3.4 Evaluation
3.4.4 Simulation Results
All of the presented plots share the same axes. Axis x represents relative time expressed in percents. Axis y shows percentage of sensor nodes within the energy hole that have enough energy for operation over the time. The ideal curve (perfect accuracy of energy needs profiling) should be a straight line at value of 100% alive sensor nodes reaching the relative time of 100% and then instantaneously
3.4. EVALUATION 49
falling to 0%. Deviations from the ideal curve imply a degree of inaccuracy. The curve falling below 100% alive sensor nodes before reaching 100% relative time indicates level of under-stocking of the network with the energy within the energy hole. The value above 0% alive sensor nodes at relative time over 100% indicates level of overstocking of the network within the energy hole.
0 20 40 60 80 100 0 20 40 60 80 100 120 140 160
[%] o
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250 nodes LEHP 500 nodes LEHP 750 nodes LEHP 250 nodes Hole 500 nodes Hole 750 nodes HoleFigure 3.3: Impact of node density
Figure 3.3 depicts the impact of density of the deployment on the accuracy of LEHP. The labels ”x nodes LEHP” and ”x nodes Hole” represent simulation with and without LEHP respectively. As expected, the network deployed with 250 sensor nodes shows slightly lower accuracy. This occurs as lower average connectivity degree degrades angular resolution. Although the network remains connected, sparse connectivity leads to the assignment of sensor nodes to an arc which is less accurate than in denser deployments. The deployment of over 500 sensor nodes assures high connectivity degree, therefore, higher angular resolu- tion is reached to result in more accurate profiling. Without energy injection, the first sensor nodes (closest to the center of hotspot) start to fail already at time of 15% of network lifetime and the rest gradually follow. The accuracy of LEHP is
highly independent from the density of the network.
The impact of the aggregation step (Alg. 2) is presented in Figure 3.4. As expected, the higher the granularity, the better the accuracy is achieved. Both r/3 and r/2 (step size r/3 and r/2) preform very well. The smaller area over which aggregation takes place the greater chances that lower variance in energy need
0 20 40 60 80 100 0 20 40 60 80 100 120 140 160 180
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step size r/3 LEHP step size r/2 LEHP step size r LEHP fixed hop LEHP Hole
fixed hop LEHP
Figure 3.4: Impact of aggregation distance
levels, therefore lower averaging error. It is to observe that applying hop distance metric (fixed hop) outperforms Euclidean distance approach if step = r. When using the hop distance, the distance corresponds to shorter Euclidean distance than r as it would be expected by multiplying the range of communication by number of hops. The curve Hole represents the failing of the sensor nodes without energy injection. Euclidean based aggregation preforms better for ring widths
smaller than communication range, otherwise it is more efficient to use hop based quantification.
The best performance is achieved for the Exponential distribution model (Fig- ure 3.5). Here, the highest energy depletion is concentrated near the epicenter of the energy hole, what assures high accuracy of profiling for this most dynamic region. For Normal and Pareto distributions the energy is more evenly distributed, degradation of network starts later but is much steeper afterwards (Pareto Hole and Normal Hole curves), therefore, the implications of lower accuracy at greater distances are more evident. The LEHP offers better accuracy for the distributions
which show smaller variance.
In order to study the impact of irregular shape on the performance of LEHP, additional simulations were conducted on energy holes as illustrated in Fig 1. These irregular energy holes result from the overlap of three symmetric energy holes. The accuracy of LEHP decreases but remains adequate (Figure 3.6). The main cause of decreased accuracy is the selection of the initiator. The selection of proper location for initiator is hard in flat regions of phenomenon distribution. The
3.4. EVALUATION 51 0 20 40 60 80 100 0 20 40 60 80 100 120 140
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Exponential LEHP Pareto LEHP Normal LEHP Exponential Hole Pareto Hole Normal HoleFigure 3.5: Impact of hole distribution
0 20 40 60 80 100 0 20 40 60 80 100 120 140
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500 nodes, step size r/2 LEHP 500 nodes, fixed hop LEHP 750 nodes, step size r/2 LEHP 750 nodes, fixed hop LEHP 500 nodes Hole 750 nodes Hole
(a) With localization (b) Without localization
Figure 3.7: Radial and angular energy need distribution
accuracy of profiling decreases on the part of the energy hole border that is furthest placed from the initiator. Despite these obstacles the LEHP still manages to keep most of the sensor nodes alive at time the original network is already partitioned.
The LEHP suffers from irregularity of energy holes, but remains robust.
Figure 3.7(a) visualizes the profiles of two simulation results of angular en- ergy need profiles. The rings represent the quantified distances from the initiator and the arcs divide the profile in directions from which data were gathered. The shades of grey represent density of energy needed. The darker the marking, the higher energy density is required. The highest density is located at the center of the hotspot and gradually decreases in direction of the hole border. The differ- ent shades of grey of arcs, shows how the density of required energy changes in different directions. Figure 3.7 highlights a slight accuracy difference for LEHP with and without position information.
3.5
Summary
This chapter provides the first necessary steps for making the energy based main- tenance a viable option for WSN functionality conservation. The developed en- ergy hole profiling algorithm, LEHP, is shown to be an efficient strategy for valu- able early warning of likelihood of energy holes. LEHP allows the accurate po- sitioning and size estimation of developing energy hole, which usually leads to sensing and communication coverage loss. The efficiently computed and accu- rate angular and radial information about the energy needs, provides means for effective maintenance actions, which optimally are long term. This assertion is substantiated by the extensive simulation results.
Chapter 4
Topology Oriented Maintenance in
Sparse Wireless Sensor Networks
4.1
Overview
The physical number of sensor nodes constitutes a major cost factor for WSN deployments. Hence, a natural goal is to minimize the number of sensor nodes to be deployed, while still maintaining the desired properties of the WSN. How- ever, sparse networks even while connected, usually suffer from topology irregu- larities that negatively impact the network lifetime and responsiveness, i.e., sen- sor data delivery reliability and latency. In addition, sensor node failures easily complicate/enforce/aggravate these irregularities. Valuable efforts have been con- ducted to discover topology specific anomalies such as coverage holes or crit- ical/bottleneck nodes. Unfortunately, these efforts suffer from at least one of the following drawbacks: (a) They are centralized and consequently inefficient in large-scale networks, (b) they are tailored to one class of anomalies, or (c) do not propose how to remedy the identified anomaly. This chapter focuses on sparse WSN which usually show varied topology irregularities and propose an in-network and localized strategy that efficiently (i) discovers generic topology irregularities, and (ii) identifies locations for minimal number of new augmented sensor deployments to remedy topology irregularities and sustain the desired op- erational requirements.
Overall, the benefits of repairing the topology irregularities can be classified into two broad classes, namely network- and functionality-centric improvements. The network operation is enhanced through shorter routing paths, reduced laten- cies, and balanced and lower energy overhead. The WSN functionalities are op- timized by maintaining a regular topology leading to uniform sampling, more accurate information extraction and hence achieving a higher Quality of Informa-
tion (QoI) level for the WSN objectives. Therefore, the proposed contribution can make potentially a significant impact on the network and application design in WSN as well as their deployment.
The TOM qualities are validated with an extensive set of evaluation studies. They show TOM effectiveness at reducing the discrepancy between hop and Eu- clidean distances, balancing the energy usage and shortening average hop distance to the sink. The evaluation studies also demonstrate that TOM effectively and effi- ciently maintains an overall regular topology by enhancing an initial sparse WSN. Thus, TOM outperforms the costly approaches that require a certain level of re- dundancy in node deployment.
4.1.1
Contributions
Topology Oriented Maintenance (TOM):
• efficiently detects topology irregularities and provides maintenance options
for a sustainable regular topology,
• is tunable and allows setting bounds on the tolerable level of topology irreg-
ularity,
• is localized, thus, allowing for efficient and sustainable WSN self-healing,
and
• provides means for a balanced and minimal initial WSN that fulfils the re-
quirements.
The chapter is structured as follows. Following the system model in Section 4.2, Section 4.3 presents a precise formulation of the proposed approach objec- tives and requirements. Section 4.4 details the proposed TOM technique as the chapters’s main contribution. The evaluation is presented in Section 4.5.
4.2
System Model
Conforming to contemporary WSN literature concerned with topology problem- atic, the system model assumes a WSN consisting of n resource constrained sen- sor nodes and a sink. The sensor nodes have finite battery energy and usually possess limited processing and storage capabilities. The communication range r is limited and fixed for a given deployment. Two neighboring sensor nodes can communicate directly only if their Euclidean distance is smaller than r. This com- munication dominates the imprint on the energy depletion of the sensor nodes. The initial sensor deployment is sparse, but the resulting network is connected