Keywords
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| Network lifetime, fuzzy based routing, Energy conservation, Wireless Sensor Network |
INTRODUCTION
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| Wireless Sensor Network (WSN) consists of spatially distributed autonomous sensors to monitor physical or environmental conditions, such as temperature, sound, pressure, etc. and to cooperatively pass their data through the network to a main location called base station. These sensor nodes have limited computation capability, limited power and small size [12]. The major components of a WSN are microcontroller, transceiver, external memory, power source and one or more sensors. WSN can be applied to a wide range of applications namely area monitoring, healthcare monitoring, environment and earth monitoring, air pollution monitoring, forest fire detection, agriculture, landslide detection, natural disaster prevention, data logging, industrial scenes and control applications etc. |
| Energy is one of the predominant features in wireless sensor networks that have to be seriously monitored. Since each sensor node has a limited battery that cannot be replaced. Many research works has been carried out for increasing the energy efficiency of WSN. Energy conservation is achieved by incorporating efficient techniques in routing, cluster head selection, topology control, congestion control etc. Various soft computing tools like Fuzzy Logic, Neural Network Genetic Algorithm were also used along with the above mentioned techniques to increase the energy efficiency of the network. An energy efficient network results in enhanced network lifetime, reduced energy consumption, reduced Channel Contention, increased packet delivery ratio and reduced Delay. |
| In this paper, various techniques and prominent parameters that are responsible for energy conservation in a WSN is studied from the existing methodologies. A comparison study is also made to understand the impact of the node parameters on the various strategies used in the literature. Fuzzy based neighbour node selection technique for providing an energy efficient routing is also proposed. |
| This paper is organised as follows. Section 2 provides various strategies related to hardware setup and its implementation. Comparative study on the existing hardware techniques is also made. Soft computing based energy efficient strategies in the literature and its comparative study is discussed in section 3. An outline on the proposed methodology is dealt in section chapter 4. Section 5 concludes this paper with a future work. |
RELATED WORKS ON HARDWARE BASED TECHNIQUES
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| Diallo, Marot and Becket (2010) proposed an efficient algorithm for reducing the Channel Contention. In this paper an analytical model is proposed based on Link Quality Indicator (LQI), to determine effective set emissary nodes that connect two cluster heads. During the clustering process, the cluster head selects a emissary node which may be its 1- hop neighbour. Then a connection between the two adjacent nodes is established by selecting the best emissary node among the respective cluster heads. All the cluster heads and the best emissary nodes are combined together to form a communication subnet. Similar results were obtained corresponding to the theoretical analysis [1]. |
| The Received Signal Strength Indicator (RSSI) has been largely realized by the WSN as an inadequate method and metric for determining link communication between any two neighbouring nodes. In this paper, Hussain and Rahman (2009) analysed the feasibility of using RSSI at receiver node to detect node replication, replacement and man-inmiddle attacks. The results show that the location change of the sender node can be easily identified by the variation in RSSI values. Also they have identified that fuzzy logic based approach can be used to find a rogue node in the network [2]. |
| Benkic et al (2008) used RSSI metric for the estimation of distance using wireless Zigbee based models. For distance evaluation and routing, RSSI has a vital role in deciding the neighbour node for data forwarding to obtain an optimum energy consumption. They have concluded that RSSI gives a precise distance value only when the terrain variables and conditions are static [3]. |
| Mon and Lin (2013) constructed a LQI based Wireless sensor network for position monitoring using Recurrent Fuzzy Neural Network (RFNN). The author analysed the performance of LQI and developed a ZigBee positioning system (ZPS) to identify the nodes location. They studied the performance of ZPS in an indoor location. Finally they combined ZPS with a RFNN to obtain dynamic response and better information storing. They have implemented the proposed WSN for children's position monitoring and achieved good performance [4]. |
| Xu et al (2010) presented a Log-normal shadowing model (LNSM) which provides an accurate relationship between RSSI and distance. Using the method of least squares, the coefficients in the model is dynamically adjusted considering the change in the environment. The results obtained by applying this concept experimentally proved that the above method reduced the probability of error and is also self-adaptable. This system can be further enhanced for position identification [5]. |
| Oliveria et al (2012) described a distance estimation system using the two metrics RSSI and LQI. RSSI is the main source for the estimation of distance and localization. In order to make more precise estimates, the authors combined RSSI and LQI for decreasing the delay and estimation cost. Fuzzy logic and Transferrable Belief Model (TBM) were used for improved decision making. The proposed model showed a 100% accurate distance estimation in an indoor scenario [6]. |
| Localization scheme is presented by Sazena, Gupta and Jain (2008), in which the Received Signal Strength is analysed for distance estimation. They used the empirical relationship between relative error and distance to obtain the RSS. It is observed that the RSSI model in wireless sensor network provides precise location estimation [7]. |
| Table 1 compares various hardware based techniques based on the problem addressed, metrics used, tools used and their inference. |
RELATED WORKS ON SOFTWARE BASED TECHNIQUES
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| Chain and Han (2009) illustrated a hierarchical approach to obtain and aggregate the residual energy in the form of energy map. The energy consumed by various sensor nodes is monitored continuously in the form of energy map. The sensor nodes are split into many clusters, with a cluster head being allotted for every cluster. The topology tree is then created based on the energy consumed by the cluster nodes, from which the energy map is obtained at the base station .The main feature is the In- network aggregation which combines the adjacent polygons of the same energy map thereby minimising energy map and reducing the message cost. Additionally by reorganizing the energy distributed evenly, the battery life of the sensor nodes are also increased [8]. |
| Chakraborty, Mitra and Naskar (2011) designed an effective genetic algorithm inspired routing protocol called "GROUP" which provides good network performance and also a good life time when compared to other available schemes. A sub-optimal energy dissipation is assumed for the random deployment of nodes. This increases the network lifetime and energy efficiency of the network. Simulated Annealing is also used to provide efficient routing paths for the further enhancement in the network performance [9]. |
| Jiang et al (2013) developed an energy efficient routing algorithm for WSN. The transceivers used in a WSN consumes large amount of energy when compared with the other components. So it is of great importance to design energy optimised routing algorithm. The author in this paper proposed an algorithm for data transceiver in which routing is done by the single-hop forwarding scheme, rather than multihop forwarding within a particular range. The energy balance can also be determined based on the energy inequality. In addition to this a fuzzy-based decision making is done, considering multi parameters observed from the sensor nodes. Proposed algorithm proves to be more energy conservative when compared with similar ones [10]. |
| Karimi et al (2013) describes the combination of fuzzy logic and chaotic based genetic algorithms to extend the lifetime of sensor nodes. Fuzzy logic is used to identify the best node as cluster head (CH) by the base station. the CH selection is based on the variables energy, density and centrality. Using Genetic algorithm the number and position of cluster heads is computed. The proposed technique is simulated in a heterogeneous WSN that has increased network life time, increased network reliability and decreased transmission delay [11]. |
| Li, Leith and Malone (2011) demonstrate the undesirable channel under-utilization or unnecessary high delays caused by the use of fixed size buffers in wireless networks. Buffers are used to accommodate short term packet bursts. Packets are dropped if many arrive in a short interval of time where there is a lack of capacity to process all of them. By the use of a dynamic buffer sizing algorithm, a high throughput and a low delay is obtained under various network conditions [12]. |
| An energy efficient mechanism is provided by Pandey and Varma (2012), which maintains the privacy of the base station's location so that intruders do not access these nodes. In the proposed technique fake packets are introduced to the traffic generated. The probability of fake packet generation by each sensor node is based on the residual energy of the neighbouring nodes. When a data packet is received by the sensor node, it generates and propagates the fake packets to all its neighbouring nodes having maximum residual energy. This method overcomes the energy overhead introduced by the previous techniques that uses uniform probability for fake packet generation. Also it is difficult to trace a packet by estimating the transmission times between the nodes [13]. |
| Kumar et al (2012) proposed Dual Fuzzy Logic Cluster Protocol (DFLCP) forming a cross layer energy efficient architecture. The approach exploit the design of hierarchical clustering with sleep scheduling, range free localization using convex optimization and Hybrid Adhoc routing protocol to maximize energy efficiency of wireless sensor nodes. The results show that DFLCP outperforms LEACH (Low-Energy Adaptive Clustering Hierarchy) in terms of network lifetime and power consumption minimization. The proposed technique provides less routing overhead and better localization estimation. |
| Table 2 compares various software based techniques based on the problem addressed, metrics used, tools used and their inference. |
PROPOSED WORK
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| From the literature surveyed, it is clear that the parameters namely Residual Energy, RSSI, LQI, Distance and Buffer Size play in reducing the energy consumption in WSN. So a new fuzzy based routing protocol that considers, the above parameters for enhancing the network life time and to reduce the energy consumption is proposed. Fuzzy Logic is used for deciding the best set of data forwarding nodes based on the key node parameters. The zigbee network scenario in which the proposed technique is to be implemented is given in figure 2. |
| In the figure 2, the end device is responsible for data gathering and forwarding it to the coordinator through any of the routers. Each router computes the values of Residual Energy, RSSI, LQI, Distance and Buffer Size. Based on fuzzy logic, best neighbour node is selected and data forwarding through that node takes place. Other nodes not involved in data forwarding are made to sleep mode. By doing this power management, the network lifetime can be enhanced. |
CONCLUSION AND FUTURE WORK
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| Network lifetime is the key characteristic in a WSN. Many research works has been carried out with an aim of enhancing the network lifetime. In this paper, a detailed survey on the existing energy efficient techniques for a WSN is made. From the analysis on the various strategies, a new fuzzy based routing protocol is also proposed for a real time environment. This methodology is to be implemented for smoke detection application using Zigbee nodes. |
Tables at a glance
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Figures at a glance
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References
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- Cherif Diallo, Michel Marot, and Monique Becker , "Using LQI to improve clusterhead locations in dense zigbee based Wireless sensornetworks",Wireless and Mobile Computing Networking and Communications, http://www-public.it-sudparis.eu, pp:137-143, Oct-2010.
- Sajid Hussain and Md Shafayat Rahman."Using Received singal strength Indicator to detect node replacement and replication at tacks inwireless sensor networks",www.cs.acadiau,ca /~shussain /wsn / publications /09-spie2009.
- K.Benkic, M.Malanjer, P.Planinsic, and Z.Cucez, "Using RSSI for idstance estimation in wireless sensor networks based on Zigbee", SystemSignals and Image processing, pp:303-306, 2008.
- Yi-jen Mon, Chih-Min Lin, Intelligent LQI based wireless sensor network applied to positioning system", www.pc.org.pl/articles,R.89 NR/5,pp-241-244, 2013.
- Jiuquiang Xu, Wei Lui, Fenggao Lang, Yuan Zhang, Chenglong Wang,"Distance measurement model based on RSSI inWSN",10.4236/wsn.2010.28072 Published Online ", pp:606-611, August 2010.
- Talmai Oliveria, Madhanmohan Raju, Dharma P.Agarwal,"Accurate Distance estimation using Fuzzy based Combined RSSI?LQI values in anIndoor Scenario Experimental verification",Macrothink Institute, Network Protocols and Algorithm,www.macrothink.org/npa, Issn:1943-3581, Vol:4, pp:174-199, 2012.
- Mohit Sazena, Puneet Gupta, Bijendra Nath Jain, "Experimental Analysis of RSSI-Based Location Estimation in Wireless sensor networks",Communication Systems Software and Middleware and Workshop Bangalore, pp:503-510S, 2008.
- Edward Chain, Song Han,"Energy Efficient Residual Energy in wireless sensor networks",International Journal Of Distributed SensorNetworks,vol- 5, pp:1-23,2009.
- Ayon Chakraborty, Swarup Kumar Mitra, Mrinal Kanti Naskar,"A Genetic algorithm inspired Routing Protocol for Wireless sensor network",International Journal of Computational Intelligence Theory and Practise, vol:6.no.1, pp:1-10, June 2011.
- Haifeng Jiang, Yanjing Sun, Renke Sun, and Hongli Xu, Research Article, "Fuzzy-Logic-Based Energy Optimsed Routing for wirelesssensor Networks", Hindwani Publishing Corporation, International journal of Distributed SEnsor Networks,Volume: 2013,http : //dx.doi.org/10.1155// 216561, pp:1-8 ,2013.
- Abbas Karimi, S.M. Abedini, Faraneh Zarafshan, S.A.R Al-Haddad, "Clusterhead selection Using Fuzzy Logic and Chaotic Based GeneticAlgorithm in wireless sensor network", Journal of Basic and Applied Scientific Research, TextRoad Publication Sci.Res.,3(4), pp:694-704,2013.
- Tianji Li, Douglas Leith, David Malone, "Buffer Sizing for 802.11 Based Networks", Networking, IEEE/ACM Transactions, Volume:19,Issue:1, pp:156-169, feb 2011.
- Manjusha Pandey, Shekhar Varma, "Residual Energy Based Anti-Traffic Analysis Privacy Preservation in WSN", I.j Computer Network andInformation Security, Published Online in MECS,http://www.mecs-press.org/, Volume:4, pp:21-29, May 2012.
- S. Swapna Kumar, M. Nanda Kumar , V. S. Sheeba , K. R. Kashwan d, " Power Management of Hybrid Scheduling Routing in Cluster Based Wireless Sensor Network", Journal of Information & Computational Science 9: 6, pp: 1555-1575, 2012.
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