Energy-aware Management in Wireless Body Area network

literature review of wireless body area network and a wireless body area network of intelligent motion sensors wireless body area network ieee paper
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I.J. Information Technology and Computer Science, 2015, 11, 74-80 Published Online October 2015 in MECS (http://www.mecs-press.org/) DOI: 10.5815/ijitcs.2015.11.09 An Energy Effective WBAN Architecture for Athletes Efforts Analysis under a Common Activity Jyoti Kumari Computer Science and Engineering, The NORTHCAP University, Gurgaon, 122017, India E-mail: josahu98gmail.com Prachi Computer Science and Engineering, The NORTHCAP University, Gurgaon, 122017, India E-mail: prachiah1985gmail.com Abstract—Wireless Body Area Network (WBAN) is one The design of WBAN includes layered architecture. of the most critical real time networks. In order to Issues related with each layer are analyzed so that increase network lifetime, several protocols have been implementation, testing, debugging and enhance network proposed in the Literature. Majority of these protocols are management can be done effectively and efficiently. based on either coverage or residual energy of the network. However, none of the protocols is based on node criticality, communication count, coverage and residual energy. In this paper, we have taken into consideration all the above stated parameters to determine the optimal path for transmission of message. In particular, we are implementing this scenario for Athlete Activity Monitoring to identify the athlete with maximum physical stamina. As per best of our knowledge, only intra-BAN routing protocols are proposed till date. However, in this paper we have simulated Athlete Activity Monitoring scenario for intra-WBAN and inter- WBAN architecture. It is clear from the simulation results that our scheme performs much better in terms of energy consumption than earlier routing schemes. Fig.1. An Example of Wireless Body Area Network Index Terms—WBAN (Wireless Body Area Network), Inter-BAN routing, Intra-BAN routing, Physical Strength, A Wireless body area network is a static network with Communication Count, Node Criticality. small no of nodes with energy specification. One of the application areas of WBAN is to monitor the health system of an athlete to identify the physical strength of I. INTRODUCTION the athlete. The presented work is focused on the same. Main motive of this paper is to study existing works in WBAN is one of the advanced form of wireless human physical movement recognition problem, and to networks that performs application specific propose and implement the work for multiple WBANs communication. It is used in various fields like research, architecture which will remove the drawbacks of existing business, industrial, defense, and viable lifestyles. There approaches. are multiple small size sensors of limited energy in a In this presented work, multiple WBAN architecture is WBAN. There are numerous applications of WBAN in defined for some athletes with the placement of effective different fields. One important application of WBAN is sensors to monitor physical strength of athlete under a health care monitoring. The health of a person can be common activity. One WBAN network is defined for monitored by the analysis of physiological data collected each athlete. For this, Inter and Intra BAN by sensors placed on the patient body. Now a day’s communication is performed. The comparative measures interest of users has increased in wearable wireless on different BAN network are taken to identify the athlete devices. Due to certain factors like topological changes, having maximum physical stamina. time-varying wireless channel, different channel The remainder of this paper is organized as follows: bandwidths, low energy nodes etc., WBANs are not able Existing works are discussed in Section II. In section III, to meet users interest level 7. Copyright © 2015 MECS I.J. Information Technology and Computer Science, 2015, 11, 74-80 An Energy Effective WBAN Architecture for Athletes Efforts Analysis under a Common Activity 75 proposed work is presented. Section IV presents the opportunistic routing for body area network. The network design of the presented work. Comparative interaction of network with outer world is defined along analysis of existing routing approach and the proposed with energy preserving communication over the network. algorithm is presented in the section V. Finally, in section The link estimation performed to provide the significant VI paper is concluded with future scope. network improvement. The author analyzed the quality of network and the links under frequently changing network so that effective WBAN communication can occur. The II. LITERATURE SURVEY analysis on problem domain was done to generate the opportunistic scheme based on body movement analysis. Lots of works is already done by different researchers To optimize routing, the life time based comparison was for body area network to optimize the capabilities under done for route generation in effective time. different aspects. These aspects include the architectural Samaneh Movassaghi 5 has defined an energy improvement, localization improvement, routing adaptive approach for power aware routing in body area approaches etc. Some of such work is defined specific to network. The author defined deployment of nodes for the the application area. Most of the work done by the body area network. The author provided the energy researchers is on health monitoring systems 8, 9 for the adaptive communication in body area network. To patients. Some work is also done in different application provide the reliable and effective communication in the areas such as soldiers monitoring 10, war area analysis, body area network. The author provided the optimization chemical plant worker analysis, athlete analysis11,12 to the system so that the reliable and effective network etc. Some of the researchers presented the network transformation will be achieved. The author provided the models and algorithms to optimize the network strength, analysis on routing protocol to improve the network communication and effectiveness. Some of such work communication and improve the network life. defined by earlier researchers is given in this section. N. Javaid 6 defined a work to measure the fatigue of Kihyun Kim 1 has defined a work on effective soldiers in the body area network. A routing protocol was routing in postural analysis in the body area network. The proposed for measuring the fatigue of a soldier. To effective communication is achieved in stationary sensor perform monitoring of specific attributes, three sensors network for human body network. The safer deployment were placed on the soldier’s body. The author proposed of nodes was done under coverage hole identification. an event driven protocol which considers three cases for The coverage perspective analysis was defined under fatigue measurement of a soldier. The parameters used body movement analysis so that the effective for the work evaluation were network lifetime, communication with network nodes can be performed. throughput, remaining energy of sensors and the fatigue Solution to the network problem, including the mobility of a soldier. analysis, fault tolerance and scalability of network was It is found that all existing works are performed for provided. The protocol level work was defined to achieve single BAN network. Work on multiple BAN networks is the communication under energy effective scenario and yet to be performed. The routing approaches have routing scheme under the global positioning system. The considered remaining energy and coverage distance author improved the energy level formation of nodes over parameters for routing. The proposed work is focused on the network. energy efficient routing in multiple BAN networks by SEA-BAN (Semi-Autonomous Adaptive Routing in considering remaining energy, coverage distance Wireless Body Area Networks) 2 was proposed which parameters, node criticality and communication count combined the advantages of both single hop transmission parameters. and multi hop transmission. In this algorithm, data of all nodes are transmitted to the BNC (Body Node coordinator) which performs computations on this data. III. PROPOSED WORK This leads to reduced computational burden on sensor nodes. All sensor nodes are assumed to be within In the existing works 2, 3, an energy effective cluster coverage range of BNC. This algorithm significantly specific multi-hop routing approach is defined to perform improved the maximum network lifetime as compared to the communication in WBAN network. The route existing direct and multi hop transmission models. selection parameters included are available energy and In 3, the author proposed an algorithm EAR-BAN the distance vector. This model is based on the priority (Energy Efficient Adaptive Routing in Wireless Body level formation of nodes so that the default Area Networks) which is an extension and modification communication to the network will be defined. This of SEA-BAN algorithm. EAR-BAN has considered the existing model includes nodes synchronization approach coverage distance along with remaining energy while to control the network communication under energy performing routing. It is clear from the simulation results parameters. The author has used the algorithmic that EAR-BAN has successfully enhanced the network framework to represent each stage of work. working lifetime better than other existing WBAN In the proposed work the author has considered node routing protocols. But there exists still some issues like criticality and communication count parameters along BNC position, node criticality, communication count, with the earlier available energy and distance vector body tissue heating etc. which need to be resolved. parameters for routing. Inclusion of these parameters has Arash Maskooki 4 has defined a work on significantly improved the network lifetime and reduced Copyright © 2015 MECS I.J. Information Technology and Computer Science, 2015, 11, 74-80 76 An Energy Effective WBAN Architecture for Athletes Efforts Analysis under a Common Activity communication energy wastage. 1. For i=1 to N In this work, the analysis on the multiple athlete's Define N nodes with the Position and Energy health will be performed. It means, the work will be Specification performed on integrated multiple BAN architecture. Each BAN architecture will be defined for each athlete and the communication analysis will be performed for each Nodes (i). Position=Random athlete. The work is defined here to handle the multiple- Nodes (i). Type=Random(1, 3) BAN architecture along with their inter-communication Nodes (i). Energy=Random and intra-communication analysis. The objective of work Nodes(i). CommCount=0; is to identify the most effective BAN architecture to analyze the physical strength of athlete. Higher the efforts 2. Set CurNode = Src made by an athlete, more energy consumption will occur Set Source Node as Current Node in BAN network. The presented work includes analysis 3. While CurNode = Dst of the multiple BAN architecture under particular Repeat Process Till Destination Node not Occur common activity to identify the athlete with maximum physical stamina. The analysis of the work will be Generate the Neighbor List for CurNode called NeighList performed in terms of communication analysis, network 4. Set nexthop=NeighList (1) life analysis and athlete efforts analysis. 5. For i=1 to NeighList. Length Table 1. Flow of Work 6. If (Nodes(i).Energy EThreshold And Distance(i, Dest) Distance(CurNode, Dest)) 1. Set up the BAN architecture with the physical placement 7. of sensors to monitor different activities. The setup will 8. If (NeighList(i) . Energy Nodes(nexthop).Energy be performed with respective to the athlete. 2. Perform the communication over the network based on And NeighList(i).Type = Info And the activity or the performance measures of the NeighList(i).CommCount Threshold And individuals. NeighList (i).Distance SensingRange ) 3. Perform the intra BAN communication to analyze the athlete's activity. 4. Generate the overall performance measure in terms of 9. Set nexthop=i; energy consumption and network life. 5. Perform the inter-BAN communication between different 10. Else If (NeighList(i).Energy Nodes(nexthop). BAN architectures if required. Energy And NeighList(i).Type = Data And 6. Perform the comparative measures between these BAN architectures to monitor the individual network NeighList (i).Distance Sensing Range) performance. 7. Perform the analysis of work in terms of athlete effort 11. Set nexthop = i; analysis and network communication analysis. 8. Identify the high performance athlete under these measures. 12. Else If (NeighList(i).CommCount Threshold And NeighList(i).Type = Data And NeighList(i).Distance Sensing Range) The network processing is done here under localization of network along with architectural specifications. Once 13. Set nexthop = i; the network is established, network communication is performed under sensing range and load vectors. The routing communication control over the network is provided. The work is defined under energy and network 14. Nodes(nexthop).Energy = Nodes(nexthop).Energy- optimization so that reliable and effective network route Forwarding Energy; over the network will be established. 15. Path. Add (next hop) Proposed Routing Algorithm For Intra BAN Routing 16. Nodes(nexthop).CommCount = Nodes(nexthop). CommCount+1 The routing within the single BAN network is defined 17. Set CurNode = nexthop by the specification of the source and the destination node over the network and to perform the communication so that the reliable communication route over the network 18. Nodes(Src).Energy = Nodes(Src).Energy - will be established. The algorithm for the route Transmission Energy; generation over the body area network is given hereunder. 19. Nodes(Dst).Energy = Nodes(Dst).Energy - Receiving Energy; Algorithm(N, Src, Dst) This algorithm has considered the communication / Define a Network with N Nodes with Src and count and node criticality along with the remaining Destination/ energy and the coverage distance parameters for routing in WBAN. These two additional parameters will help in Copyright © 2015 MECS I.J. Information Technology and Computer Science, 2015, 11, 74-80 An Energy Effective WBAN Architecture for Athletes Efforts Analysis under a Common Activity 77 achieving maximum network lifetime and will minimize V. RESULTS energy consumption in WBAN. In this section, the comparative results of existing routing approach and proposed routing approach are presented. The comparison over the network is here VI. NETWORK DESIGN presented in terms of network communication and network life parameters. Tool used for experimental In this section, the network architecture specification study is Mat lab. for the multiple BAN architecture is presented for the generation of network area in the critical application This section evaluates the performance of existing routing approach with the proposed routing approach. specification. In this work, the example of this architecture for the athlete analysis network is used. Here energy level comparative results are shown over the network. Round means network operation time in which Here proposed work is implemented for 6 WBAN networks. In each network there are 7 sensor nodes nodes send data to controller node. We assume that duration of a round is one second. The proposed attached on different body parts. Each node except the controller, collect data about one physiological parameter algorithm is expressed as proposed in the graphs and the previous algorithm represented as existing. like body temperature, glucose level, pulse rate, etc. The controller node collects data of remaining nodes and transmit it to the network coordinator. Network coordinator after receiving data of all 6 networks, determine athlete with highest physical stamina by comparing the energy consumption values of all the networks. The network with lower remaining energy value has highest higher energy consumption. Hence network with lowest remaining energy value will have athlete with highest physical stamina. Fig.3. Energy Level Analysis (Existing v/s Proposed) Here energy level comparative results are shown over the network. Here the x axis represents the communicating rounds and the y axis represents the network energy. The figure shows that the energy level for the network is about 30 J for both existing and Fig.2. Multiple WBAN Architecture proposed network. But the existing approach has given In fig 2, network design is shown. Controller node is the higher energy loss. In existing work, the network lost represented by green color circle and red color circle all its energy after 50 rounds whereas in case of proposed represent sensing nodes. work, the network energy resides for 1000 rounds. Network Scenario Here the network is defined to control the network communication in multiple BAN networks defined under the application specification. The parameters of the network are given hereunder. Table 2. Network Scenario Parameters Values Number of Nodes 7 Initial Energy 1 J Transmission Energy 50 n J Forwarding Energy 10 n J Receiving Energy 30 n J Type of Data Normal and Video Packet Size (Normal) 4K Fig.4. Dead Node Analysis (Existing v/s Proposed) Packet Size (Video) 32 K Copyright © 2015 MECS I.J. Information Technology and Computer Science, 2015, 11, 74-80 78 An Energy Effective WBAN Architecture for Athletes Efforts Analysis under a Common Activity Here fig 4 is showing the analysis of existing and proposed approach in terms of network life. The network life is here identified in terms of alive nodes. The figure shows that all nodes are alive initially but after 50 rounds no node is alive in existing approach whereas in case of proposed work, the no node is alive after 900 rounds. Fig.7. Energy Consumption Level Analysis Fig: 7 is here defined to perform network energy analysis. Here each bar represents the energy analysis on each player of the network. The figure shows that the energy consumption in most of the networks is same. But where the path establishment is critical, the proposed work is effective. The energy level of network 4 and network 6 is higher in case of proposed work. Fig.5. Alive Node Analysis (Existing v/s Proposed ) Here fig 5 is showing the analysis of existing and proposed approach in terms of network life. The network life is here identified in terms of alive nodes. The figure shows that all nodes are alive initially but after 50 rounds no node is alive in existing approach whereas in case of proposed work, the no node is alive after 900 rounds. Fig.8. Energy Consumption Analysis In fig 8 network energy analysis is performed. Here each bar represents the energy analysis on each player of the network. The figure shows that the network 1 is having the highest energy where as the least energy is on network 3. The results are implemented in Mat lab environment. The comparative results show that the presented work has improved the network communication and network life. The results shows that in case of existing work, the network energy remains only up to 50 rounds whereas in Fig.6. Aggregative Energy Analysis case of proposed work, the network life retains up to 900 rounds. It shows the significant improvement in the Here the aggregative energy analysis over the network network life From fig5, it is clear that the most of the nodes is presented. Here x axis represents the networks are having the equal energy consumption and communication rounds and the y axis represents the life for existing and proposed approach. But for critical aggregative network energy. Here energy effective networks 4 and 6, the significant improvement is communication over the network is performed. The identified in terms of network life and energy remaining aggregative energy loss in existing approach is about 23J in the network. whereas in case of proposed approach it is about 5.5J. When the individual networks in proposed work are st The results show that the proposed work has reduced the compared, it is found that the energy remaining in 1 energy consumption over the network. network is higher so that the BAN 1 has proven its Copyright © 2015 MECS I.J. Information Technology and Computer Science, 2015, 11, 74-80 An Energy Effective WBAN Architecture for Athletes Efforts Analysis under a Common Activity 79 Networks", 2009 First International Conference on strength in terms of its long lasting capability and athlete Networks & Communications 978-0-7695-3924-9/09 © stamina is weakest. In same way, the energy remaining 2009 IEEE. for BAN 3 is least which shows the athlete with highest 2 Md. Tanvir ul Huque et al., "SEA-BAN: Semi - stamina. Autonomous Adaptive Routing in Wireless Body Area Networks", 7th International Conference on Signal Processing and Communication Systems, IEEE 2013, pp. VI. CONCLUSION AND FUTURE WORK 1-7 3 Md. Tanvir ul Huque et al., "EAR-BAN: Energy Efficient In this work, an effective model is defined in a Adaptive Routing in Wireless Body Area Networks", 7th communication control mechanism for multiple BAN International Conference on Signal Processing and networks. The work is defined here to process the Communication Systems, IEEE 2013, pp. 1-10. network architecture for specialized applications such as 4 Arash Maskooki, "Opportunistic Routing for Body Area monitoring the efforts of athletes or players during some Network", 1st IEEE International Workshop on Consumer eHealth Platforms, Services and Applications 978-1-4244- activity or the game. A routing algorithm is proposed and 8790-5/11©2011 IEEE. implemented to transmit data between the source and the 5 Shruti P. Mahambre, "Decentralized Adaptive Routing for destination. This algorithm has considered the four Reliability in Event Broker Networks", 2009 15th parameters which were not adopted earlier by any International Conference on Parallel and Distributed existing routing approach. The parameters are remaining Systems 1521-9097/09 © 2009 IEEE. energy, coverage distance, node criticality and 6 N. Javaid, "Measuring Fatigue of Soldiers in Wireless communication count. Either the coverage distance or Body Area Sensor Networks", 2013 Eighth International remaining energy was considered as routing parameter by Conference on Broadband, Wireless Computing, existing routing approach. The proposed work including Communication and Applications 978-0-7695-5093-0/13 all four parameters resulted into higher network lifetime © 2013 IEEE. 7 Samaneh Movassaghi, "Wireless Body Area Networks: A and less energy wastage. Survey", IEEE Communications Surveys & Tutorials, The presented work is defined in two main phases. In 2013 IEEE, volume 16, pp. 1658-1686. the first phase, the communication in the individual body 8 Aravind Kailas, "Wireless Communications Technology in area network is being analyzed and presented with a Telehealth Systems", 1st International Conference on specification of network route constraint. In this work, an Wireless Communication, Vehicular Technology, effective communication route is generated over the Information Theory and Aerospace & Electronics Systems single body area network under multiple analytic vectors. Technology, VITAE 2009 IEEE, pp. 926-930. In the second phase, the comparative analysis between 9 Xiaoling Wu, "Optimal Routing in Sensor Networks for In- home Health Monitoring with Multi-factor Considerations", different Body Area Networks is done. The results are Sixth Annual IEEE International Conference on Pervasive implemented in Mat lab environment. The comparative Computing and Communications 0-7695-3113-X/08© results show that the presented work has improved the 2008 IEEE. network communication and network life. The results 10 N. Javaid, "Measuring Fatigue of Soldiers in Wireless shows that in case of existing work, the network energy Body Area Sensor Networks", 8th International remains only up to 50 rounds whereas in case of proposed Conference on Broadband, Wireless Computing, work, the network life retains up to 900 rounds. It shows Communication and Applications, 2013 IEEE, pp. 227-231. the significant improvement in the network life. The 11 Ashay Dhamdhere.et.al, " Experiments with Wireless analysis is also obtained in different networks. The Sensor Networks for Real Time Health Monitoring", 35th results shows that the energy remaining for different Conference on Local Area Networks, IEEE 2010, pp. 938- 945. networks and life of different network in case of proposed 12 Zongjian He .et.al, "A Wearable Wireless Body Area work is higher. Network for Human Activity Recognition", 6th The proposed work is implemented with 6 BAN International Conference on Ubiquitous and Future networks for 6 athletes. It is found that BAN 3 has Networks, 2014 IEEE, pp. 115-119. minimum remaining energy and highest energy consumption. This means higher efforts leading to higher physical stamina. So, BAN 3 athlete has highest stamina. Likewise, BAN 1 has highest remaining energy which Authors’ Profiles means BAN 3 athlete has lowest stamina. The comparative analysis is performed under decision Jyoti Kumari is M.Tech holder in vectors such as energy, network life and communication. Computer Science from ITM University In future some criticality parameters can also be used for of Gurgaon, India. Her current research analysis. In this work, the routing is considered as the interests include wireless Body Area main operation to analyze the communication behavior Networks. She received the B.Tech degree from over the network. In future some other communication B.P.S Women University Khanpurkalan aspects over the network can be considered for analysis. of Haryana, India in 2013. She is author of article "A Comprehensive Survey of Routing REFERENCES Protocols in Wireless Sensor Networks" IEEE indexed conference. 1 Kihyun Kim, "An Efficient Routing Protocol based on Position Information in Mobile Wireless Body Area Sensor Copyright © 2015 MECS I.J. Information Technology and Computer Science, 2015, 11, 74-80 80 An Energy Effective WBAN Architecture for Athletes Efforts Analysis under a Common Activity Prachi is Ph.D. holder in Computer Science from the Banasthali University of Rajasthan, India. Her current research interests include wireless sensor networks, key agreement in wireless peer-to-peer systems and security in underwater sensor networks. Prachi received the B.Tech. degree from M.D. University, Rohtak in 2007 and the M.Tech. degree with distinction in Computer Science from the Banasthali University at Rajasthan in 2009. She is author of 21 refereed articles in these areas, 9 in peer reviewed, reputed international journal indexed in international databases and 12 in IEEE and Springer indexed International Conferences. Copyright © 2015 MECS I.J. Information Technology and Computer Science, 2015, 11, 74-80

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