Journal ofScience & Technology 101 (2014) 145-149
Energy Efficiency for Wireless Sensor Networks Based on Internet of Things
Thu Ngo Quynh', Chung Nguyen Due, Anh Nguyen Quynh
Hanoi University ofScience and Technology, No 1 Dai Co Viet Sir, Ha Noi, Viet NamReceived: March 04, 2014: accepted' Aprd 22. 2014
Abstract
Recently, internet of Things (loT) enables the convergence of Wireless Sensor Networt<s with the IP wortd and the connectivity of smart objects to the Internet by using RPL routing protocol for transmitting IPv6 over small sensor nodes of WSN One most important disadvantage of RPL is - many control messages of RPL such as DIS, DIO and DAO can lead to the increase of energy consumption of sensors. That is why it IS necessary to propose different solutions for energy savings in loT, In this paper, we propose three energy efficient data processing schemes that are intergrated with RPL routing protocol and evaluate them by using Contiki operation system. The simulation result shows that our schemes can help to save from 4 to 9% energy consumed.
Keywords, Energy Efficiency, Routing protocol, Internet of Things.
1. Introduction
Recently, Internet of Things (loT) becomes a potential future scenario of the applicability and impact of technology In human life. loT can extend the concept of Internet fi-om a network of rather homogeneous devices such as computers to network of heterogeneous devices (home appliances, consumer electronics or sensors nodes of Wireless Sensor Networks). Since loT systems consist of sensor nodes that have weak processing power, to suit with new type of network, techniques used Internet are required adjustment or even new techniques, protocols or mechanisms are also suggested.
For enabling the implementation of loT over WSN or making IPv6 packets to be earned over IEEE 802.4 feasible, IETF Working Group Routing over Wireless Sensor Networks (WSN) investigated a routing protocol named RPL [1] RPL was proposed in RFC 6550 [2] because none of the existing known protocols such as AODV, OLSR or OSPF could meet the specification of Low power and Lossy Networks (LLN), Networks running RPL are connected in such a way that no cycles are present In order to have no cycles, a Destination Oriented Directed Acyclic Graph (DODAG), which is routed at a root, is built.
For establishing this DODAG, 4 types of control messages are defined in RPL specification: DIO, DAO, DIS and DAO-ACK
* Conespongdmg Author; Tel (+84)912.528.824 E-mail, [email protected],edu vn
Expenmental measurements have shown that data transmission in general costs expensive m terms of energy con sumption while data processing consumes significantly less energy [3], When transmitting a single bit the energy cost is approximately the same is thit needed for processing a thousand operations in a t^pital sensor node [4]
This important feature influences the implementation ot loT and the design ot RPL significantly because RPL framework defines many control messages as descnbed above (DIO, DAO, DIS,,,), Smce most of nodes in a WSN are typically battery powered, it is crucial to limit the amount of sent control messages over the network That is why it is necessary to propose different solutions for energy savmg of RPL m loT One possibility is to adapt the sending rate of DIO messages by extending the Trickle algorithm [5]
or using energy associated with battery index as object fimcnon of RPL [6,7] Another possibility is to implement data driven techniques that are designed to reduce the amount of sampled data by keeping the sensing accuracy within an acceptable level for the appU cation.
In this paper, we utilize the data set of Intel Berkeley research lab and present three methods (intergrated with RPL) for reducing the amount of this set. This reduced sampled data will be sent to the root by using RPL routing protocol implemented in Contiki operating system. The simulation results show that our schemes can help to save from 4 to 9%
energy consumed for RPL
This paper is organized as follows. In Section I, we present the background of data driven
Journal ofScience & Technology iwi {Miti) m-i^'i techniques for energy efficiency in WSN, Section 3
describes the proposed data processing technologies that are intergrated with RPL routing protocol.
Section 4 descnbes our simulation scenarios using Contiki operating system and evaluate the performance of these three methods. Section 5 concludes the paper and discuss our ongoing work.
2. Background
2.1 Energy conservation for WSN
In WSN, different data-driven techniques can be used to improve the energy efficiency. In fact, data .sensing impacts on sensor' energy consumption in two ways:
• Unneeded samples. Normally, sampled data have strong spatial and/or temporal correlations [7]. That is why there is no need to communicate the redundant data to the root,
• Power consumption of the sensing system.
Reducing communication is not enough when the sensor Itself is power hungry.
In the first case, redundant samples result in useless energy consumption, even if the cost of sampling is negligible because they result in unneeded communications. The second problem arises whenever the consumption of the sensing subsystem is not negligible Data driven techniques presented in the following are designed to reduce the amount of sampled data by keeping the sensing accuracy within an acceptable level depending on the requirement of applications
Data-driven techniques can be divided to: data processing and energy efficient data acquisition Specifically, data processing schemes address the case of unneeded samples, while energy-efficient data acquisition schemes often aimed at reducing the energy consummed by the sensing subsystem.
However, some of them can reduce the energy spent for communication as well.
In this paper, it is important to discuss here one more classification level related to data- processing algonthms. All these approaches aim at reducing the amount of data to be delivered to the root. However the design principles of these approaches are rather different. In-network processing performs data aggregation at intermediate sensors between the sources and the root. By doing this, the amount of data is reduced while traversing the network towards the root. The most appropnate in-network processing technique depends on the specific application and must be tailored to it. In [8]
an up-to-date survey about in-network processing approaches is presented. Data compression can be
applied to reduce the amount of data sent by sources.
This scheme includes encodmg information at sensors which generate data, and decoding it at the root There are different methods for compressing data [9-14], As compression techniques are general (i.e, not necessanly related to WSNs), we will omit a detailed discussion of them to focus on other approaches specifically tailored to WSNs. Data prediction consists of building an abstraction of a sensed phenomenon, i,e, a model descnbing the evolution of data. The model can predict the values sensed by sensors, and resides both at the sensors and at the root. On the other side, explicit communication between sensor nodes and the sink is needed when the model is not accurate enough, i.e. the actual sample has to be retrieved and/or the model has to be updated. On the whole, data prediction reduces the number of data sent by sensor nodes and the energy spent for communication as well. Data reduction are schemes that reduce the amount of data sent by source by removing unneeded samples.
In this paper, we are concentrated on developing data processing schemes intergrated with RPL routmg protocol for the data set of Intel Berkeley Research, This reduced sampled data will be sent to the root using RPL routing protocol implemented in Contiki operating system. In the next section, the characteristics of this data set are examined.
2.2 Data set of Intel Berkeley lab
The data set used in this paper is collected from Intel Berkeley Research lab It consists of temperture, humidity and light intensity that are sampled each 31 seconds. Some samples of these memcs are presented in the above table. From this table, we realize that temperature and humidity vary slowly while light intensity stays unchanged during a long period. From this point of view, we propose three data processing schemes: data reduction using difference, linear data prediction and entropy-based data reduction. These three methods are presented in the following section.
Table 1. Variation of three metrics
Tetnperatiire 19.9884 19.3024 19.1652 19.1456
Humidity 37.0933 37.0933 38.4629 38.8039
Light Intensity
45.08 45 08 45.08 45.08
Journal ofScience & Technology 101 (2014) 145-149 3. Data driven techniques
3.1 Linear data prediction
The most important characteristic of this data set is that all three metncs varies slowly according the time. That is why it is possible to implement a data linear predicted function that can predict Ihe value of a sample (temperature, humidity or light intensity) based on n data samples collected previously Affer that RPL transmits only the difference between real data value and its predicted value. By implementing an appropriate linear data predicted fimction, this difference is smaller than the original sample and transmitting only this difference can reduce energy consumption significantly. Details of this scheme are described as follows:
Step 1. Sensors collect values of n samples 5 s ( 0 with i=l..n, save these values and transmit towards root. Root receives this data and saves for later process.
Step 2. The next data value at (n+1)"' sample is collected by sensors. At this time, sensors predict also the value of this data sample by using following linear prediction function:
gin + 1) = CnflOO + c„_i5Gi - l ) -I- - + Cjg 4-
(2) where flsCO is data value at i"' sample with i=l .n, g,Oi-l- l ) f t ( « -I- l)is predicted value, q with i=l n is predicted coefficient that satisies following condition: c„ > £-„_! > ••• > c^ > 0 and I?=lCi = 1.
Step 3. Sensors evaluate difference between original and predicted value according to the following equation:
^9s = SsCn + l ) - a t ( n - l - l ) This difference is transmitted to the root using RPL routing protocol of loT
Step 4, Root receives this difference and recalculates the linear predicted fiinction:
SB (« + ! ) = c^Bs W + %-iSfi in-i} + - + c^g^ (1) It reevaluates the value of data at (n+1)''' sample as follows-
5HCn-|-l)=fl.Cn-|-l)-|-Afl«
Step 5. Sensors and root clear data at die / " sample and save data value of f>i-i-7/* sample for later use This strategy can reduce the amount of sampled data and it is more energy efficient when transmitting only this difference. However, this method requires more
memory for saving n samples and the calculation of linear predicted function consumes also much energy.
That is why we present in the next section another data reduction algorithm usmg difference between two consecutive data samples.
3.2 Data reduction using difference between consecutive samples:
As described in previous sections, three metncs of this data set (temperture, humidity and light intensity) vary slowly according the time. That IS why the difference between two consecutive samples stays small compared to original samples. By transmitting only this difference, we can achieve energy efficiency for RPL routing protocol The detail of this data reduction method is described as follows:
Step 1. Sensors collect the data value of /"' sample
§5 (l), save this value and transmit to the root At the root, this value g^ (l) is received
Step 2. Sensors collect next data sample and evaluate the difference between the P' and 2'"' values:
i & = S . < 2 ) - S . ( l )
Step 3. Sensors transmit this difference towards root Step 4. Root receives this difference and evaluate the value of 2"^ sample by implementing this equation
5fl(23=5fl(l)+Afl„
Step 5. Root and sensor clear the value of / " sample while keeping 2"'' sample.
Obviously, the difference-based algorithm is more simple than linear predicted scheme because it does not require to save n samples and the calculation of only the difference between two consecutive samples consumes less energy than the calculation of linear predicted function. In the following figure, we present the data collected at sensors when implementing these two strategies applying to 5 samples and the original data-
From this figure, we find that the difference between original data and data after precessing of the first strategy (linear prediction) is higher than the second strategy That is why the second method can achieve better energy efficiency. This comment will be reexamined in different simulation scenarios described in section 4.
Fig. 1. Data collected at sensors
Journal ofScience & Technology 101 (2014) 145-149 3.3 Entropy-based Data Reduction
In this section, we present a data reduction algorithm that utilizes an entropy-based threshold in order to decide when sensors need to transmit data towards root. In order to receive this threshold, it is necessary to calculate the entropy value of data according to the following function:
HiV^.V, ...Vi) = - 2 ] Pin-^2-Vn)l0S2pin'-^2--^)
where V^ with i=l..n is sensor node, v^ is data collected at this node (, pCi'i-i'i-.t^} is the joint probability distnbution fimction ofVi_,i?2•••!'„, After calculating this entropy value, we select an entropy- threshold based that examines the increment of the increased amount entropy AHii). The value of dW(i) can be calculated as follows:
d//CO . rHCFi...^) -WC^i ...V^_t)withi> 1
^ 1 with i = 1
Please refer to [15] for more information relating the calculation of entropy-based threshold.
Step 1. Sensors collect the value of / " data sample fljCl), save it and transmit to root by using RPL routing protocol. Root receives the value gg ( l ) . Step 2. Nodes collect 2"'' data value and evaluate the difference: Aflj = g, il) — fljCl).
Step 3. If this difference is higher than the enfropy- based threshold Ags > Hyjj.gai,oi(j, nodes will transmit this value to the root. If not, nodes do not transmit data.
Step 4. Root receives this difference Ag^ and evaluate the value of 2"'' sample by applying the equation 5^ (2) =g (l) + Ag^ ,giiC2) = g t l } -I- AgR Step 5. Nodes and root clear the value of 1" sample and while keeping the value of 2'"' sample.
ig. 2. Data collected at sensors of 3"* scheme
In the following figure, we present the value of temperature collected at sensors when applying entropy-based threshold of 0.02 and 0.01. We realize that this difference is negligible compared to the origmal data value. For humidity, the characteristic of data is similar to temperature and we can also apply an entropy-based threshold. Unlike temperature and humidity, light intensity stay unchanged during a long period.
4. Performance evaluation
In this section, we implement three above techniques intergrated with RPL routing protocol by using Contiki operating system. The simulated topology consists of 15 nodes. Nodes that have rank I are (2,3,6-8,12)andnodes that have rank 2 are (1,5,7, 9-11,13-16), Input data is the data set received at Intel Berkeley research once each 31s. Root is situated in the middle of the topology.
In this topology, we evaluate the performance of 4 following scenarios:
- In the 1" scenario, all three meh-ics temperature, humidity and light intensity are transmitted in a RPL IPv6 packet without data driven techniques.
- In the 2"'' scenario, we apply the linear predicted data reduction algonthm for M=5 samples (in case of temperature and hunidity only).
- In the J"^ scenario, data reduction algorithm using difference between consecutive samples are implemented.
- In the 4'* scenario, we implement entropy- based data reduction algorithm with the entropy threshold 0.0196 for temperature, 0.05 for humidity and 0 for light intensity.
In the following figure, energy consumed of 2"'' scenario is compared to the P' one.
From this figure, we realize that linear predicted data redimction algorithm achieves better energy efficiency compared to the scenario without data driven techniques (4.07%). However, the difference of energy consumption between two methods is not much because of the complexity of this data reduction (large memory for saving n samples and complex calculations for linear predicted function). For 3"^ scenario, energy consumed is presented in the following figure:
Fig. 3, Simulated Topology
Fig. 4. Energy of 2"'' and V scenarios
Journal ofScience & Technology 101 (2014) 145-149
Fig. 5. Energy of 3'*' and 1 ^' scenarios
Fig. 7. Energy of all 4 scenarios We realize that the data reduction method based on difference between two consecutive samples achieves also better energy efficiency than the P' scenario (5%). It can be explained simply because all three metrics change slowly and the energy consumed for transmitting difference between consecutive samples stays small.
Next, we examine energy efficiency of 4"' scenario Obviously, in the entropy-based method energy consumed is small compared to the 1"
scenario.
More concretelly, the energy efficiency of all 4 scenarios is presented together in the following.
The last scenario with entropy-based data reduction method achieves the best energy efficiency — 9%.
5. Conclusion
In this paper, we present three data processing methods, linear prediction, data reduction based on difference and data reduction based on entropy- threshold. These three methods are also intergrated with RPL routing protocol for transmitting data to the root by using IPv6 protocol. Simulation results by Contiki show that entropy-based data reduction method can save to 9% energy consummed, while method using difference between consecutive samples and linear predicted function achieve only 5% and 4%.
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