Towards Network Lifetime Enhancement of Resource Constrained IoT Devices in Heterogeneous Wireless Sensor Networks
Abstract
:1. Introduction
 We propose an efficient CH declaration scheme to reduce the energy consumption of nodes and to prolong the network lifetime. The propose scheme provides a mechanism through which a node declares itself as a CH based on the available resources such as residual energy, computational capability, and available storage. Once the CH is declared, it remains CH until the resources fall short than a certain threshold level.
 For the unassociated nodes, we employ the multicriteria decisionmaking technique known as Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to select an optimal CH.
 We also provide mechanisms of CHacquaintanceship and CHfriendship to reduce the energy consumption, optimize the workload, minimize the packet drop rate, and extend the lifetime of CH. In acquaintanceship mechanism, the CHs in the network may collaborate with each other for mutual benefits. Whereas, in CHfriendship, the low resources CH may request high resources CH to perform operation on behalf of low resources CH to avoid early failure and data loss.
 Simulations are performed in Castalia (OMNET++) to reveal the performance of the proposed scheme with relevant and state of art scheme in terms of CH lifetime, reclustering frequency, packets loss, control overhead and average energy consumption of the network.
2. Background and Related Work
2.1. Clustering Overview
 The whole network is partitioned into clusters.
 After cluster formation process, the selected CHs gather and aggregate the data received from member nodes and transmit it towards the sink node. Usually, the CHs consume more energy as compared to the other nodes and ran out of power due to high load. Therefore, to balance the energy consumption, the role of CH is switched among different sensor nodes, meaning that a CH may not be CH for longer time in the network and other high resources sensor nodes can take over the role of CH. However, in order to elect an optimal CH, the following strategies may be adopted and are discussed as follows:
 Deterministic CH election: In deterministic schemes, CHs are supernodes having high resources such as energy, storage, and computational capability etc.
 Random CH election: In these schemes a CH is elected based on randomly generated value.
 Adaptive CH election: Instead of electing CH randomly, adaptive CH election schemes provide a mechanism to elect CH based on several parameters such as residual energy, computational capability, storage, distance, etc. The combination of the multiple parameters is utilized to elect an optimal CH among several potential candidates.
 After CHs election, each CH broadcasts its information of becoming a CH to other nodes in its communication range. The receiving nodes may receive information from several CHs in its vicinity and decide which CH to join based on several metrics such as distance from CH, computational capability, residual energy and storage capability of CH, etc. After joining a particular CH, the node forwards its sensed data towards that CH.
 When resources of a current CH falls below a certain threshold, reclustering is performed to avoid the data loss. However, frequent reclustering degrades the performance of network due to control overhead.
2.2. Related Work
3. Problem Scenario
4. Proposed Scheme
4.1. Cluster Head (CH) Declaration Phase
Algorithm 1 CH Declaration Algorithm 

4.2. New NodeAssociation
 Decision Matrix DevelopmentConsider a child node having “m” CHs in its range. The child node organizes the CHs attributes in a decision matrix (X) as defined in Equation (4).$$X=\phantom{\rule{0.166667em}{0ex}}\phantom{\rule{0.166667em}{0ex}}\left(\right)open="("\; close=")">\begin{array}{ccccc}{X}_{(1,1)}& {X}_{(1,2)}& {X}_{(1,3)}& .& {X}_{(1,n)}\\ {X}_{(2,1)}& {X}_{(2,2)}& {X}_{(2,3)}& .& {X}_{(2,n)}\\ .& .& .& .& .\\ .& .& .& .& .\\ .& .& .& .& .\\ {X}_{(m,1)}& {X}_{(m,2)}& {X}_{(m,3)}& .& {X}_{(m,n)}\end{array}$$
 Resource Criteria NormalizationThe values of all these criteria do not lie in the same range (e.g., the value of residual energy is not equal to available storage), therefore, the resource criteria must have to be normalized to a common range in order to fairly select the CH. The normalized form of the decision matrix is obtained by employing Equation (5) and is defined as follows:$${{X}^{*}}_{(i,j)}=\frac{{X}_{(i,j)}}{\sqrt{{\sum}_{j=1}^{n}X{}_{(i,j)}^{2}}}where\phantom{\rule{0.166667em}{0ex}}\phantom{\rule{0.166667em}{0ex}}i=1\dots m,j=1\dots .n$$$${X}^{*}=\left(\right)open="("\; close=")">\begin{array}{cccccc}{{X}^{*}}_{(1,1)}& {{X}^{*}}_{(1,2)}& {{X}^{*}}_{(1,3)}& .& .& {{X}^{*}}_{(1,n)}\\ {{X}^{*}}_{(2,1)}& {{X}^{*}}_{(2,2)}& {{X}^{*}}_{(2,3)}& .& .& {{X}^{*}}_{(2,n)}\\ .& .& .& .& .& .\\ .& .& .& .& .& .\\ .& .& .& .& .& .\\ {{X}^{*}}_{(m,1)}& {{a}^{*}}_{(m,2)}& {{X}^{*}}_{(m,3)}& .& .& {{X}^{*}}_{(m,n)}\end{array}$$The normalized decision matrix, ${X}^{*}$ is obtained using Equation (6).
 Weights AssignmentAfter normalization, weights are assigned to each criterion as shown in Equation (7). The ${w}_{j}^{*}$ in Equation (7) is a weight value allocated to each resource criterion, j. The assignments of weights are applicationspecific (e.g., weights may vary from application to application).$${X}^{\prime}=\phantom{\rule{0.166667em}{0ex}}\phantom{\rule{0.166667em}{0ex}}\left(\right)open="("\; close=")">\begin{array}{cccccc}{w}_{1}^{*}\times {{X}^{*}}_{(1,1)}& {w}_{2}^{*}\times {{X}^{*}}_{(1,2)}& {w}_{3}^{*}\times {{X}^{*}}_{(1,3)}& .& .& {w}_{m}^{*}\times {{X}^{*}}_{(1,n)}\\ {w}_{1}^{*}\times {{X}^{*}}_{(2,1)}& {w}_{2}^{*}\times {{X}^{*}}_{(2,2)}& {w}_{3}^{*}\times {{X}^{*}}_{(2,3)}& .& .& {w}_{m}^{*}\times {{X}^{*}}_{(2,n)}\\ .& .& .& .& .& .\\ .& .& .& .& .& .\\ .& .& .& .& .& .\\ {w}_{1}^{*}\times {{X}^{*}}_{(m,1)}& {w}_{2}^{*}\times {{X}^{*}}_{(m,2)}& {w}_{3}^{*}\times {{X}^{*}}_{(m,3)}& .& .& {w}_{m}^{*}\times {{X}^{*}}_{(m,n)}\end{array}$$
 Ideal Positive Solutions ($IP{S}^{+}$) and Ideal Negative Solutions ($IN{S}^{}$)Ideal Positive Solution ($IP{S}^{+}$): The resource criteria where high attribute values such as residual energy, computational capability, and storage capacity are desired are named as $IP{S}^{+}$. The residual energy of the node is very important, as the overall lifetime of the node depends on its residual energy. Therefore, the highest value of energy is taken as $IP{S}^{+}$. Similarly, the high storage capacity is also an important criterion, since providing the large storage to a CH prevents congestion and packet drop on a CH. Likewise, the high computational capability reduces the processing delays. All these aforementioned criteria optimize the packet drop rate and enhance the performance of the network.$IP{S}^{+}$ is computed using Equation (8) and is defined as follows:$$IP{S}^{{}^{+}}=max\{({X}_{1,j}^{{}^{\prime}}),\phantom{\rule{0.166667em}{0ex}}\phantom{\rule{0.166667em}{0ex}}({X}_{2,j}^{{}^{\prime}})\dots ({X}_{m,j}^{{}^{\prime}})\},\forall \phantom{\rule{0.166667em}{0ex}}j\in \phantom{\rule{0.166667em}{0ex}}n$$Ideal Negative Solution $IN{S}^{}$: The resource criteria where the low attribute values such as traffic load on CH, distance from CH to the sink node, and distance from a child node to the CH are desired considered as $IN{S}^{}$. For instance, if there is a high traffic load on CH, the CH cannot accommodate more data packets from its child nodes due to storage limitations. Similarly, If the distance between the sink node and CH is high, the CH consumes high amount of energy in data transmission towards the sink node. Likewise, if the distance between the child node and a CH is high, the child node has to consume high mount of energy to transmit data. The high values of the aforementioned resource criteria(s) are not beneficial; therefore, these criteria are considered as $IN{S}^{}$. $IN{S}^{}$ is computed using Equation (9) and is defined as follows.$$IN{S}^{}=min\{({X}_{1,j}^{{}^{\prime}}),\phantom{\rule{0.166667em}{0ex}}\phantom{\rule{0.166667em}{0ex}}({X}_{2,j}^{{}^{\prime}})\dots ({X}_{m,j}^{{}^{\prime}})\},\forall \phantom{\rule{0.166667em}{0ex}}j\in \phantom{\rule{0.166667em}{0ex}}n$$
 Difference of each CH from $IP{S}^{+}$ and $IN{S}^{}$For each criterion, the difference of each resource criteria from the $IP{S}^{+}$ and $IN{S}^{}$ are calculated using Equations (10) and (11), respectively and are defined as follows.$$\begin{array}{c}\begin{array}{c}\hfill {X}_{i}^{+}=\sqrt{{\displaystyle \sum _{j=1}^{n}}({X}_{(i,j)}^{{}^{\prime}}\phantom{\rule{0.166667em}{0ex}}\phantom{\rule{0.166667em}{0ex}}\phantom{\rule{0.166667em}{0ex}}IP{S}^{{}^{+}}{)}^{2}}\phantom{\rule{0.166667em}{0ex}}where\phantom{\rule{0.166667em}{0ex}}\phantom{\rule{0.166667em}{0ex}}i=\phantom{\rule{0.166667em}{0ex}}1\dots m\end{array}\hfill \end{array}$$$$\begin{array}{c}\hfill {X}_{i}^{}=\sqrt{{\displaystyle \sum _{j=1}^{n}}({X}_{(i,j)}^{{}^{\prime}}\phantom{\rule{0.166667em}{0ex}}\phantom{\rule{0.166667em}{0ex}}\phantom{\rule{0.166667em}{0ex}}IN{S}^{}{)}^{2}}\phantom{\rule{0.166667em}{0ex}}where\phantom{\rule{0.166667em}{0ex}}\phantom{\rule{0.166667em}{0ex}}i=\phantom{\rule{0.166667em}{0ex}}1\dots m\end{array}$$
 Ranking Index for final decisionEquations (10) and (11) compute the difference of each CH node from $IP{S}^{+}$ and $IN{S}^{}$ respectively. By doing so, the child node obtains a deviated value of all CHs from $IP{S}^{+}$ and $IN{S}^{}$. Since the CHs are heterogeneous in terms available resource, it is highly likely that the deviated value of each CH varies from each other. Based on these deviated values, the child node computes the rank index (${R}_{i}$) of each CH node by employing Equation (12). The ${R}_{i}$ computed from Equation (12) guarantees that the CH with the best available resources is assigned a highest rank as compare to the other potential CHs. Once the child node has the list of CHs with their ranked value, the child node selects the CH which have highest rank. ${R}_{i}$ of each CH node is computed using Equation (12) and is defined as follows.$${R}_{i}=\frac{{X}_{i}^{}}{{X}_{i}^{}\phantom{\rule{0.166667em}{0ex}}+\phantom{\rule{0.166667em}{0ex}}{X}_{i}^{+}}\phantom{\rule{0.166667em}{0ex}}\phantom{\rule{0.166667em}{0ex}}\phantom{\rule{0.166667em}{0ex}}where\phantom{\rule{0.166667em}{0ex}}\phantom{\rule{0.166667em}{0ex}}i=\phantom{\rule{0.166667em}{0ex}}1\dots m$$
Algorithm 2 New Node(s) Association Algorithm 

4.3. CHAcquaintanceship
4.4. CHFriendship
5. Performance Evaluations
5.1. Performance Evaluation Metrics
 CH Lifetime: The CH lifetime is defined as the amount of time a node can act as a CH. In other words, it is the time of CH until reclustering.
 Reclustering Frequency: Reclustering is the process of electing new CH to avoid CH communication failure. Reclustering mechanism is executed when the available resources of CH go down than a certain threshold (e.g., the energy of CH). Reclustering frequency is defined as the frequency of reelecting the CHs during the entire network lifetime.
 Number of Control Packets: The control packets considered as overheads and are defined as the packets used for route establishment from a source to a destination e.g., CH announcement, Join CH and TDMA etc.
 Packet Drop Ratio: Packet drop ratio is defined as the ratio of the number of packets lost (not received at receiving node e.g., CH or Sink node) to the total number of sent packets.
 Total Energy Consumption: The energy consumption of a node mainly depends on two main factors (e.g., packet processing and transmissions or receptions) [37]. The cumulative energy consumption of a node is presented in Equation (13) and is defined as follows.$${E}_{node}=E{}_{proc}+{E}_{rx}+{E}_{tx}$$The total energy consumption of the network is directly proportional to the number of packets transmitted in the network and is defined as follows.$${E}_{network}=\phantom{\rule{0.166667em}{0ex}}\sum _{i=1}^{n}({E}_{Pro{c}_{i}}+{E}_{r{x}_{i}}+{E}_{t{x}_{i}})$$
5.2. Simulation Environment
5.3. Results and Discussions
5.3.1. CH Lifetime
5.3.2. ReClustering Frequency
5.3.3. Number of Control Packets
5.3.4. Packet Drop Ratio
5.3.5. Total Energy Consumption
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Parameter  Value 

Simulator  Castalia v3.2 
Area  100 × 100 
Total number of sensor nodes  100 
Node distribution  Random 
Initial Energy of nodes  6 J–10 J 
MAC  Tunable Mac (TMac) 
Packet rate  5 pkts/s, 10 pkts/s, 200 pkts/s 
Packet Size  4000 bits 
Energy Consumption  0.5 $\mathsf{\mu}$J/bit 
Buffer size  Max ${2}^{24}$ bits 
Propagation Model  LogNormal Shadowing Model 
Simulation time  2000 s 
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din, M.S.u.; Rehman, M.A.U.; Ullah, R.; Park, C.W.; Kim, B.S. Towards Network Lifetime Enhancement of Resource Constrained IoT Devices in Heterogeneous Wireless Sensor Networks. Sensors 2020, 20, 4156. https://doi.org/10.3390/s20154156
din MSu, Rehman MAU, Ullah R, Park CW, Kim BS. Towards Network Lifetime Enhancement of Resource Constrained IoT Devices in Heterogeneous Wireless Sensor Networks. Sensors. 2020; 20(15):4156. https://doi.org/10.3390/s20154156
Chicago/Turabian Styledin, Muhammad Salah ud, Muhammad Atif Ur Rehman, Rehmat Ullah, ChanWon Park, and Byung Seo Kim. 2020. "Towards Network Lifetime Enhancement of Resource Constrained IoT Devices in Heterogeneous Wireless Sensor Networks" Sensors 20, no. 15: 4156. https://doi.org/10.3390/s20154156