# Static Clustering Centralized Multi-hop Routing Protocol Based on Fuzzy Logic with Fish Swarm Intelligence

R. Kamatchi Iyer, Shubhada Deshpande, Deepali R. Vora, Nilima D. Zade

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R. Kamatchi Iyer, Shubhada Deshpande, Deepali R. Vora, Nilima D. Zade. Static Clustering Centralized Multi-hop Routing Protocol Based on Fuzzy Logic with Fish Swarm Intelligence. 6th International Conference on Computational Intelligence in Data Science (ICCIDS), Feb 2023, Chennai, India. pp.190-202, 10.1007/978-3-031-38296-3_15. hal-05466050

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# Static Clustering Centralized Multi-hop Routing Protocol Based on Fuzzy Logic with Fish Swarm Intelligence

Nilima D. Zade<fnref n="1" />✉, R. Kamatchi Iyer<fnref n="2" />✉, Shubhada Deshpande<fnref n="3" />✉, and Deepali R. Vora<fnref n="1" />✉

nilima.zade@gmail.com

**Abstract.** Wireless Sensor Networks are inherently subjected to restricted capabilities of processing power, battery power along with weak radio. There is always the requirement for routing protocol focusing on energy efficiency to optimize network total power consumption in the communication layer. The present research investigates distributed and centralized hierarchical routing protocols based on Fuzzy Logic with single-hop communication. A centralized hierarchical routing protocol with multiple hops for large Wireless Sensor Networks combining Fuzzy Logic and Artificial Fish Swarm algorithm is proposed and compared its performance with the distributed approach. The result obtained, demonstrates a better tradeoff between network energy balance in terms of Network Remaining Energy and Percent Node Die compared to the result obtained when the individual approach of Fuzzy Logic and Artificial Fish Swarm is implemented for the same network. The current research indicates a significant improvement in average energy consumption per round when compared with existing work.

**Keywords:** Centralized Routing Protocol · Energy Efficient Routing Protocol · Network Lifetime Optimization · Wireless Sensor Networks

## 1 Introduction

Routing Protocol's design and implementation is one of the crucial tasks in wirelessly connected Sensor Networks. Routing protocol should be designed for efficient network communication while dealing with inherent constraints of Wireless Sensor Networks (WSN) Zade N. et al. [1]. Routing protocols are categorized based on strategies that are proactive, reactive, and hybrid. Based on structure of network, routing protocols are classified as hierarchical structure, flat architecture, and location based. Hierarchical routing protocol provides many performance improvements such as energy optimization,

<footnote n="1">¹ Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune, Maharashtra, India</footnote>

<footnote n="2">² School of Management and Entrepreneurship, Atlas SkillTech University, Mumbai, Maharashtra, India</footnote>

<footnote n="3">³ University of Mumbai, Mumbai, Maharashtra, India</footnote>
traffic reduction, scalability, fault tolerance Rehan W. et al. [2]. Network is organized in clusters and the leader is selected with high energy. Cluster Leader (CL) to Base Station (BS) communication is single hop and cluster member to BS via cluster leader is done. Communication phase is steady state phase which starts after cluster formation and maintenance phase. Maintaining the length of the steady state phase is critical. If this phase is short then it may increase the protocol overhead. If it is long, then the node may deplete quickly. Many variants of basic hierarchical protocols are suggested in literature to overcome this problem.

The CL selection process either controlled centrally at BS or distributed where decision is taken locally in the cluster. Based on method of CL selection, these protocols are categorized as stochastic and attribute based. In stochastic protocol, CL selection is based on value obtained by predetermined mathematical function which is compared with preselected random value. In attribute-based protocol, computational intelligence is used such as fuzzy logic, swarm intelligence, neural network. Fuzzy control has been very effectively implemented to handle the problems of complex systems in many streams. The important parameters in routing protocol based on fuzzy system are the fuzzy logic rules, fuzzy membership functions, scaling factors, and quantitative factors. The definitions of fuzzy membership function and fuzzy rules are mostly afflicted by subjective decision. For optimal system performance, with an increasing number of variables the number of fuzzy rules grows exponentially and creates difficulty for obtaining appropriate solution in the domain. Half Node Die (HND) and First Node Die (FND) situation of network indicates balance in, how the protocol deals with the problem associated to increasing number of dead nodes and network energy consumption respectively. Fuzzy logic-based routing protocol results in balance of network total energy consumption till the FND but energy consumption decays exponentially from FND to HND. In order to address the above-mentioned problems, the fuzzy logic system can be defined as a space search problem where every point in searching space indicates the optimizing variable's the position in a group.

The evolutionary optimization algorithms such as Swarm Intelligence are good candidates for searching space problems. Artificial Fish Swarm Optimization (AFSO) based routing protocol results in linear energy consumption curve from FND and HND but very poor balance of network energy consumption in terms of Percent Node Die (PND). Evolutionary optimization techniques are subject to limitation of lower optimizing precision and speed of convergence. However, these limitations can be dealt by combing the system with fuzzy control. To combine advantages of fuzzy control with general Artificial Fish Swarm (AFS) algorithm and to overcome their limitations, fuzzy logic based AFS routing protocol is the main contribution of the research presented here.

The rest of the article is presented in the following manner. Required background work is described in Sect. 2. Mathematical model of and framework of the proposed system is mentioned in Sect. 3. Simulation related details are discussed in Sect. 3.4. Results and its analysis are discussed in Sect. 4. Conclusion and potential future scope is explained in Sect. 5.
## 2 Background

Lai et al. [3] proposed routing protocol to arrange clusters based on transmission range to deal hotspot problem and to improve network lifetime. In multi-hop routing, redundant data forwarding may occur as two or more Cluster Head (CH) may receive the same data. Sert et al. [4] has presented fuzzy clustering algorithm for non-uniformly and uniformly distributed networks. Multi-hop and direct routing are considered by assuming adjustable transmission power capability. Distributed approach is used for CH selection. Node level processing and computation energy level is disregarded in distributed approach. Wang et al. [5] presented clustering protocol for small sparse networks based on fuzzy logic. Cluster size calculation is done at sink and CH selection algorithm works in distributed approach at node level. At node, calculation of total distance to their neighbours computed which increases overhead of node power consumption. BS broadcast message to all nodes at level one and then each node broadcast message to all other nodes. Communication distance is decided based on node density and distance to sink. CH to BS one hop communication is assumed. Wang et al. [6] proposed Artificial Bee Colony (Quantum) protocol based on LEACH protocol. Centralized approach is implemented for the CH selection process. Control message communication overhead is disregarded in the study. Cao et al. [7], proposed centralized routing protocol for WSN. Each node discovers its single hop neighbours through message exchanged periodically and sink obtains the global information by sensor nodes. Algorithm has been evaluated by hardware implementation using Raspberry Pi for large and dense network. System may face the problem of traffic due to control message overload.

Balakrishnan et al. [8] proposed a Fuzzy Logic Based Energy Efficient Clustering Hierarchy (FLECH) protocol for non-uniformly wirelessly connected sensor nodes. Stochastic based distributed approach is used for CH selection after calculating fuzzy output value. Each node calculates their neighbours' distance will increase the overhead. Cuevas-Martinez et al. [9] has proposed type-II based fuzzy algorithm. CH selection is done with a distributed approach and decision of CH selection is taken stochastically with fuzzy approach. Rehman et al. [10] has proposed multi-layer clustered designing hot spot algorithm to improve network lifetime and to reduce hotspot problem. Near BS, nodes are not grouped together into clusters and kept in a flat layer. The algorithm may cause the increased frequency of cluster head rotation due to which it may deplete fast. Cuevas-Martinez et al. [11] has proposed centralized clustering algorithm based on type-II fuzzy system. CH selection process is skipped for predefined rounds to avoid the loss in unnecessary computation as the same CH may get selected in the same consecutive rounds. Hamzah et al. [12] has proposed fuzzy logic-based energy efficient clustering protocol for randomly deployed sparse network. Centralized approach is used and CH selected with highest fuzzy chance output. Energy consumption in a control message is disregarded. Yuste-Delgado et al. [13] has proposed Enhanced Unequal Distributed Type-2 Fuzzy Clustering algorithms for small sparse networks. Process completes in two parts of a round. Part A for selection of pre-candidate CH and then data transferring and in part B same alike CH are self-selected and then again data transferring. Helmy et al. [14] has proposed Artificial Fish Swarm Algorithm (AFSA) for CH selection in hierarchical routing protocol. All behaviours of artificial fish are implemented to find set of optimal CH and CH selected with best fitness among the list of optimal
CHs at BS. AFSA outperformed compared to PSO. System is implemented for small and sparse network. Communication overhead of control messages is disregarded in the study [15] proposed Cluster Head Election Mechanism (CHEF) based on fuzzy logic. Cluster member participate in CH election process if randomly generated value is greater than predetermined value and broadcast the advertising messages. Energy consumption in control messages is high in this distributed approach.

Most of the hierarchical routing protocols presented by researchers in the field of WSN have considered either one hop communication between CL to BS with the assumption of adjustable communication distance by CL or multi hop communication only through selected CL. But this increases redundant data packet transmission if multiple leaders receive same packet. Ultimately this may lead faster energy depletion of CL. In distributed control, every cluster member depends on its internal process with just local information. Also, in attribute-based functioning, node may deplete quickly if attribute to be considered are more. Algorithm implemented for small and sparse network does not give idea on how it will work for large and dense network. All these problems lead to limitation of getting optimal solution. To address above problems, centralized static clustering routing protocol based on fuzzy logic and artificial fish swarm algorithm is proposed for large and dense network.

# 3 Proposed System

In self-organized wireless sensor networks, the cluster member node in centralized routing protocol discovers its single hop neighbours through message exchange done periodically in the network. BS periodically broadcasts the rout advertisement message. All the nodes within its transmission range update their routing table based on the message received. Node which is selected as CL and router or forwarder node both propagate the routing information by broadcasting the new route advertisement message. All nodes within the CL's transmission range receive process and update the routing table. This process continues for all layers of network from BS to perimeter nodes.

## 3.1 Mathematical Modal

Network lifetime is majorly influenced by the CL selection technique. The factors affecting this technique are (i) Node's Residual Energy (RNE), (ii) its Distance from BS (DBSN), (iii) number of times the node is assigned as CL called as history (HN) and calculated as shown in (1), and (iv) Node's Compactness (CN).

$$H_{Ni} = \sum_{i=1}^{C_{RN}} \frac{T_{C_{MAlive}}}{C_{RN} \times T_{CM}} \quad (1)$$

$C_{RN} \rightarrow$ Current round number, $T_{C_{MAlive}} \rightarrow$ Total number of member nodes alive in the cluster except node $N_i$, $T_{CM} = $ Total number of member nodes at the time of formation of cluster.

These linguistic variables are considered as fuzzy input variable in the fuzzy controller to get fuzzy output variable in terms of chance to become CL (FCL). Fuzzy input
variables are represented into overlapping fuzzy membership functions. Overlaps of tri- angular membership functions for each input fuzzy variable are taken based on intuition method for fuzziness over tens of experiment [16]. Figures 1, 2, and 3 show membership functions of the input descriptors. Figure 4 shows fuzzy membership function of output descriptors. Mamdani inference model (MIM) is based on min-max composition are formulated in fuzzy controller. Min-max composition facilitates in capturing the effect of each variable on network lifetime to a different extent. IF-THEN rule equation base is formed based on MIM to map input parameters to appropriate fuzzy output parameters. De-fuzzification is done using average method. BS selects primary and auxiliary CL for each cluster based on higher and second highest value of fuzzy output chance.

<figure><img src="image_4.png" /><figcaption>Fig. 1. Fuzzy membership functions for node's R<sub>NE</sub>.</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>Fig. 2. Fuzzy membership functions for node's H<sub>N</sub>.</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>Fig. 3. Fuzzy membership functions for node's distance from base station.</figcaption></figure>

Once the list of all CLs for all clusters are generated using above technique, probable Forwarder Node (FN) list N<sub>CLi</sub> is generated for each CL<sub>i</sub> within its Visual Distance (VD) based on (2) which represents pray behaviour Artificial Fish (AF).

$$L_{CLj} = L_{CLi} + \delta \times \text{visual} \quad (2)$$

where $\delta \to \text{crowdfactor}(0 < \delta < 1)$, visual $\to$ communication distance.
<figure><img src="image_7.png" /><figcaption>Fig. 4. Fuzzy membership functions for node's fuzzy output chance.</figcaption></figure>

$L_{CLi}$ indicates the location of CL that is current state of AF at level n where i varies from 1 to total number of CL at the level n. $L_{CLi}$ is the location of CL that is future state of AF at level n + 1 (near to BS) which is in VD of $CL_i$, and j varies from 1 to total number of CL in the VD of $CL_i$ as per (2).

When list $|N_{CLi}| \neq 0$; fitness value is computed for each probable FN. A FN is selected based on minimum fitness value as per (3) which represents swarm behavior of AF.

$$f_j = \alpha f_1(R_{NEj}) + (1 - \alpha) f_2(d_{CLj} \text{ to } CL_i) \quad (3)$$

where $\alpha \to$ scaling factor ($0 < \alpha < 1$), $f_1(R_{NEj}) \to$ function of average energy of $N_{CLi}$, which is calculated as ratio of residual node energy of node under consideration to addition of residual node energy of all $CL_i$ in the list and mathematically mentioned as

$$f_1(R_{NEj}) = \frac{R_{NEj}}{\sum_{j=1}^{N_{CLj}} R_{NE_{CLj}}}$$

$f_2(d_{CLj} \text{ to } CL_i) \to$ function of average distance which is calculated as ratio of distance between $CL_j$ to $CL_i$ under consideration to the addition of distance between $CL_i$ to all $CL_j$ in the list and mathematically mentioned as

$$f_2(d_{CLj} \text{ to } CL_i) = \frac{D_{CLj} \text{ to } CL_i}{\sum_{j=1}^{N_{CLj}} D_{CLj} \text{ to } CL_i}$$

If $CL_i$ is in VD of BS then it directly communicates to BS as flat architecture as per (4) which represents follow behavior of AF.

$$f_f = \frac{D_{BS} \text{ to } CL_i}{D_{\max}} \quad (4)$$

where $D_{\max}$ is the maximum distance between BS to the farthest node in the network.
## 3.2 Framework of the System

Routing protocol works in two phases. Phase one is setup phase, which deals with the formation of static clusters and in second phase, CL and FN are selected dynamically at BS as shown in Fig. 5. The monitoring area is either in square or rectangle shape and transmission is omni directional. Generally dynamic or static clusters are formed in circular area with transmission range as the radius. If circular shape clusters are considered, then to cover rectangular or square shaped monitoring area, either there will be overlap or a hole in the coverage. To minimize above problem a method is used in which, polygons inscribed in the cluster circle are considered. For each of this extended polygon, a CL is dynamically selected.

<figure><img src="image_8.png" /><figcaption>Fig. 5. Block diagram of proposed system.</figcaption></figure>

## 3.3 Cluster Formation Phase

Clustered network is formed as per the procedure given below.

**Input:** At BS, information of complete network deployed in the monitoring area is available those are each node's Id, location and distance from BS.

**Output:** A static Clustered network.

**Steps:**

Step 1: If dc is communication range of wireless sensor node, find n hop levels of network topology,

$$n = \frac{\text{Max}(d_{\text{SN to BS}})}{d_c} \qquad (5)$$

where $d_{\text{SN to BS}}$ is the distance between farthest sensor nodes to BS in surveillance area.

If n is found to be a fractional number, then select n as approximation to higher integer always. Also, (5) indicates that BS located at center results in minimum number of hops and levels.

Step 2: Determination of size of clusters at each level.

Let $r_c$ is the radius of the circular cluster inscribed in square area and is computed as $r_c = \beta d_c$, where $\beta$ is constant to define size of cluster and varies between $0 < \beta < 1$. The $\beta$ provides provisions to make cluster size adjustable according to how many member nodes can be handled by a CL.

$$r_c = 0.5\sqrt{x^2 + y^2} \qquad (6)$$

where x and y are length and width of the inscribed polygon. By using (6), size of the cluster is computed from level 1 cluster to level n cluster.
## 3.4 Cluster Head and Forwarder Node Selection Algorithm

Algorithm is simulated using MATLAB 2014R and is based on the assumptions mentioned as: Static homogenous deterministic deployment is considered. BS has complete knowledge of network. At the time of deployment, all nodes have same initial energy and energy depletion below threshold level is the only reason for node death.

<table><tr><td>In-put:</td><td>Static clustered WSN.<br/>N→ total no. of nodes in each cluster as per size, L→ levels of clusters from perimeter to BS, C<sub>N</sub> → total no. of clusters, C<sub>l</sub> → number of clusters in the level l, l→ 1 to L. S<sub>Ni</sub> → Sensor node Id. N<sub>i</sub> → 1 to N</td></tr><tr><td>1</td><td>for each level l<br/>for each cluster C<sub>l</sub><br/>for each S<sub>Ni</sub> in C<sub>l</sub><br/>Compute F<sub>CL</sub>= fuzzy chance<br/>Ni<sub>DBSN</sub><br/>end<br/>end<br/>end<br/>Get A<sub>CL</sub> for all clusters in network and go to step 2</td></tr><tr><td>2</td><td>for level l=1 of A<sub>CL</sub><br/>for each C<sub>l</sub> where i → 1 to C<sub>l</sub><br/>select set of probable FNs within it range from level (i+1) using (2)<br/>end; Get the list |N<sub>CLi</sub>|</td></tr><tr><td>3</td><td>If |N<sub>CLi</sub>| ≠ 0<br/>set α=0.8, and Compute f<sub>i</sub> using (3)<br/>Select f<sub>imin</sub> from set of f<sub>i</sub> with minimum fitness value.<br/>Compute f<sub>f</sub> using (4)<br/>If f<sub>imin</sub> < f<sub>f</sub><br/>Select CL<sub>j</sub> as FN for CL<sub>i</sub><br/>else, CL<sub>j</sub> will directly communicate to BS<br/>end.<br/>else increase crowd factor, δ<sub>max</sub> = 1<br/>end; i = i+1, repeat till i <= C<sub>l</sub><br/>end; l = l+1; repeat till l <= L<br/>end; Exit</td></tr><tr><td>4</td><td>Get the list of all selected CH and FN for all clusters.<br/>A. Data Transmission phase<br/>Out PND, NRE, Number of rounds,<br/>put</td></tr></table>

BS selects and informs the CL and FN based on proposed algorithm. If ACK message is not received within time then BS sends message to auxiliary CL. After receiving CL selected message, each CL broadcast itself as CL along with its FN list. Perimeter nodes are not considered for CH role.
The simulation parameter as given in Table 1 and energy model considered for the study are commonly used in the references. In order to check suitability of proposed system for large and dense WSN, 200 × 200 m² monitoring area with uniform deterministic deployment of the nodes is considered as per our previous work mentioned in [17–19]. Data message packets of a single frame of 4000 bits are sent by each node in one time step. CL and FN are selected after each data transfer phase. Simulation runs till the networks remaining energy reaches to 10% of total initial energy. Energy consumption is computed based on first order energy model [8]. Algorithm executed at BS, selects CL, auxiliary CL and its FN. BS sends messages of CL selection to CL and FN. Once all CL and its FN are informed and acknowledgement message is received, data collection phase is started. Considering the use of MAC 802.11.15 protocol which supports, CSMACA and use of randomization while sending uncast, broadcast retries, jitter of just few milliseconds can be sufficient for large dense network to perform better. Cluster member sends data to CL and goes in sleep mode to conserve energy. CL aggregates data and send to BS in a frame at a time.

<table><caption>**Table 1.** Parameters considered for simulation.</caption><thead><tr><td>Parameters</td><td>Values</td></tr></thead><tbody><tr><td>Area of Monitoring</td><td>200 × 200 m²</td></tr><tr><td>No of nodes</td><td>440</td></tr><tr><td>Deployment model</td><td>Static, uniform</td></tr><tr><td>Size of the data packet</td><td>control Packet 200 of bits, 4000 bits</td></tr><tr><td>Initial energy</td><td>2 (J)</td></tr><tr><td>E<sub>elec</sub></td><td>50 (nJ/bit)</td></tr><tr><td>E<sub>fs</sub></td><td>10 (nJ/bit/m² bytes)</td></tr><tr><td>E<sub>mp</sub></td><td>0.0013 (nJ/bit/m⁴)</td></tr><tr><td>E<sub>da</sub></td><td>5 (nJ/bit)</td></tr></tbody></table>

## 4 Result Analysis

The obtained results are evaluated based on the network life time which is indicated by relationship between PND and NRE in Joules with the increasing number of CL selection rounds. The performance of network is very much degraded after death of half the nodes of network; therefore, algorithms are generally evaluated till network reaches to HND. Distributed algorithms FLECH [8], and CHEF [15] are selected and adopted to implement with same simulation parameters as reference for performance. Centralized fuzzy logic-based algorithm (CFLA) is implemented with same simulation parameter and compared with distributed algorithm. After analysis, AFSA [14] was experimented with centralized approach and represented as Centralized AFS (CAFS). Each algorithm is implemented with same simulation parameter to facilitate fair comparison. Also, the
results achieved are compared with the published results by researchers to validate the proposed work. The shortcomings of CFAS and CFLA were significant. To overcome shortcomings of both, CFAS and CFLA together is implemented in the proposed work. Figures 6 and 7 show how the algorithm has performed for PND and NRE to number of rounds respectively. Figures 6 and 7 indicates that till 800 rounds, network is 100% efficient with 400 J NRE in proposed work (FLAFA) whereas it is 75% efficient with 500 J NRE in CFAS. Almost 5% node died till 1000 round in FLAFA whereas in CFAS it increases up to 40%. After 1150 network is not useful because more than 50% nodes are died in CFAS and in FLAFA till 1500 rounds network is useful. CFLA outperforms than distributed algorithm that is CHEF and FLECH. Though it performs equally for PND compared to FLAFA, it gives lower NRE curve as shown in Fig. 7 which is not as competitive with FLAFA. Thus, FLAFA demonstrates better trade-off between CFLA and CAFS. Table 2 indicates the comparison of FLAFA with existing methods in terms of HND, FND, NRE, and average energy consumption per round. From Figs. 6 and 7 it is clearly indicated that proposed work balances the network energy consumption and deals fairly good as PND increases.

<figure><img src="image_9.png" /><figcaption>Fig. 6. Life Time of Network in terms of PND to Round Numbers.</figcaption></figure>
<figure><img src="image_10.png" /><figcaption>Fig. 7. Life Time of Network in terms of NRE to Round Numbers.</figcaption></figure>

<table><caption>Table 2. Comparison of results.</caption><thead><tr><th>Algorithm</th><th>Round at FND</th><th>NRE at FND</th><th>Round at HND</th><th>NRE at HND</th><th>Average Energy consumption per round</th></tr></thead><tbody><tr><td>CHEF [15]</td><td>486</td><td>443 J</td><td>1000</td><td>111 J</td><td>0.4946 J</td></tr><tr><td>FLECH [8]</td><td>654</td><td>420 J</td><td>1307</td><td>88 J</td><td>0.4846 J</td></tr><tr><td>CFLA</td><td>786</td><td>405 J</td><td>1483</td><td>88 J</td><td>0.47 J</td></tr><tr><td>CAFS [14]</td><td>191</td><td>767 J</td><td>1489</td><td>149 J</td><td>0.42 J</td></tr><tr><td>Proposed Work (FLAFA)</td><td>842</td><td>450 J</td><td>1527</td><td>120 J</td><td>0.4588 J</td></tr></tbody></table>

## 5 Conclusion

The WSN are used to provide solutions to numerous applications in various fields. To extend network lifetime and stability energy efficient hierarchical routing protocols are predominant. Distributed hierarchical routing protocols based on fuzzy logic with single hop communication have been investigated. Fuzzy logic and artificial fish swarm based Centralized hierarchical routing protocol is implemented and compared its performance with distributed approach. Multi-hop communication with only selected CL poses the limitation of redundant packet forwarding by multiple CL. So, to overcome shortcomings of single hop centralized routing protocol, the proposed work is presented by combining CH selection based on fuzzy logic and routing node selection based on AFSA controlled by BS. Average energy consumption per round is improved by almost 6% when compared with published work (CHEF, FLECH, CFLA). The results indicate that FLAFA demonstrates better tradeoff between network energy consumption and
PND. This research will be helpful to researchers to select appropriate routing algorithm as the domain of application. If overall longevity of the network is more important than the initial PND i.e., linear curve of network energy then one can choose to use AFSA. If requirement of active node is more important up to 10 to 15% of total node die then one can choose to use CFLA. If tradeoff is required among these two, then one can go for selecting FLAFA. Uniform deterministic deployment of nodes is the limitation of proposed work. In future the performance of FLAFA can be evaluated for randomly deployed, non-uniformly (irregularly) distributed wireless sensor networks.

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