Journal of
Sensor and Actuator Networks Article
Novel Adaptive Transmission Protocol for Mobile Sensors that Improves Energy Efficiency and Removes the Limitation of State Based Adaptive Power Control Protocol (SAPC) Debraj Basu *, Gourab Sen Gupta, Giovanni Moretti and Xiang Gui School of Engineering and Advanced Technology, Massey University, Palmerston North 4474, New Zealand;
[email protected] (G.S.G.);
[email protected] (G.M.);
[email protected] (X.G.) * Correspondence:
[email protected]; Tel.: +64-06-356-9099 Academic Editor: Asis Nasipuri Received: 30 November 2016; Accepted: 8 March 2017; Published: 15 March 2017
Abstract: In this paper, we have presented a novel transmission protocol which is suited for battery-powered sensors that are worn by a patient when under medical treatment, and allow constant monitoring of health indices. These body-wearable sensors log data from the patient and transmit the data to a base-station or gateway, via a wireless link at specific intervals. The signal link quality varies because the distance between the patient and the gateway is not fixed. This may lead to packet drops that increase the energy consumption due to repeated retransmission. The proposed novel transmission power control protocol combines a state based adaptive power control (SAPC) algorithm and an intelligent adaptive drop-off algorithm, to track the changes in the link quality, in order to maintain an acceptable Packet success rate (PSR)(~99%). This removes the limitation of the SAPC by making the drop-off rate adaptive. Simulations were conducted to emulate a subject’s movement in different physical scenarios—an indoor office environment and an outdoor running track. The simulation results were validated through experiments in which the transmitter, together with the sensor mounted on the subject, and the subject themselves were made to move freely within the communicable range. Results showed that the proposed protocol performs at par with the best performing SAPC corresponding to a fixed drop-off rate value. Keywords: adaptive transmission; energy efficiency; mobile sensors
1. Background Work The emergence of Internet of Thing (IoT) has enabled technological capabilities to exchange data and to create a healthcare system that is efficient in terms of time, energy and cost [1]. In healthcare, body wearable sensors are used to continuously monitor the vital physiological parameters of patients in hospitals and the elderly at home, allowing them to enjoy independent living [2]. Hospitals use IoT to monitor the location of medical devices, personnel and patients. The healthcare professionals are then able to use data to create a system of proactive management with this network of devices. At the same time, such procedures are effective and are cost-effective ways of monitoring age-related illnesses [3]. For example, one Texas hospital reportedly cut the re-admission rate for patients with heart failures by 50% using predictive analysis of their individual healthcare records [4]. There have been significant developments in building body wearable sensors that have low computational complexity, require little memory storage, and can be suitably implemented using simple hardware. For example, Shih-Hong Li et al. have designed a wearable sensor to detect real time wheezing [5]. In the field of medicine, the wheezing sound is usually considered as an indicator of the degree of airway obstruction. In [6], authors have developed a wearable instrumented vest J. Sens. Actuator Netw. 2017, 6, 3; doi:10.3390/jsan6010003
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posture monitoring, especially for aged people. The vest provides a portable and low-cost solution for posture monitoring, especially for aged people. The vest provides a portable and low-cost that can be used indoors and outdoors to provide long-term care at home. The recognition abilities solution that can be used indoors and outdoors to provide long-term care at home. The recognition of accelerometer-based detection and activity monitoring can can help in in assessing abilities of accelerometer-based detection and activity monitoring help assessingrheumatic rheumatic and and musculoskeletal diseases [7]. There are several other examples of similar research and musculoskeletal diseases [7]. There are several other examples of similar research and development development studies patient care care and and welfare. welfare. studies that that are are shifting shifting the the paradigm paradigm of of patient Figure showsa simple a simple representation of a wireless sensor network application used in Figure 11shows representation of a wireless sensor network application used in healthcare. healthcare. The body-wearable and implanted sensors collect vital health parameters such the The body-wearable and implanted sensors collect vital health parameters such as the pulse rate,asEEG, pulse EEG, blood level etc., andgateway. transmit The to the gateway. The gatewayinthat is shown in bloodrate, insulin level etc.,insulin and transmit to the gateway that is shown Figure 1 can be Figure 1 can be with the person or with the access point. with the person or with the access point.
Figure 1. 1. A A body body area area network network with with sensors sensors connects connects with the access point via a gateway [8,9]. Figure
Data Data communication, communication, not not just just data data collection, collection, is is an an important important component component to to build build such such success success stories. When independent living is considered, the healthcare solution should be such stories. When independent living is considered, the healthcare solution should be such that that the the monitored monitored person person is is able able to to move move seamlessly seamlessly within within the the house house with with minimum minimum loss loss of of data. data. In In order order to to support support such such aa scenario, scenario, the the transmitting transmitting module module of of the the body body wearable wearable sensors sensors should should adapt adapt with with the the changing changing radio radio link link quality. quality. Therefore, Therefore, the the radio radio transmitter transmitter must must keep keep track track of of the the changing changing environment power can can be be adjusted adjusted for for reliable reliable transmission transmission [10]. [10]. environment so so that that the the transmission transmission power There are several adaptive transmission protocols that modulate output power to meet There are several adaptive transmission protocols that modulate output power to meet the the target target E Ebb/N /N0 0and andsave saveenergy energyatatthe thesame sametime. time.InInparticular, particular,this thispaper paperfocuses focuseson on the the state-based state-based adaptive adaptive power controlprotocol protocol(SAPC) (SAPC) that uses an intelligent algorithm to transmit in an efficient energy power control that uses an intelligent algorithm to transmit data indata an energy efficient manner [11]. This protocol has been compared with fixed power transmission and other manner [11]. This protocol has been compared with fixed power transmission and other adaptive adaptive RSSI (received signal strength indication) to estimate link quality set protocolsprotocols that use that RSSIuse (received signal strength indication) to estimate link quality and and set the the transmission power [11–14]. Simulation and experimental results have shown that SAPC can save transmission power [11–14]. Simulation and experimental results have shown that SAPC can save up up to 33% energy. drawback of SAPC the SAPC protocol is the thatperformance the performance is dependent the to 33% energy. OneOne drawback of the protocol is that is dependent on theonfixed fixed drop-off (R)that anditthat it does not maintain any target PSR (packet success The value drop-off factorfactor (R) and does not maintain any target PSR (packet success rate).rate). The value of R of R does not adapt to the changing link quality during run time. In this paper, we propose an does not adapt to the changing link quality during run time. In this paper, we propose an alternative alternative to theSAPC existing that these removes these limitations by maintaining target PSR. solution tosolution the existing thatSAPC removes limitations by maintaining a target aPSR. This is This is done by modulating the value of the drop-off factor (R) based on the evaluated PSR and occurs done by modulating the value of the drop-off factor (R) based on the evaluated PSR and occurs after after a designated number of transmissions. a designated number of transmissions. 2. Description Description of of the the Adaptive Adaptive Algorithm The The adaptive adaptive algorithm algorithm consists consists of of two components. components. The The first first component component controls controls the the value value of the drop-off rate (R) depending on the current packet error rate. It maintains maintains a window window (typically set to 50 transmissions) during which it uses a fixed R value. At At the the end end of of the the window window interval, interval, the PSR is calculated. calculated. If If the the PSR PSR is is less less than than the the target target PSR, PSR, then then the the R R value value is is decremented decremented by 0.05. The The lower lower
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limit to which R can go down is set to be 0. The reason to reduce the drop-off rate is to delay the system switching togo a lower is maintained at adrop-off target level. limit to which R can down state is set so to that be 0.the ThePSR reason to reduce the rate is to delay the system On the hand, if the PSRthe is greater or equalattoa the target PSR, the R value is incremented switching toother a lower state so that PSR is than maintained target level. by 0.05. Theother uppermost that can reach 1. target In either case, the algorithm uses the On the hand, ifvalue the PSR is R greater than is orlimited equal totothe PSR, the R value is incremented new R value for all packet transmissions during the windowed interval. by 0.05. The uppermost value that R can reach is limited to 1. In either case, the algorithm uses the The second component of the algorithm can bewindowed referred tointerval. as the inner component that follows new R value for all packet transmissions during the the adaptive algorithm of SAPC [12]. SAPC is a state-based adaptive power algorithm. In each The second component of the algorithm can be referred to as the innercontrol component that follows state, the power levels of areSAPC configured in anisincreasing order of magnitude. the adaptive algorithm [12]. SAPC a state-based adaptive power control algorithm. In each Table 1 shows the power levels based in these states. In the experiments that follow the state, the power levels are configured in an increasing order of magnitude. simulation, the nRF24L01p radio modules have been used. This module has four Table 1 shows the power levels based in these states. In the experiments radio that follow the simulation, programmable outputmodules power levels. Theyused. are 0This dBm, −6 dBm, −12has dBm −18 dBm. The state the nRF24L01p radio have been radio module fourand programmable output transition model can be extended to any number of states, depending on the available power levels power levels. They are 0 dBm, −6 dBm, −12 dBm and −18 dBm. The state transition model can be of the particular radio of module. As the number states power grows, levels the algorithm can become extended to any number states, depending on the of available of the particular radio computationally expensive. It is grows, therefore power levels with expensive. a differenceIt of module. As the number of states the advisable algorithm to canchoose become computationally is approximately 5 dB in between them. therefore advisable to choose power levels with a difference of approximately 5 dB in between them. Table1. 1. States, States, power power levels levels and and retry retrylimits. limits. Table
State
1 1 Minimum (M) Minimum (M) Low Low(L) (L) Available power levels Available power levels High (H) High (H) Maximum (X) Maximum (X) Number of retries Number of retries 33 State
2
3
4
2
3
4
Low Low (L) (L) High (H) High (H) Maximum (X) Maximum (X) 22
High High(H) (H) Maximum (X)
Maximum (X)
Maximum (X) 1 1
Maximum (X) 3 3
Figure Figure 22 shows shows the the state state transition transition diagram diagram of of the theadaptive adaptivepower powercontrol controlalgorithm. algorithm. State State transition occurs depending on the power level at which the transmission is successful or has failed. transition occurs depending on the power level at which the transmission is successful or has failed.
Figure 2. 2. State State transition transition diagram diagram of of the the adaptive adaptive algorithm. algorithm. Figure
Tables22and and33explain explainthe thestate statetransition transitionmatrices. matrices. Tables Table2. 2. State State transition transition matrix matrix when whenstate statelevels levelsgo goup up[12,13]. [12,13]. Table
State
State
Available power levels Available power levels Number of retries Number of retries
1 1 Minimum (M) Minimum (M) Low Low(L) (L) High High(H) (H) Maximum (X) Maximum (X) 3 3
2
2
Low (L) Low (L) High (H) High (H) Maximum Maximum(X) (X) 22
3
3
4
4
High High (H)(H) Maximum (X) (X) Maximum (X) (X) Maximum Maximum 1 1
3
3
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Table 3. State transition matrix when state levels go down [12,13]. Next State 1 (MLHX) 1 (MLHX)
Success at state M
2 (LHX)
Probabilistic model that depends on the number of successes in level L
2 (LHX)
3 (HX)
Probabilistic model that depends on the number of successes in level L Probabilistic model that depends on the number of successes in level H
Current State 3 (HX)
4 (X)
Probabilistic model that depends on the number of successes in level H Probabilistic model that depends on the number of successes in level X
4 (X)
Probabilistic model that depends on the number of successes in level X
2.1. Choice of Hardware For experimental purposes, nRF24L01p transceiver modules from Nordic semiconductors have been used. The transceiver module at the gateway or hub has an additional power amplifier (PA) and a low-noise amplifier (LNA). The output power level of the receiver is 20 dBm. The reason for using a high power transmitter at the hub is to ensure near error-free communication between the hub and the sensor. The major specifications of the receiver and the transmitter are presented in Tables 4 and 5 respectively. Table 4. Features of nRF24L01p receiver [15]. Device: Receiver (Hub)
nRF24L01p with PA and LNA
Tx mode current Rx mode current Power Amplifier gain Low Noise Amplifier gain
115 mA 45 mA 20 dB 10 dB
Table 5. Features of nRF24L01p transmitter [16]. Device: Transmitter
nRF24L01p
Tx at 0 dBm (MIN) Tx at −6 dBm (LOW) Tx −12 dBm (HIGH) Tx −18 dBm (MAX)
11.3 mA 9 mA 7.5 mA 7 mA
A set of heart rate data from PhysioNet is preloaded into the transmitter module and set to transmit after every 2 s [17,18]. Physionet has a huge repository of human physiological data and can be downloaded from their web-based application. These values are chosen to make the target mobile scenario as realistic as possible. 2.2. Evaluation Parameters The evaluation parameters are
• •
Mean cost of successful transmission (Cmean) Protocol efficiency [19]
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One of the parameters for optimization is the energy consumed per useful bit transmitted over a wireless link [20,21]. This paper has used the following Equation (1) to evaluate Cmean . Cmean =
CTotal PS − PL
(1)
where CTotal = Sum of the cost of all transmissions; PL = Total lost packets; PS = Total packets to send. The protocol efficiency (Proteff ) includes the average number of retries [20]. Mathematically, Prote f f =
PS − PL PS + Ret T
(2)
where RetT = Sum of all retries. Here PS − PL = total successes (Psucc ). In % form, it is represented by Equation (3) when both the numerator and denominator in Equation (2) are divided by the total number of packets to send (Ps ). Prote f f (%) =
PSR 1 + Retmean
(3)
where Retmean = mean retries per packet and is defined as Ret T PS
(4)
Psucc 100 PS
(5)
Retmean = Here, PSR = 3. Simulation Design and Result
Two sets of simulations were conducted to capture two different geometrical spaces. In the first set, an indoor office environment was considered. A subject was fitted with wearable sensors, and the locations of the subject were divided into a docked position, and a number of undocked positions with respect to the base station. The docked position is defined as the location where the subject mostly stays. Occasionally, the subject changes location when he/she moves out of the docked position, for example, to visit the washroom, collect printouts, get a drink etc. This results in variable radio link conditions because of the change in the distance between the base station and the sensors, and also because the signal has to travel through intervening walls. In the second set, an outdoor environment was considered where the subject is assumed to be an athlete, and different health indices were monitored while she/he runs. An outdoor Olympic size running track was considered with the base station at the center of the field, and the subject running at an average speed along the track. 3.1. Results and Discussion—Simulation Set 1 We considered the average speed of the subject as 5 km/h and the sensors transmitting data every 2 s. Therefore, the distance covered during the 2 s was approximately 3 m. The maximum distance between the sensors and the gateway was chosen as 20 m. Therefore, the various undocked positions from the gateway were taken to be 8 m, 11 m, 14 m, 17 m and 20 m. There are partitions in between the transmitter and the receiver and the path loss (in dB) has been accounted for when the average Eb /N0 is calculated. Each of this position is considered as a state and the state transition follows a Markov process [22]. A Markov process has the following properties:
• • •
The number of possible states is finite. The outcome at any stage depends only on the outcome of the previous stage. The probabilities are constant over time.
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In the emulated walk model that is shown in Figure 3, the number of states is finite and the transitionInprobabilities constant Theinstate transition depends only on the current the emulatedare walk model over that istime. shown Figure 3, the number of states is finite and thestate, or ontransition the location of the subject. For example, thestate subject is in state or position B, then his/her probabilities are constant over time.ifThe transition depends only on the current state, next stateortransition to either A or C would only depend B andis not on any other previous states. The on the location of the subject. For example, if theon subject in state or position B, then his/her next state to in either or C would depend onthe B and not on any is other states. of probability oftransition remaining the A docked state,only given it is in docked state 0.7. previous The probability The probability of remaining in the docked state, given it is in the docked state is 0.7. The probability transiting to an undocked position, given the subject is in docked position, is 0.3. The subject does of transiting an undocked position, given the is in with docked position, is 0.3. TheNo subject does not remain in antoundocked state, but transits to asubject new state certain probability. transmission not remain in an undocked state, but transits to a new state with certain probability. No transmission happens during the transitions. This state transition model was built on the assumption that the happens during the transitions. This state transition model was built on the assumption that the subject is moderately active. The state transition probabilities are listed in Table 6. subject is moderately active. The state transition probabilities are listed in Table 6.
Figure 3. A state transition model that emulates the movement of a subject with the states defining the
Figure 3. A state transition model that emulates the movement of a subject with the states defining position from the receiving base station. the position from the receiving base station. Table 6. State transition table of the emulated walk that follows the Markov process—Activity
Table 6. State transition table of the emulated walk that follows the Markov process—Activity level: level: Moderate. Moderate. Next State
Next State Docked Undocked A Undocked B Undocked C Docked Undocked A Undocked B Undocked C Docked 0.7 0.3 0 Docked 0.7 0.3 0 0 0 A 0.7 0 0.3 0 A 0.7 0 0.7 0.3 0 0 0.3 B 0 Present Present B 0 0.7 0 0 0.7 0.3 0 C 0 State D 0 State C 0 0 0 0.7 0 0 0.7 E 0 D 0 0 0 0 0 0.7 0 E 0 0 0 0
Undocked D
Undocked D 0 0 0 0 0 0 0.3 0 0.3 10 1
Undocked E
Undocked E 0 0 0 0 0 0 0 0.3 0 0 0.3 0
The simulation was conducted in Maltab and the parameters are presented in Table 7.
The simulation was conducted in Maltab and the parameters are presented in Table 7. Table 7. Simulation parameters with Matlab.
Table 7. Simulation parameters with Matlab. Modulation Technique
BFSK
ModulationChannel Technique BFSK data Rate 250 kbps Maximum Doppler spread 20 Hz Channel data Rate 250 kbps Packet size 41 bytes Maximum Cyclic Doppler spread redundancy check CRC-1620 Hz Multi-path Fading channel model UMTS Indoor Office Test Environment [23] Packet size 41 bytes Cyclic redundancy check CRC-16 Figure 4 shows that for the same PSR of UMTS >99%, the proposed protocol can [23] provide Multi-path Fading channel model Indoor OfficeS-ATPC Test Environment
a unified, energy-efficient solution, while maintaining the same protocol efficiency as SAPC with different drop-off (R) values. The transition probabilities of the different states in Table 4 also represent Figure 4 shows that for the same PSR of >99%, the proposed S-ATPC protocol can provide a the activity levels of an individual. Based on these probability values, the activity level was categorized unified, energy-efficient solution, while maintaining the same protocol efficiency as SAPC with as moderately active. We changed the transition probability values in Table 4 to introduce more different drop-off values. The transition probabilities thethe different states in Table 4 also activeness. The (R) transition probability values were changed soofthat subject stays away from the represent the activity levels Tables of an individual. Basedthe on transition these probability values, the activity level base station more often. 6 and 7 represent probability matrices for which the was categorized as moderately We changed the transition probability values in Table 4 to introduce performances are plottedactive. in Figures 5 and 6 respectively.
more activeness. The transition probability values were changed so that the subject stays away from the base station more often. Tables 6 and 7 represent the transition probability matrices for which the performances are plotted in Figures 5 and 6 respectively.
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Figure 4. 4. Activity success Figure Activity level: level: moderate—the moderate—theprotocol protocol efficiencies efficiencies and and the the average average cost cost of of success transmissions are compared when the PSR for all the cases are equal to 100%. transmissions are compared when the PSR for all the cases are equal to 100%.
Figure 5. Activity level: level: Uncertain—the Uncertain—theprotocol protocol efficiencies efficiencies and and the the average average cost cost of success 5. Activity transmissions are compared when the the PSR PSR for for all all the the cases cases are are equal equal to to 99%. 99%.
The values suggest that that state state transitions transitions occurred with 50% Compared to to The values in in Table Table 88 suggest occurred with 50% probability. probability. Compared the simulation 6, the the subject subject was was spending spending more more time time on on average average away the simulation scenario scenario based based on on Table Table 6, away from from the base station. Therefore, the average cost per successful transmission was also greater in the base station. Therefore, the average cost per successful transmission was also greater in comparison comparison to the However, first scenario. However,from wasFigure observed Figure 5 thatcomparably S-ATPC performs to the first scenario. was observed 5 thatfrom S-ATPC performs in terms comparably in terms of energy savings. of energy savings. Table 99 suggests suggests that that the the subject subject was was highly highly active, active, as as they they were were transiting transiting to to undocked undocked states states Table more frequently frequently than than in in the the second second scenario. scenario. The more The average average cost cost of of successful successful transmission transmission at at different different R values in Figure 6 suggested that when R is low, the energy saving is the maximum. This was was R values in Figure 6 suggested that when R is low, the energy saving is the maximum. This expected because a low R value keeps the system more often in the higher power state and provides expected because a low R value keeps the system more often in the higher power state and provides best performance performance when when the the subject subject is is more more often often in in undocked undocked states states (indicating (indicating higher higher link link distance distance best and therefore poorer channel condition). The cost of successful transmission corresponding to SATPC was within 7% of that of when R = 0.01.
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and therefore poorer channel condition). The cost of successful transmission corresponding to SATPC was within 7% of that of when R = 0.01. J. Sens.Table Actuator 2017, 6, 3 8. Netw. State transition table of the emulated walk that follows Markov process—Activity8 of 13
level: Uncertain. Table 8. State transition table of the emulated walk that follows Markov process—Activity level: Uncertain.
Present Present State State
Next State Next State Docked Undocked Undocked Undocked Undocked Undocked Undocked Docked A A Undocked B B Undocked C C Undocked D D Undocked EE Docked Docked 0.5 0.5 0.5 0.5 0 0 0 0 0 0 00 A A 0.5 0.5 0 0 0.50.5 0 0 0 0 00 B B 0 0 0.5 0.5 0 0 0.5 0.5 0 0 00 C C 0 0 0 0 0.50.5 0 0 0.50.5 00 D 0 0 0 0.5 0 0.5 D 0 0 0 0.5 0 0.5 E 0 0 0 0 1 0 E 0 0 0 0 1 0
Table State transition transitiontable tableofofthe theemulated emulatedwalk walkthat that follows Markov process—Activity level: High. Table 9. 9. State follows Markov process—Activity level: High.
Present Present State State
Next State Next State Docked Undocked A Undocked B Undocked C Undocked D Undocked E Docked Undocked A Undocked B Undocked C Undocked D Undocked E Docked 0.3 0.7 0 0 0 0 Docked 0.3 0.7 0 0 0 A 0.3 0 0.7 0 0 0 0 A 0.3 0 0.7 0 0 0 B 0 0.3 0 0.7 0 0 B 0 0.3 0 0.7 0 0 C 0 0 0.3 0 0.7 0 C 0 0 0.3 0 0.7 0 DD 0 0 0 0 0 0 0.30.3 0 0 0.70.7 EE 0 0 0 0 0 0 0 0 1 1 0 0
Figure Activity level: level: High—the High—the protocol protocol efficiencies efficiencies and and the the average average cost cost of of success success transmissions transmissions Figure 6. 6. Activity are are compared compared when when the the PSR PSR for for all all the the cases cases are are equal equal to to 99%. 99%.
The steady areare plotted in in Figure 7. A The steady state state distributions distributionsofofthe thestates statesunder underthe thethree threescenarios scenarios plotted Figure 7. more active scenario means A more active scenario meansthat thatthe thesubject subjectstays staysmore moreoften oftenin inundocked undockedscenarios scenarios and and away away from from the base base station. station. the
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Figure 7. Steady state distribution of the different states under the three activity scenarios. Figure 7. Steady state distribution of the different states under the three activity activity scenarios. scenarios.
3.2. Results and Discussion—Simulation Set 2 3.2. Results and Discussion—Simulation Set 2 In this simulation, the subject ran at an average speed of 24 km/h around a track (shown in Figure at at anan average speed of 24ofkm/h around a tracka (shown in Figure In this this simulation, simulation,the thesubject subjectran ran average speed 24 km/h around track (shown in 8), and transmits data every second. Therefore, in terms of distance, the body wearable sensors 8), and8),transmits data every second. Therefore, in terms of of distance, Figure and transmits data every second. Therefore, in terms distance,the thebody bodywearable wearable sensors transmitted after an approximate distance of 7 m. Since the size of the track is not circular, the distance transmitted after after an an approximate approximate distance distanceof of 77 m. m. Since the size of the track is not transmitted not circular, circular, the distance varied with time. time. varied with time.
Figure 8. 8. Shape Shape and and dimension dimension of of aa standard standard Olympic Figure Olympic track. track. Figure 8. Shape and dimension of a standard Olympic track.
In this simulation, free space path loss model was considered, and a Rician fading channel model In this simulation, free space path loss model was considered, and a Rician fading channel model used, least line-of-sight path between thethe transmitting sensors andand the was used, as asthere therewas wasat leastone onedirect direct line-of-sight path between transmitting sensors was used, as there was atatleast one direct line-of-sight path between the transmitting sensors and the receiver (gateway) [24]. [24]. The available powerpower levels levels of the of transmitter were changed to 0 dBm, 50 dBm, the receiver (gateway) The available the transmitter were changed to dBm, receiver (gateway) [24]. The available power levels of the transmitter were changed to 0 dBm, 5 dBm, 10 dBm10and 15and dBm, as theasdistance between the the transmitter and thethe gateway higher 510dBm, dBm 15 dBm, the distance between transmitter and gatewayis higherthan than the dBm and 15 dBm, as the distance between the transmitter and the gateway isishigher than the indoor scenario. Transmission power below 0 dBm does not result in any meaningful indoor scenario. Transmission power below 0 dBm does not result in any meaningful
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indoor scenario. Transmission power below 0 dBm does not result in any meaningful communication. communication. for the sake ofcurrent simplicity, the current consumptions to the However, for theHowever, sake of simplicity, the consumptions correspondingcorresponding to the power levels power levels remained the same. This may not be practical, but the RF modules nRF24L01p does not remained the same. This may not be practical, but the RF modules nRF24L01p does not support support power levels above 0 dBm. This however did not affect the overall objective of the simulation, power levels above 0 dBm. This however did not affect the overall objective of the simulation, as the as the protocols were compared to in of their energy consumptions, and individual energy protocols were compared to in terms of terms their energy consumptions, and individual energy usage was usage was not a concern. not a concern. The result result of of the the simulation simulation is is presented presented in in Figure Figure 9. 9. The The The efficiency efficiency values values were were comparable, comparable, with the cost of successful transmission of the proposed protocol (S-ATPC) being less the with the cost of successful transmission of the proposed protocol (S-ATPC) being less than thethan average average cost for SAPC when R = 1. This result again indicated that if SAPC can be modified with cost for SAPC when R = 1. This result again indicated that if SAPC can be modified with a single Ra singlethat R value that adapts with the radio changing link we condition, we can achieve desirable energy value adapts with the changing link radio condition, can achieve desirable energy savings per savings per successful transmission. successful transmission.
Figure 9. 9. The The protocol protocol efficiencies efficiencies and and the the average average cost cost of of success success transmissions transmissions are are compared compared when when Figure the PSR for all the cases are equal to 100%. the PSR for all the cases are equal to 100%.
4. Experimental and Results Results 4. Experimental Method Method and In this this section, section, the the experimental experimental methodology methodology and and the the results results are are presented. presented. In 4.1. Experimental Methodology
experimental setup setup was a university building with the base station powered by mains, while The experimental transmittingsensor sensorwas waspiggybacked piggybacked subject. subject was allowed to freely roam inside freely the transmitting on on thethe subject. TheThe subject was allowed to roam inside the building and his routine activities were followed. The traversal path of the sensor is shown the building and his routine activities were followed. The traversal path of the sensor is shown in in Figure Figure 10.10. Data was collected for a period of approximately two hours on different days of the week. week. The sensor transmitted packet data every 2 s. The average of five such runs was used and is presented in paper. this paper.
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GATEWA GATEWA
Figure 10. Traversal path of the sensor is shown The distance between the sensor Figure 10. Traversal path red line. line.The Thedistance distancebetween between the sensor Figure 10. Traversal pathof ofthe thesensor sensoris isshown shown in in dotted dotted red the sensor and the gateway is not constant. and the gateway is not constant. and the gateway is not constant.
4.2. Experimental Results 4.2. Experimental Results 4.2. Experimental Results The results are tabulated in Table 10, and is shown shown in Figure 11. The plots show The results are tabulated diagram is shownin inFigure Figure11. 11.The The plots show The results are tabulatedin inTable Table10, 10,and and the the bar diagram plots show that for a target PSR of 99%, the proposed S-ATPC can deliver an energy efficient solution that that a targetPSR PSRofof99%, 99%,the theproposed proposed S-ATPC deliver that isisis that forfor a target deliveran anenergy energyefficient efficientsolution solution that comparable with that ofofSAPC SAPC at that corresponds to minimum minimum energy energy consumption. comparable with thatof SAPCat atRRRthat thatcorresponds corresponds to minimum comparable with that energyconsumption. consumption. Table 10. results. Table10. 10. Experimental Experimental results. Table AdaptiveTransmission Transmission Adaptive Adaptive Transmission Protocols Protocols Protocols ProposedS-ATPC S-ATPC Proposed Proposed S-ATPC SAPC SAPC SAPC SAPC SAPC SAPC SAPC SAPC SAPC SAPC SAPC SAPC SAPC SAPC SAPC
Drop-Off Drop-Off
Drop-Off Factor Factor (R) Factor (R)(R)
adaptive adaptive adaptive 0.01 0.01 0.01 0.05 0.05 0.05 0.1 0.1 0.1 0.5 0.5 0.5 1.0 1.0 1.0
PSR (%) PSR
PSR (%)
98.39 98.39 98.39 98.54 98.54 98.54 98.17 98.17 98.17 98.33 98.33 98.33 98.45 98.45 98.45 98.36 98.36 98.36
Avg. Cost Cost per perSuccessful Successful Avg. Cost per Successful Transmission (mJ) Transmission Transmission (mJ) (mJ) 0.0372 0.0372 0.0372 0.0512 0.0512 0.0512 0.0425 0.0425 0.0425 0.0397 0.0397 0.0397 0.0369 0.0369 0.0369 0.0369 0.0369 0.0369
Protocol ProtocolEfficiency Efficiency Protocol Efficiency (%) (%) (%) 86.59 86.59 86.59 87.82 87.82 87.82 86.59 86.59 86.59 86.98 86.98 86.98 87.37 87.37 87.37 86.88 86.88 86.88
Figure Experimental results: The protocol efficiencies the cost average cost transmissions of success Figure 11. 11. Experimental results: The protocol efficiencies and the and average of success Figure 11. Experimental results: The andtothe transmissions are compared when the PSRprotocol for all theefficiencies cases are equal 99%.average cost of success are compared when the PSR for all the cases are equal to 99%. transmissions are compared when the PSR for all the cases are equal to 99%.
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5. Conclusions The results of the simulations and the experiments suggest that we have a new adaptive transmission power control protocol in place, which can be implemented in mobile wireless sensor scenarios. The efficacy of the protocol has been demonstrated by two sets of simulation results in two different environments. The proposed protocol will work well in scenarios when the activity of the subject under question is moderately high. In general, the protocol is well suited for mobile sensors. This will introduce a new paradigm in the adaptive transmission domain, as this approach is unique and tested in a variable real world environment where not only the distance between the sensor and the base station affects the link quality, but also fading, due to multipath propagation of complex indoor environment and movements of other people within the vicinity of the sensors. The outdoor environment is comparably less complex, with a direct line of sight. The proposed protocol will also be tested in other outdoor environments as part of future research plans. Acknowledgments: I acknowledge the contribution of my supervisors for their support and guidance to develop this research paper. I also acknowledge Massey University for funding the cost of publication, if accepted. Author Contributions: Debraj Basu is the primary author of the paper and has done all the experimental work and analysis to support the various observations. Gourab Sen Gupta, Giovanni Moretti, and Xiang Gui are the research supervisors who have contributed with their ideas and direction to facilitate the research and develop the research paper. Conflicts of Interest: The authors declare no conflict of interest.
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