The 8th International Conference on Computer Science & Education (ICCSE 2013) April 26-28, 2013. Colombo, Sri Lanka
SaB2.8
Energy Efficient VoIP Communication Using WSN Clustering Approach Manish Tembhurkar
Dr. Latesh Malik
Student IV Sem M.E.(Mobile Technology) Department of Computer Science & Engineering G.H.Raisoni College of Engineering Nagpur, India
[email protected] [email protected]
Head of the Department Department of Computer Science & Engineering G.H.Raisoni College of Engineering Nagpur, India
[email protected] [email protected]
Nekita Chavhan Associate Professor Department of Computer Science & Engineering G.H.Raisoni College of Engineering Nagpur, India
[email protected] [email protected] This helps to conserve energy by staying in sleep mode during VoIP call over WLAN (Wireless Local Area Network) (Greencall algorithm [2]: reduces considerable energy consumption using PSM (Power save mode)). But a totally new approach is needed.
Abstract— VoIP refers to communications services —voice and/or voice-messaging applications— that are transported via the Internet, rather than the public switched telephone network (PSTN). Rate of evolution of mobile services (such as data transfer service (GPRS, EDGE, 3G, and 4G LTE), audio & video player service, camera with higher resolution, GPS) is much higher than that of energy resources or energy conservation. The wide deployment of Voice-over-IP (VoIP) over IEEE 802.11 wireless LAN causes higher rate of energy consumption which is a major issue both in wireless sensor network as well as in mobile networks. Researchers have found out several solutions to overcome this issue such as Greencall Algorithm, algorithm through trace-driven simulations as well as experiments on commodity hardware/software [Energy-Efficient VoIP over Wireless LANs]. More efficient & effective solution is needed.
Normalised Power Consumption (%)
Nowadays mobile phones with evolutionary technologies (such as 3G, 4G LTE) are dominating the market of cellular communication systems [1], [3], [4].
Keywords—Voice over IP, Wireless Sensor Network, Energy Efficiency, Clustering Approach, Mobile Communication, Android
I.
INTRODUCTION
Mobile services are evolving with much higher rate than that of energy resources or energy conservation which causes energy consumption as a major issue not only in wireless sensor network but in mobile networks. The common factor between these two networks is that they have limited resources in terms of energy and power. We cannot ignore this need and be stationary at one place for continuous power source for our devices. The issue of low battery life [1] in mobile devices such as mobile phones and laptops, cause a major concern in certain scenarios such as war, military applications, medical emergencies.
100 80 60 40 20 0
[Services]
Figure 1: Power consumption for different phone’s services normalized to the power consumption needed for downloading data using HSDPA.
In previous works, however, assume-stations were always awake during a call till in 2005, the Wi-Fi Alliance proposed a power saving mode extension which allows stations to retrieve packets from the Access Point (AP) in any mode, at any time.
978-1-4673-4463-0/13/$31.00 ©2013 IEEE
120
234
SaB2.8
implement the WSN Energy efficient routing approach into the VoIP network infrastructure to conserve considerable amount of battery life.
These phones have been provided with better hardware and are becoming more powerful day by day. Power consumption as the biggest consequence with these features does not match well with the evolution of mobile terminals which tend to have less room available for the battery in order to accommodate additional components and technologies. Power consumptions by various services are shown in figure (1) [3].
II.
A. Proposed Model The existing energy efficient model for the VoIP network shows the considerable improvement in one or more objective, to suite the specific application. Still a lot of work is needed to be done on energy efficient model in terms of low latency, real time transmission, quality of voice, clustering overhead, continuous packet delivery and reduced data fusion cost.
A. Energy Consevation Our objective is to study various technologies to reduce power consumption available in other networks and implement them more effectively in the VoIP network. Higher data rate needs more power [3], [4] which makes energy consumption as a major issue both in wireless sensor network as well as in mobile networks. VoIP service consumes the considerable amount of battery as compared to other services. TABLE I.
This paper is to consider all the factors for energy efficient VoIP communication model. The proposed model involves following steps:
POWER CONSUMPTION FOR A NOKIA N95 IN DIFFERENT
Register all the mobile devices (mobile phones and laptops) in the network with their respective parameters (such as IP address, Port number, battery status remaining, Receiving signal status)
Establishing successful VoIP communication between mobile devices
Clustering based on the algorithm
Much improved and reliable cluster head selection through RSS (Received Signal Strength) value.
Registering mobile devices to their respective cluster Heads.
Alternate CH (Cluster Head) selection for continuous packet delivery.
SCENARIOS OF VOICE SERVICE
Scenario
GSM
UMTS
Receiving a voice call
612.7 mW
1224.3 mW
Making a voice call
683.6 mW
1265.7 mW
Idle mode
15.1 mW
25.3 mW
TABLE II. EXAMPLE OF ENERGY SAVING BY USING GSM INSTEAD OF UMTS FOR DIFFERENT SCENARIOS Scenario
Time [hour
Energy saved [J]
Idle mode
8
220
Making voice calls
1
2095
PROPOSED METHODOLOGY
TABLE III. ENERGY COMPARISON USING 2G ALONE, 3G ALONE AND THE INTELLIGENT SWITCHING BETWEEN THE NETWORKS
Service
2G
3G
Switching
50 SMS of 100 bytes
90 J
110 J
90 J
100 Mbytes downloading
10006.2 J
3512.1 J
3512.1 J
5 hours of voice calls
12304.8 J
22782.6 J
12304.8 J
22401.0 J
26404.7 J
50 handoffs TOTAL
245 J 16151.9 J
B. VoIP Call Rauting Although routing algorithms (such as Location based Routing & data Centric Routing Algorithms) [6] works more effectively in wireless sensor networks (WSN) to achieve primary goal of Energy Conservation along with data routing. Clustering approach or Tree based approach is followed for more energy efficient routing in WSN. VoIP networks are very much similar to wireless networks in various aspects as follows
Wireless Infrastructure Mobile nodes All nodes reports to the centralized entity (Base Station or ISP) Major concern: Limited Battery life
Figure 1. VoIP server application module.
The only difference is that VoIP networks are based on the IP network. Hence the prior concern and the goal will be to
235
SaB2.8
III.
BACKGROUND AND RELATED WORK
A. VoIP network Voice over IP network [6] is the service to provide voice communication between two hosts in the network. In the wireless network, the VoIP device converts the dialed number into the network data packets which are transmitted over the radio waves to the wireless access point or other such wireless receivers as shown in Figure (1).
Figure 2. VoIP server application module showing the battery status as Charging.
Figure 1: Voice over Internet Protocol VoIP network has requirements as well.
various
features
along
with
VoIP applications do not require high throughput but cannot allow jitter as such applications are vulnerable to delays which may directly affect the voice quality.
Voice requires quality of service in terms of low latency, low packet loss, low jitter and high availability. Conversely, most of the power saving techniques & mechanisms do transactions with the latency and availability.
VoIP wireless phones require support for seamless roaming capabilities to enable user mobility.
Security in terms of prevention of denial of service attacks and eavesdropping is a must for the wireless media. Power consumption relies on the complexity of the algorithms.
VoIP is an isochronous traffic stream with a packetization rate of 20ms (G.711 codec).
Figure 3. VoIP server application module showing the remaining battery status (in seconds).
B. VoIP Devices VoIP application can be implemented over WiFi (IEEE802.11) and WiMax (IEEE 802.16) networks in infrastructure mode or multihop mesh mode. A WiFi VoIP (VoWiFi) device is similar in structure to a cell phone and consists of the following basic components: processor, speaker, microphone, numeric keypad and function keys,
Figure 4. VoIP server application module with Clustering feature (ON)
236
SaB2.8
lithium ion battery, screen (LCD), antenna and memory. The WiFi VoIP device uses a different radio frequency than the cell phone.
Control Overhead: This factor accounts for the power used to send and receive control packets. Reliability [8]: This element pertains to energy consumed in meeting the protocol reliability requirements, which is the data retransmissions caused from the lossy media, collisions and mobility.
The chief advantages of VoIP devices are:
Low Cost: The network is less expensive to install and maintain. Calls can be placed across the world practically free as compared to usual landline or cell phone services.
Turnaround time [10]: This is the time required to switch modes from transmit to receive and vice versa. Different power saving techniques attempts to minimize the energy consumed by the combinations of these factors.
At the enterprise level, the person need not be tied to the desk to receive calls. Allows the flexibility to roam while maintaining the low cost. This is especially useful in the enterprise environments such as the healthcare industry where the doctors and caregivers are constantly on the move in the hospital.
VoIP offers more convergence with existing data centric technologies.
Provides coverage where there may be poor or unavailable cell coverage.
Various schemes were developed and implemented successfully such as Opportunistic scheduler [4] and Greencall algorithm [2] based on the Power Saving Mode (PSM). Though the quality of the performance is maintained, it does not satisfy the requirement of saving sufficient battery power [3]. D. Energy efficient routing in WSN As energy efficiency is the major concern in wireless sensor networks, related research was already taken place in WSN. Larger size data transmission consumes more battery power of the sensor node. Hence the basic principle of smaller size data (using data fusion) transmission reduces the battery consumption is followed. There are two types of data aggregation. The first type fuses (to reduce the data size) the collected data from all the nodes to forward to the based station. But it may results in less accuracy and precision of the collected data from various nodes. Whereas the second type of approach combines the collected data and forward it as a single data packet with single header. It maintains the data redundancy to achieve accuracy. Two approaches are followed for energy efficient routing in WSN, which are,
C. Energy conservation issues in VoIP network Comparative research on VoIP network & PSTN network [7] shows that when the power consumption of VoIP network is compared with an equivalent PBX, VoIP consumes large amount of power unless an efficient power saving scheme is provided. Mostly preferred Android devices have very pleasing services, functions, and features but it unfortunately drains a lot of energy from the battery [1] as shown in figure (2). It results in limited battery life. These devices run lots of services in the background along with VoIP service. Study shows higher data rate (3G) is responsible for more energy consumption [3].
a) Elements of power consumption The power consumed by a communicating device can be factored into following elements [8], [9], [10]:
Clustering approach Tree based approach
E. Clustering Techniques in WSN: Clustering is the process of division of larger sensor network into smaller manageable group to improve the scalability of the network. Other merits apart from scalability such as bandwidth conservation within the cluster, avoid transmission of redundant message within the network, energy efficient route setup within the cluster are achieved. Further research on the clustering based approach helped to design various energy efficient routing protocols such as LEACH, HEED, and DECA.
Transmission: This accounts for the energy spent in data packet transmission. Reception: This accounts for the energy spent by a node in data reception. Idle listening: Refers to the power consumed when the radio of the node is waiting to receive potential packets but the media is idle.
a) LEACH Low energy adaptive clustering hierarchy [5] follows the clustering approach to distribute the energy consumption to all along its network. Network is divided into Clusters based on data collection and Cluster heads are elected randomly. The CH then gathers the information from the nodes which belong
Overhearing: Refers to the power used by a node when it is receiving packets on the media meant for another destination.
237
SaB2.8
to the respective cluster. Following are the steps for the LEACH protocol.
Repetition phase: Until the CH node was found with the least transmission cost, this phase was repeated. The concerned node itself was selected as the CH unless the node finds the appropriate CH.
Advertisement phase: The first step in LEACH protocol is the eligible cluster head nodes to issue a notification to the nodes in its range to become a cluster member of the cluster. All the nodes will be accepting the offer based upon the Received Signal Strength (RSS).
Finalization phase: In this phase, the selection of CH is finalized and it becomes the final CH node. c) DECA: DECA is an improved Distributed Efficient Clustering Approach [12], [13]. The basic difference between the HEED and DECA is the decision making process and the score computation. The phases in DECA operations are,
Cluster set-up phase: Nodes respond to their selected cluster heads in this phase. Schedule creation: the cluster head have to make a TDMA scheme after receiving response from the nodes. CH then sends this TDMA scheme back to its cluster members to intimate them when they have to pass their information to it.
Start Clustering: In this primary phase, all the nodes will compute its score using the function
Data transmission: The CH is provided with the data collected by the individual sensors during its time interval and on all other time, the cluster members’ radio will be off to reduce it energy consumption.
Score=w1E+w2C+w3I Where, E corresponds to the residual energy, C is the node connectivity, and I is the node identifier. After some interval time, the score value is provided to the neighboring nodes along with the node ID and the cluster ID, if the computed score is of higher value.
Here in the LEACH protocol, multi cluster interference problem was solved by using unique CDMA codes for each cluster. This method helps to prevent the energy-drain for the same sensor nodes which has been elected as the cluster leader using randomization of parameters (energy-life remaining) for each time, CH would be replaced. The CH is in charge for gathering data from its cluster members and multiplexes it to form a single packet to be forwarded to the base station. LEACH has shown a considerable improvement as compared with its previous protocols.
Receive Clustering Message: When the node receives the score value higher than it and if it is not attached to any cluster it accepts the sender node as its CH. Actual announcement: After finishing the second phase, when new nodes and already exciting nodes from some other cluster forming a cluster with a new head, the CHs ID, cluster ID and score value should be broadcasted.
b) HEED: Though the LEACH protocol is much more energy efficient, but when compared with its predecessors, the main drawbacks in this approach is the random selection of cluster head. Sometimes the CH nodes may not be distributed uniformly in the region and it will have its effect on the data gathering. To prevent the random selection of CHs, a new algorithm called Hybrid Energy Efficient Distributed clustering: HEED [11] was designed and developed, which selects the CHs based not only on the residual energy level but the communication cost. The HEED protocol follows three subsequent phases,
Finalize Clustering: As same as in HEED protocol, the new cluster with its head is decided for all other nodes. d) EDECA: EDECA is an Extended Distributed Efficient Clustering Approach [14] which is designed for making the network more reliable, dependable and efficient. A minimum cost spanning tree is designed for the transmission between wireless nodes. The basic difference between the HEED and DECA is the decision making process and the score computation. In DECA, each node periodically transmits a Hello message to identify itself, and based on which, each node maintains a neighbour list. EDECA is designed for more efficient results than that of HEED and DECA. EDECA is based on the following score function,
Initialization phase: During this phase, the initial CHs nodes’ percentage, represented by the variable Cprob which will be provided to the nodes. Every sensor node computes its respective probability to become a CH using the formula, CHprob=Cprob * Eresidual/Emax
Score = w1B + w2C + w3P + w4M
Where, Eresidual to residual energy level of the respective node, Emax represents maximum battery energy. HEED supports heterogeneous sensor nodes, where Emax may be different for different nodes according to its functions and capacity.
Where B is battery power left, C is the node connectivity and p is considered to be a probability of failure and P=1-p,
238
SaB2.8
[5]
which depends on the unfriendly & hostile environments of the network nodes.
[6]
EDECA has three phases. Cluster head selection, Cluster formation, and Cluster Conversion.
[7]
IV.
EXPECTED OUTCOME
The initial work shows the possibility of the positive result. Our main goal is to implement clustering approach and analyze the results to find energy efficient approach in VoIP communication. The implantation of this approach within an intra-network will definitely help to study on various other parameters such as latency, mobility support, cluster stability and more over that, a good quality of service as well. V.
[8]
[9] [10]
[11]
FUTURE WORK
As soon as all the mobile devices are registered in the network with their details, the server application can continuously monitor the battery status and RSS values for selection of the new CH after the predefined interval. Future work involves following goals of the model.
[12]
[13]
Alternate CH (Cluster Head) selection for continuous packet delivery using EDECA approach [14].
[14]
Compression Techniques for reduced data fusion cost. [15]
The proposed model is initially designed to be implanted for the campus or smaller network. But keeping future scope in mind, the model is designed flexible enough for the further modification to implement it on the larger network. Previous work [2], [15], [16], [17], [18] could also be useful for the effective and reliable module implementation. VI.
[16] U Shrawankar, V Thakare, “Feature Extraction for a Speech Recognition System in Noisy Environment: A Study”, ICCEA, 2010. [17] U. Shrawankar, V. Thakare “Voice Activity Detector and Noise Trackers for Speech Recognition System in Noisy Environment” IJACT, 2010. [18] N. Chavhan, S. Chhabria, “Multiple design patterns for voice over IP security”, International Conference on Advances in Computing, Communication and Control, ACM, 2009.
CONCLUSION
The limited battery life is rising up as a major concern in Mobile handheld devices. Thus the energy efficient clustering based routing approaches would certainly be taken and to be implemented in VoIP network for energy conservation and longer battery life. Precaution should be taken as the quality of the service, security, voice quality, and jitter are some important factors to consider along with elements responsible for power consumption while working with VoIP. WSN algorithms with relaxed constraints could be useful for an effective and energy efficient VoIP communication. REFERENCES [1]
[2] [3]
[4]
W.B. Heinzelman, A.P. Chandrakasan, H.Balakrishnan , ”Application specific protocol architecture for wireless microsensornetworks”, IEEE Transactions on Wireless Networking (2002), 2002. Zeadally, F. Siddiqui and P. Kubher, “Voice over IP in Intranet and Internet environments”, IEEE Proc.-Commun., Vol. 151, No. 3, June 2004. Florin Bota, Faheem Khuhawar, Marco Mellia, Michela Meo (Italy), “Comparison of Energy Efficiency in PSTN and VoIP Systems”, ACM Press, May 2012. J.Zhu, C. Qiao and X. Wang, “A Comprehensive Minimum Energy Routing Scheme for Wireless Ad hoc Networks”, IEEE INFOCOM, 2004. M.L.Sichitiu, “Cross Layer Scheduling for Power Efficiency in Wireless Sensor Networks”, IEEE INFOCOM , 2004. J. C. Chen, K. M. Sivalingam, P. Agrawal and S. Kishore, "A Comparison of MAC Protocols for Wireless Local Networks Based on Battery Power Consumption", In Proc. of IEEE INFOCOM, 1998. O. Younis, S. Fahmy, “HEED: A Hybrid,Energy- Efficient, Distributed clustering approach for Ad Hoc sensor networks”, IEEE Transactions on Mobile Computing 3 (4) (2004) 366–379, 2004. Miau Yu, Jason H.Li and renato Levy, “Mobility Resistant Clustering in Multi-Hop Wireless Networks”, Journal of Networks, Vol.1, No.1, May 2006. J. H. Li, M. Yu, and R. Levy, “A Distributed Efficient Clustering Approach for Ad Hoc and Sensor Networks”, Intelligent Automation Inc., Rockville, MD 20855, US, 2005. K. Chakraborty, A. Sengupta, “Extended Distributed Efficient Clustering Approach : Energy Efficient Data Gathering in Wireless Sensor Network”, Jadavpur University, kolkata, India. J. Surana, Rajeev G. Vishwakarma., “Energy-Saving VoIP over Wireless LANs”, ICAESM 2012, Mar 2012.
Goran Kalic, Iva Bojic and Mario Kusek “Energy Consumption in Android Phones when using Wireless Communication Technologies”, The Ministry of Science, Education and Sports of the Republic of Croatia., 2012. Vinod Namboodiri, Lixin Gao, “Energy Efficient VoIP over Wireless LANs”, ACM Press, Apr 2010. Gian Paolo Perrucciy and Frank H.P. Fitzeky and Giovanni Sassoy and Wolfgang Kellererx and J¨org Widmerx, “On the Impact of 2G and 3G Network Usage for Mobile Phones’ Battery Life”, European Wireless, 2009. Kwan-Wu Chin,“ On Maximizing VoIP Capacity and Energy Conservation in Multi-Rate WLANs ”, IEEE Communication letters, Vol. 14, No. 7, July 2010
239
