Next Article in Journal
Risk Profile Indicators and Spanish Banks’ Probability of Default from a Regulatory Approach
Next Article in Special Issue
A Performance Analysis Model of TCP over Multiple Heterogeneous Paths for 5G Mobile Services
Previous Article in Journal
A Systems Analysis of Factors Influencing Household Solar PV Adoption in Santiago, Chile
Previous Article in Special Issue
A Self-Scrutinized Backoff Mechanism for IEEE 802.11ax in 5G Unlicensed Networks
Article Menu
Issue 4 (April) cover image

Export Article

Sustainability 2018, 10(4), 1258;

5G-Based Transmission Power Control Mechanism in Fog Computing for Internet of Things Devices
Electrical Engineering Department, Sukkur IBA University, Sukkur 65200, Pakistan
Decision and Information System for Production System LAB, University Lumiere Lyon2, 69500 Bron, France
CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology (SIAT), Shenzhen 518055, China
Institute of Biomedical and Health Engineering, SIAT, Chinese Academy of Sciences (CAS), Shenzhen 518055, China
School of Computing Science and Engineering, VIT University, Vellore 632014, India
Department of English, University of Sindh, Jamshoro 71000, Pakistan
Department of Physics, Shah Abdul Latif University, Khairpur Mirs 66111, Pakistan
Department of the Computer Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
Correspondence: [email protected]
These authors contributed equally to this work.
Received: 22 February 2018 / Accepted: 4 April 2018 / Published: 14 April 2018


Fog computing has become the revolutionary paradigm and one of the intelligent services of the 5th Generation (5G) emerging network, while Internet of Things (IoT) lies under its main umbrella. Enhancing and optimizing the quality of service (QoS) in Fog computing networks is one of the critical challenges of the present. In the meantime, strong links between the Fog, IoT devices and the supporting back-end servers is done through large scale cloud data centers and with the linear exponential trend of IoT devices and voluminous generated data. Fog computing is one of the vital and potential solutions for IoT in close connection with things and end users with less latency but due to high computational complexity, less storage capacity and more power drain in the cloud it is inappropriate choice. So, to remedy this issue, we propose transmission power control (TPC) based QoS optimization algorithm named (QoS-TPC) in the Fog computing. Besides, we propose the Fog-IoT-TPC-QoS architecture and establish the connection between TPC and Fog computing by considering static and dynamic conditions of wireless channel. Experimental results examine that proposed QoS-TPC optimizes the QoS in terms of maximum throughput, less delay, less jitter and minimum energy drain as compared to the conventional that is, ATPC, SKims and constant TPC methods.
Fog computing; Internet of Things; QoS optimization; transmission power control; constant TPC; ATPC; SKim

1. Introduction

5G technology has become the center of attention in every corner of the world due to its highly resource allocation and flexible nature. In the meantime, Fog computing, edge/cloud computing and Internet of Things (IoT) and so forth, have revolutionizing many sectors such as, health, education, enterprises and industries by enhancing their resources that is, functional costs, power usage and quality management and so forth. Fog computing and IoT as a whole is portraying the clear image of the entire map and further will be explored, enriched and totally transformed from their previous conditions. It is analyzed that nearly 57% of the world’s population will be facilitated by IoT system with high resources. But one of the challenging issues of the IoT devices and networks is the high-power drain and limited battery lifetime with regular recharging from the external sources. The wireless link or channel status varies with different conditions such as, interference from same network devices, internal noise and environmental factors and so forth, also natural hindrances for example, roofs and walls are degrading the signal strength at the larger level. Poor reliability is also adversely affecting the by increasing more delay and overhead of packets during transmission of packets. To enhance the channel quality in terms of high signal delivery and less placket loss ratio (PLR) in IoT enabled sensor networks, minimizing the power drain and extending the battery lifetime of the sensor based devices is the first and foremost priority. As sensor nodes from several manufacturers such as, TelosB, MicaZ and so forth, dissipates the more power than the other parts for example, CPU, hard-drive and ROM and so forth. In addition, it increases the contention in the network. For prolonging the network lifetime and increasing transmission reliability (i.e., reducing PLR), transmission power control (TPC) mechanism is most appropriate one because it increases/decreases power according to the need of the end user and predefined threshold levels by adopting the channel conditions. Key purpose of TPC strategy is to achieve optimal transmission power, a power level that does not break the already established link between a pair of nodes and avoid the contention in the network. The decision to change transmission power level based on link quality indicator (LQI) is not appropriate, because of less convincing to get rid of environmental disturbances and deviation from the predefined threshold levels. That is another entity received signal strength indicator (RSSI) is adopted accurately analyze the quality of the receiver’s signal then adapt the transmission power in an on-demand fashion accordingly. This fluctuation in the power level in a dense network increases the interference resulting in a collision and ultimately high packet loss ratio (i.e., less reliability). This paper, therefore, investigates the impact of TPC on the quality of the entire network. It is also observed that when the fluctuation in wireless channel and difference in transmission power levels is longer than interference and re-transmission rate of packets increase. Hence, less quality of service and more power drain.
Furthermore, 5G based Fog computing and IoT have become the part and parcels of our everyday routine by examining and analyzing the entire environment to take the strong initiative for the present and future trends [1]. In order to realize the full benefits of the IoT, it will be necessary to provide sufficient networking and computing infrastructure to support low latency and fast response times in various applications. Cloud Computing in the key enabler for IoT applications due to its ample storage and processing capacity. Nonetheless, being far from end-users, cloud-supported IoT systems face several challenges including high response time, heavy load on servers and lack of global mobility.
We rigorously describe the TPC based mechanisms for QoS optimization in distinct networks with main focus at the 5G-enabled adaptive transmission power control algorithm for the QoS optimization and monitoring in the Fog computing by considering the static and dynamic channel characteristics. Many authors have already contributed significantly in revolutionizing the entire wireless and sensor worlds for efficient and closed communication between heterogeneous networks, especially, IoT, Fog and cloud computing and so forth. Whereas, very few have explored the QoS domain and still there is no proper and effective method to fix the QoS optimization and monitoring in the IoT and Fog computing with the transmission power control. Also, the static and dynamic behavior of the channel during QoS optimization in Fog and IoT networks needs to be considered. At the same time the rapid proliferation in the emerging market of the miniaturized IoT devices have facilitated the consumers on the one hand, while on the other hand, their power-hungry nature and limited battery lifetime have created several challenges in the Fog and IoT networks. So, keeping this demand into mind we have taken into account the notion of TPC-based QoS optimization in Fog computing by considering the static and dynamic wireless channel states.
As the number of connected devices increases exponentially, achieving higher network capacity and reliability with lower latency and energy consumption is challenging. It is estimated that the IoT will cause the Internet Protocol (IP) traffic to increase up to 300% by 2018 [1]. Although not all the network embedded devices (e.g., sensor nodes) will communicate simultaneously among each other and outside the network due to highly dense and multi-hop nature. Thus, it is crucial to investigate that how the QoS of IoT networks is affected with conventional methods and what changes are effectively made by the proposes algorithm.
Furthermore, remote deployment of an IoT network makes it difficult for the field technician to replace the battery sources, hence, prolonging the battery lifetime of the sensor nodes is very vital. This research aims to resolve the challenging problems, for instance, how to optimize the QoS in the Fog and IoT system by adopting the transmission power control strategy? To establish the strong and appropriate connection between the QoS metrics for example, throughput, delay, jitter and energy drain and TPC in the presence of the static and dynamic channel conditions? How to develop the state-of-the art Fog framework in-line with the IoT and TPC?
This paper contributes in two ways.
  • We propose a novel 5G enabled Transmission Power Control (TPC) algorithm for QoS optimization titled (QoS-TPC). In addition, tradeoff between Transmission Power Control and QoS metrics such as, throughput, delay, jitter and energy consumption is established by considering the static and dynamic channel features. Besides, the proposed algorithm is compared with the conventional adaptive transmission power control (ATPC), SKims and constant TPC methods.
  • Framework of the 5G-based TPC for QoS optimization in the Fog and IoT system is proposed, as shown in the Figure 1.
The rest of the paper is structured as follows. Section 2, reviews the rigorous literature about Fog computing, IoT system, QoS, 5G-based TPC and wireless channel and so forth. Section 3, proposes the 5G-based Transmission Power Control (TPC) algorithm for QoS optimization in Fog networks. Experimental results extracted in Section 4, paper is concluded in the Section 5.

2. Related Work

5G-based transmission power control (TPC) for Quality of service (QoS) provisioning is the key ingredient, while its optimization over the Fog and IoT system is the biggest hurdle to fulfil the needs of the network [1,2,3,4,5]. In the literature, various power management algorithms have been proposed such as, power control for the fair time slot allotment, adaptive and dynamic nature power control, duty-cycle enabled power control and so forth. Authors in [6,7,8,9,10] discuss that the dynamic power management (DPM) chooses the power levels in a random fashion to save the energy in the sensor networks but it has limitation to get the entire information of the past and future data which is very complex task. Similarly, the power control algorithm (PCA) in [11] adjusts the power levels by considering the needs of channel but the correlation between nodes is very weak that is why large portion of the power is dissipated in the control message transmission. While time slot control based principle offers the longer idle span, hence, more energy will be saved as compared to schemes in [12]. Authors in [13,14] design the sleep wake-up mechanism to save the more energy by increasing the sleep time of nodes, besides there is an automatic switching between these two states. Authors in [15], establish the trade-off between energy drain and he delay between transmitter and receiver nodes to manage the duty-cycle of the entire network. Authors in [16,17,18,19], design the closed-loop power control mechanism by adopting the various channel states but they do not focus at the QoS optimization in the Fog computing. Researchers in [20], present the signal to noise ratio based channel allotment scheme and they followed the work in the [21,22] for investigating the role of bandwidth and channel in effective resource allocation. Generally, in the sensor networks IoT and Fog computing reliability and TPC are inter-related and hence, the better QoS classifiers. Hence, it is very vital to build the track between QoS, channel, TPC, energy efficiency and the battery lifetime, for further details see Table 1.
Most of the traditional schemes are helpful to save the power and somehow QoS but still there is large room vacant to deal with the TPC based QoS optimization in the Fog and IoT in the presence of various wireless channel forms. Thus, a very few related works are discussed one by one. As APTC [3], develop the adaptive TPC algorithm to save the energy in the WBANs by considering the various body postures and scenarios, besides their work optimizes the channel parameters and compared with the traditional methods. But they do not focus at the joint TPC and QoS optimization approach for the Fog and IoT. Similarly, SKims [2], re-enforcement learning based TPC based method is very efficient to optimize the QoS in IoT networks but their power adaptation mechanism is very complex from computational point of view and not very effective. On the contrary our proposed TPC based QoS algorithm is very simple, effective and requires very few control packets while exchanging/transferring information between the transmitter and receiver. Moreover, their research do not broadly present the interconnection between TPC and various network metrics in the presence of the static and dynamic channel conditions. Last traditional algorithm is the constant TPC, which either saves more energy or shows more reliability and do not possess both qualities at a time, so it is not the potential candidate for the IoT and Fog computing environment.
All the aforementioned research works have worked in the diverse domains with the distinct goal and target to be achieved. Few of the emerging areas are described as, energy efficiency, resource allocations, TPC, QoS control, power monitoring and management in different direction that is, sensor networks, IoT, cognitive radios, cellular networks, wireless networks, wireless body area/sensor networks and so forth. But this paper presents very remarkable contribution by proposing 5G-based TPC algorithm for QoS optimization and adopting wireless channel’s entire features, network metrics, Fog computing, IoT and so forth. Besides, 5G-based Fog computing and IoT framework is proposed.

3. Proposed Transmission Power Control Mechanism

In this section we first propose the block diagram of the 5G-based TPC mechanism, then explain the TPC in detail with key focus at the QoS optimization in the Fog and IoT system.

3.1. Block Diagram

For the efficient and intelligent examination of the wireless channel, received signal strength indicator (RSSI) is considered as an emerging entity for the Fog computing and IoT-enabled nodes while fairly allocating the transmission power and hence the QoS optimization. It is estimated within the transceiver radio by attaining the normal value of the signal power over multiple symbol periods, that is, eight of the received information packet analogous to distinct body movements and gestures of individuals [1,2]. RSSI explains the strength of received power at the destination node and is evaluated by the time, transmission power (TP) and distance among other nodes. The reliability of channel is proportional to RSSI which is further used to characterize it. The sensor node at sender side transmits a packet after every 200 ms with TP levels in between 0 dBm and −25 dBm. Receiver sets RSSI threshold level of −100 dBm, which shows packet loss/worst channel condition. Moreover, the path loss calculation for both static and dynamic channel states are considered at the 2.4 GHz frequency. It is observed that there is relatively high path loss in dynamic channel condition and higher frequency, however, packet loss is very low in static case and lower frequency [2]. We used accelerometer sensors to detect static and dynamic channel states accordingly. Hence, this study proposes TPC based framework for considering dynamic and static cases to efficiently adapt the channel deviations for optimizing the QoS as shown in Figure 2. In the proposed framework, receiver obtains one data packet from transmitter then calculates RSSI; if its value exceeds RSSI target value, then acknowledgment (ACK) of short inter-frame space period (pSIFS) will be sent to transmitter side. Also signaling overhead is not taken into account, less power is consumed in a static case unlike the dynamic.

3.2. Transmission Power Control Based QoS Optimization

As Fog computing is one of the 5G’s intelligent services and this sub-section proposes and presents in detail the 5G-enabled TPC algorithm for QoS optimization (QoS-TPC). Our proposed QoS-TPC can be run by the receiver as well as by the transmitter sensor nodes as following the QoS requirement of sensor nodes in Figure 3. For the sake of ease, we suppose that only receiver performs TPC. The entire working principle of the proposed QoS-TPC is discussed below.
First, the receiver computes the average RSSI ( R ¯ ), (steps 2 and 4) for each latest and lowest (i.e., obtained after latest sample) received samples before determining transmission power (TP) level. Assume the TP and the corresponding RSSI at the receiver for the latest (i.e., current) sample is P t and R l a t e s t both in dBm respectively, similarly the lowest sample (i.e., received after latest sample) have transmission power and RSSI as P t Δ P i and R l a t e s t 1 respectively, where i = 1 , 2 , , N shows number of TP levels for CC2420 radio. After receiving the RSSI sample, the BS updates the average RSSI, R ¯ , according to the Equations (1) and (2).
R ¯ = R l a t e s t + ( 1 α 1 ) × R l o w e s t
R ¯ = R l a t e s t + ( 1 α 2 ) × R l o w e s t
whereas, α 1 and α 2 are the average weights of RSSI sample exhibiting good and bad state channels, accordingly.
The BS compares the value of R ¯ with the known target RSSI ( R t arg e t ) and then decides the TP level by using Equation (3).
Δ P = arg { Δ P 1 , Δ P 2 , . , Δ P N min ( ( R t arg e t R ¯ Δ P i ) 2 ) } s . t . Δ P i >   R t arg e t R ¯
whereby, N is the number of TP levels and at least log 2 ( N ) bits are required to exploit respective value for TPC.
The principle ingredients of the proposed QoS-TPC algorithms are the average RSSI ( R ¯ ), target RSSI ( R t arg e t ), α 1 ; coefficient of the good channel and coefficient of the bad channel α 2 with higher and lower threshold levels T R H , T R L , respectively.
Proposed 5G-based QoS-TPC algorithm adjusts the transmission power in an on-demand fashion by considering the entire features of the wireless channel in Fog and IoT system. If T R H greater than R t arg e t (step 6), the transmission power is decreased (step 7) to save energy. On the other hand, if T R L falls below the R t arg e t (step 6), the transmission power is increased (step 7) to improve channel reliability. Similarly, transmission power adaptation guarantees effective and reliable communication in Fog and IoT. Finally, we should also make sure that the power for each transmission shall neither exceed P max nor drop below P min , (steps 11 and 14). Proposed QoS-TPC algorithm is simple and easy to implement because small computational complexity is introduced to the receiver and the transmitter sensor nodes. Furthermore, the proposed QoS-TPC does not require large signaling overhead because only few bits are needed for the acknowledgment data packets as shown in the Figure 4.
The key aim to adopt the lowest RSSI samples is to keep consistency in the data transmission by getting feedback information about the power levels. This is the first step to introduce the TPC, Fog and QoS optimization mechanism in the IoT. We verified through experimental results in MATLAB that proposed algorithm outperforms the conventional IoT TPC such as ATPC [3], SKims [2] and constant TPC methods in terms of energy saving, RSSI stability, packet loss ratio (PLR), throughput, delay and jitter as shown in Figure 4, Figure 5 and Figure 6. Wireless channel with two experimental cases such as, static and dynamic is used and observed that proposed algorithm outperforms the traditional methods in terms of RSSI stability, acceptable reliability, average throughput, more energy saving, less delay and jitter values.

3.3. Performance Metrics for IoT SYSTEM

In this section, we present the trade-off between 5G-enabled transmission power and the different network metrics, such as throughput, delay, jitter and energy consumption level, for our proposed TPC-QoS algorithm and conventional TPCs, such as ATPC, SKims and Constant TPC methods, in the Fog and IoT system. We follow the IEEE 802.15.4 IoT’s standard MAC with physical layer [3,23], each network entity will be discussed briefly. Main notations and symbols of this section are described as:
T P H R = Transmission time of PHY header
T P = Transmission time of preamble
R D a t a = Data transmission rate
T s = CSMA slot length
T ρ S I F S = Short inter-frame spacing time
T C C A = Clear channel assessment time
M H R = MAC header
F T R = MAC footer
T A c k = Acknowledgement
τ = Propagation delay
P L R = l s
M a x i m u m T h r o u g h p u t = 8 × p l d e l a y ( p l )
d e l a y ( p l ) = W I N a v g + T D a t a + T A c k + 2 T ρ S I F S + 2 τ
W I N a v g = W I N min . T s 2 = W I N min . ( T C C A + 20 μ sec ) 2

3.3.1. Maximum Throughput

It is defined as the ratio of the payload size ( p l ), the total transmission delay that is, d e l a y ( p l ) as given in Equation (6).

3.3.2. Delay

It is defined as the time span when an event occurs while transmitting/receiving the first packet.

3.3.3. Jitter

Jitter is the deviation in delay, caused by random inter-arrival time spikes of the several transmitted and dropped/re-transmitted packets. In many cases it is defined as a measure of the variation in the packet’s delay over time in the entire network.

3.3.4. Energy Consumption

Due to energy-constrained nature of sensor nodes in Fog and IoT system the life-time of these devices will be shortened, so to remedy this problem TPC is one of the efficient and effective solutions to optimize the QoS in Fog and IoT networks.

3.3.5. Packet Loss Ratio (PLR)

It is defined as the ratio of total number of lost packets ( l ) to the transmitted packets ( s ), it always measures in (%).
Furthermore, p l , W I N a v g and T D a t a , are the payload size, average back-off time, and, transmission delay of the Physical Layer Protocol Data Unit (PPDU), accordingly. Besides, it is calculated based on the R t arg e t (−85 dBm) and threshold levels ( T R L = −88 dBm, T R H = −83 dBm) of the RSSI values.
T D a t a = T P + T P H R + 8 × ( M H R + x + F T R ) R D a t a
The Δ in Equation (9), represents the deviation in RSSI value for proposed algorithm and conventional TPC methods, R i is the RSSI latest samples, where i = 1 , 2 , , n and R t arg e t is the RSSI target.
Δ = 1 n i = 1 n ( R i R t arg e t ) 2 , n   shows   RSSI   samples

4. Experimental Environment

In this section, the performance of the typical conventional TPC methods, such as constant transmission power control, ATPC [3], the SKims method [2] and our proposed 5G-based QoS-TPC algorithm in IoT is compared and evaluated through simulations in MATLAB with respect to average values of RSSI and transmission power. We use the real-time data sets of two channel cases such as, dynamic and static provided by NICTA [22]. Table 2, presents detailed simulation parameters. In addition, we adopt average transmission power to analyze QoS optimization level of our proposed 5G-based QoS-TPC algorithm in comparison with conventional TPC methods and showed that the proposed algorithms significantly optimize the QoS in the IoT systems in terms of throughput, delay, jitter and energy saving (40.9%), hence it is the potential candidate, as shown in Figure 5 and Figure 6.
Figure 5 and Table 2, present the comparison of transmission power (dBm) and corresponding RSSI (dBm) values in the first 60 s between conventional TPC methods and our proposed TPC-QoS algorithm by considering two channel states that is, dynamic and static, respectively in frequency band 2.4 GHz. The analysis showed that the constant TPC adjusts the deviations in the channel by sacrificing more energy, so it provides more reliability and high energy consumption. Through experimental results it is clear that the proposed algorithm (transmission power= 5.67 (mW), RSSI value = −81.25 dBm, Avg. energy consumption (mJ) = 0.37, PLR = 3.63%, = 5.53 dBm), while the constant TPC (transmission power = 7.23 (mW), RSSI value = −69 dBm, Avg. energy consumption (mJ) = 3.28, PLR = 3.47%, = 8.80 dBm), ATPC (transmission power = 5.95 (mW), RSSI value = −80.29 dBm, Avg. energy consumption (mJ) = 1.27, PLR = 3.53%, = 5.60 dBm) and SKims (transmission power = 6.99 (mW), RSSI value = −78.67 dBm, Avg. energy consumption (mJ) = 1.55, PLR = 3.57%, = 5.79 dBm), at the dynamic channel state as shown in Figure 5a,b and Table 3.
Similarly, Figure 5c,d and Table 3, represents the transmission power and RSSI for the proposed algorithm (transmission power = 5.61 (mW), RSSI value = −80.96 dBm, Avg. energy consumption (mJ) = 0.34, PLR = 3.60%, = 5.43 dBm), while the constant TPC (transmission power = 7.01 (mW), RSSI value = −75.3 dBm, Avg. energy consumption (mJ) = 3.26, PLR = 3.40%, = 7.53 dBm), ATPC (transmission power = 5.83 (mW), RSSI value = −80.50 dBm, Avg. energy consumption (mJ) = 1.25, PLR = 3.50%, = 5.570 dBm) and SKims (transmission power = 6.96 (mW), RSSI value = −79.23 dBm, Avg. energy consumption (mJ) = 1.53, PLR = 3.48%, = 5.75 dBm), at static channel state. For further details see the Table 2.
Generally, there is more variation in dynamic case than the static one with proposed QoS-TPC algorithm and conventional TPC methods. Our proposed algorithm exhibits less TP, more RSSI stability, less packet loss ratio, less energy consumption than ATPC, SKims and constant TPC methods, in other words proposed QoS-TPC surpasses the typical conventional IoT TPC methods as shown in Table 3.
The Figure 6 and Table 2, show the performance of our proposed QoS-TPC algorithm and typical conventional IoT TPC methods in terms of network metrics such as, throughput, delay, jitter and energy consumption level. Figure 6a presents trade-off between transmission power (TP) and average throughput for our proposed algorithm and conventional TPC methods for IoT, in which it is verified that throughput increases with the increase of transmission power of 500 kbps, 450 kbps, 400 kbps, 250 kbps for proposed algorithm, ATPC, SKims and constant TPC methods respectively.
Experimental results show that proposed 5G-based QoS-TPC enhances performance by maximizing throughput about 500 kbps, while constant TPC has lowest throughput than other conventional TPC methods. Figure 6b, presents the relationship between TP and average delay for proposed algorithm and ATPC, SKims, constant TPC methods. The analysis shows that average delay decreases with the increase of TP and there is an average delay value of 7.5 ms for our proposed algorithm—the constant TPC method has a longer average delay of about 8.5 ms, while the ATPC and SKims methods exhibit 7.7 ms and 7.8 ms, respectively. Figure 6c illustrates the effect of TP on jitter for proposed algorithm and conventional TPC methods. Through simulation results in MATLAB it is observed that jitter decreases as the TP increases.
Apparently, proposed 5G-based QoS-TPC and ATPC method has almost same jitter of 7.4 ms and SKims method exploits 7.8 ms jitter value, while constant TPC method reveals jitter of 8.5 ms which is higher than proposed algorithm and other conventional TPC methods. Figure 6d, explores the relation between TP and average energy consumption for proposed algorithm and conventional IoT TPC methods. We analyzed that average energy consumption minimizes with the increase of TP.
It is evident from Figure 6 that the transmission power and average energy consumption of proposed QoS-TPC algorithm is less than the conventional TPC methods or in other words we can say that our proposed algorithm saves energy of 40.9% which is higher than ATPC, SKims and constant TPC methods. Nevertheless, proposed algorithm surpasses the conventional TPC methods.
Deviation in RSSI values for conventional TPC methods and our proposed algorithm with target RSSI ( R t arg e t ) is determined by using Equation (9). We analyzed that ATPC and SKims TPC methods can maintain the RSSI at a relatively stable level and constant TPC method maintains RSSI at very low level while our proposed algorithm maintains RSSI at more stable level than all conventional TPC methods as shown in Table 3. Hence, we can say that our proposed 5G-based QoS-TPC algorithm outperforms in terms of RSSI stability, throughput, delay, jitter and energy saving (see Table 3 for detail) than conventional TPC methods. In Table 3, it is shown that there is slightly more RSSI deviation and packet loss ratio in dynamic case than the static one, which affects the transmit power level and RSSI stability of conventional TPC methods more than our proposed 5G-based QoS-TPC algorithm.

5. Conclusions and Future Research Work

Due to the emerging and revolutionized role of 5G in every aspect of the human life, this paper proposes a 5G-based TPC algorithm for QoS optimization in the Fog computing and IoT system with static and dynamic wireless channel features at a frequency of 2.4 GHz. Transmission power is adapted according to the dynamic and static channel states. We examine and compare the performance of proposed 5G based QoS-TPC algorithm with traditional constant TPC, APTC and SKims methods in terms of transmission power, RSSI values and network metrics that is, throughput, delay, jitter and energy consumption and showed that constant TPC drains more energy with poor RSSI performance in both static and dynamic channel conditions. Besides, it is observed through experimental results that there is more variation in the dynamic case than in the static in the Fog computing and IoT systems. In addition, proposed 5G based QoS-TPC presents more stable RSSI value than traditional TPC methods, in the mean-time limitations of the orthodox methods are addressed with the supportive reasons while optimizing the QoS (i.e., minimum transmission power, more RSSI stability (i.e., less variation), less packet loss ratio, high throughput, less delay, less jitter and maximum energy saving of 40.9%. Finally, it can be said that the remarkable contribution of the proposed 5G-based QoS-TPC algorithm in optimizing the QoS is made unlike the conventional methods.
Following are the few limitations of the proposed QoS-TPC.
  • PLR increase due to more energy saving, which is not suitable for the critical event analysis.
  • High RSSI value is needed to perform well, which is no appropriate to QoS-sensitive applications
  • Delay and jitter values are slightly increasing
We will use our proposed algorithm with Adaptive modulation and cooperative communication to save energy in IoT and Fog computing systems.


This work is supported in part by the HEC Pakistan under the START-UP RESEARCH GRANT PROGRAM (SRGP) #21-1465/SRGP/R&D/HEC/2016 and Sukkur IBA University, Sukkur, Sindh, Pakistan. And, Natural Science Foundation of China 6171101169, Guangdong Natural Science Foundation 2015A030313782, the Science and Technology Innovation Committee Foundation of Shenzhen JCYJ20170817112037041, SUSTech Startup Fund Y01236215.

Author Contributions

Sodhro, A.H. and Pirbhulal, S. prepared the literature review and performed the experiments and and composed the manuscript, Pirbhulal, S., Sangaiah, A.K., Lohano, S. scrutinized the data, developed Methods and Experiments, Sodhro, A.H. and Sodhro, G.H. compiled the experimental results, Luo, Z. supervised the research activities and devised the systematic plans for this study.

Conflicts of Interest

There is no conflict of interest between all authors.


  1. Xin, S.; Li, S.; Hunag, Y. Internet of things for power transmission and distribution -intelligent monitoring and full lifecycle management. In Proceedings of the China International Conference on Electricity Distribution (CICED), Shenzhen, China, 23–26 September 2014. [Google Scholar]
  2. Sungwook, K. R-learning-based team game model for Internet of things quality-of-service control scheme. Int. J. Distrib. Sens. Netw. 2017, 13, 1–10. [Google Scholar]
  3. Sodhro, A.H.; Li, Y.; Shah, M.A. Energy-efficient Adaptive Transmission Power Control in Wireless Body Area Networks. IET Commun. 2016, 10, 81–90. [Google Scholar] [CrossRef]
  4. Di, M.; Ge, Y.; Mo, S.; Paul, S.; Ravichandra, N.; Chowdhury, S. Adaptive radio and transmission power selection for Internet of Things. In Proceedings of the 2017 IEEE/ACM 25th International Symposium on Quality of Service (IWQoS), Vilanova i la Geltru, Spain, 14–16 June 2017. [Google Scholar]
  5. Sodhro, A.H.; Li, Y.; Shah, M.A. Green and Friendly Media Transmission Algorithms for Wireless Body Sensor Networks. J. Multimedia Tools Appl. 2017, 76, 20001–20025. [Google Scholar] [CrossRef]
  6. Yousefpour, A.; Genya, I.; Jue, J.P. Fog Computing: Towards Minimizing Delay in the Internet of Things. In Proceedings of the IEEE International Conference on Edge Computing (EDGE), Honolulu, HI, USA, 25–30 June 2017. [Google Scholar]
  7. Rodrigues, T.G.; Suto, K.; Nishiyama, H. Hybrid Method for Minimizing Service Delay in Edge Cloud Computing Through VM Migration and Transmission Power Control. IEEE Trans. Comput. 2017, 66, 810–819. [Google Scholar] [CrossRef]
  8. Ang, L.-M.; Seng, K.P.; Zungeru, A.M. Big Sensor Data Systems for Smart Cities. IEEE Internet Things J. 2017, 4, 1259–1271. [Google Scholar] [CrossRef]
  9. Alam, F.; Mehmood, R.; Katib, I.; Albogami, N.N.; Albeshri, A. Data Fusion and IoT for Smart Ubiquitous Environments: A Survey. IEEE Access 2017, 5, 9533–9554. [Google Scholar] [CrossRef]
  10. Hashem, I.A.T.; Chang, V.; Anuar, N.B.; Adewole, K.; Yaqoob, I.; Gani, A.; Ahmed, E.; Chiroma, H. The role of big data in smart city. Int. J. Inf. Manag. 2016, 36, 748–758. [Google Scholar] [CrossRef]
  11. Mehmood, Y.; Ahmad, F.; Yaqoob, I.; Adnane, A.; Imran, M.; Guizani, M. Internet of Things based Smart Cities: Recent Advances and Challenges. IEEE Commun. Mag. 2017, 55, 16–24. [Google Scholar] [CrossRef]
  12. Global IoT Market to Reach USD 1.7 tln in 2020-IDC. Available online: (accessed on 20 October 2016).
  13. Zhang, N.; Chen, X.; Chen, J. Semantic Framework of Internet of Things for Smart Cities: Case Studies. Sensors 2016, 16, 1501. [Google Scholar] [CrossRef] [PubMed]
  14. Mahapatra, C.; Sheng, Z.; Leung, V.C.M.; Stouraitis, T. A reliable and energy efficient IoT data transmission scheme for smart cities based on redundant residue based error correction coding. In Proceedings of the 12th Annual IEEE International Conference on Sensing, Communication, and Networking—Workshops (SECON Workshops), Seattle, WA, USA, 22–25 June 2015. [Google Scholar]
  15. Arasteh, H.; Hosseinnezhad, V.; Loia, V.; Tommasetti, A.; Troisi, O.; Shafie-khah, M.; Siano, P. IoT-based smart cities: A survey. In Proceedings of the 2016 IEEE 16th International Conference on Environment and Electrical Engineering (EEEIC), Florence, Italy, 7–10 June 2016. [Google Scholar]
  16. Batalla, J.B.; Krawiec, P.; Mavromoustakis, C.X.; Mastorakis, G.; Chilamkurti, N.; Negru, D. Efficient Media Streaming with Collaborative Terminals for the Smart City Environment. IEEE Commun. Mag. 2017, 55, 98–104. [Google Scholar] [CrossRef]
  17. Liu, G.Z.; Huang, B.Y.; Wang, L. A Wearable Respiratory Biofeedback System Based on generalized Body Sensor Network. Telemed. J. e-Health 2011, 17, 348–357. [Google Scholar] [CrossRef] [PubMed]
  18. Preejith, S.P.; Dhinesh, R.; Joseph, J.; Sivaprakasam, M. Wearable ECG platform for continuous cardiac monitoring. In Proceedings of the 38th IEEE Annual International Conference of the Engineering in Medicine and Biology Society (EMBC), Orlando, FL, USA, 16–20 August 2016. [Google Scholar]
  19. Jhuang, J.W.; Ma, H.P. A patch-sized wearable ECG/respiration recording platform with DSP capability. In Proceedings of the International Conference on E-Health Networking, Application and Services (HealthCom), Boston, MA, USA, 14–17 October 2015. [Google Scholar]
  20. Shen, Y.; Li, C.; Du, Y.; Liu, G. The Design of Wearable Integrated Physiological Monitoring System. In Proceedings of the International Conference on Man-Machine-Environment System Engineering MMESE 2017: Man–Machine–Environment System Engineering, Chendgu, China, 22 August 2017; pp. 315–321. [Google Scholar]
  21. Von Rosenberg, W.; Chanwimalueang, T.; Goverdovsky, V.; Looney, D.; Sharp, D.; Mandic, D.P. Smart Helmet: Wearable Multichannel ECG and EEG. IEEE J. Transl. Eng. Health Med. 2016, 4, 1829–1832. [Google Scholar] [CrossRef] [PubMed]
  22. National ICT Australia. Available online: (accessed on 1 January 2018).
  23. Sodhro, A.H.; Shah, M.A. Role of 5G in Medical Health. In Proceedings of the IEEE International Conference on Innovations in Electrical Engineering and Computational Technologies (ICIEECT), Karachi, Pakistan, 5–7 April 2017. [Google Scholar]
  24. Scalise, L.; Leo, A.D.; Primiani, V.M.; Russo, P.; Shahu, D.; Cerri, G. Non contact monitoring of the respiration activity by electromagnetic sensing, Medical Measurements and Applications Proceedings (MeMeA). In Proceedings of the 2011 IEEE International Workshop on 2011, Bari, Italy, 30–31 May 2011. [Google Scholar]
  25. Sprager, S.; Zazula, D. Heartbeat and Respiration Detection From Optical Interferometric Signals by Using a Multimethod Approach. IEEE Trans. Biomed. Eng. 2012, 59, 2922–2929. [Google Scholar] [CrossRef] [PubMed]
  26. Sweeney, K.T.; Kearney, D.; Ward, T.E.; Coyle, S.; Diamond, D. Employing Ensemble Empirical Mode Decomposition for Artifact Removal: Extracting Accurate Respiration Rates from ECG Data during Ambulatory Activity. In Proceedings of the 35th Annual International Conference of the IEEE EMBS, Osaka, Japan, 3–7 July 2013; pp. 977–980. [Google Scholar]
  27. Pascoli, S.T.; Puntin, D.; Pinciaroli, A.; Balaban, E.; Pompeiano, M. Design and Implementation of a Wireless In-Ovo EEG/EMG Recorder. IEEE Trans. Biomed. Circuits Syst. 2013, 7, 832–840. [Google Scholar] [CrossRef] [PubMed]
  28. Kesper, K.; Canisius, S.; Penzel, T.; Ploch, T.; Cassel, W. ECG signal analysis for the assessment of sleep-disordered breathing and sleep pattern. Med. Biol. Eng. Comput. 2012, 50, 135–144. [Google Scholar] [CrossRef] [PubMed]
  29. Mohammadi-Koushki, N.; Memarzadeh-Tehran, N.; Goliaei, S. A wearable device for continuous cardiorespiratory System Monitoring. In Proceedings of the IEEE 41st Conference on Local Computer Networks Workshops (LCN Workshops), Dubai, UAE, 7–10 November 2016; pp. 230–235. [Google Scholar]
  30. Zhu, Z.; Liu, T.; Li, G.; Li, T.; Inoue, Y. Wearable Sensor Systems for Infants. Sensors 2015, 15, 3721–3749. [Google Scholar] [CrossRef] [PubMed]
  31. Gaetano, D.; Gargiulo, A. Wearable Contactless Sensor Suitable for Continuous Simultaneous Monitoring of Respiration and Cardiac Activity. J. Sens. 2015, 2015, 1–7. [Google Scholar]
  32. Charlton, P.H.; Bonnici, T.; Tarassenko, L.; Clifton, D.A.; Beale, R.; Watkinson, P.J. An assessment of algorithms to estimate respiratory rate from the electrocardiogram and photoplethysmogram. Physiol. Meas. 2016, 37, 610–626. [Google Scholar]
  33. Molnar, G.; Vucic, M. Bernoulli Low-Pass Filters. IEEE Trans. Circuits Syst. 2014, 61, 85–89. [Google Scholar] [CrossRef]
  34. Brugarolas, R.; Latif, P.; Dieffenderfer, J.; Walker, J.; Yuschak, S.; Sherman, B.L.; David, L.R. Wearable Heart Rate Sensor Systems for Wireless Canine Health Monitoring. IEEE Sens. J. 2016, 16, 3454–3464. [Google Scholar] [CrossRef]
  35. Sodhro, A.H.; Fortino, G. Energy Management during Video Transmission in WBSNs. In Proceedings of the 14th IEEE International Conference on Networking, Sensing and Control (ICNSC), Calabria, Italy, 16–18 May 2017. [Google Scholar]
  36. Dieffenderfer, J. Low-Power Wearable Systems for Continuous Monitoring of Environment and Health for Chronic Respiratory Disease. IEEE J. Biomed. Health Inform. 2016, 20, 1251–1264. [Google Scholar] [CrossRef] [PubMed]
  37. Dieffenderfer, J.; Goodell, H.; Bent, B. Wearable wireless sensors for chronic respiratory disease monitoring. In Proceedings of the 12th IEEE International Conference on Wearable and Implantable Body Sensor Networks (BSN), Cambridge, MA, USA, 9–12 June; 2015; pp. 1–6. [Google Scholar]
  38. Majunder, S.; Mondal, T.; Deen, M. Wearable Sensors for Remote Health Monitoring. Sensors 2017, 17, 130. [Google Scholar] [CrossRef] [PubMed]
  39. Sodhro, A.H.; Ye, L. Battery-Friendly Packet Transmission Strategies for Wireless Capsule Endoscopy. In Proceedings of the IFMBE The International Conference on Health Informatics, Internation Federation for Medical and Biological Engineering (IFMBE) Proceedings, Lisbon, Portugal, 10–12 June 2014; Volume 42, pp. 236–239. [Google Scholar]
  40. Sodhro, A.H.; Li, Y. Novel Key Storage and Management Solution for the Security of Wireless Sensor Networks. TELKOMNIKA Indones. J. Electr. Eng. 2013, 11, 3383–3390. [Google Scholar] [CrossRef]
  41. Siedenburg, K.; Dorfler, M. Audio Denoising by generalized time-frequency thresholding. In Proceedings of the 45th International Conference, Helsinki, Finland, 1–4 March 2012. [Google Scholar]
  42. Radhika Bhagat, R.; Kaur, R. Improved Audio Filtering Using extended High Pass filters. Int. J. Eng. Res. Technol. 2013, 2, 1–10. [Google Scholar]
  43. Sodhro, A.H.; Ye, L. Medical Quality-of-Service Optimization in Wireless Telemedicine System Using Optimal Smoothing Algorithm. E-Health Telecommun. Syst. Netw. (ETSN) J. 2013, 2, 1–8. [Google Scholar]
  44. Agrawal, P.; Verma, J.S. A Survey of linear & non-linear filters for noise reduction. Int. J. Adv. Res. Comput. Sci. Manag. Stud. 2013, 1, 18–25. [Google Scholar]
  45. Singh, M.; Garg, E.N.K. Audio noise reduction using Butterworth filter. Int. J. Comput. Org. Trends 2014, 4, 20–23. [Google Scholar]
  46. Singla, E.M.; Singh, H. Paper on frequency based audio noise reduction using butterworth, Chebyshev & Elliptical filters. Int. J. Recent Innov. Trends Comput. Commun. 2015, 3, 5989–5995. [Google Scholar]
  47. Porle, R.R.; Ruslan, N.S.; Ghani, N.M.; Arif, N.A.; Ismail, S.R.; Parimon, N.; Mamat, M. A survey of filter design for audio noise reduction. J. Adv. Rev. Sci. Res. 2015, 12, 26–44. [Google Scholar]
  48. Andersen, K.T.; Moonen, M. Adaptive Time-frequency Analysis for Noise Reduction in an Audio Filter Bank with Low Delay. Available online: (accessed on 1 April 2018).
  49. Afroz, F.; Huq, A.; Ahmed, F.; Sandrasegaran, K. Performace analysis adaptive noise cancellor employing NLMS algorithm. Int. J. Wirel. Mobile Netw. 2015, 7, 45–58. [Google Scholar] [CrossRef]
  50. Ali Ahmed, E.S.; Elatif, R.E.A.; Alser, Z.T. Median filter performance based on different window sizes for salt and Copper noise removal in gray and RGB Images. Int J. Signal Process. Image Process. Pattern Recognit. 2015, 8, 343–352. [Google Scholar]
  51. Tahir, M.; Iqbal, A.; Khan, A.S. A review paper of various filters for noise removal in MRI brain image. Int. J. Innov. Res. Comput. Commun. Eng. 2016, 4, 21711–21715. [Google Scholar]
  52. Salih, A.O.M. Audio Noise Reduction Using Low Pass Filters. Open Access Library J. 2017, 4, 1–7. [Google Scholar] [CrossRef]
  53. Djurovic, I. BM3D filter in salt-and-pepper noise removal. EURASIP J. Image Video Process. 2017, 13, 1–11. [Google Scholar] [CrossRef]
  54. Sonia Mirdha, S. Noise Reduction Techniques using Bilateral Based Filter. Int. Res. J. Eng. Technol. 2017, 4, 1093–1098. [Google Scholar]
  55. Priyanka, S.; Naveen, A.S. Noise Removal in the Remote Sensing Image using Kalman Filter Algorithm. Int. J. Adv. Res. Comput. Commun. Eng. 2016, 5, 894–897. [Google Scholar]
  56. Biswas, S.; Ghoshal, D. A model of noise reduction using Gabor Kuwahara Filter. In Proceedings of the 4th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India, 6–7 January 2017. [Google Scholar]
  57. Gupta, A.; Sulata, B. ECG noise reduction by different filters: A comparative analysis. IJRCCT 2015, 4, 424–431. [Google Scholar]
  58. Bernard, T.; Amir, N. Adaptive ecg signal filtering using bayesian based evolutionary algorithm. Metaheuristics Med. Biol. 2017, 17, 187–211. [Google Scholar]
  59. Khaliq, A.; Waseem, A.; Munir, M.F. Comparison of adaptive noise cancelers for ECG signals in wireless biotelemetry system. In Proceedings of the IEEE International Conference on Intelligent Systems Engineering (ICISE), Islamabad Pakistan, 15–17 January 2016; pp. 181–184. [Google Scholar]
  60. Gong, Y.; Gao, P.; Wei, L. An enhanced adaptive filtering method for suppressing cardiopulmonary resuscitation artifact. IEEE Trans. Biomed. Eng. 2017, 64, 471–478. [Google Scholar] [CrossRef] [PubMed]
  61. Dasgupta, A.; Chakraborty, S.; Routray, A. A two-stage framework for denoising electrooculography signals. Biomed. Signal Process. Control 2017, 31, 231–237. [Google Scholar] [CrossRef]
  62. Belchandan, A.; Khmerj, D.; Jitendra, K. Removal of noises in ECG signal by using a digital fir-iir filter in vhdl. Digit. Signal Process. 2016, 8, 135–139. [Google Scholar]
  63. Joao, M.L.P. Caldeira, Toward ubiquitous mobility solutions for body sensor networks on healthcare. IEEE Commun. Mag. 2012, 50, 108–115. [Google Scholar]
  64. Haghi, M.; Thurow, K.; Habil, I.; Stoll, R.; Habil, M. Wearable Devices in Medical Internet of Things: Scientific Research and Commercially Available Devices. Healthc. Inform. Res. 2017, 23, 4–15. [Google Scholar] [CrossRef] [PubMed]
  65. Sodhro, A.H.; Sekhari, A.; Ouzrout, Y. Energy-efficient Comparison between Data Rate Control and Transmission Power Control Algorithms for WBSNs. Int. J. Distrib. Sensor Netw. 2018, 14, 1–18. [Google Scholar] [CrossRef]
  66. Pirbhulal, S.; Wu, W.; He, Z.; Zhang, Y.T. A novel secure IoT-based Smart Home Automation System using WSN. Sensors 2017, 17, 69. [Google Scholar] [CrossRef] [PubMed]
  67. Sodhro, A.H.; Kumar, A.; Sekhari, A.; Ouzrout, Y.; Pirbhulal, S. Green Media-Aware Medical IoT System, Multimedia Tools and Applications, Springer. 2018. Available online: (accessed on 29 January 2018).
  68. Pirbhulal, S.; Wu, W.; He, Z.; Zhang, Y.T. HRV-based Privacy-perserving and Security Mechanism for BSN. In Wearable Sensor: Application, design and Implementation; Subhas, C.M., Tarikul, I., Eds.; IOP Publishing Ltd.: Bristol, UK, 2017. [Google Scholar]
  69. Wu, W.; Sandeep, P.; Zhang, H. Assessment of Biofeedback Training for Emotion Mnagament through wearable textile Monitoring System. IEEE Sens. J. 2015, 15, 7087–7095. [Google Scholar] [CrossRef]
Figure 1. Proposed 5G-enabled Fog Computing and Internet of Things (IoT) based Architecture.
Figure 1. Proposed 5G-enabled Fog Computing and Internet of Things (IoT) based Architecture.
Sustainability 10 01258 g001
Figure 2. Block diagram of the 5G-based Transmission Power Control (TPC) in Fog and IoT system.
Figure 2. Block diagram of the 5G-based Transmission Power Control (TPC) in Fog and IoT system.
Sustainability 10 01258 g002
Figure 3. Flowchart of the Quality of Service (QoS) requirement in Fog and IoT systems.
Figure 3. Flowchart of the Quality of Service (QoS) requirement in Fog and IoT systems.
Sustainability 10 01258 g003
Figure 4. Proposed 5G-based TPC Algorithm for QoS optimization in the Fog Networks.
Figure 4. Proposed 5G-based TPC Algorithm for QoS optimization in the Fog Networks.
Sustainability 10 01258 g004
Figure 5. Transmission power level and Received Signal Strength Indicator (RSSI) in Dynamic and Static Channel states (a) and (c) Transmission power level, (b) and (d) RSSI of each data packet.
Figure 5. Transmission power level and Received Signal Strength Indicator (RSSI) in Dynamic and Static Channel states (a) and (c) Transmission power level, (b) and (d) RSSI of each data packet.
Sustainability 10 01258 g005
Figure 6. Transmission power vs. network metrics (a) Average throughput, (b) average delay, (c) jitter, (d) average energy consumption.
Figure 6. Transmission power vs. network metrics (a) Average throughput, (b) average delay, (c) jitter, (d) average energy consumption.
Sustainability 10 01258 g006
Table 1. Summary of Existing Works.
Table 1. Summary of Existing Works.
Ref. NoApplicationsProposed TechniquesComponent Being OptimizedResults
[1,2,3,4,5]QoS optimization, TPCReviewQoS metricsQoS aware Fog computing and IoT
[6,7,8,9,10]IoT-enabled Fog computing, QoSTPC driven and MAC basedData rate and throughputTPC-aware QoS control
[11,12,13,14,15]IoT, 5G, smart mobile security TPC and data rate basedSecurity key, resource allocationLow power consumption
[16,17,18]IoT, Security and ECGAlgorithms, ArchitectureAuthentication key, Duty cycleMinimize cost and energy consumption
[19,20,21]IoMT, Telemedicine Battery-friendly, rate controlBattery charge and modulation levelIoT longer media transmission
[22,23,24,25]Wireless capsule, IoMTPredictive techniquesBattery charge, data ratesImprove mobility, battery lifetime
[26,27,28,29]IoT, DLM, wireless ad-hoc networksWindow-based algorithms, Battery modelsTransmission ratesBattery-friendly and energy-efficient
[30,31,32]Medical media, 5GEnergy optimization algorithmsNovel FrameworksSmart medical healthcare system
[33,34,35]IoT, PLM, Smart Cities, IndustriesOntology-based methods and architecturesBattery lifecycleSmart PLM, Smart CPS
[36,37,38]IoT, QoS, WSN, Security, Dynamic game-based and energy managementTPC and data rateTo extend the lifecycle of medical devices
[39,40,41,42,43,44,45]IoT, Energy Harvesting, PLM and WPTBattery-friendly and Energy HarvestingBattery chargeLifetime of smart IoMT and BSN devices
[46,47,48,49,50,51,52]IoT, EEG, Security in BANRouting-based power control techniquesTo optimize QoS in PLMTo monitor IoT based healthcare
[53,54,55,56,57,58,59,60,61]IoT, Artificial Intelligence, security QoS and Energy management FrameworksFuzzy-logic, HRV and energy-efficient techniquesTo optimize, transmission power and battery chargeTo obtain energy-efficient IoT based
[62,63,64,65,66,67,68,69]IoT, BSN, ECG, PLM data sourcesTPC, energy harvestingTo optimize, manage the TPC and duty-cycleSmart healthcare
Table 2. Experimental Parameters.
Table 2. Experimental Parameters.
TRH−83 dBm
TRL−88 dBm
Rtarget−85 dBm
Carrier frequency2.4 GHz
Channel Bandwidth1 MHz
Maximum Transmit power level0 dBm
Minimum Transmit power level−25 dBm
Channel ModelReal-time [28]
Data packet size100 Bytes
Data packet interval100 ms
Data Rate250 Kbps
Noise figure5 dB
Noise PSD−174 dBm/Hz
Node speed1.5 km/h
Pc12.5 mW
Ttotal100 ms
Number of packets4000
Table 3. Summary of Experimental Results.
Table 3. Summary of Experimental Results.
AlgorithmQoS ParametersWireless Channel
TPC Constant Experimental Analysis
Transmission power (mW)7.237.01
Average RSSI (dBm)−69.0−75.3
Avg. energy_consump (mJ)3.283.26
PLR (%)3.473.4 in RSSI (dBm)8.87.53
ATPC MethodTransmission power (mW)6.136.09
Average RSSI (dBm)−80.29−80.50
Avg.energy_consump (mJ)6.270.25
PLR (%)3.533.5 in RSSI (dBm)5.65.57
SKims MethodTransmission power (mW)5.955.83
Average RSSI (dBm)−78.67−79.23
Avg. energy_consump (mJ)1.551.53
PLR (%)3.573.48 in RSSI (dBm)5.795.76
Proposed Algorithm (QoS-TPC)Transmission power (mW)5.675.61
Average RSSI (dBm)−81.85−80.96
Avg. energy_consump (mJ)0.370.34
PLR (%)3.633.6 in RSSI (dBm)5.535.43

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (
Sustainability EISSN 2071-1050 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top