ABRM: In-Network Aggregation Based Routing Protocol for Mobile Sensor Networks with Multiple Mobile Sinks

Maged S. Soliman1, Hossam M. A. Fahmy2, Ashraf E. Salem3

Computer and Systems Engineering Department Faculty of Engineering Ain Shams University

Cairo, Egypt 1magedsalah@msn.com, 2hossam.fahmy@ieee.org,3ashraf.salem@eng.asu.edu.eg

Abstract— The recent technological advances in the field of wireless sensor networks (WSN) have expanded the range of WSN applications. In some of these applications, sensor nodes are mobile rather than static. Also, the recent advances in personal digitals assistants (PDAs) allow the existence of multiple mobile sinks to collect the sensors data. These characteristics require the design of new routing protocols to meet the existence of mobile sensors and multiple mobile sinks while taken into consideration the limited resources for sensor nodes especially energy. In-network aggregation is one of the important techniques used to save power consumption. This paper presents ABRM which is an in-network aggregation based routing protocol for mobile sensor networks with multiple mobile sinks. Compared to CCBR, ABRM yields good aggregation results in addition to a great reduction in power consumption and routing cost.

Keywords- aggregation; content; context; mobility; routing; WSN

I. INTRODUCTION The latest developments in WSNs have expanded the range of WSN from traditional static sensors, deployed in an area, to collect some measurements for a single static sink, to mobile sensors with multiple mobile sinks. This is convenient for applications like animal or elderly people monitoring, where the sensors are attached to the moving bodies and data are collected through PDAs moving around. For mobile sensor networks (MSNs), CCBR is the sole protocol that targets MSNs with multiple mobile sinks [1]. CCBR is a context and content-based routing protocol that takes into consideration the context (i.e. properties) of the sensor nodes e.g. the sensor holder type and its age in data filtering besides filtering based on the data type and its content. In order to collect data, each sink disseminates its interests across the sensor nodes in the network. Sensor nodes store these interests in case the interest context matches the node properties. When a sensor node generates a data, it forwards it towards the interested sinks. In order to overcome continuous topology changes, CCBR adopts a receiver based approach where each neighbor decides to forward the received packet or not. The main drawback of CCBR is the overhead resulting from data packets duplication towards the sinks due to the adoption of the receiver based approach. Protocols for sensor networks should be carefully designed to make the most efficient use of the limited

resources in terms of energy, computation, and storage. The communication is often several orders of magnitude higher than the computation cost. In-network data aggregation [2] is considered an effective technique to reduce communications cost by eliminating the inherent redundancy in raw data collected from the sensors. In-network aggregation based routing protocols are divided to the following approaches: tree-based, cluster-based, multipath, and hybrid. Multipath approach as proposed in [3],[4],[5],[6] is the most applicable approach for dynamic networks. The main idea of the approach is that each node can broadcast its packet to its neighbors taking the advantages of wireless medium approach. Hence, packets can flow from source nodes to the sinks along multiple paths and packets can be aggregated by each node within these paths. Synopsis diffusion [3] is a general framework for data aggregation based on multipath approach and uses algorithms to avoid the double counting problem. In synopsis diffusion, sensor nodes are organized as concentric rings around the sink, each ring represents a distance (hops) to the sinks. In addition to this simple ring topology, synopsis diffusion introduced another topology called adaptive rings to increase the robustness of the network due to nodes failure or movement. Synopsis diffusion is suitable for MSNs but it lacks introducing a full routing protocol. In the meantime, it ignores the existence of multiple mobile sinks. Tributaries and deltas [4] combines the features of both tree-based and multipath approaches. The protocol uses data aggregation tree with the network stable portions (that have low packet loss rate). In order to provide robustness, the protocol uses the multipath approach in the network portions that face high packet loss rate, or the network portions that carry partial aggregation results accumulated from many sensor readings. The major weakness of this hybrid approach is the possible overhead required to maintain the data aggregation structure in addition to the lack of dealing with mobility of sensor nodes. CMR [5] (Content-based Multipath Routing) is designed for in-network processing of grouped aggregation queries. CMR decides at each node how to forward aggregates to different candidate parents where each node forwards aggregates in such a way that in-network processing among aggregates belonging to the same group is performed more

2013 IEEE 27th International Conference on Advanced Information Networking and Applications

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DOI 10.1109/AINA.2013.39

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frequently and early. CMR does not consider the mobility of nodes and sink and the existence of multiple of sinks as well. In [6] an extension for CCBR is proposed to provide in-network aggregation, but it lacks considering the existence of multiple mobile sinks. So far, limited research has dealt with in-network aggregation routing protocols for MSNs, and the existence of multiple mobile sinks has been overlooked as well. This paper proposes ABRM, an in-network aggregation based routing protocol designed for MSNs with multiple mobile sinks. The organization of the paper is as follows: Section II presents a description of the proposed ABRM protocol, Section III presents the performance evaluation of the ABRM protocol, finally, section IV concludes the paper and provides directions for future work.

II. ABRM DESCRIPTION ABRM routing protocol is built upon CCBR routing protocol. ABRM is a receiver based routing protocol for mobile sensors network where the data is aggregated towards the sinks. Both the sensors and sinks are mobile. Sinks transmit a beaconing message to the network nodes, every beaconing period, in order to update their costs (number of hops) towards the sinks and save it in their distance table. Figure 1(a) shows the beaconing message format. A beaconing message consists of three fields each of 8 bits size. The three fields are as follows: sequence number, time to live (TTL), and source address. The sinks may encapsulate an interest message inside the transmitted beaconing message to announce the data it is interested in from the sensors. Figure 1(b) shows the format of the beaconing message with interest message encapsulated. The interest message fields and their sizes may vary based on the application. The interest message contains the following fields: • Data ID, which identifies the required data type, e.g.

temperature or pressure. • Data Filter Min and Data Filter Max, which identify the

boundaries of the data values that a sink is interested to receive, e.g. temperature between 40 C and 50 C.

• Property filter to select the sensor nodes properties a sink is interested to receive, e.g. animal type = cow, horse, or sheep.

• The aggregation function, the function that determines how data is to be aggregated, e.g. Min, Max, Sum, and Count.

• The activation time, the time when the interest will be activated on sensor nodes in order to start collecting data that match the interest.

When a node receives the interest message that matches its properties, it updates its interest table with the new data. When a sensor node generates a data and there is at least one interested sink, it creates a data packet as the one shown in Figure 1(c). The data packet size varies based on the data type it holds. The data packet contains the following fields: • Sequence number.

• Sinks vector of 8 bits, where each sink is represented by one bit; when set, the corresponding sink is interested in the data.

• Distance vector, which represents the distance to the sinks from the last sensor node that transmitted the packet.

• Data ID, which presents the data type e.g. temperature or pressure.

• Data field. • The retransmission credit C is initially set to a value

decremented by one every time the packet is retransmitted. The data packet cannot be transmitted when C reaches zero or initially set to zero.

• The source address, which identifies the data originator. • ToSink is a field set to one only if the packet will be sent

from a sink to other sink(s) as will be described later in ABRM description, and it is set to zero otherwise.

• Timestamp contains the generation time of the data. The acknowledge packet shown in Figure 1(d) is used to acknowledge the data packet received by a sensor node or sink in case the packet is to be stored (not dropped). The packet contains the following fields: the sequence number of the acknowledged packet in addition to the source address.

Figure 1. ABRM Packets Formats (a) Beaconing Message Format. (b) Beaconing Message Format with Interest Message encapsulated. (c) Data Packet Format. (d) Ack. Packet Format ABRM protocol aggregates the data messages during each aggregation epoch TE, considering that the start and the end of TE are as in the following equations: e1k= ta + (k-1) TE (1)

e2k=e1k+TE (2)

Where ta is the activation time of the interest that has been sent, and k is the epoch number which is incremented after each aggregation epoch. The nodes towards the nearest sink are considered into rings where each ring represents a distance to the sink as shown in Figure 2. The nodes in each ring wait until esk to aggregate the data messages stored in the aggregation queue (the queue where the packets that will be aggregated are stored) and forward them to the nodes in the lower ring towards the nearest sink. esk is calculated as

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in (3) where d is the number of hops to the nearest sink, and HMAX is a constant which represents the max number of hops that can be reached, it is calculated as a factor of the number of nodes in the network. It is assumed that sensor nodes clocks are synchronized with sinks clocks. A timing diagram for an aggregation epoch is shown in Figure 3. esk= e2k d(TE/HMAX) (3)

Figure 2. Overview of ABRM on MSN with multiple mobile sinks.

Figure 3. Aggregation Epoch Timing Diagram.

During each aggregation epoch the network acts as follows: 1) When a node has a new generated data, it checks the

interest table to check if there are sinks interested in the generated data.

2) The node chooses the nearest sink to aggregate the data towards it (if there are more than one sink with the same lowest distance, one is randomly chosen).

3) The node marks the chosen sink inside the generated packet and forwards the packet to its neighbors.

4) If the current time t <= esk, the generated data and/or the received packets will be stored in the aggregation queue until t=esk.

5) When t=esk, the packets in the aggregation queue are aggregated according to the aggregation function and sent towards the chosen sink.

6) If a sensor node receives a data packet in t > esk and its distance to the sink is lower than the packet distance, it forwards the data packets received to the lower ring towards the chosen sink. Otherwise, it drops it.

7) If the sensor node does not receive an acknowledgement packet, or does not hear the retransmission of its transmitted packet by at least one of its neighbors, and the packet’s retransmission credit C >0, it retransmits the packet again towards the chosen

sink after increasing the packet distance to the sink by one.

8) These steps are repeated in each ring towards the sink. 9) In t= e2k, the sink node aggregates the packets received

and stored in the aggregation queue. 10) Any sink drops any data packet received with a

timestamp < e1k. 11) The sink stores the aggregated packet in the final

aggregation queue, and forwards it to the other interested sinks after updating the distances to those sinks in the aggregated packet and setting ToSink field to one.

12) Each sink waits a time TW which is a constant set to be the maximum time required to deliver a data packet from one sink to the other sinks.

13) After elapse of TW, each sink aggregates the packets received from the other sinks.

III. ABRM EVALUATION ABRM has been validated through performance analysis and simulations experiments as shown in the following subsections.

A. Performance Analysis WSN's have very limited power sources, so, one of the most important performance measures is the total energy consumed in the network. The following theorem shows that ABRM gives lower energy consumption than CCBR. Theorem: Aggregating packets to the nearest sink then sending the partial aggregation to the other interested sinks is more energy efficient than sending the packets for each interested sinks to aggregate. Proof: Consider a number of sensors N, number of sinks K, Di is the distance between node n and sink k, data generation frequency F, aggregation epoch E, and max distance HMAX. The energy consumed can be considered through the number of packets transmitted over the network. In CCBR, (with aggregation on the sinks), the number of packets generated across an aggregation epoch from one sensor node is equal to E*F per each sensor node. Then, the best case of the number of packets transmitted across the network PCCBR is equal to N*E*F+K, while the worst case equals K*N*E*F. Accordingly, PCCBR is as follows: N*E*F + K <= PCCBR <= K*N*E*F (4) In ABRM, each node transmits its aggregated packet on esk. After that, the sensor node transmits the packets individually without aggregation until the end of the aggregation epoch. Thus, the number of packets transmitted across an aggregation epoch for one sensor node is 1+ F*E*(D/HMAX), in addition to K aggregated packets transmitted between the sinks. Then, the number of transmitted packets across the network per each aggregation epoch E in ABRM (PABRM) will be:

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KH

DEFNPNi MAX

iABRM ++ �

∈= )(** (5) QED

It can be concluded from (4) and (5) that PABRM < PCCBR. This means that ABRM gives lower overhead and energy consumption rather than CCBR.

B. Simulation Environment ABRM and CCBR protocols have been implemented on OMNET++ [7] network simulator using the mobility framework [8] to simulate the mobile environment according to the random way mobility model. A path loss model has been fully used, while considering interferences from other parallel transmissions, to calculate the signal to noise ratio (SNR) of each frame at runtime. It is assumed that sensors provide real measurement uniformly drawn from the interval I = [0,100] generated with frequency F equal to one measurement per second. The properties of nodes have been set to satisfy the sinks context filters. The message filter has been developed to make the generated data match sinks interest with probability 0.9. The simulation time beaconing period, and aggregation epoch have been chosen to be 150 seconds, 30 seconds, and 15 seconds respectively. In the simulation experiments, ABRM has been tested versus CCBR with CCBR aggregate on the sink(s) at the end of the aggregation epoch. To measure ABRM performance, different simulation scenarios have been considered by changing the following simulation parameters: density of the network (the number of nodes per km2), number of sinks, retransmission credit C, different aggregation functions, and different mobility scenarios based on random way mobility model as follows: • Static scenario: All sensors and sinks are static across the

simulation. • Low mobility scenario: Speed varies between 1 and 2 m/s

and pause time up to 10 seconds. • Medium mobility scenario: Speed varies between 3 and 5

m/s and pause time up to 2 seconds. • High mobility scenario: Speed between 5 and 10 m/s

without pause.

C. Simulation Results

The metrics used to measure the performance of ABRM against CCBR are as follows: • Average Aggregation Error: It is the average of the

difference between the true aggregated value and the value computed by the sink(s) on the final aggregation across all the aggregation epochs.

• Number of Nodes Involved: Average number of nodes involved per each packet transmission across the simulation.

• Total Energy consumed (in joule): The total energy consumed across the network.

• Routing Cost: The number of bits transmitted across the network divided by the number of packets that should be delivered to the sinks.

The following subsections show the simulation results of ABRM and CCBR versus: 1) different nodes densities 2) different number of sinks. The simulations have been done across the mobility scenarios stated in Section III.B. ABRM and CCBR have been simulated considering two retransmission credits C = 0 and 2. The aggregation functions used across the simulation are the minimum and average functions; each of them represents an aggregation functions category. The minimum function represents the duplicate in-sensitive category. This is the category of the aggregation functions that are not affected by the double counting problem (counting the sensor measurement more than once) which happens due aggregating across multiple paths. Minimum and maximum functions fall into this category. The average function represents duplicate sensitive category. This category is for the aggregation functions that are affected by the double counting problem. Average, sum, and count fall into this category.

1) Impact of Nodes Density This subsection presents the simulations results for ABRM and CCBR for five different nodes densities ranging from 25 Nodes/km2 to 400 Nodes/km2. The number of sinks has been chosen to be three sinks and the simulation area is 1 km2 across the simulation runs. The average aggregation error for minimum function is shown in Figure 4. Overall, the increase in density decreases the aggregation error due to the increase of the coverage area, which leads to reducing packet loss rate, and hence the aggregation error decreases. In the meantime, the increase in the mobility speed increases the aggregation error as nodes/sinks mobility increases packet loss rate. In the static scenario of Figure 4(a), ABRM gives higher aggregation error than CCBR, with the variance between them decreases with the increase in density until the variance disappears starting from a density of 200 Nodes/km2. The variance is higher in low densities because the packet loss sensitivity of ABRM is higher than CCBR due to the aggregation of packets. In the meantime, the variance between ABRM and CCBR decreases as the mobility speed increases until CCBR with C=0 becomes higher than ABRM, and CCBR with C=2 become almost identical to ABRM with C=2. This shows that the effect of mobility on CCBR becomes higher than in ABRM as the nodes wait until t = esk to aggregate and then transmit the aggregated packets, while CCBR forwards the packet as soon as they are generated. This leads CCBR to have higher probability of packet loss than ABRM in high mobility scenarios. Both protocols give aggregation error in C=2 lower than in the C=0 case, as the retransmissions reduce the probability of packet loss and hence the aggregation error is reduced.

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Figure 4. The Average Aggregation Error of Minimum Aggregation Function vs. Nodes density, (a) Static Scenario (b) Low Mobility Scenario (c) Medium Mobility Scenario (d) High Mobility Scenario. The average aggregation error for the average function is shown in Figure 5. It is noted in all mobility scenarios that CCBR with C=0 and C=2 gives an average aggregation error lower than ABRM. At the same time, ABRM with C=2 gives lower average aggregation error than ABRM with C=0 with a slight variance, the same result holds for CCBR. Generally, the average aggregation error decreases proportionally to the density increase. CCBR is more affected by the mobility as the average aggregation error for CCBR increases with the increase in mobility speed especially in lower densities. On the other side, the average aggregation error in ABRM case slightly increases with the increase in mobility speed. The aggregation error in ABRM is higher than in CCBR because a packet may be aggregated more than once in different nodes, which affects the aggregation error of duplicate sensitivity aggregation functions like average function. The average number of nodes involved in one packet transmission for ABRM and CCBR with C=0 and 2 is shown in Figure 6. The average number of nodes involved in each packet transmission is identical across all the mobility scenarios. The exception is the slight increase in CCBR proportionally to the increase in the mobility speed. The number of involved nodes in CCBR in both C=0 and 2 increases proportionally to the increase in density while the number of nodes involved in CCBR with C=2 is higher than CCBR with C=0. The variance between the two cases slightly increases proportionally to the density increase and is constant between 200 and 400 nodes/km2. On the other hand, the number of nodes in ABRM with C=0 and 2 is much lower than in CCBR, and is almost constant across all the densities considering that the average number of nodes involved in ABRM with C=2 is slightly higher than ABRM with C=2 . This is due to the fact that ABRM targets one sink at a time, while CCBR targets all the interested sinks at the same time. This makes the number of involved nodes on message transmission in CCBR higher, than in ABRM, because of forwarding the packets through more neighbors

that find themselves nearest to one interested sink or more. While in ABRM the nodes that forward packets are the neighbors that consider themselves nearest to the sink targeted for aggregation. Therefore the number of nodes involved in transmission increases proportionally to the increase in density in CCBR while ABRM is not affected by the increase in density. The number of nodes involved increases in C=2 rather than in C=0 due to retransmission when failing to deliver the packet from the first attempt, while no retransmission happens in C=0.

Figure 5. The Average Aggregation Error of Average Aggregation Function vs. Nodes density (a) Static Scenario (b) Low Mobility Scenario (c) Medium Mobility Scenario (d) High Mobility Scenario.

Figure 6. The average number of nodes involved in packet transmission vs. Nodes density, (a) Static Scenario (b) Low Mobility Scenario (c) Medium Mobility Scenario (d) High Mobility Scenario. The total energy consumed, as shown in Figure 7, is identical across all mobility scenarios. The exception is the slight increase in CCBR proportionally to the increase in mobility speed. The total energy consumed in CCBR with C=0 and C=2 increases proportionally to the increase in density, considering that the total energy consumed in CCBR with C=2 is higher than CCBR with C=0 with a small variance that slightly increases proportionally to the

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density increase. On the other hand, the total energy consumed in ABRM with C=0 and C=2 is much lower than in CCBR and it slightly increases proportionally to the density increase. This returns to that ABRM makes in-network aggregation from node to node, while CCBR nodes send raw data packets without aggregation until final aggregation is done on the sinks. Moreover, the number of nodes involved in CCBR is much higher than ABRM as seen in Figure 6.

Figure 7. Total Energy Consumed vs. Nodes density, (a) Static Scenario (b) Low Mobility Scenario (c) Medium Mobility Scenario (d) High Mobility Scenario. The routing cost for delivering a packet to an interested sink for ABRM and CCBR with C=0 and C=2 is shown in Figure 8. The routing cost is almost identical across all the mobility scenarios. The exception is the slight increase in CCBR proportionally to the increase in mobility speed. The highest routing cost is for CCBR with C=2 where the curve from 25 to 100 nodes/km2 increases proportionally to the density increase, while the curve saturates between 100 - 400 nodes/km2. Then comes CCBR at C=0 curve with a slight increase proportional to the density increase. On the other side, the routing cost for ABRM with C=0 and 2 is much lower than in CCBR with the curves slightly decreasing as the density increases considering that ABRM with C =2 is a little higher than ABRM with C =0. These results are due to the reduction in the total bits transmitted across the network in ABRM because of in-network aggregation. While CCBR sends raw data packets and aggregates on the sinks only, and this increases the total bits transmitted across the network. In the meantime, CCBR with C=2 is higher in routing cost than in C=0 due to the effect of data packets retransmissions. This is revealed in low densities (25-100 nodes/km2) where the routing cost increases proportionally to the increase in density due to the need to more packet retransmissions. Whereas on high densities (100-400 nodes/km2), the routing cost saturates as the packets retransmissions do not increase with the increase in densities in these dense networks.

Figure 8. Routing Cost vs. Nodes Density, (a) Static Scenario (b) Low Mobility Scenario (c) Medium Mobility Scenario (d) High Mobility Scenario.

2) The Impact of the Number of Sinks This subsection presents the simulations results for ABRM and CCBR versus four different sinks numbers ranging from 1 to 7 sinks. The number of nodes has been chosen to be 100 nodes and simulation area of 1 km2 across the simulation runs. The average aggregation error in minimum function case for ABRM and CCBR with C=0 and C=2 is shown in Figure 9. It has been shown that the average aggregation error increases with the increase in the mobility speed due to the increase in packets loss. CCBR gives lower aggregation error than ABRM in low number of sinks (1-3 sinks) with variance narrowed as well as the mobility speed increased. The performance of ABRM does not appear on static networks with sole sink. The only difference between ABRM and CCBR in this case will be in-network aggregation, which makes ABRM more affected by packets loss than CCBR. In the meantime, ABRM is more resistant to packet loss due to mobility than CCBR and that is why the variance between them decreases as the mobility speed increases. On the other side, CCBR on high number of sinks (5-7 sinks) gives aggregation error higher than ABRM with C=2 and lower than ABRM with C=0 in the static and low mobility scenarios. The variance between CCBR and ABRM with C=0 gets narrowed with the increase in the mobility speed until CCBR is higher than ABRM with C=0 in the highest mobility scenario (Figure 9 (d)). Overall, ABRM with C=2 gives the lowest aggregation error and specifically in high number of sinks (5-7 sinks). ABRM aggregation error is lower than CCBR in high number of sinks, as ABRM targets aggregation towards one sink only until aggregating on the sink, and then sends the aggregated packet to the other sinks for final aggregation. While CCBR targets all the sinks at one time, which increases the probability of packets loss than in ABRM.

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Figure 9. Average Aggregation Error of Minimum Aggregation Function vs. Number of Sinks, (a) Static Scenario (b) Low Mobility Scenario (c) Medium Mobility Scenario (d) High Mobility Scenario. The average aggregation error in average function case for ABRM and CCBR with C=0 and C=2 is shown in Figure 10. It has been shown that ABRM with C=0 and C=2 gives higher aggregation than in CCBR with C=0 and C=2. In the meantime, the variance between ABRM and CCBR decreases as the number of sinks increases. ABRM results returns to the duplicate sensitivity of the average function as stated before.

Figure 10. Average Aggregation Error of Average Aggregation Function vs. Number of Sinks, (a) Static Scenario (b) Low Mobility Scenario (c) Medium Mobility Scenario (d) High Mobility Scenario. The average number of nodes involved per each data packet transmission for ABRM and CCBR with C=0 and C=2 is shown in Figure 11. It has been shown that CCBR with C=2 gives the highest number of nodes involved. CCBR with C=0 comes second with half the number of nodes. ABRM with C=2 comes third with about one-third of the number of nodes involved in CCBR with C=0. ABRM with C=0 gives the lowest number of nodes involved in packet transmission with a small variance from ABRM with

C=2. This variance slightly increases with the increase of the number of sinks. Overall, the number of nodes involved per packet transmission slightly increases with the increase of mobility speed. These results come from the fact that ABRM executes in-network aggregation for data packets towards the nearest sink. Thereafter, the nearest sink forwards the aggregated packet towards other interested sinks. This reduces the number of nodes involved in packet transmission, as compared to the number of nodes involved per packet transmission when CCBR forwards the raw packets towards all the sinks at the same time.

Figure 11. The average number of nodes involved per packet transmission vs. Number of Sinks, (a) Static Scenario (b) Low Mobility Scenario (c) Medium Mobility Scenario (d) High Mobility Scenario. The total energy consumed for ABRM and CCBR with C=0 and 2 is shown in Figure 12. It can be shown that in CCBR with C=0 and C=2, the energy consumed increases proportionally to the increase of the number of sinks, while for ABRM with C=0 and C=2 it increases slightly with the increase in the number of sinks. CCBR with C=2 gives the highest total energy consumed, while CCBR with C=0 comes next with about two-thirds of the total energy consumed. ABRM with C=2 comes third with total energy consumed at fifth of the total energy consumed in CCBR with C=0. ABRM with C=0 produces the lowest total energy consumption with a small variance than ABRM with C = 2. Overall, the total energy consumed slightly increases with the increase of mobility speed. The similarity between Figure 11 and Figure 12 is noticed since the total energy consumed comes from the number of nodes involved per packet transmission only as the number of nodes is fixed across these simulation runs. The routing cost of ABRM and CCBR, with C=0 and 2 is shown in Figure 13. The routing cost decreases with the increase in the number of sinks. The routing cost is the total number of bits transmitted across the network divided by the number of data packets to be delivered. This number of data packets is the product of the number of data packets generated multiplied by the number of sinks whose interest

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matches these generated data packet. Therefore, the routing cost is reduced as the number of sinks increases while the data generated is constant. It has been shown that CCBR with C=2 gives the highest routing cost. CCBR with C=0 follows with half the routing cost. The variance between CBBR with C=2 and CCBR with C=0 decreases with the increase of the number of sinks. ABRM gives the lowest routing cost considering ABRM with C=2 slightly increases than ABRM with C=0. These results are interpreted by the fact that ABRM makes in-network aggregation towards the nearest sink, and sends the sinks aggregated packet to the other interested sinks to make the final aggregation. On the other hand, CCBR forwards raw data packets and aggregate on the sinks only. In the meantime, CCBR targets all the interested sinks at the same time, which means duplicating data packets through the paths to the different sinks.

Figure 12. Total Energy Consumed vs. Number of Sinks, (a) Static Scenario (b) Low Mobility Scenario (c) Medium Mobility Scenario (d) High Mobility Scenario.

Figure 13. Routing Cost vs. Number of Sinks, (a) Static Scenario (b) Low Mobility Scenario (c) Medium Mobility Scenario (d) High Mobility Scenario.

IV. CONCLUSION This paper presents the ABRM protocol, which is an in-network aggregation based routing protocol for mobile sensor networks with multiple mobile sinks. The simulation results have been executed on different mobility speed scenarios, different nodes densities, and different sinks numbers. The simulation results show that ABRM, compared with CCBR, produces efficient aggregation results for duplicate in-sensitive aggregation functions. Also, ABRM produces an aggregation error higher than CCBR in duplicate sensitive aggregation functions. In the meantime, the performance analysis and simulation results show that ABRM produces very low power consumption and low routing cost if compared with CCBR. It has also been shown that packet retransmissions enhance the aggregation results outcome in ABRM with a neglected increase in the energy consumed and routing cost. Future work in ABRM protocol is to find new techniques to enhance the aggregation results in case of duplicate sensitive aggregation functions.

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