ScienceDirect. Comparison of Energy Efficient Data Collection Techniques in Wireless Sensor Network

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1 Available online at ScienceDirect Procedia Computer Science 57 (2015 ) rd International Conference on Recent Trends in Computing 2015 (ICRTC-2015) Comparison of Energy Efficient Data Collection Techniques in Wireless Sensor Network Rupali Rohankar a*, C. P. Katti b, Sushil Kumar c a Student, Jawaharlal Nehru University, Delhi, India, rupalir25@gmail.com b Professor, Jawaharlal Nehru University, Delhi, India, cpkatti@jnu.ac.in c Assistant Professor, Jawaharlal Nehru University, Delhi, India, skdohare@jnu.ac.in Abstract Application of wireless devices is increasing day-by-day in all areas of human life throughout the world. Most of these devices are based on tiny sensors that collect information from surrounding environment automatically without any human intervention when they are deployed in the environment thus creating wireless sensor networks. These tiny sensors are highly energy constrained due to their limited battery, storage, processing power and communication capacity. The constraint makes Energy Efficiency one of the most investigated themes among researchers in wireless sensor networks. In the past, various energy efficient data collection techniques have been suggested in the literature. This paper presents a qualitative review of recent advances in data collection techniques in WSN. The review classifies each of the considered techniques based on underlying topology. A second level categorization of these techniques is done based on energy saving scheme used. A comparison of these techniques is provided for qualitatively evaluating these techniques. The review concludes with a discussion on limitations of the considered techniques The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license 2015 The Authors. Published by Elsevier B.V. ( Peer-review Peer-review under under responsibility responsibility of organizing of organizing committee committee of the of 3rd the International 3rd International Conference Conference Recent on Trends Recent in Computing Trends in 2015 (ICRTC-2015) Computing 2015 (ICRTC-2015). Keywords WSN; data collection; energy efficiency; network lifetime maximization; energy saving. 1. Introduction Wireless Sensor Network (WSN) have emerged as networking technologies deploying large number of low power, inexpensive sensor nodes distributed randomly to monitor, usually, a remote environment [1].WSNs have been developed from early 70s and have several applications like structural monitoring, wildlife habitat monitoring, and forest fire detection and so on. The basic task of a sensor node is to collect the sensed data and disseminate it to the sink node. Due to limited battery supply and high transmission energy consumption, balancing energy expenditure of a node during data collection and forwarding becomes vital for maximizing The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license ( Peer-review under responsibility of organizing committee of the 3rd International Conference on Recent Trends in Computing 2015 (ICRTC-2015) doi: /j.procs

2 Rupali Rohankar et al. / Procedia Computer Science 57 ( 2015 ) the network lifetime [2]. The sensor nodes usually cover a large area and have limited transmission range. Therefore, multi-hop communication is used to relay the data to the sink node. But the intermediate nodes may get exhausted due to relay transmission cost, resulting in a disrupted network aka funneling effect [3]. Alternatively, mobile nodes can be used to collect data and save battery time. But data freshness, mobility aware power management and localization issues need to be addressed [4]. Despite the inherent challenges of the network, data collection protocol mainly aims to maximize network lifetime, guarantee coverage & connectivity and minimize latency. The Energy Efficient data collection strategies have been widely implemented. They include data aggregation, sleep-wake scheduling, and transmission power adjustment to name a few. The factors like number of control packets and available residual energy of a node are particularly considered while developing the energy efficient protocols. In the earlier surveys, taxonomy of data collection protocols is presented based on multipath routing, mobility support, deployment, dissemination and data delivery and MAC scheduling [5-9]. The energy efficient protocols are surveyed and compared based on aggregation, mobility, data delivery methods in [10]. In [11], a survey of logical topologies and their comparison based on energy consumption, lifetime, scalability, control overhead and latency is done. Similar to [11], this paper surveys recent advances in routing protocols based on underlying topology. Additionally, synchronization method (centralized, distributed and local) and multipath routing topologies like connected dominating set (CDS) or coversets are also considered for classification. A second level categorization is based on energy saving techniques used. The performance comparison between the energy efficient protocols is done using factors like energy consumption, load balancing, coverage & connectivity, scalability, network lifetime and control overhead. We further discuss the limitations of these protocols. Section II describes the energy efficiency that can be achieved in WSN data collection. The recent energy efficient techniques for data collection and related issues are described. Section III compare and discusses the outcome of the surveyed techniques. 2. Energy Efficiency in WSN Data Collection Energy efficiency of WSN affects its network lifetime. Network lifetime is defined as the time elapsed from the network operation starts until the first node (or the last node) in the network depletes its energy (dies). A node may perform useful operations like receiving or transmitting operations. On the contrary, it can perform unnecessary operations like overhearing, retransmission due to collisions, redundant control packets broadcast, and also listening to media when idle. The energy efficiency can be thus increased by avoiding unnecessary operations of a node. Table I. Survey of energy efficient protocols References Topology Energy Saving Technique Synchronization 1. [27] Tree Mobile Sink Local 2. [12], [21] Aggregation + Scheduling Local

3 148 Rupali Rohankar et al. / Procedia Computer Science 57 ( 2015 ) [22] Wake up Scheduling Centralized & distributed 4. [15], [26] Load balancing Distributed 5. [13] Available residual energy Centralized & distributed 6. [18] Cluster Data Compression Distributed 7. [16] Aggregation Local 8. [14] Aggregation + Scheduling Local 9. [20] Flat Sleep /wake scheduling Centralized 10. [23] Scheduling + mobility Local 11. [17], [25] CDS / Coversets Mobility Sink + residual energy Centralized 12. [29], [30] Aggregation + degree Constrained Centralized distributed & 13. [19] Residual Energy + Mobile elements + Scheduling Distributed 14. [28] Scheduling Distributed 15. [24] Energy available + Transmission Range Local

4 Rupali Rohankar et al. / Procedia Computer Science 57 ( 2015 ) Energy Saving Techniques Used in Data Collection In-network data aggregation method is implemented to reduce correlated data packets being forwarded to the sink maximizing lifetime. It mainly solves the problem of implosion, overlapping and resource blindness [3-4]. Data compressive sensing is an alternative approach for efficient correlated data transmission. It allows for signal recovery with high probability using random projections [11-12]. Data is often sensed periodically. Therefore, nodes may be put off /on depending on the activity. The low duty cycle is energy efficient way for maximizing the network lifetime [13-17]. Alternatively, path controlled traversal mobile sink or nodes method improves efficient data collection and network lifetime. Mobility of nodes provides ubiquitous data collection which is also scalable and provides continuous data delivery. [18-20]. 2.2 Topology The inherent topology described by the routing protocol also affects the energy consumption. Most widely used topologies are cluster based for aggregating and fusing the spatially correlated data [21-23]. Tree based data collection creates a hierarchy of nodes and aggregation of gathered data is performed at each level [24-27]. Coversets or backbone formation methods are implemented to avoid any loss due to link failure in tree based topology. Coversets or backbone formation methods are implemented to avoid any loss due to link failure in tree based topology [28-31]. A summary of surveyed techniques is given in Table Comparison & Discussion The protocols surveyed have addressed several problems with the aim to maximize network lifetime. Innetwork data aggregation and fusion used by different protocols under different topologies reduces energy consumption while data forwarding.[13], [17-18], [25-27] consider residual energy of node or path to reduce energy consumption while data dissemination in the network. Often aggregating data at a single node is erroneous due several network constraints. To solve this problem and to increase longevity of the network the sensors may be activated in rounds, so that their energy is not wasted in monitoring those areas that are monitored by some other sensors [12], [14], [20-23]. The challenges in network lifetime maximization are further augmented due to the distinct features of WSNs like large network size, stochastic node distribution with high density, dynamic topology and unreliability, and severe energy constraints. Rotating CDS or coversets are multipath routing techniques that enable energy load balancing of data forwarding paths [17], [19], [24-25], [28-30]. Table 2 depicts a comparison of the energy efficient protocols surveyed based on the issues addressed A: Node energy available, B: Reduced control overhead, C: Coverage & connectivity, D: Improved scalability, E: Network lifetime maximization and F: Reduced latency. Table II. Comparison of energy efficient protocols S.N. Protocols Surveyed Issues Addressed Limitations 1. [12] E F

5 150 Rupali Rohankar et al. / Procedia Computer Science 57 ( 2015 ) [13] A, B, E F 3. [14], [16] C, D E, F 4. [15] E, F B, C 5. [18] A, E F 6. [17], [19], [23-30] A, B, C, D, E F Depending on the energy saving techniques used and the results described in the protocols the issues addressed are determined. The limitations are defined based on similar work. The protocols using CDS or coversets topology address maximum energy efficient issues whereas those using single path routing based on tree topology are not much energy efficient. Cluster topology is better in performance when compared to tree topology but not better than CDS. Nevertheless, in CDS topology the certain areas like reconstruction of backbone, control overhead are still open issues for research. 4. Conclusion In this paper, a qualitative study of recent advances in energy efficient data collection techniques in WSN has been performed. The considered techniques have been classified based on the topology such as tree, cluster, flat, CDS or cover sets. These techniques have been further differentiated on the basis of the scheme considered for energy saving. A comparison and discussion is followed considering qualities and limitations in these techniques. Based on the comparison and discussion, it is concluded that coversets or connected dominating sets are more suitable for addressing most of the issues such as energy efficiency, coverage & connectivity and latency in wireless sensor networks. References [1] F. Akylidiz, W. Su, Y. Sankarasubramaniam, E. Cayirciv (2004):Wireless Sensor Network: Survey, Computer Networks, Vol. 38, Issue 4. [2] W. Dargie, Christian P. (2010): Fundamentals Of Wireless Sensor Networks: Theory And Practice, Wiley Series on Wireless Communications and Mobile Computing, pages [3] Li J., Mohapatra P. (2007): Analytical modeling and mitigation techniques for the energy hole problem in sensor networks. Pervasive Mob. Comp. 3, Vol. 3, [4] I. Chatgzigiannakis, A. Kinalis, Sotiris N. (2006):Sink Mobility Protocols for Data Collection in Wireless Sensor Networks. Proceedings of the 4th ACM international workshop on Mobility management and wireless access. pp ACM Newyork. [5] D. Francesco, M., Das, S. K., and Anastasi, G. (2011): Data collection in wireless sensor networks with mobile elements: A survey. ACM Trans. Sen. Netw. 8, 1, Article 7, 31 pages [6] R. ANNIE UTHRA and S. V. KASMIR RAJA (2012):QoS Routing in Wireless Sensor Networks A Survey, ACM Computing Surveys, Vol. 45, No. 1, Article 9.

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