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Energy consumption and network connectivity based on Novel-LEACH-POS protocol networks
Computer Communications 149 (2020) 90–98
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Computer Communications journal homepage: www.elsevier.com/locate/comcom
Energy consumption and network connectivity based on Novel-LEACH-POS protocol networks Moorthi a , R. Thiagarajan b,c ,∗ a
Saveetha Engineering College, India Department of Computer Science and Engineering, Prathyusha Engineering College, India c Anna university, Chennai, India b
ARTICLE
INFO
Keywords: Energy consumption Novel routing algorithm Clustering LEACH Network connectivity
ABSTRACT Wireless sensor network (WSN) is used for a large number of sensor nodes and energy efficiency is important to constrained resources in wireless sensor networks. Wireless sensors networks use multiple approaches such as data cycling, energy optimal scheduling and energy aware routing to reduce energy consumption throughout the network. Energy awareness is an essential design for WSNs hence, data broadcasting protocols, routing and power management are specially designed to meet their requirements. The past decade was mitigating the issues of energy dissipation to ensure energy efficient routing and clustering. To address these issues, a novel idea is presented where the energy efficiency will incorporate the selection mechanism of nodes. In Wireless Sensor Networks, path routing for nodes is a difficult and tricky task. It is a crucial role to increase the stability and network lifetime. The LEACH Protocol is a measured level of the sensor networks lifetime by pairing the node energy consumption. Simulation results show that protocol outperform its routing protocols Low Energy Adaptive Clustering Hierarchy (LEACH) and Novel-LEACH in terms of network connectivity and power consumption. Also, a new energy efficient clustering scheme for WSNs with the use of Particle Swarm Optimization (PSO) is proposed. The clustering algorithm is implemented with the goal of concurrently minimizing the intra-cluster distance along with optimizing the usage of network energy.
1. Introduction Wireless sensor network is a collection of number of nodes for processing and communication system with each and every node gathering records and reversing the information back to sink node. Wireless sensor networks come under a unique kind ad-hoc networks having applications in areas such as environment monitoring, target tracking etc... Wireless sensor network, a novel and a greatest technique, is known for its salient feature of processing the group of information to the base station with limited power capacity. However, a major hindrance in employing the WSNs is increasing the network lifetime of nodes, since they possess different categories of networks. They are low sampling rate magnetic, visual, thermal and infrared, radar and acoustic. The main applications coming under the operation of wireless sensor networks are related to scanning a wide selection of ambient stages. Humidity, temperature, vehicular movement, lightning condition, pressure, soil makeup, noise levels, earth monitoring, target field imaging, fire alarm sensors, disaster management are those kind of stages. The alternative method lies in providing a hunt for criminal acts, measures of precision in agriculture field and performing sensor activities for detecting unknown intrusion.
On the other hand, a wireless ad-hoc network is basically a collection of heterogeneous network nodes. This network, as the term ad-hoc implies, is formed temporarily by not concentrating on any infrastructure or any centralized administrator. As there could be restrained range of transmission from wireless host, a packet is forwarded to the destination node. Fig. 1 demonstrates the structure of Wireless ad-hoc networks communicating in a self-organized way. Wireless sensor networks may be filled with sensor nodes numbering in hundreds or in thousands. However, a comparative attempt shall be made to show that ad-hoc network will contain only fewer nodes and that too without any specified structure. Table 1 illustrates the comparative features of WSN and Ad hoc networks. As mentioned earlier, WSN faces a handicap in maintaining less power consumption during transmission. As a result, reducing the power consumption during the transmission is enabled by applying the clustering technique in routing. This yields a considerable reduction of power consumption when WSN sends data from one node to the other nodes. In this sense, WSN is deemed to be data-centric, as it involves in responding to the base station as per the instructions from the Base-station (BS). This means that WSN does not involve in transmitting the data between or among the sensors.
∗ Correspondence to: Department of Computer Science and Engineering, Prathyusha Engineering College, Part Time Research scholar, Anna university, Chennai, India. E-mail address: [email protected] (R. Thiagarajan).
https://doi.org/10.1016/j.comcom.2019.10.006 Received 5 June 2019; Received in revised form 16 August 2019; Accepted 2 October 2019 Available online 10 October 2019 0140-3664/© 2019 Elsevier B.V. All rights reserved.
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Computer Communications 149 (2020) 90–98
Fig. 1. Wireless ad-hoc networks.
Table 1 Contrast between wireless sensor network and ad hoc networks. Parameters
Wireless sensor networks
Ad hoc networks
Number of sensor nodes Identifiers Topology Deployment Failure rate Communication paradigm Fusion/aggregation Centric Battery Computational capacities and memory Data rate Redundancy
Large No unique identifiers Changes repeatedly Heavily deployed Prone to failures Broadcast Communication Possible Data centric Not replaceable/Not rechargeable Limited Short High
Medium Unique identifiers Very rare changes Spreader Very rare Point-to-Point communications Not suitable Address centric Replaceable Not limited High Short
clusters. This organization facilitates data aggregation and compression within the networks. Thus, each cluster is provided with cluster heads, in the hierarchical pattern. It shall be reminded that optimization of power consumption by routing algorithm/adopting Wireless Ad Hoc Networks can increase the lifetime of the sensor network. Basically, a Wireless Sensor Network (WSN) is structured with a base station, communication satellites, and sensor nodes. The sensor nodes are evenly distributed along the transmission range within the network. The link between the network and the end subscriber is enabled by the base station. There is a categorization of routing within the WSNs, which is based on the structure of the network. The categorized routing patterns are location-based routing, hierarchical-based routing, and flat-based routing. The chief purpose of the hierarchical routing is to effect the power consumption in sensor nodes by adding multi-hop communication within a specific cluster. Clustering is the best routing algorithm which aims at decreasing the power consumption in sensor networks. The LEACH is the first algorithm to be focused by clustering. LEACH is the identity-organizing and adaptive clustering procedure. The operation of the LEACH consists of setup phase-cluster formation and steady phase that transfer to sink
Wireless Sensor Network has another, yet a major, hitch in the way of redundancy in the sensed data. WSNs send data to the end user, instead of sending them to the stipulated node, which consumes large power consumption. Moreover, it is a known process when lot of message packets containing second data is transmitted; it will end up with reduced transmission of packets but result in de-duplication of data. In practice, small sized sensors and those are at cheap cost are capable of securing various applications. Generally, sensor networks consist of different types of mechanisms for sensing, by which data can be gathered out of a given physical surrounding. One of the major functions of WSNs is (sensing/sending) the data to the particular destination node. Routing algorithm is a novel technique along with cluster based routing protocols. Wireless Ad Hoc networks are identified as a structure having a group of two or more communication devices, enabling capable networking facility. In this Wireless Ad Hoc networks, the lifetime of each network is split on fixed time span that provides a select cluster of nodes to be in operating or sleep mode. This time span interval is known as a round, which, in other way, creates an operative problem in the case of clustering. Within sensor networks, there could be an organized form of nodes in smaller groups, which are otherwise treated as 91
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heads as routers to the sink node. The application of LEACH does the action of saving energy whereas Cluster Head performs the transmission of data to the sensor nodes. 2. Related works Ramamurthy [1] describe the wide survey on WSNs and a variety of approaches are used for energy consumption similar to data driven approaches, duty cycling and various mobility based approaches. Other authors have applied a clustering based energy efficient algorithm. But this author structured the WSNs in a two-layer manner with clustering algorithm, and then recovered the loss of data based on this two-layer structure. Wireless sensor networks have sensing devices to use in ad hoc network and battery operated computing. In order to increase the energy consumption, network lifetime should be optimized. In clusterbased WSNs, cluster heads or gateways perform behaviors, such as data collection from their member nodes, data exchange and data aggregation with the base station. This makes the load balancing of gateways to be a crucial parameter in optimization of the network lifetime in WSNs. Rathi and Dagar [2] proposed a clustering algorithm called Low Energy Adaptive Clustering Hierarchy (LEACH), which is applicable for sensor networks. This distributed algorithm involved clustering the LEACH and leaving the nodes independent, not to be controlled by any center. Among the algorithms found under LEACH, the hierarchical routing is considered as the best preferred. In this algorithm, the clusters of the sensor nodes are constructed with the help of local cluster heads (CHs). These CHs act as routers, linking the sink node and enable deriving signal strength. Through these routers only CHs perform the transmission meant for specified nodes, instead of every node in the network. By this select transmission, through chosen routing; energy is saved when the cluster retains temporary data processing, which is otherwise viewed as data fusion and aggregation. In fact, there is a possible change in CHs at random level, which is for balancing the dissipation of energy nodes. CHs perform this balancing by way of choosing a node random number between 0 and 1. Each node is provided with a Cluster Head for the current round and this carries out the task of checking whether the number node is less than the threshold. In Darabkh et al. [3] LEACH involves in partitioning the nodes into number of clusters, wherein each node makes use of a cluster head along with certain common nodes as its members. The cluster head undertakes rotation based functioning at stipulated time span, getting contact with other nodes at every round. This makes even distribution of rounds which, in turn, secures evenly distributed energy consumption within the network. A major disadvantage of LEACH is that it is not feasible for functioning in networks of wider environment. This is because, the cluster heads in LEACH process effect communication with the far off base station, thereby consuming more power for data transmission. The ultimate effect of this is the reduced lifetime of the network as well. However, LEACH has the feasibility of making randomized rotation of cluster-head throughout the network. By this way, less energy is consumed for transmission when LEACH chooses the specific cluster head. Hence, there is a possible increase in lifetime of the network. Singh and Sharma [4] examined a novel energy efficient clustering algorithm for WSNs with help of the PSO algorithm that maximize the life span of the network. The most focused objective of the proposed algorithm is the selection of a corresponding CH, to minimize the intracluster distance and optimizing the energy efficiency of the network. The authors proposed the method which has a high energy node as CH and made the clusters by placing them in a uniform pattern in the sensor region. The authors attempted at optimization of the energy consumption by the nodes by simultaneous maximization of data transmission. However, the main difference between this approach and the earlier works lie in the choice of PSO for picking the optimum nodes as CHs. Thus, they proved the objective of enhancing the lifetime of the network.
Fig. 2. Cluster approach in Leach.
node that occurs. Cluster head is elected by sensor nodes based on probabilistic approach. Sensor nodes are micro-electro-mechanical systems (MEMS) that produce a measurable response to a change in some physical condition like temperature and pressure. Sensor nodes sense or measure the physical data of the area that are to be monitored. Sensor nodes are of very small size; they consume extremely low energy; they are operated in high volumetric densities, and they can be autonomous and adaptive to the environment. As wireless sensor nodes are typically very small electronic devices, they can only be equipped with a limited power source. The general cluster approach is shown in Fig. 2. There are three categories of sensor nodes: (i) Passive, Omni Directional Sensors: passive sensor nodes sense the environment without manipulating it by active probing. In this case, the energy is needed only to amplify their analog signals. There is no notion of ‘‘direction’’ in measuring the environment. (ii) Passive, narrow-beam sensors: these sensors are passive and they are concerned about the direction when sensing the environment. (iii) Active Sensors: these sensors actively probe the environment. For more decades, routing protocols are designed with a focus and stress on the power efficiency and robustness of the network, by enhancing the lifetime. LEACH is a widespread application under dynamic clustering hierarchical routing protocol in sensor networks. LEACH turns out to be the first protocol, introduced on clustering hierarchy in WSNs. LEACH is known for its novel approach in energy efficiency in sensor networks, yet with restrained applications. This reduction of power consumption, invoking the efficiency, is aided by applying Low-Energy Adaptive Clustering Hierarchy. This concept paves way for forming or constructing the clusters in sensor networks, in accordance with the derived signal strength and already consumed built-in cluster 92
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Fig. 3. Transmission areas in adhoc.
3. Theoretical background 3.1. Ad hoc network An ad hoc network is compared by focusing the infrastructure based wireless networks, such as cellular network and WLAN. The transmission of a data packet to a destination node in the infrastructure based wireless networks, take place via access point only (in cellular network like GSM, it is called base station). This access point is noted for its establishment a network area, so that the nodes in this area can only make use of the services offered by the access point. The wireless ad hoc networks are designed with nodes having a transceiver and are independent of an infrastructure. This makes the nodes to establish their respective networks and it is to be noted that a sensor node is expected to communicate with other nodes in a stipulated range only. However, the uniqueness of the infrastructure based wireless network is that nodes have the feasibility to communicate with other nodes at any range. The data thus sent to this access point, is relayed to the desired node by the access point. In every aspect, the effective transmission of data is achieved when routing algorithm is applied. Fig. 3 shows how the nodes form a transmission cloud.
Fig. 4. Cluster based WSN with gateways.
3.3.1. Issues with cluster based routing protocols Regarding the efficiency of cluster based routing protocols still there are certain pertinent issues that need to be dealt with proper concern. The issues are: 1. The importance of selecting a node as a cluster head which must properly be done by considering the energy left over in every node. In addition, there should be a provision for a threshold energy level below which the network must readily hop from node to node. 2. During the data transmission, few nodes in the network would remain unattended when the deployment is over a wide area. Therefore, focus should be made on increasing the attention on the governing area. 3. Network topology is subject to change based on the functioning of the particular network which is in accordance with the terrestrial area. Hence, this requires better efficient clustering methods and mechanisms for improving the network efficiency. 4. The sensor nodes are expected to handle minor faults at sufficient levels. 5. Network should be structured by incorporating minimum number of possible nodes and there shall not be any compromise on the accuracy and security.
3.2. Cluster based WSN Cluster based WSN system is the one where all the sensor nodes get combined in forming a couple of gateways. These sensor nodes are assigned within the range of data transmission and communication. This makes each sensor node to avail a couple of gateways. Usually, the local information is collected by the sensor nodes and is duly transferred to the corresponding or the destination nodes. There will be a wireless communication between these two nodes. Fig. 4 shows the cluster based WSN, that have the sensor nodes, gateway and base station connection.
4. Methodology of energy consumption and network connectivity based on novel-LEACH protocol networks
3.3. Cluster based routing protocols It is a well-known fact that the wireless network has the constituent nodes which are provided with varied range of energy. This makes the smart clustering based approach to choose the nodes in accordance with their residual energy pattern. The efficient energy management is based on two factors viz. when the nodes having considerably higher energy range are selected for transmission process and data communication, and when the nodes having considerably less energy are made use for data sensing.
4.1. LEACH protocol LEACH is viewed as a self-organizing protocol performing adaptive clustering. It involves two phases namely, set-up phase where CHs are formed, and steady-state phase where data is transferred from nodes to CHs and on to BS. Each node is capable of becoming a cluster head, with the probability p exactly once every 1 p rounds considering the 93
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following threshold ⎧ (𝑝 ) ⎪ 𝑇 (𝑛) = ⎨ 1 − 𝑝 𝑟. 𝑚𝑜𝑑1 𝑝 ⎪ 0, ⎩
𝑖𝑓 𝑛 ∈ 𝐺 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
(1)
Here, r represents current round and G is indicated as set of nodes, which it has not selected for CHs in the last 1p rounds. So, the node chooses an arbitrary number between 0 and 1. If the selected number is lesser than the threshold T(n), then the node is CH for the current round r. When the BS is situated at the center of the field with nodes uniformly M × M distributed, free space channel model is used for sending l - bit message. Transmitting energy is , (𝑙, 𝑑) = 𝑙.𝐸𝑒𝑙𝑒𝑐 + 𝑙.√𝑒𝑓 𝑠 . 𝑑 2 , as the distance d of any node to BS or its CH is less than 𝑑𝑜 = 𝑒𝑓 𝑠∕𝑒𝑚𝑝, where 𝐸𝑒𝑙𝑒𝑐 is electronics energy, 𝑒𝑓 𝑠 and 𝑒𝑚𝑝 represent transmitter amplifier for free space and for multipath channel. Optimal number of clusters in each round is: √ √ 𝑛 𝑀 𝑛 2 = 𝑘= (2) 2𝜋 𝑑𝑡𝑜𝐵𝑆 2𝜋 0.765
Fig. 5. Clustering in LEACH.
By this calculation the optimal probability that a node becomes CH can be arrived: 𝑃 =
𝑘 𝑛
4.1.2. Shortcomings of LEACH protocol LEACH Protocol is always aware that every node has data to be forwarded and that the nodes in CH are chosen with the residual or initial energy. In general, the number of CHs to be pre-determined comes in the range of 5% or 10% of total nodes. Yet, this is insufficient to fulfill the wider area of sensor nodes, since the nodes are randomly dispersed. The choice of CHs is based on random or rotational manner and equally significant is that the Residual energy does not come under the consideration in the formation of clusters. The random dispersal of CHs in the network makes the focus towards the CHs on any one region of the network. This makes other nodes in the network which are not covered under the CHs region, which creates lack of proper CH location within the network. As mentioned earlier, the aggregated data is transferred by CH to the BS in a single-hop way. As a result, this factor makes LEACH as invalid in the case of deploying the networks for wider regions. Further, LEACH protocol is designed with rounds, wherein every round, there is a reconstruction of novel clusters by all the sensor nodes. This clustering process consumes a lot of energy. Fig. 5 shows clustering in LEACH. In LEACH-Centralized set-up phase, information on current location and the energy level at the base station are forwarded by every node. In fact, LEACH-C decides a centralized clustering algorithm and the same steady-state protocol. The BS is responsible in locating the clusters, CH and non-CHs of every cluster. In addition, the BS uses its global information of the network and creates better clusters which will consume less energy for data transmission.
(3)
LEACH Protocol provides the formation of round by creating or providing several rounds. Of these, each round is said to consist two states of which the first state is cluster setup state while the other is steady state. In cluster setup state, the CHs process is chosen by the clusters while the data is transmitted to the clusters in steady state. The cluster state is typically significant wherein there is a decrease of the overhead, due to the difference of time gap between the processes in the second state time and the first state time. This is to say that the process time in the second state is longer than that of the first state. However, the rounds decide the functioning of LEACH as there is a repetition of clusters by the system and transmission of data in every round. The Two Phases in LEACH Protocols: (1) Set-up phase: In set-up phase, CHs are chosen depending on T(n) threshold. The ADV message to the entire Non-CH nodes is broadcast by the CHs and based on this, the Non-CHs choose their CHs. The RSSI of ADV message also plays a significant role in Non-CHs choosing the CHs. Once the Non-CHs select the clusters, there is a transfer of Join-REQ back to CH by Non-CH nodes. This is followed by generation of TDMA schedule by CHs and ultimate transfer of the schedule to the whole Non-CH nodes. (2) Steady-state phase: The sensing and transfer of data by the sensor nodes to CHs occur in this phase as per the schedule. This is done by CHs, after gathering the data and merge them to the Base station by one-hop way. This results in the decrease in the number of transmission and subsequently the reduction of energy consumption. With the passage of a particular time span, the network retards to its set-up phase and continues the round process. For the purpose of reducing the disorder from any other cluster nodes, dissimilar CDMA is utilized by cluster communication.
4.1.3. Limitation of LEACH protocols In LEACH Protocols, the nodes are sufficiently provided with power to reach the base station, as nodes always either receive or send information. There is a possibility for nodes to have easy communication, if they are nearer between or among themselves. Yet, they may not be aware of the predetermined CHs which could be distributed in an even manner within the network. This necessitates the CHs to choose the exact point within the system; thereby the choice of CHs is random in the LEACH protocol. Consequently, the CHs select nodes with less strength that could lead these nodes to die too quick.
4.1.1. Advantages of LEACH protocol LEACH protocol has a lot of advantages and chief among them is obtaining the factor of 7 reductions in energy dissipation in comparison with direct communication and comparison of 4–8 factors till the last transmission energy routing protocol. In case the nodes face death selectively, there is an increase in the life time of the dynamic clustering network. Nevertheless, LEACH is deemed to be a wholly distributed network and it does not require any worldwide information of network.
4.2. Particle Swarm Optimization (PSO) Particle Swarm Optimization (PSO) is evinced from the pattern and support of natural lifecycles, like fish schooling, bird flocking and arbitrary search techniques of evolutionary procedure. This concept is based on the natural pattern which shows the winged creatures fly or travel together in a random search for shelter and food. Their 94
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The 𝒑𝒅𝒈 is represents the global fitness of dimension d. Lbest PSO It can be seen that both types are capable of searching in global as well as local. Local search is provided in PSO by 𝑝𝑑𝑖 and 𝑖th represents the individual best fitness position in overall iteration t+1. The other way is instead of estimating global best fitness till last looping t+1 the local best fitness can be calculated in the place of 𝑝𝑑𝑔 . The above equation is combined and become as ) ( 𝑣𝑑𝑖 (𝑡 + 1) = 𝑤 + 𝑣𝑑𝑖 (𝑡) + 𝜑1 × 𝑟𝑛𝑑() × 𝑝𝑑𝑖 − 𝑥𝑑𝑖 (𝑡) + 𝜑2 × 𝑟𝑛𝑑()× ( d ) pl − xdi (t) (6) Hybrid PSO Both types of Gbest and Lbest are proposed with new PSO. To use both types of equation the two parts 𝜑2 and 𝜑3 are sub-divided and velocity at t+1 represents the three functions which are individual best positions, global best position and last iteration best position. ) ( 𝑣𝑑𝑖 (𝑡 + 1) = 𝑤 + 𝑣𝑑𝑖 (𝑡) + 𝜑1 × 𝑟𝑛𝑑() × 𝑝𝑑𝑖 − 𝑥𝑑𝑖 (𝑡) + 𝜑2 × 𝑟𝑛𝑑()× ( ) ( ) 𝒑𝒅𝒈 − 𝑥𝑑𝑖 (𝑡) + 𝜑3 × 𝑟𝑛𝑑() × 𝒑𝒅𝒍 − 𝑥𝑑𝑖 (𝑡) (7) Fig. 6. Gbest and Pbest Movements of PSO.
The difference between the Gbest and Pbest are Gbest is changes the value only when Pbest comes closer to the target then Gbest. pBest value only indicates the closest the data has ever come to the target since the algorithm started.
flocking represents a cluster without any collision among themselves. This is possible when every member of the cluster is conscious of the location, flying speed and the cluster information shared. All these factors contribute in reducing the energy exertion among these creatures. PSO is defined as the one having a swarm of a pre-specified size of particles. The purpose of particle swarm algorithm (PSO) is to optimize the LEACH protocol most commonly used in WSN routing protocol, in order to reduce the energy consumption of WSN data transmission and optimize the data transmission routing. The peculiarity of PSO is that each of its element supplies an exact solution to the issues related to the multi-dimensional optimization. This makes an assumption that the dimension D of the considerable number of particles is equal. A particle Pi 1 < I < Np has position Xid , 1 < d < D and velocity Vid in the dth dimension of the hyperspace. The notation for demonstrating the 𝑖th particle Pi of the inhabitants as follows: [ ] 𝑃𝑖 = 𝑋𝑖,1 , 𝑋𝑖,2 , 𝑋𝑖,3 , … … … , 𝑋𝑖,𝐷 (4)
4.3. PSO with LEACH protocol Sensing elements are like sensors deployed randomly along with some gateways. The proposed system is processed by using the following steps: Step 1: PSO approach with clustering is applied to get the optimal selection of cluster head and improve the progression in the remaining energy of node by sending a data packet to the cluster head which is situated closest to the Base station. The cluster head is selected using PSO approach based on the remaining energy of that node and distance from the cluster member node to base station. Step 2: Calculate the distance between transmitter and receiver. If the distance is less than a threshold value d0 and free space (fs) model is applied, otherwise, the multipath (mp) model is applied. Step 3: If sensing element is within the communication range, then sensing element is easily allocated to any gateway. The sensing element can allocate the individual sensor nodes by using some predefined gateways. The below Fig. 7 shows the flow of PSO with LEACH Protocols
PSO is capable of solving the issue as it offers the superiority of the solution and estimates the fitness function of each particle. To reach the total best position, the particle Pi monitors its each particles best, i.e., personal best called Pbest and global best called Gbest that changes its individual position and velocity. In every duplication particle, its position Xid and velocity Vid and in the dth dimension are changed by utilizing their associated conditions. Fig. 6 illustrates the PSO particle movements in a two-dimensional space. It represents the current optimum particle represented as ‘‘pbest’’. From the figure, it can be observed that there is a best optimization of the results from overall reading and the tracing of the location as lbest. In addition, the velocity of each particle is represented as pbest and lbest. PSO is known for its maintaining of the record of outputs in three global attributes viz target value or condition, gbest and stopping value.
4.4. Algorithm Set-Up Phase 1.CH N:idCH ,crc,adv 2.ni CH: idni , idCH ,crc, join_req 3. CH N: idCH ,(. . . ,( idni , Tni ). . . ), crc, sched 4. GW N: idgw , crc, adv Steady State Phase 5. ni CH: idCH , crc 6. ni GW: idgw , crc 7. CH – BS: idCH , idBS , GW BS: idgw , idBS 8. Base Station get information from all the cluster heads and Gateways. The symbol used in proposed algorithm signifies: CH, 𝐧𝐢 , BS: Cluster Head, ordinary node, base station N: Set of all nodes in the network. Adv,join_req,sched: String identifiers for message sorts Crc: Cyclic redundancy check idni , idCH , idBS : Nodes ni, CH, BS id’s respectively ⟨𝐲, 𝐓𝐲⟩: A node id y & its active slot Ty in the clusters TDMA schedule , : Unicast, broadcast transmissions, respectively.
Three Types of PSO Algorithms Gbest PSO The equation above is generated t+1 of velocity and used for global fitness and it continues the iteration process till the end of the function. Here, below representation equation is easily a show the execution. ( ) 𝑣𝑑𝑖 (𝑡 + 1) = 𝑤 + 𝑣𝑑𝑖 (𝑡) + 𝜑1 × 𝑟𝑛𝑑() × 𝑝𝑑𝑖 − 𝑥𝑑𝑖 (𝑡) + 𝜑2 × 𝑟𝑛𝑑()× ( ) 𝑝𝑑𝑔 − 𝑥𝑑𝑖 (𝑡) (5) 95
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Table 2 Experiment of PSO with Lbest, Gbest.
Table 4 Performance metrics of LEACH AND NOVEL-PSO-LEACH.
S.No
Type of PSO
Attributes
Performance metrics
LEACH [5]
NOVEL-PSO-LEACH
1 2 3
Gbest PSO Lbest PSO PSO hybrid
Size = 35 Size = 35
Total number of energy consumed Total number of data transmitted(bits) Number of first node dies Network lifetime(rounds)
309.101 47 854 370 497
490.20 98 665 700 834
Table 5 Energy dissipation of 100 sensor nodes. LEACH [5]
NOVEL-PSO-LEACH
12 25 55 110 170 225 267 335 375 435
23 35 75 135 189 245 289 315 365 415
Table 6 Data transmitted for 100 sensor nodes. Fig. 7. Network Lifetime of PSO LEACH.
LEACH [5]
NOVEL-PSO-LEACH
10 023 10 070 10 120 10 165 20 220 20 275 20 320 30 360 40 425 40 460 50 520 50 570 50 640 60 680 70 720 70 760 80 820
10 045 10 090 10 145 10 189 20 245 20 290 20 340 30 390 40 410 40 480 50 540 50 580 50 610 60 640 70 740 70 780 80 108
The proposed scheme is evaluated with a comparison of LEACH protocol. An assumption is put on the network model of 100 nodes with equal initial energy and network of 100*100 m2 is selectively deployed. Base Station was placed at the center of the network. The result analysis shows that the proposed algorithm outperforms LEACH Protocol in respect of energy consumption, network throughput and network lifetime. The simulation results used in some of the computation procedures are, stability period, lifetime of the system, and number of data transmitted to the BS. Stability period: The stability period refers the level of energy underwent dissolution in the network till the death of the entire network or until the network time becomes unstable. System lifetime: The system lifetime is measured as number of rounds till the whole system dies or the number of rounds from network lifetime is initialized till all nodes die. Number of Data transmitted to BS: This metric defines the cumulative volume of data which either CHs or Non-CHs directly transmit to BS.
Fig. 8. Energy of PSO LEACH after 70%.
5. Experiment results Table 2 displays the result derived from the experiment as per the setup. PSO algorithm measured the efficiency with total number of rounds till network survives find the better hybrid PSO than the Lbest, Gbest PSO. The remaining energy node of WSN and efficiency of the network have been verified and better hybrid PSO is observed. Table 3 shows the network lifetime, number of node dead in the first stage and then in 50%; the residual energy at 70% of the dead nodes dead is shown. By measuring the three algorithm rounds, the efficiency of PSO is found out. Figs. 7 and 8 show the graphs of the above table values. Table 3 Experiment results of hybrid PSO. WSN routing
Network lifetime (rounds)
First node dead
50% node dead
Total rounds sent packets
Remaining energy after 70% node deads
Gbest PSO LEACH Lbest PSO LEACH Hybrid PSO LEACH
1895 1923 2349
665 684 693
1354 1367 909
11 671 11 689 12 123
2.23 2.49 1.91
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Fig. 9. Analysis of energy dissipation of 100 sensor nodes.
Table 7 Network lifetime for 100 sensor nodes. LEACH [5]
NOVEL-PSO-LEACH
100 100 100 100 100 100 100 100 90 45 10 45 8 8 8 8 8 8
100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 85 18 8
there is a reduction in the energy consumption while transmitting the data from one node to another. Similarly, there is an increase in the network efficiency as the energy consumed is directly proportional to the nodes. 6. Conclusion Wireless sensor networks are progressively being utilized in the fields of healthcare, transportation, manufacturing, and much more. It provides multi-dimensional and existing routing protocols along with their classifications. Of these, cluster based routing protocols can serve as a useful tool for various tasks in modifying and optimizing the various routing algorithms used in wireless sensor networks. In future, protocols may be proposed to identify the dead nodes before they actually die and improve the efficiency by hopping. The performance of LEACH protocol lies entirely on the random selection of Cluster Head (CHs). This indicates that there could be no significant impact on the efficiency of the network lifetime or the power consumption if the selection of CHs is not proper. The results observed with the application of this algorithm prove that there is an increase in the network lifetime and energy consumption among sensor network nodes. PSO with clustering is found to achieve the best solution of cluster head and this solution increases the successive residual energy in transmitting the data packets to their nearest base station. The cluster head significantly balances the power energy and enhances the lifetime of the system. The experimental results show that the proposed LEACH performs better than the existing LEACH in terms of system lifetime, stability period and the total data transmission.
The experimental Table 4 shows the performance metrics of LEACH and NOVEL-PSO-LEACH for energy consumed, data transmitted and network lifetime while Table 5 shows the 100 nodes energy dissipation Fig. 9 above describes the result analysis of total energy dissipation in network using 100 sensor nodes. The image clearly explains that NOVEL-PSO-LEACH consumes less energy than LEACH, and Table 6 shows 100 sensor nodes data transmission values. Fig. 10 shows the network lifetime for 100 sensor nodes and Table 7 shows the network lifetime for 100 sensor nodes. The first node dies at 370 and at 700 rounds for LEACH and NOVEL-PSO-LEACH respectively. Likewise, the first node other nodes dies at 497 and 834 rounds for LEACH and NOVEL-PSO-LEACH. The experimental result proves that NOVEL-PSO-LEACH lifetime is more than LEACH due to cluster head optimization by using PSO approach.
Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Throughput and Energy Consumption Simulation result shows that the proposed algorithm is better than the LEACH in respect of throughput and stability. The network performance is achieved by taking the last dead node as well as the first dead node as the key parameter. The selection of CH is based on its ability to reduce the communicating distance between the nodes. Subsequently,
Ethical approval This article does not contain any studies with human participants or animals performed by any of the authors. 97
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Fig. 10. Analysis of network lifetime for 100 sensor nodes.
References
[4] Prerna, Sanjay Kumar, Energy efficient clustering algorithm for WSN, in: International Conference on Signal Processing and Integrated Networks, 2015. [5] Guisheng Yin, Guang Yang, Wu Yang, Bingyang Zhang, Wenjin Jin, An Energy-Efficient Routing Algorithm for Wireless Sensor Networks, IEEE, 2008. [6] Asish P. Mathew, Abey Abraham, K.G. Preetha, A novel algorithm for traffic reduction in wireless sensor networks, in: First International Conference on Computational Systems and Communications, ICCSC, 2014. [7] Martinus Dipobagio, An Overview on Ad Hoc Networks, Institute of Computer Science (ICS), 2009. [8] Guey-Yun Chang, Wei Li, Jen-Feng Huang, A Novel Node Movement Detection Scheme in Wireless Sensor Networks, IEEE, 2014. [9] Prerna, Sanjay Kumar, Energy Efficient Clustering Algorithm for WSN, IEEE, 2015. [10] Ali shokouhi Rostami, Mohammad Rasoul tanhatalab, Hossein Mehrpour bernety, Komeil Nosrati, Novel Algorithm of Energy-Aware in asymmetric wireless sensor networks routing for In-Point Coverage, in: International Conference on Computational Intelligence and Communication Networks, 2010. [11] S. Sharmila, T. Shanthi, A Survey on Wireless Ad Hoc Network, IEEE, 2016. [12] Divya Garg, Pardeep Kumar, Kompal, Energy efficient clustering protocol for wireless sensor networks using particle swarm optimization approach, Int. Res. J. Eng. Technol. (2017). [13] Jasvir Kaura, Sukhchandan Randhawab, Sushma Jain, A novel energy efficient cluster head selection method for wireless sensor networks, Int. J. Wirel. Microw. Technol. (2018).
[1] Garimella Ramamurthy, Energy Efficient Design of Wireless Sensor Network: Clustering, International Institute of Information Technology, 2018. [2] Jyoti Rathi, M.S. Dagar, A novel algorithm for improve low energy adaptive clustering hierarchy (LEACH) protocol in wireless sensor network, Int. J. Innov. Res. Comput. Commun. Eng. (2016). [3] Khalid A. Darabkh, Noor J. Al-Maaitah, Iyad F. Jafar, Ala’ F. Khalifeh, Energy efficient clustering algorithm for wireless sensor networks, in: IEEE WiSPNET, 2017. [4] Santar Pal Singh, S.C. Sharma, A PSO based improved localization algorithm for wireless sensor network, 2018. [5] Vesna Glavonjic, Aleksandar Neskovic, Ljerka Beus-Dukic, LEACH-reformed clusters: A novel cluster formation algorithm in LEACH protocol, 2016.
Further reading [1] Kalyan Krishna Awasthi, Arun Kumar Singh, Shailendra Tahilyani, Study of cluster based routing protocols in wireless sensor networks, Int. J. Comput. Appl. 163 (1) (2017) 0975–8887. [2] Joilson Alves Junior, Emilio C.G. Wille, Routing in vehicular ad hoc networks: Main characteristics and tendencies, J. Comput. Netw. Commun. (2018). [3] Zhongyuan Qin, Yuying Wang, Hengkang Wang, Jie Huang, A Novel Key Pre-Distribution Scheme in Wireless Sensor Networks, IEEE, 2014.
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Computer Communications journal homepage: www.elsevier.com/locate/comcom
Energy consumption and network connectivity based on Novel-LEACH-POS protocol networks Moorthi a , R. Thiagarajan b,c ,∗ a
Saveetha Engineering College, India Department of Computer Science and Engineering, Prathyusha Engineering College, India c Anna university, Chennai, India b
ARTICLE
INFO
Keywords: Energy consumption Novel routing algorithm Clustering LEACH Network connectivity
ABSTRACT Wireless sensor network (WSN) is used for a large number of sensor nodes and energy efficiency is important to constrained resources in wireless sensor networks. Wireless sensors networks use multiple approaches such as data cycling, energy optimal scheduling and energy aware routing to reduce energy consumption throughout the network. Energy awareness is an essential design for WSNs hence, data broadcasting protocols, routing and power management are specially designed to meet their requirements. The past decade was mitigating the issues of energy dissipation to ensure energy efficient routing and clustering. To address these issues, a novel idea is presented where the energy efficiency will incorporate the selection mechanism of nodes. In Wireless Sensor Networks, path routing for nodes is a difficult and tricky task. It is a crucial role to increase the stability and network lifetime. The LEACH Protocol is a measured level of the sensor networks lifetime by pairing the node energy consumption. Simulation results show that protocol outperform its routing protocols Low Energy Adaptive Clustering Hierarchy (LEACH) and Novel-LEACH in terms of network connectivity and power consumption. Also, a new energy efficient clustering scheme for WSNs with the use of Particle Swarm Optimization (PSO) is proposed. The clustering algorithm is implemented with the goal of concurrently minimizing the intra-cluster distance along with optimizing the usage of network energy.
1. Introduction Wireless sensor network is a collection of number of nodes for processing and communication system with each and every node gathering records and reversing the information back to sink node. Wireless sensor networks come under a unique kind ad-hoc networks having applications in areas such as environment monitoring, target tracking etc... Wireless sensor network, a novel and a greatest technique, is known for its salient feature of processing the group of information to the base station with limited power capacity. However, a major hindrance in employing the WSNs is increasing the network lifetime of nodes, since they possess different categories of networks. They are low sampling rate magnetic, visual, thermal and infrared, radar and acoustic. The main applications coming under the operation of wireless sensor networks are related to scanning a wide selection of ambient stages. Humidity, temperature, vehicular movement, lightning condition, pressure, soil makeup, noise levels, earth monitoring, target field imaging, fire alarm sensors, disaster management are those kind of stages. The alternative method lies in providing a hunt for criminal acts, measures of precision in agriculture field and performing sensor activities for detecting unknown intrusion.
On the other hand, a wireless ad-hoc network is basically a collection of heterogeneous network nodes. This network, as the term ad-hoc implies, is formed temporarily by not concentrating on any infrastructure or any centralized administrator. As there could be restrained range of transmission from wireless host, a packet is forwarded to the destination node. Fig. 1 demonstrates the structure of Wireless ad-hoc networks communicating in a self-organized way. Wireless sensor networks may be filled with sensor nodes numbering in hundreds or in thousands. However, a comparative attempt shall be made to show that ad-hoc network will contain only fewer nodes and that too without any specified structure. Table 1 illustrates the comparative features of WSN and Ad hoc networks. As mentioned earlier, WSN faces a handicap in maintaining less power consumption during transmission. As a result, reducing the power consumption during the transmission is enabled by applying the clustering technique in routing. This yields a considerable reduction of power consumption when WSN sends data from one node to the other nodes. In this sense, WSN is deemed to be data-centric, as it involves in responding to the base station as per the instructions from the Base-station (BS). This means that WSN does not involve in transmitting the data between or among the sensors.
∗ Correspondence to: Department of Computer Science and Engineering, Prathyusha Engineering College, Part Time Research scholar, Anna university, Chennai, India. E-mail address: [email protected] (R. Thiagarajan).
https://doi.org/10.1016/j.comcom.2019.10.006 Received 5 June 2019; Received in revised form 16 August 2019; Accepted 2 October 2019 Available online 10 October 2019 0140-3664/© 2019 Elsevier B.V. All rights reserved.
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Fig. 1. Wireless ad-hoc networks.
Table 1 Contrast between wireless sensor network and ad hoc networks. Parameters
Wireless sensor networks
Ad hoc networks
Number of sensor nodes Identifiers Topology Deployment Failure rate Communication paradigm Fusion/aggregation Centric Battery Computational capacities and memory Data rate Redundancy
Large No unique identifiers Changes repeatedly Heavily deployed Prone to failures Broadcast Communication Possible Data centric Not replaceable/Not rechargeable Limited Short High
Medium Unique identifiers Very rare changes Spreader Very rare Point-to-Point communications Not suitable Address centric Replaceable Not limited High Short
clusters. This organization facilitates data aggregation and compression within the networks. Thus, each cluster is provided with cluster heads, in the hierarchical pattern. It shall be reminded that optimization of power consumption by routing algorithm/adopting Wireless Ad Hoc Networks can increase the lifetime of the sensor network. Basically, a Wireless Sensor Network (WSN) is structured with a base station, communication satellites, and sensor nodes. The sensor nodes are evenly distributed along the transmission range within the network. The link between the network and the end subscriber is enabled by the base station. There is a categorization of routing within the WSNs, which is based on the structure of the network. The categorized routing patterns are location-based routing, hierarchical-based routing, and flat-based routing. The chief purpose of the hierarchical routing is to effect the power consumption in sensor nodes by adding multi-hop communication within a specific cluster. Clustering is the best routing algorithm which aims at decreasing the power consumption in sensor networks. The LEACH is the first algorithm to be focused by clustering. LEACH is the identity-organizing and adaptive clustering procedure. The operation of the LEACH consists of setup phase-cluster formation and steady phase that transfer to sink
Wireless Sensor Network has another, yet a major, hitch in the way of redundancy in the sensed data. WSNs send data to the end user, instead of sending them to the stipulated node, which consumes large power consumption. Moreover, it is a known process when lot of message packets containing second data is transmitted; it will end up with reduced transmission of packets but result in de-duplication of data. In practice, small sized sensors and those are at cheap cost are capable of securing various applications. Generally, sensor networks consist of different types of mechanisms for sensing, by which data can be gathered out of a given physical surrounding. One of the major functions of WSNs is (sensing/sending) the data to the particular destination node. Routing algorithm is a novel technique along with cluster based routing protocols. Wireless Ad Hoc networks are identified as a structure having a group of two or more communication devices, enabling capable networking facility. In this Wireless Ad Hoc networks, the lifetime of each network is split on fixed time span that provides a select cluster of nodes to be in operating or sleep mode. This time span interval is known as a round, which, in other way, creates an operative problem in the case of clustering. Within sensor networks, there could be an organized form of nodes in smaller groups, which are otherwise treated as 91
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heads as routers to the sink node. The application of LEACH does the action of saving energy whereas Cluster Head performs the transmission of data to the sensor nodes. 2. Related works Ramamurthy [1] describe the wide survey on WSNs and a variety of approaches are used for energy consumption similar to data driven approaches, duty cycling and various mobility based approaches. Other authors have applied a clustering based energy efficient algorithm. But this author structured the WSNs in a two-layer manner with clustering algorithm, and then recovered the loss of data based on this two-layer structure. Wireless sensor networks have sensing devices to use in ad hoc network and battery operated computing. In order to increase the energy consumption, network lifetime should be optimized. In clusterbased WSNs, cluster heads or gateways perform behaviors, such as data collection from their member nodes, data exchange and data aggregation with the base station. This makes the load balancing of gateways to be a crucial parameter in optimization of the network lifetime in WSNs. Rathi and Dagar [2] proposed a clustering algorithm called Low Energy Adaptive Clustering Hierarchy (LEACH), which is applicable for sensor networks. This distributed algorithm involved clustering the LEACH and leaving the nodes independent, not to be controlled by any center. Among the algorithms found under LEACH, the hierarchical routing is considered as the best preferred. In this algorithm, the clusters of the sensor nodes are constructed with the help of local cluster heads (CHs). These CHs act as routers, linking the sink node and enable deriving signal strength. Through these routers only CHs perform the transmission meant for specified nodes, instead of every node in the network. By this select transmission, through chosen routing; energy is saved when the cluster retains temporary data processing, which is otherwise viewed as data fusion and aggregation. In fact, there is a possible change in CHs at random level, which is for balancing the dissipation of energy nodes. CHs perform this balancing by way of choosing a node random number between 0 and 1. Each node is provided with a Cluster Head for the current round and this carries out the task of checking whether the number node is less than the threshold. In Darabkh et al. [3] LEACH involves in partitioning the nodes into number of clusters, wherein each node makes use of a cluster head along with certain common nodes as its members. The cluster head undertakes rotation based functioning at stipulated time span, getting contact with other nodes at every round. This makes even distribution of rounds which, in turn, secures evenly distributed energy consumption within the network. A major disadvantage of LEACH is that it is not feasible for functioning in networks of wider environment. This is because, the cluster heads in LEACH process effect communication with the far off base station, thereby consuming more power for data transmission. The ultimate effect of this is the reduced lifetime of the network as well. However, LEACH has the feasibility of making randomized rotation of cluster-head throughout the network. By this way, less energy is consumed for transmission when LEACH chooses the specific cluster head. Hence, there is a possible increase in lifetime of the network. Singh and Sharma [4] examined a novel energy efficient clustering algorithm for WSNs with help of the PSO algorithm that maximize the life span of the network. The most focused objective of the proposed algorithm is the selection of a corresponding CH, to minimize the intracluster distance and optimizing the energy efficiency of the network. The authors proposed the method which has a high energy node as CH and made the clusters by placing them in a uniform pattern in the sensor region. The authors attempted at optimization of the energy consumption by the nodes by simultaneous maximization of data transmission. However, the main difference between this approach and the earlier works lie in the choice of PSO for picking the optimum nodes as CHs. Thus, they proved the objective of enhancing the lifetime of the network.
Fig. 2. Cluster approach in Leach.
node that occurs. Cluster head is elected by sensor nodes based on probabilistic approach. Sensor nodes are micro-electro-mechanical systems (MEMS) that produce a measurable response to a change in some physical condition like temperature and pressure. Sensor nodes sense or measure the physical data of the area that are to be monitored. Sensor nodes are of very small size; they consume extremely low energy; they are operated in high volumetric densities, and they can be autonomous and adaptive to the environment. As wireless sensor nodes are typically very small electronic devices, they can only be equipped with a limited power source. The general cluster approach is shown in Fig. 2. There are three categories of sensor nodes: (i) Passive, Omni Directional Sensors: passive sensor nodes sense the environment without manipulating it by active probing. In this case, the energy is needed only to amplify their analog signals. There is no notion of ‘‘direction’’ in measuring the environment. (ii) Passive, narrow-beam sensors: these sensors are passive and they are concerned about the direction when sensing the environment. (iii) Active Sensors: these sensors actively probe the environment. For more decades, routing protocols are designed with a focus and stress on the power efficiency and robustness of the network, by enhancing the lifetime. LEACH is a widespread application under dynamic clustering hierarchical routing protocol in sensor networks. LEACH turns out to be the first protocol, introduced on clustering hierarchy in WSNs. LEACH is known for its novel approach in energy efficiency in sensor networks, yet with restrained applications. This reduction of power consumption, invoking the efficiency, is aided by applying Low-Energy Adaptive Clustering Hierarchy. This concept paves way for forming or constructing the clusters in sensor networks, in accordance with the derived signal strength and already consumed built-in cluster 92
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Fig. 3. Transmission areas in adhoc.
3. Theoretical background 3.1. Ad hoc network An ad hoc network is compared by focusing the infrastructure based wireless networks, such as cellular network and WLAN. The transmission of a data packet to a destination node in the infrastructure based wireless networks, take place via access point only (in cellular network like GSM, it is called base station). This access point is noted for its establishment a network area, so that the nodes in this area can only make use of the services offered by the access point. The wireless ad hoc networks are designed with nodes having a transceiver and are independent of an infrastructure. This makes the nodes to establish their respective networks and it is to be noted that a sensor node is expected to communicate with other nodes in a stipulated range only. However, the uniqueness of the infrastructure based wireless network is that nodes have the feasibility to communicate with other nodes at any range. The data thus sent to this access point, is relayed to the desired node by the access point. In every aspect, the effective transmission of data is achieved when routing algorithm is applied. Fig. 3 shows how the nodes form a transmission cloud.
Fig. 4. Cluster based WSN with gateways.
3.3.1. Issues with cluster based routing protocols Regarding the efficiency of cluster based routing protocols still there are certain pertinent issues that need to be dealt with proper concern. The issues are: 1. The importance of selecting a node as a cluster head which must properly be done by considering the energy left over in every node. In addition, there should be a provision for a threshold energy level below which the network must readily hop from node to node. 2. During the data transmission, few nodes in the network would remain unattended when the deployment is over a wide area. Therefore, focus should be made on increasing the attention on the governing area. 3. Network topology is subject to change based on the functioning of the particular network which is in accordance with the terrestrial area. Hence, this requires better efficient clustering methods and mechanisms for improving the network efficiency. 4. The sensor nodes are expected to handle minor faults at sufficient levels. 5. Network should be structured by incorporating minimum number of possible nodes and there shall not be any compromise on the accuracy and security.
3.2. Cluster based WSN Cluster based WSN system is the one where all the sensor nodes get combined in forming a couple of gateways. These sensor nodes are assigned within the range of data transmission and communication. This makes each sensor node to avail a couple of gateways. Usually, the local information is collected by the sensor nodes and is duly transferred to the corresponding or the destination nodes. There will be a wireless communication between these two nodes. Fig. 4 shows the cluster based WSN, that have the sensor nodes, gateway and base station connection.
4. Methodology of energy consumption and network connectivity based on novel-LEACH protocol networks
3.3. Cluster based routing protocols It is a well-known fact that the wireless network has the constituent nodes which are provided with varied range of energy. This makes the smart clustering based approach to choose the nodes in accordance with their residual energy pattern. The efficient energy management is based on two factors viz. when the nodes having considerably higher energy range are selected for transmission process and data communication, and when the nodes having considerably less energy are made use for data sensing.
4.1. LEACH protocol LEACH is viewed as a self-organizing protocol performing adaptive clustering. It involves two phases namely, set-up phase where CHs are formed, and steady-state phase where data is transferred from nodes to CHs and on to BS. Each node is capable of becoming a cluster head, with the probability p exactly once every 1 p rounds considering the 93
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following threshold ⎧ (𝑝 ) ⎪ 𝑇 (𝑛) = ⎨ 1 − 𝑝 𝑟. 𝑚𝑜𝑑1 𝑝 ⎪ 0, ⎩
𝑖𝑓 𝑛 ∈ 𝐺 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒
(1)
Here, r represents current round and G is indicated as set of nodes, which it has not selected for CHs in the last 1p rounds. So, the node chooses an arbitrary number between 0 and 1. If the selected number is lesser than the threshold T(n), then the node is CH for the current round r. When the BS is situated at the center of the field with nodes uniformly M × M distributed, free space channel model is used for sending l - bit message. Transmitting energy is , (𝑙, 𝑑) = 𝑙.𝐸𝑒𝑙𝑒𝑐 + 𝑙.√𝑒𝑓 𝑠 . 𝑑 2 , as the distance d of any node to BS or its CH is less than 𝑑𝑜 = 𝑒𝑓 𝑠∕𝑒𝑚𝑝, where 𝐸𝑒𝑙𝑒𝑐 is electronics energy, 𝑒𝑓 𝑠 and 𝑒𝑚𝑝 represent transmitter amplifier for free space and for multipath channel. Optimal number of clusters in each round is: √ √ 𝑛 𝑀 𝑛 2 = 𝑘= (2) 2𝜋 𝑑𝑡𝑜𝐵𝑆 2𝜋 0.765
Fig. 5. Clustering in LEACH.
By this calculation the optimal probability that a node becomes CH can be arrived: 𝑃 =
𝑘 𝑛
4.1.2. Shortcomings of LEACH protocol LEACH Protocol is always aware that every node has data to be forwarded and that the nodes in CH are chosen with the residual or initial energy. In general, the number of CHs to be pre-determined comes in the range of 5% or 10% of total nodes. Yet, this is insufficient to fulfill the wider area of sensor nodes, since the nodes are randomly dispersed. The choice of CHs is based on random or rotational manner and equally significant is that the Residual energy does not come under the consideration in the formation of clusters. The random dispersal of CHs in the network makes the focus towards the CHs on any one region of the network. This makes other nodes in the network which are not covered under the CHs region, which creates lack of proper CH location within the network. As mentioned earlier, the aggregated data is transferred by CH to the BS in a single-hop way. As a result, this factor makes LEACH as invalid in the case of deploying the networks for wider regions. Further, LEACH protocol is designed with rounds, wherein every round, there is a reconstruction of novel clusters by all the sensor nodes. This clustering process consumes a lot of energy. Fig. 5 shows clustering in LEACH. In LEACH-Centralized set-up phase, information on current location and the energy level at the base station are forwarded by every node. In fact, LEACH-C decides a centralized clustering algorithm and the same steady-state protocol. The BS is responsible in locating the clusters, CH and non-CHs of every cluster. In addition, the BS uses its global information of the network and creates better clusters which will consume less energy for data transmission.
(3)
LEACH Protocol provides the formation of round by creating or providing several rounds. Of these, each round is said to consist two states of which the first state is cluster setup state while the other is steady state. In cluster setup state, the CHs process is chosen by the clusters while the data is transmitted to the clusters in steady state. The cluster state is typically significant wherein there is a decrease of the overhead, due to the difference of time gap between the processes in the second state time and the first state time. This is to say that the process time in the second state is longer than that of the first state. However, the rounds decide the functioning of LEACH as there is a repetition of clusters by the system and transmission of data in every round. The Two Phases in LEACH Protocols: (1) Set-up phase: In set-up phase, CHs are chosen depending on T(n) threshold. The ADV message to the entire Non-CH nodes is broadcast by the CHs and based on this, the Non-CHs choose their CHs. The RSSI of ADV message also plays a significant role in Non-CHs choosing the CHs. Once the Non-CHs select the clusters, there is a transfer of Join-REQ back to CH by Non-CH nodes. This is followed by generation of TDMA schedule by CHs and ultimate transfer of the schedule to the whole Non-CH nodes. (2) Steady-state phase: The sensing and transfer of data by the sensor nodes to CHs occur in this phase as per the schedule. This is done by CHs, after gathering the data and merge them to the Base station by one-hop way. This results in the decrease in the number of transmission and subsequently the reduction of energy consumption. With the passage of a particular time span, the network retards to its set-up phase and continues the round process. For the purpose of reducing the disorder from any other cluster nodes, dissimilar CDMA is utilized by cluster communication.
4.1.3. Limitation of LEACH protocols In LEACH Protocols, the nodes are sufficiently provided with power to reach the base station, as nodes always either receive or send information. There is a possibility for nodes to have easy communication, if they are nearer between or among themselves. Yet, they may not be aware of the predetermined CHs which could be distributed in an even manner within the network. This necessitates the CHs to choose the exact point within the system; thereby the choice of CHs is random in the LEACH protocol. Consequently, the CHs select nodes with less strength that could lead these nodes to die too quick.
4.1.1. Advantages of LEACH protocol LEACH protocol has a lot of advantages and chief among them is obtaining the factor of 7 reductions in energy dissipation in comparison with direct communication and comparison of 4–8 factors till the last transmission energy routing protocol. In case the nodes face death selectively, there is an increase in the life time of the dynamic clustering network. Nevertheless, LEACH is deemed to be a wholly distributed network and it does not require any worldwide information of network.
4.2. Particle Swarm Optimization (PSO) Particle Swarm Optimization (PSO) is evinced from the pattern and support of natural lifecycles, like fish schooling, bird flocking and arbitrary search techniques of evolutionary procedure. This concept is based on the natural pattern which shows the winged creatures fly or travel together in a random search for shelter and food. Their 94
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The 𝒑𝒅𝒈 is represents the global fitness of dimension d. Lbest PSO It can be seen that both types are capable of searching in global as well as local. Local search is provided in PSO by 𝑝𝑑𝑖 and 𝑖th represents the individual best fitness position in overall iteration t+1. The other way is instead of estimating global best fitness till last looping t+1 the local best fitness can be calculated in the place of 𝑝𝑑𝑔 . The above equation is combined and become as ) ( 𝑣𝑑𝑖 (𝑡 + 1) = 𝑤 + 𝑣𝑑𝑖 (𝑡) + 𝜑1 × 𝑟𝑛𝑑() × 𝑝𝑑𝑖 − 𝑥𝑑𝑖 (𝑡) + 𝜑2 × 𝑟𝑛𝑑()× ( d ) pl − xdi (t) (6) Hybrid PSO Both types of Gbest and Lbest are proposed with new PSO. To use both types of equation the two parts 𝜑2 and 𝜑3 are sub-divided and velocity at t+1 represents the three functions which are individual best positions, global best position and last iteration best position. ) ( 𝑣𝑑𝑖 (𝑡 + 1) = 𝑤 + 𝑣𝑑𝑖 (𝑡) + 𝜑1 × 𝑟𝑛𝑑() × 𝑝𝑑𝑖 − 𝑥𝑑𝑖 (𝑡) + 𝜑2 × 𝑟𝑛𝑑()× ( ) ( ) 𝒑𝒅𝒈 − 𝑥𝑑𝑖 (𝑡) + 𝜑3 × 𝑟𝑛𝑑() × 𝒑𝒅𝒍 − 𝑥𝑑𝑖 (𝑡) (7) Fig. 6. Gbest and Pbest Movements of PSO.
The difference between the Gbest and Pbest are Gbest is changes the value only when Pbest comes closer to the target then Gbest. pBest value only indicates the closest the data has ever come to the target since the algorithm started.
flocking represents a cluster without any collision among themselves. This is possible when every member of the cluster is conscious of the location, flying speed and the cluster information shared. All these factors contribute in reducing the energy exertion among these creatures. PSO is defined as the one having a swarm of a pre-specified size of particles. The purpose of particle swarm algorithm (PSO) is to optimize the LEACH protocol most commonly used in WSN routing protocol, in order to reduce the energy consumption of WSN data transmission and optimize the data transmission routing. The peculiarity of PSO is that each of its element supplies an exact solution to the issues related to the multi-dimensional optimization. This makes an assumption that the dimension D of the considerable number of particles is equal. A particle Pi 1 < I < Np has position Xid , 1 < d < D and velocity Vid in the dth dimension of the hyperspace. The notation for demonstrating the 𝑖th particle Pi of the inhabitants as follows: [ ] 𝑃𝑖 = 𝑋𝑖,1 , 𝑋𝑖,2 , 𝑋𝑖,3 , … … … , 𝑋𝑖,𝐷 (4)
4.3. PSO with LEACH protocol Sensing elements are like sensors deployed randomly along with some gateways. The proposed system is processed by using the following steps: Step 1: PSO approach with clustering is applied to get the optimal selection of cluster head and improve the progression in the remaining energy of node by sending a data packet to the cluster head which is situated closest to the Base station. The cluster head is selected using PSO approach based on the remaining energy of that node and distance from the cluster member node to base station. Step 2: Calculate the distance between transmitter and receiver. If the distance is less than a threshold value d0 and free space (fs) model is applied, otherwise, the multipath (mp) model is applied. Step 3: If sensing element is within the communication range, then sensing element is easily allocated to any gateway. The sensing element can allocate the individual sensor nodes by using some predefined gateways. The below Fig. 7 shows the flow of PSO with LEACH Protocols
PSO is capable of solving the issue as it offers the superiority of the solution and estimates the fitness function of each particle. To reach the total best position, the particle Pi monitors its each particles best, i.e., personal best called Pbest and global best called Gbest that changes its individual position and velocity. In every duplication particle, its position Xid and velocity Vid and in the dth dimension are changed by utilizing their associated conditions. Fig. 6 illustrates the PSO particle movements in a two-dimensional space. It represents the current optimum particle represented as ‘‘pbest’’. From the figure, it can be observed that there is a best optimization of the results from overall reading and the tracing of the location as lbest. In addition, the velocity of each particle is represented as pbest and lbest. PSO is known for its maintaining of the record of outputs in three global attributes viz target value or condition, gbest and stopping value.
4.4. Algorithm Set-Up Phase 1.CH N:idCH ,crc,adv 2.ni CH: idni , idCH ,crc, join_req 3. CH N: idCH ,(. . . ,( idni , Tni ). . . ), crc, sched 4. GW N: idgw , crc, adv Steady State Phase 5. ni CH: idCH , crc 6. ni GW: idgw , crc 7. CH – BS: idCH , idBS , GW BS: idgw , idBS 8. Base Station get information from all the cluster heads and Gateways. The symbol used in proposed algorithm signifies: CH, 𝐧𝐢 , BS: Cluster Head, ordinary node, base station N: Set of all nodes in the network. Adv,join_req,sched: String identifiers for message sorts Crc: Cyclic redundancy check idni , idCH , idBS : Nodes ni, CH, BS id’s respectively ⟨𝐲, 𝐓𝐲⟩: A node id y & its active slot Ty in the clusters TDMA schedule , : Unicast, broadcast transmissions, respectively.
Three Types of PSO Algorithms Gbest PSO The equation above is generated t+1 of velocity and used for global fitness and it continues the iteration process till the end of the function. Here, below representation equation is easily a show the execution. ( ) 𝑣𝑑𝑖 (𝑡 + 1) = 𝑤 + 𝑣𝑑𝑖 (𝑡) + 𝜑1 × 𝑟𝑛𝑑() × 𝑝𝑑𝑖 − 𝑥𝑑𝑖 (𝑡) + 𝜑2 × 𝑟𝑛𝑑()× ( ) 𝑝𝑑𝑔 − 𝑥𝑑𝑖 (𝑡) (5) 95
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Computer Communications 149 (2020) 90–98
Table 2 Experiment of PSO with Lbest, Gbest.
Table 4 Performance metrics of LEACH AND NOVEL-PSO-LEACH.
S.No
Type of PSO
Attributes
Performance metrics
LEACH [5]
NOVEL-PSO-LEACH
1 2 3
Gbest PSO Lbest PSO PSO hybrid
Size = 35 Size = 35
Total number of energy consumed Total number of data transmitted(bits) Number of first node dies Network lifetime(rounds)
309.101 47 854 370 497
490.20 98 665 700 834
Table 5 Energy dissipation of 100 sensor nodes. LEACH [5]
NOVEL-PSO-LEACH
12 25 55 110 170 225 267 335 375 435
23 35 75 135 189 245 289 315 365 415
Table 6 Data transmitted for 100 sensor nodes. Fig. 7. Network Lifetime of PSO LEACH.
LEACH [5]
NOVEL-PSO-LEACH
10 023 10 070 10 120 10 165 20 220 20 275 20 320 30 360 40 425 40 460 50 520 50 570 50 640 60 680 70 720 70 760 80 820
10 045 10 090 10 145 10 189 20 245 20 290 20 340 30 390 40 410 40 480 50 540 50 580 50 610 60 640 70 740 70 780 80 108
The proposed scheme is evaluated with a comparison of LEACH protocol. An assumption is put on the network model of 100 nodes with equal initial energy and network of 100*100 m2 is selectively deployed. Base Station was placed at the center of the network. The result analysis shows that the proposed algorithm outperforms LEACH Protocol in respect of energy consumption, network throughput and network lifetime. The simulation results used in some of the computation procedures are, stability period, lifetime of the system, and number of data transmitted to the BS. Stability period: The stability period refers the level of energy underwent dissolution in the network till the death of the entire network or until the network time becomes unstable. System lifetime: The system lifetime is measured as number of rounds till the whole system dies or the number of rounds from network lifetime is initialized till all nodes die. Number of Data transmitted to BS: This metric defines the cumulative volume of data which either CHs or Non-CHs directly transmit to BS.
Fig. 8. Energy of PSO LEACH after 70%.
5. Experiment results Table 2 displays the result derived from the experiment as per the setup. PSO algorithm measured the efficiency with total number of rounds till network survives find the better hybrid PSO than the Lbest, Gbest PSO. The remaining energy node of WSN and efficiency of the network have been verified and better hybrid PSO is observed. Table 3 shows the network lifetime, number of node dead in the first stage and then in 50%; the residual energy at 70% of the dead nodes dead is shown. By measuring the three algorithm rounds, the efficiency of PSO is found out. Figs. 7 and 8 show the graphs of the above table values. Table 3 Experiment results of hybrid PSO. WSN routing
Network lifetime (rounds)
First node dead
50% node dead
Total rounds sent packets
Remaining energy after 70% node deads
Gbest PSO LEACH Lbest PSO LEACH Hybrid PSO LEACH
1895 1923 2349
665 684 693
1354 1367 909
11 671 11 689 12 123
2.23 2.49 1.91
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Fig. 9. Analysis of energy dissipation of 100 sensor nodes.
Table 7 Network lifetime for 100 sensor nodes. LEACH [5]
NOVEL-PSO-LEACH
100 100 100 100 100 100 100 100 90 45 10 45 8 8 8 8 8 8
100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 85 18 8
there is a reduction in the energy consumption while transmitting the data from one node to another. Similarly, there is an increase in the network efficiency as the energy consumed is directly proportional to the nodes. 6. Conclusion Wireless sensor networks are progressively being utilized in the fields of healthcare, transportation, manufacturing, and much more. It provides multi-dimensional and existing routing protocols along with their classifications. Of these, cluster based routing protocols can serve as a useful tool for various tasks in modifying and optimizing the various routing algorithms used in wireless sensor networks. In future, protocols may be proposed to identify the dead nodes before they actually die and improve the efficiency by hopping. The performance of LEACH protocol lies entirely on the random selection of Cluster Head (CHs). This indicates that there could be no significant impact on the efficiency of the network lifetime or the power consumption if the selection of CHs is not proper. The results observed with the application of this algorithm prove that there is an increase in the network lifetime and energy consumption among sensor network nodes. PSO with clustering is found to achieve the best solution of cluster head and this solution increases the successive residual energy in transmitting the data packets to their nearest base station. The cluster head significantly balances the power energy and enhances the lifetime of the system. The experimental results show that the proposed LEACH performs better than the existing LEACH in terms of system lifetime, stability period and the total data transmission.
The experimental Table 4 shows the performance metrics of LEACH and NOVEL-PSO-LEACH for energy consumed, data transmitted and network lifetime while Table 5 shows the 100 nodes energy dissipation Fig. 9 above describes the result analysis of total energy dissipation in network using 100 sensor nodes. The image clearly explains that NOVEL-PSO-LEACH consumes less energy than LEACH, and Table 6 shows 100 sensor nodes data transmission values. Fig. 10 shows the network lifetime for 100 sensor nodes and Table 7 shows the network lifetime for 100 sensor nodes. The first node dies at 370 and at 700 rounds for LEACH and NOVEL-PSO-LEACH respectively. Likewise, the first node other nodes dies at 497 and 834 rounds for LEACH and NOVEL-PSO-LEACH. The experimental result proves that NOVEL-PSO-LEACH lifetime is more than LEACH due to cluster head optimization by using PSO approach.
Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Throughput and Energy Consumption Simulation result shows that the proposed algorithm is better than the LEACH in respect of throughput and stability. The network performance is achieved by taking the last dead node as well as the first dead node as the key parameter. The selection of CH is based on its ability to reduce the communicating distance between the nodes. Subsequently,
Ethical approval This article does not contain any studies with human participants or animals performed by any of the authors. 97
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Computer Communications 149 (2020) 90–98
Fig. 10. Analysis of network lifetime for 100 sensor nodes.
References
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[1] Garimella Ramamurthy, Energy Efficient Design of Wireless Sensor Network: Clustering, International Institute of Information Technology, 2018. [2] Jyoti Rathi, M.S. Dagar, A novel algorithm for improve low energy adaptive clustering hierarchy (LEACH) protocol in wireless sensor network, Int. J. Innov. Res. Comput. Commun. Eng. (2016). [3] Khalid A. Darabkh, Noor J. Al-Maaitah, Iyad F. Jafar, Ala’ F. Khalifeh, Energy efficient clustering algorithm for wireless sensor networks, in: IEEE WiSPNET, 2017. [4] Santar Pal Singh, S.C. Sharma, A PSO based improved localization algorithm for wireless sensor network, 2018. [5] Vesna Glavonjic, Aleksandar Neskovic, Ljerka Beus-Dukic, LEACH-reformed clusters: A novel cluster formation algorithm in LEACH protocol, 2016.
Further reading [1] Kalyan Krishna Awasthi, Arun Kumar Singh, Shailendra Tahilyani, Study of cluster based routing protocols in wireless sensor networks, Int. J. Comput. Appl. 163 (1) (2017) 0975–8887. [2] Joilson Alves Junior, Emilio C.G. Wille, Routing in vehicular ad hoc networks: Main characteristics and tendencies, J. Comput. Netw. Commun. (2018). [3] Zhongyuan Qin, Yuying Wang, Hengkang Wang, Jie Huang, A Novel Key Pre-Distribution Scheme in Wireless Sensor Networks, IEEE, 2014.
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