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Abstract

Due to the increased demand of wireless sensor networks for their characteristics like low energy consumption, robustness, and low cost in several demanding and complex applications like smart grid, health and safety, traffic and weather updates, there is need of monitoring the infrastructure in a timely manner with high reliability. In this article, a cross-layer data communication scheme target-aware cross-layer technique is proposed to enhance reliability and to reduce the latency in wireless sensor networks. The proposed scheme uses connected dominating set at network layer where nodes in the connected dominating set are directly connected to all the other nodes in the network. Each sensor node sends the data to the nodes present in the connected dominating set, which forwards it to their respective destinations. The proposed scheme reduces the chances of collision resulting reduced delays. Higher packet delivery ratio is achieved with the proposed scheme results in improved reliability. The proposed scheme is outperforming other state-of-the-art schemes in terms of packet delivery ratio, latency, and data throughput with the default 802.15.4 and delay-responsive cross layer in both static and mobile scenarios using network simulator tool.

Keywords

Wireless sensor networks prioritization of data target-aware cross-layer technique (TACT)connected dominating set (CDS)multichannel communication critical delay smart grids Internet of things (IoT)

Introduction

Wireless sensor networks (WSNs) are becoming a prevailing technology nowadays. This is due to their wonderful features like robustness and their vast applications in real-life scenarios. The applications are control processes, monitoring smart grids, and various applications of Internet of Things (IoTs). However, to achieve this target, WSNs must have to promise to provide a certain set of characteristics, which includes a robust Quality of Service (QoS) support. In smart grids, reliable online information becomes the key aspect for reliable transfer of power from the generation unit to the end users. The effect of equipment failure, capacity limitations, and natural accidents generally lead to power failure. This can be avoided to a large extent by online power monitoring, control, diagnostics resulting in power system protection. In a critical application like smart grid, utilities and customers are considered as intelligent nodes of a communication network that are able to distribute real-time information. For this reason, the existence of an efficient communication system becomes the main factor in the successful working of the smart grid. Recent developments^1–5 in wireless technologies have made WSNs a prime candidate for smart grid communication.

However, issues like packet loss due to either failure of coordinator in star topology or unreachable destination in WSNs still need to be resolved effectively. Similarly, another issue is packet delay. In a time-critical application like fault monitoring or protection, if a packet reaches its destination after the specified threshold time, the damage is done and the packet is of no use.

The rest of the article is presented as follows: In “Related work” section, the related work along with their limitation is presented. In “Materials and methods” section, we have discussed the methods and techniques to overcome the limitations and proposed a comprehensive algorithm, TACT (target-aware cross-layer technique). Further, in “Simulation and results” section, the simulation methodology and results are presented and discussed. The article is concluded in “Conclusions and future directions” section.

Related work

To address the problem of delay in WSNs, two medium access schemes are presented in Al-Anbagi et al.’s work,¹ which target the service requirement and delay. These schemes are delay-aware cross layer (DRX) and fair delay-aware cross layer (FDRX). FDRX induces fairness in DRX. It is done to limit some specific nodes to occupy and dominate the communication channel. However, the concept of implementing cross-layer approaches among the network and the MAC (medium access control) layers was not investigated in their work. Second, the authors have used star topology, in which all transmission is through the coordinator. If the coordinator fails or breaks down, all the transmission in the network would fail, hence, causing reliability issues.

In the last few years, WSNs have been used in smart grids in using various communication protocols and different network topologies.^2–4,6 In Tahir and Mazumder’s work,⁵ the authors have presented an approach to achieve balance between demand and supply for congestion control of packets by employing a deadline-ordered scheduler with packet concatenation.

A network structure by organizing sensor nodes into different size of clusters, which communicate with fusion center, has been proposed in Cheng et al.’s work.⁷ Reduction in latency is showed by their results. However, the effect of packet arrival rate is not considered by the author.

To guarantee QoS in smart grid communication network, an adaptive wireless resource allocation (AWRA) algorithm has been proposed⁸ in which resources are allocated adaptively, assuming that if the delay of packets is higher than the predefined delay threshold then the packet will be discarded. However, the problem of retransmission of the packet was a major limitation that the author did not discuss. In Lee and Ke’s study,⁹ authors deployed 19 LoRa mesh networking devices and achieved an average of 88.49% packet delivery ratio (PDR), whereas the star network topology used by LoRa achieved only 58.7% under the same settings.

Dong et al.¹⁰ presented a protocol that is based on data gathering on the necessities of a sensing application. This is through trade-offs between the source-to-sink transport latency and energy consumption. Ding and Hong¹¹ proposed an algorithm that determines the node and network parameters by taking real-time requirements and length of data of periodic messages. One of the major limitations of the algorithm is that the author did not take network size into account.

To avoid congestion, broadcasting periodic control data packets were used by Bhuiyan et al.,¹² which allows neighbors to utilize that data. Their protocol performed better than previous techniques for congestion control.

To support service differentiation, a QoS-aware-based MAC protocol for WSNs was designed and implemented in Yigitel et al.’s work¹³ that used fragmentation, adaptive duty cycling, and intraqueue prioritization. The advantage of their work was that one can allocate resources dynamically. But, the author laid the foundation of their protocol on RTS/CTS operation, but IEEE 802.15.4¹⁴ does not support this operation.

To reduce collisions, CSMA/CA is used with slotted back-off (BO) approach in IEEE 802.15.4 protocol.^15–17 There are two channel-access schemes that are defined in the standard of IEEE 802.15.4. A stable model for slotted IEEE 802.15.4 has been presented and its performance is analyzed by Park et al.¹⁸ To improve the throughput in WSNs, a multiple channel approach is analyzed by Simon et al.¹⁹ Different smart grid applications have different network requirements in terms of data payloads, sampling rates, latency, and reliability.²⁰ To effectively address the smart grid communication requirements the use of WSNs in smart grids is investigated in U.S. Department of Energy.²⁰

All the work done by the respective authors have focused on either of the issues faced by a WSN. No cross-layer approach has been deployed to optimize each and every layer of the network. An approach is much needed that can tackle maximum number of issues. This research article addresses the issues of reliability, throughput, and delay in WSNs using a cross-layer data transmission technique. The effectiveness of our proposed technique is validated through its application in static scenarios like smart grids and mobile scenarios for IoT.

Materials and methods

The proposed, TACT, aims to reduce the end-to-end delay by using connected dominating set (CDS) on the network layer, which is applied to the adaptive MAC layer techniques used in Al-Anbagi et al.¹ Minimal CDS (MCDS) makes a set from a given number of nodes. Each and every node in the whole network is connected to at least one member of this MCDS set. This ensures fast route discovery and thus delay is minimized. TACT also ensures reliability, and makes it suitable to be used in the most critical scenarios where a packet loss or delay cannot be tolerated. To ensure reliability, data are communicated on multiple channels as shown in Figure 1. This results in reduction in the number of collisions and reduction in the latency of data propagation, by enabling different nodes to use different channels for transmission of data packets. Two major issues in the multichannel scheme need to be addressed. First is the requirement that nodes have to advertise and request data among each other so that they can take decision at the time of transmission, and personal area network (PAN) coordinator should have the knowledge and control over them. Second, issue is that all nodes need to agree on the same channel for the transmission of data between them. The McTorrent protocol¹⁷ is used in our proposed technique, and it helps to bypass both these issues. Different steps of the proposed technique are discussed below.

Figure 1.

Showing channel allocation for multichannel communication.

CDS

The issue of finding a CDS can be mapped into a set covering problem. The set covering problem is in essence a problem, concerning bipartite graphs. The CDS algorithm and its working can be better understood by an example, shown in Figure 2.

Figure 2.

Connected dominating set for sensor node A.

CDS algorithm:

Calculate the total number of covering paths.

Select a node B with maximum covering paths.

Remove the selected node B and all other nodes that are connected to B.

Select a node C from the nodes that remained unused in the previous step.

Remove that node C and all nodes connected to it.

Repeat steps 4 and 5 unless all the nodes in a network are covered.

Multichannel protocol description

Each node in a multichannel network is able to change its channel for communication, from the list of available channels. Thus, it can transmit and receive packets, on any of the channel available in the network, except the shared channel for control messages. For better understanding, we denote the total number of channels available with Ƈ. This Ƈ includes the shared channel (Cs) for control messages between all nodes and PAN coordinator. Four types of messages are used in McTorrent protocol used for our multiple channel communication. These messages are advertisement (ADV), request (REQ), channel (ch), and Data. The first three messages are control messages and are transmitted over the shared channel Cs. Data are sent on a channel, defined by the transmitting node, after getting all the information from all the nodes and coordinator in the WSN. So McTorrent is a four-phase data exchange protocol. Initially, a node that needs to transmit some data sends an ADV packet. On reception of ADV, the sink/coordinator replies with REQ packet. Information of the source addresses, that is, the address of the node that will transmit, and the channel (Ƈj) (where j is any channel) are sent to PAN coordinator in ADV, which is forwarded as broadcast in the network. This Ƈj will be used by that specific node for transmission. Similarly, destination address along with the address of source is sent in the REQ packet. Echoed REQ contains Ƈj that is received in ADV packet. During this process, no other node will try to transmit on that channel. Hence, number of collision and latency of the network is reduced.

Channel selection

In McTorrent protocol, before transmitting an ADV, the transmitter must choose a channel for the data transmission. Ideally, the transmitter should choose a channel that does not match with the channels already being used by transmitters one or two hops away. A contention-based scheme is used here, in which every node monitors the ADV and CHANNEL messages, broadcast by its neighbor nodes to keep record of the channels that are in use. Then the transmitting node tries to choose an unused channel for its transmissions. If there is no such unused channel, it selects the channel that was recently least used, by another node. Every node updates a counter for all the channels. Counter keeps the track that how many times a channel is used. So when channel selection is required, a channel with 0 counter is randomly selected from the available ones. If no channel with 0 (zero) counter is present, then channel with minimum counter is used.

Proposed TACT

The proposed technique TACT, initially builds a set of nodes known as connected dominating set (CDS). The members of these nodes are connected to all other nodes directly. This is done at network layer. Then on the MAC layer, our scheme performs estimation of delay (D). If the estimated delay is beyond the delay necessities of the critical application, then a flag is placed in application layer, so that the MAC layer understands that the delay is high for that particular application node. Then by reducing clear channel assessment (CCA) duration, from 128 us to 64 us, that specific node gets high priority of transmitting data. In addition, the proposed scheme uses the phenomena of multiple channel data communication. There are three channels. One is shared for all nodes, for all the time, for control messages to PAN coordinator. The other two channels are for data traffic. If a node finds out that no channel is available and all are busy in transmission, it initiates the slotted back-off CSMA/CD algorithm as explained by Erol-Kantarci and Mouftah.² We have assumed that no other devices are transmitting at the same frequency, 2.4 GHz in our case. This assumption is made to avoid problems of any possible coexistence. FTACT induces fairness into TACT. Similar to TACT, FTACT starts by building a set of nodes known as Connected Dominating Set (CDS). Then it estimates delay. The node specified higher priority to access the channel if the estimated delay is higher than the delay necessities of the application. To stop one node occupying the channel for a long period of time, FTACT induces fairness, by periodically permitting all other nodes in the PAN to fairly access the channel. The proposed TACT algorithm is summarized as follows.

TACT algorithm

//Estimate the delay [D] for the application //

[D]>TH then

// Enables TACT by placing a flag in the application layer header//

•APPHeader=APPHeader (flag)

//At network layer,

•Node Applies CDS and exchanges information with the PAN Coordinator

//MAC layer Changes

•MAC=CSMA/CA

•CCA duration=4 symbol durations

•Specify one Channel particular for control messages.

•Add multiple channels for data transmission.

else

•CCA duration=8 symbol durations

•MAC=CSMA/CA

end if

•(Implement IEEE 802.15.4 CSMA/CA)

Here, TH represents the minimum threshold for delay and is different for different components.¹⁶ The value of TH is equal to 1 ms for initial simulations.

Simulation and results

For evaluation of the performance of the proposed schemes, the TACT and FTACT schemes are implemented with the Network simulator NS 3.19 in Ubuntu 12.04 LTS installed on a virtual machine VMware. Our two modified schemes are tested with different number of nodes and traffic conditions. Mesh topology having N nodes and a single coordinator is used. Assuming that all sensor nodes are working in the 2.4 GHz band and maximum bit rate is 250 kbps. Initially, one node is assumed to receive high priority packets during the simulation time. The transmission range for all the nodes in the same PAN is 30 m. Simulation time for each scenario is 50 s. The threshold delay [D] is taken as 1 ms. All nodes in the PAN can hear each other. In all simulations, each node is given 1 joule of energy. Noise factor is kept constant throughout the whole simulation. Default parameters used in the simulations are shown in Table 1. Based on actual critical scenarios, network densities are selected in our work, (i.e., numbers of nodes vary from 10 to 50 nodes). Moreover, simulation settings are being varied as per application and scenario. More details can be found out later in “Scenarios” section.

Table 1.

Initial simulation parameters.

Parameter name	Parameter value
Simulation time	50 s
Energy given per node (joule)	1
Noise factor (dB)	10.0
Contention window	2
Packet size (byte)	120
Number of nodes	10–50
Transmission range of sensor nodes	30

Performance metrics

To validate the performance of our proposed technique TACT, following metrics are used.

Power consumption

The average energy consumed in transmitting a packet ( $E_{t o t a l}$ ) is computed by adding the average energy consumed during BO (beacon order) ( $E_{BO}$ ), channel sensing ( $E_{sc}$ ), packet transmission ( $E_{t}$ ), idle state ( $E_{Q}$ ), buffering ( $E_{B}$ ), and wake-up ( $E_{W}$ ). The energy consumed in transmitting a packet from an end node is given by

$E_{t o t a l} = E_{B O} + E_{s c} + E_{t} + E_{Q} + E_{B} + E_{W}$ (1)

PDR

This is the ratio of data packets successfully delivered to the number of data packets sent by sources. We transmitted fixed number of known packets from a transmitting node, applied our settings, and then counted the total number of received packets at the destination node. In this manner, PDR is calculated for different scenarios. As PDR is a ratio of same quantities, hence, it is unitless and thus presented as a percentage.

End-to-end delay

The average end-to-end delay for a successful packet transmission is defined as the duration from the instance when the packet reaches the head of MAC layer queue until an ACK from the destination is received. Now if due to either reaching the maximum BO limits or the maximum retry limits, a packet is dropped, its delay is not included into the average delay.

Scenarios

We considered two scenarios for validation of effectiveness of our proposed techniques. First scenario is in which the sensor nodes are at rest and in second case, sensor nodes are in motion. To prove and validate our work, default 802.15.4, DRX, FDRX,¹ TACT, and FTACT are applied on the same scenario and the results are collected from the simulations. Scenario with respect to number of nodes and their mobility are discussed below.

Static scenario

A scenario is considered in which numbers of sensor nodes is varied from 10 to 50 as discussed above. The sensor nodes are taken from 10 to 50 and applied Default 802.15.4; DRX, FDRX, TACT, and FTACT with all nodes as well as their coordinator are at rest. The results are discussed below.

The results in Figures 3 –5 and Table 2 show that our schemes TACT and FTACT outclass the previous techniques with respect to reliability, latency, energy efficiency (in some cases), and throughput. In the scenario where there were 10 nodes, TACT improved the reliability 21% from Default IEEE 802.15.4 and 7% from DRX. Latency is reduced by 5% from Default IEEE 802.15.4 and 15% from DRX. Throughput has increased by 25% from default and 9% from DRX. Similarly in case of 20 nodes, TACT reduces the latency by 32% and 45% from Default 802.15.4 and DRX, respectively. Reliability has improved by 25% and 10% from Default 802.15.4 and DRX respectively. Energy efficiency is improved by 14% and 10% from Default 802.15.4 and DRX, respectively. TACT performed better than the schemes used before. Overall packet loss in a network is reduced, thus reliability has improved. There was minimum packet loss because there was more than one channel for control and data packets. Similarly, latency has reduced by average 20% to 40%. This was achieved because of the use of CDS. Thus, by keeping the energy consumption low, the latency has been reduced and reliability and energy efficiency of the network is improved.

Figure 3.

Impact of number of sensor nodes on packet delivery ratio for static scenario.

Figure 4.

Impact of number of sensor nodes on end-to-end delay (ms) for static scenario.

Figure 5.

Impact of number of sensor nodes on throughput (kbps) for static scenario.

Table 2.

Comparison of average energy (joule) saved per node for static scenario.

Technique vs. node density	10 nodes	20 nodes	30 nodes	40 nodes	50 nodes
Default 802.15.4	0.085 J	0.04 J	0.028 J	0.02 J	0.015 J
DRX	0.084 J	0.042 J	0.028 J	0.021 J	0.016 J
FDRX	0.086 J	0.043 J	0.028 J	0.021 J	0.017 J
TACT	0.084 J	0.042 J	0.028 J	0.02 J	0.017 J
FTACT	0.084 J	0.042 J	0.028 J	0.021 J	0.017 J

DRX: delay-aware cross layer; FDRX: fair delay-aware cross layer; TACT: target-aware cross-layer technique; FTACT: fair target-aware cross-layer technique.

Mobile scenario

The number of nodes is varied from 10 to 50 and Default 802.15.4, DRX, FDRX, TACT, and FTACT were applied, considering random way point mobility model. This means that all nodes as well as their coordinator are at mobile in random directions in the grid. The results are discussed below.

In the scenario where the nodes were mobile, the results in Figures 6 –8 and Table 3 show that our schemes TACT and FTACT outclass the other compared techniques, with respect to reliability, latency, energy efficiency (in some cases), and throughput. In the scenario where there were 30 nodes, TACT improved the reliability by 13% from Default IEEE 802.15.4 and 11% from DRX. TACT reduced the latency by 29% and 5% from Default 802.15.4 and DRX, respectively. Throughput has increased by 18% from default and 14% from DRX. Similarly, when there were 40 nodes, there is 20% and 2% more reliability than Default 802.15.4 and DRX, respectively. There is about 3% and 26% reduction in end-to-end delay of the packet from Default IEEE 802.15.4 and DRX, respectively. Energy consumed at each node is same in all the schemes. It means that with the same energy, when the sensor nodes are mobile, our scheme reduced the latency and improved the reliability and throughput of the network. Hence, energy is more efficiently utilized.

Figure 6.

Impact of Number of sensor nodes on packet delivery ratio for mobile scenario.

Figure 7.

Impact of number of sensor nodes on end-to-end delay (ms) for mobile scenario.

Figure 8.

Impact of number of sensor nodes on throughput (kbps) for mobile scenario.

Table 3.

Comparison of average energy (joule) saved per node for mobile scenario.

Technique vs. node density	10 nodes	20 nodes	30 nodes	40 nodes	50 nodes
Default 802.15.4	0.085 J	0.042 J	0.025 J	0.02 J	0.017 J
DRX	0.085 J	0.042 J	0.028 J	0.021 J	0.016 J
FDRX	0.085 J	0.043 J	0.027 J	0.021 J	0.016 J
TACT	0.085 J	0.042 J	0.028 J	0.021 J	0.017 J
FTACT	0.085 J	0.043 J	0.028 J	0.021 J	0.016 J

DRX: delay-aware cross layer; FDRX: fair delay-aware cross layer; TACT: target-aware cross-layer technique; FTACT: fair target-aware cross-layer technique.

Case study

To validate our proposed schemes and their results, some critical scenarios of smart grid and IoT are chosen. In smart grid, majority of nodes are static. So most of the scenarios discussed above are static. In IoT, most of the applications are composed of nodes that are mobile. So performance with respect to mobility is discussed in IoT applications.

Smart grid applications

For smart grid scenario, where majority of nodes are static, different applications are considered. The applications are capacitor bank control, fault current indicator, line protection and control, emergency response, and transformer monitoring. To study our scheme, these applications are taken one by one.

Our simulation results has showed that in static nodes scenario, node density is increased from 10 to 50, the delay in every respective scenario, our proposed scheme performs better than the compared schemes. In scenario of 40 nodes, there is zero packet loss thus packet delivery ratio is 100%, showed in Table 4. In other scenarios showed in Table 5, our proposed scheme shows better results. Delay has been reduced in our scheme. Figure 4 shows the end-to-end delay with respect to increasing number of nodes.

Table 4.

PDR comparison of different schemes in different smart grid applications.

Application	Reliability requirement	Default IEEE 802.15.4	DRX	FDRX	TACT	FTACT
Capacitor bank control	96.0%–99.99%	91%	97%	93%	100%	99%
Fault current indicator	99.99%–99.99%	91%	97%	93%	100%	99%
Transformer monitoring	99.99%–99.99%	91%	97%	93%	100%	99%
Line protection and control	99.99%–99.99%	91%	97%	93%	100%	99%
Emergency response	99.99%	91%	97%	93%	100%	99%

DRX: delay-aware cross layer; FDRX: fair delay-aware cross layer; TACT: target-aware cross-layer technique; FTACT: fair target-aware cross-layer technique; PDR: packet delivery ratio.

Table 5.

Reliability requirements for different applications.^20,21

Application	Reliability
AMI	> 99.99%
Demand response	> 99.99%
Wide-area situational awareness	> 99.99%
Distribution energy resources and storage	> 99.99%
Electric transportation	> 99.99%
Distribution grid management	> 99.99%
Wide-area protection	> 99.99%
Predictive under frequency load shedding	> 99.99%
Adaptive islanding	> 99.99%
Wide-area voltage stability control	> 99.99%
FACTS and HVDC control	> 99.99%
Cascading failure control	> 99.99%
Precalculation transient stability control	> 99.99%
Closed-loop transient stability control	> 99.99%
Wide-area power oscillation damping control	> 99.99%
Wide-area monitoring	> 99.99%
Local power oscillation monitoring	> 99.99%
Wide-area power oscillation monitoring	> 99.99%
Local voltage stability monitoring	> 99.99%
Wide-area voltage stability monitoring	> 99.99%
PMU-based state estimation	> 99.99%
Dynamic state estimation	> 99.99%
PMU-assisted state estimation	> 99.99%

IoT applications

For IoT scenario, majority of nodes are mobile, two applications are considered. The applications are health safety and traffic update. In health safety, consider a scenario where a patient gets unconscious and now has to be taken to the hospital by an ambulance. So the ambulance in charge will put a smart jacket on the patient. This jacket has different sensors in it like heart beat sensor, blood pressure sensor etc. So these sensors will take the readings in this WSN in the ambulance toward the hospital. Then, these data are transmitted to a coordinator, which sends it to the doctor who is on duty in hospital, by an application through the Internet. So before the arrival of patient at the hospital, the doctor can have an idea about the condition of the patient and thus time is saved to check and take all the readings in the hospital. The WSN for health monitoring is shown in Figure 9.

Figure 9.

Wireless sensor network in Internet of things for health safety.

Similarly, in our second application, that is, traffic update is shown in Figure 10. Suppose there is accident or traffic jam on a highway. But the people coming to highway do not know about the traffic blockage. This can create a huge traffic mess. To avoid such a scenario, sensors are placed across the roads, which take periodic readings to check if there is a jam or not. Then these data are sent to a coordinator in this WSN. The coordinator forwards this information to the Internet. In both these applications, nodes are mobile. To study these applications, Default IEEE 802.15.4, DRX, FDRX, TACT, and FTACT are applied one by one.

Figure 10.

Wireless sensor network in Internet of things for traffic monitoring and update.

We choose random way point motion model for these cases. Nodes and coordinator can move anywhere in the specified grid. Results have showed that in such a scenario where nodes are in random motion, TACT has a much better overall performance. The results in Tables 6 and 7 show that in comparison with the previous protocols, latency is reduced with the increase in reliability and throughput with approximately the same amount of energy consumed.

Table 6.

Comparison of PDR with respect to sensor nodes in IoT.

	10 nodes	20 nodes	30 nodes	40 nodes	50 nodes
Default IEEE 802.15.4	57%	73%	81%	72%	68%
DRX	82%	81%	83%	90%	83%
FDRX	79%	78%	85%	83%	82%
TACT	87%	95%	94%	92%	91%
FTACT	88%	88%	86%	89%	96%

IoT: Internet of things; DRX: delay-aware cross layer; FDRX: fair delay-aware cross layer; TACT: target-aware cross-layer technique; FTACT: fair target-aware cross-layer technique; PDR: packet delivery ratio.

Table 7.

Comparison of throughput (kbps) with respect to sensor nodes in IoT.

	10 nodes	20 nodes	30 nodes	40 nodes	50 nodes
Default IEEE 802.15.4	44.71	55.68	64.96	57.09	52.73
DRX	67.5	64.98	68.3	75.51	69.6
FDRX	65.39	60.3	70.85	78.89	66.66
TACT	72.25	80.12	79.3	77.2	77.5
FTACT	73.85	71.26	70.43	75.09	80.5

IoT: Internet of things; DRX: delay-aware cross layer; FDRX: fair delay-aware cross layer; TACT: target-aware cross-layer technique; FTACT: fair target-aware cross-layer technique.

Conclusions and future directions

A TACT for data communication was proposed in this article with a goal to achieve low latency, lower end-to-end packet delay, lower energy consumption, and good throughput with enhanced reliability in WSNs. To check the effectiveness of the proposed technique, TACT was applied to both mobile scenario like IoT and static scenarios like smart grid with variable number of sensor nodes. Results in terms of PDR, data throughput, end-to-end delay and energy consumption per transmission showed that the proposed scheme TACT/FTACT performed better than Default IEEE 802.15.4, DRX, and FDRX. In TACT and FTACT, overall packet loss in a network is reduced; thus reliability has improved by average of 18% and 6% in static scenario and 21% and 8% in mobile scenario from Default 802.15.4 and DRX, respectively. The reduction in packet loss is due to the availability of multiple channels. Latency has been reduced by average 30% by the implementation of CDS. Throughput has increased by 20% and 7% from default and DRX, respectively, in static scenario and 29% and 10% in mobile scenario. Thus, by keeping the energy consumption low or same as before, latency and packet loss have been reduced. Thus, energy efficiency of the WSN is enhanced. In future, we are building a test bed to optimize the cost of the proposed schemes on the basis of mentioned performance metrics.

Footnotes

Handling Editor: SooKyun Kim

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research,authorship,and/or publication of this article.

Funding

The author(s) received no financial support for the research,authorship,and/or publication of this article.

ORCID iD

Javed Iqbal

References

Al-Anbagi

Erol-Kantarci

Mouftah

HT.

Priority and delay aware medium access for wireless sensor networks in the smart grid. IEEE Syst J 2014; 8: 608–618.

Erol-Kantarci

Mouftah

HT.

Wireless multimedia sensor and actor networks for the next generation power grid. Ad Hoc Netw J 2011; 9: 542–551.

Erol-Kantarci

Mouftah

HT.

Wireless sensor networks for cost-efficient residential energy management in the smart grid. IEEE Trans Smart Grid 2011; 2: 314–325.

Gungor

Hancke

GP.

Opportunities and challenges of wireless sensor networks in smart grid. IEEE Trans Ind Electron 2010; 57: 3557–3564.

Tahir

Mazumder

SK.

Event- and priority-driven coordination in next-generation grid. IEEE J Emerg Select Top Power Electron 2016; 4: 1186–1194.

Al-Anbagi

Mouftah

Erol-Kantarci

. Design of a delay-sensitive WSN for wind generation monitoring in the smart grid. In: Proceedings of the IEEE CCECE, Niagara Falls, ON, Canada, 8–11 May 2011, pp.1370–1373. New York: IEEE.

Cheng

Cai

Cross-layer schemes for reducing delay in multihop wireless networks. IEEE Trans Wirel Commun 2013; 12: 928–937.

Ruiyi

Xiaobin

Jian

et al . An adaptive wireless resource allocation scheme with QoS guaranteed in smart grid. In: Proceedings of the IEEE PES innovative smart grid technologies conference, Washington, DC, 24–27 February 2013, pp.1–6. New York: IEEE.

Lee

KH.

Monitoring of large-area IoT sensors using a LoRa wireless mesh network system: design and evaluation. IEEE Trans Instrum Meas 2018; 67: 2177–2187.

10.

Dong

Ota

Liu

et al . Joint optimization of lifetime and transport delay under reliability constraint wireless sensor networks. IEEE Trans Parall Distrib Syst 2016; 27: 225–236.

11.

Ding

Hong

. CFP scheduling for real-time service and energy efficiency in the industrial applications of IEEE 802.15.4. J Commun Netw 2013; 15: 87–101.

12.

Bhuiyan

Gondal

Kamruzzaman

. CODAR: congestion and delay aware routing to detect time critical events in WSNs. In: Proceedings of the IEEE international conference on information networking, January 2011, pp.357–362, https://www.researchgate.net/publication/252058108_CODAR_Congestion_and_Delay_Aware_Routing_to_detect_time_critical_events_in_WSNs

13.

Yigitel

Incel

Ersoy

Design and implementation of a QoS-aware MAC protocol for wireless multimedia sensor networks. Comput Commun J 2011; 34: 1991–2001.

14.

IEEE 802.15.4:2006. Wireless medium access control (MAC) and physical layer (PHY) specifications for low-rate wireless personal area networks (WPANs).

15.

Pollin

Ergen

et al . Performance analysis of slotted carrier sense IEEE 802.15.4 medium access layer. IEEE Trans Wirel Commun 2008; 7: 3359–3371.

16.

Misic

Sha

Misic

. Performance of a beacon enabled IEEE 802.15.4 cluster with downlink and uplink traffic. IEEE Trans Parall Distrib Syst 2006; 17: 361–376.

17.

Buratti

. Performance analysis of IEEE 802.15. 4 beacon-enabled mode. IEEE Trans Veh Technol 2010; 59: 2031–2045.

18.

Park

Di Marco

Soldati

et al . A generalized Markov chain model for effective analysis of slotted IEEE 802.15.4. In: Proceedings of the IEEE 6th international conference on mobile adhoc and sensor systems, Macau, China, 12–15 October 2009, pp.130–139. New York: IEEE.

19.

Simon

Huang

Farrugia

et al . Using multiple communication channels for efficient data dissemination in wireless sensor networks. In: Proceedings of the 2nd IEEE international conference on mobile adhoc and sensor systems, Washington, DC, 7 November 2005, pp.7–10. New York: IEEE.

20.

U.S. Department of Energy. Communications requirements of smart grid technologies. Report, U.S. Department of Energy, Washington, DC, 5 October 2010.

21.

Kuzlu

Pipattanasomporn

Rahman

Communication network requirements for major smart grid applications in HAN, NAN, WAN. Comput Netw 2014; 67: 74–88.

Wireless sensor network design for smart grids and Internet of things for ambient living using cross-layer techniques

Abstract

Keywords

Introduction

Related work

Materials and methods

CDS

Multichannel protocol description

Channel selection

Proposed TACT

TACT algorithm

Simulation and results

Performance metrics

Power consumption

PDR

End-to-end delay

Scenarios

Static scenario

Mobile scenario

Case study

Smart grid applications

IoT applications

Conclusions and future directions

Footnotes

Declaration of conflicting interests

Funding

ORCID iD

References