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1. Introduction
Internet of Things (IoT) [1–3] and cloud computing [4–6] leverage the ubiquity of sensor-equipped devices (e.g., smart portable devices, smart sensor nodes) to collect information at low cost and provide a new paradigm for solving the complex sensing applications from the significant demands of critical infrastructure such as surveillance systems [7–10], remote patient care systems in healthcare [11, 12], intelligent traffic management [13], automated vehicles in transportation environmental [14], and weather monitoring systems. Data gathering is one of the important functions for such sensor based networks, in which each smart sensor based device is sensing information from the surrounding environment and sends data to the sink via multihop routing. After data have been acquired, the sink can make decisions about perceived events, thus bringing smart life to people [8, 10, 11]. But, due to the limited energy of sensor devices, how to ensure that data is gathered to the sink quickly as well as in an energy-efficient way is an important issue [15–17].
Although the smart sensor based devices (e.g., wireless sensor nodes, smart phone, and tablets) have powerful computing and communication capabilities, the data produced are getting more and more as the scope of application expands and the variety increases, so the energy of the device is drained out much faster than before. Green communications play a critical role in or are expected to be an indispensable part of the smart sensor based networks [1, 10, 13, 18]. Due to the diversity and complexity of applications running over the networks, green communications under delay or quality of service guarantee in such networks gain increasing attention in the research community [19, 20] and face a great challenge.
(1)
The challenge of green communication: communication consumption is the largest energy consumption in sensor based networks, so the key to green communications is to effectively reduce the energy consumption in communication. Overall, the main ways to reduce energy consumption are as follows:
(a)
Using asynchronous sleep/waking working mode can effectively reduce the energy consumption of nodes. In this mode, the sensor nodes periodically rotate between sleeping and waking states, and the ratio of the duration that the nodes are in the waking state to the unit cycle is called the duty cycle [11, 21]. In general, the energy consumption of the node in the sleeping state is 1/100–1/1000 of that in waking state [8, 12, 21]; thus, in order to save energy, the node should be in sleeping state as much as possible. However, the node can communicate only when it is in waking state, so the small duty cycle of node can reduce its energy consumption effectively, but when it has data to send, it needs to wait for the waking up of the receiver to communicate. And the time required from the preparation of data at the sender to the waking up at the receiver is called sleeping delay. This means that to reduce the data transmission delay it is necessary to increase the duty cycle, but at the same time it will increase the energy consumption of the node, so how to balance the delay and energy consumption is a tricky problem.
(b)
Another way to effectively reduce energy consumption is data fusion. Reference [22] points out that event information has relevance both in time and in space, so data fusion can be used to fuse multiple event information, thus greatly reducing the amount of data and reducing energy consumption. From the perspective of reducing energy consumption, more data packets should be fused so that the redundancy is minimized. Although a lot of routing strategies for data fusion have been proposed, most of them have not been applied to sensor networks. What is more, applications often require that the delay of the data’s arrival is less than a specified threshold. For example, in a fire monitoring system, “alarm” message is required to be transmitted to the control center as soon as possible; otherwise it will cause significant losses. The same situation exists in the medical systems, the sensor node attached to the patient is generally used to record the patient’s daily vital signs, but, in an emergency where the patient’s heart rate changes dramatically, the “alarm” packet of the data needs to be transmitted to the doctor in time to take the appropriate first-aid measures [5, 12, 23]. Obviously, in such cases, the strategies proposed for maximizing data fusion are not able to ensure that the delay satisfies the requirements. Therefore, it is another important issue to make the data fusion under the precondition of guaranteeing the application delay and reduce the network energy consumption effectively.
(2)
The delay or quality of service (QoS) guarantees for sensor networks: as an important part of information infrastructure, sensor based networks often collect data for a variety of applications. Different applications have different requirements for QoS, and even the same applications may have different requirements due to the different events [20, 22], where delay is the basic metrics in data gathering. Generally speaking, delay-sensitive data requires a small delay to the sink, and delay-tolerant data can tolerate a certain delay. The best way to optimize network performance is to use a differentiated service to distinguish different transmissions to meet application requirements. But, to the best of our knowledge, there is no research that is able to provide differentiated services in green communications.
In summary, how to combine the settings of duty cycles with data fusion technologies and differentiated services to establish efficient routings to ensure energy-efficient and delay-guaranteed data gathering is of great significance and also has broad application prospects. Thus, a delay Differentiated Services based Data Routing (DSDR) scheme is creatively proposed, in which the energy consumption can be reduced and delay can be guaranteed for applications. The contributions of the DSDR scheme are as follows.
(1) The DSDR scheme has high energy efficiency and reduces transmission delay greatly by establishing energy-efficient routing paths (E2RPs). First, multiple E2RPs are established in different locations of the network to forward data, and the duty cycles of nodes on E2RPs are increased to 1, so the data is forwarded by E2RPs without the existence of sleeping delay, which greatly reduces transmission latency. What is more, the E2RPs are built in the region with adequate residual energy, and the residual energy of nodes on E2RPs is periodically measured, and once the remaining energy is below the threshold, the E2RP where the node located is immediately removed. Therefore, the DSDR scheme enhances the energy utilization of network effectively without affecting the network lifetime.
(2) The DSDR scheme adopts delay differentiated services, which makes the scheme have a better performance under the premise of guaranteeing the service quality. In DSDR, for delay-sensitive data, the direct-forwarding strategy is used to ensure the minimal end-to-end transmission delay, while, for delay-tolerant data, the wait-forwarding method and data fusion are adopted to reduce energy consumption.
(3) The DSDR scheme has the capability of global event data fusion, which greatly reduces the data amount needed to be transmitted and improves the energy efficiency of the network. Firstly, E2RPs are naturally data fusion paths, which can greatly increase the probability of data fusion. Secondly, the data are all routed through E2RPs, making the data fusion under DSDR scheme global and the largest possible, which greatly reduces data redundancy and energy consumption. To the best of our knowledge, the DSDR is the first more perfect combination of data fusion, differentiated services, and duty cycle setting and integrates an efficient data gathering in a number of performances far better than the previous studies.
(4) Through our extensive theoretical analysis, we demonstrate that the DSDR scheme proposed in this paper has better performances. It realizes synchronous optimization in latency and energy, reduces the transmission delay of delay-sensitive data by 44.31%, reduces the transmission delay of delay-tolerant data by 25.65%, and improves network energy utilization by 30.61%. Compared with the previous schemes, DSDR has obvious advantages in delay optimization and energy utilization.
The rest of this paper is organized as follows: in Section 2, a literature review related to this work is introduced. Then the system model and problem statement are described in Section 3. In Section 4, we proposed an efficient DSDR scheme. The performance analysis of the DSDR scheme is provided in Section 5. Finally, Section 6 presented the conclusion and future perspectives of our work.
2. Related Work
Different applications running on the same network platform may have different requirements on the service of quality; the basic requirement is network delay [19, 21, 24, 25]. In this section, we introduce some state-of-the-art researches for delay differentiated service.
Considering the network delay simply, the duty cycle is the key to reduce delay [26–29]. In WSNs, the node is awake periodically; the longer the node in the waking state, the smaller its sleeping delay, and the smaller the network latency. Based on this idea, the authors of [14] proposed a distributed duty cycle allocation algorithm. The algorithm uses the network residual energy to gradually increase the duty cycles of nodes in the far-sink region based on the distance between the node and the sink and makes the delay of the network external nodes decrease. In [16], the authors also consider the adjustment of the node duty cycle. However, the adjustment under this study is based on the load of the entire network. This kind of duty cycle adjustment scheme has some innovation, but it increases the complexity of the network system, especially when the network is large and the parameter configuration will be tricky.
In addition, since the data are transmitted to the sink through multihop relays, reducing the hops of relay can effectively reduce the end-to-end delay. The authors of [15] realize this and propose a communication scheme called T-LOGF to select the next hop based on the shortest distance. The scheme uses one-step-look-ahead data transmission principle, each time selects the node with the smallest distance from the sink to forward data, so that the total relay hops can be minimized, and reduces the delay, but the scheme ignores the waiting delay caused by the sleep/waking mode.
In [30], the authors studied the issue of network latency requirements for differential services in content-centric networking (CCN), while optimizing the content overall transmission performance. In this paper, in order to guarantee the network delay, the authors first proposed a simple and comprehensive network model; the network delay of routing content is characterized to different locations of the client. Ensure that each content provider has optimized content and latency by combining network locations with content popularity. Then, the content distribution model is incorporated into the proposed global network model, and the delay is addressed as the nonlinear integer programming (NIP) problem in the given network resource and traffic access mode.
The above studies have been considered unilaterally from the network latency, but in fact the data integrity is valued by most applications. In [3], a multipath dynamic routing algorithm called IDDR is proposed to satisfy these two requirements simultaneously. In IDDR, according to the sensitivity of the data to delay and integrity, on the basis of weights of each packet and different QoS requirements, the data packets in the network are separated, and different data are transferred to the sink through different paths to improve data fidelity and integrity and minimize the end-to-end transmission latency. This dynamic routing for data integrity and delay differentiated service can meet the quality requirements of different data, but it is necessary to create a route for each data transfer, increasing the complexity of the network.
Similar to [3], authors of [31] evaluate the quality of the network from the timeliness and reliability. The authors proposed a packet delivery mechanism called Multipath and Multi-SPEED Routing Protocol (MMSPEED) to improve data latency and reliability. For timeliness, MMSPEED provides multiple QoS levels to ensure the transmission speed of multiple packets. For reliability, probabilistic multipath forwarding supports the various reliability requirements of data. These mechanisms for QoS provisioning are realized in a localized way without global information by adopting localized geographic packet forwarding augmented with dynamic compensation, which compensates for local decision inaccuracies as a packet travels towards the destination.
In a more complex system, the quality of service of the network may involve multiple parameters [32–36]. In [20], the authors proposed RPL for the application of traffic prioritization and differentiation for bandwidth, delay, reliability, and security. RPL addresses QoS channel access through queue scheduling mechanism or the priority of media access control (MAC). Specifically, multiple instances of RPL implement the validity of QoS differences based on different objective functions. At the same time, three variants of RPL: standard RPL, multi-instance RPL (RPL-M), and multi-instance RPL with prioritized channel backoffs (RPL-M+) along with two distinct traffic classes have been examined as data traffic rate and composition was varied.
A three-layer QoS scheduling model is proposed for service-oriented IoT in [37]; it selects different scheduling schemes from the application layer, network layer, and sensing layer. In application layer, it uses the service of each component to explore the best QoS-aware service composition. In network layer, the model mainly deals with the scheduling of a heterogeneous network environment. In sensing layer, it is responsible for different service information collection and resource allocation scheduling. The scheme can reduce the costs of network resources in a certain extent.
WSNs are highly complex systems, and energy consumption in resource-constrained WSNs is also an important parameter to consider when reducing network latency. In-network data fusion and clustering are important technologies to reduce energy consumption in wireless sensor networks; the authors of [38] proposed a delay-aware network structure for WSNS with in-network data fusion by utilizing this technique, which divides the sensor nodes into different size clusters, so that each cluster can communicate with the fusion center in an interleaved manner. At the same time, an optimization process is proposed to optimize the communication distance within the group. The proposed network can reduce the latency of the data aggregation process, provided that the data can only be partially fused.
Villas et al. of [22] also use data fusion to achieve the performance requirements of the network and propose an in-network data aggregation routing strategy. The strategy establishes routing paths in the nonhotspot area of the network, and then a number of events that occur in different area are merged into the path, and when the packets of these events go through the same node, data fusion is performed to reduce redundancy. However, this strategy can only be part of the local event information together, but no global network event information fusion capabilities.
In relatively differentiated services, the network traffic is divided into a small number of service classes based on their packets transmission quality, in terms of per-hop metrics for queuing delays and packet losses. In [39], the authors improved and quantified relatively differentiated services and proposed proportional differentiation model for network operators providing “tuning knobs” to adjust the quality interval between classes. The model has a strict priority or capacity differentiation and does not consider the class load and loss differences.
It can be seen from the above that duty cycle and data fusion are all key technologies to improve network performance. Therefore, in the DSDR scheme, we first reduce the transmission delay of delay-sensitive data by increasing duty cycles of relay nodes and then decrease the energy consumption of delay-tolerant data by data fusion.
3. The System Model and Problem Statement
3.1. System Model
The network model of this paper is a typical planar periodic data gathering wireless sensor network, which is similar to [14, 40]; its model structure is as follows.
(1)
(2) Sensor nodes sleep/waking work periodically, nodes monitor the target and transmit the data in the waking state. All the monitoring targets are randomly distributed in different positions of the network, so the probability that each node monitors target and generates data is equal.
(3) The lossless step-by-step multihop aggregation model is used in this paper [22]. In this model, the result of data fusion of nodes
where
3.2. Network Parameters
A sensor node is composed of a sensing unit and a communication unit [17], the sensing unit is responsible for sensing the monitoring event and invoking the communication unit after sensing the event, and then the communication unit initiates its internal communication mechanism to transmit data to the sink. Due to the fact that the energy of nodes is strictly limited, the sensor nodes use sleep/waking cycling mode to save energy [15]. In one unit cycle, the nodes are in the sleeping and waking state periodically, and only when the node is in the waking state can it perceive events and transmit data. In one unit cycle, the ratio of the node in the waking state to the whole cycle is called duty cycle; suppose that
The energy consumption model of this paper is similar to [14, 41], and the energy consumption of nodes is composed of event sensing, data transmission, data receiving, and low power listening. Therefore, the energy consumption model can be expressed as
Table 1 and the Parameters Related to Calculation list the relevant parameters used in this paper, and the parameters values are derived from the internal data tables of the prototype sensor nodes as in [7].
Table 1
Network parameters.
Parameter | Value | Description |
| 0.5 | Initial energy (J) |
| 15 | Sensing cycle (s) |
| 100 | Communication cycle (ms) |
| 0.26 | Preamble duration (ms) |
| 0.26 | Acknowledge window duration (ms) |
| 0.93 | Data packet duration (ms) |
| 0.0511 | Power consumption in transmission (w) |
| 0.0588 | Power consumption in receiving (w) |
| 0.0036 | Power consumption in sensing (w) |
| | Power consumption in sleeping (w) |
3.3. Problem Statements
The main objective of this paper is to design an efficient communication scheme suitable for wireless sensor networks. In the quality of service, the scheme can meet different delay requirements. In terms of network performance, the scheme is able to maintain the network lifetime while strengthening the effective use of energy. It can be expressed as follows.
( 1) Minimize End-to-End Delay for Delay-Sensitive Data. The end-to-end transmission delay of data refers to the time required from the preparation of data at the sender to its arrival at the sink [17, 42]. Assuming that the transmission delay of kth hop is
( 2) Balance Energy Consumption and Maximize Energy Utilization. All data in the network are transmitted to the sink via multihop relays. This sink-centered data gathering model results in a large amount of data and energy consumption in the area that is relatively close to the sink [11, 43, 44], while, in the area far away from the sink, the energy consumption is very small, which contributed to an imbalance in the energy consumption. Assuming that there are N nodes in the network, the energy consumption of node
Energy utilization is the ratio of the energy consumed by the network to the initial energy. Assuming that the initial energy of the node
( 3) Maximize Network Lifetime. Network lifetime is the data transmission rounds that have been performed before the death of the network, which is defined in most studies as the death time of the first node in the network [12, 45, 46]. In wireless sensor networks, the node is powered by the battery, and when the energy of the node is exhausted, the node dies [47]. Therefore, the network lifetime depends on the maximum energy consumption of the network. Assuming that the energy consumption of the node
In summary, the research objectives are as follows:
4. DSDR Scheme
4.1. Research Motivation
Applications running on the same network platform may have different latency requirements due to the differences in events [3, 48], and most existing schemes cannot intelligently select appropriate data routing based on the latency sensitivity of the events, thus failing to meet the requirements of different data. For example, in a network applied to biological monitoring, the sensor nodes are usually deployed to record the daily behaviors of the organism, but if the organism gets into danger and its vital signs change dramatically, it is required to transfer the urgent data to decision-makers as soon as possible in order to take corresponding rescue measures in time. Therefore, for such networks, the difference of its service quality is embodied in the following: for delay-sensitive data, it is required to transmit information to the sink as soon as possible through a reliable transmission mechanism, and for delay-tolerant data only the normal transmission is required. As shown in Figure 1,
In a sparse network with
This can also be seen from Figure 2, compared with the lower duty cycle 0.2, when the duty cycle is 0.5, the network delay is greatly reduced, and the maximum delay in far-sink area is shortened from 167.25 ms to 69.75 ms.
Increasing the duty cycle can effectively reduce the transmission delay, but this is at the expense of energy consumption. Because the energy consumption of nodes in the waking state is 100–1000 times that in the sleeping state [5, 12, 23], therefore, if the duty cycle of all nodes in the network increases, it will greatly exacerbate the network energy consumption and shorten the network lifetime. According to the energy consumption model of (4), the energy consumption of nodes is caused by event sensing, data transmission, data receiving, and low power listening, where energy consumption in sensing and low power listening are related to the node’s sensing and communication cycle, and the cycles are constant. The energy consumption in data transmission and receiving are affected by data amount. When the node communication duty cycle is 0.3, 0.4, and 0.5, respectively, the energy consumption of the network under different data amount is given in Figure 3. As shown in Figure 3, the energy consumption increases linearly as the amount of data increases, and the larger the data amount is, the more energy it consumes, and, at the same time, the larger the duty cycle is, the faster the energy consumption increases.
It can be seen from Figure 3 that reducing the amount of data is the key to saving energy. Numerous studies have shown that network data are correlated with each other in time and space, and there is redundancy among the data detected by multiple nodes of the event [3, 22]. Therefore, data fusion technology can be used to aggregate the information of multiple events and then send it to the sink, thus greatly reducing the amount of data and energy consumption. When the probability of generating data is 0.1, the data fusion rate is between 0.1 and 0.3, and the comparison of data amount before and after data fusion is shown in Figure 4. Since the fusion rate between any two data packets is random and uncertain, the amount of data after fusion for different data varies. However, in general, compared with the original data, the data amount is greatly reduced after fusion.
As mentioned in Figure 4, improving the node duty cycle can effectively reduce delay, while using data fusion can alleviate the energy consumption caused by the increase of duty cycle. However, from the perspective of data generation, in general, the data transmitted in the network are delay-tolerant data; that is, the probability of generating delay-sensitive data is relatively small, and for these small probability data improving duty cycles of all nodes, obviously, is not the most reasonable solution. Therefore, it can only increase the duty cycles of a small part of nodes and establish energy-efficient routing paths by these nodes and finally relay the delay-sensitive data to the sink via these paths; as for delay-tolerant data, data fusion is performed at each hop on the path to save energy, and we called this communication scheme DSDR (delay Differentiated Services for Data Routing).
In the same network scenario as Figure 2, if the DSDR scheme is adopted, then the comparison between DSDR scheme and DR-GA scheme in terms of latency and network lifetime is given in Figures 5 and 6, where the DR-GA (General Data Routing Approach) represents the routing scheme widely used in other studies, the data transmission under this scheme also uses multihop relaying, and the relay node selection is based on the shortest distance. As can be seen from Figure 5, compared with the DR-GA scheme, the transmission delay under the DSDR scheme is greatly reduced, and the latency of delay-sensitive data is smaller than that of delay-tolerant data, which means that DSDR can provide differentiated services while ensuring low transmission latency. More importantly, although the DSDR scheme can effectively reduce the network delay, as shown in Figure 6 it has a longer network lifetime than the DR-GA scheme, which also shows the advantage of the DSDR scheme.
4.2. General Design of DSDR
The main idea of DSDR is to establish energy-efficient routing paths (E2RPs) at different locations of the network, and the communication duty cycles of nodes on E2RPs are increased to 1 (except for the nodes in the near-sink area), and then transmit data to the sink through these E2RPs by adopting different routing methods for different data.
In general, the data communication under DSDR includes the following phases.
Phase 1 (establishment of energy-efficient routing paths (E2RPs)).
Each node in the network is required to store two values:
Phase 2 (delay differentiated services for different data).
After E2RPs are established, all the data is transferred to the sink through E2RPs, and different transmission methods are adopted according to different delay requirements of the data. First, for the delay-sensitive data, a direct-forwarding routing method is adopted, the specific process is as follows: starting from the sending node, each time the node selects a node whose
Phase 3 (data fusion for delay-tolerant data).
Starting from the sending node, data fusion is performed at each relay hop. Firstly, the maximum waiting time at each hop is obtained according to the total waiting time of the data and the distance from the sending node to the sink and the E2RP. Then, the data stops for corresponding time at each relay to merge with other packets received by the relay. After the waiting duration is reached, the fused data are transmitted to the next hop. And the above process is repeated until the data are transferred to the sink.
Figure 7 is the data routings for different data in DSDR. The data routing of delay-tolerant data and delay-sensitive data is described with an E2RP as an example. Compared to delay-tolerant data, delay-sensitive data has a higher transmission priority. When the delay-sensitive data are perceived, the sensing node immediately transmitted it to the sink via E2RP, and there is no stop during the transmission. And for delay-tolerant data, such as data in Area 1 and Area 2, after the node senses the data, it is transmitted to the next hop, and then the next hop node waits for more packets around it to arrive. When the corresponding waiting time for the data is up, the relay node merges all the data it receives with the packet to remove the redundant information and then forwards it to the next hop. As we can see, the delay-tolerant data is fused at each hop it passes.
[figure omitted; refer to PDF]4.3. Implementation of DSDR
In DSDR, each E2RP forwards events in its vicinity; therefore, the transmission quality of the path directly affects the performance of the entire network. In addition, the amount of data carried by the E2RP is very large, and the nodes on the path are awake all the time; therefore, the energy consumption of E2RPs is very serious, so the establishment and switching of the E2RPs are an important issue. On the other hand, how to determine the urgency of data, define its data type, and set the maximum waiting time at each hop is essential for data routing. And, for the delay-tolerant data in the network, the specific calculation of data fusion is also an important issue, and we mainly discuss the implementation of these in this section.
( 1) Establishment and Switching of E2RP. Not every node that generates a packet will establish an E2RP, and the establishment of E2RPs only happens in two phases: (a) network initialization and (b) when an E2RP is removed. The specific establishment of an E2RP is divided into three scenarios:
As shown in Figure 8, there are four E2RPs in the network: Paths 1–4.
Algorithm 1: Establishment of energy-efficient routing path.
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The energy consumption of the E2RPs is serious. Because the lifetime of network is determined by the maximum energy consumption of nodes, in order to avoid the energy consumption of E2RPs shortening the network lifetime, we measure the residual energy of the E2RPs periodically and switch it at appropriate time. The principle of switching and reconstructing the E2RPs is as follows: for each E2RP in the network, the residual energy of its nodes is measured at intervals of time, and once the energy is lower than the threshold, which is set to 20%–30% of the initial energy, the path is immediately removed and the parameters of nodes on it are restored to the system initial values, and all nodes on the path no longer act as E2RPs nodes. Then find new relay nodes which are located in the far-sink area and have a large amount of residual energy. And the establishment of the new E2RP is similar to Scenario 2 in Algorithm 1, but the remaining energy is taken into account when selecting relay nodes on E2RPs. If the residual energy of the node is below the threshold, then it will not serve as a relay node on E2RPs.
( 2) Setting of Data Urgency and Waiting Time. There are two types of data under the DSDR scheme, one is delay-sensitive data, which is denoted as
Theorem 1.
Suppose that
Proof.
The total waiting time of
After the data type and one-hop waiting duration are confirmed, the data can be routed as in Figure 9. The waiting time of delay-sensitive data in the network is 0, and once the data are sensed, they are quickly transmitted to the sink via E2RP, such as Data 1 and other delay-sensitive packets on E2RP in the figure. When delay-tolerant data are sensed, they are waiting for
( 3) Related Implementation of Data Fusion. Data fusion rules of the delay-tolerant data are as follows: data stay
In this paper, the nodes have different data amount due to the different positions, and the trend of data amount is as follows: (A) the closer the node is to the sink, the larger its data amount is, the data amount of the nodes in the near-sink area is large, and that of far-sink area is small. (B) The closer the node is to the E2RP, the larger its data amount is, and the data amount of nodes that are far away from the E2RP and the sink is small.
According to [14], suppose that the network radius is
Theorem 2.
In DSDR, suppose that the network radius is R, the node communication radius is r, the probability of generating data is
Proof.
Equation (11) gives the data packets of nodes in a network with the sink as the center:
In DSDR, the data fusion is performed after each relay node located on the transmission path receives a delay-tolerant packet. Suppose that the number of delay-tolerant packets received by
According to (13), suppose that the number of delay-tolerant packets is
In a circular network with a radius of 500 and a node communication radius of 100, a data fusion rate of 0.01–0.15, the data amount of the network under different fusion times is given in Figure 10. It can be seen from the figure that data fusion can effectively reduce data amount, but, because of the uncertainty of the data correlation of packets in the network, the data fusion rate between packets is also different. Generally, data relevance for the packets sensed at the same time and space is higher. Observing the circumstances under different data fusion times, it is found that the data amount decreases with the increase of the fusion times. Since the data redundancy becomes less and less in the process of continuous fusion, when the number of fusions reaches a certain level, the reduction in the amount of data is less obvious.
[figure omitted; refer to PDF]After the E2RPs are established and the data fusion rules are confirmed and the routing rules are shown in Figure 11, consider the following.
[figure omitted; refer to PDF](1) For data in the near-sink area, they are sent directly to the sink regardless of the data type, as Data 1 in Figure 11.
(2) For delay-sensitive data in far-sink area, they are transmitted to the sink via the E2RP, as Data 2 in Figure 11.
(3) For the delay-tolerant data in the far-sink area, they are also transmitted to the sink through the E2RP, but the
Suppose that the node that senses the data is
Algorithm 2: Data routing under DSDR scheme.
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5. Performance Analysis of DSDR
In this section, the effectiveness of DSDR scheme is analyzed from three aspects: network delay, energy efficiency, and network lifetime. At the same time, the performance of DSDR scheme is compared with the General Data Routing Approach (DR-GA), which is a routing scheme based on the shortest distance and multihop relaying. Without additional descriptions, the network parameters are set as follows:
5.1. Transmission Delay
According to [17], suppose that
In DR-GA, suppose that
In DSDR, one-hop delay varies depending on the different types of data, suppose that
Theorem 3.
In DSDR, suppose that
Proof.
Nodes have different one-hop delay due to different types. When
Theorem 4.
In DSDR, suppose that
Proof.
When the data transmitted is
Theorem 5.
In DSDR, suppose that
Proof.
In DSDR, the transmission delay of
Definition 6.
Total network delay (denoted as
Figures 12–16 show the transmission delays in DR-GA and DSDR. Figure 12 is the one-hop delay of them. In DR-GA, the one-hop delay of all nodes is the same due to the constant parameter values. In DSDR, the one-hop delay of nodes that transmit delay-sensitive data is divided into two cases. After the data reach the E2RP, the communication duty cycles of relay nodes are 1 and the delay is small; otherwise the one-hop delay of the nodes is the same as that in DR-GA. The delay of delay-tolerant data in DSDR is similar to that of delay-sensitive data, but the nodes stay for a short period of time for data fusion at each relay, so the delay is larger than the delay-sensitive data.
[figure omitted; refer to PDF] [figure omitted; refer to PDF] [figure omitted; refer to PDF] [figure omitted; refer to PDF] [figure omitted; refer to PDF]Figure 13 shows the end-to-end delay under DR-GA scheme. It can be seen that the transmission delay increases rapidly with the distance from the sink, and the maximum delay in the network is about 4–6 times the minimum delay.
Figure 14 shows the end-to-end delay for different types of data under DSDR scheme. As can be seen from the figure,
Figures 15 and 16 show the transmission delay at different duty cycles and communication radius under DR-GA and DSDR scheme. As can be seen from Figure 15, when the node duty cycle is 0.2, the transmission delay under the DSDR scheme is much lower than the DR-GA, which means that the DSDR can improve the transmission speed with less energy cost. And as shown in Figure 16, obviously, compared with the DR-GA scheme, the DSDR scheme proposed in this paper has high efficiency in transmitting both delay-sensitive data and delay-tolerant data, especially in the transmission of delay-sensitive data, and it can reduce the delay by 44.31%.
5.2. Energy Utilization
According to (11), in DR-GA, suppose that
Similarly, according to (12), in DSDR, suppose that
Theorem 7.
In DSDR, the direct-forwarding is adopted for delay-sensitive data and wait-forwarding is adopted for delay-tolerant data, and suppose that
Proof.
The amount of data received by
The amount of data transmitted by
Definition 8.
Average data amount of an E2RP (denoted as
Figure 17 shows the data amount of different nodes in the far-sink area under the DSDR scheme. As we can see from the figure,
Equation (4) is the energy consumption model:
According to [17], suppose that the node communication duty cycle is
The first item is the transmission consumption to notify the receiving node of the periodic preamble when the packet arrives, and
With the same duty cycle and communication cycle, the energy consumption to receive a packet can be expressed as follows:
The energy consumption of event sensing is composed of its sleep consumption and sensing consumption. Suppose that the sensing duty cycles of nodes are
The energy consumption of low power listening is relatively small, according to [14], suppose that
The energy consumption of nodes in low power listening consists of listening consumption and sleeping consumption, that is,
The calculations for
At this point, the energy consumption of each part for the node has been given. According to the energy consumption model of this paper, suppose that
Figures 18–20 show the energy consumption under DR-GA and DSDR. Among them, Figures 18 and 19 describe the energy consumption corresponding to different duty cycles under DR-GA and DSDR, respectively. As shown in Figure 18, the energy consumption under DR-GA decreases gradually with the distance from the sink, and the energy consumption of near-sink area is serious, while the consumption of far-sink are is slight, and the maximum energy consumption in the network is about 3 times the minimum energy consumption, which indicates that the energy consumption under DR-GA is not balanced. While, in the DSDR scheme shown in Figure 19, the energy consumption of the entire network is much more balanced, the maximum energy consumption in the network is less than twice the minimum, and the energy consumption of the entire network presents such a law: the energy consumption of regions near the sink or E2RPs is serious, and it decreases with the distance from them. However, whether DR-GA or DSDR, the node with the most serious energy consumption is still the node closest to the sink, and the maximum energy consumption in DSDR is slightly smaller than DR-GA due to the reduction of redundancy by data fusion, which also shows the efficiency of DSDR.
[figure omitted; refer to PDF] [figure omitted; refer to PDF] [figure omitted; refer to PDF]Figure 20 shows the energy consumption of different data relevance under the DSDR scheme, where
Definition 9.
Energy difference (denoted as
Definition 10.
Energy utilization (denoted as
Figure 21 is the energy consumption difference under DSDR and DR-GA, and the smaller the difference is, the more balanced the energy consumption is; otherwise it shows that the energy of some area in the network is not fully utilized. It is clear that compared with DR-GA the DSDR proposed in this paper has a lower energy difference, which shows that the energy consumption under DSDR is more balanced. Figure 22 shows the energy utilization of DSDR and DR-GA. Compared with DR-GA, the DSDR can improve the energy utilization by 30.61%, and especially when the duty cycle is large it has significant advantages in energy utilization.
[figure omitted; refer to PDF] [figure omitted; refer to PDF]5.3. Network Lifetime
Definition 11.
Network lifetime (denoted as
Figure 23 shows the network lifetime under different data fusion rate, and the higher the data fusion rate is, the less data are needed to transmit, the less energy the nodes consume, and the longer the network lifetime is.
[figure omitted; refer to PDF]Figure 24 is the lifetime comparison of DSDR and DR-GA, although the energy utilization greatly improved under DSDR scheme, the increasing consumption has no impact on network lifetime. What is more, it makes the lifetime have a certain improvement. This is explained by the fact that, under the DSDR proposed in this paper, we choose different routings for different areas. In the far-sink area we use
5.4. Effect of Other Parameters on the Performance
( 1) The Proportion of Delay-Sensitive Data
Theorem 12.
Suppose that there are
Proof.
The transmission delay of
Similar to Theorem 12, suppose that there are
Figures 25 and 26 show the transmission delay and energy consumption at different proportion of delay-sensitive data under DSDR scheme. In Figure 25, because the delay of delay-sensitive data is less than delay-tolerant data, then the higher the proportion of delay-sensitive data is, the smaller the average transmission delay of the network is. In Figure 26, the greater the proportion of delay-sensitive data is, the more the serious energy the network consumes, because the delay-tolerant data are transmitted after data fusion and the data redundancy is greatly reduced, so energy consumption of delay-sensitive data is greater than delay-tolerant data. From Figures 25 and 26, we can see the effectiveness of DSDR: when there are more delay-sensitive data in the network, the DSDR can transfer data to the sink quickly and has a high transmission efficiency; when there are more delay-tolerant data in the network, the DSDR can reduce energy consumption and improve network lifetime.
[figure omitted; refer to PDF] [figure omitted; refer to PDF]( 2) The Probability of Data Generation. Figures 27 and 28 show the effect of the probability of data generation on network performance. When the probability is higher, the network needs to transmit more data, and the data amount each node needs to forward is greater, thus the energy consumption is more serious, and the network energy utilization is higher, but this also shortens the network lifetime. From Figure 27, we can see that the energy utilization increases with the increase of data generation rate and duty cycle. Contrary to the energy utilization, it can be seen from Figure 28 that the network lifetime is shortened as the data generation rate increases.
[figure omitted; refer to PDF] [figure omitted; refer to PDF]( 3) Network Radius and Node Communication Radius. The relationship between communication radius of nodes and network radius is also an important factor that affects the performance of the network. When the communication radius of the node is larger, it means that the node can forward farther at each hop, and the number of relay hops is less.
Figures 29 and 30 show the effect of the node communication radius on the transmission delay. It can be seen that when the communication radius is greater, the number of relay hops is less and the transmission delay is smaller. Similarly, when the network radius is small, the node needs to be relayed multiple hops before its arrival to the sink, the energy consumption is more serious, and the energy consumption directly affects the energy utilization, and also it has an impact on the network lifetime, as shown in Figures 31 and 32.
[figure omitted; refer to PDF] [figure omitted; refer to PDF] [figure omitted; refer to PDF] [figure omitted; refer to PDF]6. Conclusions and Future Work
Applications running on the same network may have different service requirements depending on the different backgrounds of their events, and the basic requirement is the transmission delay. Traditional wireless sensor networks lack intelligence and cannot dynamically select appropriate data routing according to the delay sensitivity of the data, so it cannot meet the requirements of the events. Then, a delay Differentiated Services for Data Routing scheme (DSDR) is creatively proposed in this paper, which establishes energy-efficient routing paths (E2RPs) at different locations of the network to forward data. In DSDR, data routing is intelligently selected according to the delay requirements of data. For delay-sensitive data it provides direct-forwarding method to minimize the transmission delay. For delay-tolerant data it uses wait-forwarding method and reduces redundancy by data fusion, thus to reduce network energy consumption. A comprehensive performance analysis shows that DSDR has significant advantages in terms of transmission delay and energy utilization. Compared with the general approaches, DSDR can reduce the transmission delay of delay-sensitive data by 44.31%, reduce transmission delay of delay-tolerant data by 25.65%, and improve the energy utilization by 30.61%, while also enabling the network to have a lifetime that is not less than the previous studies.
With the depth of the relevant research, the content of this paper can be extended. In this paper, we mainly study the delay differentiated services of data; in fact, the integrity of the data can also be differentiated by establishing different transmission routings in the network according to the delay and integrity requirements. Therefore, in the next working plan, we consider data integrity in the guarantee of transmission latency.
Conflicts of Interest
The authors declare that there are no conflicts of interest regarding the publication of this article.
Acknowledgments
This work was supported in part by the National Natural Science Foundation of China (61772554, 61379110, 61370229, and 61370178); the National Basic Research Program of China (973 Program) (2014CB046305); the National Key Technology R & D Program of China under Grant no. 2014BAH28F02; the S & T Projects of Guangdong Province under Grant nos. 2014B010103004, 2014B010117007, 2015B010110002, and 2016B010109008; and the S & T Project of Guangzhou Municipality under Grant no. 201604010054.
Glossary
Parameters Related to Calculation
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Abstract
Energy-efficient data gathering techniques play a crucial role in promoting the development of smart portable devices as well as smart sensor devices based Internet of Things (IoT). For data gathering, different applications require different delay constraints; therefore, a delay Differentiated Services based Data Routing (DSDR) scheme is creatively proposed to improve the delay differentiated services constraint that is missed from previous data gathering studies. The DSDR scheme has three advantages: first, DSDR greatly reduces transmission delay by establishing energy-efficient routing paths (E2RPs). Multiple E2RPs are established in different locations of the network to forward data, and the duty cycles of nodes on E2RPs are increased to 1, so the data is forwarded by E2RPs without the existence of sleeping delay, which greatly reduces transmission latency. Secondly, DSDR intelligently chooses transmission method according to data urgency: the direct-forwarding strategy is adopted for delay-sensitive data to ensure minimum end-to-end delay, while wait-forwarding method is adopted for delay-tolerant data to perform data fusion for reducing energy consumption. Finally, DSDR make full use of the residual energy and improve the effective energy utilization. The E2RPs are built in the region with adequate residual energy and they are periodically rotated to equalize the energy consumption of the network. A comprehensive performance analysis demonstrates that the DSDR scheme has obvious advantages in improving network performance compared to previous studies: it reduces transmission latency of delay-sensitive data by 44.31%, reduces transmission latency of delay-tolerant data by 25.65%, and improves network energy utilization by 30.61%, while also guaranteeing the network lifetime is not lower than previous studies.
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1 School of Information Science and Engineering, Central South University, Changsha 410083, China
2 School of Computer Science, National Huaqiao University, Quanzhou 362000, China
3 School of Information Technology in Education, South China Normal University, Guangzhou 510631, China