International Journal of Computer Networks & Communications (IJCNC)

AIRCC PUBLISHING CORPORATION

IJCNC 003

Improvements In Routing Algorithms To Enhance Lifetime Of Wireless Sensor Networks

1Naga Ravikiran1 and C.G. Dethe21Research Scholar, ECE Department, Priyadarshini Institute of Engineering and Technology (PIET), Nagpur, Maharashtra.2Director, UGC-Human Resource Development Centre, RTM Nagpur University, Nagpur, India.

ABSTRACT:


Wireless sensor network (WSN) brings a new paradigm of real-time embedded systems with limited computation, communication, memory, and energy resources that are being used fora huge range of applications. Clustering in WSNs is an effective way to minimize the energy consumption of sensor nodes. In this paper improvements in various parameters are compared for three different routing algorithms. First, it is started with Low Energy Adaptive Cluster Hierarchy (LEACH)which is a famed clustering mechanism that elects a CH based on the probability model. Then, work describes a Fuzzy logic system initiated CH selection algorithm for LEACH. Then Artificial Bee Colony (ABC)which is an optimisation protocol owes its inspiration to the exploration behaviour of honey bees. In this study ABC optimization algorithm is proposed for fuzzy rule selection. Then, the results of the three routing algorithms are compared with respect to various parameters.

KEYWORDS

Wireless Sensor Network (WSN), LEACH, Clustering, Artificial Bee Colony (ABC), Fuzzy logic system.

1. Introduction

Wireless Sensor Networks (WSN) is a group of spatially dispersed dedicated sensors to monitor/record an environment’s physical conditions and to organize collected data at a central location (Xiao 2004).

Clustering is used for communication between nodes and BS, as it is energy efficient compared to single/multi-hop routing. In clustering, a sensor node in a cluster is elected Cluster Head (CH) and relays data from a sensor to a remote receiver [3]. Few CH nodes are heavily loaded, in clustering when energy depletion occurs.CH performs aggregation function on data received and sends it to BS where it is needed. LEACH is a popular routing protocol using cluster-based routing to reduce energy consumption [1].LEACH divides communication into rounds with a round including a set-up phase and a steady-state phase [4].<a


Figure 1. Cluster-Based Wireless Sensor Network

Clustering based schemes are the most energy efficient routing protocols. In a cluster, a node is elected as CH while others are member nodes who in their respective clusters sense ambient conditions in the environment and transmit measured data to corresponding CHs [7]. CHs collect data from member nodes, aggregate them, and finally forward it to either a neighboring CH (multi-hop) or directly (single hop) to BS. Clustering leverages advantages of small transmit distances for most nodes, requiring only a few to transmit farther distances to a BS [29].

Every sensor node in the group is associated to a single cluster and interacts only with the respective CH [17]. Hence, this means that the appropriate CH should be selected to optimize the consumption of energy by the CH; If not so, it may cause the death of CH because of additional load for data collection and forwarding. Many of the routing algorithms that are based on cluster technique first select CH at random or by probability and thereafter form the cluster.

This paper gives a view about comparing the results with respect to fuzzy rule selection with the use of LEACH and ABC optimization algorithms. Section 2 lists the literature of previous work, Section 3 illustrates the used technique, Section 4 explains the comparison of the result and Section 5 includes the conclusions.

2. Literature Review 

An energy efficient CH election protocol (LEACH-HPR) proposed by Han [8] used a minimum spanning tree algorithm to construct inter-cluster routing. An improved LEACH called partition-based LEACH (pLEACH) which partitioned a network into an ideal number of sectors and chose a node with the highest energy as sector head was proposed by Gou and Yoo [9] using centralized calculations. A Multi-hop Routing with LEACH (MR-LEACH) protocol was presented by [10] to prolong WSN life. MR-LEACH partitioned a network into different cluster layers. BS selected upper layer CHs to act as super CHs for lower layer CHs.

Singh, et al., (2010) surveyed and summarized recent research works focused mainly on the energy efficient hierarchical cluster-based routing protocols for WSNs [6]. Due to the scarce energy resources of sensors energy efficiency was the main challenge in the design of protocols for WSNs. The ultimate objective of the protocol design is to keep the sensors operating for as long as possible, thus extending the network lifetime.

MS-LEACH was proposed by [11] to enhance S-LEACH security by giving data confidentiality, and a node to CH authentication using pairwise keys shared by CHs and cluster members. The new MS-LEACH’s security analysis showed that it had efficient security properties achieving all WSN security goals compared to LEACH protocol’s current security solutions. MS-LEACH’s simulation-based performance evaluation proved the effectiveness of new MS-LEACH preserving the energy efficiency was a critical and a challenging task.

Biradar, et al., (2009) analyzed the design issues of WSNs and presented a classification and comparison of routing protocols. Recent advances and convergence of micro-electro-mechanical systems technology wireless communications, integrated circuit technologies, microprocessor hardware and nanotechnology, distributed signal processing, Ad-hoc networking routing protocols and embedded systems created the WSN concept where nodes were limited regarding energy supply, restricted computational capacity, and communication bandwidth [18].

Elrahim et al (2016) proposed an energy efficient data forwarding protocol [16]  called  Energy  Aware  Geographic Routing  Protocol  (EAGRP)  for WSNs to extend the lifetime of the network. In EAGRP, both position information and energy were available at nodes used to route packets from sources to destination. This prolongs the lifetime of the sensor nodes hence the network lifetime, higher packet delivery ratio and minimal compromise of energy efficiency were performed. The routing design of EAGRP was based on two parameters: location and energy levels of nodes.

Hancke, et al., (2007) introduced a Simple Energy Efficient Routing Protocol (SEER) to improve network lifetime by limiting the number of messages that were sent through the network [25]. The nature of WSN necessitates specific design requirements, of which energy efficiency is paramount. SEER uses a flat network structure for scalability and source initiated communication, along with event-driven reporting to reduce the number of message transmissions. Computational efficiency was achieved by using a relatively simple method for routing path selection. Routing decisions were based on the distance to the base station as well as on remaining battery energy levels of nodes on the path towards the base station.

Zogović, et al., (2010) research on WSN focused mostly on providing energy-efficient operation for every node that ensures a long life for WSN. It was important to consider QoS provisioning in addition to taking into account energy-efficiency. Keeping in mind, that throughput, average delay and jitter  (delay variance) were important QoS parameters at  Medium  Access  Control (MAC)  layer,  it led to reviews of fundamental energy-efficiency vs. delay36 trade-off, and throughput vs. capacity in wireless communications.

Nikravan, et al., (2011) affirmed a routing protocol in wireless sensor networks to achieve real-time communication besides the energy efficiency [24]. With demand increasing for real-time WSN services, Quality of Service (QoS) based routing is now an emerging research topic. Providing QoS guarantee in sensor networks is challenging. A fuzzy logic-based Energy Efficient scheme for real-time packet transmission in WSN was proposed. Here a Fuzzy Logic System (FLS) was used as a decision mechanism for next hop node selection.  Both transmission rate and energy were chosen parameters for choosing the next-hop node in real-time packet transmission. Simulation results showed that this scheme provided improvement in real-time transmission and energy efficiency performance, low energy consumption, and high packet delivery ratio within deadline compared with some other routing protocols when operating in varying real-time environment.

Kumar, et al., (2015) proposed a novel approach with an energy efficient hierarchical clustering technique using the Fuzzy Logic method [26]. The Fuzzy search algorithm was applied for cluster formation and cluster head selection in the distributed hierarchical clustering environment. The fuzzification functions and rules optimized the simulation [27]. The proposed approach results in Mat lab simulation outperformed the existing results. The evaluation of the proposed approach was compared with LEACH protocol. The result showed the algorithm scaled well in dynamic and energy deficient wireless sensor networks

Sobral, et al., (2013) proposed a Fuzzy Inference System to help the Directed Diffusion routing protocol to choose a route for the communication between any nodes in the network. A new approach helped to choose the best route based on Fuzzy Inference Systems and Ant Colony Optimization (ACO). The Fuzzy System was used to estimate the degree of the route quality, based on the number of hops and the lowest energy level among the nodes that form the route. The ACO algorithm was used to adjust the rule base of the fuzzy system in order to improve the classification strategy of the route, and hence increased the energy efficiency and the survivability of the network. The simulations showed that it was effective from the point of view of the energy, the number of received messages, and the cost of received messages when compared to other approaches.

Zhou, et al., (2017) proposed a multiple dimensional tree routing protocol for Multi-sink WSNs based on listening and ACO. Taking into consideration hops, packet losses, retransmission, and delay account, a distributed ant colony algorithm was proposed. When nodes selected routes in the data transmission, the algorithm was utilized to realize the real-time optimization by coordination between nodes. The simulation results showed that the ACOMSR protocol realized the QoS optimization for Multi-sink wireless sensor networks, and its performance was better than the routing protocol of minimum hop numbers.

3. Proposed Method

This section discusses fuzzy-based CH selection, ABC and fuzzy ABC-based selection of CH. Figure 2 shows the flowchart of the proposed methodology.

Figure 2: The flowchart of the proposed methodology.

3.1 Fuzzy Logic System

Fuzzy logic implements decision using fuzzy decision sets, each given by a separate term like “small”, “medium”, or “large”. Fuzzy logic system performs de-fuzzification and fuzzy inference. Fuzzification converts a crisp input to a fuzzy value. Generally, used membership functions use triangular/trapezoidal membership functions.

Fuzzy input parameters are Remaining energy, farthest node distance in the cluster from Cluster head and hops to sink. Heinzelman’s energy model for sensor networks, Energy required to transmit a k-bit message to distance d is given by:

3.2 Fuzzy-Based CH Selection

In this technique, it is assumed that a node in WSN node receives its coordinates. LEACH-FL organization comprises three sections, four fuzzification functions, an inference engine (concluding 27 rules) as well as defuzzification module [13]. Defuzzification module is a mere formula and hence is not depicted in the figure and will be given later (Ran et al., 2010).

  • Fuzzification module: Consider that three different node attributes influences the selection of CH, in order to utilize the input functions to change the system input into the fuzzy set, namely distance, the density of node and battery power level. Every input function consists of three membership functions to exhibit the various degrees of function. The count after the membership functions exhibits the membership function degree and illustrates the relationship between the input function is arithmetical [20].
  • Knowledgebase: Any system consists of 27 rules in fuzzy inferences. The forms of regulations are: IF A and B and C THEN D. Here, A, B, C, and D represent battery level, node density, distance as well as probability correspondingly. The rules are with respect to the equation (1):

Probability = battery level *2 + node density + (2 − distance) ……….                                      (1)

The formula illustrates the association among various input function. Battery level is the key feature in the CH election probability.

  • De-fuzzification module: After collating the conclusion by every rule, defuzzification technique is needed to obtain crisp value. General defuzzification technique is the centroid that gives the value of the center area in a fuzzy set to obtain collated conclusion [22].

As illustrated in Figure 2 node in G1 starts to try to become CH at time 0, later the node from G2  starts with a setback, and then the node from G3 starts with a setback after the nodes in G2 is completed. At the time of execution, a node sends station information to BS:

Si → BS: Loc(Si ) …………                                                                          (2)

The BS transmits information on delay to the remaining nodes:

Bs→Si: Delay (Si )……….                                                                            (3)

Set Num(Give up) to 0. Start with nodes in G1. If a CH is produced from G1, forward a Hello package and Num(Give up).

Hj →broadcast: Hello, Num(Give up)…..                                               (4)

Fig 3: selecting CH’s

Well-organized CH selection has an influence on cluster organization. Using FRD to choose CH is different from current methods like CBRP, WACA, SCAM, and secured clustering algorithm (SCA). Selecting CH is difficult and imprecise in CBRP, with respect to Lower ID, MOBIC as per mobility, and SCA based on the value of trust. Existing methods select CH based on one of the following variables: ID, mobility, as well as trust value. The suggested technique applies variables together to choose CH. CH is elected by fuzzy relevance degree (FRD) and decided by power availability, the strength of signal and distance between the nodes.

FRD of a node represented a degree of consistency that a network neighbor node provides. Fuzzy relevance-based CH selection algorithm (FRCA) system selects CH with respect to fuzzy relevance, power availability, mobility, and internodes distance [23].Available node power, internode distance, and node mobility are responsible for the maintenance of energy consumption balance of a node. Inter-node distance and mobility are responsible for the equilibrium of cluster. FRCA executes clustering as per parameters detailed above and selects a CH for effective clustering.

After the employment of sensor node, it obtains information on the location of the node (via GPS technology or known before employment) and informs it to a BS. The BS determines Delay (Si) distribution of the sensor node. Delay (Si) = 0 for those in regions to begin first (Abad and Jamali, 2011).

3.3 Low Energy Adaptive Clustering Hierarchy (Leach)

LEACH is a cluster-based algorithm uses distributed clustering formation algorithm which is a cluster routing based data aggregation algorithm [14]. This algorithm is represented in rounds with two phases: setup phase and steady state phase. In setup phase, 𝑝 % of 𝑛 sensors are randomly chosen as CHs based on a threshold [28].

Here𝑝 is a required number of CHs, 𝑡 current round, and 𝐺 a set of nodes that are not CHs in the last 1/𝑝rounds ensuring that a sensor chosen to be CH is not selected in next rounds till all network sensors become CHs [12]. This leads to fair energy consumption and increases network life. LEACH functioning is represented in rounds, which starts with a set-up phase when clusters are organized, then steady-state phase [21].

Set-up phase: Nodes in LEACH takes independent decisions to define clusters using a distributed algorithm without centralized control.

Steady-State Phase: Steady-state operation is divided into frames with nodes sending data to CH at one per frame during given transmission slot. Set-up phase will not assure nodes being evenly distributed among CH nodes. So, nodes per cluster vary in LEACH, and data a node can send to a CH depends on the number of nodes in a cluster [15].

3.4 ABC Algorithm

The ABC protocol was invented by Karaboga in 2005 for optimization of numerical problems that comprises three groupings of bees: employed bee, onlooker bee, finally, scout bee. The bee that carries out a search in random is called a scout bee. The bee moving to food source visited by it previously and sharing the information with other types of bees is named as an employed bee and the bee that waits in the dancing area is called onlooker bee. The onlooker bee, as well as scout bee, is also known as unemployed bees. The quality of nectar of food sources represents the fitness cost of the related solutions.

3.5 Fuzzy ABC-Based CH Selection

Inthe fuzzy method, FRD to select CH varies from existing method like CBRP, WACA, SCAM, as well as SCA. Selecting CH is difficult and imprecise in CBRP, with respect to Lower ID, MOBIC as per mobility, and SCA based on the value of trust. Existing methods select CH based on any one of the variables: ID, mobility, as well as trust value. The suggested technique applies variables together to select CH. CH is elected by FRD and decided by power availability, the strength of signal and distance among the nodes. FRD of a node represented a degree of consistency that a network neighbor node provides. FRCA system selects CH with respect to fuzzy relevance, power availability, mobility, and internodes distance [19].

Available nodes power, internode distance, and node mobility are responsible for the maintenance of energy consumption balance of a node. Inter-node distance and mobility are responsible for the equilibrium of cluster. FRCA executes clustering as per parameters detailed above and selects a CH for effective clustering. ABC is used for optimizing the rule selection. ABC refers to a population-based technique that imitates the behavior of honey bees [5].

Bees in a hive are classified as employed bees, onlookers, as well as scout bees.

1 Employed bee: Employed bee hunts for nectar and gets linked with the source. The collected information is shared with the onlooker bee by waggle dance.

2 Onlooker bee: Onlooker bee waits for employed bee in the dancing area of the hive to get the detail of food source and decides on selecting the food source.

3 Scout bee: Scout bee carries out the search randomly near the quality food source. In short, they conduct local searches.

The Fuzzy-ABC algorithm flow chart

 

4. Results And Discussion

Parameters considered for improvement in each routing algorithm implementation are Number of clusters formed, an Average end to end delay, Packet loss rate and Number of nodes alive. The software tool used to implement different algorithms was using MAT Lab and OPNET simulator.

(i) Number of clusters formed

Table 1: Number of clusters formed

From the above table 1 it is clear that Number of clusters formed increases by 9% due to using fuzzy based ABC algorithm in multihop wireless sensor networks.

(ii) Average end to end delay

Table 2: Average end to end delay

From the above table 2 it is clear that end delay decrease by 5% due to the implementation of fuzzy based ABC algorithm in multi-hop wireless sensor networks.

(iii) Packet loss rate

Table 3: Packet loss rate

From the table 3 it is clear that packet loss rate decreases by 5% due to implementation of fuzzy based ABC algorithm in multi hop wireless sensor networks.

(iv) Number of nodes alive

Table 4: Number of nodes alive


From the table 4 it is clear that Number of nodes alive increases by good number after completion of so many numbers of rounds of iteration due to the implementation of fuzzy based ABC algorithm in multi hop wireless sensor networks.

5.Conclusion

Wireless network is considered as most common service used in industrial and commercial application due to its technological enhancement in the process, interaction and utilisation of low power embedded computational devices. Energy consumption and lifespan are the most vital concerns in heterogeneous WSN as it increases the energy consumption equilibrium and hence increase in the lifespan of network. In the current study, a novel hybrid fuzzy ABC is suggested that is built through the integration of fuzzy logic with ABC to optimise the CH selection [2]. ABC is utilised to optimise the rule selection. The ABC optimisation with fuzzy rule selection approach is given for improving the network lifetime. The experimental results compared with other routing algorithms proved that the suggested method enhances the network’s lifespan as well as reduces the delay from end to end delays and rate of packet loss in comparison with other selection methods.

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