Energy-efficient and reliable data delivery in ... - Semantic Scholar

Download PDF
4 downloads 370 Views 647KB Size Report
Comment
Shukor Abd Razak. Published online: 10 July 2012 ... BS through the most reliable and energy-efficient route. Keywords Wireless sensor networks а Routing а ... Faculty of Computer Science and Information Systems,. Universiti Teknologi ...
Wireless Netw (2013) 19:495–505 DOI 10.1007/s11276-012-0480-x

Energy-efficient and reliable data delivery in wireless sensor networks Mohammad Hossein Anisi • Abdul Hanan Abdullah Shukor Abd Razak

•

Published online: 10 July 2012  Springer Science+Business Media, LLC 2012

Abstract Clustering has been used as one of energyefficient mechanisms for data routing in wireless sensor networks. In hierarchical routing approaches, cluster heads are responsible for management (e.g. data aggregation, queries dispatch) and transmission of the collected data in the region controlled by them. For efficient data delivery, several researches have proposed various mechanisms for cluster organization and cluster head selection. However, less focus is given in the area of data transmission associated with Base Station (BS). In such a situation, any failure or packet loss may lead to considerable packet loss. For solving this problem, we propose an efficient data routing scheme for controlling data delivery from nodes to BS. In our proposed approach every node is aware about the link quality of all nodes and is able to deliver data to the BS through the most reliable and energy-efficient route. Keywords Wireless sensor networks  Routing  Energy consumption  Reliability

1 Introduction In wireless sensor networks (WSNs), due to the limited sources of energy, delivering sensory data to the BS requires an energy-efficient routing solution [1–7, 10, 11]. In general, routing protocols are classified into three i.e. data-centric, hierarchical and location-based protocols. Data-centric protocols are usually query-based and work based on the requested data. Hierarchical approaches aim at clustering the M. H. Anisi (&)  A. H. Abdullah  S. A. Razak Faculty of Computer Science and Information Systems, Universiti Teknologi Malaysia (UTM), 81310 Johor, Malaysia e-mail: [email protected]

nodes so that cluster heads are capable of doing data aggregation and decreasing data overhead for conserving energy. Location-based protocols utilize the location of data to relay the information to the required areas. In hierarchical routing protocols, densely deployed sensor nodes are grouped together to form a cluster-based topology. By forming such clusters, the network can be managed in a distributed manner [7]. In each cluster, one of the members will be selected as the cluster head to control and manage the data exchanged between nodes in the cluster and report the collected data to a BS through one or more clusters. Each cluster head maintains neighbour table that includes information about its member nodes and adjacent cluster heads. In addition, other members will be synchronized with their cluster head. Hence, several approaches have been proposed for performing efficient cluster formation, cluster head selection and synchronization [7, 8, 10, 11]. Cluster heads gather information about their clusters and later transmit the information to the sink node. Here, we should aware about the data delivery from cluster heads to the BS as each cluster head needs to transfer large amount of data. In this area, some intermediate nodes act as bridges between cluster heads and the BS which should frequently transfer large amount of data. However, performing this task gradually exhausts intermediate nodes energy and makes them die sooner than other nodes. Therefore, loosing these bridges leads to disconnection between the nodes and the BS and reduces the network lifetime significantly. In [9] authors discussed a similar problem in wireless sensor networks focusing on a hotspot. In their proposed approach, hotspot is defined as the area in the interior of the maximum transmission distance of the BS node. They mentioned that failure of the nodes in the hotspot disconnects connection between BS and other nodes. The authors suggest using a multi-hop routing approach in hotspot area to decrease the

123

496

transmission distance of the nodes. However, there is a drawback in their approach. The area of hotspot only contains nodes which are located in the maximum transmission range of the BS and there are some other nodes which may be located between the cluster heads and the nodes inside the hotspot. Thus, failure of such nodes could again disconnect data communication between cluster heads and the BS. Moreover, the authors did not suggest any solution for performing reliable and energy-efficient data delivery in the hotspot area. In our proposed approach, hotspot is defined as the whole area from the farthest cluster head to the BS and known as critical region (CR). We have proved that rather than considering the hotspot, by covering all the cluster heads in the CR and using our proposed data forwarding algorithm, energy-efficiency, reliability and network lifetime could be improved. Figure 1(a) demonstrates the whole area of CR in which data of all cluster heads is transmitted towards the BS. Figure 1(b) depicts a subset of CR wherein each cluster head transmits the data of its cluster members to the BS through a multi-hop path. Such a path must provide sufficient energy-efficiency and reliability to increase the rate of data delivery. In this paper, we propose an efficient data routing approach which reduces the energy consumption of the network and provides reliable data delivery to the BS by controlling data transmission in the CR. Existing flat routing approaches could reduce the total energy consumption of the network but such approaches did not consider the energy depletion of the nodes located in the CR in transferring large amount of data. In addition, cluster-based approaches could reduce the amount of traffic by using data aggregation technique. However, in these approaches, large transmission distance from cluster heads

Fig. 1 Data delivery in CR. a The whole view of CR, b a subset of CR

123

Wireless Netw (2013) 19:495–505

to the BS exhausts the energy of the cluster heads in CR which leads to the failure and reduction of network lifetime. On the other hand, in our proposed approach, we have used an efficient data routing mechanism in which, sensor nodes are organized into several clusters but unlike the ordinary cluster based routing mechanisms, data of the cluster heads is delivered to the BS using a QoS-aware flat routing scheme. Therefore, the amount of data which should be transmitted by the nodes in CR is reduced in the clusters and by using the proposed flat routing scheme, the required transmission power for cluster heads is decreased. Moreover, contrary to [9], the intermediate nodes which are located outside the hotspot area are also covered. Therefore, the energy consumption of the nodes is reduced and the network lifetime is prolonged. In addition, in the proposed QoS-aware flat routing scheme, by considering the link quality metrics of the nodes, data of cluster heads is transmitted through the most reliable paths. This paper is organized as follows: Sect. 2 reviews current data routing approaches in WSNs; in Sect. 3, the proposed link cost function is discussed and our reliable and energy-efficient data delivery scheme is proposed. Results of the study are discussed and analyzed in Sect. 4. The paper is concluded in Sect. 5. 1.1 Assumptions We assume N sensor nodes are distributed randomly and exponentially in the sensing field centred by the base station. The random distribution in the proposed approach can be either uniform or exponential. However, this research chose exponential distribution since the problem of critical region can be more highlighted in this form of distribution.

Wireless Netw (2013) 19:495–505

The exponential distribution is also chosen because this research is compared with previous works that utilize exponential distribution method. There is only one BS in the network and it is connected to the power supply. Each node in the network is assigned a unique ID and all nodes are willing to participate in communication process by forwarding data. Furthermore, we assume that the sensor nodes are stationary for the whole of their lifetime. Additionally, we assume at any time, each sensor node is able to compute its residual energy (the remaining energy level), and its available buffer size (remaining memory space to cache the sensory data while it is waiting for servicing). The entire network is divided into several clusters, each having a cluster head that is responsible for data reporting control. Each cluster head collects data from its cluster members, performs data aggregation, and forwards the results to the BS. 1.2 Network model The proposed network is a heterogeneous network in which cluster heads with higher communication capabilities manage each of the clusters. Normal nodes have short transmission ranges and are inexpensive that allows for a large number of sensors to be deployed in the network. These nodes operate as cluster members and report their sensing results to the cluster head which has more energy, higher processing capability, and longer communication ranges so that each cluster head can directly reach the adjacent cluster heads in a single- hop manner.

2 Related works In this section, according to the hybrid structure of the proposed approach, we presented some of the famous hierarchical and data-centric approaches which are related to our proposed approach. Clustering is a useful approach for energy-efficient data delivery in WSNs. Hierarchical multi-hop routing algorithms successfully utilize the data aggregation technique to decrease the volume of data flowing in the network. There are several data routing approaches in this area which proposed effective solutions for cluster formation and cluster head selection. In [10], the authors have proposed a clusterbased protocol which reduces the energy consumption of the networks by selecting the best cluster formation. For choosing the cluster head, they have considered different parameters such as remaining energy and position of the nodes as the weight (i.e. selection criteria of the nodes). According to the calculated weight, nodes with the minimum weight will be selected as the cluster heads. The approach in [11] uses three-tier architecture to propose cluster-based routing algorithm. In such hierarchical

497

scheme the cluster formation will be done before networking process. Cluster heads which were called gateways have more energy than other sensor nodes and it is assumed that they know the location of other nodes. Thus, gateway nodes establish multi hop routes for data collection based on the maintained states of the nodes. The aim of authors in Hierarchical Geographic Multicast Routing (HGMR) for wireless sensor networks [12] is enhancing efficient data forwarding and increasing the scalability to a large-scale network. HGMR incorporates the key design concepts of the Geographic Multicast Routing (GMR) [13] and Hierarchical Rendezvous Point Multicast (HRPM) protocols [14] and optimizes the two routing protocols in the wireless sensor network environment. Energy-Efficient and Fast Data Gathering Protocols for Indoor Wireless Sensor Networks [15] focus on some specific applications which need prompt reactions. The proposed protocols considered for indoor environment and it is suitable for alert services such as warning poisonous gases in the rooms. This paper, proposes two hierarchical protocols known as R-EERP and S-EERP based on LEACH [16] with different clustering structures. In R-EERP nodes are deployed randomly but in S-EERP their structure is sequential. In both protocols nodes are fixed during the cluster change time and their cluster head selection is done based on LEACH. In this approach, similar to TEEN, two threshold values known as critical threshold and base threshold are defined. Base threshold demonstrates the minimum required value which should be sensed and the values below this threshold are not acceptable. On the other hand, critical threshold relates to emergency situations and values above this threshold are considered as real-time values which cannot tolerate any delay. Thus, cluster heads attempt to transmit such values with a minimum delay. In data-centric approaches, usually a query will be sent from BS to certain regions in the network and waits for the responses of sensor nodes. Such queries have different attributes and data will be responded according to the specified attributes. M-SPIN [17] is the improved version of SPIN protocol [18] which could reduce the number of packets transmission in comparison with SPIN. Similar to SPIN, M-SPIN performs negotiation before data transmission. However, in such approach, the number of nodes which transmit REQ messages in response to ADV message is limited only to the nodes which are adjacent to the BS. In addition, the neighbours of the BS in this approach are identified by a distance discovery algorithm which operates based on the hop counts of the nodes to BS. Therefore, by finding the BS’s neighbours, instead of broadcasting the data packet, it is transmitted towards the BS or its neighbours. The main problem of this approach is the considerable loads on BS’s neighbours which may lead to their failure.

123

498

In Directed Diffusion (DD) protocol [19], receivers and resources using some attributes for recognizing the produced or required information. The goal of this approach is to find an efficient multi path route between senders and receivers. In this approach, each task is represented as an interest and each interest is a set of attribute-value pairs. For performing a task, the related task will be propagated to the network. There are other similar approaches which have also been proposed based on Directed Diffusion. Data query protocol with restriction flooding (DQPRF) [20] is an improved version of directed diffusion approach which disseminates the negative effect of flooding by controlling the interest messages. The proposed approach controls the flooding by using a cache in each node to check whether the interest message has been received previously or not. Energy-efficient differentiated directed diffusion (EDDD) [21] is also an extended version of DD which obtains energy efficiency by using two kinds of gradients; each one is used for different kinds of applications. Whenever the delay is the main issue, real-time filter forwards data through the shortest path between source and BS in order to perform load balancing among the nodes. On the other hand, best effort BE filters will be selected which choose longer but more energy-available paths towards the BS node. Energy-efficient and Reliable Routing Scheme has been proposed in [22] to improve the directed diffusion protocol. This data routing approach selects the next hop neighbour by taking advantage of radio information. In EARS, instead of broadcasting the packets into the network, the most suitable neighbour among other candidates is selected and the data is transmitted only to that node. This strategy resulted in better average energy consumption in EARS in comparison to the Directed Diffusion. In this approach, each node uses the radio-aware metric of MAC layer to evaluate the quality of links. The lowest value of this metric demonstrates the lowest data rate and Frame Error Rate (FER) of MAC layer. Therefore, the neighbour with the lowest radio-aware metric will be the candidate of the next hop. Moreover, to perform more reliable data transmission, a Request to Send (RTS) message is transmitted to the neighbours to check whether it can be received correctly or not. If this packet becomes lost, another candidate according to its link quality will be selected as the next hop.

3 Proposed approach In our proposed approach, for solving CR problem, sensor nodes which are located outside the CR are organized into several clusters to reduce the volume of data entering the CR. In addition, unlike the previous approaches, data of

123

Wireless Netw (2013) 19:495–505

cluster heads is routed toward the BS using a QoS-aware flat routing scheme to reduce the transmission power inside the CR. The proposed flat routing scheme utilizes a link cost function which enable of the selection of the most reliable and energy-efficient paths. Therefore, by using the proposed approach, the intermediate nodes which are located outside the hotspot area are also covered, the energy consumption of the nodes is reduced and the network lifetime is prolonged. In the following subsections, firstly, the proposed link cost function is discussed then our reliable and energy-efficient data delivery scheme is presented. 3.1 Link cost function The link cost function is used by the nodes to calculate the link cost of their next hop neighbours during the initialization phase. The link cost function in the proposed approach is calculated according to the following metrics. In sensor networks, the nodes which participate in routing operation more than others may die sooner than other nodes due to depletion of their energy. Such a problem could disrupt the routing process. Therefore, for achieving reliable data delivery and increasing network lifetime, nodes should be aware about the remaining energy of their neighbouring nodes and select the neighbours with highest residual energy. Therefore, residual energy of the neighbours is one of the main factors which should be considered in selecting the next hop neighbour. Moreover, for selecting the shortest path from nodes to BS and minimizing the utilization of energy, the hop distance of nodes to the BS is an important factor which can be considered in benchmarking a link. Furthermore, when a sensor node receives more data than it could forward, the excess data has to be buffered. However, when the limited buffer space of the node is full, congestion occurs and the received data has to be dropped. Hence, for performing a reliable packet transmission, the nodes should be aware of their neighbours’ buffer size as well to prevent congestion. The proposed link cost function for performing efficient next hop selection considers the energy state of the sensor nodes as well as their hop count distance from the BS and their available buffer size. These metrics are based on our objectives to increase network lifetime, selecting energyefficient paths and reducing the congestions. Some previous approaches have used complicated link cost functions for selecting the desired next hop or path. Although, their proposed functions are effective in term of next hop selection, but it should be also noted that performing such complex computation on the nodes which own limited power could reduce a considerable amount of their energy. Moreover, the results of these functions are usually not much different since normally there are limited

Wireless Netw (2013) 19:495–505

499

options around the nodes to be selected as the next hop. Therefore, the function should satisfy simplicity and effectiveness together. In this paper, we have proposed an efficient function which is able to select the most appropriate links by considering the most effective link cost factors and performing simple computations. The cost of transmitting packet (C) from node y to node x is defined as follow:     EresðxÞ 2 BavaðxÞ 2 C1 ¼ 1  þ 1 ð1Þ EiniðxÞ Btotal ðxÞ C2 ¼

ððdx  dy Þ þ 1Þ dx

ð2Þ

C ¼ aðC1 þ C2Þ:

ð3Þ

Table 1 shows the notations of the functions. In the proposed equations, C1 considers residual energy and  2 E when the available buffer size of the nodes. In 1  EresðxÞ iniðxÞ residual energy of node x becomes less, the result tends to 1. Oppositely, when the residual energy is high, this value tends to zero and the cost become lower. Also, when there is no change in the energy of the node (i.e. same as the initial energy), the cost of energy will be equal to 0.   BavaðxÞ 2 Similarly, in 1  Btotal when there is enough space in ðxÞ the buffer size, the cost is approximately 0 and when the buffer size is near to full, the cost tends to 1. On the other hand, C2 checks the distance cost in selecting the next hop neighbour. Since the proposed approach needs to keep only the information of the one hop neighbours, the next hop candidates can be one hop nearer or farther to the BS or they may have the same hop distance ððd d Þþ1Þ

with the sender. Therefore, in x dxy when node x is one hop nearer to the BS, the cost is 0, when it is farther to the BS, the cost is 2 and when they have same hop distance, the cost is 1. Finally, by obtaining C1 and C2 and combining them together, we can achieve C which is the total cost of transmitting packet from node y to node x. In Eq. 3, a is a positive scale adjustment factor which limits the maximum value of the link cost to a desirable value. Table 1 Notations of link cost function

Now, let us define fq as a set of adjacent cluster heads of cluster head q. q selects its adjacent cluster head Z as the next hop if Z ¼ min ðCb Þ:

ð4Þ

befq

3.2 Energy-efficient and reliable data delivery scheme In the proposed approach, we need an initialization algorithm to calculate the required metrics of link cost to select the most appropriate neighbour as the next hop. Hence, we could ensure reliable and energy-efficient data transmission from cluster heads to the BS in the critical region. The initialization algorithm begins by broadcasting a packet from BS. Along with the general fields in the packet, it contains three key parameters including the residual energy of the nodes, hop count from nodes to BS and available buffer size of the nodes. Therefore, sensor nodes upon receiving the initialization packet, maintain the information of their neighbours as their possible next hop selections in their routing tables. In the proposed scheme, the initialization stage is done immediately after the nodes are deployed. Neighbours table is initially empty and it is initialized by this process. Information stored in the neighbours table are node identifier (Node_ID), Hop Distance (HD), residual energy (NE) and the available buffer size (AB). Table 2 presents the packet header of our algorithm. To compute the effectiveness of routing paths, Hop Distance (HD), Node Energy (NE) and Available Buffer size (AB) are the main parameters which are used by the nodes. The whole mechanism works as follow: • • • •

Once the nodes are deployed, the BS broadcasts an initialization packet to the nodes. When the initialization packet starts to move from the BS, its hop distance is set to 0. The available buffer size is considered maximum. Finally, the node energy is set to the actual value.

Once the neighbouring nodes of the BS get the initialization packet, the following algorithm is followed:

Table 2 Packet header Pkt_seq

Sequence number of the packet

Pkt_src

Packet source

Residual energy of node x Initial energy of node x

Pkt_dst Pkt_st

Packet destination Packet start time

Bava(x)

Available buffer size of node x

Node_ID

ID of the node

Btotal(x)

Total buffer size of node x

NE

Energy of the node

dx

Hop distance from node x to BS

HD

Hop Distance from the node to BS

a

Adjustment Factor

AB

Available Buffer size of the node

Term

Description

Eres(x) Eini(x)

123

500

Upon receiving the initialization packet, a node perform the following tasks: incrementing HD by one, calculating its residual energy after one transmission and updating the NE field, calculating its available buffer size and updating the AB field and finally propagating the packet to other neighbours. This node also updates its neighbour table with Node_ID, HD, NE and AB of all the neighbours whose packets are received. Algorithm 1 illustrates this process. This process is repeated until the entire cluster heads are covered. The outcome of the initialization phase is that, each cluster head will know all its neighbours as its possible parents in its radio frequency (broadcast) region, hop distance to reach the BS from each of those nodes, their residual energy and their available buffer size. Therefore, each node can easily transmit its data packet toward the BS with the help of the data available in the neighbour table. By doing so, the time to spend for route discovery or creation between the source and the BS is reduced. In the proposed scheme, the neighbour table is created at each node by just one transmission. Also, there is no need for determination of the entire path from source to destination for routing—each node needs to know only its neighbours. Therefore, it requires less memory as the routing table has the data of the neighbours only. Moreover, it is easy to implement as the algorithm has no complex computations. This reduces the load on the nodes and could increase the lifetime of the network. Finally, after initialization phase, sensor nodes can compute the final link cost function of their neighbouring nodes. Therefore, according to the proposed link cost function, each node selects the neighbour with the least link cost as its parent; thus, data of the clusters could be sent to the BS through the most energy-efficient and reliable path.

Wireless Netw (2013) 19:495–505

4.1 Analyzing energy consumption in critical region In this section, similar to [9], we analyze the energy consumption of the proposed approach. The energy consumption of the CR relates to all the energy consumed for packet transmission by the nodes inside the critical region. The formula is as follows: ECR ¼ HCR  E  DCR :

ð5Þ

In this equation, HCR demonstrates the number of hop count distance a packet should pass to reach the BS in CR, E denotes the energy required to send a unit of data over distance d as the average transmission distance between sender and receiver and DCR is the volume of data transmitted in the CR. The number of hop distance is obtained as the radios of the CR circle on the average transmission distance. The relation is obtained as follow: HCR ¼

1 Cd  : 2 d

ð6Þ

According to the equation, the radius of CR is the half of CR circle’s diameter, Cd, which is the distance from the BS and its farthest cluster head. The energy consumption at the nodes is dependent on transmission, reception and computation. However, for all of these functions, when the energy consumption is considered, it is observed that energy consumed for transmission is considerably greater than that for reception and computation. Therefore energy consumption of the node is considered as E = ed2 which directly depends on the square of the distance between the sender and receiver. Moreover, the volume of data in CR is computed as follow:   ACR CR D ¼sn 1 2 ð7Þ 4l

4 Performance evaluations In this section, the energy consumption of the proposed approach in CR is analyzed. Then, its performance is evaluated through simulation experiments.

Algorithm 1 Initialization The Base Station broadcasts the initialization message 1. For each initialization message receiver: 2. insertNeighbourtable (HD of previous hop, Energy, Available Buffer size, Node_id) 3. HD = HD ? 1 4. NE = NE - 1 5. packetBroadcast(HD, NE,AB, Node_id)

123

where s denotes packet size, n is the number of nodes, l demonstrates the length of the whole sensing area and ACR is the circle area of CR which is equal to: ACR ¼ p

Cd2 : 2

ð8Þ

Therefore, the energy of CR is calculated as below:   1 Cd ACR CR 2 E ¼   ed  s  n 1  2 2 d  4l  Cd ACR ¼ e dsn 1  2 ð9Þ 2 4l In [9], hotspot is defined as the area in the interior of the maximum transmission distance of the BS node. If we consider C2d = r1 as the radius of CR and r2 as the radius of hotspot; hence we can achieve the relations below:

Wireless Netw (2013) 19:495–505

501

r1 [ r2 then ACR [ Ahotspot     ACR Ahotspot ACR [ Ahotspot then 1  2 \ 1  4l 4l2 n

CR

[n

hopspot

ð10Þ

Property

ð11Þ ð12Þ

:

From the relations 10, 11 and 12 we can conclude that when the majority of the cluster heads are in the interior of the maximum transmission distance of the BS node (hotspot), since the volume of transmitted data becomes more, according to the relations 10 and 11, it can be approximated that: CR

D \D

hotspot

CR

then E \E

hotspot

:

Table 3 Simulation settings

ð13Þ

Moreover, the main problem of hotspot is that it covers only some cluster heads in the transmission distance of the BS and other cluster heads which have the same significance as those in the hotspot are ignored. Therefore, the cluster heads which are located outside the hotspot area may fail by depletion of their energy which leads to considerable data loss. On the other hand, to cover all the cluster heads and provide a fully reliable data delivery, we have proposed and defined CR as the whole area in which data of cluster heads is transmitted to the BS. Moreover, in the proposed approach, according to the necessity of reliable data delivery in CR, the link costs of the nodes are taken into consideration and data is transmitted through the most energy-efficient and reliable paths. 4.2 Simulation experiments The proposed approach was simulated and evaluated using J-SIM (Java-Based simulator) [23]. This simulator was chosen because it is component-based where it uses the concept of components instead of the concept of having an object for each individual node; this feature enables users to modify or improve it. J-SIM uses three top level components: the target node which produces stimuli, the sensor node that reacts to the stimuli, and the BS node which is the ultimate destination. For stimuli reporting, each component is broken into parts and modelled differently within the simulator; this eases the use of different protocols in different simulation runs. In our simulation settings, sensor nodes were randomly distributed in a 2,000 m 9 2,000 m area and each simulation run is in 15, 30 and 60 min. Other settings are as listed in Table 3. First, we have compared our approach with the Hybrid approach [9] to present the advantages of covering CR area rather than hotspot. Then, the performance of the proposed routing approach is evaluated and compared with Energyefficient Differentiated Directed Diffusion (EDDD) [21] which is the improved version of Directed Diffusion.

Value

Underlying MAC protocol

IEEE 802.15.4

Channel

Wireless

Propagation

OneRayGround

Network type

WirelessPhy

Queue

DropTail/PriQueue

Antenna

OmniAntenna

Topography

700; # X dimension of the topography 700; # Y dimension of the topography

Queue length Traffic types

40; # max packet in ifq CBR 10 1 10000 512 1S CBR 5 1 10000 512 2S CBR 15 1 10000 512 1S

Figure 2 illustrates the transmission distance of the nodes according to their distances to the BS. The hybrid approach could reduce the transmission distance up to distance boundary of 500 (hotspot area). However, after this boundary, since flat routing strategy was not utilized, transmission distance is increased. On the other hand, in the proposed approach, all the cluster heads in the CR (boundary of 800) can transmit their data with low transmission distance. Figure 3 depicts the difference between energy consumption of the nodes in CR and hotspot according to the number of nodes in the hotspot area. Results as in Fig. 3 suggested that the energy consumption in hotspot is dependent to the number of nodes it covers. When the number of nodes located in the hotspot is low, the hybrid approach consumes less energy but when the number of nodes goes higher, the volume of data and traffic load is increased correspondingly and the excess data has to be buffered. However, when the limited buffer space of a node is full, congestion which causes energy waste is occurred. Moreover, as the buffer size of the nodes is not considered

Fig. 2 Transmission distance in each node

123

502

Fig. 3 Energy consumption versus number of nodes in hotspot

in the hybrid approach, when the number of transmissions in hotspot area is increased, the number of packet loss due to the lack of available buffer space is also increased. Hence, retransmission of lost packets gradually uses up the energy of the nodes and reduces the network lifetime. On the other hand, according to Fig. 3, since CR covers all the cluster heads, increasing the energy consumption is almost uniform. Moreover, because of considering the available buffer size of the nodes in the proposed approach, packet loss is controlled and the energy consumption is reduced. Figure 4, illustrates the energy consumption of the circular area centred on the BS node with the radius of d. When d is lower than 500 m (the radius of hotspot), the energy consumption of both approaches is almost the same. However, the region between radius of 500 and 800 is the area where flat routing is not supported by the hybrid approach. Thus, the link quality and energy consumption of the nodes in this area are not taken into consideration. On the other hand, this area is a part of CR which is completely covered in the proposed approach. Hence, in addition to the common area with hotspot, the proposed approach optimizes the data routing in this area and outperforms the hybrid approach in term of energy consumption.

Fig. 4 Energy consumption versus distance to BS

123

Wireless Netw (2013) 19:495–505

In Fig. 5, we have compared the energy consumption of EDDD and the proposed approach in CR with different number of nodes in CR. In addition, the energy consumption in these two approaches with different distances from the BS is evaluated and the results are as illustrated in Fig. 6. In EDDD, creating gradients is a complex task. Also, as a diffusion-based approach, it loads the networks with more number of packets. As can be seen in both Figs. 5 and 6, when the number of nodes or distance from the BS is increased and more number of nodes involved in the routing process, the negative effects of the mentioned disadvantages is obvious. On the other hand, unlike EDDD which transmits high number of messages, in the proposed approach, less energy consumption could be achieved by transmitting packets directly toward the BS through the shortest paths and considering the remaining energy of the nodes to balance energy among the nodes. Packet Delivery Ratio (PDR) is the ratio of the number of data packets delivered to the BS to the number of packets generated by the source nodes as below: PDR:

Total Number of Packets Received at BS Total Packets Sent

ð14Þ

Total Number of Packets Received at BS ¼ n X Number of Source Nodes  Number of Packets i¼1

 Recieved at BS by each node Total Packet Sent ¼

n X

ð15Þ

Number of Nodes

i¼1

 Number of Packets sent by each node: ð16Þ In Fig. 7, the PDR of the proposed approach and hybrid approach in CR and hotspot area is compared. When the number of nodes in hotspot is increased, more number of

Fig. 5 Energy consumption versus number of nodes inside the critical region

Wireless Netw (2013) 19:495–505

Fig. 6 Energy consumption versus distance to BS

packets is transmitted and consequently more congestion, more packet drop and more energy waste occurred. On the other hand, in the proposed approach, since hotspot is a subset of CR, PDR is not much varied. Figure 8 states the PDR of the circular area centred on the BS node with the radius of d. By increasing the radius of the circle, more number of nodes is covered and the number of transmissions is increased. In the proposed approach, according to the considered metrics in calculating the link cost of the nodes, it transmit data through the paths with minimum hop distances and more available buffer sizes and energy to ensure efficient data delivery. Therefore, it outperforms the hybrid approach in term of PDR. Moreover, we have evaluated and compared the PDR of the proposed approach and EDDD in CR according to the number of nodes inside the CR (Fig. 9). Then, in Fig. 10, we have measured PDR with different distances from the BS. In EDDD, the usage of data diffusion loads the network with more number of packets. Therefore, increasing the distance to the BS or the number of nodes inside the CR

503

Fig. 8 Average packet delivery ratio versus distance to BS

Fig. 9 Average packet delivery ratio versus number of nodes inside the critical region

Fig. 10 Average packet delivery ratio versus distance to BS

Fig. 7 Average packet delivery ratio versus Number of nodes inside the hotspot

leads to more diffusion which increases the number of transmission but decreases the number of packet received at the BS. However, the proposed approach does not use diffusion; instead, it directly sends the sensed data to the

123

504

BS through other nodes by considering the link quality of the nodes. 5 Conclusions In cluster-based routing approaches, usually cluster heads transmit the collected information from the sensor nodes directly to the BS. This requires a high transmission power to transmit the large amount of gathered data, especially for the cluster heads which are far away from the BS. Therefore, a reliable routing mechanism must be applied between the cluster heads and the BS to reduce the energy consumption and prevent node failures and packet loss. In this paper, we have considered the region in which cluster heads transmit their data to the BS as the critical region and use a multi-hop data routing approach between the cluster heads and the BS rather than using direct transmission to the BS. The proposed routing approach by considering the link quality metrics of the nodes including residual energy, available buffer size and number of hops to the BS selects the most reliable and energy-efficient paths and increases the rate of data delivery at the BS node. According to our performance evaluation, we have proved that the proposed approach can outperform EDDD in term of energy consumption and packet delivery ratio. Also, in comparison with the hybrid approach, it has been shown that only when the number of nodes in hotspot area is few; the hybrid approach can achieve almost better energy consumption and PDR than the proposed approach. But when the number of nodes inside the hotspot area goes higher, the proposed approach exceeds in performance, significantly.

References 1. Yilmaz, O., Demirci, S., Kaymak, Y., Ergun, S., & Yildirim, A. (2012). Shortest hop multipath algorithm for wireless sensor networks. Computer Communications, 63, 48–59. 2. Moser, C., Thiele, L., Brunelli, D., & Benini, L. (2010). Adaptive power management for environmentally powered systems. IEEE Transactions on Computers, 59, 478–491. 3. Alsalih, W., Hassanein, H., & Akl, S. (2010). Placement of multiple mobile data collectors in wireless sensor networks. Ad Hoc Networks, 8, 378–390. 4. Gianluca, M., Gabriele, M. (2011). W-Grid: A scalable and efficient self-organizing infrastructure for multi-dimensional data management, querying and routing in wireless data-centric sensor networks. Journal of Network and Computer Applications, in press. 5. Alippi, C., Camplani, R., Galperti, C., & Roveri, M. (2011). A robust, adaptive, solar-powered WSN framework for aquatic environmental monitoring. IEEE Sensors Journal, 11, 45–55. 6. Papadopoulos, A., Navarra, A., Navarra, J., Pinotti, C. (2011). VIBE: An energy-efficient routing protocol for dense and mobile

123

Wireless Netw (2013) 19:495–505

7.

8.

9.

10.

11.

12.

13.

14.

15.

16.

17.

18.

19.

20.

21.

22.

23.

sensor networks. Journal of Network and Computer Applications, in press. Dimokas, N., Katsaros, D., & Manolopoulos, Y. (2010). Energyefficient distributed clustering in wireless sensor networks. Journal of Parallel and Distributed Computing, 70, 371–383. Jung, W. S., Lim, K. W., Ko, W. B., & Park, S. J. (2011). Efficient clustering-based data aggregation techniques for wireless sensor networks. Wireless Networks, 17, 1387–1400. Abdulla, A., Nishiyama, H., Kato, N. (2011). Extending the lifetime of wireless sensor networks: A hybrid routing algorithm. Computer Communications, in press. Cheng, L., Qian, D., & Wu, W. (2008). An energy efficient weight-clustering algorithm in wireless sensor networks. In Japan-China Joint Workshop on Frontier of Computer Science and Technology (FCST), Nagasaki. Younis, M., Youssef, M., Arisha, K. (2002). Energy-aware routing in cluster-based sensor networks. In Proceedings of the 10th IEEE/ACM International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems (MASCOTS), Fort Worth, TX. Koutsonikola, D., Das, S., Charlie, H. Y., & Stojmenovic, I. (2010). Hierarchical geographic multicast routing for wireless sensor networks’. Wireless Networks, 16, 449–466. Sanchez, J. A., Ruiz, P. M., & Stojmenovic, I. (2006). GMR: Geographic multicast routing for wireless sensor networks’. In Proceedings of 2006 3rd Annual IEEE Communication Society Conference on Sensor and Ad Hoc Communications and Networks, Reston, VA. Das, S. M., Pucha, H., & Hu, Y. C. (2008). Distributed hashing for scalable multicast in wireless ad hoc networks. In. IEEE Transactions on Parallel and Distributed Systems, 19, 347–362. Tumer, A. E., & Gunduz, M. (2010). Energy-efficient and fast data gathering protocols for indoor wireless sensor networks. Sensors, 10, 8054–8069. Heinzelman, W., Chandrakasan, A and Balakrishnan, H. (2000). Energy-efficient communication protocol for wireless microsensor networks. In Proceedings of the 33rd International Conference on System Sciences (HICSS), Hawaii. Heinzelman, W., Kulik, J., & Balakrishnan, H. (1999). Adaptive protocols for information dissemination in wireless sensor networks. In proceedings of the 5th Annual ACM/IEEE International Conference on Mobile Computing and Networking (MobiCom 1999), Seattle, WA. Rehena, Z.; Roy, S.; Mukherjee, N. A modified SPIN for wireless sensor networks. In Third international conference on communication systems and networks (COMSNET), January 2011, Bangalore, India. Intanagonwiwat, C., Govindan, R., & Estrin, D. (2000). Directed diffusion: A scalable and robust communication paradigm for sensor networks. In Proceedings of the 6th Annual ACM/IEEE International Conference on Mobile Computing and Networking (MobiCom 2000), August 2000, Boston, MA. Yan-rong, C. (2009). Data Query Protocol with Restriction Flooding in Wireless Sensor Networks. In International conference on networks security, wireless communications and trusted computing, Wuhan, Hubei. Chen, M., Kwon, T., & Choi, Y. (2006). Energy-efficient differentiated directed diffusion (EDDD) in wireless sensor networks. Computer Communications, 29(2), 231–245. Tariq, M., Kim, Y. P., Kim, J. H., Park, Y. J., Jung, E. H. (2009). Energy efficient and reliable routing scheme for wireless sensor networks. In Proceedings of the IEEE international conference on communication software and networks, Macau. J-Sim: Java Simulator, Available at http://www.j-sim.zcu.cz/.