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International Journal of Innovative Research in Computer and Communication Engineering
An ISO 3297: 2007 Certified Organization
Vol.3, Special Issue 3, April 2015
2nd National Conference On Emerging Trends In Electronics And Communication Engineering (NCETECE’15)
Organized by
Dept. of ECE, New Prince Shri Bhavani College Of Engineering & Technology, Chennai-600073, India during 6th & 7th April 2015
Optimization of Energy in Wireless Sensor
Network Using Weighted Rendevous Planning
K.Ramasubramanian, P.Manikandan
PG Scholar, Dept of ECE, S.Veerasamy Chettiar College of Engg and Tech, Puliangudi, Tamil Nadu, India
Assistant Professor, Dept of ECE, S.Veerasamy Chettiar College of Engg and Tech, Puliangudi, Tamil Nadu, India
ABSTRACT: Over the last decade a large number of routing protocols has been designed for achieving energy
efficiency in data collecting wireless sensor networks. It has been shown that a mobile sink may improve the energy
dissipation and to prevent the formation of energy holes. However, these benefits are dependent on the path taken by
the mobile sink, particularly in delay-sensitive applications, as all sensed data must be collected within a given time
constraint. An approach proposed to address this challenge is to form a hybrid moving pattern in which a mobile-sink
node only visits rendezvous points (RPs), as opposed to all nodes. The fundamental problem then becomes computing a
tour that visits all these RPs within a given delay bound. To address this problem, a heuristic called weighted
rendezvous planning (WRP) is proposed, whereby each sensor node is assigned a weight corresponding to its hop
distance from the tour and the number of data packets that it forwards to the closest RP. Simulation results show that
WRP reduces energy consumption and also increases network lifetime.
I. INTRODUCTION
WIRELESS sensor networks (WSNs) are composed of a large number of sensor nodes deployed in a field. They have wideranging [1]–[3], environment monitoring [4], [5], agriculture [6], [7], home automation [8], smart transportation [9], [10], and
health [11]. Each sensor node has the capability to collect and process data, and to forward any sensed data back to one or
more sink nodes via their wireless transceiver in a multihop manner. In addition, it is equipped with a battery, which may be
difficult or impractical to replace, given the number of sensor nodes and deployed environment. These constraints have led to
intensive research efforts on designing energy-efficient protocols [12].
In multihop communications, nodes that are near a sink tend to become congested as they are responsible for forwarding data
from nodes that are farther away. Thus, the closer a sensor node is to a sink, the faster its battery runs out, whereas those
farther away may maintain more than 90% of their initial energy [13]. This leads to nonuniform depletion of energy,
which results in network partition due to the formation of energy holes [14], [15]. As a result, the sink becomes
disconnected from other nodes, thereby impairing the WSN. Hence, balancing the energy consumption of sensor nodes
to prevent energy holes is a critical issue in WSNs. In WSNs with a mobile sink, one fundamental problem is to determine
how the mobile sink goes about collecting sensed data.
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ISSN(Online): 2320-9801
ISSN (Print): 2320-9798
International Journal of Innovative Research in Computer and Communication Engineering
An ISO 3297: 2007 Certified Organization
Vol.3, Special Issue 3, April 2015
2nd National Conference On Emerging Trends In Electronics And Communication Engineering (NCETECE’15)
Organized by
Dept. of ECE, New Prince Shri Bhavani College Of Engineering & Technology, Chennai-600073, India during 6th & 7th April 2015
Fig. 1. Example showing a mobile sink performing data collection in a WSN. A source node determines and sends all data to a suitable site.
To this end, previous works such as [15] and [16] employ one or more mobile sinks. These mobile sinks survey and collect
sensed data directly from sensor nodes and thereby help sensor nodes save energy that otherwise would be consumed by
multihop communications. Fig. 1 shows the feasible sites of a mobile sink in an example WSN. Specifically, the squares
denote the feasible sites that the mobile sink will visit and stop for data collection. The data forwarding path from sensor
nodes to the sink is dependent on the sink’s current position. This requires sensor nodes to dynamically plan one or more data
forwarding paths to each feasible site whenever the sink node changes its position over time. As demonstrated by [15], a
mobile sink that moves at the periphery of a sensor field max-imizes the lifetime of sensor nodes. Intuitively, by changing the
position of the sink over time, the forwarding tree will involve a different set of sensor nodes and, hence, will help to balance
energy consumption [17]–[21].
The traveling path of a mobile sink depends on the real-time requirement of data produced by nodes. For example, in
hard real-time applications such as a fire-detection system [22], environmental data need to be collected by a mobile
sink quickly. Moreover, a mobile-sink node may change its position after a certain period of time and select another
data collection/ feasible site. The feasible sites and corresponding sojourn time are dependent on the residual energy of
sensor nodes [23]– [27]. In general, limitations such as the maximum number of feasible sites [28], maximum distance
between feasible sites, and minimum sojourn time [24] govern the movement of a mobile sink.
One approach is to visit each sensor node to receive sensed data directly [29]. This is essentially the well-known traveling
salesman problem (TSP) [30], where the goal is to find the shortest tour that visits all sensor nodes. However, with an
increasing number of nodes, this problem becomes intractable and impractical as the resulting tour length is likely to violate
the delay bound of applications. To this end, researchers have proposed the use of rendezvous points (RPs) to bound the tour
length [31], [32]. This means a subset of sensor nodes are designated as RPs, and non-RP nodes simply forward their data to
RPs. A tour is then computed for the set of RPs, as shown in Fig. 2. As a result, the problem, which is called rendezvous
design, becomes selecting the most suitable RPs that minimize energy consumption in multihop communications while meeting a given packet delivery bound. A secondary problem here is to select the set of RPs that result in uniform energy
expenditure among sensor nodes to maximize network lifetime.
In this paper, we call this problem the delay-aware energy-efficient path (DEETP). We show that the DEETP is an NPhard problem and propose a heuristic method, which is called weighted rendezvous planning (WRP), to determine the
tour of a mobile-sink node. In WRP, the sensor nodes with more con-nections to other nodes and placed farther from
the computed tour in terms of hop count are given a higher priority. Thus, this paper is summarized as follows.
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ISSN(Online): 2320-9801
ISSN (Print): 2320-9798
International Journal of Innovative Research in Computer and Communication Engineering
An ISO 3297: 2007 Certified Organization
Vol.3, Special Issue 3, April 2015
2nd National Conference On Emerging Trends In Electronics And Communication Engineering (NCETECE’15)
Organized by
Dept. of ECE, New Prince Shri Bhavani College Of Engineering & Technology, Chennai-600073, India during 6th & 7th April 2015
We define the problem of finding a set of RPs to be visited by a mobile sink. The objective is to minimize energy onsumption
by reducing multihop transmissions from sensor nodes to RPs. This also limits the number of RPs such that the resulting
tour does not exceed the required
deadline of data packets.
•
We propose WRP, which is a heuristic method that finds a near-optimal traveling tour that minimizes the energy
consumption of sensor nodes. WRP assigns a weight to sensor nodes based on the number of data packets that they
forward and hop distance from the tour, and selects the sensor nodes with the highest weight.
•
We mathematically prove that selecting the sensor node that forwards the highest number of data packets and have
the longest hop distance from the tour reduces the network energy consumption, as compared with other nodes. Moreover, we show that, in contrast to cluster-based (CB) [32], rendezvous design for variable tracks (RD-VT) [33], and
rendezvous planning utility-based greedy (RP-UG) [31] algorithms, WRP is guaranteed to find a tour if the latter exists.
•
We demonstrate via computer simulation the properties and effectiveness of WRP against the CB [32], RD-VT
[33], and RP-UG algorithms [31]. Our results show that WRP achieves 14% more energy savings and 22% better
distribution of energy consumption between sensor nodes than the said algorithms.
The remainder of this paper is structured as follows. Section II reviews methods that employ a hybrid model to collect sensed
data. Section III presents DEETP. In Section IV, we illustrate WRP and present a detailed analysis of WRP and key
properties. Finally, in Section V, we compare the performance of WRP with previous works before concluding in Section VI.
II. LITERATURE REVIEW
Existing methods on using a mobile sink in WSNs can be grouped into two categories: 1) direct, where a mobile sink
visits each sensor node and collects data via a single hop; and 2) rendezvous, where a mobile sink only visits nodes
designated as RPs. The main goal of protocols in category 1 is to minimize data collection delays, whereas those in
category 2 aim to find a subset of RPs that minimize energy consumption while adhering to the delay bound provided
by an application [17]. In the following, we review the challenges faced by these protocols.
A. Direct
Initial studies used a mobile sink that visits sensor nodes randomly and transport collected data back to a fixed sink node. An
example is the use of animals as mobile-sink nodes to assist in data collection from sensor nodes scattered on a large farm
[16]. To reduce the latency of visiting each sensor node randomly, researchers have proposed TSP-based data col-lection
methods. In essence, the problem is reduced to finding the shortest traveling path that visits each sensor node [29]. For
example, TSP with neighborhood [34] involves finding the shortest traveling tour for a mobile-sink node that passes through
the communication range of all sensor nodes. Another TSP-based algorithm [35] called label-covering considers a WSN as a
complete graph. For each edge, it calculates a cost and associates a label set. The cost of an edge is the Euclidean distance
between nodes, whereas the label set contains sensor nodes whose transmission range intersects with the given edge. The
label-covering algorithm selects the minimum number of edges where their associated label set covers all sensor nodes.
B. Rendezvous
The problem with collecting data directly from sensor nodes is that it becomes impractical when there are a large
number of sensor nodes. Visiting each sensor node increases the mobile sink’s traveling path length and results in
sensor nodes experi-encing buffer overflow due to data collection delays. To address this problem, researchers have
proposed a rendezvous-based model, in which a mobile sink only visits a subset of sensor nodes called RPs. The sensor
nodes outside the mobile sink path send their data via multihop communications to these RPs. Studies [31]–[33], [36]–
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Vol.3, Special Issue 3, April 2015
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[38] deploying this approach can be classified according to the mobile sink’s trajectory, i.e., whether it moves along a
fixed path or its path is unconstrained by any external factors.
1) Fixed: In the studies conducted in [36]–[38], the path of the mobile sink is fixed, and sensor nodes are randomly
deployed near the sink’s traveling path. Sensor nodes that are inside a mobile sink’s communication range play the role
of RPs and collect data from other sensor nodes. An example application is a traffic management system where mobile
sinks are public buses that roam a city to collect data from sensor nodes placed on buildings [36]. In these approaches,
the length of the traveling path is not dependent on the buffer size of sensor nodes or application deadline. Hence, the
buffer of RPs may overflow or packets may expire before they are collected by the sink.
Xing et al. [33] propose RD-FT, where the movement of a mobile sink is governed by application deadline. They also
consider obstacles that restrict the movement of a mobile sink along a predefined path. The objective is to find a set of RPs on
the fixed path such that the length of data forwarding paths from sensor nodes to RPs is minimized and that the traveling time
between RPs is limited to the required packet delivery time.
Unconstrained: In [33], a WSN with a static sink node and a mobile element (ME) is assumed to collect data from RPs.
Moreover, RPs perform data aggregation. An algorithm called RD-VT is proposed with the objective of identifying a
traveling path that is shorter in duration than the packet delivery time. The algorithm first constructs a Steiner minimum
tree (SMT) rooted at the sink node. RD-VT then starts from the sink’s position and traverses the SMT in preorder until
the shortest distance between visited nodes is equal to the required packet delivery time. Since, in an SMT, a Steiner
point may be a physical position and does not correspond to the position of a sensor node, RD-VT replaces these virtual
RPs with the closest sensor nodes. A major limitation of RD-VT is that traversing the SMT in preorder leads to the
selection of RPs that in turn results in long data forwarding paths to sensor nodes located in different parts of the SMT.
As a result, RD-VT causes nodes to have an unbalanced data forwarding load and energy consumption.
C. Contributions of This Paper
In this paper, we propose a hybrid unconstrained movement pattern for a mobile sink with the aim of balancing the
energy consumption of sensor nodes. Our approach makes the follow-ing contributions, as compared with the work
reported in the literature.
•
We prefer nodes that have a high degree. This is critical as sensor nodes in dense parts of a WSN generate the
highest number of packets. Hence, giving priority to sensor nodes in these parts during tour computation will help to
reduce congestion points, and in turn, reduces energy consump-tion and improves WSN lifetime. In addition, it helps to
mitigate the energy-hole problem.
•
We consider hop distance between sensor nodes and RPs as fewer hop counts reduce multihop transmissions. RPUG [31] minimizes network energy consumption by re-ducing the physical distance between sensor nodes and RPs.
However, due to the existence of obstacles, physical distance is not a reliable indicator of energy consumption.
Apart from node density and hop count, when using an SMT, we use virtual RPs in the final tour to increase
performance, and we do not replace them with real sensor nodes. Replacing a virtual RP with a real sensor node in RDVT [33] and RP-UG [31] effectively approximates an SMT with a shortest-path tree (SPT). We will show through
simulation that, when SMT is used and virtual nodes are in the final tour, the energy consumption of nodes can be
reduced considerably, as compared with using an SPT.
III. PROBLEM FORMULATION
Let us consider a WSN in which sensor nodes generate data packets periodically. Each data packet must be delivered to the
sink node within a given deadline. There is a mobile sink that roams around a WSN to collect data from a set of RPs. The
objective is to determine the set of RPs and associated tour that visits these RPs within the maximum allowed packet delay.
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Vol.3, Special Issue 3, April 2015
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Dept. of ECE, New Prince Shri Bhavani College Of Engineering & Technology, Chennai-600073, India during 6th & 7th April 2015
A. Assumptions
Before describing DEETP, we first outline some assumptions.
1)
The communication time between the sink and sensor nodes is negligible, as compared with the sink node’s
traveling time. Similarly, the delay due to multihop com-munications including transmission, propagation, and queuing
delays is negligible with respect to the traveling time of the mobile sink in a given round.
2)
Each RP node has sufficient storage to buffer all sensed data.
3)
The sojourn time of the mobile sink at each RP is suffi-cient to drain all stored data.
4)
The mobile sink is aware of the location of each RP.
5)
All nodes are connected, and there are no isolated sensor nodes.
6)
Sensor nodes have a fixed data transmission range.
7)
Each sensor node produces one data packet with the length of b bits in time interval D.
B. Notation
We model a WSN as G(V, E), where V is the set of homoge-neous sensor nodes, and E is the set of edges between
nodes in V (see Table I for a summary of each notation). If sensor node i sends b bits to node j, its energy consumption
is [23]
ETX(i, j) = b α1 + α2 × di,jγ
(1)
where di,j is the physical distance between sensor node i and j, and α1 is the energy consumption factor indicating the
power per bit incurred by the transmitting circuit. The expression α2dγi,j indicates the energy consumption of the
amplifier per bit, where α2 is the energy consumption factor of the amplifier circuit. Here, γ is the path-loss exponent,
which usually ranges between 2 and 4, depending on the environment. Moreover, the power consumption incurred by
node i to receive b bits from node j is
ERX(i, j) = b × β
(2)
IV. WEIGHTED RENDEZVOUS PLANNING
WRP preferentially designates sensor nodes with the highest weight as a RP. The weight of a sensor node is calculated
by multiplying the number of packets that it forwards by its hop distance to the closest RP on the tour. Thus, the weight
of sensor node i is calculated as
Wi = NFD(i) × H(i, M ).
(6)
Based on (6), sensor nodes that are one hop away from an RP and have one data packet buffered get the minimum
weight. Hence, sensor nodes that are farther away from the selected RPs or have more than one packet in their buffer
have a higher priority of being recruited as an RP.
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From (1) and (2), the energy consumption is proportional to the hop count between source and destination nodes, and
the number of forwarded data packets. Hence, visiting the highest weighted node will reduce the number of multihop
A. Analysis
The time complexity of our algorithm is dependent on how many times WRP calls the TSP solver to calculate a tour that
visits all RPs. The worst case is when all sensor nodes are marked but not selected as an RP, which means WRP will iterate
for |V | times to check the possibility of adding nodes into a tour. After a node is selected as an RP, WRP again unmarks other
sensor nodes and restarts the search process (see lines 12–37). This means our algorithm uses the TSP solver for a
maximum
2
where
. Therefore, the time complexity
of n times,
n = |V |
3
of WRP is O(n × O(TSP)). Hence, if we use Christofides’s heuristic [39], which has time complexity of O(n ), the
result2
5
ing time complexity is O(n ). In our experiments, we used a local-search-based heuristic TSP solver [40].
We like to point out that WRP always finds a tour when there is at least one possible tour in the network. This is
because WRP checks the possibility of adding all sensor nodes to the tour. This is significant when compared with CB
and RD-VT because the latter two algorithms fail in the following scenario. In CB, if the only possible tour consists of
only the sink and a neighbor in the same cluster, CB will not be able to find this tour because two sensor nodes from
the same cluster cannot be in the final tour. As for RD-VT, it will return no tour if the distance of the first sensor node
in the SMT, as it starts its depth-first traversal, exceeds lmax.
We now prove that visiting the most weighted nodes by a mo-bile sink results in the least energy consumption, as
compared with visiting any other nodes.
Theorem 1: Visiting sensor node P with weight wp reduces energy consumption more than visiting sensor node Q with
weight wq , where wp > wq .
Proof: Recall that sensor node P forwards NFD(P ) data packets to its closest RP. Therefore, the energy consumption
of sensor nodes on the path from node P to the closest RP is
Ep = (ETX + ERX) × (NFD(P ) × H(P, M )) . (7)
However, if sensor node P becomes an RP, the energy con-sumption incurred by data packets at P is zero. Similarly,
for sensor node Q that forwards NFD(Q) data packets to its closest RP, we have these problems. it involves
subproblems such as interference and coordination between rovers. Having said that, we note that WRP remains
applicable if a large WSN is partitioned into smaller areas where each area is assigned a mobile sink. WRP can be thus
run in each area. We defer the evaluation of such an approach to a future paper. less than Lc as the tour connecting the
set of nodes cannot be longer than the tour containing all nodes by the triangle inequality. Hence, WRPop will add
mi=2 and so forth until i = n. WRPop then terminates as Tn = |V |. Note that, in Lemma 1, the requirement on there
being anoptimal TSP solver can be relaxed if we assume that_i ≤lmax. In other words, the sum of all distances between
nodes is less than the required tour length. Theorem 2: Assume a sensor node P that has the longest hop distance from the
sink, and the average hop distance between sensor nodes and the sink is k; then, the maximum difference between the network
energy consumption of WRPop and the optimal is within ((2 × K × (|V | − 1) + 1)/(|V | + 2)).
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International Journal of Innovative Research in Computer and Communication Engineering
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Vol.3, Special Issue 3, April 2015
2nd National Conference On Emerging Trends In Electronics And Communication Engineering (NCETECE’15)
Organized by
Dept. of ECE, New Prince Shri Bhavani College Of Engineering & Technology, Chennai-600073, India during 6th & 7th April 2015
Proof: The network energy consumption when the mobile sink visits only sensor node P is
ENetwork(p) = (ETX + ETX × ((|V | − 1) × k))
+ (ERX × ((|V | − 1) × k)) . (10)
On the other hand, the minimum amount of energy consumed by visiting all sensor nodes except node P is
E
= E × (|V | + 1) + E .
Network(|V |−1)
TX
RX
(11)
This means sensor node P has to send all its data packets to the closest RP, whereas other sensor nodes send their data
packets directly to the mobile sink. From (10) and (11), the ratio of energy consumption in WRP in comparison to the
optimal model is converted.
VI. CONCLUSION
In this paper, we have presented WRP, which is a novel algo-rithm for controlling the movement of a mobile sink in a WSN.
WRP selects the set of RPs such that the energy expenditure of sensor nodes is minimized and uniform to prevent the
formation of energy holes while ensuring sensed data are collected on time. In addition, we have also extended WRP to use
an SPT and an SMT. Apart from that, we have also considered visiting virtual nodes to take advantage of wireless coverage.
Our re-sults, which are obtained via computer simulation, indicate that WRP-SMT reduces the energy consumption of tested
WSNs by 22% in comparison to CB. We also benchmarked WRP against existing schemes in terms of the difference between
sensor-node energy consumption. Our simulation results show that WRP uniformly distributes energy consumption by 39%
and 44% better than CB and RD-VT, respectively.
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