Smart Wireless Sensor Networks Part 8 ppt

Optimizing Coverage in 3D Wireless Sensor Networks 199 Network Size 200 – 600 nodes Area Dimensions 100 x 100 x 100 m Sensing Range (ݎ ௦ ሻ 15 – 25 m Communication Range (ݎ ௖ ሻ 2* (ݎ ௦ ሻ Probabililty p 0.15 Initial Energy 0.5 J Message Size 25 Bytes .Elect E - Energy spent in electronics 50 n J /bit f s  - Constant for free space propagation 10 p J/bit/m 2 mp  - Constant for multi-path propagation .0013 p J/bit/m 4 Table 1. Simulation Parameters Figure 4 demonstrates results from a series of experiments performed for different network sizes (200 to 600 nodes). A sensing range of 20 m was used in these experiments over 20 random topologies. Our metric of interest here is the number of nodes in the active cover set. For each network size both mean and standard deviation are reported. It can be clearly observed that significant improvements are made by reducing the number of nodes in the active cover set. For 200 nodes the cover set is 60 nodes and for 400 nodes the cover set is about 72 nodes. If the network size is increased to 600, the cover set contains about 80 nodes resulting in a saving of 86.6%. It is not surprising to notice an improvement of approximately 17 % when the network size is increased from 200 nodes to 600 nodes. The DCA algorithms ensures that there is only one active nodes within one sensing range, therefore an increase in the network size (more node density per unit area) yields a little increase in the active cover set. The resulting topology produced by the algorithm with respect to connectivity was also evaluated. We define connectivity of a node as its ability to communicate either directly or indirectly to at least one of its neighbors. Figure 5 shows results where nodes use a sensing range that varies between 5 and 25 meters. These experiments were conducted for network sizes of 200, 300 and 400 nodes. It can be seen that a sensing range of 15m (or greater) results in a topology where 99.9% connectivity is achieved. These results corroborate perfectly with the analytical estimates discussed in the previous section. Fig. 4. Number of nodes in the active cover set for different network sizes Fig 5. Percentage of connected nodes in the active cover set vs. sensing range ሺݎ ௦ ሻ An important evaluation criteria of coverage alogorithms is how well the target region is covered by the sensor nodes. Figure 6 presents results for the observed coverage. 200 300 400 500 600 0 20 40 60 80 100 Total No. of Nodes No. of Nodes in Active Cover Set 5 10 15 20 25 0 20 40 60 80 100 Sensing Radius (r s ) Percentage of Connecetd Nodes in Active Set n=200 n=300 n=400 Smart Wireless Sensor Networks200 (a) Network Size=200 (b) Network Size=300 (c) Network Size=400 Fig. 6. Percentage of point covered with respect to Observed coverage k in a) N=200, b)N=300 and c) N=400 nodes As discussed in Section 4, a simple case is when a point is covered by at least one sensor, the resultant coverage is said to be of the order 1. Although the DCA is designed with the object to provide best 1-coverage (k=1) in the target region, we ran a number of experiments to estimate the coverage of higher oders i.e k > 1. For this set of experiments, three network sizes of 200, 300 and 400 nodes were selected. Simulations for each network size were 1 2 3 4 5 6 0 20 40 60 80 Observed coverage k % of points covered r s = 15 m 1 2 3 4 5 6 0 20 40 60 80 100 Observed coverage k % of points covered r s = 20 m 1 2 3 4 5 6 0 20 40 60 80 100 Observed coverage k % of points covered r s = 25 m 1 2 3 4 5 6 0 50 100 Observed coverage k % of points covered r s = 15 m 1 2 3 4 5 6 0 50 100 Observed coverage k % of points covered r s = 20 m 1 2 3 4 5 6 0 50 100 Observed coverage k % of points covered r s = 25 m 1 2 3 4 5 6 0 50 100 Observed coverage k % of points covered r s = 15 m 1 2 3 4 5 6 0 50 100 Observed coverage k % of points covered r s = 20 m 1 2 3 4 5 6 0 50 100 Observed coverage k % of points covered r s = 25 m further repeated with three different values of sensing radius. The results from these experiments are presented in Figure 6. It can be observed that these results are in agreement with our analytical results presented in Section 4, we observe that for a sensing range of 25 m provides us a toplogy where 99% of nodes are covered by at least one sensor node. Moreover, the the same value of sensing range yield the topolgy where approximately 60% of the points are 2-covered (i.e k=2). Figure 7 and Figure 8 depict the resultant topology and connectivity graph before and after the execution of DCA. It can be clearly seen that the DCA preserves connectivity while reducing extra nodes within a given deployment region. Fig. 7. Network topology and connectivity graph before the execution of DCA (network size =300 nodes, ݎ ௦ =20 m) 0 50 100 0 50 100 0 20 40 60 80 100 x y z Optimizing Coverage in 3D Wireless Sensor Networks 201 (a) Network Size=200 (b) Network Size=300 (c) Network Size=400 Fig. 6. Percentage of point covered with respect to Observed coverage k in a) N=200, b)N=300 and c) N=400 nodes As discussed in Section 4, a simple case is when a point is covered by at least one sensor, the resultant coverage is said to be of the order 1. Although the DCA is designed with the object to provide best 1-coverage (k=1) in the target region, we ran a number of experiments to estimate the coverage of higher oders i.e k > 1. For this set of experiments, three network sizes of 200, 300 and 400 nodes were selected. Simulations for each network size were 1 2 3 4 5 6 0 20 40 60 80 Observed coverage k % of points covered r s = 15 m 1 2 3 4 5 6 0 20 40 60 80 100 Observed coverage k % of points covered r s = 20 m 1 2 3 4 5 6 0 20 40 60 80 100 Observed coverage k % of points covered r s = 25 m 1 2 3 4 5 6 0 50 100 Observed coverage k % of points covered r s = 15 m 1 2 3 4 5 6 0 50 100 Observed coverage k % of points covered r s = 20 m 1 2 3 4 5 6 0 50 100 Observed coverage k % of points covered r s = 25 m 1 2 3 4 5 6 0 50 100 Observed coverage k % of points covered r s = 15 m 1 2 3 4 5 6 0 50 100 Observed coverage k % of points covered r s = 20 m 1 2 3 4 5 6 0 50 100 Observed coverage k % of points covered r s = 25 m further repeated with three different values of sensing radius. The results from these experiments are presented in Figure 6. It can be observed that these results are in agreement with our analytical results presented in Section 4, we observe that for a sensing range of 25 m provides us a toplogy where 99% of nodes are covered by at least one sensor node. Moreover, the the same value of sensing range yield the topolgy where approximately 60% of the points are 2-covered (i.e k=2). Figure 7 and Figure 8 depict the resultant topology and connectivity graph before and after the execution of DCA. It can be clearly seen that the DCA preserves connectivity while reducing extra nodes within a given deployment region. Fig. 7. Network topology and connectivity graph before the execution of DCA (network size =300 nodes, ݎ ௦ =20 m) 0 50 100 0 50 100 0 20 40 60 80 100 x y z Smart Wireless Sensor Networks202 Fig. 8. Network topology and connectivity graph after the execution of DCA (network size =300 nodes, ݎ ௦ =20 m) Besides coverage and conenctivity, network lifetime is also an important performance metric for WSNs. To estimate network lifetime we used the following operation model. For each experiment nodes are deployed randomly over the target region. After the intial neighnor discovery step the operation proceeds in rounds. In each round a set of active nodes is selected according to the proposed DCA. This selection of active nodes is followed by data transmission where each active node sends 10000 bytes. Modeling the network operation in this manner allows measurement of the network life in number of rounds until the very first node runs out of its energy or a percentage of nodes completely exhaust their battery and die. The lifetime on an individual sensor node is measured in the number of rounds before its energy is depleted. The lifetime of a network can be defined in either the number of rounds until the first node dies or a certain percentage of nodes die. We ran a number of experiments to estimate network lifetime in percent of alive nodes for network sizes of 200, 300, 400 and 500 nodes. These results for metric were collected using a sensing radius of 15 m and p=0.15. While it is intutive to note that selecting a subset of active node will significantly improve over the case where all nodes remain active, the results present in Figure 9 provide insight to the perfromance of the network with different network sizes. We observe that all cases display a fairly consistent behavior with respect to the first node deatth. We also note that the rate at which node exhust their energy is also consistent. To elaborate, 50% of nodes die in round 238, 280, 336 and 390 for network size of 200, 300, 400 and 500 respectively. This gradual increase is attributed to more nodes present in the system. 0 50 100 0 50 100 0 20 40 60 80 100 x y z Fig. 9. Network lifetime in percentage of alive nodes for N=200, N=300, N=400 and N=500 6. Conclusions In this work we presented a distributed algorithm for coverage and connectivity in three dimensional WSNs. The DCA algorithm presents a solution to the problem of selecting a minimum set of nodes from random deployment such that nodes remain connected while maximizing the coverage. The key feature of the algorithm is its simplicity and ability to be executed in a distributed manner. Sensor nodes executing this algorithm exchange messages with their one-hop neighbors to decide the nodes in the active cover set. We derived mathematical relations that were used to estimate the sensing range ݎ ௦ , a key parameter for DCA. Simulation results provide strong evidence that for appropriate values of ݎ ௦ , DCA maximizes both coverage and connectivity. Our future work will include incorporating real world deployment models and into the current framework. We plan to extend the current DCA framework to provide higher order coverage in our future work. 7. Rererences Akyildiz, I. F., D. Pompili, et al. (2005). "Underwater acoustic sensor networks: research challenges." Ad Hoc Networks 3(3): 257-279. Alam, S. M. N. and Z. J. Haas (2006). Coverage and connectivity in three-dimensional networks. 12th annual international Conference on Mobile Computing and Networking Los Angles, CA, USA, ACM New York, NY, USA. 0 100 200 300 400 500 0 50 100 150 200 No. of Rounds % of Alive Nodes N=200 0 200 400 600 800 0 100 200 300 No. of Rounds % of Alive Nodes N=300 0 200 400 600 800 0 100 200 300 400 No. of Rounds % of Alive Nodes N=400 0 200 400 600 800 0 100 200 300 400 500 N=500 No. of Rounds % of Alive Nodes Optimizing Coverage in 3D Wireless Sensor Networks 203 Fig. 8. Network topology and connectivity graph after the execution of DCA (network size =300 nodes, ݎ ௦ =20 m) Besides coverage and conenctivity, network lifetime is also an important performance metric for WSNs. To estimate network lifetime we used the following operation model. For each experiment nodes are deployed randomly over the target region. After the intial neighnor discovery step the operation proceeds in rounds. In each round a set of active nodes is selected according to the proposed DCA. This selection of active nodes is followed by data transmission where each active node sends 10000 bytes. Modeling the network operation in this manner allows measurement of the network life in number of rounds until the very first node runs out of its energy or a percentage of nodes completely exhaust their battery and die. The lifetime on an individual sensor node is measured in the number of rounds before its energy is depleted. The lifetime of a network can be defined in either the number of rounds until the first node dies or a certain percentage of nodes die. We ran a number of experiments to estimate network lifetime in percent of alive nodes for network sizes of 200, 300, 400 and 500 nodes. These results for metric were collected using a sensing radius of 15 m and p=0.15. While it is intutive to note that selecting a subset of active node will significantly improve over the case where all nodes remain active, the results present in Figure 9 provide insight to the perfromance of the network with different network sizes. We observe that all cases display a fairly consistent behavior with respect to the first node deatth. We also note that the rate at which node exhust their energy is also consistent. To elaborate, 50% of nodes die in round 238, 280, 336 and 390 for network size of 200, 300, 400 and 500 respectively. This gradual increase is attributed to more nodes present in the system. 0 50 100 0 50 100 0 20 40 60 80 100 x y z Fig. 9. Network lifetime in percentage of alive nodes for N=200, N=300, N=400 and N=500 6. Conclusions In this work we presented a distributed algorithm for coverage and connectivity in three dimensional WSNs. The DCA algorithm presents a solution to the problem of selecting a minimum set of nodes from random deployment such that nodes remain connected while maximizing the coverage. The key feature of the algorithm is its simplicity and ability to be executed in a distributed manner. Sensor nodes executing this algorithm exchange messages with their one-hop neighbors to decide the nodes in the active cover set. We derived mathematical relations that were used to estimate the sensing range ݎ ௦ , a key parameter for DCA. Simulation results provide strong evidence that for appropriate values of ݎ ௦ , DCA maximizes both coverage and connectivity. Our future work will include incorporating real world deployment models and into the current framework. We plan to extend the current DCA framework to provide higher order coverage in our future work. 7. Rererences Akyildiz, I. F., D. Pompili, et al. (2005). "Underwater acoustic sensor networks: research challenges." Ad Hoc Networks 3(3): 257-279. Alam, S. M. N. and Z. J. Haas (2006). Coverage and connectivity in three-dimensional networks. 12th annual international Conference on Mobile Computing and Networking Los Angles, CA, USA, ACM New York, NY, USA. 0 100 200 300 400 500 0 50 100 150 200 No. of Rounds % of Alive Nodes N=200 0 200 400 600 800 0 100 200 300 No. of Rounds % of Alive Nodes N=300 0 200 400 600 800 0 100 200 300 400 No. of Rounds % of Alive Nodes N=400 0 200 400 600 800 0 100 200 300 400 500 N=500 No. of Rounds % of Alive Nodes Smart Wireless Sensor Networks204 Andersen, T. and S. Tirthapura (2009). Wireless sensor deployment for 3D coverage with constraints. Sixth International Conference on Networked Sensing Systems (INSS). Bai, X., S. Kumar, et al. (2006). Deploying wireless sensors to achieve both coverage and connectivity. ACM Mobihoc, ACM New York, NY, USA. Cardei, M. and J. Wu (2006). "Energy-efficient coverage problems in wireless ad-hoc sensor networks." Computer communications 29(4): 413-420. Cayirci, E., H. Tezcan, et al. (2006). "Wireless sensor networks for underwater survelliance systems." Ad Hoc Networks 4(4): 431-446. Chen, F., P. Jiang, et al. (2008). "Probability-Based Coverage Algorithm for 3D Wireless Sensor Networks." Advanced Intelligent Computing Theories and Applications. With Aspects of Contemporary Intelligent Computing Techniques, Communications in Computer and Information Science 15. Heinzelman, W. B., A. P. Chandrakasan, et al. (2002). "An application-specific protocol architecture for wireless microsensor networks." IEEE Transactions on wireless communications 1(4): 660-670. Huang, C. F., Y. C. Tseng, et al. (2004). The coverage problem in three-dimensional wireless sensor networks. IEEE Global Telecommunications Conference. I. F. Akyildiz, W. Su, et al. (2002). " A Survey on Sensor Networks." IEEE Communications Magazine 40(8): 102-114. Iyengar, R., K. Kar, et al. (2005). Low-coordination topologies for redundancy in sensor networks, ACM. Kim, S., S. Pakzad, et al. (2006). "Wireless sensor networks for structural health monitoring." Proceedings of the 4th international conference on Embedded networked sensor systems: 427-428. Liu, B. and D. Towsley (2004). A study of the coverage of large-scale sensor networks. IEEE International Conference on Mobile Ad-hoc and Sensor Systems (MASS) Lynch, J. P. and K. J. Loh (2006). "A summary review of wireless sensors and sensor networks for structural health monitoring." Shock and Vibration Digest 38(2): 91-130. Mainwaring, A., D. Culler, et al. (2002). Wireless sensor networks for habitat monitoring, ACM. MEMSIC. (2011). "IRIS Mote Data Sheet." from http://www.memsic.com/products/wireless-sensor-networks/wireless- modules.html. MEMSIC. (2011). "TelosB Data Sheet." from http://www.memsic.com/products/wireless- sensor-networks/wireless-modules.html. Poduri, S., S. Pattem, et al. (2006). Sensor network configuration and the curse of dimensionality. The Third IEEE Workshop on Embedded Networked Sensors (EmNets), Cambridge, MA, USA. Szewczyk, R., E. Osterweil, et al. (2004). "Habitat monitoring with sensor networks." Communications of the ACM 47(6): 34-40. Xing, G., X. Wang, et al. (2005). "Integrated coverage and connectivity configuration for energy conservation in sensor networks." ACM Transactions on Sensor Networks (TOSN) 1(1): 36-72. Yang, S., F. Dai, et al. (2006). "On connected multiple point coverage in wireless sensor networks." International Journal of Wireless Information Networks 13(4): 289-301. Zhang, H. and J. C. Hou (2005). "Maintaining sensing coverage and connectivity in large sensor networks." Ad Hoc & Sensor Wireless Networks 1(1-2): 89-124. Quality of Service Management and Time synchronization Part 3 Quality of Service Management and Time synchronization Mechanism and Instance: a Research on QoS based on Negotiation and Intervention of Wireless Sensor Networks 207 Mechanism and Instance: a Research on QoS based on Negotiation and Intervention of Wireless Sensor Networks Nan Hua and Yi Guo X Mechanism and Instance: a Research on QoS based on Negotiation and Intervention of Wireless Sensor Networks Nan Hua 1 and Yi Guo 2 1 Institute of Telecommunication Engineering, Air Force Engineering University China 2 East China University of Science and Technology China 1. Introduction The definition of QoS (Quality of Service) varies with the concerned network techniques (wired networks, wireless access networks, wireless Ad hoc networks or wireless sensor networks, etc) and the viewpoint of observation (application level or network level) (Chen & Varshney, 2004; Crawley et al.,1998). The concerned topics of QoS in traditional networks are all end-to-end, and the bandwidth utilization is a core issue of QoS mechanism due to the requirements of multimedia applications. Although there are differences among the specific realization techniques, the research models of QoS are similar and the metrics for evaluating and describing QoS are roughly the same (Chen & Varshney, 2004). Today, the research on the QoS of traditional networks is mature considerably in theory and practice. In wireless sensor networks (WSN), due to the features such as the limited resource (including energy, bandwidth, cache ability, storage capacity, processing capacity, transmission power, etc), high data redundancy, dynamic topology of network and specific application, the QoS problems are different from that of the traditional networks in the design and implementation. For example, in IP networks, a primary intention of QoS is to ensure that the traffic streams which have different grades or types can get corresponding and predictable transmission services. The grade of service can be classified into best-effort service, differentiated service and guaranteed service. In WSN, because of the unpredictable behavior of edge-to-edge, it is not realistic to provide predictable and reliable transmission service for traffic stream. Hence the QoS of WSN is based on unreliable and best-effort data transmission, but it does not exclude the expression method of traffic (task) stream based priority level. Moreover, WSN reduces the requirements for the packet loss rate to a certain degree; the main concerned issues are no longer the efficient utilization of bandwidth, and the QoS is not always end-to-end. The researches on QoS mainly involve two aspects: mechanisms and metrics. The classical QoS research results of WSN were summarized by Chen and Shearifi. (Chen & Varshney, 12 Smart Wireless Sensor Networks208 2004; Sharifi et al., 2006). In addition, the issues about QoS of WSN are involved or taken into account in many papers in recent years, while conducting the research on the routing and clustering (topology control) protocol, MAC protocol, as well as application issues, etc (Fapojuwo & Cano-Tinoco, 2009; Hoon & Sung-Gi, 2009; Zytoune et al., 2009; Peng et al., 2008; Chen and Nasser, 2008; Yao et al., 2008; Gelenbe & Ngai, 2008; Navrati et al., 2008; Youn et al., 2007; Zhang et al., 2007; Zhang & Xiong, 2007). The QoS issues involved mainly focus on the instantaneity, fault tolerance capacity and energy consumption of networks, and are studied with the respective research fields of these papers conjointly. All these researches on QoS mentioned above belong to the research field of metrics, these researches neither focus on the QoS mechanism nor discuss the QoS issues of WSN specially and systematically from the basis and architecture. To the best of our knowledge, in the research field of QoS mechanisms of WSN, few distinctive researches are conducted at the present time. In these researches, some QoS schemes based on cross-layer QoS optimization (Cai and Yang, 2007), adaptable mobile agents (Spadoni et al., 2009), cloud model (Liang et al., 2009) and limited service polling discipline analytical model (Aalsalem et al., 2008), and so on, were presented, but are not very mature yet. In this chapter, we focus our research domain on the mechanisms, the concrete QoS metrics is beyond our discussion scope. In this chapter, we bring forward an Active QoS Mechanism (AQM), the core of it is the negotiation between applications and network and the active intervention for them. On this basis, we conduct a further research, present and realize a common QoS infrastructure as an instance of AQM, named QISM (QoS Infrastructure base on Service and Middleware). The application, state and role oriented QoS optimization scheme, the middleware and service based architecture, the Topic and functional domain based expression method are important characteristics of QISM. Proved by simulation of a typical scenario, QISM has good QoS control ability and flexibility, can support complex applications, and is independent of network architectures. The rest of chapter is organized as follows. In section 2, we present two QoS levels of WSN and analyze the relationship between the essential problems and QoS. In section 3, we bring forward the concept of AQM, and the working processes, the fundamental of state evaluation and strategy generation are discussed. In section 4, the design philosophy and important characteristics of QISM are studied. In section 5, the infrastructure and realization of QISM are presented and analyzed from four aspects in detail. Then, the simulation results are illustrated in section 6. Finally, we conclude this chapter in section 7. 2. Essential Problems and QoS of WSN 2.1 Three Essential Problems of WSN We present three essential research problems which should be considered seriously in the applications of WSN through a representative application scenario: In order to deploy WSN nodes in hostile battlefield or terrible conditions, we normally use airdrop to execute this task. After the nodes bestrewn, it is possible that quite part of them cannot work properly, which leads to heterogeneous distribution of the nodes. Furthermore, it is impossible to supply power when the node energy is exhausted. So, when the network is established, we should face three essential problems as follows: 1) Network Organization When old nodes invalidated or new nodes joined, the network will be reorganized. Reorganization of network involves many complex processes, such as route rebuilding (the route optimization), topology reconstruction (the selection between the plane architecture and the hierarchical architecture of network, and the transformation from one to another) and task transference (new joined nodes or other working nodes resume the tasks of the disabled nodes), etc. 2) Lifetime of Network and Nodes To prolong the lifetime of whole network, nodes should work in an energy-efficient way, which includes node dormancy and exchanges of node roles (for example, cluster head, cluster member and router node are three different roles of the nodes, which node acts as which role can be decided through elections and the role of node should alternate periodically). Through these methods, it is mostly possible to average energy consumption of the nodes and ensure the lifetime of key nodes. 3) Quality of Service We must get tradeoff between lifetime and QoS demand of the network. For example, for the nodes in a lower-density region or executing key tasks, we should find a way to get the necessary tradeoff between application quality and node energy consumption, ensure the achievement of application and the maximum lifetime of network. 2.2 Two QoS Levels of WSN WSN is a fully distributed network, the QoS of it can be divided into two correlative levels as follows: 1) Network (Application) QoS Level This level focuses on the whole network, and considers quality of service with a global view of network. The concerned issues involve network organization, network lifetime, and so on. Since Application is a concept correlative with Network, the issue about the analyses of application quality and network state should also be considered in this level. 2) Node (Task) QoS Level This level focuses on the network nodes, regulates nodes based on the analyses of metrics and data of concrete nodes under the direction of network (application) QoS level, and feeds back data to it for the problem solving of network (application) QoS level. Since Task is a concept correlative with Node, the issue about the analyses of task quality and node state should also be considered in this level. These two levels of QoS are correlative. For example, the node energy consumption (an issue in node (task) QoS level) is closely related to the network lifetime (an issue in network (application) QoS level), while the energy saving strategy of network (an issue in network (application) QoS level) would affect the lifetime of single node (an issue in node (task) QoS level). The problems in network (application) QoS level have no way to be solved just through the data of some isolated nodes, but the acquisition and analyses of global network situation. The problems in node (task) QoS level generally are the basis of the problems solving of network (application) QoS level, but it is also independent to a certain extent. 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Shock and Vibration Digest 38( 2): 91-130. Mainwaring, A., D. Culler, et al. (2002). Wireless sensor networks. http://www.memsic.com/products /wireless- sensor- networks /wireless- modules.html. MEMSIC. (2011). "TelosB Data Sheet." from http://www.memsic.com/products /wireless- sensor- networks /wireless- modules.html Service) varies with the concerned network techniques (wired networks, wireless access networks, wireless Ad hoc networks or wireless sensor networks, etc) and the viewpoint of observation (application