of target object in the sensing area. This work results reduce power consumption of WSN and also save energy consumption of highly loaded cluster head. Dongmei Van et al. [32] proposed a coordination method for target tracking WSN which is based on cluster and game theory. Three important parameters of target tracking are: target location, target direction and speed. To perform target tracking: firstly, there is a need to select CH, as per distance and cost factor then classification is done according to target tracking. This can be predicted by target location, target direction and speed which are measured by sensors. Then, all the information are gathered to perform tracking and passed on to the CHs. Finally, transmit this information to the BS. They also suggest that payoff function is composed of two factors: cooperation and reputation. This work shows that desired objectives like locate target accurately, acquire more reliable data communication are achieved. The CROSS algorithm needs the global information that how many nodes participate in the game at every round. So, Dongfeng Xie et al. [33] proposed a localized game theoretical clustering algorithm (LGCA) which is based on game theory. In LGCA each node selfishly plays a localized clustering game only within a communication radius. Here every node has three states: normal state, potential CH state and real CH state. The initialization phase is used to collect localized neighbour’s information for every node. During potential CHs election every node selfishly decides to be a CH. The node who win the clustering games, switches to potential CH and the node finally serves as a real CH stay in real CH state. In addition, those failed potential CHs must give up opportunity to bid for real CHs and return to normal state. During real CH contention all potential CHs have to further contend for real CHs to ensure that one real CH in region. When all real CHs have been determined then announces the election. The remaining normal nodes join the nearest CH and form cluster. Data transmission is done and again the next round starts. The simulation result shows that CROSS algorithm improve network lifetime save more energy and also suggest solution of “left-behind node” problem. But not concentrate on clustering problem of heterogeneous sensor network. D. Swarm intelligence based approaches In [34], authors proposed an algorithm which is a combination of Bacterial foraging optimization algorithm (BFO) which is a Bio-Inspired algorithm. LEACH and HEED protocols which enhanced the lifetime of a network by dissipating minimum amount of energy. This algorithm consists of three phase: The sensors nodes are moved within in the cluster so that a proper inter-node distance is maintained then select the cluster head according to the LEACH protocol and the HEED protocol. The more is the residual energy there are more chances of a sensor node to become a CH and finally the actual transmission of the data is done by the cluster heads which gather the data sensed by the sensor nodes in their cluster region and then it will send the data to the base station. This work enhanced the lifetime of a network by dissipating minimum amount of energy. Moreover, some parameters are still pending to check whether the algorithm is enhancing the lifetime of the network or not. Wei-Lun Chang et al. [35] proposed an Artificial Bee Colony – based (ABC) path finding algorithm which is significant to plan a data collection path with minimum length to complete data collection task. In ABC algorithm, there are three groups of bees: employed bees, onlooker bees, scout bees. This algorithm consists of four phases: Initialization phase, employed bees phase, onlooker bee’s phase and the scout bee’s phase. Employer bee’s search for the food sources within the neighbour and share the information regarding new food to the onlooker bees. At the beginning of ABC algorithm generates a large number of food sources randomly means possible solution, the employed bees did some modifications in food sources and then fitness value is calculated. Then, an onlooker bee selects the food source with the largest probability value and modifies the position of the food source. The fitness value of the new and old food source is calculated and compared. Then, the food source with the largest fitness value © Elsevier Publications 2014.
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is recorded as the temporarily best solution. The best food source is iteratively updated for certain iterations. Authors suggested shortest route with least cost is discovered but it lack when deal with multiple mobile robots. In [36], authors described improved Ant Colony Optimization Algorithm (ACO) which is used to find number of optimal solution, number of ants on path, which will lead to reduced the network node on the path energy drastically. When ants move forward in search for food, they secrete pheromones and other companion on the way secretes this pheromones. This pheromone is gradually volatile in nature. The path which has more concentration of pheromones acts as a shortest and best route. As it has some demerits which is overcome by improved ACO in which pheromones concentration can be increase or decreases as it is volatile in nature. Also contains the features of real and non-real ants so has better ability to search for better solutions. Now, ACO also used in clustering. Hence authors found that this approach reduces energy drastically. But they did not try hybrid algorithm on the ant colony algorithm. Dexin Ma et al. [37] presented an Adaptive Node Partition Clustering protocol using Particle Swarm Optimization (ANPC-PSO). PSO is population based like evolutionary algorithm (EA) but there is no mutation and crossover. PSO updates each particle’s position, according to its present velocity, its previous best position and the best position found by its neighbour. In ANPC-PSO, network is partition efficiently and adaptively. In tialization of position and velocity of each particle is done randomly. Fitness of each particle is calculated which consider residual energy of the node, distance among the cluster, distance between CH and BS should be considered because CH is responsible for transmitting data to BS. Update each particle position, velocity and map the updated new position with the closest coordinate. This will repeat until maximum number of iteration reached and select best solution as CH and then broadcast the advertisement message using CSMA MAC protocol. Therefore provides higher network lifetime and improvement in data delivery. E. Security based approaches Multi input Multi output (MIMO) routing algorithm is given by D. Sathian et al. [38] which is used for energy efficient communication and decrease transmission power consumption. This algorithm is based on game theory in which performance is analyzed in terms of energy consumption. In this scheme the CH cooperates transmit data cooperatively. Game theory is used for selecting appropriate cluster heads with sufficient residual energy. The purpose of clustering is to minimize power consumption and increase energy efficiency. During data exchange each CH node gets paired with other sensor node and takes place in transmission. The simulation result shows that it consumes less energy for data transmission, number of nodes live is more in CH-C-TEEM, increase path security and reduce number of retransmission.
Figure 1. Classification of clustering approaches.
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A Comprehensive Survey of Clustering Approaches in Wireless Sensor Networks
In [39], Winston K. G. Seahet al. suggested Wireless Sensor Network Powered by Ambient Energy Harvesting (WSN-HEAP). WSN -HEAP is used which convert the ambient energy from the environment into electricity to power the sensor nodes. As there are some challenges in designing networking protocols for such WSNs arising from the characteristics of the energy source. These challenges are topology control, MAC, reliable data delivery, maximize throughput and minimize delay. But there is a problem in energy harvesting from environment. Bo Chen et al. [40] proposed an Incentive Detection Mechanism (IDM) which consists of detection module and punishment module. The detection module determined if there is a selfish behaviour in the network by recording data from retransmission numbers of nodes itself. Calculate NBmax-NBi (NB=retransmission number of packets) whether the value is greater than the threshold, if satisfied, indicates that is a selfish node; if not satisfied, indicates that the node is a normal node. The punishment module change the strategy of selfish nodes, thus all nodes can fairly complete the channel resources. The simulation results show that it can significantly improve the detecting rate and reduce false detecting rate. Punishment modules promote selfish nodes to cooperate with normal nodes, and thus improve the performance of the network. Renita Machado et al. [41] discussed problems related to energy efficiency, security and detection in WSNs with the help of game theory. Since sensors are equipped with limited energy sources, they should be programmed to achieve energy efficiency. In heterogeneous networks, a selfish routing game within an objective to increase cooperation is used. In this, node act selfishly to maximize their profit. This problem is solved by considering two different authorities having different action profiles. If sink has common resources, then cooperative forwarding is beneficial. When multiple authorities are there in the network then routing approach is followed. They also give an idea regarding security and pursuit-evasion games in WSN. 4. Comparison Summary of Various Approaches Many proposals have been presented in existing scenario that provides some good level of energy efficient mechanisms with some constraints, but further more researches need to be done towards developing a system that can be used without or less constraint. Table 1 shows the comparison table of different approaches based on their performance parameters. The parameters are: approach type, mobility, network model type, CH selection (probability based or non-probability based), intra cluster topology, intra-cluster topology, location aware for node location. 4.1 Research gaps in present works Base on literature review, few research gaps are identified and discussed below: 1. In Game Theoretic Clustering, there is an assumption that there is only one cluster head in each region, and this assumption is somehow unreasonable, and it also limit the applicability of game theoretic clustering. 2. A lot of research has focused to provide energy efficiency but they do not deal with the cross layer optimization between query and routing strategies. 3. The energy efficient algorithms available in literature is not considered heterogeneous environment which greatly affect the energy efficiency as well as network lifetime. 4. The energy efficient algorithms available in literature have not considered sensor nodes mobility factor means the network is assumed static which is unrealistic assumption in most the heuristic based clustering methods. 5. A lot of research has been done in static environments, still there is need to do all this research work in dynamic environments where all the requirements keeps on changing and are not in stable condition. 5. Conclusion and Future Work Wireless sensor networks (WSNs) can be employed in wide spectrum of industrial and civilian application areas, healthcare applications, home automation, habitat monitoring, medicine health monitoring, engineering applications etc. We have seen that there are some WSN routing protocols that have better results than others. We also proposed a comprehensive survey work by focusing on detailed description of clustering approaches. Moreover, this paper found most crucial WSNs problems like energy efficiency, network lifetime and security with the help of routing protocols, game theory, genetic algorithm, swarm optimization and incentive detection mechanisms. As per our literature review we analyzed that clustering approaches in WSNs conserve energy and improved the network lifetime. For this reason our future work is based on energy conservation mainly in heterogeneous environments. We will try to propose a new optimized approach based on present research gaps and try to evaluate in WSNs simulation environment based on © Elsevier Publications 2014.
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Bhawna, et al. Table 1. Clustering approaches with their performance metrics.
S. No 1.
Approach Name and Reference
2. 3.
Energy efficient routing through balanced clustering [3] Chain based routing Protocol [9] CH selection strategy [10]
4. 5.
EEBCDA [11] EELC [12]
6.
Fuzzy logic cluster formation protocol [13] Energy based clustering algorithm [14] Energy efficient CH selection algorithm [15] Performance analysis of different protocol [16] Improved LEACH [4–6] Intelligent hierarchical clustering technique [18] Multi-objective GA [19]
7. 8. 9. 10. 11. 12. 13.
20.
Optimized multi-hop routing algorithm [21] Adaptive clustering hierarchy based on GT technique [22] Energy saving through cooperation using GA [26] Energy aware clustering algorithm via Game Theory [25] Adaptive energy aware cooperation strategy in heterogeneous multi-domain SNs [28] Localized game theoretic approach [33] Game theory based routing approach [14] Artificial bee colony [30]
21.
Incentive detection mech. [40]
14. 15. 16. 17.
18. 19.
Approach Type
Intra Cluster Topology
Node Deployment
Network Model Type
Protocol
Multi-hop
Uniform
Protocol Algorithm Algorithm Hierarchical Protocol Protocol
Multi-hop Single-hop with clustering Single-hop Multi-hop
CH Selection Probability Based (P) or Non Probability Based (NP)
Location Aware (Y/N)
Base Station Mobility
Homogeneous
No
Fixed
NP
Random Random
Homogeneous Heterogeneous
– –
Fixed Fixed
NP NP
Random Uniform
Homogeneous Homogeneous
Yes –
Fixed –
NP NP
–
Uniform
–
–
Fixed
NP
Algorithm Algorithm
– –
Random Uniform
Heterogeneous Heterogeneous
Yes
Fixed –
NP NP
Protocol
–
–
Homogeneous
No
–
–
Protocol Genetic algorithm Genetic algorithm Genetic algorithm Genetic algorithm Genetic Algorithm Game theory
Multi-hop Single-hop
Random Random
Heterogeneous –
Yes –
– –
NP NP
Multi-hop
Random
Homogeneous
Yes
Fixed
NP
Multi-hop
Random
–
Yes
No
NP
Multi-hop
Random
–
Yes
No
NP
Single -hop
Cooperative Game Uniform
–
–
–
NP
–
–
–
NP NP
Multi-hop
Game theory
Multi-hop
Cooperative game
Heterogeneous
–
Fixed
Game theory
–
–
–
–
Game theory
Single-hop
–
–
NP
Swarm Intelligence Security
Multi-hop
Non-cooperative Homogeneous game Non-cooperative – game Random –
–
–
–
–
–
–
–
NP
Cooperative game
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