An Efficient Centroid-based Routing Protocol for Energy ... - IEEE Xplore

This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2017.2749606, IEEE Access JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015

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An Efficient Centroid-based Routing Protocol for Energy Management in WSN-Assisted IoT Jian Shen, Member, IEEE, Anxi Wang, Chen Wang, Patrick C. K. Hung, Member, IEEE, and Chin-Feng Lai, Senior Member, IEEE

Abstract—Wireless sensor networks (WSNs) distribute hundreds to thousands of inexpensive micro-sensor nodes in their regions, and these nodes are important parts of Internet of Things (IoT). In WSN-assisted IoT, the nodes are resource constrained in many ways, such as storage resources, computing resources, energy resources and so on. Robust routing protocols are required to maintain a long network lifetime and achieve higher energy utilization. In this paper, we propose a new energyefficient centroid-based routing protocol (EECRP) for WSNassisted IoT to improve the performance of the network. The proposed EECRP includes three key parts: a new distributed cluster formation technique that enables the self-organization of local nodes, a new series of algorithms for adapting clusters and rotating the cluster head (CH) based on the centroid position to evenly distribute the energy load among all sensor nodes, and a new mechanism to reduce the energy consumption for long-distance communications. In particular, the residual energy of nodes is considered in EECRP for calculating the centroid0 s position. Our simulation results indicate that EECRP performs better than LEACH, LEACH-C and GEEC. In addition, EECRP is suitable for networks that require a long lifetime and whose base station (BS) is located in the network. Index Terms—Internet of Things, wireless sensor networks, energy management, cluster.

I. I NTRODUCTION

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NTERNET of Things (IoT) is the inter-networking of physical devices, vehicles, buildings, and other items that contain embedded electronics, software, sensors, actuators and so on. IoT enables these intelligent objects to collect and exchange data [1], [2], [3] for different purposes. For example, wireless sensor networks (WSNs) are a typical type of IoT network, in which the sensors can detect and monitor the network area. WSNs are developed based on microelectromechanical systems (MEMS), system on chip (SoC), wireless communications and low-power embedded technology. At present, WSNs are widely used in military applications [4], J. Shen is with Jiangsu Engineering Center of Network Monitoring, School of Computer and Software, Nanjing University of Information Science & Technology, Nanjing, China, 210044 and State Key Laboratory of Information Security (Institute of Information Engineering), Chinese Academy of Sciences, Beijing 100093. E-mail: s [email protected] A. Wang and C. Wang are with the school of Computer and Software, Nanjing University of Information Science & Technology, Nanjing, China, 210044. E-mail: Anxi [email protected], [email protected], [email protected] P. Hung is with the Faculty of Business and IT, University of Ontario Institute of Technology, 2000 Simcoe Street North, Oshawa, ON L1H 7K4, Canada. E-mail: [email protected] C. F. Lai (corresponding author) is with the Department of Engineering Science, National Cheng Kung University, 62102 Chia-Yi, Taiwan, ROC. Email: [email protected] Manuscript received April XX, XXXX; revised August XX, XXXX.

intelligent transportation [5], civilian domains [6] and other fields [7], [8], [9]. In addition, WSNs can be used for collecting data in IoT [10]. With the help of cloud computing, this network can provide great convenience for our daily lives [11], [12]. WSN-assisted IoT in a wired network has the advantages of low cost, convenient deployment and good scalability. However, the defects cannot be ignored. One of the main drawbacks is limited energy resources. In fact, sensor nodes are generally powered by batteries, and it is difficult to add energy to the sensor nodes due to the rugged environments where they operate, which makes energy management an important task for WSN-assisted IoT. Due to the temporary validity of sensors and the high price of replacement, replacing the node components is not worthwhile. Hence, prolonging the lifetime of the network and managing the energy resources throughout the entire network are the most important aspects when improving the performance of WSN routing protocols. The low-energy adaptive clustering hierarchy (LEACH) [13] protocol is one of the most common protocols in this field. A considerable number of new protocols have been designed to improve the performance of LEACH [14], [15], [16], [17], [18], [19], [20], [21] by reducing the energy of the cluster head (CH) nodes or finding a multihop path from CHs to the base station (BS) [22], [23], [24], [25], [26], [27], [28]. To simplify the management of the network, the concept of cluster is proposed, and the CH nodes are the managers of local clusters. A CH node takes the responsibilities of organizing the cluster, establishing the routing table, and collecting, compressing, and transmitting data. Because of its high frequency of utilization, the energy consumption of a CH node is the fastest among the entire network. Searching for a multi-hop path from the CHs to the BS is fundamental for reducing the energy consumption of CH nodes. The energy consumption of long-distance node communication is very large. If the network can find a node that does not considerably contribute to the network and that is located at the edge of the cluster to be the forwarder, then the energy dissipation rate of CH nodes will be significantly reduced. This approach will not reduce the life cycle of the entire network. As mentioned in [29], at the end of the entire sensor network life cycle, the energy consumption of sensor nodes at the edge of the network is only 10%. Thus, selecting an edge node as an intermediate node for communication between a CH and BS will not reduce the life cycle of the entire network. To summarize, controlling the energy consumption of CH nodes plays an important role

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in prolonging the lifetime of the network and achieving energy efficiency. Aiming at a higher energy efficiency for the entire network, a new protocol named energy-efficient centroid-based routing protocol (EECRP) is proposed to manage energy resources in WSN-assisted IoT. The main contributions of this paper are as follows: • A clustering algorithm that operates according to the position of the energy centroid and the residual energy of nodes is constructed. • An optimization algorithm that is based on the number of dead nodes and CH nodes is added to the protocol. • To reduce the number of long-distance communications, a protection mechanism for EECRP to save the energy of CH nodes is established. Notably, the above contributions substantially broaden the field of applications of the energy-efficient routing protocol by applying an energy centroid with local management and global control. Moreover, the average energy consumption of each round is reduced without impacting the network lifetime. The remainder of this paper is organized as follows. In section II, the related works and the motivation for our paper are discussed. Our proposed EECRP is described in detail in section III. In section IV, the energy consumption model of EECRP is presented. In section V, the performance evaluations of our protocol and comparisons with other protocols are presented. Conclusions are drawn in section VI, and future works are also mentioned. II. R ELATED W ORK Cluster routing protocols work on the network layer, which connects the data link layer and the transport layer. When deployed in the network, these protocols can avoid the ”hot spot” problem and obtain better network performance. In previous studies, clustering algorithms play a very important role in designing cluster routing protocols. Here, we mainly discuss the clustering and CH selection algorithms in hierarchical networks. LEACH [13] is the first hierarchical routing protocol designed for WSNs. The main characteristics of LEACH include local cluster generation and dynamic CH node rotation combined with data fusion technology. In LEACH, the node uses a stochastic algorithm, which is shown as Eq. (1), to decide whether to be a CH node. The details of this stochastic algorithm are discussed in [27]. ( P , if n N 1 (1) T (n) = 1−P ·(r mod P ) 0 , otherwise However, the process of CH node selection leads to a significant amount of energy consumption. The large amount of transmitted data leads to a short lifetime of nodes and a lack of monitoring of the area filled with dead nodes. Note that the extendibility of LEACH is not suitable for large-scale networks. LEACH-C is an improved version of LEACH. In LEACHC, at the beginning of each round, nodes send information of

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their position and residual energy to the BS. After receiving this information, the BS calculates the average energy of all nodes. Nodes whose energy is lower than the average energy will not be selected to be a candidate CH node. In this way, the energy consumption resulting from the CH node selection process and the overhead caused by data transmission are significantly reduced. However, the large amount of data transmission tasks at the beginning of each round still cause considerable overhead. In addition, LEACH-C does not perform as well as LEACH when the BS is located inside of the sensor network. Lindsey et al. [30] introduced a protocol called powerefficient gathering in sensor information systems (PEGASIS), in which a chain is formed for all nodes to transfer data packets to the BS. In [31], Loscri et al. proposed a two-level hierarchy routing protocol (TL-LEACH), which uses random rotations of local cluster base stations. In this way, TL-LEACH can better distribute the energy load among the sensors in the network, particularly when the density of the network is higher. In [32], Wei et al. proposed a distributed clustering algorithm (EC) that determines a suitable cluster size based on the hop distance to the BS. By applying EC, the network makes a good balance between the cluster’s energy consumption and the lifetime of nodes. In 2016, Razaque et al. [33] combined the features of LEACH and PEGASIS to improve the energy efficiency in routing. In the same year, Razaque et al. [34] designed H-LEACH, which is used to solve problems of energy considerations while selecting a CH. H-LEACH considers the residual and maximum energies of nodes for every round while selecting a CH using threshold conditions. Lin et al. [35] took advantage of a game model to select CH nodes. In addition, a routing protocol named game-theory-based energy-efficient clustering (GEEC) was proposed. GEEC, which is a type of clustering routing protocol, adopts an evolutionary game theory mechanism to achieve energy balance and longevity. From the above analysis, note that CH node selection algorithms should take four aspects into consideration: the local autonomy, network coverage, node location and the remaining energy of the node. Previous CH node selection algorithms mostly meet one or two with respect to three aspects, and there is no comprehensive consideration of the influence of all four aspects. III. E NERGY-E FFICIENT C ENTROID -BASED ROUTING P ROTOCOL (EECRP) In this section, the centroid-based routing protocol is discussed in detail. Subsequently, the energy consumption model is introduced. Moreover, some definitions, terminologies and assumptions are presented for a better understanding. A. System Model 1) Some assumptions and notations: We assume that wireless sensor nodes are randomly distributed in the network. Once the arrangement of the entire sensor network is completed, the positions of the sensor nodes will not be changed. In addition, the location information of the node is already loaded into the node when the network is deployed. We also



assume that each node knows the position of the BS and its remaining energy at any time. The shape of the entire tested area that the nodes are distributed in is a rectangle. We create a Cartesian coordinate system with its origin point located at the lower left corner of the entire region. In EECRP, CH nodes have direct communication with the BS. We set the number of CH nodes at the percentage of 5% during the simulation. 2) Sensor Energy Consumption Model: The sensor energy model is an important metric used to measure the performance of EECRP. The model that we choose is a popular one mentioned in previous works [36]. The model is shown as Eq. (2). ( l · (er + et + f s d2 ) if d < dT h E= (2) l · (er + et + mp d4 ) if d > dT h In Eq. (2), E is the total energy consumption when delivering a single l-bit packet from a sender to a receiver. The baseline energy consumption levels at the sender and receiver radios are indicated by er and et , respectively. The value of d is the distance of the link between the sender and receiver. The transmission energy consumption is denoted by either f s d2 or mp d4 depending on the distance d and the distance threshold dT h . For d ≤ dT h , f s is used to reflect “free-space” conditions, while mp represents longer links potentially affected by “multi-path” fading. B. Clustering Algorithm Scheme The clustering algorithm is used to find the most appropriate CH node for the cluster. The algorithm includes three phases: the initialization phase, the first cluster head selection phase and the rotate phase. 1) Initialization phase: A LOCATION message is first sent to the BS from every sensor node. The format of a LOCATION message is shown in Fig. 1. The field Message type indicates that it is a LOCATION message. The field Sender ID contains the node ID of the message sender. The X coordinate represents the abscissa of the node0 s location. The Y coordinate provides the ordinate of the node0 s location. The Energy level provides the energy status of the node.

Fig. 1: The format of the LOCATION message At the end of this phase, the BS will calculate the distance between each node and the BS itself in the network. The BS clusters based on the distances. Subsequently, the BS will update the node table, including each node0 s position and energy. Then, the BS broadcasts a FEEDBACK message to the nodes in one cluster specifically. The format of the message is shown in Fig. 2. Message type is used to specify the type of the message to inform the receiver. The MAX-dist field, which is calculated by the BS, delivers the maximum broadcast range to each node in the network. The CH0 s ID represents the ID

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of the CH node in each cluster. The Avg-energy indicates the average energy of the network. The receiver will update all the information of the FEEDBACK message in its routing table.

Fig. 2: The format of the FEEDBACK message The main task of the initialization phase is to exchange messages between the BS and the sensor nodes. The messages mainly contain the location and energy information of the nodes, the average energy of the entire network, the CH nodes chosen by the BS in the first round and the longest transmission distance. After implementing the entire phase, the exchanged information will be stored in the routing tables of the BS and sensor nodes. In addition, the information of the routing table will update in real time as the entire network operates. 2) First cluster head selection phase: After the LOCATION message and FEEDBACK message are received, the BS determines the node whose energy is greater than the Avgenergy. Specifically, the first round of CH node selection is random because the energy levels of each node are identical. The selection criteria include the most suitable percentage P and the entire network, which should be monitored by being covered by clusters. In fact, in the first CH node selection phase, each node in a cluster checks its own ID to determine whether it is a CH node. The first cluster head selection phase is mainly to complete the identification of the broadcast information of the BS. When the ID of the node is the same as that of the CH node, the node confirms that the node itself is the CH node and opens the transmitting antenna to prepare for the next phase. When the IDs are different, the node closes the transmitting antenna to save energy and opens the receiving antenna to prepare for the arrival of information from the CH node. 3) Rotate phase: After the first cluster selection phase, the first CH node broadcasts the schedule message to the neighbor nodes. The CH node0 s ID and position are contained in this message. All CH nodes in the network send this message. When the neighbor nodes receive this message, they will determine whether it belongs to this cluster based on the CH node0 s ID in the FEEDBACK message and the schedule message. At this point, the clustering is completed. The general nodes send the information about their locations and energy to the associated CH node. The CH node calculates the location of the energy centroid of the cluster. The node that is nearest to the energy centroid will be chosen to be the candidate CH node. The rotate phase is established to choose a candidate CH node. In this way, the network can uniformly distribute the energy consumption to all nodes in the network. EECRP simultaneously meets the four aspects that were mentioned in section II. In the first round, the CH nodes are chosen by the



BS. Thus, the BS has an overall view of the network. When the network is running, the CH node is selected in the local cluster, which means that the network is self-adapting. Moreover, the selected CH node is the closest node to the energy centroid that can maximize the network coverage. From the calculation of the position of the energy centroid, which is described in detail below, we find that the calculation is based on the node location and the remaining energy of the node. In other words, the cluster algorithm in EECRP can make a improvement to the existing algorithms. Then, a general node is added to the candidate CH node sequence. The added nodes must meet the following two conditions. 1) 1) Its energy level is more than the average energy level of the clusters. 2) The distance from the energy centroid of the network to the node is less than the average distance of the entire nodes to the energy centroid. Moreover, in the process of CH node rotation, the numbers of CH nodes and dead nodes are taken into consideration. In other words, the number of clusters decreases as the number of dead nodes increases to keep the value of P unchanged. Here, note that P represents the desired percentage, which is defined in section II. C. Centroid Algorithm Scheme In the field of mathematics, the centroid is the center of weight, which is the imaginary point of mass concentration. Centroid position is important in engineering fields. In our study, we use the concept of the cluster energy centroid rather than the traditional weight centroid. The reasons for using the cluster energy centroid are as follows. First, the weight of sensor nodes in the network is meaningless. Second, the center of the weight centroid of nodes for the entire cluster is meaningless because node location and weight do not change in the operation of the network. Finally, in the entire network, the energy of the sensor nodes is the only factor that changes. The energy centroid can intuitively display the distribution of residual energy in the network. Note that Eqs. (3) and (4) are used to calculate the position of the weight centroid in the field of mathematics. Eqs. (5) and (6) are used to calculate the position of the energy centroid. Clearly, we take the residual energy and the position of node i into consideration to calculate the energy centroid. Next, the meanings of the parameters used in the equations are introduced. µ is the density of nodes’ weights in the cluster, S is the measure of cluster area, dσ is the differential of weight, and dMx and dMy are the static moment to the x-axis and y-axis, respectively. Ei rs represents the residual energy level of node i, E0 is the initial value of energy, and X and Y are the X coordinate and the Y coordinate of node i, respectively. Xwc and Ywc are the results of the weight centroid. Xec and Yec are the results of the energy centroid RR x · µdσ My Xwc = = D RR (3) M µdσ D

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RR Ywc =

Mx = M

D

x · µdσ RR µdσ

(4)

D n X

Xec =

i=0

Ei rs · X E0

n X Ei

Yec =

(5)

N

i=0

rs

E0

· Y

(6) N If the weights of sensor nodes in the cluster are known and evenly distributed, then we can use Eqs. (3) and (4) to calculate the position of the weight centroid [37]. However, in the real case, the effect of the node weight of the nodes in the network is not important for the network lifetime. Hence, with node location information and the residual energy level, we can use Eqs. (5) and (6) to calculate the position of the energy centroid. The energy centroid can reflect the energy distribution during smooth operation of the network. D. Protective Mechanism Scheme As mentioned above, the MAX-dist value is broadcast to each node by the BS with the FEEDBACK message. This value is the protective mechanism of EECRP. The MAX-dist, calculated by the BS, is the communication threshold. Note that the MAX-dist value is calculated by taking the Avg-energy in Eq. (2). When CH nodes are ready to send packets to the BS, the CH nodes compare their own distances to the BS with the MAX-dist sent by the BS in the FEEDBACK message. If the distance is smaller than the MAX-dist, then CH nodes will turn on their antennas and send packets. Conversely, if the distance is larger than the MAX-dist, then CH nodes will stop transmitting the data to the BS and store the packets, waiting for the next round. Although this mechanism will lead to a loss of data packets in the short term, it can avoid the long-distance communications of CH nodes, which can reduce the energy consumption of the network, as shown in Eq. (16) in section IV. From the perspective of monitoring the entire network, it is more good than harm to utilize the protective mechanism. The details of EECRP are presented in Algorithm 1. IV. E NERGY C ONSUMPTION M ODEL In this section, we provide the value of energy consumption Eround of the network during a round period. This value is needed by the EECRP algorithm to calculate the equalized lifetime value and to determine the effect of the protective mechanism. The total initial deployment energy in P the region can be represented as E0 all = j Ci E0 (j) = P 2 j Ci E0 (j) · π ri σ = E0 · abσ, where E0 is the average deployment energy and ri denotes the radius of cluster i. The 0 all lifetime of the entire network is L ≈ EEround . The round energy is the sum of energy consumption values for cluster formation, route discovery and data communication events. The round energy is given by Eround = Ecluster + Ecomm + Ein−comm . In the following parts of this section, the individual phases of EECRP are separately calculated.



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Algorithm 1 The process of EECRP

lower energy consumption.

BS ← LOCATION messages Calculate: Avg-energy and MAX-dist N odes ← FEEDBACK message if (Candidate CHs exist) then Clustering Recalculate centroid Create schedule if (Alive node number > number of clusters) then if (Round time is not over then CH ← data (normal nodes) Fusion data BS ← data (CH nodes) return end if Recalculate: Avg-energy and MAX-dist N odes ← FEEDBACK message Select candidate CH nodes return end if return end if

Eout1 = lloc {(ab − π d2T h )σ et (ab−π d2T h )σ

X

+

(8)

mp [(XB − Xi )2 + (YB − Yi )2 ]2 }

i=1

Eqs. (9) and (10) describe the energy consumed by nodes to receive the FEEDBACK message from the BS. The condition of receiving the LOCATION message is the same as that of receiving the FEEDBACK message. Eq. (9) and Eq. (10) are related to the receiving energy. We can determine that in this phase, when the nodes are located far from the BS, the number of long-distance communications is two times the number of nodes. This result demonstrates that the conclusion drawn from Eq. (8) holds. Ein2 = lf d {π d2T h σ er π d2T h σ

+

X

(9)

f s [(XB − Xi )2 + (YB − Yi )2 ]}

i=1

Eout1 = lf d {(ab − π d2T h )σ er A. Energy Consumption in Cluster Formation

(ab−π d2T h )σ

The energy consumption of nodes sending a LOCATION message to the BS is calculated in this section. The energy consumption can be described as Eq. (7) when the distance of the link between the sender and receiver is less than dT h . We assume that the area is circular with the centroid as the center and dT h as its radius. In Eq. (7), lloc represents the size of the LOCATION message. lf d represents the size of the FEEDBACK message. Clearly, the relationship between lloc and lf d can be described as lf d = 0.8· lloc . σ indicates the density distribution of sensor nodes. XB and YB are the location information of the BS. Xi and Yi are the location information of ordinate nodes. XC and YC are the location information of CH nodes. ab shows the size of the entire sensor network. P indicates the desired percentage of CH nodes. ldata is the size of the packet that has been compressed by the CH node. ldata0 is the size of the packets that are sent to the CH node by common nodes. The symbols used in the following equations are the same as in Eq. (7).

+

X

f s [(XB − Xi )2 + (YB − Yi )2 ]}

X

mp [(XB − Xi )2 + (YB − Yi )2 ]2 }

i=1

(10) After exchanging LOCATION and FEEDBACK messages, normal nodes join the nearest cluster. The value of l0 is the size of the control message. The energy consumption can be calculated as Eq. (11). In contrast to the formulas for calculating information exchange, Eq. (11) is used to calculate the energy consumptions of both the sender and the receiver. This is because the BS is independent of the entire network in our calculations. In addition, the energy of the BS is limitless, which indicates that the broadcast and receive energy consumptions of the BS do not need to be calculated.

Enon

ch

= l0 {ab(1 − P )σ et + abP σ er ab(1−P )σ

+

X

f s [(XC − Xi )2 + (YC − Yi )2 ]}

(11)

i=1

The total energy consumption in the cluster formation phase is shown in Eq. (12), which is the sum of Eqs. (7) ∼ (11).

Ein1 = lloc {π d2T h σ et π d2T h σ

+

(7)

Ecluster = Ein1 + Eout1 + Ein2 + Eout2 + Enon−ch (12)

i=1

The energy consumption can be described as Eq. (8) when the distance of the link between the sender and receiver is larger than dT h . The energy consumption of transmitting packages by nodes with a larger d in Eq. (8) is the exponential time of that in Eq. (7). A conclusion can be drawn that shorter long-distance transmission times can contribute to

After a simple mathematical reduction using l = 0.8· lloc = lf d , Eq. (12) can be simplified as Eq. (13). The relationship between LOCATION and FEEDBACK messages is shown in subsection III(B). Here, the problem of long-distance communication must be mentioned again. From Eq. (13), we can observe that the energy consumption of the communication channel is an important component of the energy consumption



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of the entire network. Thus, it is essential to reduce the channel energy consumption.

Eround = Ecluster + Ecomm + Ein−comm = l · {1.25abσ et + abσ er π d2T h σ

Ecluster = l · {1.25abσ et + abσ er X

+ 2.25

f s [(XB − Xi )2 + (YB − Yi )2 ]}

i=1

i=1

X

mp [(XB − Xi )2 + (YB − Yi )2 ]2 }

i=1

i=1

+ l0 {ab(1 − P )σ et + abP σ er +

f s [(XC − Xi )2 + (YC − Yi )2 ]}

i=1

mp [(XB − Xi )2 + (YB − Yi )2 ]2 }

ab(1−P )σ

ab(1−P )σ

+

X

+ 2.25

+ l0 {ab(1 − P )σ et + abP σ er X

f s [(XB − Xi )2 + (YB − Yi )2 ]}

(ab−π d2T h )σ

(ab−π d2T h )σ

+ 2.25

X

+ 2.25

π d2T h σ

X

f s [(XC − Xi )2 + (YC − Yi )2 ]}

i=1

+ ldata {abP σ et (13) +

abP Xσ

f s [(XB − XCi )2 + (YB − YCi )2 ]}

i=1

+ ldata0 {ab(1 − P )σ et + abP σ er

B. Energy Consumption in Transmitting Data to the BS In this part, the energy consumption model is very simple because only cluster head nodes consume energy during transmission. Moreover, the protective mechanism has a considerable influence on the performance. The result is shown in Eq. (14). Thanks to the protective mechanism, the longdistance communication is greatly reduced in Eq. (14). The purpose of doing so is to prolong the survival time of CH nodes, thereby prolonging the network life cycle. Ecomm = ECH−BS = ldata {abP σ et +

abP Xσ

f s [(XB − XCi )2 + (YB − YCi )2 ]}

(14)

i=1

C. Energy Consumption in Transmitting Data to the CH nodes in Clusters In this part, the normal nodes transmitting data packets consume their own energy, and the cluster head nodes receiving data also consume energy. Because data packets are transmitted in one cluster, the value of d is smaller than that of dT h . The energy consumption model is shown in Eq. (15). The calculation in Eq. (15) is similar to that in Eq. (11). Sending and receiving information in Eq. (15) is the data information perceived by sensors rather than control information. Ein−comm = Ein−cluster = ldata0 {ab(1 − P )σ et + abP σ er ab(1−P )σ

+

X

f s [(XC − Xi )2 + (YC − Yi )2 ]}

i=1

(15)

D. Total Energy Consumption in a Round The total energy consumption in a round is the sum of Ecluster , Ecomm and Ein−comm , which is shown in Eq. (16).

ab(1−P )σ

+

X

f s [(XC − Xi )2 + (YC − Yi )2 ]}

i=1

(16) As shown in Eq. (16), the energy consumption of the network using EECRP is the information exchange in the network initialization phase. This means that when the network enters the stable operation phase, the energy consumption of the normal nodes can be ignored. First, a considerable amount of energy consumption occurs in the initial phase of the network. As mentioned in section III, the nodes need to send the LOCATION message to the BS at the very beginning. The distance between the node and the BS is relatively larger than that between the node and the CH node in one cluster. In addition, nodes also need to receive the FEEDBACK message sent from the BS in the initialization phase. For most of the nodes, these two communications belong to long-distance communication; thus, the energy consumption will be very significant. Second, the energy consumption of the nodes in the stable operation phase is relatively low. Hierarchical sensor networks are known to achieve local clustering; thus, the local normal node only communicates with the CH node of the cluster. The average transmission distance in the cluster is highly related to the location of the CH node. In this paper, we propose a CH node selection algorithm based on the location of the energy centroid, where the CH node is located in the energy center of the cluster and the most concentrated area of the nodes. In LEACH, the CH node is selected by Eq. (1). If the CH node is located at one edge of the network, then the transmission distance of nodes on the other side of the network will be greatly increased. As time passes, the network’s coverage rate will be greatly reduced, which will directly lead to a higher error rate of data. Regarding the stable operation phase of the proposed EECRP, the CH node of the next round is calculated by the CH node of the previous round. Note that the candidate CH node is located in the center of the energy network; thus, it can greatly improve the ability of real-time monitoring. Moreover, when the distance between the CH node and



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TABLE I: Parameters used in simulation Parameter

Value

Network size BS’s locations Number of sensor nodes Initial energy (J) f s mp Transmitting/Reception energy l (length of data) Mac Bandwidth Beam forming energy (nJ/bit)

100×100 inside 100 2 10pJ/bit/m2 0.0013pJ/bit/m4 50 nJ/bit 500 bit 802.11 200 Kbps 5

the local node is larger than the MAX-dist, the data will not be sent and will be stored locally until a neighbor node is selected to be a new CH node. The number of long-distance communications is reduced by the protective mechanism in EECRP. In addition, the number of long-distance communications between CH nodes and the BS is also greatly reduced. The stored data packets will be sent to new CH nodes in the following rounds. The influence of the energy consumption of long-distance communication on the survival time of cluster heads is remarkable. The protective mechanism plays a very important role, as indicated by the simulation results and comparisons.

Fig. 3: The rounds at which the first and last nodes die versus different values of Ps

V. P ERFORMANCE E VALUATION Taking the location of the BS into consideration, the selection of the value of P in Eq. 1 and the comparison between LEACH and LEACH-C are implemented using the ns-2 simulator [38]. The version of ns-2 used in our simulation is ns-2.35. The software platform that we run the protocols on is Ubuntu 12.04.

Fig. 4: Average data transmission versus different values of Ps

A. Simulation Parameters In the simulation part, the network is implemented in a 100 m × 100 m field with 100 nodes randomly distributed in the network area. Each node is set to have 2 J as its initial energy. Furthermore, the BS is located in the sensor network. The detailed parameters that were used in our simulation are listed in TABLE I. In addition, the simulations among LEACH, LEACH-C, GEEC and EECRP are also implemented using the ns-2 simulator. The following three performance measurements are considered: the total number of nodes that are alive, the total energy dissipation and the number of messages received at the BS. The lifetime of the network is defined as the round number when the remaining nodes are not sufficient to form clusters.

As vividly shown in Fig. 3, when the value of P is small, which represents a small number of CH nodes, the nodes begin to die in a very short time. This result might be due to the shortage of small Ps, although the lifetime of the entire network becomes longer. This results in large parts of the network being not under monitoring in the later rounds. In addition, Fig. 3 indicates that the average data transmission of networks with fewer CH nodes is larger, which means that the data integrity is poor.

B. Simulation Results

Taking all aspects into consideration, setting the percentage of CH nodes to 5% leads to a good condition of the network. The energy of nodes dissipates slowly, and as the nodes die, network area that is not being monitored can hardly be found. In other words, considering the influence on lifetime and the rate of energy dissipation, 5% should be the best setting of CH nodes.

The results of part 1 are shown in Figs. 3, 4, and 5. To evaluate the setting of the value of P in Eq. (1), we consider the number of nodes that are alive, the size of transmitted data and the total energy dissipation through the entire network with the BS located in the network area. The value of P ranges from 1% to 12%.

However, when there is a large number of CH nodes in the sensor network, all aspects of the sensor network are in poor condition. In detail, shorter lifetime, smaller data transmission and quicker energy dissipation become the shortages of the networks with large Ps, as shown in Figs. 3, 4, and 5.



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Fig. 6: The number of sensor nodes that are alive in the four protocols Fig. 5: The total energy dissipated versus different values of Ps

C. Comparison Results EECRP and the other three protocols (LEACH, LEACHC and GEEC) are simulated in this part. Each of the four protocols is analyzed from three different aspects, which are the number of sensors still alive, the number of data signals received by the BS and energy dissipation. In this part, as illustrated in Figs. 6, 7, and 8, several metrics are taken into consideration to evaluate the performances of LEACH, LEACH-C, GEEC and EECRP when the BS is located inside of the network. The detailed comparison of the four protocols is as follows. The number of nodes that are alive: The number of nodes that are alive indicates the lifetime of WSNs, which is the most important parameter of routing protocols. As shown in Fig. 6, there are considerable differences in the number of sensor nodes that are alive among EECRP, LEACH, LEACH-C and GEEC. First, the first node dies at approximately the 100th round in LEACH-C. Regarding LEACH, GEEC and EECRP, the first node dies at approximately the 400th round. Second, the nodes in LEACH-C die in a very short time due to the large amount of communicating control messages, whereas the network using EECRP has a very long lifetime. The lifetime of EECRP is longer than that of LEACH by approximately 100 rounds and longer than that of GEEC by approximately 50 rounds. The number of messages received by the BS: Fig. 7 shows that LEACH-C delivers considerably fewer messages to the BS than any other protocols. The numbers of messages received by the BS in the network using EECRP are equal to those using LEACH and GEEC before the 400th round. However, after the 400th round, EECRP0 s packet delivery slowly increases, which is due to the setting of MAX-dist in EECRP. When increasingly more nodes die, the distance between two nodes in the network increases. The node will stop sending information to the CH node and the BS to save energy when the distance to the CH node or BS exceeds the value of MAX-dist. Nodes will store the data packets in their storage spaces. However, the messages that are stored will be sent in the following round until the communication distance

Fig. 7: The number of messages received by the BS in the four protocols

is less than the MAX-dist. The details about this protective mechanism are presented in section III. Energy dissipation: As shown in Fig. 8, the speed of total energy dissipation of LEACH-C remains at a very high level. In other words, LEACH-C performs not as well as LEACH, GEEC and EECRP in this aspect. In addition, the speed of energy consumption of EECRP is slower than that of LEACH after the 400th round, which means that nodes in EECRP can monitor the network for a long period. In addition, GEEC and EECRP have similar performance in terms of energy consumption. D. Comparison of EECRP and the Conventional Protocols The performance and comparison of the characteristics of LEACH, LEACH-C, GEEC and EECRP are presented in TABLE II. The characteristics used to compare the protocols are life cycle, scalability, computation and communication overhead, path selection, location awareness and mobility. As shown in TABLE II, EECRP performs better than LEACH, LEACH-C and GEEC in terms of life cycle, scalability, computation and communication overhead and location awareness. However, with static sensor nodes and a single hop between the BS and CH nodes, EECRP is suitable for the networks where sensor nodes do not need to change locations. Finally,



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TABLE II: Comparison among EECRP, LEACH and LEACH-C Protocol Properties

LEACH

LEACH-C

GEEC

EECRP

Life cycle

540 rounds

480 rounds

670 rounds

720 rounds

Scalability

Bad

Bad

Good

Good

Computation & communication overhead

Setting up and maintaining cluster




Path selection

Single hop

Single hop

Single hop

Single hop

Location awareness

No

Yes

Yes

Yes

Mobility

Fixed

Fixed

Fixed

Fixed

R EFERENCES

Fig. 8: The total energy dissipation in the four protocols

it is very suitable for us to deploy EECRP in networks where the BS is located inside the network. VI. C ONCLUSION In this paper, we propose a new energy-efficient centroidbased routing protocol (EECRP) to manage the energy of WSN-assisted IoT by solving the problem of forming clusters, which is based on the distance to the energy centroid. We also propose an optimization algorithm based on the number of dead nodes and the number of cluster head nodes. From the simulation results, when the BS is located in the network, the EECRP could transmit a considerable amount of data with very low energy dissipation. Meanwhile, the network lifetime of EECRP is longer than that of LEACH, LEACH-C and GEEC. In future work, we want to improve the protocol by finding the multi-hop path from the CH nodes to the BS. The multi-hop path is used by CH nodes to transmit data packets. We hope that our future protocol can perform well when the BS is located outside of the network. ACKNOWLEDGMENT This work is supported by the National Science Foundation of China under Grant No. 61672295, No. 61300237 and No. U1405254, the State Key Laboratory of Information Security under Grant No. 2017-MS-10, the 2015 Project of six personnel in Jiangsu Province under Grant No. R2015L06, the CICAEET fund, and the PAPD fund.

[1] C. Floerkemeier, M. Langheinrich, E. Fleisch, F. Mattern, and S. E. Sarma, “The internet of things,” Electronics World, vol. 297, no. 6, pp. 949 – 955, 2017. [2] M. Az, Mart, N. Cristian, and B. Rubio, “State-of-the-art, challenges, and open issues in the integration of internet of things and cloud computing,” Journal of Network and Computer Applications, vol. 67, no. C, pp. 99–117, 2016. [3] S. Misra, M. Maheswaran, and S. Hashmi, “Securing the internet of things,” Computer Fraud and Security, vol. 2016, no. 4, pp. 15–20, 2016. [4] T. Alhmiedat, A. Abu Taleb, and M. Bsoul, “A study on threads detection and tracking systems for military applications using wsns,” International Journal of Computer Applications, vol. 40, no. 15, pp. 12–18, 2012. [5] G. Xing, M. Li, T. Wang, W. Jia, and J. Huang, “Efficient rendezvous algorithms for mobility-enabled wireless sensor networks,” IEEE Transactions on Mobile Computing, vol. 11, no. 1, pp. 47–60, 2012. [6] A. Liu, J. Ren, X. Li, Z. Chen, and X. S. Shen, “Design principles and improvement of cost function based energy aware routing algorithms for wireless sensor networks,” Computer Networks, vol. 56, no. 7, pp. 1951–1967, 2012. [7] J. Shen, H. Tan, J. Wang, J. Wang, and S. Lee, “A novel routing protocol providing good transmission reliability in underwater sensor networks,” Journal of Internet Technology, vol. 16, no. 1, pp. 171–178, 2015. [8] C. F. Lai, S. Zeadally, J. Shen, and Y. X. Lai, “A cloud-integrated appliance recognition approach over internet of things,” Journal of Internet Technology, vol. 16, no. 7, pp. 1157–1168, 2015. [9] J. Zhu, J. Liu, Z. Hai, and Y. G. Bi, “Research on routing protocol facing to signal conflicting in link quality guaranteed wsn,” Wireless Networks, vol. 22, no. 5, pp. 1739–1750, 2016. [10] Q. Chi, H. Yan, C. Zhang, Z. Pang, and L. D. Xu, “A reconfigurable smart sensor interface for industrial wsn in iot environment,” IEEE Transactions on Industrial Informatics, vol. 10, no. 2, pp. 1417–1425, 2014. [11] J. Shen, T. Zhou, D. He, Y. Zhang, X. Sun, and Y. Xiang, “Block design-based key agreement for group data sharing in cloud computing,” IEEE Transactions on Dependable and Secure Computing, 2017, DOI: 10.1109/TDSC.2017.2725953. [12] J. Shen, J. Shen, X. Chen, X. Huang, and W. Susilo, “An efficient public auditing protocol with novel dynamic structure for cloud data,” IEEE Transactions on Information Forensics and Security, 2017, DOI: 10.1109/TIFS.2017.2705620. [13] W. R. Heinzelman, A. Chandrakasan, and H. Balakrishnan, “Energyefficient communication protocol for wireless microsensor networks,” in Proc. 33rd HICSS, 2000, pp. 10–20. [14] A. Yektaparast, F. H. Nabavi, and A. Sarmast, “An improvement on leach protocol (cell-leach),” in Proc. IEEE ICACT, 2012, pp. 992–996. [15] Y. Wu, S. Fahmy, and N. B. Shroff, “Energy efficient sleep/wake scheduling for multi-hop sensor networks: Non-convexity and approximation algorithm,” in Proc. IEEE INFOCOM, 2007, pp. 1568–1576. [16] D. Kandris, P. Tsioumas, A. Tzes, G. Nikolakopoulos, and D. D. Vergados, “Power conservation through energy efficient routing in wireless sensor networks,” Sensors, vol. 9, no. 9, pp. 7320–7342, 2009. [17] C. Lung and C. Zhou, “Using hierarchical agglomerative clustering in wireless sensor networks: An energy-efficient and flexible approach,” Ad Hoc Networks, vol. 8, no. 3, pp. 328–344, 2010. [18] B. Wang, X. Gu, L. Ma, and S. Yan, “A variable threshold-value authentication architecture for wireless mesh networks,” International Journal of Sensor Networks, vol. 23, no. 4, pp. 265–278, 2017.



10

[19] P. Raja and P. Dananjayan, “Game theory-based efficient energy consumption routing protocol to enhance the lifetime of wsn,” International Journal of Information and Communication Technology, vol. 8, no. 4, pp. 357–370, 2016. [20] J. Shen, S. Chang, J. Shen, Q. Liu, and X. Sun, “A lightweight multi-layer authentication protocol for wireless body area networks,” Future Generation Computer Systems, 2016, DOI: 10.1016/j.future.2016.11.033. [21] P. Marappan and P. Rodrigues, “An energy efficient routing protocol for correlated data using cl-leach in wsn,” Wireless Networks, vol. 22, no. 4, pp. 1415–1423, 2016. [22] J. Zhang, C. K. Jeong, G. Y. Lee, and H. J. Kim, “Cluster-based multi-path routing algorithm for multi-hop wireless network,” Future Generation Communication and Networking, vol. 1, pp. 67–75, 2007. [23] J. Shen, A. Wang, C. Wang, Y. Ren, and X. Sun, “A rfid based localization algorithm for wireless sensor networks,” in Proc. ICCCS, 2016, pp. 275–285. [24] M. Chen, V. C. Leung, and S. Mao, “Directional controlled fusion in wireless sensor networks,” Mobile Networks and Applications, vol. 14, no. 2, pp. 220–229, 2009. [25] J. Shen, D. Liu, J. Shen, Q. Liu, and X. Sun, “A secure cloud-assisted urban data sharing framework for ubiquitous-cities,” Pervasive and Mobile Computing, 2017, DOI: 10.1016/j.pmcj.2017.03.013. [26] Y. Yuan, Z. He, and M. Chen, “Virtual mimo-based cross-layer design for wireless sensor networks,” IEEE Transactions on Vehicular Technology, vol. 55, no. 3, pp. 856–864, 2006. [27] J. Shen, A. Wang, C. Wang, Y. Ren, and J. Wang, “Performance comparison of typical and improved leach protocols in wireless sensor network,” in Proc. IEEE CCITSA, 2015, pp. 187–192. [28] Y. Zhang, X. Sun, and B. Wang, “Efficient algorithm for k-barrier coverage based on integer linear programming,” China Communications, vol. 13, no. 7, pp. 16–23, 2016. [29] A. Liu, Z. Zheng, C. Zhang, Z. Chen, and X. Shen, “Secure and energyefficient disjoint multipath routing for wsns,” IEEE Transactions on Vehicular Technology, vol. 61, no. 7, pp. 3255–3265, 2012. [30] S. Lindsey and C. S. Raghavendra, “Pegasis: Power-efficient gathering in sensor information systems,” in Proc. Aerospace Conference, 2002, pp. 1125–1130. [31] V. Loscri, G. Morabito, and S. Marano, “A two-levels hierarchy for low-energy adaptive clustering hierarchy (tl-leach),” in Proc. IEEE VTC, 2005, pp. 1809–1813. [32] D. Wei, Y. Jin, S. Vural, and K. Moessner, “An energy-efficient clustering solution for wireless sensor networks,” IEEE Transactions on Wireless Communications, vol. 10, no. 11, pp. 3973–3983, 2011. [33] A. Razaque, M. Abdulgader, C. Joshi, F. Amsaad, and M. Chauhan, “Pleach: Energy efficient routing protocol for wireless sensor networks,” in Proc. IEEE LISAT, 2016, pp. 1–5. [34] A. Razaque, S. Mudigulam, K. Gavini, and F. Amsaad, “H-leach: Hybrid-low energy adaptive clustering hierarchy for wireless sensor networks,” in Proc. IEEE LISAT, 2016, pp. 1–4. [35] D. Lin and Q. Wang, “A game theory based energy efficient clustering routing protocol for wsns,” Wireless Networks, pp. 1–11, 2016. [36] J. Yick, B. Mukherjee, and D. Ghosal, “Wireless sensor network survey,” Computer Networks, vol. 52, no. 12, pp. 2292–2330, 2008. [37] J. Shen, C. Wang, A. Wang, X. Sun, S. Moh, and P. C. K. Hung, “Organized topology based routing protocol in incompletely predictable ad-hoc networks,” Computer Communications, 2016. DOI: j.comcom.2016.07.009. [38] M. Khosrowpour, “Network simulator ns-2,” Journal of the Institute of Image Information and Television Engineers, vol. 65, pp. 946–949, 2011.
