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OPTMAL DATA COLLECTION IN WIRELESS SENSOR NETWORKS WITH PATH CONSTRAINED MOBILE SINKS

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OPTMAL DATA COLLECTION IN WIRELESS SENSOR NETWORKS WITH PATH CONSTRAINED MOBILE SINKS Powered By Docstoc
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National Conference on Role of Cloud Computing Environment in Green Communication 2012




  OPTMAL DATA COLLECTION IN WIRELESS SENSOR NETWORKS WITH PATH CONSTRAINED MOBILE SINKS

                  Anil Kumar N V
   ME Student: Department of Computer Science and                            S. Jeba Anand M.E.,PhD
                    Engineering                              Assistant Professor, Department of Computer Science and
     Sun College of Engineering and Technology                                      Engineering
               Nagercoil, Kanyakumari                               Sun College of Engineering and Technology
             Kumarnv.anil@gmail.com                                           Nagercoil, Kanyakumari
                                                                              anand.jeba@gmail.com

     Abstract—Improving network lifetime is the               where the sink trajectories are random. In the
     fundamental challenge of wireless sensor networks.       scenarios where the trajectories of the mobile sinks
     One possible solution consists in making use of          are constrained or predetermined [4], [5], efficient
     mobile sinks. Sink mobility along a constrained path     data collection problems are often concerned to
     can improve the energy efficiency in wireless sensor     improve the network performance. The path
     networks. However, due to the path constraint, a         constrained sink mobility is used to improve the
     mobile sink with constant speed has limited              energy efficiency of single-hop sensor networks
     communication time to collect data from the sensor       which may be infeasible due to the limits of the path
     nodes deployed randomly. This poses significant          location and communication power [9], [10].
     challenges in jointly improving the amount of data
     collected and reducing the energy consumption. This          This paper focuses on large-scale dense WSNs
     paper propose a novel data collection scheme, called     with path-constrained mobile sinks that may exist in
     the Maximum Amount Shortest Path (MASP), to              real world applications, such as ecological
     address this issue, that increases network throughput    environment monitoring and health monitoring of
     as well as conserves energy by optimizing the            large buildings [10]. Let a mobile sink M installed on
     assignment of sensor nodes.                              a transportation vehicle move along a fixed trajectory
                                                              L periodically. Assume that sensor nodes are
         MASP is formulated as an integer linear              randomly deployed in the neighborhood of the
     programming problem and then solved with the help        trajectory. When M arrives at the end point of its path
     of improved ant colony optimization. Zone based          once and returns back to the start point, now it has
     partition is applied to implement the MASP scheme.       completed one round. The mobile sink collects data
     The residual energy of each node is calculated and       from sensor nodes while moving close to them.
     the optimal path is selected by considering the          According to the communication range of M, the
     shortest path, residual energy, channel noise, and       monitored region can be divided into two parts, the
     delay. This approach is validated through simulation     direct communication area (DCA), and the multihop
     experiments using NS2                                    communication area (MCA) for far-off sensors.
                                                              Sensor nodes within the DCA, called subsinks, can
        Keywords—Mobile sinks, constrained path, data         directly transmit data to the mobile sink due to their
     collection, sensor nodes, residual energy, channel       closer proximity of the trajectory. On the other hand,
     noise, delay.                                            sensors within the MCA, called members, must first
                                                              relay data to the subsinks which complete the final
     1. INTRODUCTION                                          data transmission to the mobile sink. The
         Existing work has shown that sink mobility can       communication time (or duration) between each
     improve the performance of wireless sensor networks      subsink and the mobile sink is assumed to be fixed
     [2], [3], [5]; mobile sinks are mounted on some          due to the fixed movement path and constant speed of
     people or animals moving randomly to collect             M. So each subsink has an upper bound on the
     information of interest sensed by the sensor nodes




Department of CSE, Sun College of Engineering and Technology
  National Conference on Role of Cloud Computing Environment in Green Communication 2012
                                                                                                                         350


 amount of data that can be transmitted to the mobile         communication is designed to improve the amount of
 sink in one round.                                           data and reduce energy consumption. In [6], [8], the
     The throughput of the WSN is dependent on the            authors propose mobile sensor networks with a path-
 relationship between the upper bound on the data             constrained      sink     supporting      multihop
 collected and the number of members belonging to             communication.
 each subsink. The main challenge here is to find an
 efficient assignment of members to the subsinks that                  A communication protocol and a speed
 improves the data delivery performance as well as            control algorithm of the mobile sink are suggested to
 reduces energy consumption.
                                                              improve the energy performance and the amount of
4.1 RELATED WORK                                              data collected by the sink. In this protocol, a shortest
    Based on the trajectory of the mobile sink, existing      path tree (SPT) is used to choose the cluster heads
 research on sink mobility can be classified into three       and route data, which may cause imbalance in traffic
 categories: random path, constrained path, and               and energy dissipation. To address the imbalance
 controllable path. In sensor networks where the path         problem, the MASP scheme proposed in this paper is
 is random [1], [2], the mobile sinks are often               designed to enhance data collection from the
 mounted on some people or animals moving                     viewpoint of choosing cluster heads more efficiently.
 randomly to collect interested information sensed by         Moreover, if a mobile sink is mounted on public
 the sensor nodes. Due to random mobility, it is              transportation, e.g., a bus, the speed cannot often be
 difficult to bound the data transfer latency and the         changed freely to the purpose of data collection. In
 data delivery ratio. On the other hand, it is possible to    [7], a routing protocol, called MobiRoute, is
 guarantee the data delivery efficiency with the help         suggested for WSNs with a path predictable mobile
 of efficient communication protocols and data                sink to prolong the network lifetime and improve the
 collection schemes while the trajectories of the             packet delivery ratio, where the sink sojourns at some
 mobile sinks are constrained or controllable.                anchor points and the pause time is much longer than
                                                              the movement time. Accordingly, the mobile sink has
    This section reviews the data collection                  enough time to collect data, which is different from
 approaches in WSNs with path-constrained mobile              our scenario. Moreover, in MobiRoute all sensor
 sinks and path-controllable mobile sinks, which can          nodes need to know the topological changes caused
 be sub classified according to the communication             by the sink mobility.
 mode (single or multiple hops) and the number of
 mobile sinks.                                               4.1 PATH-CONTROLLABLE SINK MOBILITY
                                                                        Most of the current work about path-
4.1 PATH-CONSTRAINED SINK MOBILITY                            controllable sink mobility has focused on how to
          Predictable sink mobility is exploited in [3]       design the optimal trajectories of mobile sinks to
 to improve energy efficiency of sensor networks. A           improve the network performance. Mobile element
 mobile sink is installed on a public transport vehicle       scheduling problem is studied in [11], where the path
 which moves along a fixed path periodically.                 of the mobile sink is optimized to visit each node and
 However, all sensor nodes can only transmit data to          collect data before buffer overflows occur. The work
 the single mobile sink in one-hop mode. Actually,            in [11] is extended to support more complex scenario
 single-hop communication between all sensor nodes            with multiple sinks in [13]. A partitioning-based
 and the mobile sink may be infeasible due to the             algorithm is presented in [12] to schedule the
 limits of existing road infrastructure and                   movements of the mobile element to avoid buffer
 communication power. An architecture of wireless             overflow. In [11], [12], [13], the mobile sinks need to
 sensor networks with mobile sinks (MSSN) is                  visit all sensor nodes to collect data and the path
 proposed in [4] for a traffic surveillance application.      optimization is based on the constraint of buffer and
 However, it is also assumed that all sensor nodes in         data generation rate of each node. In [14], the path
 MSSN are located within the direct communication             selection problem of a mobile device is focused to
 range of the mobile sink. In this paper, a data              achieve the smallest data delivery latency in the case
 collection scheme based on the multi-hop                     of minimum energy consumption at each sensor. It is

 Department of CSE, Sun College of Engineering and Technology
 National Conference on Role of Cloud Computing Environment in Green Communication 2012
                                                                                                                       351


assumed that each sensor node sends its data directly      round of the mobile sink. Assume that all sensor
to the mobile device.                                      nodes forward data along the shortest path trees to
                                                           their destinations.
         Single-hop communication is not feasible
due to the limitation of road infrastructure and           Equation (2) describes the relationship between the
requirement on delivery latency. A rendezvous-based        total amount of data received by all nodes and the
data collection approach is proposed in [15] to select
                                                           sum of hops.
the optimal path due to the delay limitation in WSNs
with a mobile base station. In this work, the mobile
                                                                             n       n

                                                                             Kri   hi .q
element visits exact locations, called rendezvous
points, according to the pre computed schedule to                                                            (2)
                                                                            i 1    i 1
collect data. The rendezvous points buffer and
aggregate data originated from the source nodes
through multihop relay and transfer to the mobile          Where    hi is the shortest hop from sensor node i to its
element when it arrives.                                   destination subsink.
3 PROBLEM FORMULATION
    In this scenario, let n sensor nodes be deployed       The total amount of data,       qtotal   collected by the
randomly and let ns nodes close to the trajectory of
the mobile sink be chosen as subsinks. The other n m       mobile sink in one round consists of the data
nodes away from the mobile sink choose different           collected from all subsinks as follows:
subsinks as their destinations. The mobile sink moves
along a fixed path periodically with constant speed to                ns
collect data. Assume that the mobile sink has
unlimited energy, memory, computing resources and
                                                           qtotal =  qi                                     (3)
                                                                     i 1
has enough storage to buffer data. Each sensor node
continuously collects data and transmits them either
directly to the mobile sinks or to one of the subsinks     Where    q i is the amount of data from subsink i per
which finally delivers the data to the mobile sink.        round.
    The members within the multihop communication          4     PROPOSED SOLUTION
area need to choose one and only one subsink as its                 The proposed solution focuses efficient data
destination. A highly dense sensor network is              collection and network lifetime maximization of
considered, in which all members can reach the             wireless sensor networks. Here the predictable
subsinks    through    single-hop   or    multihop         mobile sink path is considered. The objective is to
communication. Assume each sensor node transmits           improve the energy efficiency for data gathering,
and receives data with fixed transmission and              which minimizes the energy consumption of entire
reception power, respectively. So the power                network under the condition of maximizing the total
consumption is independent of the transmission             amount of data collected by the mobile sink. Network
distance between adjacent nodes. To calculate the          lifetime can be improved by optimal subsink
power consumption [4]:                                     selection which depends on the residual energy of the
                                                           nodes. The problem is solved by using improved ant
         P ≈ e ( kr + kt )                          (1)    colony optimization.

                                                                     Ant Colony Optimization, a swarm
Where p denotes the total energy consumption of one
                                                           intelligence based optimization technique, [19] is
node for receiving k r bits and transmitting   k t bits,   widely used in network routing. A novel routing
and e is a factor indicating the energy consumption        approach using Improved Ant Colony Optimization
per bit at the receiver circuit. Let q denote the total    algorithm is proposed for Wireless Sensor Networks
amount of data sensed by each node per traversal           consisting path constrained mobile sink. Ant colony

Department of CSE, Sun College of Engineering and Technology
 National Conference on Role of Cloud Computing Environment in Green Communication 2012
                                                                                                                             352


optimization (ACO) algorithms simulating the                    across. The probability choice is carried on several
behavior of ant colony have been successfully                   times and then the rotary table choice is carried on.
applied in many optimization problems such as the               The probability choice can protect the excellent
asymmetric traveling salesman, vehicle routing and              solution and the rotary table choice can produce the
WSN routing.                                                    opportunity of the better solution.

         Let   bi t    , (i = 1... n) be the number of
                                                               4.1 EXPLANATION OF THE ALGORITHM
                                                                (1) Input source node, destination node sets,
                                         i1 bi (t )
                                             n
ants in town i at time t and let m =                     be     maximum delay, delay jitter, minimum bandwidth
                                                                and cost of the routing request; NC=0, NC is cycle
the total number of ants.
                                                                counter.
         Each ant is a simple agent with the
                                                                (2) Initialize the network. The link bandwidth is
characteristics: it chooses the town to go to with a
                                                                judged. If it is not satisfied, the link is not considered
probability that is a function of the town distance and
                                                                in this routing request.
of the amount of trail present on the connecting edge;
to force the ant to make legal tours, transitions to            (3) Initialize tabuk1, allowdk1 and tabub1. Source
already visited towns are disallowed until a tour is            node joins the taboo table tabuk1. All nodes except
completed (this is controlled by a tabu list); when it          the source join the allowed node set allowdk1. m ants
completes a tour, it lays a substance called trail on           are on the source node. The previous received
each edge (i,j) visited. Let    ij (t ) be the intensity of    optimal path and the probability of the selected path
                                                                join the table babub1.
trail on edge (i,j) at time t. Each ant at time t chooses
the next town, where it will be at time t+1.                    (4) Initialize tabuk2, allowdk2 and tabub2.
                                                                Destination node joins the taboo table tabuk2. All
         The moves carried out by the m ants in the
                                                                nodes except the destination join the allowed node set
interval (t, t+1), then every n iterations of the
                                                                allowdk2. m ants are on the destination node. The
algorithm (a cycle) each ant has completed a tour. At
                                                                previous received optimal path and the probability of
this point the trail intensity of each edge is calculated
                                                                the selected path join the table babub2.
and which is compared with the residual energy of
the node to find the optimal path.                              (5) Each ant chooses the next node j from the current
                                                                node i based on the formula
         An improved ant colony algorithm is
proposed in the paper for the limitation of the basic           (6). Nodes j will be inserted into the tabuk1.
ant colony algorithm. In the searching process, two             Similarly, the next node which is chosen by the ant
group ants carry out searching separately. One finds            from destination to source is inserted into tabuk2.
the optimal path from the source to destination, and            After a period of searching time, the path is searched
the other finds path from the destination to source.            by rotary table. The strategy makes the search
After one search, they alternate information each               constringency quickly in the beginning because the
other. All ants are assigned to two paths so as to              probability search is adopted, and makes the search
avoid stagnating by choosing one. Meanwhile, in                 diverse and avoids stagnation in the later part of the
every searching process, the previous received                  search because the rotary table search is adopted.
optimal path and the probability of the path are
saved.                                                          (6) The information amount of each path is changed
                                                                by each ant’s walking path
         When each ant chooses the next node, the
probability of previous search is introduced to speed           (7) If NC < NCmax, all tabuk1 and tabuk2 tables are
up the search speed. After a period of time, ants               emptied, go to Step 2.
choose the path according to rotary table, which can
ensure diverse solution and avoid stagnation. The               (8) The objective function in tabuk1, tabuk2 is
probability choice and rotary table choice carry on             calculated by formula (10)[10]. The path which

Department of CSE, Sun College of Engineering and Technology
    National Conference on Role of Cloud Computing Environment in Green Communication 2012
                                                                                                                 353


meets QoS constraints and has the minimum cost is       satisfies multi constrains: delay, delay jitter,
the optimal path.Then the multicast tree with the       bandwidth and cost although it increases delay
smallest cost is put out.                               compared with mini delay algorithm and increases
                                                        cost compared with mini cost algorithm.

                                                                  For future work, validate the proposed
                                                        schemes on different scenarios with various
                                                        movement trajectories of mobile sinks. Considering
                                                        that minimizing the total energy consumption may
                                                        not lead to the maximum network lifetime, also plan
                                                        to study the subsink selection problem with network
                                                        lifetime maximization as the optimization objective
                                                        as future work.



                                                        6   REFERENCES


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Department of CSE, Sun College of Engineering and Technology
 National Conference on Role of Cloud Computing Environment in Green Communication 2012
                                                                                                                354


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