A review on routing protocols and non uniformity

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A review on routing protocols and non uniformity

  1. 1. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 – INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING 6367(Print), ISSN 0976 – 6375(Online) Volume 3, Issue 3, October-December (2012), © IAEME & TECHNOLOGY (IJCET)ISSN 0976 – 6367(Print)ISSN 0976 – 6375(Online)Volume 3, Issue 3, October - December (2012), pp. 348-354 IJCET© IAEME: www.iaeme.com/ijcet.aspJournal Impact Factor (2012): 3.9580 (Calculated by GISI) ©IAEMEwww.jifactor.com A REVIEW ON ROUTING PROTOCOLS AND NON UNIFORMITY WITH WIRELESS SENSOR NETWORKS Mrs.R.Rajasreea, Dr.G.Kalivarathanb a Research Scholar, CMJ University, Meghalaya, Shillong, b Principal, PSN Institute of Technology and Science, Tirunelveli, Tamilnadu, Supervisor, CMJ university, Shillong. Email: sakthi_eswar@yahoo.com,vasanth.sukir@gmail.com ABSTRACT In this review, it is proposed to analyse the uneven areas related to Wireless Sensor Networks (WSNs) for parallelizing the complicated issues under the purview of research oriented activities for various specific applications. In general, almost the WSNs research is based on a hidden uniformity assumption under uneven routing strategies. This assumption involves deployment, distribution and radio transmissions including the complexities of networking strategies. Since, the real world is not uniform. In the work, break the uniformity assumption and study the non uniformity influence in WSNs. In particular, it has been shown that addressing common WSN problems taking non uniformity into account can provide results that are sensibly different from the ones achieved in a uniform world. It focuses on the influence of non uniformity on a particular aspect of WSNs: data management. It has been pointed out that even widely accepted solutions based on the uniformity assumption are not able to survive inside a non uniform scenario. Then, it is proposed in such a way that the ideologies of data management and its allied solutions can cause successful predominance for useful solutions. The approach to the data management and its details may leads to get a solution which can eliminate the non uniformity consistently to the core. This feature facilitate to capture the fundamental aspects of non-uniformity in WSNs and to cope with non uniformity strategies for various applications as a generic methodology. In the review, it is evident that the models and solutions are competitive in a uniform scenario and it continues to work properly in a non uniform issue always. Keywords: Wireless sensor Networks (WSN s), Non Uniformity, Data Management, Routing, Distribution, Ideological complexity, Tracking. 1.0 INTRODUCTION In this chapter, we start with some general consideration about the uniformity which target is to disrupt the uniformity dogma: we show how the uniformity is rarely present in nature and, as a consequence of this, in every human artifact as WSNs. Once this task is performed, we review non-uniformity: we describe it formally and we present four models of non-uniformity that may influence WSNs, namely the deployment non-uniformity, the geographical non-uniformity, the functional non uniformity and finally the movement non- 348
  2. 2. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 –6367(Print), ISSN 0976 – 6375(Online) Volume 3, Issue 3, October-December (2012), © IAEMEuniformity. These models present a rough taxonomy of non-uniformity and we do not pretendthat this taxonomy is the best one or the only one: we define this taxonomy because it is anaurea mediocritas for the model developed in the rest of the thesis and a reasonablerepresentation of the main characteristics of non uniformity for WSNs.1.1 MODELS OF NON UNIFORMITYThe deployment non-uniformity, the geographical non-uniformity, the functional non-uniformity and finally the movement non uniformity. Table 1. Our taxonomy of non-uniformity in WSNs.We developed this taxonomy for the models of non-uniformity because, in our opinion, this isa reasonable high level representation of the possible kinds of non uniformity. Moreover, ourtaxonomy catches four aspects of the possible distributions. Table 1 shows the four aspects.1.2 GEOGRAPHICAL NON-UNIFORMITY WSNs’ distributions can be affected by the geographical characteristics of the deploymentarea in which the sensors lay on. In this non-uniformity models, we can distinguish two cases. In thefirst case, the deployment region is full of obstacles (walls, building, people, environmental radiationsetc.) that prevent nodes physically close to communicate. In this situation, the sensors have an alteredperception of the space that can bring to problems where, for instance, nodes are equipped with GPSantennas and interpret these connection errors as GPS errors or worst as security attacks frommalicious users. In the second case, sensors dropped on a terrain tend to follow the terrain shape andto fall into soil depressions. (a) Terrain (b)Hill Fig. 1: Geographical non-uniformity. (a) Represents a sample geographic terrain conformation with a central valley and (b) Example of a network deployed accordingly to a Hill distribution with the sensors more dense in an angle. This situation can bring to networks in which the nodes are more dense in the valleysand less dense at high points. If we add the fact that hills can represents obstacles that preventthe sensor-to-sensor communications, we can have an highly partitioned network. For thecase in which sensors follow the terrain shape, we explore two different distributions. Thefirst one, fully generic and related only to the geography, and the second one, that is adistribution function that approximates reality. 349
  3. 3. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 –6367(Print), ISSN 0976 – 6375(Online) Volume 3, Issue 3, October-December (2012), © IAEME1.3 FUNCTIONAL NON-UNIFORMITY The functional non-uniformity is related to functional patterns of the network itself. Asingle network can be made up by multiple kinds of sensors, with different capabilities andconstraints, or can be formed by a single kind of nodes behaving in different ways. Thesefunctional patterns are related to two basic concepts. In the first one, the nodes of a WSN canadopt some kind of sleeping pattern to enlarge life time. The effect of these sleeping patternsis to create an effect of dynamic non-uniformity in which the sensors’ distribution is not onlya function of the deployment method but also a function of time, the sensor “disappears” forsome time and then “appears” again. Depending on the sleeping pattern, we can experiencean effect of the same region that is alternatively monitored by two or more networks sharingonly few nodes. In the second one, a single WSN can be made up of different kind of nodeswith different capabilities and constraints. An example of this kind of networks in which twokinds of nodes are present: 1. Sensing nodes, which are small and with low power, and perform data acquisitionfrom the environment. 2. Communication nodes, which are more powerful and with a high powercommunication system, that can communicate at a larger distance providing a bridge likeservice between sub-areas of the network.1.4 MOVEMENT NON-UNIFORMITY In the first one, the sensors are free to move in an autonomous and predictable way, forinstance, the sensors that are piggybacked on moving objects such as industrial robots that the sensorsare intended to monitor and control. In the second one, the sensors are supposed to be stable and/orthe movement is not predictable, for instance, the sensors are dropped in the water to track the flowsor sensors that are attached to animals and are used to monitor the wildlife. The sensors distributionsthat describe movement non-uniformity are functions of both time and space. Moreover, we can havesituations in which the sensors can converge to a single point and create an unbalanced network for ashort period of time, or the sensors can move randomly changing network topology continuously.2.0 DATAMANAGEMENT All the possible uses of WSNs deal with the idea of data acquisition and data retrieval. Thesetwo concepts are strictly related because data retrieval is the response to a user query. The user shouldbe able to actively program the network, via control programs, to retrieve data that is considereduseful. In traditional WSN systems, these tasks consist in transmitting sensed data to a powerful node(the sink) which performs data analysis and storage. However, these models resulted unsuitable tokeep the pace with technological advances which granted to WSNs significant (although still limited)processing and storage capabilities. For this reason, more recent paradigms for WSN introduced database approaches to define the tasks of data sampling and processing, and the concept of data-centricstorage for efficient data access. In this chapter, we revise the main research contributions in the datamanagement field.2.1 DATA CENTRIC STORAGE AND DATABASE SUPPORT SYSTEMS Both the data centric storage and the database models need some basic systems tooperate in a correct and efficient way. These support systems can be grouped into three mainareas: the localization to be able to associate a position in space to data or to nodes, therouting to find paths to move data from one node to another and finally redundancy toachieve fault tolerance.2.2 LOCALIZATION SYSTEMS Localization systems are used to find the position of a sensor. The position of a sensorcan be useful for two different reasons:1. Queries request data from specific locations.2. A datum must be stored in a specific location for later retrieval. The localization systemsare grouped into three main categories: GPS-based, approximate, and virtual. 350
  4. 4. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 –6367(Print), ISSN 0976 – 6375(Online) Volume 3, Issue 3, October-December (2012), © IAEME2.3 GPS-BASED SYSTEMS GPS-based systems assign real geographical coordinates to sensors. In this model,each sensor is equipped with a GPS antenna and it is able to receive the GPS signal and tolocate itself. This solution is not always applicable in current WSNs because GPS receiversare expensive both in terms of money and energy and they may fail if the nodes are locatedinside buildings or in areas in which obstacles prevent the GPS signal to arrive. The growingcapabilities in hardware could provide, in a near future, cost effective and energy inexpensiveGPS receivers. If this happens, solutions that now seem poorly applicable could becomefuture standards.2.4 ROUTING SYSTEMS Traditional reactive approaches to routing in ad hoc networks appear unsuitable towireless sensor networks due to the limitations of the sensors in terms of energy, memory andprocessing capabilities. For this reason, recent approaches in WSN focus on geographic,stateless routing which does not burden the sensors with routing tables and caches. Amongthese protocols, we mention GPSR and VPCR, which are the most widely used in WSNs.3.0 Q-NIGHT: NON-UNIFORMITY AWARE DATA MANAGEMENT The data management in WSNs, as presented in the last chapter, is a wide topic and itis made up of different approaches and techniques. In this thesis, we choose focus on thedata-centric storage model. Our main contribution in this area is Q-NiGHT. Q-NiGHToriginated by the analysis of the experimental results that we performed on GHT in non-uniform sensor networks. These results pointed out the inability of GHT to provide goodresults in non-uniformly distributed WSNs. In this chapter, we revise Q-NiGHT using thefollowing approach. We start with the analysis of GHT in both uniformly and non-uniformlydistributed networks. Our aim is to point out that GHT is unable to provide good load balancein uniform networks and things get worse in non-uniform environments. GHT’s problems aredue to the poor control of the perimeters where the data is stored on and the lack of flexibilityof the hash function used to provide the coordinates of the point used to store data. Then, wemove into the study of our improvements of GHT to provide a better load distribution. Thistask is achieved using two different techniques. The first one dismisses the use of perimetersto store data and the second one uses a new, adaptive, hash function to deal with non-uniformity.4.0 EXPERIMENTAL RESULTS We have performed experiments for measuring the effectiveness of our servicelocalization architecture with respect to the energy cost of querying with and without caches,as well as the cost of the look-up operation. In the simulation setting, we have considered5000 WSNs where 5000 sensor nodes are scattered randomly and uniformly in a square areawith a side of 1000m. Each node has communication range of 30m. Power consumption fortransmission is set to 24mW and that for reception is set to 14.4mW as in the EYES sensorprototypes (Having a et al., 2003). The sensors that perform a look-up operation and thensend a message to the server are uniformly chosen between the deployed sensors. All theexperiments are aimed at showing the effectiveness of the architecture in providing promptand energy efficient response to sensor queries. In particular, we show here that caching isparticularly useful in providing a more balanced energy consumption, and therefore betterperformance of the network. For this reason, all the presented experiments are provided withand without nodal cache enabled. All tests are performed starting from the same seeds togenerate the same scenarios with different architectural parameters. The results we showachieve a statistical confidence of 95%, with a precision within 5%. Figure 2, Figure 3andFigure 4 depict the cost for a sensor to contact server locators and servers. In Figure 2 the 351
  5. 5. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 –6367(Print), ISSN 0976 – 6375(Online) Volume 3, Issue 3, October-December (2012), © IAEMEcost is defined as the energy spent by a node to send a packet to the server locators, to get theserver location back and then to perform one communication to the server. In other words, wecompute the total energy to deliver/receive three packets. In Figure 3, the cost is defined onlyas the cost to send a message from a sensor to the server locators and to get the serverlocation back (that is the energy to send two packets). This provides up with a more detailedidea of how much it costs to the sensor the use of the intermediate tier provided by the sensorlocators. Figure 4 shows the cumulative cost of the look-up operation, to have an idea ofdifference in the growth of the energy cost. The cumulative cost is computed as follows: thecost of the qth look-up is given by its cost and summed to the cost of all the previous q − 1look-ups. As mentioned, each set of experiments is performed with and without the cachemechanism enabled. The network is observed for a time long τ to take into account themaximum usage Fig. 2 Cost for server look-up plus the cost for contacting the servers. Figure 3: Cost for servers look-up operations only. 352
  6. 6. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 –6367(Print), ISSN 0976 – 6375(Online) Volume 3, Issue 3, October-December (2012), © IAEME Fig. 4: Cumulative cost for servers look-up operations.5.0 CONCLUSIONS Q-NiGHT uses a modified version of the GPSR protocol to route messages inside thenetwork. The key differences between the Q-NiGHT and GHT are in the assumption made bythe protocol to work. GHT assumes that the network is uniform and the hash function used tofind the position of a datum is always the same, independently from the real distribution ofthe nodes. On the other hand, Q-NiGHT uses the Rejection Hash algorithm distribute data inthe network with a distribution that is an approximation of the network distribution. Q-NiGHT showed up its flexibility and its ability to work properly in non-uniformly distributednetworks. The extensive simulations performed on Q-NiGHT showed up that all theimprovements aimed at providing fair load distribution and QoS features to GHT have a costand that this cost is not very high if compared to the advantages in terms of load distributionand memory resources usage. Finally, it is described and evaluated the performances of apossible application scenario involving Q-NiGHT. We provide a fault tolerant and load-balanced localization system for services in WSNs. The server-locators are selected using Q-NiGHT: during the network initialization phase, the servers hash the name of the services thatthey provide and store their position in the point returned by the hash function. This operationdefines a set of server-locators around that point. When a sensor needs a service it simplyhashes the service name, sends a query to the server locator position and as soon as a replayarrives it begins communicating with the server. This approach is totally generic and can beused to locate any kind of service provided in a simple and reliable way. In all, we usedQ-NiGHT pretending to know the correct network distribution functions, for instance insidethe Rejection Hash function. This assumption is not realistic but it made simpler thedescription of Q-NiGHT. Obviously, we have also addressed the problem of finding out thedistribution of a WSN, which is the central problem for non-uniformly distributed networks.It is found that the reliability of routing protocols is normally based on selective assemblyprocess and survey of mapping in the context of routing can perform complex resolutions in alogical manner to identify the representations of the wireless sensor networks. 353
  7. 7. International Journal of Computer Engineering and Technology (IJCET), ISSN 0976 –6367(Print), ISSN 0976 – 6375(Online) Volume 3, Issue 3, October-December (2012), © IAEMEREFERENCES[1] Akkaya, K. and Younis, M. (2005). A survey on routing protocols for wireless sensornetworks. Ad Hoc Networks, 3(3):325–349.[2] Akyildiz, I., Su, W., Sankarasubramaniam, Y., and Cayirci, E. (2002a). A survey onsensor networks. IEEE Communications Magazine, 40(8):102–114.[3] Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., and A survey on sensor networks. IEEECommunications Magazine, 40(8):102–114.[4] Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., and Cayirci, E. (2002c). Wireless sensornetworks: a survey. Computer Networks, 38(4):393–422.[5] Al-Karaki, K. (2004). Routing techniques in wireless sensor networks: a survey. IEEEWireless Communications, 11(6):6–28.[6] Albano, M., Chessa, S., Nidito, F., and Pelagatti, S. (2006a). Geographic hash tables withQoS in non uniform sensor networks. In ACM Mobihoc 2006 Poster Proceedings, Firenze,Italy.[7] Albano, M., Chessa, S., Nidito, F., and Pelagatti, S. (2006b). Q-NiGHT: Adding QoS todata centric storage in non-uniform sensor networks. Technical Report TR-06-16,Dipartimento di Informatica, Universit`a di Pisa.[8] Albano, M., Chessa, S., Nidito, F., and Pelagatti, S. (2007). Q-NiGHT: Adding QoS todata centric storage in non-uniform sensor networks. In Proceedings of The 8th InternationalConference on Mobile Data Management (MDM’07), Mannheim, Germany.[9] Aly, M., Pruhs, K., and Chrysanthis, P. K. (2006). KDDCS: a load-balanced in-networkdata-centric storage scheme for sensor networks. In Proc. of the 15th ACM internationalconference on Information and knowledge management, pages 317–326, New York, NY,USA. ACM Press.[10] Bollobas, B. (1985). Random Graphs. Academic Press.[11] Bonnet, P., Gehrke, J., and Seshadri, P. (2000). Querying the physical world. IEEEPersonal Communications, 7(5):10–15.[12] Cormen, T. H., Leiserson, C. E., and Rivest, R. L. (1990). Introduction to Algorithms.[13] Dorogovtsev, S. and Mendes, J. (2003). Evolution of Networks: From Biological Nets tothe Internet and WWW. Oxford University Press.[14] Kaplan, E. D., editor (1996). Understanding GPS: Principles and Applications. ArtechHouse, Boston, MA. 354

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