green architecture for dense home area networks based on ...€¦ · 27342, and 17687 people per...

JOURNAL OF ELECTRONIC SCIENCE AND TECHNOLOGY, VOL. 14, NO. 2, JUNE 2016

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AbstractThe high-density population leads to

crowded cities. The future city is envisaged to encompass a large-scale network with diverse applications and a massive number of interconnected heterogeneous wireless-enabled devices. Hence, green technology elements are crucial to design sustainable and future-proof network architectures. They are the solutions for spectrum scarcity, high latency, interference, energy efficiency, and scalability that occur in dense and heterogeneous wireless networks especially in the home area network (HAN). Radio-over-fiber (ROF) is a technology candidate to provide a global view of HAN’s activities that can be leveraged to allocate orthogonal channel communications for enabling wireless-enabled HAN devices transmission, with considering the clustered-frequency-reuse approach. Our proposed network architecture design is mainly focused on enhancing the network throughput and reducing the average network communications latency by proposing a data aggregation unit (DAU). The performance shows that with the DAU, the average network communications latency reduces significantly while the network throughput is enhanced, compared with the existing ROF architecture without the DAU.

Index TermsData aggregation unit, dense home area network, green architecture, heterogeneous network, radio-over-fiber.

Manuscript received March 16, 2016; revised April 27, 2016. This work

was supported by the Ministry of Higher Education, Malaysia under Scholarship of Hadiah Latihan Persekutuan under Grant No. KPT.B.600-19/3-791206065445.

M. S. Abdullah, A. H. F. A. Hamid, N. Fisal, A. Lo, R. A. Rashid (corresponding author), and S. K. S. Yusof are with the Department of Communication, Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 Johor Bahru, Malaysia (e-mail: [email protected]; [email protected]; [email protected]; [email protected]; [email protected]; anthonylo@ ieee.org ).

M. A. Sarijari is with the Faculty of Electrical Engineering, Mathematics and Computer Sciences, Delft University of Technology, 2600 AA Delft, the Netherlands (e-mail: [email protected]).

Digital Object Identifier: 10.11989/JEST.1674-862X.603163

1. Introduction In the twenty-first century, cities have evolved

significantly and become key companions in social and economic development as over 50% of world population lives in cities and this trend may rise to 80% by 2020[1]. For instance, in Canada, most people live in urban areas, which are about 81% of the population[2]. On top of that, most of the famous cities in the world have a very high density of population, such as Paris, City of Westminster (London), and San Francisco, which comprise of more than 52590, 27342, and 17687 people per square mile[3],[4], respectively.

The high density population leads to transforming the cities with a high density of buildings and housings, heavy traffics on the road, complex drain and sewage systems, as well as high demands for electricity, water, and information and communications technology (ICT) services. All these must be efficiently planned and managed by the government, utilities companies, and technology providers to create safe, convenient, and sustainable cities.

Therefore, many research work has been carried out in order to propose the best model of smart devices or intelligent systems that have the capability to monitor and control certain infrastructures and facilities as well as autonomous reaction based on current situation, for example automated systems[5], smart homes[6], smart grids (SGs)[7], and intelligent transport management systems[8]. These ‘smart’ and ‘intelligent’ features depend on the communications system between the cognitive engine, sensors, and infrastructures or facilities. The communications backbone is enabled by heterogeneous wireless technologies, such as WiFi[9], ZigBee[10], Bluetooth[11], Universal Mobile Telecommunications System (UMTS), and Long Term Evolution (LTE)[12]. Hence, reliable, robust, and autonomous wireless communications play ever important roles in meeting the communications requirements and diverse quality-of-service (QoS).

In fact, in the era of Internet-of-Things (IoT), it is envisaged that a rapid decline occurs in the global usage of non-smart devices from 64% in 2015 to 33% by 2020, as the trend shifts to smart devices with about 8.2 billion

Green Architecture for Dense Home Area Networks Based on Radio-over-Fiber with

Data Aggregation Approach

Mohd Sharil Abdullah, Mohd Adib Sarijari, Abdul Hadi Fikri Abdul Hamid, Norsheila Fisal, Anthony Lo, Rozeha A. Rashid, and Sharifah Kamilah Syed Yusof


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handled or wireless-enabled devices by 2020[13]. Note that, the number of wireless-enabled devices can be assumed to be proportional to the population density. Thus, the density of wireless networks would be proportional to the rise in population and in the housing and building density of certain residential and office areas.

Consequently, the above aforementioned scenario will contribute to a dense heterogeneous wireless networks deployment especially at the user’s side: home and office. Since, people spend more and more time at home[14], a future urban home area network (HAN) is expected to be connected with a large number of wireless-enabled devices via a license-exempted frequency band for network communications with heterogeneous wireless networking technologies, in particular with WiFi, ZigBee, and Bluetooth[15],[16].

Hence, a dense and heterogenous HAN is a significant issue that needs to be addressed as soon as possible due to the fast growth in population and residential densities. As these wireless technologies are continuously evolving, the infrastructure has to be replaced each time. Obviously, it is very costly in terms of both the capital expenditure (CAPEX) and operational expenditure (OPEX), to keep up-to-date with current developments.

Thus, the exploration of a future-proof technology becomes vital, and the radio-over-fiber (ROF) is seen as a promising solution. Besides, a HAN also needs to support various applications, such as safety and security systems, home-entertainment, telehealth. These applications have diverse sets of QoS requirements including low to high data rates, real-time and delay-tolerant communications, and numerous data traffic patterns.

This phenomenon has introduced the problem of communications congestion and interference. Worse, in the near future, it is envisioned that every device in the house will need to communicate in order to support several functions (for example in the SG and IoT visions).

The clustered-topology is a key technique that can be used to address these issues. Therefore, a HAN can be divided into different clusters and each cluster can operate independently, while the global view of the whole network is managed by a controller. Consequently, the clustered-ROF-based network can provide sustainable, future-proof, and scalable home communications, which are the main characteristics of this green network architecture proposal and are also in line with the future features of green IoT[17],[18].

To the best our knowledge, this manuscript is the first to present clustered-frequency-reuse for spectrum management in a dense HAN based on ROF technology and introduce a new architectural component, called data aggregation unit (DAU) as the data concentrator in order to reduce communications latency and enhance network throughput. Our architecture caters for dense and

heterogeneous wireless-enabled device network communications in the HAN environment.

The remainder of the manuscript is organized as follows. Section 2 and Section 3 present an overview of state-of-the-art of the HAN architecture and the existing ROF-based HAN architecture, respectively. Section 4 explains the challenges and future communications requirements. Then, Section 5 describes our proposed green architecture for the dense HAN based on ROF with the DAU approach. In Section 6, the performance analysis of the proposed network architecture is presented as well as the comparison with the existing ROF architecture network performance in terms of average communications latency and normalized network throughput. Finally, we provide future work in Section 7 and conclude the manuscript in Section 8.

2. State-of-the-Art of HAN Architecture

In the near future, a HAN has to be able to support tremendous capacity and various QoS in the dense wireless-enabled devices environment. This scenario makes HAN architecture design and communications system become more crucial and challenging in enabling various applications including smart grid, security, voice call, video streaming, home entertainment, home smart appliances, and telehealth, as depicted in Fig. 1.

Fig. 1. Heterogeneous wireless technologies in HAN.

These wireless-enabled devices are located in a small physical space (i.e., limited by the size of the house), creating a dense network in a HAN. Therefore, any wireless-enabled devices in a HAN might interfere with one another unless an orthogonal frequency channel or different communications time slot is allocated for each wireless-enabled device. However, it is difficult to be

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implemented in a dense scenario, specifically in a high density residential area in a populated city as illustrated in Fig. 2. This means a wireless-enabled device also receives interfering signals from any HAN in neighboring residential units.

Fig. 2. Scenario of dense HAN in a high residential density area in a dense city.

Furthermore, due to the indoor environment[19], there is a possibility that each different area of the house is affected by interference from different neighboring HANs. Therefore, the conventional communications approach is impractical and inefficient to be used for HAN communications in the entire house. In essence, wireless network architectures as discussed in [20], [21], [22], and [23], proposed that a HAN can be operated either in an infrastructure mode or an infrastructure-less mode that is also known as an ad-hoc mode, depending on its application. For the ad-hoc mode, a wireless-enabled device communicates locally among each other within the same coverage area.

While, in the infrastructure mode, each HAN needs a dedicated access-point (AP) as the communications interface that allows the connection between wireless-enabled devices and the backbone of the network. As the wireless medium is shared, the AP plays a significant role in choosing the best frequency channel to operate in. However, most of APs may be deployed in a spontaneous manner by non-expert personnel without considering its neighboring networks due to the network ownership. Thus, it leads to a crucial coexistence issue which may result in interference problems from the neighboring HANs[24],[25].

Moreover, the current design of APs is dedicated for the single wireless networking technology. This means every wireless networking technology has its own AP in enabling network communications among wireless-enabled devices from the same wireless networking technology. Due to this limitation, it is difficult to integrate different wireless

networking technologies and technology upgrading in the future, which incurs a very high cost for replacing the outdated AP with a new one. For example, in the case of WiFi technology, if the cost of an AP is €10 and the number of subscribers is 1 million, the service provider has to spend a huge sum of money at least €10 million.

Apart from that, the OPEX is also too high for a new AP deployment and maintenance matters. To date, the latest technology of APs can perform spectrum sensing activities in order to choose the best frequency channel to operate in. However, the channel assignment can be done during the deployment only and then its cost is very expensive.

Hence, the existing network architecture design for HANs is not an economical and future-proof technology. The next generation of wireless communications is widely anticipated to have cross-technology network communications that integrate various wireless networking technologies and network architectures to provide green and sustainable wireless communications that meet the diversity of consumers’ QoS demands, especially in terms of throughput, latency, and mobile wireless device lifetime[26]-[29].

3. Radio-over-Fiber for HAN The ROF technology can be seen as the best suitable

candidate technology to solve all aforementioned problems and it also can be the underlying technology to design the desired HAN communications architecture[30]. Mainly, an ROF architecture, as shown in Fig. 3, encompasses two main components: a HAN communications controller (HCC) and multiple radio access units (RAUs). Basically, the fundamental principle of ROF technology is to convey the radio frequency (RF) signal via an optical fiber by converting the RF signal to optical signal. One of the main objectives is to extend the propagation of the RF signal from an HCC to multiple RAUs and vice versa.

This approach is advantageous because the HCC has a global view of the local frequency channel mapping and has a full control on frequency channel assignment. Thus, the coexistence issues can be eliminated by doing frequency-reuse planning[31]. Besides, the optical fiber technology[20],[27] offers a low signal attenuation, high capacity transmission, and flexibility to the HAN communications to enhance its performance, such as a high data rate, low latency, large coverage area, and line of sight (LOS) operation.

However, the existing ROF architecture is not suitable for dense and heterogeneous applications which encompass diverse sets of communications characteristics and requirements as shown in Fig. 4. The ROF is originally engineered for gigabit communications, thus transmission of small and infrequent packets, such as machine-to- machine (M2M) communications, is inefficient and


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impractical. The problem of inefficiency is further aggravated by massive M2M devices in a cell.

Fig. 3. ROF based HAN architecture.

4. Challenges and Future Communications Requirements

As discussed in previous sections, reliable, robust, and autonomous wireless communications play ever-more important roles in creating safe, convenient, and sustainable cities. Therefore, each constraint that exists due to dense and heterogeneous wireless-enabled devices needs to be resolved and future communications requirements must be taken into account to propose the best solution with a green and future-proof technology.

4.1 Challenges of Dense HAN

The dense wireless network will introduce several challenges[16],[32]-[34] in the forms of spectrum scarcity, latency, interference, capacity, energy consumption, and scalability[35], which have to be addressed as soon as possible because both the population and housing density are increasing every year.

The spectrum scarcity occurs when the radio spectrum demand is much higher than the allocated spectrum band. This spectrum outage often occurs in licensed-exempted bands in the case of a dense wireless network, particularly in the industrial, scientific, and medical (ISM) 2.4-GHz band. This is due to the fact that the bandwidth size of the ISM band is only 100 MHz and shared by thousands of wireless-enabled devices. Due to that, it also introduces the problem of communications latency, where each wireless-enabled device has to queue for a long time to access the communications channel.

The coexistence issue also results in the interference problem, i.e., homogenous and heterogeneous interference. Furthermore, in the dense HAN, the interference can be

further classified as in-house or neighbourhood interference. The in-house interference can be managed by using frequency planning and transmission scheduling method, but neighbourhood interference is very difficult to control, owing to the ownership of the network. Hence, even though when a HAN successfully manages in-house interference, it may suffer a power drain problem, due to the interference-combating activities to overcome neighboring interference.

Moreover, energy consumption is a critical issue for a battery-powered wireless-enabled device as the small battery pack is designed in order to make the wireless-enabled device compact and handy in line with the market demand and trend. There are two major activities that cause high-energy consumption in wireless-enabled devices: computational processing and interference mitigation. Therefore, an energy-aware communications protocol is specifically required to mitigate the interference and efficiently access the spectrum.

On the other hand, as mentioned previously, the size of the wireless network is growing every year, therefore a scalability feature needs to be embedded in the wireless network design to cope with the dynamic network topology. Besides that, the feature of self-organizing or plug and play is also important to make the wireless network more autonomous and less maintenance.

Apart from the above-mentioned challenges, another challenge is the diversity of communications requirements and characteristics from different wireless-enabled devices and applications that are based on different wireless technologies. The possible different communications requirements and characteristics in a heterogeneous HAN are shown in Fig. 4.

Fig. 4. Heterogeneous HAN communications characteristics and requirements.

In order to overcome the above-mentioned challenges, an orthogonal frequency channel has to be allocated between two or more neighboring networks for each wireless-enabled device. However, this approach is very difficult to achieve because a spectrum band has a very limited number of the orthogonal channels and may overlap


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with other wireless networking technologies. For example[36], IEEE802.11-Wireless Local Area Network (WLAN) and IEEE802.15.4-Wireless Personal Area Network (WPAN) have only 3 and 16 orthogonal channels in the ISM 2.4-GHz frequency band, respectively. If these wireless networking technologies coexist in the same physical space and at the same time only 3 WPAN channels will remain orthogonal. Since the WLAN signal strength is higher than that of the WPAN signal, the performance of a WPAN device will be significantly degraded by coexistence with WLAN signals. Therefore, all wireless-enabled devices should avoid operating in the same frequency channel that is used by others, and instead, operate in the available channel only. In order to achieve that, the ROF technology can be used as leverage.

4.2 Future Communications Requirements

The fifth generation (5G) network communications aim at higher capacity, lower latency, and more consistent gigabit experience in dense deployment. There are a lot of proposals in the literatures on technology vision of 5G[33],[34], and we summarize the 5G network communications requirements as flexibility, data rate, latency, reliability, and security.

Flexibility is a key design principle in 5G network communications, which is to support diverse devices’ QoS. For instance traffic pattern variations and various packet sizes require a dynamic resource allocation in term of frequency channel assignment. Besides that, the 5G network communications design needs to be easy and economical for future extension and enhancement.

Data rate determines the speed in gigabits per second (Gbps) at which information is churned out. For most of the 5G applications, the data-rate demand ranges from 1 Gbps to higher with the exception of machine-to-machine applications.

Latency is the time (in seconds) taken by a data packet to traverse from the source through communications networks to the destination. Latency depends on the data rate, physical source-destination distance, processing and queuing delay. In 5G scenarios, the tactile internet has the most stringent latency requirement as low as 1 ms, and multimedia and voice based applications also necessitate delay less than 10 ms and 100 ms, respectively.

Reliability refers to the dependability of the 5G network communications infrastructure. As mentioned in the literature, the communications infrastructure reliability should be at least 99.999% for security and safety applications and 99% for the other applications.

Security refers to the ability of a communications infrastructure to support secure end-to-end communications in order to make the communications free from external denial of service attacks and intrusion. For instance, smart grid is a huge system that spreads over large distances and comprises of many distributed electric components/field devices located in remote areas, which make these devices

vulnerable to the physical-attack. The communications infrastructure should be able to prevent, if not, detect the theft and vandalism as early as possible. All of the smart-grid applications expect a high security requirement.

5. Proposed Green Architecture for Dense HAN Based on ROF

with DAU In this manuscript, we propose a green architecture for

the dense HAN based on ROF with a DAU (GROFHAN) as shown in Fig. 5. This proposed network architecture meets the characteristics of green IoT as discussed in [17] and [18]. The main ‘green’ characteristics of this proposal are energy efficiency and sustainable technology solutions. The energy efficiency can be obtained by reducing the network communications latency, as high latency can cause high power consumption for communications activities such as medium access, re-transmission, and computational processing. Since this HAN’s architecture is based on ROF, the technology upgrading in the near future becomes easier and lower cost. These benefits lead to a future-proof technology of the ROF HAN (ROFHAN) and sustainable technology solution.

The network communications model can be classified as the hybrid architecture (i.e., a combination of infrastructure-based and infrastructure-less architectures). This model uses tree and mesh topologies for inter-tier and intra-tier communications among wireless-enabled devices in the network, respectively. This section, we consider three categories of wireless-enabled HAN devices which are based on devices’ characteristics: Category A (low power, low data rate, and delay-tolerant wireless-enabled HAN devices), Category B (low power, low data rate, and delay-sensitive wireless-enabled HAN devices), and Category C (high data rate, high power, and delay-sensitive wireless-enabled HAN devices), as also listed in Table 1.

Table 1: Category of wireless-enabled HAN devices

Category Power transmit Data rate Latency

Category A Low Low Tolerant

Category B Low Low Sensitive

Category C High High Sensitive

In this proposed GROFHAN architecture, it has four major components: a gateway, an HCC, multiple RAUs, a DAU, and wireless-enabled HAN devices. To date, in the existing ROF architecture as discussed in the Section 3, the DAU component is not proposed yet. The deployment of the DAU is scattered throughout the home and is connected to a dedicated RAU via a single communications. Each DAU forms a cluster of wireless-enabled devices of Category A within its coverage range.

While wireless-enabled HAN devices of Category B and Category C are connected directly to RAU, in this work,


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there are only two wireless networking technologies that will be considered: IEEE 802.11 (WiFi) and IEEE 802.15.4 (WPAN-ZigBee).

The functionalities of each network component (HAN gateway, HCC, RAU, DAU, and wireless-enabled HAN devices) are described as below:

Fig. 5. Proposed green network architecture for dense HAN communications based on ROF.

The HAN gateway plays the role as an interface between a HAN and a communications backbone/ISP or cloud. Currently, every technology has a dedicated gateway and is connected to the internet service provider (ISP) or cloud for internet access through an Ethernet or optical fiber cable. The other possible connection is via a wireless link, e.g., the Worldwide Interoperability for Microwave Access (WiMAX) or LTE network.

An HCC is responsible to extend the propagation of the RF signal to wireless-enabled devices and vice versa by using optical fiber, and at the same time keep its modulation scheme. Besides that, an HCC plays a vital role as the network communications engine that manages and coordinates the spectrum usage of the HAN. For this, the HCC needs to construct a spectrum map database for the particular HAN environment. This database consists of a list of channels that are used by the RAU and DAU in their cluster as well as the condition of each channel, i.e., the

statistics of channel activities including channel utilization. It is constructed from the information fed by the DAU.

From this database, the HCC will provide the RAU or DAU with the channels that they could use for their cluster. Therefore, the channels that are assigned to network components are optimal and not random. In addition, in this way, the HCC also knows which channels are being utilized by which network components, and which channels are still unallocated.

In this work, the channel allocation is based on the resource block approach. Channels that are allocated to the RAU or DAUs are called in-band resource elements, while channels that are not allocated are called candidate resource elements, seeing Section 5.2 for more details.

An RAU is a communications interface between the optical fiber and wireless medium. All the RF signals from any wireless networking technologies are converted to light wave for optical fiber communications and re-converted to


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radio signals at the RAU and vice versa. The assumption in this RAU design is that the antenna has the capability to receive any 2.4 GHz signal and it is a directional antenna.

A DAU is responsible for managing the usage of the allocated resource elements and performing the data aggregation activity from its wireless-enabled HAN devices of Category A. Once the buffer limit is exceeded or time out, the aggregated-data will be flushed to RAU. The DAU can request for more channels from the HCC if the current

in-band resource element is not enough to support its network cluster demand. Each DAU will utilize different channels from other DAUs, creating a distributed multichannel network in the HAN. The DAU is also required to report the channel utilization and occupancy to the HCC periodically in order for the controller to construct and keep the spectrum map database up to date. The DAU operation will be explained in more detail in Section 5.3 and as illustrated in Fig. 6.

Fig. 6. Fractional frequency reuse (FFR) deployment scheme in a cell of GROFHAN.

Wireless-enabled HAN devices are the devices that carry out various HAN applications including smart grid, security and safety, and home automation. These devices will connect to one of the clusters to get access and communicate with the HAN network based on mesh topology. We consider two types of wireless-enabled HAN devices: home and guest devices. Home devices are devices which belong to the HAN-owner, while guest devices do not belong to the HAN-owner. An example of a guest device is a neighbor’s device which needs to off-load its traffic, e.g., due to congestion in its own HAN network. Another example is a device that passes through the house that can take advantage of this feature to get Internet access.

5.1 GROFHAN Operation

The flowchart of Fig. 7 shows the operation of the proposed GROFHAN communications network. During the network startup, a DAU will establish a connection with the

HCC and a dedicated RAU. This will be the backbone of the HAN communications infrastructure. Then, for wireless-enabled HAN devices to get access to the HAN network, they will perform the network joining a procedure in which they need to connect to one of the existing RAUs or DAUs. It depends on the category of wireless-enabled HAN devices: whether Category A, Category B, or Category C (see Table 1 for details).

After successfully joining the network, wireless- enabled HAN devices will enter the network operating state. In this state, the device will be able to perform its communications task based on the designed communications protocol. Finally, when a device wants to leave the HAN network, it will execute the network leaving procedure informing the dedicated RAU or DAU that it is leaving the network. This is important for the HCC to take a note as it will then update its cluster member information for communications and spectrum allocation optimization.

(a)

(b)


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Fig. 7. Flowchart of the proposed GROFHAN communications network.

5.2 Spectrum Management in GROFHAN

As the ROF technology offers a global view of frequency channel assignment activities in a HAN, we propose to adopt the concept of fractional frequency reuse as in [37] to design a spectrum management scheme for GROFHAN based on clustered-approach. Since the RAU and DAU are infrastructure-based and stationary, we can strategically deploy these components to achieve good coverage and signal quality for the entire house as depicted in Fig. 6 (a). Each coverage of RAU is divided into 4 sectors, namely Center area, Area 1, Area 2, and Area 3, as illustrated in Fig. 6 (b). In order to do that, we assume that each RAU has 1 omnidirectional antenna for Center area and 3 directional antennas with 120° angle for other sectors.

The channel allocation for each network component is managed by a spectrum management mechanism that is located in HCC. The mechanism uses a spectrum map database, which consists of channel occupancy and channel state information. These parameters are used for decision making on channel assignment to meet the network demand. In the case of dense and heterogeneous network, it is difficult to allocate the orthogonal channel for each wireless-enabled HAN devices without considering the frequency reuse approach.

Therefore, in this proposed scheme, we use a method of resource block to develop a spectrum map database, as illustrated in Fig. 8. For instance, a HAN has 49 resource elements available to be allocated to the network components for enabling HAN communications and divided into 4 groups; i.e., Group 1 for Center area, Group 2 for Area 2, Group 3 for Area 3, and Group 4 for Area 4 as shown in Fig. 9 (a). Since the HCC collects the information of network demands from each cluster, segmentation of candidate resource elements is dynamic and it will be allocated according to the cluster demand.

There are two types of communications: i) the 1st Tier Communications that are the communications between DAU and wireless-enabled HAN devices with Category A characteristics and ii) the 2nd Tier Communications which are direct communications to RAU from DAU or wireless-enabled HAN devices under Category B or Category C. In this proposal, each cluster will be allocated with the orthogonal channel for any types of communications, as depicted in Fig. 9 (b).

Fig. 8. Spectrum map database.

(a) (b)

(c)

Fig. 9. Proposed clustered-frequency reuse for scheme on GROFHAN: (a) communications direct to RAU (2nd Tier), (b) clustered-frequency reuse scheme, and (c) communications through DAU (1st Tier).

For instance, for the 2nd Tier Communications, in T1, CH1 (RE1), CH2 (RE8), and CH3 (RE15) are allocated to G1, CH4 (RE22) and CH5 (RE29) are assigned to G2, while CH6 (RE36) and CH7 (RE43) are granted to G3 and G4, respectively. In this case, G3 and G4 are only allocated with a single resource element but the others with more resource elements. It is due to the demand from each group. Basically, the single resource element can support the wireless-enabled HAN device under Category A or Category B.

In order to maximize the network capacity, the frequency reuse approach is applied to G1 resource element for the 1st Tier Communications, as shown in Fig. 9 (c).

By using this clustered frequency reuse scheme, it helps to overcome the problem of spectrum scarcity and to avoid co-tier interference and cross-tier interference issues, which occur in dense wireless networks with a limited available spectrum bandwidth.

5.3 Proposed DAU Operation Design

Fig. 10 shows the proposed DAU architecture that is a new network component of the existing ROF architecture. The DAU acts as a data aggregator that aggregates packets


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from multiple wireless-enabled HAN devices (Category A) into a single large packet to the dedicated RAU. As a relay, the DAU increases the network capacity, and more importantly, reduces the transmission power of the devices that have power constraints (e.g., smart meters), and also decreases communications latency particularly in accessing the wireless channel for transmission.

This aggregation process is only performed at the physical layer in order to keep the design simple and future-proof, regardless of any wireless networking technologies. In this design, the intra-tier communications is allowed to operate in the single channel communications. To improve the uplink transmission efficiency, every RF signal, received at the antenna will be down converted to baseband signal and the baseband signal will be stored into the buffer for a certain time or threshold. The buffered-signal will be modulated with OFDM-like modulation, in which each signal is modulated by an orthogonal carrier (i.e. 0 1 1, , , nf f f , which are

orthogonal from one another), before they are aggregated (summed) and up converted to RF signal, then transmitted to RAU.

Fig. 10. Proposed DAU architecture.

On top of that, there is a limitation of the DAU, i.e., the buffer size. In the case of the extreme dense occurs at the DAU, the buffer size may be not able to cope with transmission demands from the larger number of connected wireless-enabled devices. This scenario will affect the network performance in terms of latency and throughput. Therefore, the off-loading mechanism is important in order to overcome this issue by controlling the maximum number of HAN’s devices that are allowed to connect with a DAU and transfer the current load to other DAUs which are located in the same coverage area with less transmission activities. However, in this manuscript, the mentioned limitation is not considered.

6. Performance Analysis 6.1 System Model and Simulation Setup

Let us consider a network model as shown in Fig. 11. It consists of an RAU, 3 DAUs, 360 Category A wireless-enabled HAN devices of which 120 are connected to each DAU, and 40 Category B wireless-enabled HAN devices. An event-driven simulation is set up to evaluate

the performance of the proposed HAN architecture. In this simulation, the following parameters are considered: maximum back off time to be 5, transmission duration to be 10 clock cycles, and the probability of packet generated at each HAN device to be 0.0002. The simulation is executed for 10000 clock cycles. We assume that the access mechanism uses carrier sense multiple access/collision avoidance (CSMA/CA), and each device communicates by using a single carrier system.

Fig. 11. Network model.

6.2 Simulation Results and Analysis

Fig. 12 shows the average latency for the proposed ROF-based HAN architecture with and without the DAU. It can be seen that in general the average communications latency increases with time. This is due to more packets generated and queued to be transmitted as time passes. By using a DAU in the communications system, the number of HAN devices that needs to communicate with the RAU can be significantly reduced (i.e., Category A HAN devices will be connected through a DAU). Subsequently, the number of contention is also reduced, as well as the latency, as observed in this figure. Therefore, power consumption is also reduced due to lesser communications overheads.

Fig. 12. Average communications latency with and without DAU.

The throughput performance of the proposed solution is shown in Fig. 13. During this time, more packets are


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generated, consequently increasing the channel contentions. For a system without the DAU, at a certain point during this time, no packet can be successfully transmitted to the RAU, causing the throughput to be exponentially decreased. In contrast, for the system with the DAU, the delay-tolerant packets are aggregated and sent through the DAU, and this will reduce the number of channel contentions significantly. As a result, the average communications throughput can be maintained throughout the time.

Fig. 13. Normalized communications throughput for the proposed radio-over-fiber based HAN.

It is very important to ensure the latency and the throughput to be guaranteed in order to ensure the delivery of any important information such as a fire alert, i.e., carried by a Category B HAN device. Furthermore, in the case of a high density HAN, the number of DAUs can be increased to further reduce any possibly-high communications latency and sustainably maintain the communications throughput.

7. Future Work In order to improve the proposed network architecture

design, there is some future work which needs to be taken into account to enhance the proposed HAN’s architecture based on ROF with the DAU approach. One of the biggest tasks is the resource allocation. This is because a house consists of multiple clusters which utilize multiple channels that can be different from one another, hence allocating the optimal channels to each cluster and the corresponding devices in the cluster have become very important. Besides that, the capability of off-loading mechanism will help to avoid the congestion problem in the certain area of HAN. On top of that, the coexistence issue for guest-HAN’s devices also needs to be further investigated to provide a complete solution for the dense HAN scenario. Therefore, this proposed architecture needs to be further analyzed to find the optimal buffer size, maximum number of devices for each DAU and RAU, and optimal channel allocation for each cluster.

8. Conclusions HAN is becoming more challenging from year to year,

because of a rapid development of communications technologies, a high number of devices available in a house that is situated in a dense city, and the existence of heterogeneous types of devices. A sustainable solution has to be drawn to ensure the continuous-success of this communications. In this paper, we proposed a green communications architecture for the dense HAN in a dense city based on the ROF technology. The proposed architecture can provide a future-prove solution, and sustainably support the ever-increasing communications demands in a house. We also have shown that with the proposed DAU in the communications system, the communications latency can be reduced significantly and the network throughput is enhanced, compared with the existing ROF architecture.

Acknowledgment The authors would like to thank all those who contributed

toward making this research successful and all the reviewers for their insightful comments.

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Mohd Sharil Abdullah was born in Pahang, Malaysia. He received his bachelor (with honors) and master degrees in electrical engineering from University Teknologi Malaysia (UTM), Johor Bahru, Malaysia in 2003 and 2009, respectively. Currently, he is a Ph.D. candidate with UTM-MIMOS Telecommunication Technology Research Group, Faculty of


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Electrical Engineering, UTM. He has worked with UTM since 2004. His current research interests include in the fields of wireless communication networks, radio-over-fiber, software-defined networking and wireless sensor networks, and ad-hoc communications systems.

Mohd Adib Sarijari was born in Johor, Malaysia in 1984. He received the bachelor degree in engineering (first class) in 2007, and the master degree of science in electrical engineering in 2011 both from UTM. He is currently working with the Department of Communication Engineering, Faculty of Electrical Engineering, UTM. Since 2012, he

has been pursuing the Ph.D degree with the Circuits and Systems Group, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Delft, the Netherlands. His general research interests include the fields of communications, optimization, electronics, and programming. In particular, he is interested in cognitive radio, home area networks, wireless sensor networks, software defined radio, and smart city.

Abdul Hadi Fikri Abdul Hamid was born in Baling Kedah, Malaysia in 1984. He received the B.Eng. and M.S. degrees from UTM in 2007 and 2011, respectively, both in electrical engineering. He is currently pursuing the Ph.D. degree with the Telematics Research Group, UTM. His research interests include wireless sensor networks, cognitive radio, and embedded systems. Norsheila Fisal received her B.S. degree in electronics communication from University of Salford, Manchester, United Kingdom in 1984, the M.S. degree in telecommunication technology, and the Ph.D. degree in data communication, both from University of Aston, Birmingham, United Kingdom in 1986 and 1993, respectively. Currently, she is a professor with

the Faculty of Electrical Engineering, UTM, and the Head of the UTM-MIMOS Center of Excellence in Telecommunication Technology. Her research interests include wireless communication networks, next-generation networks, and Internet-of-Things.

Anthony Lo received the combined B.S. and B.E. degree and the Ph.D. degree in 1992 and 1996, respectively from La Trobe University, Bundoora, Australia. He is currently a senior 5G research and standards engineer at Nokia. He has 18 years of experience in wireless communications with Huawei Technologies (R & D Center) Sweden, Ericsson Eurolab

Netherlands, Center for Wireless Communications Singapore, and Delft University of Technology in the Netherlands, respectively. His research interests include M2M communications, intelligent transportation systems, smart grids, and the wireless networks

Rozeha A. Rashid received her B.S. degree in electrical and electronic engineering from University of Michigan, Ann Arbor, USA in 1989. She received her M.E.E. and Ph.D. degrees in telecommunication engineering from UTM in 1993 and 2015, respectively. She is a senior lecturer with the Department of Communication Engineering, Faculty of Electrical Engineering,

UTM. Her research interests include wireless communications, sensor networ, cognitive radio, and Internet-of-Things.

Sharifah Kamilah Syed Yusof received her B.S. degree (cum laude) from George Washington University, Washington, USA in 1988. She received her M.E.E. and Ph.D. degrees in electrical engineering from UTM in 1994 and 2006, respectively. Currently, she is an associate professor with UTM where she has been since 1988. Her current research interests include

fields of wireless communication networks, cognitive radio systems, and molecular communications.

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