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EGRPM: Energy efficient geographic routing protocol based on mobile sink in wireless sensor networks
Sustainable Computing: Informatics and Systems 25 (2020) 100377
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Sustainable Computing: Informatics and Systems journal homepage: www.elsevier.com/locate/suscom
EGRPM: Energy efficient geographic routing protocol based on mobile sink in wireless sensor networks Maryam Naghibi, Hamid Barati ∗ Department of Computer Engineering, Dezful Branch, Islamic Azad University, Dezful, Iran
a r t i c l e
i n f o
Article history: Received 26 April 2019 Received in revised form 29 October 2019 Accepted 31 January 2020 Available online 4 February 2020 Keywords: Wireless sensor network Routing Data gathering Mobile sink Network lifetime
a b s t r a c t A wireless sensor network consists of a large number of nodes, sending sensed data to the base station or sink, either directly or through intermediate nodes. Multi-hop communication results in increased volume of traffic and depleting the energy of nodes adjacent to static sinks. A method of dealing with this challenge is using mobile sinks. Mobile sinks balance the load and distribute energy consumption throughout the network. This paper suggests a method to divide the network into some cells in a geographic way and applies two mobile sinks to gather the data sensed by these cell nodes. Based on the communication between cells and mobile sinks, the cells are divided into two categories: single-hop communication cells (SCCs) and multi-hop communication cells (MCCs). Mobile sinks move over two concentric diamond-shaped orbits in such a way that each half of the network is covered by a sink at a time. Initially, both sinks move in one direction and stay at particular intervals in the corners of the orbits to gather data from sensor nodes. When sinks are stationary, SCCs send data to the sinks directly, but MCCs apply the proposed routing algorithm (EGRPM) to send data to mobile sinks. The proposed approach is simulated by NS2 software. A comparison between the performance of EGRPM and conventional methods shows that applying EGRPM results in a significant decrease in average energy consumption and data delivery delay and causes a substantial increase in packet delivery rate and network lifetime. © 2020 Elsevier Inc. All rights reserved.
1. Introduction A WSN includes a large number of small sensor nodes with limited communication and calculation abilities; the network is employed to gather and transfer data from an environment towards a user or the base station [1,2]. Recent developments in the technology of making small integrated circuits and developing the technology of wireless communication have led to designing WSNs. The major difference between these networks and conventional ones is due to their relationship with environment and physical phenomena. Conventional networks connect people and data bases, while sensor networks are directly connected with physical world. These networks observe physical environment through sensors and then decide and implement an appropriate operation [3,4]. WSNs are applied in many military and non-military applications including monitoring the environment and habitats, healthcare programs, home automation, and controlling traffic [5,6]. The major task of a sensor node is to sense and gather data from a particular
∗ Corresponding author. E-mail addresses: [email protected] (M. Naghibi), [email protected] (H. Barati). https://doi.org/10.1016/j.suscom.2020.100377 2210-5379/© 2020 Elsevier Inc. All rights reserved.
area and then process and transfer it to the sink. However, ensuring a direct communication between sensor nodes and sink might require nodes to broadcast their messages strongly, which results in a fast depletion in their resources [7]. Since the energy of nodes is limited and radio receiving and transferring require a lot of energy, the level of energy consumption of sensor nodes is a major issue [5]. Thus, the power of battery is one of the vital parameters in designing routing algorithms to increase the lifetime of nodes. In addition to maximizing the lifetime of sensor nodes, a proper and uniform distribution of energy throughout WSNs for maximizing the performance of the network is desirable [5,6]. In WSNs, the data packet, generated by sensors, reaches the base station through multi-hop paths. Due to the nature of WSNs, sensors are required to interact to transfer data to the sink. Since the energy of sensors is limited, transferring data to and receiving data from the sink cause increased energy consumption in the intermediate nodes; in fact, sensors adjacent to the sink (at a one-hop distance) show a greater involvement in transferring and receiving sensed data. Thus, the energy of sensors in an area near the sink is depleted faster, and they are removed from the network so that network nodes are incapable of transferring data to the sink. This issue is called hotspot and causes the network to die, while other sensors are supplied with enough energy. A mobile
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sink can keep the balance between sensors and is an appropriate solution for the hotspot problem [8–10]. When the sink travels, its neighboring nodes change. Also, in a multi-hop transferring, nodes transfer data across a shorter distance so that the energy is distributed throughout the network. For this reason, a uniform energy consumption pattern is available, and the lifetime of the network is increased. Applying a mobile sink causes sensor nodes to consume less energy for transferring data to the sink, and the displacement of the sink does not result in energy holes. Moreover, a mobile sink can travel in areas where the energy of sensor nodes is insufficient for transferring data. Thus, the lifetime and operation power of the network rise [11,12]. This paper proposed a mobile sink based routing method, named EGRPM. Here, the network is divided into cells in a geographic way, and two mobile sinks with the same mobility pattern for gathering sensed data by the cell nodes are used as well. Based on the type of the communication between cells and mobile sinks, the cells are categorized into two groups: single-hop communication cells (SCCs) and multi-hop communication cells (MCCs). SCCs are at a one-hop distance from the anchor points of mobile sinks, while MCCs are at a distance of more than one hop. Mobile sinks stop at particular intervals to gather data from sensor nodes. When a sink remains still, SCCs send data directly to it, but farther cells, MCCs, transfer data through the proposed routing algorithm. In EGRPM, according to the mobility pattern of sinks, each half of the network is covered by a sink. Thus, sending data to sinks is performed in half of the network, which results in saving sensor nodes’ energy and increased network lifetime. The rest of the paper is organized as follows: Section 2 represents an overview of the related work. In Section 3, the details of the proposed method, which is based on mobile sink, are presented. Then we provide the simulation of the proposed protocol and the analysis of its performance in Section 4. Finally, Section 5 concludes this paper. 2. Related work To avoid the problem of WSNs with static sinks (e.g. energy holes), mobile sinks are applied [13,14]. A mobile sink can use various mobility patterns in the field of sensor networks, which leads to energy efficiency and different data gathering strategies. Different mobility patterns can be categorized into three general groups: randomized, predictable, and controlled mobility [15,16]. Each pattern applies different functions and methods to calculate the future position of the sink. • Randomized mobility: according to this pattern, sinks move randomly in the field of sensors. The most characteristic of this pattern is simplicity and unpredictability of the future position of the sinks. Different methods of randomized mobility define different degrees of freedom to the movement of the sinks [17]. • Predictable mobility: in this pattern, the mobile sink needs a certain pattern, which can be determined. These patterns can be periodic movements along a predefined path. Here, the sensor nodes can become aware of the time the sink is adjacent to them and will optimize sensing task and data gathering [17,18]. • Controlled mobility: according to this pattern, the mobility pattern of the sink can be programed to provide more efficiency. One pattern is moving in a programed path. Controlled movement can be predictable such as observing a certain number of sensors in a particular period of time. The major issue of the controlled mobility is the definite programing of the sink movement to optimize the network lifetime [17]. Some routing algorithms with mobile sinks will be discussed in the following.
Kim et al. [19] suggested that to avoid overhead and energy consumption, caused by the movement of the sink node, other nodes should set their path to a static node, called agent node. Then the agent node sets its path to the new position of the sink node. A long distance data delivery is performed through multi hops. The sensor nodes and mobile sinks are aware of their own locations. On appearing a stimulus, the surrounding sensors jointly process the signal, and one of them changes into a source for generating data reports. Finally, the sinks search the network to gather sensed data. Sink-Trial protocol employs the logical coordinate space to calculate distance. A protocol, suggested by Liu et al. [20], has solved the problem of not knowing the geographical position of sensors through the messages sent by mobile sinks to the network at intervals. Each source node of the network saves a vector, known as trail vector, indicating the logical coordinate of that node. The vector contains the number of last hops of the node to the sink. Source nodes apply a greedy forwarding method to transfer their data. Saved trail vectors help to find the shortest distance to the sink. It is essential to discover a protocol for finding effective paths from mobile sink to sensor nodes without consuming too many resources. Thus, Jiang et al. [21] suggested an algorithm, known as Virtual-node Greedy Embedding (VGE). The algorithm provides each node with a virtual coordinate, which ensures the greedy forwarding of the packets from sensor nodes to the sink when there is no failed node in the network or even if there are some obstacles. On initial embedding, new nodes join the network, and VGE maintains the greedy characteristics and does not change the node coordinate, which is predetermined. Knowing the destination coordinate (the mobile sink), sensor nodes send packets to the destination through a greedy forwarding algorithm. While gathering data in the sink node is not static so that data delivery delay is increased. Tang et al. [22] represented a substitution heuristic algorithm to minimize delivery delay, which is an NP-Complete problem. This algorithm solves the problem through reducing route length, moving the mobile sink, and gathering data simultaneously. The substitution heuristic algorithm includes four steps. In the first step, the positions of sensor nodes are selected as the initial positions of anchor points. The second step is to apply a classic heuristic algorithm to generate the initial path. In the third step, point substitution and line substitution are employed to minimize the initial path. The fourth step is to determine the visiting schedule through solving a linear programing problem. Zhao et al. [23] investigated the lifetime of WSNs. In this paper, the mobile sink periodically gathers data in a predefined path, and each sensor node sends its data to the mobile sink over a multi-hop communication path. The purpose of establishing a mobile sink is to reduce communication cost among sensor nodes. In fact, the mobility of sink increases the rate of data delivery and decreases energy consumption. However, there is an increase in gathering delay due to the physical speed of mobile sinks. Wichman et al. [24] focused on faster utilization of mobile sinks. They expanded a smooth path construction algorithm, based on travelling salesman algorithm. Then the algorithm was extended through selecting path based on the required time in each node. In RKM algorithm, represented by Kaswan et al. [25], a set of potential positions of RPs is initialized by k-means algorithm. The purpose of minimizing potential positions of RPs is to cover all sensors with the least possible hop distance. Thus, the energy is saved. In each iteration of RKM, all possible positions of RP covering a maximum of one sensor node is removed. Then the weight of the remaining potential positions of RPs is computed, and the highest value is selected. Next, the selected RP and the sensors covered by it are removed. This procedure continues until the initial set is empty. On acquiring the final set of RP positions, the path of sinks is obtained through the travelling salesman method.
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Table 1 Notations. Notation
Description
m L n w SCC MCC T t A, B, C, D a, b, c, d
Number of sensor nodes Network length Number of rows or columns Side length of each cells Single-hop communication cell Multi-hop communication cell Stop duration of the sink at anchor points Transferring timeout Anchor points for sink 1 Anchor points for sink 2 Sound speed Distance from cell agent to the sink Current cell agent Neighboring cell agent Distance from node V to the anchor of the sink Distance from current cell agent to the sink A set of neighbors with a cell agent, closer to the sink compared with current cell agent A set of neighbors with a cell agent, further to the sink compared with current cell agent
v X i V dV di Nnear Nfar
Sharma et al. [26] suggested Rendezvous-based routing protocol. The protocol creates a virtual cross area in the middle of the network and then a tree is formed in it. The tree nodes transfer data between the source node and the sink. The mobile sink transfers its position to the tree. The sensor node investigates the area and generates data. In this protocol, source nodes send data to the sink once it is required. The source node recovers the location of the sink from the tree and transfers data to it. 3. Proposed method In WSNs, gathering and delivering data to the sink node with the least energy consumption are essential issues. One way to resolve these issues is applying mobile sinks. This paper proposed the EGRPM method to reduce energy consumption and increase the lifetime of sensor nodes in WSNs. In EGRPM, the network is divided into some cells in a geographic way, and an agent is selected for each cell. The agent of each cell gathers data from the nodes of that area. Based on the communication between cells and mobile sinks, the cells are divided into two categories: single-hop communication cell (SCC) and multi-hop communication cell (MCC). SCCs are at a one-hop distance from the anchor points of mobile sinks, while MCCs are at a distance of more than one hop. In the proposed method, there are two mobile sinks, which move over two concentric diamond-shaped orbits. Due to using at least two mobile sinks with a 180◦ difference between their initial locations in two orbits, each sink covers half of the network at a time. Increased number of sinks causes a rise in the cost of the hardware of the network. The sinks move in the orbits in a clockwise direction with different speeds; they remain still at anchor points at particular intervals to gather data from sensor nodes. When the sinks remain still, the agents of SCCs send data to the sink directly, but the agents of MCCs send their data to the agents of their neighboring SCCs to be transferred to the sink. If there is no neighboring SCC, they search for the closest agent to the sink with the highest level of remaining energy among their neighbors. This procedure continues in the next cell until the data is delivered to the sink. The notations used in the proposed method are listed in Table 1. 3.1. Network model in EGRPM In EGRPM method, m homogeneous static sensors with limited energy are constantly distributed in an area of n × n size and each node can be considered as a source node or a router. When a node
Fig. 1. The overall flowchart of EGRPM.
senses data, it acts as a source node; but in a routing process, when the node sends the data of other nodes to the destination, it is considered as a router. All nodes are equipped with GPS so that they are aware of their positions in the network. There is a one-hop communication between sensor nodes and the cell agent. Also, the cell agent communicates with the agent of neighboring cells in the same way. The wireless sensor network is homogeneous and there is a similarity between the cell agents and other nodes of the network in terms of hardware, processing power, and energy. Both mobile sinks move in their own orbits inside the network to gather the data of cell nodes from the cell agents. The location of each mobile sink can be predicated by sensor nodes through a loose time synchronization, which has been proven in [27]. Also, Liu et al. [20] proven that after completing the loose clock synchronization, the bias between mobile sink clock and node clock could be ignored. Sinks are powerful nodes with unlimited resources and energy. The base station is statically located outside the field of the network, and its location is known to the mobile sinks.
3.2. EGRPM EGRPM consists of seven steps. The overall flowchart is illustrated in Fig. 1.
3.2.1. Step 1: Geographically celluralizing the network area For increasing network scalability and reducing communication overhead in the proposed method, the network area is geographically divided. As shown in Fig. 2, the network area is divided into n rows, and each row is consisted of n columns. Each cell is a square
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Fig. 2. Celluralizing the network area.
shaped orbit, concentric with orbit 1 is taken as orbit 2, the side of which is half the size of that of orbit 1. There is a 180◦ difference between the initial locations of sinks in these two orbits. So each sink covers half of the network at a time. As shown in Fig. 6, the sinks move in these orbits in a clockwise direction with different speeds.
Fig. 3. The hello packet.
of w × w size. The length of each cell (w) is obtained through Eq. (1). w=
L n
(1)
Where L denotes the network length, and n indicates the number of rows or columns. On cellularzing, an identifier is assigned into each cell. Each identifier includes an organized pair of row and column numbers. The number of rows and columns range from 0 to n − 1. 3.2.2. Step 2: Selecting the agents of cells In each cell, the node with the highest remaining energy is selected as the cell agent. All nodes send a Hello packet, containing their ID, remaining energy, and cell identifier, to their neighbors in the same cell. The hello packet is shown in Fig. 3. Receiving the hello packet, each node compares its remaining energy level with that of its neighbors. Since each node knows the remaining energy of all nodes in the same cell, the node with the highest level of remaining energy is selected by other nodes as an agent node. After selecting the cell agents, the network is as shown in Fig. 4. Whenever the remaining energy level of the agent node falls below 10%, the agent node sends an agent notification packet to other nodes in the same cell. The agent notification packet is shown in Fig. 5. This packet type field is set as zero and is considered as a relinquishment packet. When the relinquishment packet is sent, the process of selecting the cell agent is repeated. Thus, the agent is replaced to maintain its good performance and perform its tasks properly. 3.2.3. Step 3: Determining orbits for the movement of sinks There are two concentric diamond-shaped orbits for the movement of sinks. Orbit 1 (the larger one) is selected in such a way that four corners of the diamond are exactly located in the middle of the latitude and longitude of the network area. Another diamond-
3.2.4. Step 4: Determining anchor points and resting time of sinks for data gathering As shown in Fig. 7, the corners of the orbits are considered as anchor points, where the mobile sinks remain still for a particular period of time (T) to gather data from the cells around the anchor. T value is defined by the network manager based on the network application. In orbit 1, A, B, C, and D indicate the points. Also, a, b, c, and d denote the points in orbit 2. Since the side of orbit 2 is half that of orbit 1, mobile sinks’ traveling speed in orbit 1 is considered twice faster so that both sinks simultaneously reach the anchor points in two opposed directions. In fact, one sink is present within each half of the network at a time. 3.2.5. Step 5: Scheduling the resting time of sinks and announcing it to sensor nodes In this step, the resting time is scheduled and reported to all cell agents. Thus, the cell agents become aware of the location of anchors, resting time, and the time of reaching anchors so that the cell agents can make the best decision for sending data to the most suitable sink at each point. The mobile sinks report the schedule to the cell agents. In fact, they travel in their orbits and on reaching the anchor points, they report the schedule to the cell agents around themselves. Thus, all cell agents of the network become aware of the movement and rest schedule of the sinks. 3.2.6. Step 6: Sending sensed data to mobile sinks Sensor nodes send sensed data to the cell agents of their cell. Then cell agents integrate the data and decide about the receiver sink based on their own location, schedule, current location of sinks, remaining resting time of the sink, and transferring timeout (t). To ensure the data is received by the sink at current anchors, (t) should be considered. Transferring timeout (t) is dynamically calculated through Eq. (2) based on the distance from the cell agent to
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Fig. 4. Selecting the agent of cells.
from the sink; there is no neighbor at a one-hop distance from the sink. In the following, the modes are discussed. Fig. 5. Agent notification packet.
the sink. A further distance between the cell agent and sink results in a longer (t), and vice versa. t =
x
v
(2)
Where x denotes the distance from the cell agent to the sink, and v indicates the speed of sound. According to the schedule, there are three modes to transfer data to the sink: there is a one-hop distance from the current cell to the sink; the current cell has a neighbor, which is at a one-hop distance
• There is a one-hop distance from the cell agent to the sink (SCCs). According to the schedule, the sink location is defined. When the sink is at A, B, C, or D anchor point, the location of its neighbors is obtained by Eqs. (3)–(6), respectively. Similarly, if the sink is at a, b, c, or d anchor point, the location of its neighbors is calculated by Eqs. (7)–(10), respectively. If the identifier of the current cell and the cell of the sink’s neighbors are similar, the cell agent recognizes that it is at a one-hop distance from the anchor of the sink so that it sends the data to the sink directly.
0,
n 1 ± 2 2
Fig. 6. Determining orbits for the movement of sinks.
(3)
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Fig. 7. Determining anchor points to rest the mobile sinks.
As shown in Eq. (3), anchor A is located in the 0th row and the number of its column is between cell 2n + 1 and 2n − 1. So that the lower bound of 2n ± 12 is calculated to find its neighbors (n denotes the number of the rows or columns of the network).
n
1 ± , n − 1 2 2
(4)
Since the row number of anchor B is between cell 2n + 1 and n − 1, and it is located in the last column (n − 1), the neighbors 2 are determined by Eq. (4).
n − 1,
1 n ± 2 2
(5)
As shown in Eq. (5), anchor C is located in the last row, and its column number is between cell 2n + 1 and 2n − 1. So that the lower bound of 2n ± 12 is obtained to define its neighbors.
n
2
±
1 , 0 2
(6)
Since the row number of anchor D is between celll 2n + 1 and n − 1, and its column number is 0, Eq. (6) is applied to define its 2 neighbors.
n
4
±
1 1 n , ± 2 2 2
n 4
The row number of anchor a is between cell + 1 and − 1, and its column number is between cell 2n + 1 and 2n − 1. So that its neighbors are defined through Eq. (7).
2
±
3n 1 1 , ± 2 4 2
(8)
Since the row number of anchor b is between cell 2n + 1 and − 1, and its column number is between cell 3n + 1 and 3n − 1, 4 4 Eq. (8) is applied to define its neighbors. n 2
3n
4
±
n
2
±
n 1 1 , ± 2 4 2
(10)
The row number of anchor d is between cell 2n + 1 and 2n − 1, and its column number is between cell 4n + 1 and 4n − 1, so that Eq. (10) is applied to define its neighbors. • There is a neighboring cell at a one-hop distance from the sink (MCCs with a neighbor which is at a one-hop distance from the sink). When the current cell is not at a one-hop distance from the respective sink, it seeks to find its neighbors through Eq. (11). Then according to the schedule and the sink location, it seeks to find the neighbors of the sink through Eq. (3)–(10). If one of the neighbors of the current cell is at a one-hop-distance from the sink, the data is sent to it to be delivered to the sink. ((| Xi − Xnib |== 0 and (| Xi − Xnib |== 1 and
| Yi − Ynib |== 1) or | Yi − Ynib |== 0)
(11)
or (| Xi − Xnib |== 1 and | Yi − Ynib |== 1) | 0 X n − 1, 0 Y n − 1)
(7) n 4
n
Since the row number of anchor c is between cell 3n + 1 and 4 − 1, and its column number is between cell 2n + 1 and 2n − 1, its neighbors are defined through Eq. (9). 3n 4
n 1 1 , ± 2 2 2
(9)
Where (Xi , Yi ) and (Xnib , Ynib ) denote the cell identifier of the current cell and neighboring cell, respectively. Since the network dimensions range from 0 to n − 1, X and Y should not be out of this range. • There is no neighboring cell at a one-hop distance from the sink (MCCs with no neighbors at a one-hop distance from the sink) Here, the current cell agent sends an agent notification packet to its neighbors as a Req packet, calculated through Eq. (11). This agent notification packet is illustrated in Fig. 5. If the packet type is one, it means that the packet requests for the remaining energy and the distance of the neighboring agents to the respective sink. The agent of the neighboring cells measures its own distance to the respective sink through Eq. (12) and sends a Rep packet to the requested cell agent. As shown in Fig. 8, the Rep packet
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Fig. 8. Rep packet.
Fig. 9. Different modes of transferring data in EGRPM.
contains the agent node’s ID, cell identifier, remaining energy, and the distance of the agent node to the sink. dv =
(xv − xS )2 + (yv − yS )2
(12)
Where (xv , yv ) and (xS , yS ) denote the location of the neighboring cell agent and sink, respectively. On receiving the requested data, the cell agent divides its neighbors into Nnear and Nfar sets. Nnear set consists of neighbors closer to the sink compared with the current cell agent. Nfar set includes neighbors further to the sink compared with the current cell agent.
Table 2 Simulation parameters. Parameters
Value
Network area MAC protocol Number of sensor nodes Initial energy of sensor nodes Data packet size Communication range of each sensor node Number of mobile sinks Speed of mobile sink1 Speed of mobile sink2 Simulation time
500*500 m2 IEEE-802.11 50–300 2j 512 byte 30 m 2 4 m/s 2 m/s 600 s
Nnear (i) = {v ∈ Neighbors(i) | d(v) d(i) }
(13)
4. Simulation and results
Nfar (i) = {v ∈ Neighbors(i) | d(v) d(i) }
(14)
This section provides the results of simulation and evaluation of EGRPM algorithm. The simulation has been performed by NS2 software in Linux operating system. EGRPM method has been compared with RKM [25] and Rendezvous-based routing [26] in terms of different parameters such as average energy consumption, network lifetime, packet delivery rate, and end-to-end delay. To make a reasonable comparison between the protocols, EGRPM is compared with other protocols in the same environmental circumstances. The parameters of simulation are listed in Table 2. The results of simulation and the average energy consumption in EGRPM and other methods are illustrated in Fig. 11. As shown in Fig. 11, in EGRPM method, the average energy consumption of the network is lower than RKM and Rendezvous-based Routing. Since data is sent to the cell agent, which is close to other nodes, the nodes of each cell consume less energy. On the other hand, due to the presence of two mobile sinks and their movement models and orbits, there is just one sink in each half of the network area at a time. Thus, the cell agents transfer data at a short distance and consume less energy.
Where i, v, d(v) , and d(i) denote the current cell agent, the neighboring cell agent, the distance between node v and the sink, and the distance from the current cell agent to the sink, respectively. Then the cell agent with the highest level of energy is selected from Nnear set, and the data is sent to it. The procedure repeats in the next cell until the data is delivered to the respective sink. These three modes of transferring data are illustrated in Fig. 9. Fig. 10 illustrates the flowchart of the proposed routing algorithm.
3.2.7. Step 7: Sending sensed data to the base station through mobile sinks Completing their rotation in the orbit and gathering data from various nodes of the network, mobile sinks transfer the gathered data to the base station by one hop.
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Fig. 10. Flowchart of proposed routing algorithm in EGRPM.
Fig. 12. Average energy consumption in proportion to different node numbers. Fig. 11. Average energy consumption at different times.
Fig. 12 illustrates the energy consumption in proportion to different node numbers. Normally, increased number of nodes leads to a rise in energy consumption. However, on increasing the number of nodes and developing the network,
the energy consumption is still low in comparison to other methods. The average time required to transfer a data packet between two network nodes presents the delay of packet, called end-to-end
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Fig. 13. Average end to end delay.
Fig. 15. Packet delivery rate at different times.
Fig. 14. Packet delivery rate in proportion to different node numbers.
delay. Fig. 13 illustrates the results of simulation and comparing end-to-end delay in the aforementioned protocols. As shown in Fig. 13, increased number of the network nodes causes an increase in data delivery delay. However, the data delivery delay of the proposed method is lower compared with other methods. In EGRPM, there are three models for transferring data due to the presence of mobile sinks and the location of cell: the cell is at a one-hop distance from the sink (SCC) so that the cell agent immediately delivers data to the sink; the cell is at a two-hop distance from the sink (MCC with a neighbor at a one-hop distance from the sink) so that the data is transferred to the cell agents at a one-hop distance from the sink (SCCs) and then it is immediately delivered to the sink; the distance between the cell agents and the sink is more than two hops (MCCs do not have a neighbor at a onehop distance from the sink) so that the best path, the shortest one, is immediately selected. Therefore, the end-to-end delay is reduced. Fig. 14 illustrates the rate of data packet delivery in RKM, Rendezvous-based routing, and EGRPM. As shown in Fig. 14, on increasing the number of nodes, packet delivery rate of EGRPM is significantly increased compared with other protocols. In EGRPM, mobile sinks travel to the cell agents based on their mobility patterns and receive data in a reliable way. Also, reliable paths are selected through the routing process (the nodes of high energy and closer distance to the sink are selected). Similarly, the packets are only transferred within half of the network. Thus, the possibility of the collision between pack-
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Fig. 16. Network lifetime.
ets is minimized, and the rate of packet delivery is significantly increased. Fig. 15 illustrates the rate of packet delivery at different time. On passing time, the rate of packet delivery does not significantly change, which shows the constancy of the network. The reduction of energy consumption in nodes and load balance in sensor nodes are two significant, effective factors for the network lifetime. Fig. 16 shows that the lifetime of the network is improved in the proposed method compared with others. In EGRPM, the network is divided into some cells and an agent is assigned into each cell. The cell nodes transfer data to the cell agent, which is closer to other nodes. Thus, energy consumption is reduced. In addition, there is a sink in each half of the network area at a time, and the sinks are close to the cell agents as well and consume a low level of energy. Also, the shortest paths are selected to deliver data. Therefore, reduced energy consumption leads to increased lifetime of the network. 5. Conclusion In WSNs, transferring data from sensor nodes to the sink is a challenging issue. One method to resolve the issue is applying mobile sinks. This paper provides a routing method based on mobile sinks. In fact, two mobile sinks and an appropriate mobility pattern have been applied. Thus, the cells are offered access to
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one sink at a time, which causes cell agents transfer data to the sinks with the least energy consumption and more quickly. Dividing the network into cells leads to decreased energy consumption and increased scalability in the network. In this method, on determining the orbits, anchor points are selected for sinks to remain still and gather data from sensor nodes. During resting time, the data gathered in SCCs is directly sent to the sinks, and the data gathered in MCCs is send to the sinks through the proposed routing method. Utilizing some communication models causes a quick and reliable transmission of data to the sinks. The results of simulation reveals that EGRPM has significantly improved in terms of the average energy consumption, End-to-end delay, packet delivery rate, and network lifetime compared with conventional methods.
[12]
[13]
[14]
[15]
[16]
[17]
Conflicts of interest [18]
None declared. References ˇ cej, Single-hop vs. ´ J.J. Mohorko, K. Benkiˇc, Zˇ .F. Cuˇ [1] U.M. Peˇsovic, multi-hop-energy efficiency analysis in wireless sensor networks, 18th Telecommunications Forum, TELFOR (2010). [2] S. Saginbekov, A. Jhumka, Many-to-many data aggregation scheduling in wireless sensor networks with two sinks, Comput. Netw. 123 (2017) 184–199. [3] N.A. Pantazis, S.A. Nikolidakis, D.D. Vergados, Energy-efficient routing protocols in wireless sensor networks: A survey, IEEE Commun. Surv. Tutor. 15 (2) (2013) 551–591. [4] P. Rawat, K.D. Singh, H. Chaouchi, J.M. Bonnin, Wireless sensor networks: a survey on recent developments and potential synergies, J. Supercomput. 68 (1) (2014) 1–48. [5] S.K. Singh, M. Singh, D.K. Singh, et al., Routing protocols in wireless sensor networks – a survey, Int. J. Comput. Sci. Eng. Surv. (IJCSES) 1 (2) (2010) 63–83. [6] D. Yi, H. Yang, Heer-a delay-aware and energy-efficient routing protocol for wireless sensor networks, Comput. Netw. 104 (2016) 155–173. [7] O. Ahmad, Survey of Routing Protocols in Wireless Sensor Networks, 2014. [8] E.B. Hamida, G. Chelius, Strategies for data dissemination to mobile sinks in wireless sensor networks, IEEE Wirel. Commun. 15 (6) (2008) 31–37. [9] R. Yarinezhad, Reducing delay and prolonging the lifetime of wireless sensor network using efficient routing protocol based on mobile sink and virtual infrastructure, Ad Hoc Netw. 84 (2019) 42–55. [10] J. Wang, Y. Cao, B. Li, H.-J. Kim, S. Lee, Particle swarm optimization based clustering algorithm with mobile sink for wsns, Future Gener. Comput. Syst. 76 (2017) 452–457. [11] Y. Saleem, F. Salim, M.H. Rehmani, Resource management in mobile sink based wireless sensor networks through cloud computing, in: Resource
[19]
[20] [21]
[22]
[23]
[24]
[25]
[26]
[27]
Management in Mobile Computing Environments, Springer, 2014, pp. 439–459. C. Zhu, K. Quan, G. Han, J.J. Rodrigues, A high-available and location predictive data gathering scheme with mobile sinks for wireless sensor networks, Comput. Netw. 145 (2018) 156–164. J. Zhang, J. Tang, T. Wang, F. Chen, Energy-efficient data-gathering rendezvous algorithms with mobile sinks for wireless sensor networks, Int. J. Sens. Netw. 23 (4) (2017) 248–257. S. Ghafoor, M.H. Rehmani, S. Cho, S.-H. Park, An efficient trajectory design for mobile sink in a wireless sensor network, Comput. Electr. Eng. 40 (7) (2014) 2089–2100. C. Tunca, S. Isik, M.Y. Donmez, C. Ersoy, Distributed mobile sink routing for wireless sensor networks: a survey, IEEE Commun. Surv. Tutor. 16 (2) (2014) 877–897. A. Agrawal, V. Singh, S. Jain, R.K. Gupta, Gcrp: Grid-cycle routing protocol for wireless sensor network with mobile sink, AEU-Int. J. Electron. Commun. 94 (2018) 1–11. I. Chatzigiannakis, A. Kinalis, S. Nikoletseas, Efficient data propagation strategies in wireless sensor networks using a single mobile sink, Comput. Commun. 31 (5) (2008) 896–914. M. Faheem, V.C. Gungor, Mqrp: mobile sinks-based qos-aware data gathering protocol for wireless sensor networks-based smart grid applications in the context of industry 4.0-based on internet of things, Future Gener. Comput. Syst. 82 (2018) 358–374. J.-W. Kim, J.-S. In, K. Hur, J.-W. Kim, D.-S. Eom, An intelligent agent-based routing structure for mobile sinks in wsns, IEEE Trans. Consum. Electron. 56 (4) (2010). X. Liu, H. Zhao, X. Yang, X. Li, Sinktrail: a proactive data reporting protocol for wireless sensor networks, IEEE Trans. Comput. 62 (1) (2013) 151–162. Y. Jiang, W. Shi, X. Wang, H. Li, A distributed routing for wireless sensor networks with mobile sink based on the greedy embedding, Ad Hoc Netw. 20 (2014) 150–162. J. Tang, S. Guo, Y. Yang, Delivery latency minimization in wireless sensor networks with mobile sink, 2015 IEEE International Conference on Communications (ICC) (2015) 6481–6486. H. Zhao, S. Guo, X. Wang, F. Wang, Energy-efficient topology control algorithm for maximizing network lifetime in wireless sensor networks with mobile sink, Appl. Soft Comput. 34 (2015) 539–550. A. Wichmann, T. Korkmaz, Smooth path construction and adjustment for multiple mobile sinks in wireless sensor networks, Comput. Commun. 72 (2015) 93–106. A. Kaswan, K. Nitesh, P.K. Jana, Energy efficient path selection for mobile sink and data gathering in wireless sensor networks, AEU-Int. J. Electron. Commun. 73 (2017) 110–118. S. Sharma, D. Puthal, S.K. Jena, A.Y. Zomaya, R. Ranjan, Rendezvous based routing protocol for wireless sensor networks with mobile sink, J. Supercomput. 73 (3) (2017) 1168–1188. C. Zhu, Y. Wang, G. Han, J.J. Rodrigues, J. Lloret, Lpta: location predictive and time adaptive data gathering scheme with mobile sink for wireless sensor networks, Sci. World J. 2014 (2014).
Contents lists available at ScienceDirect
Sustainable Computing: Informatics and Systems journal homepage: www.elsevier.com/locate/suscom
EGRPM: Energy efficient geographic routing protocol based on mobile sink in wireless sensor networks Maryam Naghibi, Hamid Barati ∗ Department of Computer Engineering, Dezful Branch, Islamic Azad University, Dezful, Iran
a r t i c l e
i n f o
Article history: Received 26 April 2019 Received in revised form 29 October 2019 Accepted 31 January 2020 Available online 4 February 2020 Keywords: Wireless sensor network Routing Data gathering Mobile sink Network lifetime
a b s t r a c t A wireless sensor network consists of a large number of nodes, sending sensed data to the base station or sink, either directly or through intermediate nodes. Multi-hop communication results in increased volume of traffic and depleting the energy of nodes adjacent to static sinks. A method of dealing with this challenge is using mobile sinks. Mobile sinks balance the load and distribute energy consumption throughout the network. This paper suggests a method to divide the network into some cells in a geographic way and applies two mobile sinks to gather the data sensed by these cell nodes. Based on the communication between cells and mobile sinks, the cells are divided into two categories: single-hop communication cells (SCCs) and multi-hop communication cells (MCCs). Mobile sinks move over two concentric diamond-shaped orbits in such a way that each half of the network is covered by a sink at a time. Initially, both sinks move in one direction and stay at particular intervals in the corners of the orbits to gather data from sensor nodes. When sinks are stationary, SCCs send data to the sinks directly, but MCCs apply the proposed routing algorithm (EGRPM) to send data to mobile sinks. The proposed approach is simulated by NS2 software. A comparison between the performance of EGRPM and conventional methods shows that applying EGRPM results in a significant decrease in average energy consumption and data delivery delay and causes a substantial increase in packet delivery rate and network lifetime. © 2020 Elsevier Inc. All rights reserved.
1. Introduction A WSN includes a large number of small sensor nodes with limited communication and calculation abilities; the network is employed to gather and transfer data from an environment towards a user or the base station [1,2]. Recent developments in the technology of making small integrated circuits and developing the technology of wireless communication have led to designing WSNs. The major difference between these networks and conventional ones is due to their relationship with environment and physical phenomena. Conventional networks connect people and data bases, while sensor networks are directly connected with physical world. These networks observe physical environment through sensors and then decide and implement an appropriate operation [3,4]. WSNs are applied in many military and non-military applications including monitoring the environment and habitats, healthcare programs, home automation, and controlling traffic [5,6]. The major task of a sensor node is to sense and gather data from a particular
∗ Corresponding author. E-mail addresses: [email protected] (M. Naghibi), [email protected] (H. Barati). https://doi.org/10.1016/j.suscom.2020.100377 2210-5379/© 2020 Elsevier Inc. All rights reserved.
area and then process and transfer it to the sink. However, ensuring a direct communication between sensor nodes and sink might require nodes to broadcast their messages strongly, which results in a fast depletion in their resources [7]. Since the energy of nodes is limited and radio receiving and transferring require a lot of energy, the level of energy consumption of sensor nodes is a major issue [5]. Thus, the power of battery is one of the vital parameters in designing routing algorithms to increase the lifetime of nodes. In addition to maximizing the lifetime of sensor nodes, a proper and uniform distribution of energy throughout WSNs for maximizing the performance of the network is desirable [5,6]. In WSNs, the data packet, generated by sensors, reaches the base station through multi-hop paths. Due to the nature of WSNs, sensors are required to interact to transfer data to the sink. Since the energy of sensors is limited, transferring data to and receiving data from the sink cause increased energy consumption in the intermediate nodes; in fact, sensors adjacent to the sink (at a one-hop distance) show a greater involvement in transferring and receiving sensed data. Thus, the energy of sensors in an area near the sink is depleted faster, and they are removed from the network so that network nodes are incapable of transferring data to the sink. This issue is called hotspot and causes the network to die, while other sensors are supplied with enough energy. A mobile
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sink can keep the balance between sensors and is an appropriate solution for the hotspot problem [8–10]. When the sink travels, its neighboring nodes change. Also, in a multi-hop transferring, nodes transfer data across a shorter distance so that the energy is distributed throughout the network. For this reason, a uniform energy consumption pattern is available, and the lifetime of the network is increased. Applying a mobile sink causes sensor nodes to consume less energy for transferring data to the sink, and the displacement of the sink does not result in energy holes. Moreover, a mobile sink can travel in areas where the energy of sensor nodes is insufficient for transferring data. Thus, the lifetime and operation power of the network rise [11,12]. This paper proposed a mobile sink based routing method, named EGRPM. Here, the network is divided into cells in a geographic way, and two mobile sinks with the same mobility pattern for gathering sensed data by the cell nodes are used as well. Based on the type of the communication between cells and mobile sinks, the cells are categorized into two groups: single-hop communication cells (SCCs) and multi-hop communication cells (MCCs). SCCs are at a one-hop distance from the anchor points of mobile sinks, while MCCs are at a distance of more than one hop. Mobile sinks stop at particular intervals to gather data from sensor nodes. When a sink remains still, SCCs send data directly to it, but farther cells, MCCs, transfer data through the proposed routing algorithm. In EGRPM, according to the mobility pattern of sinks, each half of the network is covered by a sink. Thus, sending data to sinks is performed in half of the network, which results in saving sensor nodes’ energy and increased network lifetime. The rest of the paper is organized as follows: Section 2 represents an overview of the related work. In Section 3, the details of the proposed method, which is based on mobile sink, are presented. Then we provide the simulation of the proposed protocol and the analysis of its performance in Section 4. Finally, Section 5 concludes this paper. 2. Related work To avoid the problem of WSNs with static sinks (e.g. energy holes), mobile sinks are applied [13,14]. A mobile sink can use various mobility patterns in the field of sensor networks, which leads to energy efficiency and different data gathering strategies. Different mobility patterns can be categorized into three general groups: randomized, predictable, and controlled mobility [15,16]. Each pattern applies different functions and methods to calculate the future position of the sink. • Randomized mobility: according to this pattern, sinks move randomly in the field of sensors. The most characteristic of this pattern is simplicity and unpredictability of the future position of the sinks. Different methods of randomized mobility define different degrees of freedom to the movement of the sinks [17]. • Predictable mobility: in this pattern, the mobile sink needs a certain pattern, which can be determined. These patterns can be periodic movements along a predefined path. Here, the sensor nodes can become aware of the time the sink is adjacent to them and will optimize sensing task and data gathering [17,18]. • Controlled mobility: according to this pattern, the mobility pattern of the sink can be programed to provide more efficiency. One pattern is moving in a programed path. Controlled movement can be predictable such as observing a certain number of sensors in a particular period of time. The major issue of the controlled mobility is the definite programing of the sink movement to optimize the network lifetime [17]. Some routing algorithms with mobile sinks will be discussed in the following.
Kim et al. [19] suggested that to avoid overhead and energy consumption, caused by the movement of the sink node, other nodes should set their path to a static node, called agent node. Then the agent node sets its path to the new position of the sink node. A long distance data delivery is performed through multi hops. The sensor nodes and mobile sinks are aware of their own locations. On appearing a stimulus, the surrounding sensors jointly process the signal, and one of them changes into a source for generating data reports. Finally, the sinks search the network to gather sensed data. Sink-Trial protocol employs the logical coordinate space to calculate distance. A protocol, suggested by Liu et al. [20], has solved the problem of not knowing the geographical position of sensors through the messages sent by mobile sinks to the network at intervals. Each source node of the network saves a vector, known as trail vector, indicating the logical coordinate of that node. The vector contains the number of last hops of the node to the sink. Source nodes apply a greedy forwarding method to transfer their data. Saved trail vectors help to find the shortest distance to the sink. It is essential to discover a protocol for finding effective paths from mobile sink to sensor nodes without consuming too many resources. Thus, Jiang et al. [21] suggested an algorithm, known as Virtual-node Greedy Embedding (VGE). The algorithm provides each node with a virtual coordinate, which ensures the greedy forwarding of the packets from sensor nodes to the sink when there is no failed node in the network or even if there are some obstacles. On initial embedding, new nodes join the network, and VGE maintains the greedy characteristics and does not change the node coordinate, which is predetermined. Knowing the destination coordinate (the mobile sink), sensor nodes send packets to the destination through a greedy forwarding algorithm. While gathering data in the sink node is not static so that data delivery delay is increased. Tang et al. [22] represented a substitution heuristic algorithm to minimize delivery delay, which is an NP-Complete problem. This algorithm solves the problem through reducing route length, moving the mobile sink, and gathering data simultaneously. The substitution heuristic algorithm includes four steps. In the first step, the positions of sensor nodes are selected as the initial positions of anchor points. The second step is to apply a classic heuristic algorithm to generate the initial path. In the third step, point substitution and line substitution are employed to minimize the initial path. The fourth step is to determine the visiting schedule through solving a linear programing problem. Zhao et al. [23] investigated the lifetime of WSNs. In this paper, the mobile sink periodically gathers data in a predefined path, and each sensor node sends its data to the mobile sink over a multi-hop communication path. The purpose of establishing a mobile sink is to reduce communication cost among sensor nodes. In fact, the mobility of sink increases the rate of data delivery and decreases energy consumption. However, there is an increase in gathering delay due to the physical speed of mobile sinks. Wichman et al. [24] focused on faster utilization of mobile sinks. They expanded a smooth path construction algorithm, based on travelling salesman algorithm. Then the algorithm was extended through selecting path based on the required time in each node. In RKM algorithm, represented by Kaswan et al. [25], a set of potential positions of RPs is initialized by k-means algorithm. The purpose of minimizing potential positions of RPs is to cover all sensors with the least possible hop distance. Thus, the energy is saved. In each iteration of RKM, all possible positions of RP covering a maximum of one sensor node is removed. Then the weight of the remaining potential positions of RPs is computed, and the highest value is selected. Next, the selected RP and the sensors covered by it are removed. This procedure continues until the initial set is empty. On acquiring the final set of RP positions, the path of sinks is obtained through the travelling salesman method.
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Table 1 Notations. Notation
Description
m L n w SCC MCC T t A, B, C, D a, b, c, d
Number of sensor nodes Network length Number of rows or columns Side length of each cells Single-hop communication cell Multi-hop communication cell Stop duration of the sink at anchor points Transferring timeout Anchor points for sink 1 Anchor points for sink 2 Sound speed Distance from cell agent to the sink Current cell agent Neighboring cell agent Distance from node V to the anchor of the sink Distance from current cell agent to the sink A set of neighbors with a cell agent, closer to the sink compared with current cell agent A set of neighbors with a cell agent, further to the sink compared with current cell agent
v X i V dV di Nnear Nfar
Sharma et al. [26] suggested Rendezvous-based routing protocol. The protocol creates a virtual cross area in the middle of the network and then a tree is formed in it. The tree nodes transfer data between the source node and the sink. The mobile sink transfers its position to the tree. The sensor node investigates the area and generates data. In this protocol, source nodes send data to the sink once it is required. The source node recovers the location of the sink from the tree and transfers data to it. 3. Proposed method In WSNs, gathering and delivering data to the sink node with the least energy consumption are essential issues. One way to resolve these issues is applying mobile sinks. This paper proposed the EGRPM method to reduce energy consumption and increase the lifetime of sensor nodes in WSNs. In EGRPM, the network is divided into some cells in a geographic way, and an agent is selected for each cell. The agent of each cell gathers data from the nodes of that area. Based on the communication between cells and mobile sinks, the cells are divided into two categories: single-hop communication cell (SCC) and multi-hop communication cell (MCC). SCCs are at a one-hop distance from the anchor points of mobile sinks, while MCCs are at a distance of more than one hop. In the proposed method, there are two mobile sinks, which move over two concentric diamond-shaped orbits. Due to using at least two mobile sinks with a 180◦ difference between their initial locations in two orbits, each sink covers half of the network at a time. Increased number of sinks causes a rise in the cost of the hardware of the network. The sinks move in the orbits in a clockwise direction with different speeds; they remain still at anchor points at particular intervals to gather data from sensor nodes. When the sinks remain still, the agents of SCCs send data to the sink directly, but the agents of MCCs send their data to the agents of their neighboring SCCs to be transferred to the sink. If there is no neighboring SCC, they search for the closest agent to the sink with the highest level of remaining energy among their neighbors. This procedure continues in the next cell until the data is delivered to the sink. The notations used in the proposed method are listed in Table 1. 3.1. Network model in EGRPM In EGRPM method, m homogeneous static sensors with limited energy are constantly distributed in an area of n × n size and each node can be considered as a source node or a router. When a node
Fig. 1. The overall flowchart of EGRPM.
senses data, it acts as a source node; but in a routing process, when the node sends the data of other nodes to the destination, it is considered as a router. All nodes are equipped with GPS so that they are aware of their positions in the network. There is a one-hop communication between sensor nodes and the cell agent. Also, the cell agent communicates with the agent of neighboring cells in the same way. The wireless sensor network is homogeneous and there is a similarity between the cell agents and other nodes of the network in terms of hardware, processing power, and energy. Both mobile sinks move in their own orbits inside the network to gather the data of cell nodes from the cell agents. The location of each mobile sink can be predicated by sensor nodes through a loose time synchronization, which has been proven in [27]. Also, Liu et al. [20] proven that after completing the loose clock synchronization, the bias between mobile sink clock and node clock could be ignored. Sinks are powerful nodes with unlimited resources and energy. The base station is statically located outside the field of the network, and its location is known to the mobile sinks.
3.2. EGRPM EGRPM consists of seven steps. The overall flowchart is illustrated in Fig. 1.
3.2.1. Step 1: Geographically celluralizing the network area For increasing network scalability and reducing communication overhead in the proposed method, the network area is geographically divided. As shown in Fig. 2, the network area is divided into n rows, and each row is consisted of n columns. Each cell is a square
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Fig. 2. Celluralizing the network area.
shaped orbit, concentric with orbit 1 is taken as orbit 2, the side of which is half the size of that of orbit 1. There is a 180◦ difference between the initial locations of sinks in these two orbits. So each sink covers half of the network at a time. As shown in Fig. 6, the sinks move in these orbits in a clockwise direction with different speeds.
Fig. 3. The hello packet.
of w × w size. The length of each cell (w) is obtained through Eq. (1). w=
L n
(1)
Where L denotes the network length, and n indicates the number of rows or columns. On cellularzing, an identifier is assigned into each cell. Each identifier includes an organized pair of row and column numbers. The number of rows and columns range from 0 to n − 1. 3.2.2. Step 2: Selecting the agents of cells In each cell, the node with the highest remaining energy is selected as the cell agent. All nodes send a Hello packet, containing their ID, remaining energy, and cell identifier, to their neighbors in the same cell. The hello packet is shown in Fig. 3. Receiving the hello packet, each node compares its remaining energy level with that of its neighbors. Since each node knows the remaining energy of all nodes in the same cell, the node with the highest level of remaining energy is selected by other nodes as an agent node. After selecting the cell agents, the network is as shown in Fig. 4. Whenever the remaining energy level of the agent node falls below 10%, the agent node sends an agent notification packet to other nodes in the same cell. The agent notification packet is shown in Fig. 5. This packet type field is set as zero and is considered as a relinquishment packet. When the relinquishment packet is sent, the process of selecting the cell agent is repeated. Thus, the agent is replaced to maintain its good performance and perform its tasks properly. 3.2.3. Step 3: Determining orbits for the movement of sinks There are two concentric diamond-shaped orbits for the movement of sinks. Orbit 1 (the larger one) is selected in such a way that four corners of the diamond are exactly located in the middle of the latitude and longitude of the network area. Another diamond-
3.2.4. Step 4: Determining anchor points and resting time of sinks for data gathering As shown in Fig. 7, the corners of the orbits are considered as anchor points, where the mobile sinks remain still for a particular period of time (T) to gather data from the cells around the anchor. T value is defined by the network manager based on the network application. In orbit 1, A, B, C, and D indicate the points. Also, a, b, c, and d denote the points in orbit 2. Since the side of orbit 2 is half that of orbit 1, mobile sinks’ traveling speed in orbit 1 is considered twice faster so that both sinks simultaneously reach the anchor points in two opposed directions. In fact, one sink is present within each half of the network at a time. 3.2.5. Step 5: Scheduling the resting time of sinks and announcing it to sensor nodes In this step, the resting time is scheduled and reported to all cell agents. Thus, the cell agents become aware of the location of anchors, resting time, and the time of reaching anchors so that the cell agents can make the best decision for sending data to the most suitable sink at each point. The mobile sinks report the schedule to the cell agents. In fact, they travel in their orbits and on reaching the anchor points, they report the schedule to the cell agents around themselves. Thus, all cell agents of the network become aware of the movement and rest schedule of the sinks. 3.2.6. Step 6: Sending sensed data to mobile sinks Sensor nodes send sensed data to the cell agents of their cell. Then cell agents integrate the data and decide about the receiver sink based on their own location, schedule, current location of sinks, remaining resting time of the sink, and transferring timeout (t). To ensure the data is received by the sink at current anchors, (t) should be considered. Transferring timeout (t) is dynamically calculated through Eq. (2) based on the distance from the cell agent to
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Fig. 4. Selecting the agent of cells.
from the sink; there is no neighbor at a one-hop distance from the sink. In the following, the modes are discussed. Fig. 5. Agent notification packet.
the sink. A further distance between the cell agent and sink results in a longer (t), and vice versa. t =
x
v
(2)
Where x denotes the distance from the cell agent to the sink, and v indicates the speed of sound. According to the schedule, there are three modes to transfer data to the sink: there is a one-hop distance from the current cell to the sink; the current cell has a neighbor, which is at a one-hop distance
• There is a one-hop distance from the cell agent to the sink (SCCs). According to the schedule, the sink location is defined. When the sink is at A, B, C, or D anchor point, the location of its neighbors is obtained by Eqs. (3)–(6), respectively. Similarly, if the sink is at a, b, c, or d anchor point, the location of its neighbors is calculated by Eqs. (7)–(10), respectively. If the identifier of the current cell and the cell of the sink’s neighbors are similar, the cell agent recognizes that it is at a one-hop distance from the anchor of the sink so that it sends the data to the sink directly.
0,
n 1 ± 2 2
Fig. 6. Determining orbits for the movement of sinks.
(3)
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Fig. 7. Determining anchor points to rest the mobile sinks.
As shown in Eq. (3), anchor A is located in the 0th row and the number of its column is between cell 2n + 1 and 2n − 1. So that the lower bound of 2n ± 12 is calculated to find its neighbors (n denotes the number of the rows or columns of the network).
n
1 ± , n − 1 2 2
(4)
Since the row number of anchor B is between cell 2n + 1 and n − 1, and it is located in the last column (n − 1), the neighbors 2 are determined by Eq. (4).
n − 1,
1 n ± 2 2
(5)
As shown in Eq. (5), anchor C is located in the last row, and its column number is between cell 2n + 1 and 2n − 1. So that the lower bound of 2n ± 12 is obtained to define its neighbors.
n
2
±
1 , 0 2
(6)
Since the row number of anchor D is between celll 2n + 1 and n − 1, and its column number is 0, Eq. (6) is applied to define its 2 neighbors.
n
4
±
1 1 n , ± 2 2 2
n 4
The row number of anchor a is between cell + 1 and − 1, and its column number is between cell 2n + 1 and 2n − 1. So that its neighbors are defined through Eq. (7).
2
±
3n 1 1 , ± 2 4 2
(8)
Since the row number of anchor b is between cell 2n + 1 and − 1, and its column number is between cell 3n + 1 and 3n − 1, 4 4 Eq. (8) is applied to define its neighbors. n 2
3n
4
±
n
2
±
n 1 1 , ± 2 4 2
(10)
The row number of anchor d is between cell 2n + 1 and 2n − 1, and its column number is between cell 4n + 1 and 4n − 1, so that Eq. (10) is applied to define its neighbors. • There is a neighboring cell at a one-hop distance from the sink (MCCs with a neighbor which is at a one-hop distance from the sink). When the current cell is not at a one-hop distance from the respective sink, it seeks to find its neighbors through Eq. (11). Then according to the schedule and the sink location, it seeks to find the neighbors of the sink through Eq. (3)–(10). If one of the neighbors of the current cell is at a one-hop-distance from the sink, the data is sent to it to be delivered to the sink. ((| Xi − Xnib |== 0 and (| Xi − Xnib |== 1 and
| Yi − Ynib |== 1) or | Yi − Ynib |== 0)
(11)
or (| Xi − Xnib |== 1 and | Yi − Ynib |== 1) | 0 X n − 1, 0 Y n − 1)
(7) n 4
n
Since the row number of anchor c is between cell 3n + 1 and 4 − 1, and its column number is between cell 2n + 1 and 2n − 1, its neighbors are defined through Eq. (9). 3n 4
n 1 1 , ± 2 2 2
(9)
Where (Xi , Yi ) and (Xnib , Ynib ) denote the cell identifier of the current cell and neighboring cell, respectively. Since the network dimensions range from 0 to n − 1, X and Y should not be out of this range. • There is no neighboring cell at a one-hop distance from the sink (MCCs with no neighbors at a one-hop distance from the sink) Here, the current cell agent sends an agent notification packet to its neighbors as a Req packet, calculated through Eq. (11). This agent notification packet is illustrated in Fig. 5. If the packet type is one, it means that the packet requests for the remaining energy and the distance of the neighboring agents to the respective sink. The agent of the neighboring cells measures its own distance to the respective sink through Eq. (12) and sends a Rep packet to the requested cell agent. As shown in Fig. 8, the Rep packet
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Fig. 8. Rep packet.
Fig. 9. Different modes of transferring data in EGRPM.
contains the agent node’s ID, cell identifier, remaining energy, and the distance of the agent node to the sink. dv =
(xv − xS )2 + (yv − yS )2
(12)
Where (xv , yv ) and (xS , yS ) denote the location of the neighboring cell agent and sink, respectively. On receiving the requested data, the cell agent divides its neighbors into Nnear and Nfar sets. Nnear set consists of neighbors closer to the sink compared with the current cell agent. Nfar set includes neighbors further to the sink compared with the current cell agent.
Table 2 Simulation parameters. Parameters
Value
Network area MAC protocol Number of sensor nodes Initial energy of sensor nodes Data packet size Communication range of each sensor node Number of mobile sinks Speed of mobile sink1 Speed of mobile sink2 Simulation time
500*500 m2 IEEE-802.11 50–300 2j 512 byte 30 m 2 4 m/s 2 m/s 600 s
Nnear (i) = {v ∈ Neighbors(i) | d(v) d(i) }
(13)
4. Simulation and results
Nfar (i) = {v ∈ Neighbors(i) | d(v) d(i) }
(14)
This section provides the results of simulation and evaluation of EGRPM algorithm. The simulation has been performed by NS2 software in Linux operating system. EGRPM method has been compared with RKM [25] and Rendezvous-based routing [26] in terms of different parameters such as average energy consumption, network lifetime, packet delivery rate, and end-to-end delay. To make a reasonable comparison between the protocols, EGRPM is compared with other protocols in the same environmental circumstances. The parameters of simulation are listed in Table 2. The results of simulation and the average energy consumption in EGRPM and other methods are illustrated in Fig. 11. As shown in Fig. 11, in EGRPM method, the average energy consumption of the network is lower than RKM and Rendezvous-based Routing. Since data is sent to the cell agent, which is close to other nodes, the nodes of each cell consume less energy. On the other hand, due to the presence of two mobile sinks and their movement models and orbits, there is just one sink in each half of the network area at a time. Thus, the cell agents transfer data at a short distance and consume less energy.
Where i, v, d(v) , and d(i) denote the current cell agent, the neighboring cell agent, the distance between node v and the sink, and the distance from the current cell agent to the sink, respectively. Then the cell agent with the highest level of energy is selected from Nnear set, and the data is sent to it. The procedure repeats in the next cell until the data is delivered to the respective sink. These three modes of transferring data are illustrated in Fig. 9. Fig. 10 illustrates the flowchart of the proposed routing algorithm.
3.2.7. Step 7: Sending sensed data to the base station through mobile sinks Completing their rotation in the orbit and gathering data from various nodes of the network, mobile sinks transfer the gathered data to the base station by one hop.
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Fig. 10. Flowchart of proposed routing algorithm in EGRPM.
Fig. 12. Average energy consumption in proportion to different node numbers. Fig. 11. Average energy consumption at different times.
Fig. 12 illustrates the energy consumption in proportion to different node numbers. Normally, increased number of nodes leads to a rise in energy consumption. However, on increasing the number of nodes and developing the network,
the energy consumption is still low in comparison to other methods. The average time required to transfer a data packet between two network nodes presents the delay of packet, called end-to-end
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Fig. 13. Average end to end delay.
Fig. 15. Packet delivery rate at different times.
Fig. 14. Packet delivery rate in proportion to different node numbers.
delay. Fig. 13 illustrates the results of simulation and comparing end-to-end delay in the aforementioned protocols. As shown in Fig. 13, increased number of the network nodes causes an increase in data delivery delay. However, the data delivery delay of the proposed method is lower compared with other methods. In EGRPM, there are three models for transferring data due to the presence of mobile sinks and the location of cell: the cell is at a one-hop distance from the sink (SCC) so that the cell agent immediately delivers data to the sink; the cell is at a two-hop distance from the sink (MCC with a neighbor at a one-hop distance from the sink) so that the data is transferred to the cell agents at a one-hop distance from the sink (SCCs) and then it is immediately delivered to the sink; the distance between the cell agents and the sink is more than two hops (MCCs do not have a neighbor at a onehop distance from the sink) so that the best path, the shortest one, is immediately selected. Therefore, the end-to-end delay is reduced. Fig. 14 illustrates the rate of data packet delivery in RKM, Rendezvous-based routing, and EGRPM. As shown in Fig. 14, on increasing the number of nodes, packet delivery rate of EGRPM is significantly increased compared with other protocols. In EGRPM, mobile sinks travel to the cell agents based on their mobility patterns and receive data in a reliable way. Also, reliable paths are selected through the routing process (the nodes of high energy and closer distance to the sink are selected). Similarly, the packets are only transferred within half of the network. Thus, the possibility of the collision between pack-
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Fig. 16. Network lifetime.
ets is minimized, and the rate of packet delivery is significantly increased. Fig. 15 illustrates the rate of packet delivery at different time. On passing time, the rate of packet delivery does not significantly change, which shows the constancy of the network. The reduction of energy consumption in nodes and load balance in sensor nodes are two significant, effective factors for the network lifetime. Fig. 16 shows that the lifetime of the network is improved in the proposed method compared with others. In EGRPM, the network is divided into some cells and an agent is assigned into each cell. The cell nodes transfer data to the cell agent, which is closer to other nodes. Thus, energy consumption is reduced. In addition, there is a sink in each half of the network area at a time, and the sinks are close to the cell agents as well and consume a low level of energy. Also, the shortest paths are selected to deliver data. Therefore, reduced energy consumption leads to increased lifetime of the network. 5. Conclusion In WSNs, transferring data from sensor nodes to the sink is a challenging issue. One method to resolve the issue is applying mobile sinks. This paper provides a routing method based on mobile sinks. In fact, two mobile sinks and an appropriate mobility pattern have been applied. Thus, the cells are offered access to
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one sink at a time, which causes cell agents transfer data to the sinks with the least energy consumption and more quickly. Dividing the network into cells leads to decreased energy consumption and increased scalability in the network. In this method, on determining the orbits, anchor points are selected for sinks to remain still and gather data from sensor nodes. During resting time, the data gathered in SCCs is directly sent to the sinks, and the data gathered in MCCs is send to the sinks through the proposed routing method. Utilizing some communication models causes a quick and reliable transmission of data to the sinks. The results of simulation reveals that EGRPM has significantly improved in terms of the average energy consumption, End-to-end delay, packet delivery rate, and network lifetime compared with conventional methods.
[12]
[13]
[14]
[15]
[16]
[17]
Conflicts of interest [18]
None declared. References ˇ cej, Single-hop vs. ´ J.J. Mohorko, K. Benkiˇc, Zˇ .F. Cuˇ [1] U.M. Peˇsovic, multi-hop-energy efficiency analysis in wireless sensor networks, 18th Telecommunications Forum, TELFOR (2010). [2] S. Saginbekov, A. Jhumka, Many-to-many data aggregation scheduling in wireless sensor networks with two sinks, Comput. Netw. 123 (2017) 184–199. [3] N.A. Pantazis, S.A. Nikolidakis, D.D. Vergados, Energy-efficient routing protocols in wireless sensor networks: A survey, IEEE Commun. Surv. Tutor. 15 (2) (2013) 551–591. [4] P. Rawat, K.D. Singh, H. Chaouchi, J.M. Bonnin, Wireless sensor networks: a survey on recent developments and potential synergies, J. Supercomput. 68 (1) (2014) 1–48. [5] S.K. Singh, M. Singh, D.K. Singh, et al., Routing protocols in wireless sensor networks – a survey, Int. J. Comput. Sci. Eng. Surv. (IJCSES) 1 (2) (2010) 63–83. [6] D. Yi, H. Yang, Heer-a delay-aware and energy-efficient routing protocol for wireless sensor networks, Comput. Netw. 104 (2016) 155–173. [7] O. Ahmad, Survey of Routing Protocols in Wireless Sensor Networks, 2014. [8] E.B. Hamida, G. Chelius, Strategies for data dissemination to mobile sinks in wireless sensor networks, IEEE Wirel. Commun. 15 (6) (2008) 31–37. [9] R. Yarinezhad, Reducing delay and prolonging the lifetime of wireless sensor network using efficient routing protocol based on mobile sink and virtual infrastructure, Ad Hoc Netw. 84 (2019) 42–55. [10] J. Wang, Y. Cao, B. Li, H.-J. Kim, S. Lee, Particle swarm optimization based clustering algorithm with mobile sink for wsns, Future Gener. Comput. Syst. 76 (2017) 452–457. [11] Y. Saleem, F. Salim, M.H. Rehmani, Resource management in mobile sink based wireless sensor networks through cloud computing, in: Resource
[19]
[20] [21]
[22]
[23]
[24]
[25]
[26]
[27]
Management in Mobile Computing Environments, Springer, 2014, pp. 439–459. C. Zhu, K. Quan, G. Han, J.J. Rodrigues, A high-available and location predictive data gathering scheme with mobile sinks for wireless sensor networks, Comput. Netw. 145 (2018) 156–164. J. Zhang, J. Tang, T. Wang, F. Chen, Energy-efficient data-gathering rendezvous algorithms with mobile sinks for wireless sensor networks, Int. J. Sens. Netw. 23 (4) (2017) 248–257. S. Ghafoor, M.H. Rehmani, S. Cho, S.-H. Park, An efficient trajectory design for mobile sink in a wireless sensor network, Comput. Electr. Eng. 40 (7) (2014) 2089–2100. C. Tunca, S. Isik, M.Y. Donmez, C. Ersoy, Distributed mobile sink routing for wireless sensor networks: a survey, IEEE Commun. Surv. Tutor. 16 (2) (2014) 877–897. A. Agrawal, V. Singh, S. Jain, R.K. Gupta, Gcrp: Grid-cycle routing protocol for wireless sensor network with mobile sink, AEU-Int. J. Electron. Commun. 94 (2018) 1–11. I. Chatzigiannakis, A. Kinalis, S. Nikoletseas, Efficient data propagation strategies in wireless sensor networks using a single mobile sink, Comput. Commun. 31 (5) (2008) 896–914. M. Faheem, V.C. Gungor, Mqrp: mobile sinks-based qos-aware data gathering protocol for wireless sensor networks-based smart grid applications in the context of industry 4.0-based on internet of things, Future Gener. Comput. Syst. 82 (2018) 358–374. J.-W. Kim, J.-S. In, K. Hur, J.-W. Kim, D.-S. Eom, An intelligent agent-based routing structure for mobile sinks in wsns, IEEE Trans. Consum. Electron. 56 (4) (2010). X. Liu, H. Zhao, X. Yang, X. Li, Sinktrail: a proactive data reporting protocol for wireless sensor networks, IEEE Trans. Comput. 62 (1) (2013) 151–162. Y. Jiang, W. Shi, X. Wang, H. Li, A distributed routing for wireless sensor networks with mobile sink based on the greedy embedding, Ad Hoc Netw. 20 (2014) 150–162. J. Tang, S. Guo, Y. Yang, Delivery latency minimization in wireless sensor networks with mobile sink, 2015 IEEE International Conference on Communications (ICC) (2015) 6481–6486. H. Zhao, S. Guo, X. Wang, F. Wang, Energy-efficient topology control algorithm for maximizing network lifetime in wireless sensor networks with mobile sink, Appl. Soft Comput. 34 (2015) 539–550. A. Wichmann, T. Korkmaz, Smooth path construction and adjustment for multiple mobile sinks in wireless sensor networks, Comput. Commun. 72 (2015) 93–106. A. Kaswan, K. Nitesh, P.K. Jana, Energy efficient path selection for mobile sink and data gathering in wireless sensor networks, AEU-Int. J. Electron. Commun. 73 (2017) 110–118. S. Sharma, D. Puthal, S.K. Jena, A.Y. Zomaya, R. Ranjan, Rendezvous based routing protocol for wireless sensor networks with mobile sink, J. Supercomput. 73 (3) (2017) 1168–1188. C. Zhu, Y. Wang, G. Han, J.J. Rodrigues, J. Lloret, Lpta: location predictive and time adaptive data gathering scheme with mobile sink for wireless sensor networks, Sci. World J. 2014 (2014).