- Research Article
- Open Access
Collaborative Event-Driven Coverage and Rate Allocation for Event Miss-Ratio Assurances in Wireless Sensor Networks
© H. Ozgur Sanli and H. Çam. 2010
- Received: 2 November 2009
- Accepted: 30 March 2010
- Published: 10 May 2010
Wireless sensor networks are often required to provide event miss-ratio assurance for a given event type. To meet such assurances along with minimum energy consumption, this paper shows how a node's activation and rate assignment is dependent on its distance to event sources, and proposes a practical coverage and rate allocation (CORA) protocol to exploit this dependency in realistic environments. Both uniform event distribution and nonuniform event distribution are considered and the notion of ideal correlation distance around a clusterhead is introduced for on-duty node selection. In correlation distance guided CORA, rate assignment assists coverage scheduling by determining which nodes should be activated for minimizing data redundancy in transmission. Coverage scheduling assists rate assignment by controlling the amount of overlap among sensing regions of neighboring nodes, thereby providing sufficient data correlation for rate assignment. Extensive simulation results show that CORA meets the required event miss-ratios in realistic environments. CORA's joint coverage scheduling and rate allocation reduce the total energy expenditure by 85%, average battery energy consumption by 25%, and the overhead of source coding up to 90% as compared to existing rate allocation techniques.
- Sensor Node
- Wireless Sensor Network
- Neighboring Node
- Cluster Member
- Rate Allocation
Wireless sensor networks usually consist of a large number of sensor nodes that collaboratively gather data from a target region of interest. A sensor network is expected to provide event miss-ratio guarantees, while minimizing the energy consumption of its sensor nodes. The miss-ratio for an event is equal to the fraction of those occurrences that are missed by wireless sensor nodes in a given time interval. While a sensor network can dynamically adapt its aggregate coverage in response to event misses, providing such assurances with minimum energy consumption is not easy due to the following problems. First, event occurrences may follow a nonuniform distribution. Consequently, two sensor nodes may detect a different number of events in a given interval even though their contribution to aggregate coverage of the network is the same. Second, minimizing the number of active nodes by reducing the overlapping areas among sensing regions of neighboring nodes often sacrifices their correlation. However, sensor data correlation can be exploited by distributed source coding for reducing the energy spent for data transmission which is a major component in overall system energy consumption [1, 2]. Inspired by these problems, this paper is the first of its kind to exploit the relationship between coverage scheduling (i.e., deciding which sensing unit(s) of each node should be activated) and source coding rate allocation (i.e., determining the number of bits that a sensor node should transmit for its sensor reading), and to show how they should cooperate. The main contributions of this research include the following.
We demonstrate the existence of an ideal correlator radius within cluster of sensor nodes around which on-duty node selection will minimize the amount of data sensed and transmitted under both uniform and nonuniform event distributions.
CORA (COverage and Rate Allocation) Protocol is proposed to exploit ideal correlator radius concept via cooperation between event-based scheduling and rate allocation. CORA schedules on-duty nodes to achieve better accuracy on the coverage of the monitored area than existing algorithms and provides event miss-ratio assurances even when nodes have no location information and there's noise in sensing ranges of nodes.
This paper develops a novel technique called differential data transmission to remove those data redundancies which cannot be eliminated via distributed source coding. Nonredundant data gathered by distributed source coding are compared with known reference data and then only its difference from the reference data are transmitted to the clusterhead.
We noticed that certain cluster members are critical for meeting the required event miss-ratios energy efficiently due to their following two features. First, by taking advantage of their location in cluster topology, they can apply distributed source coding for other cluster members and significantly reduce data redundancy. Second, due to their proximity to the hotspots (i.e., regions with high event occurrence probabilities), they can detect majority of the event occurrences which are condensed around hotspots. Therefore, in CORA, rate allocation requests coverage scheduling to activate such critical cluster members within the ideal proximity of the clusterhead which we refer as correlator radius. Similarly, coverage scheduling helps rate allocation assign various rates to sensor nodes by determining which neighboring sensor nodes of a given sensor node should be put in sleep mode.
CORA's rate allocation first identifies the uniform/nonuniform distribution of event occurrences using a continuously updated histogram. Under nonuniform event distribution, those hotspot locations where the event occurrence probability is very high are determined. For both uniform and nonuniform event distributions, we then demonstrate the existence of an ideal radius around the clusterhead where the source coding gain is maximized. At the distance of ideal radius from clusterhead, our rate allocation algorithm forms groups of active nodes. Each group has a so-called correlator node that gathers data from its group members and transmits the aggregated data to its clusterhead. To further improve data reduction, we propose a new differential data transmission technique for correlator nodes. To the best of our knowledge, there is no previously known technique that assigns rates in accordance with the occurrence of the observed phenomena.
CORA's coverage scheduling is unique of its kind in guaranteeing the miss-ratio required for each event without any need for location information of nodes. While activating less number of nodes than existing coverage scheduling algorithms to provide such assurances, coverage scheduling also relieves sensor nodes computation and storage overhead. To be of practical value, the coverage scheduling algorithm takes into account the effects of realistic environments on coverage such as the changes in sensing ability of nodes and the noise in distance measurements between node pairs.
The preliminary results of this research have appeared in [3, 4]. The rest of the paper is organized as follows. The related work is presented in Section 2. In Section 3, system model and assumptions are given. The problem we tackle is formally described in Section 4. Section 5 presents the CORA protocol. The performance evaluation of CORA is discussed in Section 6. The concluding remarks are made in Section 7.
Coverage scheduling algorithms and distributed source coding techniques are usually addressed as two separate research fronts in the literature. CORA distinguishes itself from the existing solutions in closing the loop between these two research fronts.
Coverage scheduling in wireless sensor networks has received increasing attention in the recent years [5–15]. Some research efforts have considered coverage scheduling in conjunction with connectivity requirements [16, 17], bandwidth constraints , and target localization . But, in none of the previously known coverage scheduling techniques, the impact of coverage scheduling on distributed source coding is addressed, even though distributed source coding efficiency depends on which set of sensor nodes gather data for an event. In comparison, CORA identifies the nodes that are critical for distributed source coding by keeping track of the event distribution. Then, only such critical nodes are put on duty to provide the variable coverage needed over the monitored area for minimizing event misses. CORA also uses less number of active nodes to meet the coverage needed than existing coverage scheduling techniques .
Distributed source coding [20, 21] and explicit communication  reduce the amount of data transmitted by taking advantage of the correlation structure between the sensor measurements. Let and be the data sources at two neighboring sensor nodes that have overlapping sensing areas and belong to the same cluster. In case of distributed source coding, the data of are sent as to the clusterhead which then informs about the correlation structure between the data of and . Finally, the data of are encoded as with side information in accordance with the Slepian-Wolf theorem . In case of explicit communication, the node with source receives explicit side information from the node with source and decodes . Then, the data of is encoded as and sent to their clusterhead in addition to .
Recently, rate assignment algorithms are developed to determine the coding rates to be used by each sensor node [22–24]. These rate assignment algorithms usually focus on minimizing energy consumption and do not consider the overhead of distributed source coding. In CORA however, cooperative coverage scheduling and rate assignment activates and maintains correlation information only for those critical nodes that would maximize the energy saving of distributed source coding.
The main drawback of aforementioned existing rate allocation algorithms is that they usually assume uniform distribution of event occurrences. In our work, we address both uniform and nonuniform event distributions and show the ideal correlator radius for each case. In light of the ideal correlator radius, our rate allocation algorithm partitions on duty nodes into groups, each with a designated correlator node, and assigns rates within each group in response to the uniform/nonuniform distribution of event occurrences.
Rate allocation and network lifetime are very related to each other. For instance, in , rate allocation in wireless sensor networks is addressed under a given network lifetime requirement. This research work advocates the use of lexicographical max-min rate allocation for all sensor nodes in order to avoid a severe bias in rate allocation when the objective is to maximize the sum of rates of all nodes.
Event Miss-Ratio Assurances
The problem of event-misses have been addressed by several node level scheduling algorithms [26–29]. The objective of such research efforts is to keep a node in low power modes as much as possible while processing event occurrences within their deadlines. However, these existing techniques do not provide any event miss-ratio assurances and schedule events at a sensor node separately from its neighboring nodes. Such algorithms can not relieve a sensor node from processing those events that are also detected by its neighboring nodes. In comparison, CORA assigns each event only to some sensor nodes within a group of cluster members. Therefore, CORA significantly reduces the event processing load at each node.
Network and Node Model
This paper focuses on dense wireless sensor networks which consists of multiple clusters that are formed based on node residual energy levels. We consider single hop clusters where each cluster member is within the direct transmission range of the clusterhead. The sensor nodes are static and have the same hardware capabilities. The distance between any two sensor nodes within a cluster is assumed to be known based on standard methods such as Received-Signal-Strength-Indicator (RSSI) and Time of Arrival (ToA).
We consider the recent commercial off-the-shelf sensor nodes that have multiple sensing units . The sensor nodes do not have an attached GPS device but their positions within a cluster are estimated based on internode distances. The power control circuit of each sensing unit enables putting it in sleep or active mode . The sensing unit consumes constant power level in active mode. The radio unit has dynamic transmission power control capability which enables sensor nodes to control their transmission range under the effect of environmental factors and multipath propagation. In order to transmit bits between two sensor nodes separated by a distance , the required power is modeled as for transmission and for reception where and are constants .
Coverage and Event Model
In sensor networks, different types of coverage models exist, (i) the monitored area consists of a discrete set of points and each point should be within the sensing region of at least one active node , (ii) the monitored area is a predefined contiguous region and the active sensor nodes should be covering each point within this region , and (iii) the monitored area is a contiguous region defined by the union of all individual nodes' sensing regions, and a subset of active nodes should cover the sensing regions of others . In this paper, we focus on the third coverage model. We consider both uniform and nonuniform event distribution models. In uniform case, each node has the same event occurrence probability. To model nonuniform event distribution, however, random hotspot locations are created within the monitored area, and event occurrences follow the Gaussian distribution around these hotspot locations.
Data Correlation Model
For active sensor nodes that participate in source coding within the same group, a vector of entries keeps the mean values of their sensor readings and an matrix maintains their correlation. , where the random variable corresponds to the source sensor readings at active cluster member for . denotes the correlation degree between and active cluster member for .
The existing research efforts consider only internode distances while modeling sensor data correlation . Given a set of nodes , their model calculate the nonredundant data produced by node based on the minimum Euclidean distance between node and any node for . However, this distance-based approach faces problems when node is very close to more than one member of the node set . In this case, the amount of nonredundant data generated by is actually much less than the amount that is approximated by their model. To address such cases and represent correlation more accurately, this paper models the data correlation among sensor nodes based on the overlapping area of their sensing regions. Specifically, the data correlation of active cluster members and is expressed as , where and denote the sensing regions of and active cluster members, respectively, for and is the sensing range.
The transmission range of any given node.
The sensing range of any given node.
The sensing region of any given node.
An event to be sensed by the wireless sensor network.
The set of sensor nodes in any given cluster.
The number of sensor nodes in any given cluster.
The entropy of any given random variable .
There are two extreme cases for . At one extreme, the observed event miss-ratio is becoming equal to one when all event occurrences are missed. The other extreme occurs when all event occurrences are detected, thereby making the observed event miss-ratio equal to zero.
We first give in this section an overview of how CORA provides event miss-ratio assurances with minimum energy consumption. The network lifetime is divided into consecutive rounds and CORA performs coverage scheduling and rate assignment at the beginning of each round. The round-based operation enables CORA to take the recent node residual energy information into account. At each location , coverage scheduling and rate assignment control and respectively, for . Meanwhile, CORA enables event-driven cooperation of coverage scheduling and rate assignment in two phases.
In Phase II, those critical nodes which significantly improve the energy consumption of the cluster are identified from their assigned rates. Coverage scheduling then activates only those critical nodes for improving energy consumption and meeting the required event miss-ratios. Since Phase I differs for uniform and nonuniform event distributions, the steps of Phase I are described next separately for uniform and nonuniform event distributions.
5.1. Phase I for Uniform Event Distribution
The expected amount of data generated by each node is the same under uniform event distribution. Therefore, the locations of sensor nodes within the cluster topology plays the key role for overall energy consumption. In this section, we study how distance of correlator nodes to the clusterhead affects the energy expenditure. Let us refer to this distance as where the subscript denotes the correlator radius and . Let be the ideal value which maximizes the energy gain of distributed source coding by correlator nodes relative to the case when cluster members transmit data directly to the clusterhead. In what follows, we will explain how is found in Step I of Protocol CORA by formulating this energy gain as a function of . We will then describe the allocation of coding rates in accordance with .
We now analyze the energy consumption for a cluster of wireless sensor nodes when distributed source coding is applied at a given distance from the clusterhead (i.e., when correlator nodes have distance to the clusterhead). The first question we answer in this analysis is the following: For which cluster members would correlator nodes yield any improvement in energy consumption? Especially within the close proximity of the clusterhead, transmitting data directly to the clusterhead (i.e., bypassing the correlators) should be preferable over sending data through the correlator nodes.
To identify , we find the relationship between and . Specifically, we consider the energy consumed for gathering data from any cluster member with distance to the clusterhead where . Note that the energy consumption of node should be the same in direct data transmission and distributed source coding cases since node is located on the boundary of . Let us refer to the energy consumption of node when it uses direct transmission to the clusterhead as . Let be the energy consumption of when its data reach the clusterhead indirectly through correlator nodes.
When correlator nodes apply distributed source coding at distance from the clusterhead, sends only nonredundant data to the correlator node which are then forwarded to the clusterhead. Thus, we next consider the expected amount of data transmitted by node when data redundancy is eliminated by distributed source coding.
where represents the ratio of the rate control message size to the data message size. First, rate information is transmitted from the correlator to node over distance . Then, node codes sensor data with the assigned rate. The data received by the correlator node are transmitted to the clusterhead over distance .
Let refer to the value substitution of which to (7) and (11) yields the same energy consumption for and , respectively. The term is included in subscript to indicate that is dependent on the value. Since refers to the radius of , a cluster member uses direct data transmission to the clusterhead if its distance to the clusterhead is less than . Otherwise, the data of the cluster member are sent to its designated correlator node. In light of this selection criteria for data transmission, we next define the energy gain function of correlator nodes for any given value.
Algorithm Coding Rate Assignment describes how rate allocation groups are formed under guidance of and rates are assigned to group members at Step II and Step III of Protocol CORA, respectively. To select the correlator nodes, the average residual energy of cluster members is calculated first. A cluster member within becomes a correlator node if (i) it has higher residual energy than the average residual energy, and (ii) its distance to the clusterhead is closer to than any one of its neighboring nodes. The remaining active nodes join the group of the closest correlator node.
Within each group, the data transmissions from group members to the correlator node are ordered in descending order of node residual energy levels. Thus, a sensor node uses the data of other group members that have higher residual energy as side information. Note that, having a high residual energy node transmit data earlier than others does not guarantee that it will use the highest coding rate. This is especially true when such a node has highly overlapping sensing region with other high energy nodes that have already transmitted data. However, when a high energy node uses low coding rate, its residual energy would not change significantly while other nodes would be spending more energy. Consequently, CORA would schedule such group members to transmit data earlier than others in the following rounds.
The correlator node applies the proposed differential data transmission technique to further reduce the size of the data before sending it to the clusterhead. The basic motivation behind differential data transmission is that the data gathered from group members exhibit not only spatial correlation but also temporal correlation. To exploit the correlation in temporal domain, both clusterhead and correlator node maintain a common reference data which captures the characteristic values of last data values transmitted from the correlator node to the clusterhead.
Let be the reference data and be the raw nonredundant data gathered at the correlator node. Let denote the difference operator and refer to its inverse. As an example, if the sensor readings are mapped to numbers than and may correspond to the subtraction and addition, respectively. Then, the correlator node transmits the differential data with the value . After receiving , the clusterhead recovers the original raw data .
5.2. Phase I for Nonuniform Event Distribution
In the analysis, we do not follow a particular event distribution but assume that event occurrence probabilities are nonzero within the local proximity of hotspots only. In particular, let be the event occurrence probability at a point with distance to the hotspot center. can be calculated either by using exponential and Pareto distributions depending on the nature of the event occurrences. In exponential distribution, the probability density function is where the event occurrence probability decreases rapidly as the distance to the hotspot center increases. For those hotspots with a slowly decreasing effect, Pareto distribution is more suitable due to its density function where is the shape parameter and is the initial value.
As in uniform event distribution analysis, we will express the energy gain function in terms of where . However, unlike the uniform event distribution case, the analysis is shaped by those locations near hotspots that have nonzero event occurrence probabilities rather than all cluster locations. Let us refer to the maximum distance between the center of the hotspot and any node within the hotspot as the hotspot range and denote it by .
In computation of both and , we will consider each possible distance that a sensor node can have to the hotspot center where . In Figure 5, a red circle labeled illustrates the possible node locations with distance to the hotspot center. For the upper semicircle of as an example, such locations are defined by the coordinates where and . Let denote the expected energy consumption of the nodes located on when they use correlator nodes. Let refer to the expected energy expenditure of the same nodes on when they apply direct data transmission to the clusterhead. We will integrate and over all within the hotspot to find and , respectively.
and . In words, when there is a sensor node on , the probability of event data transmission from is equal to the probability that the node is placed at (i.e., ) and the probability that an event occurs (i.e., ). The energy consumption for sending data from to the clusterhead consists of two components. First, the data are transmitted from to the correlator node over distance , and then sent by correlator node to the clusterhead over distance .
Given a set of hotspots with the local ideal correlator radius for the hotspot referred to as , the global ideal correlator radius is set as where . Once the value of is obtained, the selection of correlator nodes and their group members are the same with the uniform event distribution case. Within a group however, the rates are allocated based on the proximity of nodes to the hotspots. Specifically, a sensor node uses the data of other group members that have larger distances to the hotspot as side information. The motivation here is that those group members that are near the hotspots consume more energy due to their high event detection activity. Thus, CORA alleviates the burden on such nodes by increasing their side information in distributed source coding.
5.3. Phase II for Both Uniform and Nonuniform Event Distributions
This section presents the steps of Phase II that are implemented in the same way for both uniform and nonuniform event distributions. CORA employs a priority-based coverage scheduling technique to select the active sensor nodes for each event . In coverage scheduling, a node that is considered earlier for activation has better chance of going to sleep mode since those nodes that are waiting to be activated can still meet the required event miss-ratio. Based on this observation, CORA implements node priorities to have critical nodes considered later in coverage scheduling, thereby forcing them to remain active. In Step IV, each cluster member is associated with a score that reveals how much it improves the energy expenditure of the cluster for . is computed by estimating the rates within the neighborhood of node for the following two cases:
The rate predictions are made according to the proposed rate assignment algorithms. Then, is set as the ratio of the expected average residual energy of the neighboring nodes for Case I to the one for Case II. Consequently, the critical nodes get assigned the highest scores.
Algorithm Coverage Scheduling implements Step V of Protocol CORA for the assignment of events to cluster members. The clusterhead first sorts the nodes in ascending order of their scores such that low score nodes are considered first. Then, each node is evaluated for coverage scheduling in the order of its score where . The status of node for event is determined according to its sensing region's overlap with the sensing regions of its neighboring nodes. To control event misses, either (i) node remains active and covers the physical area within its sensing region on its own, or (ii) node goes to sleep mode and lets its neighboring nodes provide coverage over its sensing region.
Once its eligible set of neighboring nodes are identified, node computes the uncovered part of its sensing region for event denoted by where . If , then node goes to sleep mode for event since its sensing region is covered sufficiently by neighboring nodes that would remain active. Otherwise, node is scheduled active to preserve for event .
where . Consequently, CORA provides variable coverage over the monitored area with the worst and best coverage defined by the following two cases. When is equal to , node meets the required event miss-ratio threshold by setting . On the other hand, if is equal to , then node detects all event occurrences by using .
Proof of Lemma 1.
The proof is given in the Appendix.
We next present an efficient algorithm to compute the area covered within a node's sensing region by its neighboring nodes. The coverage computation is used to verify that a node's sensing region has sufficient overlap with its neighboring nodes before the node goes to sleep mode. While computing coverage, a sensor node considers its neighboring nodes in ascending order of their identifiers. For each neighboring node, the area of intersection that's not covered by lower id nodes is determined. This helps the overlapping areas between the sensing regions of neighboring nodes to be considered only once.
For a given neighboring node, the segment with the lowest id that intersects with the sensing region of the neighboring node is determined. Then, for each segment with a higher id than the lowest id segment, the covered part of the segment by the neighboring node is represented with an angle . This angle is defined with respect to the intersection points of the segment and the neighboring node's sensing region as illustrated in Figure 6. By finding the overlap with the sensing regions of all neighboring nodes, the covered portions of any given segment are represented with a set of angles where . The covered portion of segment is found based on the comparison of and the sum of magnitudes for the angles in . The total covered portion of the sensing region is determined by incorporating each segment in proportion with its area.
The accuracy of the proposed coverage computation technique improves with increasing number of segments. However, using a large number of segments also comes at the expense of higher computational and storage overhead. We next describe how the overhead of the segment-based representation is minimized. The key idea is to limit the application of segment-based division scheme to specific parts of the sensing region only. The remaining parts of the sensing region are divided into subregions at a higher level of granularity than segments, specifically into four quadrants. These quadrants have the special property that the full coverage of each quadrant can be verified in a very simple manner. The segment-based representation is then applied only within those quadrants for which the full coverage cannot be verified.
Figure 7(b) shows these three fields separately for each one of the quadrants where the quadrant name is indicated in parentheses and appended to the field name.
Let us also consider the random placement of more than one neighboring node over the quadrant. In this case, either one of the conditions below satisfies full coverage of the quadrant.
We next give an important lemma which describes the coverage relationship between different quadrants of the same sensing region. The key observation here is that the existence of active neighboring nodes in a quadrant can compensate for the uncovered parts of its adjacent quadrants. Two quadrants are referred to as adjacent if they have a common boundary of length and they constitute a contiguous .
Let and be the set of active nodes that are located over the adjacent quadrants and , respectively, where , and . An element of placed in field has an equal coverage effect on as compared to an element of placed in . This property is also symmetric and is applicable to fields of adjacent quadrants.
Proof of Lemma 2.
The proof is available in the Appendix.
From Lemma 2, Condition 2 for the full coverage of each quadrant is rephrased as follows. Let the neighboring subregions of be denoted by and according to the type of adjacent field that has with its neighboring quadrant where and .
The remaining question that needs to be answered is how to determine the field and the quadrant that a neighboring is located.
We next address the effect of environmental factors on coverage computation. The changes in weather and ground status result in differences between the effective sensing ranges of nodes and their nominal sensing ranges. To incorporate effective sensing ranges into coverage computation, CORA considers both single-node detectable and multi-node detectable environmental factors such as humidity level and rain, respectively. Let denote the set of all such environmental factors taken into account by the sensor nodes. For each sensing unit, a correction constant is associated with environmental factor for where . The magnitude and sign of each correction constant is determined by experimental evaluation with sensor hardware and different settings for the environmental factor it is associated with. The negative and positive sign for represents the degradation and improvement in the sensing ability of the sensing unit due to the environmental factor, respectively, .
Once the effective sensing range is determined, it is broadcast to the neighboring nodes at the beginning of each round before the coverage scheduling takes place. In order to check the full coverage of its quadrants, a sensor node compares its distance to each one of its neighboring nodes with its effective sensing range. Then, among those neighboring nodes within the sensing region, the sensor node considers only the ones which have larger effective sensing ranges than itself while checking Condition 1 and Condition 2. For the quadrants that are not fully covered, the coverage of the segments are determined based on the intersection of effective sensing ranges of the node itself and its neighboring node.
5.3.1. Noise in Internode Distance Measurements
The clusterhead uses the distances between pairs of cluster members to create a local coordinate system. The coordinate assigned to each cluster member is not exact and has an error margin . This section considers how such errors in node location estimates can be incorporated into coverage computation. In the absence of exact node coordinate information, we express coverage and event miss-ratio in terms of expected values rather than absolute values.
Let us represent the maximum deviation on the and coordinates of sensor nodes as and , respectively. A cluster member with coordinate estimates may be located at any point within the ellipsis bounded by a box of width and of height . In what follows, we denote the set of all candidate points for the exact location of the node in this ellipsis by .
Let us now consider the coverage of any given cluster member 's sensing area by its neighboring nodes where . We refer to node 's location as and the neighboring nodes that can contribute to the coverage of its sensing region as the set . In the case of location errors, we define two sets of angles for the coverage of each segment within 's sensing region. These sets of angles, namely, and , represent the worst and best possible cases for the coverage of the segment by the neighboring sensor nodes in , respectively, where .
CORA is evaluated in terms of event miss-ratios, total energy expenditure, average node battery consumption, and the storage/update overhead for the application of source coding. In the simulations, we consider sensor networks with multiple clusters in which the clusters are formed based on residual energy of sensor nodes. Each network is simulated for a single round with data gatherings. For uniform event distribution, every sensor node within a cluster is assumed to detect an event per data gathering. For nonuniform event distribution, we have randomly selected hotspot centers. At each data gathering, the event occurrences are modeled around these hotspot centers following the Gaussian distribution.
In the simulation graphs, the results for uniform and nonuniform event distributions are illustrated with solid and dashed lines, respectively. Each point shown in the graphs is obtained by averaging the simulation outcomes from a large number sensor networks. The sensor networks are deployed randomly over a target area of side length 100 m. The number of sensor nodes in each network is selected from the interval [ ] to control node/coverage redundancy. As network population increases, the sensing ranges of nodes overlap more, thereby increasing both the number of nodes redundant for coverage and the data correlation among neighboring sensor nodes.
The sensing range and transmission range of the nodes are set to 10 m and 30 m, respectively. The initial residual energy level of each node is assigned as 10 Joules. To calculate the energy expenditure, we have used the radio power consumption model described in Section 3 with settings and . The entropy of the source at a single cluster member is modeled as bits. The ratio of the control message size to the data message size is set to . Unless specified otherwise, the required event miss-ratio threshold is equal to .
Figure 8(b) illustrates the empirical verification of the assurances provided by CORA under different event miss-ratio requirements. For a particular value, a large number of simulations are run while keeping the network population constant. The observed event miss-ratios (shown in axis of Figure 8(b)) always remain below the required event miss-ratios (illustrated in axis of Figure 8(b)).
CORA assigns rates to the active nodes only and therefore is highly scalable. Figure 8(c) shows the results of the simulations that are run with the same required event miss-ratio value but with varying number of nodes. Even though the network population increases, CORA limits the number of nodes that are scheduled active and get assigned rates. Having only a smaller set of nodes active prolong the time event miss-ratio assurances can be met as those sensor nodes which are put in off-duty mode can save their energy to go active in the subsequent scheduling phases.
The simulations in Figure 8(c) also evaluate the accuracy of Algorithm Coverage Computation. For comparison, we have used the sponsored sector-based coverage computation technique proposed in  which we refer to as in the results. considers those neighboring nodes that are located within the sensing region of a node while checking whether the node's sensing region is covered by its neighboring nodes. If the node's sensing region overlaps sufficiently by its neighboring nodes, then it becomes eligible to go off-duty. In particular, for a neighboring node with distance closer than the sensing range represents the region covered by the neighboring sensor node with a central angle which is referred to as the sponsored sector where . If the union of sponsored sectors by all neighboring nodes is equal to , then the sensing region of the node is covered completely by its neighboring nodes and the node becomes eligible for going to sleep mode.
Figure 8(c) shows that CORA approximates the coverage of the monitored area with better accuracy than and hence activates less number of nodes to meet the same coverage requirement. This is attributed to the fact that, CORA can consider neighboring nodes of a sensor node during coverage computation, including those with a distance to the sensor node larger than the sensing range . While achieving more accurate coverage computation, CORA introduces little overhead with the help of its quadrant-based and segment-based coverage computation techniques described in Section 5.3.
To evaluate the performance of differential data transmission, we consider the energy spent for gathering data from the correlator nodes to their clusterhead. We model the sensor data sent by correlator nodes as numbers, and define arithmetic subtraction ( ) as the difference operator and arithmetic addition ( ) as its reverse. The reference data used between the clusterhead and a particular correlator node is calculated based on the arithmetic average of the data values transmitted by that correlator node. The data value to be transmitted from a correlator node is selected as 100 plus a random differential data value for each data gathering. The upper bound used in each experiment for the differential data values are shown in the axis of Figure 9(c). When upper bound value is 40 as an example, the sensor data sent by correlator nodes are selected randomly from the range [ ].
Let us consider a scenario where the reference data value used by a particular correlator node is 110, and the correlator node has sensor data value of 130. The correlator node would apply the difference operator (subtraction) with respect to the reference data and would send 20 as the differential data value. Clusterhead would then recover the original data 130 using the same reference data value by applying the inverse operator (addition) to the reference data value 110 and the differential data 20 received from the correlator node. CORA in Figure 9(c) refers to the energy consumption when original data values, such as 130 in the example above, are sent by the correlator nodes without any differential data transmission. As simulation results show, differential data transmission takes advantage of the temporal correlation of data sent by correlator nodes to the clusterhead for reducing energy consumption.
Master-Slave Rate Assignment. This rate allocation represents the low efficiency, low-overhead end of all possible rate assignments to sensor nodes. The cluster members code their data according to their correlation with the data of the clusterhead. For clarity, let us label active node as the clusterhead. Then, master-slave rate assignment allocates rate to active node for . This rate allocation has low storage overhead as only the correlation between the data of each cluster member and the clusterhead needs to be maintained. However, while being scalable, Master-Slave scheme sacrifices the potential gains achievable from the correlation of a cluster member with other cluster members for .
Clustered Slepian-Wolf Rate Assignment. This rate assignment has the characteristic of efficient rate allocation at the cost of high storage overhead of correlation information. The cluster members code their data based on their correlation with the cluster members that have higher residual energy. Let us consider active nodes as labeled in descending order of their residual energy where active node has more remaining energy than for with clusterhead having the highest residual energy. Slepian-Wolf Rate Assignment then assigns rate to active node . To achieve this rate allocation, data correlation need to be maintained between node and node(s) . Consequently, clustered Slepian-Wolf brings significant storage/update overhead in order to increase the side data available to sensor nodes.
Figure 10(a) shows the total energy consumption of sensor nodes when CORA Protocol, Master-Slave, and Clustered Slepian-Wolf schemes are applied on the same network instances. As node redundancy increases, CORA's ability to identify noncritical sensor nodes and to put them off duty yields better energy consumption than Clustered Slepian-Wolf. As expected from the increase in side data, Figure 10(b) shows that node redundancy improves the average battery consumption for both CORA and Clustered Slepian-Wolf. For Master-Slave, the battery consumption of nodes remains almost the same since data is coded at each cluster member without considering its correlation with other cluster members. In terms of storage/update overhead, CORA gets close to Master-Slave with the increase in node density as illustrated in Figure 10(c).
In this paper, we have addressed the problem of meeting event miss-ratio requirements with minimum energy expenditure. We identify a graceful cooperation between coverage scheduling and rate allocation techniques, which are addressed separately in the literature. For both uniform/ nonuniform (hotspot) event distributions, we show the existence of a radius within the cluster for ideal application of distributed source coding. With the help of this radius, rate allocation guides coverage scheduling to activate only those cluster members that are critical for minimizing energy consumption. The coverage scheduling technique of the proposed protocol CORA assists rate assignment by enabling overlapping sensing regions and correlation among neighboring nodes while minimizing the overhead of distributed source coding. In comparison with existing rate allocation algorithms, CORA significantly improves energy expenditure by 85%, node battery energy consumption up to 25% and source coding overhead by 90%.
This paper has addressed the impact of environmental factors on the size of circular-shaped sensing regions. As for future work, identifying the irregular-shaped sensing regions due to environmental factors and considering their effect on event miss-ratio assurances remain interesting and challenging problems.
Algorithm 2: Protocol CORA.
Each clusterhead finds its correlator radius.
Those cluster members that are located around the correlator radius form rate assignment groups, each consisting of a correlator node and a number of sensor nodes.
Each clusterhead identifies the critical nodes of rate assignment groups, based on their assigned rates.
Algorithm 3: Coding rate assignment.
Algorithm 4: Coverage scheduling.
Algorithm 5: Coverage computation.
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