Impact of redundant sensor deployment over data gathering performance: A model based approach
Introduction
Data gathering and processing in a distributed environment is one of the most widely used applications for wireless sensor networks (WSN). A number of efficient data gathering approaches have been proposed in the literature, such as collection tree protocol (Gnawali et al., 2009) and its variants, minimum cost tree (Zhang et al., 2005), differentiated tree protocol (Naderi and Mazinani, 2012), TREEPSI (Satapathy and Sarma, 2006), fast data collection (Durmaz Incel et al., 2012), and source routing tree (Sergiou and Vassiliou, 2012). It has been well studied that tree based data collection, particularly breadth first search (BFS) tree, is most efficient for sensor networks, as it provides fast collection with minimum duplicate delivery and data loss (Chen et al., 2012, Li et al., 2011). As shown in Chen et al. (2012), a simple BFS tree based data collection method can lead to order-optimal performance for any arbitrary sensor networks. Further, most of the tree based data collection methods, as mentioned earlier, converge to a BFS tree like structure at the steady state, and therefore, BFS tree based data collection can be considered as the mostly used and most effective data gathering protocol for a general purpose sensor network.
A sensor lifetime directly depends on the traffic load that it forwards (Chen et al., 2012). For a tree based data aggregation, the energy dissipation for a sensor node gradually increases from the leaf of the tree towards the sink. To save critical sensor resources, a number of schemes has been proposed in the literature, like data gathering medium access control (DMAC) (Lu et al., 2007) and its variants, where sensors follow a periodic sleep wakeup schedule to save battery power. In this mode of operation, the nodes that does not have data to transmit or receive go to the sleep state. However, sleep–wakeup based data gathering schedule is not applicable for all sensor applications, as it increases data delivery delay, as well as the sensory activities depend on the scheduling strategy (Kim et al., 2011, Suriyachai et al., 2012, Chao and Hsiao, 2014, Chakraborty et al., 2014). Based on this, sensor operation mode can be classified into two types – periodic sensing that supports sleep–wakeup based scheduling, and steady sensing where nodes remain active throughout the lifetime, and participate in data gathering activities all the time, until they die-out of energy or crash. Both of these sensing modes have their direct implication over the network lifetime:
- (a)
For periodic sensing, sensor nodes that neither transmit nor receive, go to sleep state to save energy. The amount of sleep duration increases gradually from the sink to the leaf nodes of a data gathering tree where in-network data aggregation is not possible (Zheng et al., 2011). Thus, energy dissipation of the leaf nodes is significantly less compared to the nodes near the sink. This results in an early die-out of sensors near the sink.
- (b)
For steady sensing, the sensors that neither transmits nor receive, remains in the idle state. However, the energy dissipation for idle state is less than that of transmit or receive state. For tree based data gathering, the nodes near the leaf spends more idle time compared to the nodes near the sink. As a consequence, the nodes near the sinks dies out early compared to the nodes near the leaf.
Both periodic sensing and steady sensing result in an early die-out of sensors near the sink, and affects both the connectivity and the sensing coverage in the network. On failure of a sensor node, the maintenance of the data gathering tree by reconstructing the tree from the scratch is not at all cost-effective (Zhang et al., 2005, Diallo et al., 2012, Ammari and Das, 2009, Turau and Weyer, 2009). Additionally, an involvement of all nodes in the maintenance activities introduces a global freeze, which in effect, degrades QoS for the application. Tree maintenance in reactive approach charges a significant cost in terms of control message communication as well as repairing delay. Proactive repairing with a low cost serves better in this scenario. However, considering an irregular topology, the proactive repairing also fails to perform well. Moreover, multiple simultaneous node failures in a close vicinity would be difficult to incorporate as this type of maintenance scheme increases per node load after repairing the tree on every node failure.
Redundancy in sensor deployment is an efficient method to ensure uninterrupted data delivery and improved network lifetime (Shen and Wu, 2011). However, a proper estimation of redundancy is required based on the traffic load at different level of data gathering tree to ensure balanced energy dissipation throughout the network. Further, in real life, the area of interest or the terrain may be irregular as well as inaccessible in nature. Homogeneous deployment density would not be suitable for an irregular terrain as the deployed redundant nodes might not be able to serve the faulty node in this case (Zou and Chakrabarty, 2004). Therefore, the initial deployment of sensor nodes plays a crucial role in prolonging the network lifetime while maintaining the connectivity and the coverage.
In this paper, a gradient based node deployment framework, termed as MoDGraDIT, has been introduced considering both the cases of sensor energy dissipation model – the periodic sensing and the steady sensing. Considering irregular terrain, MoDGraDIT deploys a sufficient number of redundant nodes that can replace a faulty node on failure, while maintaining the sensing coverage and network connectivity. Based on the energy dissipation model for tree based data gathering, MoDGraDIT calculates sensor density as a function of the distance from the sink. The proposed scheme designs a model to estimate the number of nodes in the rooted subtree of an intermediate node in the data gathering tree, assuring the network connectivity and the sensing coverage during a node failure. Based on the estimation, the number of redundant nodes required to be placed is calculated. Applicability of the proposed mathematical model and the trade-off among the connectivity, coverage, fault-tolerance and the redundancy are justified through analysis using sensor network calculus (Schmitt and Roedig, 2005). Finally, the performance of the proposed gradient based sensor deployment framework is compared with the deployment frameworks proposed in Liao and Lin (2011) and Yun et al. (2010) through the simulation results. An initial version of this concept has been presented in Chakraborty et al. (2013), where the gradient deployment effect has been computed and analyzed for periodic sensing only. This paper extends the previous version through rigorous analysis of the deployment strategy for both steady sensing and periodic sensing, with performance results from a more realistic scenario.
The rest of the paper is organized as follows: Section 2 gives a brief description of state of the art works on sensor deployment strategies. Section 3 provides the system model and assumptions for the proposed framework. Few concepts and definitions required to establish the proposed theory are provided in Section 4. Section 5 describes the mathematical analysis for the estimation of the proposed gradient based deployment density. The theoretical analysis through sensor network calculus is provided in Section 6. Finally, Section 8 presents the simulation results followed by a conclusion of the contribution in Section 9.
Section snippets
Sensor deployments strategies in the literature: a brief survey
Initial deployment of sensor nodes plays a crucial role in prolonging the network lifetime while maintaining the connectivity and the coverage. A set of works has been proposed to deploy the sensor nodes in the network such that some predefined QoS requirements are satisfied (Bojkovic and Bakmaz, 2008, Ma et al., 2011, Gajbhiye and Mahajan, 2008, Oldewurtel and Mä, 2012, Liu et al., 2013, Tarng et al., 2009). Most of the applications require the area of interest to be sensing covered by enough
System model and assumptions
Let a large number of sensor nodes be deployed in the field of interest or terrain. Each sensor node is assumed to be identical in terms of transceiver power, memory and processing capacity. Deployed sensors can be categorized into two types, a set of primary nodes and a set of redundant nodes. The primary nodes are assumed to construct a BFS tree, denoted by , rooted at the sink and then perform the tree based data gathering. The proposed deployment scheme does not depend on the tree
Deployment model for MoDGraDIT: the gradient calculation
The area of interest for environment sensing (or terrain) can form any irregular polygon as shown in Fig. 1. It is assumed that the sink is placed at an optimal position according to Poe and Schmitt (2009). The network lifetime can be defined as the duration of time from the initial setup of the network till the network becomes partitioned or a network hole is created due to failure of a single node or a set of nodes. To achieve an improved lifetime, sensor nodes are deployed in high
MoDGraDIT: estimation of deployment density
This section gives the details of estimating sensor deployment density for MoDGraDIT as a function of the distance from the sink. The density of deployment ρ, defined in Definition 5, dictates the level of tolerance for node failures. Definition 5 The density of deployment, denoted by ρ, is defined to be the number of nodes in the network (including both the redundant and primary), deployed per unit area of interest.
However, the value of ρ is not homogeneous over the network. Sensor nodes are deployed in
Why EDF works as a suitable metric for MoDGraDIT? A theoretical analysis
The basic idea behind the deployment of sensor nodes in an irregular terrain lies in the fact that the nodes closer to the sink are required to forward more traffic compared to the ones near the terrain periphery. As a result, considering tree based data gathering rooted at the sink, nodes closer to the sink die out faster than the leaf nodes due to energy exhaustion. To improve the network lifetime satisfying the connected coverage criteria, MoDGraDIT uses a gradient based deployment strategy
Properties of MoDGraDIT—some theoretical insights
This section analyzes some of the properties of MoDGraDIT deployment model from theoretical foundations. MoDGraDIT uses redundancy to increase network lifetime while maintaining connectivity and coverage during node failures. This section analyzes the trade-off among different design parameters of MoDGraDIT, like redundancy, connectivity, coverage and the effect of dmin and dMAX over protocol design parameters.
MoDGraDIT: performance analysis and comparison from simulation
Standard MicaZ MicaZ (2014) sensor motes specifications are used for numeric and simulation analysis. The power consumption for receive, transmit, sleep and idle mode are 0.053 W, 0.047 W, 0.001 W and 0.0216 W, respectively. The channel bandwidth and per-hop latency are assumed as 1 Mbps and 0.01 s respectively. For simulation analysis, the proposed deployment framework is implemented in Qualnet-5.0.1 (Qualnet, 2014) network simulation framework. The performance of the proposed deployment scheme is
Conclusion
In this paper, a gradient based node deployment framework with redundancy has been proposed to assure the connected-coverage criteria on potential node failure. Considering tree based data gathering in sensor network, MoDGraDIT uses a strategy where the deployment density decreases from the root towards the leaf nodes of the tree. The density of nodes at a particular level of the tree depends on the number of nodes in the subtree rooted at a node in that particular level. The estimated
References (44)
- et al.
Design of structure-free and energy-balanced data aggregation in wireless sensor networks
J. Netw. Comput. Appl.
(2014) - et al.
Real-time data management on wireless sensor networksa survey
J. Netw. Comput. Appl.
(2012) - et al.
ReviewCoverage and connectivity issues in wireless sensor networks: a survey
Pervasive Mobile Comput.
(2008) - et al.
A pre-determined node deployment strategy to prolong network lifetime in wireless sensor network
Comput. Commun.
(2011) - et al.
Deployment guidelines for achieving maximum lifetime and avoiding energy holes in sensor network
Inf. Sci.
(2013) - et al.
Computational geometry based distributed coverage hole detection protocol for the wireless sensor networks
J. Netw. Comput. Appl.
(2011) - et al.
A relay node deployment method for disconnected wireless sensor networksapplied in indoor environments
J. Netw. Comput. Appl.
(2009) - et al.
Fault tolerance measures for large-scale wireless sensor networks
ACM Trans. Auton. Adapt. Syst.
(2009) - et al.
A survey on wireless sensor networks deployment
WSEAS Trans. Commun.
(2008) - Chakraborty, S., Chakraborty, S., Nandi, S., Karmakar, S., 2013. Exploring gradient in sensor deployment pattern for...
Topology management ensuring reliability in delay sensitive sensor networks with arbitrary node failures
Int. J. Wirel. Inf. Netw.
Capacity of data collection in arbitrary wireless sensor networks
IEEE Trans. Parallel Distrib. Syst.
Fast data collection in tree-based wireless sensor networks
IEEE Trans. Mobile Comput.
Is sensor deployment using Gaussian distribution energy balanced?
Algorith. Archit. Parallel Process.
Dimensioning and worst-case analysis of cluster-tree sensor networks
ACM Trans. Sensor Netw.
Optimal any cast technique for delay-sensitive energy-constrained asynchronous sensor networks
IEEE/ACM Trans. Netw.
Complexity of data collection, aggregation, and selection for wireless sensor networks
IEEE Trans. Comput.
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