IEEE PotEntIals March/Apr i l 2016 n 9

IWSN—Standards, challenges and future

Humaira Abdus Salam and Bilal Muhammad Khan

0278-6648/16©2016IEEE

IWSN—Standards, challenges, and the future

Humaira Abdus Salam and Bilal Muhammad Khan

ireless sensor net-work (WSN) is a sys-tem that comprises thousands of wire-less nodes spread around. These nodes

use radio frequency (RF) channels to share their information and data, which may be further processed to monitor or control the system.

One of the emerging applica-tions of WSNs is the industrial WSN (IWSN), where the wireless system is used for controlling and monitor-ing various industrial tasks. Wire-less networks show tremendous potential and advantages over con-ventional wired industrial networks in terms of infrastructure (no long cable runs), cost reduction, ease of troubleshooting, and ease of re-pair, replacement, and upgradation. IWSN has the ability to operate in harsh environments and provide ef-ficient performance through rapid and accurate results with enhanced reliability.

Along with these gains, the chal-lenges posed by industrial control systems are very unique. To operate such networks in an efficient way, wireless technology should meet the demands of industrial networks like reliability, real-time communication, robustness, and energy efficiency. IWSN not only fulfills these de-mands, but it also provides flexibil-ity to the overall industrial network.

IWsn structure architectureA WSN is an open network that pro-vides great flexibility to the system during installation and network operation. Nodes in a wireless net-work are arranged in a star, tree, or mesh topology, which is controlled by a network manager or a personal area network (PAN) coordinator through radio communication links. Mesh topology is used to provide better reliability to the system. The

deployment of mesh is very complex and costly in wired networks, how-ever, wireless networks can organize it with low cost and less complexity. Figure 1 shows different network topologies for both wired and wire-less networks.

Unlike a conventional point-to-point connection-oriented wired network, a wireless network, due to its inherent connection-free na-ture, increases the flexibility of the

Digital Object Identifier 10.1109/MPOT.2015.2422931 Date of publication: 7 March 2016

iMAge licenSed By ingrAM puBliSHing

W

10 n March/Apr i l 2016 IEEE PotEntIals

network. This infrastructure-less architecture makes the wireless network scalable, portable, and reliable for both static and mobile nodes. Nodes in a wireless network are self-organizing and self-healing and can adapt to environmental changes to perform efficiently.

IWSN provides convenient net-work management, security, and mesh networking in industry, along with a highly reliable system. Data from field devices and remote sites can communicate to supervisory control and data acquisition (SCA-DA) systems for process and control-ling purposes. The central manager tracks and monitors the devices and the activity throughout the plant. Based on these observations, the central manager can control pro-cesses or can direct commands to a mobile worker at the plant. This offers an efficient way of managing and controlling industrial process in real time with reduced cost. Figure 2 shows an architecture of an indus-trial wireless network where the cen-tral manager is monitoring and con-trolling several processes at a plant as well as at remote sites.

applications of IWsnThe enhanced reliability, simple installation, and convenient man-agement of IWSNs contribute to improved productivity with a high quality of services. In industry, wireless networks are used in differ-ent safety, supervisory, data acqui-sition, and control applications ranging from alarms, monitoring, automation, logging, uploading, downloading, and research purpos-es. A WSN can improve these pro-cesses by collecting data within a bounded time interval. It also lowers the safety risk by remotely access-ing hazardous locations at the plant and dealing with unplanned system failures. IWSNs offer an economical solution for difficult-to-wire areas. For moving and rotating machines in industries, a wired network requires an additional mechanical parts for proper functioning. In con-trast, a wireless network provides tangible cost saving by moving sen-sors itself along with the rotat-ing part.

Considering these advantages and the need of WSNs, these net-works are widely used in several

industrial applications (as shown in Fig. 3), some of which include: ■■ Environmental sensing: In indus-

try, WSNs are used to monitor environmental changes at fields and plants. The agricultural industry monitors relative humidity, soil moisture, tempera-ture, and other environmental conditions for the better growth of crops. WSNs are also used in greenhouse monitoring and in nuclear power plants to further enhance processes. The use of sensor nodes in the oil and gas industries constantly monitors resources, pumping, leakage, and production at fields. These appli-cations demand sensors at the field to be energy efficient and robust toward climate conditions to work effectively.

■■ Monitoring: IWSNs are also being used in industry to monitor atmospheric conditions and to analyze the behavior of plants and equipment, which is required to enhance the yield and product quality. Data recorded through monitoring is also useful for the research and development of industrial networks. Industrial equipment performance deterio-rates with time, which affects optimal system performance. This equipment must be moni-tored to maintain quality of ser-vice and avoid system malfunc-tion. Monitoring applications in an IWSN demands reliable com-munication with accurate infor-mation from all devices.

■■ Control and automation: The automation industry utilizes the robustness and reliability of IWSNs. Mostly, these networks comprise sensors and actuators for controlling critical and non-critical processes operating in a closed loop. An IWSN used in automation can increase the production gain by making the process fast and reliable. Such a system requires a protocol that can provide better quality of ser-vice in terms of robustness, reli-ability, and the real-time com-munication of data.

Wired StarTopology

Hub

WorkstationWorkstation

Workstation

Wireless StarTopology

Wireless MeshTopology

WiredMesh

Topology

fIg1 The star and mesh topology of a wired and wireless network.

One of the emerging applications of WSNs is the industrial wireless sensor network (IWSN), where the wireless system is used for controlling

and monitoring various industrial tasks.

IEEE PotEntIals March/Apr i l 2016 n 11

■■ Safety: Robustness and low laten-cy of IWSNs are also required for safety-critical application in industry. A WSN can be used at hazardous locations to decrease injuries and equipment damage. It eliminates the need of human interaction near heavy machinery, chemicals, and unsafe areas.

the challenges of IWsnsIn comparison with other wireless networks, IWSNs require high reli-ability and real-time performance, which is challenging due to noisy surroundings. Latency, fault toler-ance, synchronization, real-time constraints, network security, and cross-layer design are critical issues posed by industrial applications (as shown in Fig. 4). These challenges an affect the network services and other performance metrics. The fol-lowing are some of the factors that can degrade network efficiency in an industrial domain.■■ Reliability: Reliability is con-

cerned with how much data is received successfully at the receiver end with minimum delay. In WSNs, it is one of the most important factors that is considered in analyzing network performance. The transmission of data packets in WSN generally is affected by interference, an abrupt change of channel state, weak link availability, and proto-col overheads. Also, highly reli-able communication is required in industry to monitor and con-trol the process with maximum accuracy (and also to lower the risk of equipment damage and injury). The industrial environ-ment offers critical interference issues and extreme conditions such as high temperatures, noise, and climate changes. Node’s mobility reduces the net-work reliability by frequently breaking the communication link. The reliability of the network can be improved by considering the environmental loss and other factors by using control mecha-nisms in a communication proto-col. Existing data transportation

schemes in IWSNs should be more robust to resolve the critical issues of reliability in industry.

■■ Latency and real-time communi-cation: Latency is defined as the time required by a data packet to reach its destination. To provide real-time communication, data should be transmitted with mini-mum delay. Delay in a network is introduced through number of causes including network setup time, retransmission, and con-gestion, as well as medium ac-cess control (MAC) delays, such as a MAC backoff delay and con-tention delays. A harsh industrial environment introduces more de-lays to the wireless network and makes it more challenging to pro-vide real-time communication in the automation industry. To im-prove latency in an industrial en-vironment, network traffic and interference reduction proce-dures should be added. The net-work setup should be robust, using few control packets and less overheads. Currently, indus-

trial standards are providing soft real-time communication, there-fore they cannot be used most of the time for critical industrial ap-plications. More robust protocols are needed to provide hard real-time communication in industrial environments.

■■ Climate: Climatic changes such as wind, rain, snow, humidity, and temperature affect the per-formance of sensor nodes in large fields. Dust, dirt, high

People/AssetTracking

Wireless Plant Network

Wireless Field Network

Process/PhysicalSecurity

MobileCommunications

MobileWorker

Operations

Plant Network

Control Network

FieldDevices

Self-OrganizingNetworks

Remote Sites

Asset Management

fIg2 The network architecture of an iWSn.

IWSNApplications

EnvironmentalSensing

Monitoring

Automationand Control

Safety

ns

EnvSen

Mo

Autand

Saf

fIg3 Application of an iWSn.

Unlike a conventional point-to-point connection-oriented wired network, a wireless network,

due to its inherent connection-free nature, increases the flexibility of the network.

12 n March/Apr i l 2016 IEEE PotEntIals

humidity level, and solar radia-tion cause the nodes to malfunc-tion and also reduce the signal strength. These faulty nodes then generate false data and transmit intermittently. Further-more, rain, fog, and snow have a negative impact on link connec-tivity and transmission range. To make the nodes robust toward climatic changes, onboard self-test and error recovery should be introduced.

■■ Dynamic network: WSNs are based on infrastructureless architecture, so they do not fol-low one configured network topology. Nodes present in most wireless networks are mobile in nature and, due to their mobility, the network topology changes at every instant. Also, weak com-munication links and noise causes the network to discover new connections among nodes and deliver data successfully. The dynamic network topology also reduces the network lifetime by increasing the routing pro-cess. Therefore, controlling the network topology is one primary

task in designing a routing proto-col for a WSN. For various appli-cations, standards are required that can successfully adapt and significantly enhance perfor-mance through self-configuration and self-healing characteristics.

■■ Power and energy: Network life-time is one critical issue in wire-less networks. Due to limited resources and the small size of a wireless node, the network cannot support a complex and computa-tionally exhaustive protocol for communication. Power consump-tion by the network is dependent on network size, traffic load, the number of retransmissions, mobil-ity, and cluster size. In a large net-work, the number of transmis-sions increases, which also boosts congestion, collision, delay, and packet loss. Energy consumption in industry can decrease by incor-porating clustering, reducing net-work traffic, and by maintaining the most active link.

■■ Security: A WSN is an open system, where an unauthor-ized user can access data easi-ly and provide serious security

threats to industry. Most of the routing protocols are simple to keep the communication less complex, but this makes the network susceptible to attack-ers. An intruder can easily pick up the data by designing a powerful receiver. Also, it can capture any particular node and generate a false alarm and erroneous data. A security proof protocol with a strong encryption technique, such as AES-128 encryption, can be used to secure data over a communication link.Due to the aforementioned limita-

tions, it is challenging to establish and maintain an acceptable quality of service for industrial networks. To overcome these challenges, several IWSN protocols and standards have been produced. These protocols re-solved some of the issues posed by industrial networks; however, there are still areas that need significant improvement.

IWsn protocolsCurrently, two standards, Wireless HART and International Society of Automation (ISA) 100.11a, are uni-versally accepted for industrial wire-less networks. These standards are used in several industrial applica-tions and are of significant impor-tance. These standards overcome the industrial challenges to a great extent; however, there are some limi-tations that should be addressed for more efficient results.

Wireless HARTWireless HART is the first industrial standard, released in 2007, for con-trol and measurement that outper-forms all other existing industrial protocols of its time. Wireless HART commissions a self-organizing and self-healing mesh network with easy setup capability. Some basic fea-tures of Wireless HART protocol are shown in Fig. 5.

HART uses IEEE 802.15.4 stan-dard under a 2.4-GHz frequency spectrum. It consumes low power for a short-range network to provide a data rate maximum up to 250 Kb/s.

fIg4 challenges for iWSns.

In comparison with other wireless networks, IWSNs require high reliability and real-time performance,

which is challenging due to noisy surroundings.

IEEE PotEntIals March/Apr i l 2016 n 13

For real-time communication, HART uses a synchronized time slot, each of a duration of 10 ms. The concept of superframe is also introduced to send several data frames in their respective time slots. It uses direct sequence spread spectrum to frag-ment the information and frequency hopping spread spectrum to select alternate channels for data trans-mission. To route the data packet, this protocol uses graph routing, which contains redundant path for nodes. Data transmission over bad links is avoided by using a black-listing mechanism. Security is also preserved in wireless HART through hop-by-hop data integrity using mes-sage integrity code with AES-128 at the MAC layer. The network also uses different keys for confidential data integrity, such as public keys, joining keys, and network keys, which en-able a secure and authenticated data transmission within a network.

LimitationsHART uses time-division multiple access (TDMA) slotting, which requires all of the nodes to be synchronized. More-over, there is a high possi-bility that nodes are not utilizing the complete time slot, thus decreasing bandwidth utilization. Blacklisting of channels also reduces the available channels for transmission. This can affect the band-width utilization and can increase interference.

Graph routing increases the overhead on each node by means of demanding huge memory for redundant paths and frequent updates to maintain an active route table. Wireless HART also poses a device compatibil-ity issue and can work only with HART-specific field de-vices.

ISA100.11aIn September 2009, the International Society of Automation proposed the

comprehensive standard ISA100.11a for monitoring and control applica-tion in industry. This standard does not make wireless HART obsolete but stands along with it in various industrial applications. Similar to wireless HART, it uses 802.15.4 at the physical layer but with variable TDMA slots.

In addition to the properties of traditional 802.15.4, ISA100.11a uses time, frequency, and spatial di-versity. With a slotted and adaptive channel-hopping scheme, the com-munication channel switches be-tween consecutive time slots within a superframe and among consecu-tive superframes. The standard uses graph and source routing, and it is influenced by the specification of the IPV6 LoWPAN protocol to provide compatibility. Use of IPV6 provides

the merits of Internet protocol (IP) to the wireless system.

To provide better service between end-to-end users, the transport layer uses additional authentica-tion techniques with better integ-rity checks and encryption. ISA100 also supports a counter with cipher block chaining message authentica-tion code along with an advanced encryption standard (AES-128) to block cipher using symmetric keys. The application processes of the ISA standard are used to handle hard-ware, perform computation, and support protocol tunneling. The ma-jor features of ISA100.11a are pre-sented in Fig. 6.

LimitationsThe variable timeslot property in ISA100 is not useful for applications

with constant data length because it introduces addi-tional complexity to the network. Like wireless HART, it also offers a limit-ed number of radio chan-nels for communication due to blacklisting. The memory exhausting graph tables for routing are sometimes also unaccept-able. The use of IPV6 LoW-PAN in ISA100 introduces a large size of header that increases complication in system design and its operation, whereas the use of compressing header techniques also provides some additional overheads. The inclusion of IP connec-tivity may result in a secu-rity threat to these net-works, since there are sev-eral mature IP-hacking methods available. For real-time communication, ISA100 uses an additional contract based priority

Wireless HART

Graph and Source Routing

MeshNetwork

Single-Channel

FrequencyHopping

TDMA–10-msFixed

Time Slot

802.15.4MAC

AES128 Cipher

fIg5 The feature blocks of wireless HArT.

ISA100.11a

Graph andSource Routing,IPV6 LoWPAN

TimeFrequencyand Spatial

Diversity

MeshNetwork

TDMA–VariableTime Slot

802.15.4MAC

AES128Cypher

fIg6 The feature blocks of iSA100.11a.

Currently two standards, Wireless HART and International Society of Automation (ISA)

100.11a, are universally accepted for industrial wireless networks.

14 n March/Apr i l 2016 IEEE PotEntIals

scheme which incurs complexity and overhead for its management.

Both industrial standards have some limitations, therefore a new ap-proach is needed that can overcome the challenges of these standards. Several techniques introduced can be implemented to achieve optimized results under harsh industrial envi-ronments. The following are some of the techniques that can be used to in-crease the quality of service of exist-ing conventional IWSN protocols and standards.

Methods to enhance industrial network performanceSeveral control mechanisms are avail-able that can be added to existing standards or used with some other novel approach to develop an efficient protocol for harsh industrial sur-roundings. Protocol design for IWSNs using proposed schemes/methods will be less complex and provide ener-gy-efficient communication.■■ Cognitive radio approach: This is

the increased trend of wireless applications demands an opportu-nistic utilization of radio channels to reduce interference. In this regard, the cognitive radio approach is an emerging technol-ogy that intelligently manages the spectrum for communication. In a wireless network, some of the available frequency channels are used very often and called spec-trum holes. The introduction of the cognitive spectrum can improve the bandwidth utilization of such networks by making use of these spectrum holes. In this method, the transmitter detects the occu-pied radio channels and then uti-lizes the one that is vacant to improve the channel capacity of the network. Using cognitive radio, these spectrum holes utilize frequency spectrum efficiently.

■■ Clustering: In dense networks, the most serious issues are

packet collision and contention for accessing the medium. These issues can be resolved by mak-ing clusters in which a single node among a group is selected as a cluster head. The cluster head is selected on the basis of different metrics, such as its dis-tance from the gateway, the node’s power, the number of its neighboring nodes, and its loca-tion. All of the nodes under a specific cluster send data to their cluster head, and the cluster head is then responsible for for-warding the data of the whole cluster toward the gateway. This reduces the contention of the channel at the gateway, and more data can be transmitted successfully. Clustering can also provide energy-efficient routing by transmitting aggregated node information.

■■ Aggregation: Aggregation is a technique that combines data of two or more nodes before for-warding. It can be done at the physical or the network layer using several coding techniques. Superposition coding, collabora-tive, and XOR coding are a few examples available for data aggre-gation that can enhance network performance by sending data in a compact form. Fewer transmis-sions will be required to transmit all data, which also improves net-work latency. It further reduces the energy consumption and traf-fic load in a network.

■■ Distributed graph routing: Instead of using conventional graph and source routing for an industrial standard, the graph table can be distributed among nodes according to their charac-teristics, which also reduces the complexity and improves energy efficiency. The introduction of three graph tables can reduce the load at a node and provide

better communication. The up-link graph can be created, which provides a path from all devices to a gateway for sending a node’s data. A down-link graph contains a link of the path from the gate-way to the device to send individ-ual control messages to each device. A broadcast graph is used to maintain a route for the trans-mission of common configuration and control signals from the gateway to all nodes.

■■ Cooperate spectrum: Communi-cation among networks, for channels within a cognitive radio network, can improve the chan-nel capacity. Different networks at the same industrial site should negotiate the frequency spectrum to avoid interference among them. Suitable protocols of spectrum sensing for the selection of the next frequency channel between different net-works can enhance performance by reducing collision, data loss, and interference.

■■ Add relaying nodes: For reliable communication in a dynamic network, adding excessive relaying nodes can enhance network performance. These nodes are not responsible for generating data or managing network; instead, they only relay data among nodes. This can maintain reliability in a network even when sensor nodes reach a black hole.

■■ Cooperative diversity: This is a technique usually used at physi-cal layers to exploit diversity in a wireless network. Methods like decode and forward, amplify and forward, and compress and for-ward are used to relay data of several nodes reliably over the network. This method provides a limitation over a cooperating node’s data rate at the physical layer, but the same procedure can be implemented at the upper layers of protocol stack to achieve higher gain with a larger number of nodes.

■■ Adaptability: One way to get the optimized result in a dynamic

Wireless HART commissions a self-organizing and self-healing mesh network with easy

setup capability.

IEEE PotEntIals March/Apr i l 2016 n 15

network is to introduce adaptable protocol. Control packet interval, network traffic, and MAC param-eters are adaptive and can improve the system performance. The adaptive nature of these parameter will adjust the values according to network surround-ings. It avoids the excess trans-mission of control packets and packet collision due to high net-work traffic, and it also manages delays at the MAC layer including backoff exponential, contention delay, and retrial limit. Using this scheme, the network will config-ure the parameters according to the surrounding atmosphere and provide a high data delivery ratio with reduce packet loss.

■■ Energy efficiency: One technique used to design an energy-efficient protocol is to introduce cluster hierarchy in the network. Clus-tering can conserve energy by reducing network traffic and sending information toward the network manager via cluster heads only. Proactive routing and routing table overheads also drain most of the node’s energy. Therefore, a less-complex routing strategy with on-demand routing should be incorporated to reduce the load of routing computations. Cooperation can also improve energy efficiency by forwarding the data of several nodes over a single path. An energy-aware routing schedule can also save network energy by transmitting data intermittently. Moreover, low-power-consuming compo-nents on sensor nodes can also extend the network lifetime.

Energy-harvesting techniquesEnergy harvesting can be done at nodes by using devices that can serve as energy sources. Nodes, placed in open field areas, can make use of photovoltaic modules. These modules utilize solar energy and serve as an unlimited energy resource for the network. Since industrial environ-ments are surrounded by vibrations, vibration harvesters can be used to transduce the vibrational energy and

improve the network life. Nodes in an industrial environment can harvest energy from their surroundings in different forms to increase the net-work’s lifetime.

Future workIWSNs are a growing area and pro-vide several challenges and prob-lems on which to work. The following are some future research directions in which these areas can be further explored and improved.

After the development of wire-less HART and ISA100.11a, IWSNs have overcome several challenges and gained a significant reputation. Currently, these two IWSN stan-dards are implemented as default protocols for industrial applications. However, there is a demand for a unique comprehensive and robust universal solution.

One of the significant problems of the industrial domain is the in-terference on radio channels caused by surrounding noise. To maintain reliability in industrial networks, existing standards use graph rout-ing, however, these table-driven pro-tocols incur high overhead, much memory, and waste valuable energy.

These issues of graph routing in both wireless HART and ISA100.11a can be minimized by using distrib-uted graphs for separate commu-nication tasks. Distributed graph routing also reduces the system complexity and the load of the rout-ing table at each node. Delay and performance can also be improved by updating the concerned graph only on network changes. For dy-namic networks, the overhead of a routing table can be eliminated by designing a reactive cross-layer pro-tocol, which can enhance the reli-ability by considering the most re-cent network status and active links. This will reduce energy and memory consumption of the system, which in turn increases the network lifetime.

To avoid communication over bad links, wireless HART and ISA100 use a channel blacklisting mecha-nism. The blacklisting of radio links reduces the number of available channels for communication, which can degrade network efficiency. However, these unused channels can be further utilized for some other applications or networks to increase spectrum efficiency. This can be achieved by introducing co-operation among the networks and assign unused channels to other network setups. The standards also use frequency hopping schemes to avoid collision and transmit maxi-mum data, which can improve fur-ther by using cognitive radio. A cog-nitive approach utilizes the channel bandwidth efficiently by fair alloca-tion of frequency channels among source nodes. Cooperative spectrum techniques and cognitive radio ap-proaches enhance the channel ca-pacity by making use of spectrum holes in the frequency band.

Reducing traffic in a network improves the chance for successful data transmission. To achieve this, the clustering of nodes and data aggregation can be used. This will require little amendment in exist-ing standards and result in higher gains at the receiver. The aggrega-tion of data packets also preserves the time slot of the superframe and  allows more devices to join the network.

Clustering, aggregation, and co-operation, all make use of compact data packets to improve the network latency. Furthermore, the introduc-tion of a variable time slot, accord-ing to the length of the data packet, can improve network latency. Due to the soft real-time communication of current industrial standards, these protocols are best suited for moni-toring and controlling noncritical processes. Hard real-time commu-nication is still in demand for most

The increasing use of wireless networks in several industrial applications demands new products,

which will be emerging in the next few years.

16 n March/Apr i l 2016 IEEE PotEntIals

of the critical industrial applica-tions; therefore, robust routing tech-niques must be used for quick link discovery.

Adaptive control parameters can also reduce delay and provide enhanced reliability for real-time communication—it maintains the network performance under dif-ferent network states. The adapt-ability of the protocol provides high efficiency by managing and con-figuring the parameters according to the network dynamics. Reliable communication is essential for the automated control of industrial pro-cesses to prevent severe equipment and plant damage. Cooperation and coding techniques at the network layer offer highly reliable commu-nication by transmitting redundant data. This also provides security to the data by encoding the data packets, which also eliminates the need for using energy-consuming encryption techniques.

Due to the limited resources of sensor nodes, the high energy re-quirement for graph routing is some-times unacceptable. Industries can harvest energy from solar radiations, wind, and surrounding vibrations us-ing different devices. Another way to reduce energy consumption is to use an energy constraint protocol and low-power devices in the network. The introduction of clustering and adapt-ability are also energy-efficient tech-niques, along with enhancing the net-work reliability. Reducing the network traffic and lowering the transmission power can conserve network energy and improve its lifetime.

The increasing use of wireless networks in several industrial ap-plications demands new products, which will be emerging in the next few years. More intelligent equip-ment will be produced for intelligent process automation systems in the near future. IWSNs can also pro-vide an excellent opportunity for an extended network to use it with a wired network as a backbone. Lead-ing companies are now designing intelligent and smart sensor nodes, programmable logic controllers, small wireless devices (Motes), and

low-power wireless sensor platforms to improve the network performance in industries.

ConclusionThis article provides an overview of industrial wireless networks along with the needs and standards that are currently in use. Although faced with several challenges, IWSNs are preferred in industry over wired net-works due to their numerous advantages. IWSNs are providing efficient and affordable performance at several industrial locations where wired networks fall short.

Currently, two standards are be-ing used in industry, but none can be declared as the one universal so-lution. Wireless networks in an in-dustrial environment require much tougher, more reliable, and robust networks that can assure the maxi-mum quality of service for end users in industry.

We highlighted the challenges of-fered by IWSNs in this domain and their possible solutions. By using the proposed methods, one can lead the industrial standard toward more effective and efficient performance. Out of several issues pointed out in the article, real-time communication and reliability are the critical issues in industrial environments and are still open areas for research and de-velopment.

Read more about it•J. Song, S. Han, A. K. Mok, D.

Chen, M. Lucas, M. Nixon, and W. Pratt, “WirelessHART: Applying Wire-less Technology in Real-Time Industri-al Process Control,” in Proc. IEEE Real-Time Embedded Technology Applica-tions Symp., 2008, pp. 22–24.

•D. Christin, P. S. Mogre, and M. Hollick, “Survey on wireless sensor network technologies for industrial automation the security and quality of service perspectives,” Future Internet, vol. 2, no. 2, pp. 96–125, 2010.

•A. Low, “Evolution of wireless sensor networks for industrial control,” Technol. Innovation Manage. Rev., pp. 5–12, May 2013.

•A. N. Kim, F. Hekland, S. Petersen, and P. Doyle, “When HART goes wire-less: Understanding and implementing the wirelessHART standard,” in Proc. IEEE Int. Conf. Emerging Technologies Factory Automation, 2008, pp. 899–907.

•G. Cena, A. Valenzano, and S. Vitturi, “Hybrid wired/wireless net-works for real-time communication,” IEEE Industrial Electron. Mag., vol. 2, no. 1, pp. 8–20, 2008.

•S. Petersen and S. Carlsen, “Wireless HART versus ISA100.11a: The format war hits the factory floor,” IEEE Industrial Electron. Mag., vol. 5, no. 4, pp. 23–34, 2011.

•K. Somappa, K. Ovsthus, and L. Kristensen, “An industrial perspective on wireless sensor networks—A survey of requirement, protocols and chal-lenges,” IEEE Commun. Surveys Tuts, vol. 16, no. 3, pp. 1391–1412, 2014.

•N. Azmi, L. M. Kamarudin, M. Mahmuddin, A. Zakaria, A. Y. M. Shakaff, S. Khatun, K. Kamarudin, and M. N. Morshed, “Interfernce issues and mitigitaion method in WSN 2.4GHz ISM band: A survey,” in Proc. IEEE 2nd Int. Conf. Electronic design, 2014, pp. 403–408.

•T. Hasegawa, H. Hayashi, T. Kita, and H. Sasajima, “Industrial wireless standardization—Scope and imple-mentation of ISA SP100 standard,” in Proc. IEEE SICE Annu. Conf., 2011, pp. 2059–2064.

•R. Randriatsiferana, R. Lorion, F. Alicalapa, and F. Harivelo, “Energy-effi-cient clustering algorithm based on energy variance for wireless sensor networks,” in Proc. IEEE Int. Conf. Smart Communications Network Tech-nologies, 2013, vol. 1, pp. 1–5.

•B. Thenral and T. Sikamani, “Proficient routing method that exploits the lifetime of wireless sensor network,” in Proc. IEEE Int. Conf. Cur-rent trends Engineering Technology, 2013, pp. 231–234.

about the authorsHumaira Abdus Salam (humaira.abdus.salam@pnec.nust.edu.pk) is currently working toward her M.S. degree in communication and net-working from the National University of Sciences and Technology, Pakistan. She earned her B.E. degree in elec-tronics from NED University of Engi-neering and Technology.

Bilal Muhammad Khan (bmkhan@pnec.nust.edu.pk) earned his Ph.D. and postdoc degrees in wireless com-munication networks from the Uni-versity of Sussex, United Kingdom. He is currently working as assistant professor at National University of Sciences and Technology, Pakistan.