Design and Implementation Energy-Efficient
Data Collection in Multiple Mobile Gateways
WSN-MCN Convergence System
Ms. Mama...
MCN, WSN can extend the intelligent application
range of MCN.
The mobile UE moves in WSN area and
activates the sensors in...
to the corresponding sub sinks within the range of the
sink, thus improving the network throughput.
However, this kind of ...
sensors reply their addresses to the mobile UE. The
mobile UE determines the current highest parent
nodes (C-HPNs) based o...
If the one-hop parent node of the
determining CCHPN is a CSC, it means that this one-
hop parent node originally must be a...
interval leads to the less data collections during the
data validity period. It means that the collected data
will have be...
our proposed system is much more energy efficient
than the existing system. In the future we consider
using this system in...
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design & implementation of energy effecient data collection in multiple mobile gateways wsn mcn

  1. 1. Design and Implementation Energy-Efficient Data Collection in Multiple Mobile Gateways WSN-MCN Convergence System Ms. Mamatha . C 1 Mamatha29gmail.com Department of Computer science and Engineering DBIT, Bangalore Mrs. Shruthi.G 2 , Asso. professor shruthigmysore @gmail.com Department of Computer science and Engineering DBIT, Bangalore Abstract:-We analyze an architecture based on mobility to address the problem of energy efficient data collection in a sensor network. Our approach exploits User equipment (UEs) which acts a mobile gateway present in the sensor field as forwarding agents. As a UEs moves in close proximity to sensors, data is collected by the mobile gateway and transfer to the Base station which acts as a sink. Data gathering is a fundamental task of wireless sensor network (WSN). Recently, mobile sink has been exploited for data gathering in WSN to reduce and balance energy expenditure among sensors. How to energy-efficiently collect and transmit the data in case of multiple mobile sinks is a hot research topic. In this paper, we propose a multiple mobile sinks energy-efficient data collection (MSE2 DC) scheme for query-driven data delivery in tree-topology WSN and mobile cellular network (MCN) convergence system. The WSN sensors are purposely activated for data delivery. By implementing the MSE 2 DC, only necessary sensors should be activated for data delivery while the other sensors could keep sleeping to save energy consumption. Simulation results demonstrate that the significant energy saving of MSE 2 DC. Keywords- WSN, MCN, convergence, mobile UEs, data delivery, energy saving I. INTRODUCTION A Wireless Sensor Network (WSN) consists of spatially distributed autonomous devices that cooperatively sense physical or environmental conditions, such as temperature, sound, vibration, pressure, motion, or pollutants at different locations. WSNs have been used in applications such as environmental monitoring, homeland security, critical infrastructure systems, communications, manufacturing, and many other applications that can be critical to save lives and assets[9] This paper addresses the topic of energy efficient data collection in wireless sensor networks (WSNs) consisting of sensor nodes deployed randomly in large number and several mobile gateways used to collect data from the sensors. Sensors send their data to the sinks using multi-hop communication. mobile sinks have been proposed as a solution for data gathering in WSN to geographically balance the energy consumption among the sensors throughout the network [1-3]. This not only solves early death problem of the one- hop neighbors of the sink but also extends the network lifetime by distributing the responsibility of relaying data to the sink among many sensors in WSN [4-5].here we focus on the query-driven data delivery model: data is requested by the sink and pushed by the sensor nodes. In WSNs, sensors closer to a sink tend to consume more energy than those farther away from the sinks. This is mainly because, besides transmitting their own packets ,they forward packets on behalf of other sensors that are located farther away. As a result, the sensors closer to the sink will drain their energy resources first, resulting in holes in the WSN. This uneven energy consumption will reduce network lifetime[4]. Current approaches involve forming an ad-hoc network among the sensor nodes to send data. However, this faces the following energy related issues. Firstly, in a sparse network, the energy required for transmitting data over one hop is quite large. This is because sensors may be far from each other and the transmission power required increases as the fourth power of distance. Secondly, in an ad-hoc network sensors have to not only send their data, but also forward data for other sensors. Thirdly, the network has routing hotspots near the access points. Sensors that are near the access points have to forward many more packets and drain their battery much more quickly[1]. heterogeneous networks consisting of MCN and WSN [6] appear in many application areas where mobile terminals of MCN are equipped with WSN air-interface and provide backhaul data links for the WSN. Heterogeneous networks consisting of MCN and WSN appear in many application areas such as electricity, transportation, industrial control, e-health, environment monitoring, . In the converged MCN and WSN, MCN can be used in the supervisory control for WSN data transmission system, where the nodes of WSN can overhear the downlink signaling from the MCN directly. WSN in the applications can be managed and optimized with the aid of MCN. Because MCN has the advantages of large coverage and powerful user terminals. The convergence of MCN and WSN can benefit each other: (I) For WSN, the MCN can provide optimization to save the WSN’s energy consumption, prolong the WSN life time and provide quality of service (QoS) for WSN services; (II) For
  2. 2. MCN, WSN can extend the intelligent application range of MCN. The mobile UE moves in WSN area and activates the sensors in its one-hop range to collect data. These activated sensors activate their topological child sensors for data collection via traditional multi-hops methods. With the movement of mobile UE, this procedure repeats at different physical locations. If the WSN’s topology is fixed, it is highly possible that some sensors may be repeatedly activated even the mobile UE stops at different physical locations. in most use cases such as air/soil/water pollution monitoring, the data collected from a sensor could be valid as a reference during a pre-configured period and there is no necessary to reactivate this same sensor to collect data in this period. Otherwise, the frequent activations results in extra energy consumption and then shorten the sensor’s lifetime. if there are multiple mobile UEs acting as mobile gateways to collect data simultaneously in the same WSN area, the possibility of frequent activation of same sensors will significantly increase since the sensors may be activated by different mobile sinks. Hence, the convergent interactive control and joint optimization technologies of MCN and WSN to energy-efficiently collect/transmit data is a critical problem and need to be researched and developed. In this paper, to solve the aforementioned problems, we propose a multiple sinks energy-saving data collection scheme (MSE2 DC) in WSN-MCN convergence system. We first provide an overview of related work of mobile sink data gathering in Section II. Then, in Section III, we introduce the network model and describe proposed MSE 2 DC scheme in detail. In Section IV, we discuss and analyze the simulation results. Finally, we conclude the work in Section V. II.RELATED WORK Among numerous challenges faced while designing WSNs and protocols, maintaining connectivity and maximizing the network lifetime stand out as critical considerations[6]. The network lifetime, is directly related to how long the power sources in sensor nodes will last. The network lifetime can be increased through energy conserving methods such as using energy-efficient protocols and algorithms, and battery replenishment techniques. The mobile devices can also be used as an orthogonal method to address the connectivity and lifetime problems in WSNs . With communication devices on mobile platforms, the connectivity and energy efficiency (hence, network lifetime) problems can be addressed as follows:• Connectivity: I)Mobile platforms can be used to carry information between isolated parts of WSNs .II)• Energy efficiency: Information carried in mobile devices can reduce the energy consumption of sensor nodes by reducing multi-hop communication. The data collection algorithms based on path-controllable mobile sink focus on how to design the optimal trajectories of mobile sinks to improve the network performance. Mobile element scheduling problem is studied in [7], where the path of the mobile sink is optimized to visit each sensor and collect data on the constraint of buffer and data generation rate of each sensor. a mobile base station is used to reduce the energy consumption. Clustering is done by the fuzzy method based on different priorities. In first clustering is done by energy and cluster centric priorities then the base station has moved with fuzzy logic approach on a predetermined paths for collecting data. In second, clustering is done by distance, energy and cluster centric priorities. clustering is done based on distance and energy priorities[8]. Increasing network lifetime generally fall into two categories: The first category has a fixed base station and Network lifetime is calculated. The novel two-phase clustering (TPC) scheme for energy- saving and delay-adaptive data gathering in wireless sensor networks. The scheme partitions the network into clusters in phase I each with a cluster head, forming a direct link between cluster member and cluster head. In phase II, each cluster member searches for a neighbor closer than the cluster head within the cluster to set up an energy- saving data relay link. The sensors use either the direct link or the data relay link for their sensed data forwarding depending on the requirements specified by the users or applications . the authors aim to find the best way to relocate sinks inside buildings by determining their optimal locations and the duration of their sojourn. Therefore an Integer Linear Program for multiple mobile sinks which directly maximizes the network lifetime instead of minimizing the energy consumption or maximizing the residual energy, which is what was done in previous solutions? Path-constrained sink mobility is exploited in [9], in which a mobile sink is installed on a public transport vehicle which moves along a fixed path periodically. All sensors can only transmit data to the single mobile sink in one-hop mode which maybe infeasible due to the limits of existing road infrastructure and communication power. Gao et al. in propose a novel data collection scheme with sink moving along a constrained path, called Maximum Amount Shortest Path (MASP), which assigns the members out of the range of the sink
  3. 3. to the corresponding sub sinks within the range of the sink, thus improving the network throughput. However, this kind of data collection schemes is based on the condition that the mobile sink has a planned mobility path or the path can be accurately predicted. In sparse sensor networks where the path is random [1], the mobile sinks are often mounted on some people or animals to collect interested information sensed by the sensors. However, latency is increased because a sensor has to wait for a mobile sink before its data can be delivered. Liu et al. in [3] proposes a proactive data reporting protocol, SinkTrail, which achieves energy efficient data forwarding to multiple mobile sinks with broadcasting sink location messages. But this kind of data collection schemes is undesirable, especially when the sensor network scale increases, as frequent message flooding will cause serious congestion in network communication and significantly impair the sensor network lifetime. In order to solve the problem of frequent activation, Yin et al proposes an Energy-Efficient Data Collection Scheme (E2 DCS) to collect data from the sensors when single mobile UE moves randomly in WSN, , in realistic applicative scenario, only one single mobile UE in the WSN area for data collection is not reasonable, there may be multiple mobile UEs acting as gateways to collect data simultaneously. In this scenario, if each mobile UE individually adopts E2 DCS to collect data, the frequent activation of the same sensors by different UEs is inevitable. Algorithm:.collecting data using single UEs Step 1 :- UE activates the sensors in its WSN one- hop range Step 2 :- The activated sensors reply the address Step 3 :- UE determines the C-HPNs from the address provided by activated sensors Step 4 :- Based on the DNT and ANT, UE determines sensors for data collection (CSC) or Transmission (CST) If sensor == CSC Then Extract data from C-HPNs Else If sensor == DSC Then Activate Child EndIf EndIf Step 5:- After determining, UE activates CSC/CST for data collection/transmission Step 6:- Accordingly UE updates ANT and DNT Involving the coordination among the mobile UEs is necessary, which can not only avoid the frequent activation of the same sensors for data collection but also some appreciated mobile UEs can act as mobile gateways to overhear and directly transmit the collected data to the MCN instead of activating some WSN sensors for data transmission. The MCN entity such as base station (BS) coordinates the multiple mobile UEs to determine which mobile UEs can assist to collect or transfer data from which WSN sensors, and keeps the other sensors sleeping to save the energy consumption. III. NETWORK MODEL AND PROPOSED SCHEME The proposed MSE2 DC is supposed to work in WSN-MCN converged networks. The WSN sensors are deployed randomly and networked based on tree topology. The multiple mobile UEs act as mobile gateways and randomly move in the WSN area to collect data from the sensors and transfer the collected data directly to the MCN. For each data collection stop, the BS coordinates the information from the multiple mobile UEs and determines the mobile UEs and the WSN sensors for data collection and transmission is illustrated in Figure 1. Figure 1:The scenario of data collection via mobile UEs The main steps of MSE2 DC scheme are shown in Figure 2.If one mobile UE stops for data collection, it activates the sensors in its one- hop range with its WSN interface. The activated
  4. 4. sensors reply their addresses to the mobile UE. The mobile UE determines the current highest parent nodes (C-HPNs) based on the filiation among these sensors. Then the mobile UE sends a REQUEST message including its C-HPNs to the BS to ask for data collection assistance. We called this request mobile UE as RU and its C-HPNs as RCHPNs. we make the following assumptions for the network model: The WSN sensors are networked based on IEEE 802.15.4 [13] and ZigBee [13] tree topology. All the sensors are initially in sleep state, and go to sleep after finishing the data collection or transmission. The sensors are allocated the address based on the allocation mechanism defined in ZigBee specifications, and the UE is aware of the address allocation mechanism. A. Protocol Design In our setup, as the mobile UEs move in the sensing field randomly, the data collection is repeated every time a fixed time period. A new round begins when a mobile UEs enters the sensing field, or when the previous round ends. Each round includes the following three phases: A. Phase 1:Node Deployment the sensor nodes are deployed randomly in wireless sensor network region. the user equipment moves randomly in the sensing region and collects the data when it passes near the sensor .the data collected by UEs are forwarded when the request is made by the base station . B.Phase II:. Selection of CSs the BS determines the candidate sensors (CSs) which may be activated for data collection (CSC) or transmission (CST) based on the RCHPNs and the information in DNT and ANT. Then, the BS broadcasts a HELP message to all mobile UEs in the WSN area. the base station selects the UEs based on the current highest parent node(C-HPN).Base station maintains two lists of its appreciation node table(ANT) (one hop away from the mobile node and the nodes which are active) and depreciation node table(DPT-the nodes which are dead) C. Phase III: Selection of UEs Once received the HELP message from the BS, if the mobile UEs would like to provide the help of data transmission, the mobile UEs stop and activate the sensors in their one-hop range. They determine the C-HPNs and report the C-HPNs to the BS, respectively. We called these mobile UE as candidate mobile UEs (CUs) and the reported C- HPNs as candidate C-HPNs (CCHPNs). Based on the physical location of RUs and CUs, the CCHPNs and RCHPNs may be related topologically. For example, as shown in Figure 1, the UE1’s RCHPN #140 is the topological parent of the UE2’s CCHPN #141. After recognized the topological relationship between the CCHPNs and the RCHPNs, the BS determines the necessary mobile UEs (NUs) which can give a helping hand for data transmission. Then, the BS determines the necessary sensors (NSs) which should be activated based on the NUs and the information in ANT and DNT. After the determination, the BS multicasts an ASSIST message including the corresponding CCHPNs to the NUs to confirm the assistance and sends a RESPONSE message including the NSs to the RU. Finally, the BS updates the information in DNT and ANT. After received the RESPONSE message, the RU begin to activate the NSs for data collection. After received the ASSIST message, the NUs begin to collect data from the corresponding CCHPNs and transmit to the MCN. The other mobile UEs do not receive the message can continue their primary movements. Algorithm for the main steps of MSE2 DC scheme Step 1:- RU sends “REQUEST” message including RCHPNs to BS for assistance after activating one- hop nodes. Step 2:- BS maintains two table Deprecated Node Table (DNT) and Appreciated Node Table Step 3:- By adopting the E2DCS , the BS determines the candidate sensors (CSs) which may be activated for data collection (CSC) or transmission (CST) based on the RCHPNs and the information in DNT and ANT Step 4:- BS broadcasts a HELP message to all mobile UEs in the WSN area Step 5:- Once received HELP message If mobileUE == dataTransmission Then mobile UEs stop and activate the sensors in their one-hop range. Step 6:- Determines C-HPNs, report C-HPNs to BS EndIf Step 6 :- Based on the physical location of RUs and CUs BS determines the necessary mobile UEs (NUs Step 7:- The BS determines the necessary sensors (NSs) based on the NUs and ANT and DNT. Step 8 :- the BS multicasts an ASSIST message including the corresponding CCHPNs to the NUs to confirm the assistance Step 9:- BS sends a RESPONSE message including the NSs to the RU. Step 10 :- the BS updates the information in DNT and ANT
  5. 5. If the one-hop parent node of the determining CCHPN is a CSC, it means that this one- hop parent node originally must be activated for data collection. Hence, this CCHPN may not be helpful to reduce the number of CSTs and could be deprived the candidature. If the one-hop parent node of the determining CCHPN is neither a CSC nor a CST, it means that there is no data need to be collected and transmitted. This CCHPN may not be helpful for energy saving and could be deprived the candidature. If the one-hop parent node of the determining CCHPN is a CST, it means that this one-hop parent node originally has to be activated for data transmission. To determine whether this CCHPN can really help for data transmission, which data to be transferred by this CST is needed. We have to check this CST’s entire child branches, except for the branch where the determining CCHPN belongs to, to find out the data source. In each child branch, we only consider the sensor nodes from this CST downwards to the first emerged CCHPNs. If the one- hop parent node of the determining CCHPN is a CST, it means that this one-hop parent node originally has to be activated for data transmission. To determine whether this CCHPN can really help for data transmission, which data to be transferred by this CST is needed. We have to check this CST’s entire child branches, except for the branch where the determining CCHPN belongs to, to find out the data source. In each child branch, we only consider the sensor nodes from this CST downwards to the first emerged CCHPNs. If there are CSCs in the child branches, it means that this CST is activated to also transmit the collected data from other child branches. This CST must be activated regardless of the situation of the branch where the determining CCHPN belongs to. Hence, the determining CCHPN may not be helpful to assist to data transmission and could be deprived the candidature. If there is no CSC in the other child branches, it means that this CST is activated to only transmit the collected data from the branch where the determining CCHPN belongs to. We need further to consider the determining CCHPN’s child branches. In each child branch of the determining CCHPN, we only consider the sensor nodes from the determining CCHPN downwards to the first emerged CCHPNs.  If there are CSCs from the determining CCHPN downwards to the next emerged CCHPN, this determining CCHPN is determined to be able to assist for data transmission. Otherwise, this CCHPN may but is not necessary to be activated to transfer the collect data and could be deprived the candidature. IV. SIMULATION RESULT AND ANALYSIS In this section, a series of numerical simulations are implemented by using San surya simulator to verify the energy saving gains of the proposed MSE 2 DC scheme and E 2 DC[11], comparing to the conventional scheme that the mobile UE randomly moves and freely stops for data collection without considering time-validity. We construct a 1200m*1200m WSN area, in which 1000 sensors are randomly deployed and networked based on tree-topology. The WSN sink is in the middle of the area. There are 20 mobile UEs moving randomly in the WSN area to collect data from the sensors and their velocity is 60 m/min. The time intervals between sequent two data collections are random and the collected data could be valid before the data validity period expired. In our simulation scenario, we assume that there are random several mobile UEs stop to collect data simultaneously and all the other mobile UEs would like to provide assistant. A parameter GE is introduced to evaluate the energy saving gains: where N b is the number of activated sensors of the conventional scheme and N p is the number of activated sensors of the proposed MSE2 DC scheme or E2 DCS scheme. In the simulations, the G E is evaluated by varying the time interval of data collection and the data validity period, respectively. We first simulate the scenario of varying the time interval but keeping the data validity period fixed. In the simulation, the data validity period is 10 min and the time interval varies among 2~9min.Table I shows the simulation result, where the x-axis is the time interval and the y-axis is the value of G E . Varying time interval E2 DCS MSE2 DC 0.407 0.372 0.329 0.291 0.260 0.238 0.222 0.210 0.884 0.873 0856 0.840 0.825 0.812 0.800 0.789 Table I:varying time intervals We can easily find out that the values of G E of both schemes are high when the time interval is small and it degrades as the time interval increases. It is easy to understand that the longer time
  6. 6. interval leads to the less data collections during the data validity period. It means that the collected data will have been valid during less data collections. We can also observe in the figure that the MSE2 DCscheme can less activate almost 80%- 90% sensors comparing the conventional mechanism and less activate almost 50%-60% sensors comparing the E2 DCS scheme. Table I shows the simulation result, where the x-axis is the time interval and the y-axis is the value of G E . Figure 2:value of GE when varying time interval We also simulate the scenario of varying the data validity period but keeping the time interval fixed. For simplicity, we assume that the time interval is 3 min and the data validity period varies among 4~10 min. Figure 6 shows the simulation result, where the x-axis is the data validity period and the y-axis is the value of G E . Table II shows the simulation result, where the x- axis is the data interval and the y-axis is the value of G E . Varying data validity period E2 DCS MSE2 DC 0.078 0.078 0.078 0.097 0.122 0.133 0.152 0.694 0.706 0.723 0.738 0.751 0.763 0.773 Table II: value of GE when varying data Figure 3:value of GE when varying data period As illustrated in Figure 6, the MSE 2 DC scheme can less activate almost 70%-80% sensors comparing the conventional mechanism; and less activate almost 60% sensors comparing the E 2 DCS scheme. As the ME2 DC scheme can make use of some appreciated mobile UEs for data transmission to avoid activating the CSTs, the values of G E of the MSE2 DC scheme is obvious higher than that of the E2 DCS scheme. Based on the illustrations in Figure 2 and Figure 3, we know that the value of G E degrades as the time interval increases but increases as the data validity period increases. Hence, it is easy to understand that the varying of time interval and the varying of data validity period almost have equivalent influences on the values of G E , which makes the value of G E keeps unchanged. In conclusion, the MSE2 DC scheme can achieve much higher energy saving gains by avoiding activating many sensors for data transmission. V. CONCLUSION AND FUTURE WORK Here we proposed a MSE 2 DC scheme in WSN-MCN convergence system for data collection and transmission. In the system, the BS coordinates the information from multiple mobile UEs to determine the necessary sensors to be activated for data collection and the necessary appreciated mobile UEs to help transmit the collected data from the WSN to the MCN. Instead of transmitting data using single hop ,multi-hop is used to for transmission through mobile gateways. By adopting this scheme, only the necessary sensors should be activated while other sensors can keep sleeping to save energy. On the other hand, the MSE2 DC scheme also introduces additional signaling overheads for the BS and the mobile UEs to inform/activate the necessary sensors. Based on our simulation result we have analyzed that
  7. 7. our proposed system is much more energy efficient than the existing system. In the future we consider using this system in different topology and mobile sink is used to enhance our system. REFERENCES [1] Sushant Jain, Rahul C.Shan, et al, “Exploiting Mobility for Energy Efficient Data Collection in Wireless Sensor Networks,” Mobile Networks and Applications, vol. 11, no. 3, pp. 327-39, Jun. 2006. [2] Shuai Gao, Hongke Zhang, Sajal K. Das, “Efficient Data Collection in Wireless Sensor Networks with Path-Constrained Mobile Sinks,” IEEE Transactions on Mobile Computing, vol. 10, no. 5, pp. 592-608, 2011. [3] Xinxin Liu, Han Zhao, et al, “Trailing Mobile Sinks: A Proactive Data Reporting Protocol for Wireless Sensor Networks,” IEEE Transactions on Computers, pp. 214-223, 2011. [4] Mirela Marta, Mihaela Cardei, “Using Sink Mobility to Increase Wireless Sensor Networks lifetime,” 2008 International Symposium on a World of Wireless, Mobile and Multimedia Networks, pp. 10-18, 2008. [5] Z. Maria Wang, Stefano Basagni, et al, “Exploiting Sink Mobility for Maximizing Sensor Networks Lifetime,” Proceedings of the Annual Hawaii International Conference on System Sciences, pp. 287-295, 2005. [6] Mobility-Based Communication in Wireless Sensor Networks Eylem Ekici, Yaoyao Gu, and Doruk Bozdag, Ohio State University [7] A. Somasundara, A. Ramamoorthy, and M. Srivastava, “Mobile Element Scheduling with Dynamic Deadlines,” IEEE Trans. Mobile Computing, vol. 6, no. 4, pp. 395-410, Apr. 2007.[8] Energy-saving in Wireless Sensor Networks based on Sink Movement Control using fuzzy logic Mozhgan Toulabi 1 , Shahram Javadi 2 [8] A. Chakrabarti, A. Sabharwal, “Communication Power Optimization in a Sensor Network with a Path-Constrained Mobile Observer,” ACM Trans. Sensor Networks, vol. 2, no. 3, pp. 297-324, Aug. 2006. [9] .F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cyirci, “Wireless Sensor Networks: A Survey,” Computer Networks, vol. 38, no. 4, pp. 393-422, Mar. 2002. [10] F.Yin, H.Wang, Z.Li, J.Xiao, F.Liu, P.Wang, “Mobile Data Collection in A Wireless Sensor Network”, PCT Patent, No. PCT/CN2012/074280. [11] J.Xiao, F.Yin, H.Wang, Z.Li, J.Xiao, F.Liu, P.Wang, “Energy-Efficient Data Collection in WSN-MCN Convergence Architecture”, IEEE GlobeCom 2012, submitted [12] IEEE Std 802.15.4™-2006, Part 15.4: Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low-Rate Wireless Personal Area Networks (WPANs), 2006 [13] ZIGBEE SPECIFICATION, ZigBee Document 053474r17, 2008

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