In this section, we suppose that a virtual field is monitored by a CWSN with some POIs. The numbers of nodes and POIs are predetermined by the administrator. Each POI and sensor node is stationary after either random deployment or predetermined deployment. All the simulations are conducted on the MATLAB platform.
4.2. Network Lifetime Prolongation and Coverage Preservation
In this section, we present the evaluation results for the performance of the CoCMA with respect to network lifetime prolongation and coverage preservation. The CoCMA is applied to the sink and used to control the activation or inactivation of sensor nodes to maintain higher coverage ratio of POIs with energy efficiency. In this scenario, we suppose that there are 64 POIs and 400 nodes distributed uniformly in a sensing field with size of 100 m × 100 m. The initial energy for every node is the same, and all the nodes are assumed homogenous. Similarly, the nodes share the same sensing range of 17.675 meters and can communicate directly with the sink and other nodes in the sensing field. The corresponding parameters of this simulation scenario are summarized in Table 2
. Additionally, the nodes are deployed to form a CWSN [26
For the simulation scenario of uniform deployments, the redundant nodes can be efficiently found by the CoCMA and then be inactivated to conserve energy. Figure 8
(left) shows the initial deployment of nodes and POIs. Based on the proposed MA-based schedule for nodes, the redundant nodes are determined and inactivated. The remaining nodes are still activated and they are responsible to perform sensing tasks. Figure 8
(right) shows the coverage for POIs after applying the MA-based optimal schedule. In this simulation case, only 2.75% of nodes need to be activated to cover all POIs, which are all 1-coverage.
shows the random deployment for a CWSN with POIs. The adopted parameters for the network are summarized in Table 2
. In this simulation, there are 100 nodes and 100 POIs randomly deployed in a 50 m × 50 m sensing field, as shown in the left of Figure 9
. At this stage, the CoCMA is not applied yet. We note that there are many redundant sensor nodes in this sample network. After applying the CoCMA to the network, the new coverage for POIs is depicted in the right of Figure 9
. It is clearly seen from this figure that a better energy-efficient coverage is achieved. The simulation data shows that 72% of the sensor nodes have been switched to a sleeping mode by the assistance of the CoCMA. In this case, only 28% of sensor nodes need to be active. This simulation demonstrates that the given CWSN using the CoCMA is able to monitor all 1-covered POIs and achieves a full coverage.
In the next simulations, we will verify the feasibility of applying the proposed algorithm to a CWSN [26
]. The sink node with sufficient power is able to arrange the optimal schedule for every node in the sensing field. When one node exhausts its energy, the wake-up scheme in the CoCMA can determine which neighboring nodes need to be awakened to recover the uncovered POIs. Therefore, the sensing coverage ratio and the number of active nodes are managed simultaneously. To compare the performance of the CoCMA with other four algorithms, simulations were conducted using the same sample network mentioned earlier under uniform deployments of nodes and POIs. The simulation results are compared to those of the Grid-Based Data Gathering Scheme (EGDG) [27
], LEACH-Coverage-U [28
], LEACH [26
] and PEGASIS [29
]. Figure 10
depicts the sensing coverage ratio versus the number of rounds. It clearly indicates that PEGASIS, LEACH-Coverage-U and LEACH provide poor capabilities in maintaining the sensing coverage ratio. The proposed CoCMA maintains the sensing coverage ratio at 100% until the 2,000th
round which doubles that provided by the PEGASIS. The proposed CoCMA and EGDG yielded similar results in the interval between the 2,000th
and the 3,500th
round, but the sensing coverage ratio of the EGDG method decreases to around 97% at the 2,000th
round. The advantage of the proposed CoCMA becomes significant starting from the 3500th
round. Moreover, the network lifetime is prolonged to 5,118 rounds by the CoCMA, which lasts about 1,000 rounds longer compared to the EGDG method.
shows the number of rounds versus the percentage of dead nodes. The EGDG, PEGASIS, LEACH-Coverage-U, and LEACH methods lose their 50% of nodes at the 1950th
, and 300th
round, respectively. The simulation result demonstrates that the proposed CoCMA significantly prolongs the lifetime of the network, even outperforms the EGDG method. We observe that the nodes die rapidly using PEGASIS, LEACH-Coverage-U and LEACH methods, since they do not have node-scheduling strategies. On the contrary, the EGDG and the proposed CoCMA can inactivate the redundant nodes via node-scheduling strategies that save much energy, so the longer network lifetime can be obtained. A longer network lifetime and higher sensing coverage are important for coverage-preserving sensor networks. The simulation results show that the CoCMA is able to achieve these missions. Thereby, the QoS of the network can be improved by using the CoCMA.
In order to compute the energy distribution over the network, a 2-D Gaussian distribution is utilized as a point spread function to estimate the spatial strength of energy. Figure 12
depicts the energy distribution over the entire network at different rounds of the simulation. In CWSNs, cluster heads consume most energy since they are responsible for data collection, fusion and packet transmission with a sink. As a result, the energy density on a specific sensing area will decrease rapidly if a large number of nodes congregate at a sub-region of the sensing field as time passes. If the cluster is far away from the sink, the energy level of the cluster will also be lowered. Since the sink is located at (50, 200) (which is far from the sensing field) in this simulation case, the nodes located at the bottom of the given CWSN exhausted their energy rapidly because of the long distance between these nodes and sink. An inspection of Figure 12
indicates that most sensor nodes have died in the sensing field from the sub-figure of the 4,900th
round except for the sensor nodes at the top of the sensing field.
Clearly, the position of a sink will significantly affect the distribution of the remaining energy for the entire CWSN. If the sink is placed at the center of the CWSN, the remaining energy will be distributed over the field evenly, which can be seen in Figure 13
. Since the average distance between nodes and sink in Figure 13
is less than that in Figure 12
, the total remaining energy in Figure 13
is higher than that in Figure 12
. Hence, the network lifetime in Figure 13
is longer than that in Figure 12
. In addition, the energy congregated in the surrounding regions of the sink as time passes because the distance between these nodes and the sink is shorter. In Figure 13
, some light red regions appear at the 500th
round, which implies that some of the active nodes have consumed much energy. At the 1500th
round, the original light red regions extended outward, and their centers become yellow-green representing lower remaining energy. After the 1,500th
round, we observe the same phenomenon in the extension of light regions. This is because the nearby nodes were awakened to replace the original dying ones. Thus, these awakened sensor nodes will start consuming energy and result in the extension of light regions.
Through the proposed CoCMA, preserving the sensing coverage and prolonging the lifetime can be carried out successfully in the CWSN. In CWSNs applying the CoCMA, the position of the sink will affect the performance of the CWSN according our simulation results. The spatial distribution of energy is more even if the position of the sink approximates the geometric center of the CWSN. This is caused by the innate restriction in CWSNs. We will keep studying the related issues in the future.