Enhanced approach for wireless sensor network based on localization, time synchronization and quality of service routing Sathyaprakash Palaniappan & Prakasam Periasamy
Cluster Computing The Journal of Networks, Software Tools and Applications ISSN 1386-7857 Cluster Comput DOI 10.1007/s10586-017-1488-x
1 23
Your article is protected by copyright and all rights are held exclusively by Springer Science+Business Media, LLC, part of Springer Nature. This e-offprint is for personal use only and shall not be self-archived in electronic repositories. If you wish to selfarchive your article, please use the accepted manuscript version for posting on your own website. You may further deposit the accepted manuscript version in any repository, provided it is only made publicly available 12 months after official publication or later and provided acknowledgement is given to the original source of publication and a link is inserted to the published article on Springer's website. The link must be accompanied by the following text: "The final publication is available at link.springer.com”.
1 23
Author's personal copy Cluster Computing https://doi.org/10.1007/s10586-017-1488-x
Enhanced approach for wireless sensor network based on localization, time synchronization and quality of service routing Sathyaprakash Palaniappan1 · Prakasam Periasamy2 Received: 28 September 2017 / Revised: 22 November 2017 / Accepted: 6 December 2017 © Springer Science+Business Media, LLC, part of Springer Nature 2017
Abstract Recent technological advances have enabled the development and increasing deployment of low-cost, low-power, and multifunctional sensor networks. They are multifunctional sensor nodes with varied sensing capabilities. They find application over a diversified spectrum of domains on the large scale to domestic security, patient health care defense, industrial, agriculture etc. on the small scale. Their specification and requirements do vary accordingly as some multimedia based application tolerate accuracy .but cannot afford to lose grounds on speed and defense applications require precision and timeliness. Health care systems require high reliability and fast reporting whenever certain event of reference values being crossed occurs. Certain other sensing networks can afford to go on and off alternating as they need to conserve energy. Hence, there are set of criteria to be satisfied from the point of view of localization, Time Synchronization and Service guarantees on specific quality parameters. In this work a protocol is proposed, that considers localization, time synchronization and Quality of service based routing. The designed protocol is simulated using NS-2.34 and comparative analysis is performed on parameters with two existing protocols namely Energy efficient and Quality of service based routing protocol for Wireless Sensor Networks and Location aware event driven multipath routing in Wireless Sensor Networks. Keywords Localization · Synchronization · Quality of service · Multipath routing · Wireless sensor networks
1 Introduction Wireless sensor network (WSN) [1,2] consists of mobile nodes constrained by processing power, energy. Usually they are deployed in hostile physical conditions [3,4], scattered over a geographical region of importance, for the purpose of monitoring the environment around them. Each node is equipped with necessary sensors performing collaborative measurement process. The measured parameters temperature, vibrations, pressure etc. they were expected to be wirelessly transmitted to a special node, usually identified as sink node through multiple intermediate nodes, for further processing [5]. The exchange of data requires that the nodes be aware of the positions on some common coordinate system, known to
B
Sathyaprakash Palaniappan
[email protected]
1
Department of Computer Science and Engineering, Maha Barathi Engineering College, Chinnasalem, India
2
Department of Electronics and Communication Engineering, SNS College of Engineering, Coimbatore, India
be localization. There are many localization algorithms used for this purpose [6]. Thus it becomes paramount important to design efficient localization algorithms (Table 1). Data fusion is yet another important primary operation. The data thus collected by various sensors exchanged to get a single meaningful result. This warrants the individual node’s time need to be synchronized to a global clock. This is known as synchronization [7]. The two issues namely localization and synchronization along with Quality of Service based routing are addressed in this work.
1.1 Architecture The network architecture can either be a flat or planar, multitier hierarchical comprising a set of heterogeneous devices that communicate in an ad hoc manner. Communicating set of mobile nodes along with a set of stationary nodes give raise to the two-tier architecture. The three-tier architecture consists of a set of stationary sensor nodes, a set of mobile devices and a set of access points (AP). The availability data originates at the sensor network that is broadcasted and to be received by mobile devices. The mobile devices then forward
123
Author's personal copy Cluster Computing Table 1 List of notations and abbreviations L QVirj
Link quality value of rout between node i and j
Pr e f
The reference value to obtain best RSSI nodes
Q O Sri j
The QoS based metric for routing between nodes i and j
W ts
Weights for QoS factors (d—distance, l—loss, delay and bandwidth) for traffic
REVr
Route efficiency Value
AV G S r Average and S DSr
Standard deviation
RREQ
Route request
RREP
Route reply
as a percentage. Network life time [11] the first death of any of the nodes of the network. The rest of the paper is organized as follows: existing works related with localization, synchronization, quality of service research presented in Sect. 2. Section 3 presents the idea and the actual work of Localization Time synchronization and Quality of service routing. In Sect. 4, the simulation results of proposed methodology are presented .finally, the last section concludes the paper.
2 Related works 2.1 Localization and mobility
Fig. 1 Wireless sensor network arrangement
the received data to access points. The Centralized database server gets uploaded with this data [8] (Fig. 1). Data collection mechanism determines when a data collection and transmission should take place in a network. Energy demand model characterizes the energy requirements of the sensor network. Energy consumption (average energy consumption) for a sensor network may be due to maintaining the architecture [9], collecting and communicating the data. This even determines the lifetime of the network. The lifetime of a network [10] varies in its definition which range from the time of deployment of the network to the time at which the network is rendered nonfunctional or the instance at which death of any one of the nodes. Routing overhead, the consumption of a node’s resources is due to the mobility and consequent update of routing information. The throughput of a wireless network indicates the rate of propagation of information in a network. When the distances between all pairs of source-destination are of the same order, the throughput is proportional to the sum rate of the links across the network. Average delay is the average time delay for data packets from the source node to the destination node. Packet Loss Ratio the number of packets lost per number of packets sent expressed
123
Localization is the act of determining the physical coordinates of a group of sensor nodes in a wireless sensor network (WSN) to report data that is geographically meaningful. Due to various reasons, use of GPS becomes unrealistic; hence, sensors need to conform to a coordinate system and compute their coordinates [12]. In contrast to statically deployed networks, when nodes are mobile and traversing across sensing and transmitting regions, it becomes necessary to continuously obtain their position dynamically. The mobility of nodes demand greater energy, time and built in high-speed localization services. Localization service can be implemented in three distinct phases namely (1) distance/angle estimation, (2) position computation, (3) running localization algorithm. Distance/angle estimation is responsible for estimating information about the distances and/or angles between two nodes. Position computation phase is for computing a node’s position based on available information pertaining to distances/angles and positions of reference nodes. Running localization algorithm is the third phase that determines how the thus obtained information need be manipulated in order to estimate their positions by the nodes of a WSN. Out of many methods that are used to estimate the position are received signal strength indication (RSSI), the angle of arrival (AoA), and time of arrival or time difference of arrival (ToA/TDoA) [13]. 2.1.1 Localization algorithms It is of great importance to design efficient localization algorithms as in large scale ad hoc networks, node localization can aid in routing and most of the applications depend on a successful localization. Executing certain localization algorithms are limited by the inherent resource constraints in WSN. There are many ways of classification of algorithms for localization. Predominantly, they fall in either of these two broad classes namely, centralized localization, and distributed localizations. A centralized localization algorithms
Author's personal copy Cluster Computing
run on a base station and every mobile node forwards its data to the base station [14,15]. In the case of distributed localization algorithms each node carries out computations and communicates among them. Each approach has its own merits and drawbacks, making it suitable for different applications [16]. The distance between two nodes can be estimated using RSSI [17]. Based on the received signal strength of the distant node the distance between them can be computed. The signal strength is inversely proportional to squared distance, and any suitable model can be used to convert the signal strength into distance. In real-world environments, factors like noises, obstacles, and the type of antenna have greater influence on this resulting in higher inaccuracies of distance estimations. Cesare Alippi, et al, proposes a three stages scheme to localize nodes that use the attenuation of Electromagnetic waves in RF. They are (1) RF mapping of the network, (2) Creation of the ranging model and (3) Centralized localization model [18]. Sutagundar et al proposes link quality estimation along with localization named location aware multipath routing. It achieves better in terms of localization alone. It fails address remaining parameters. [19]
2.2 Synchronization Time synchronization is an important issue in sensor networks. Many applications of sensor networks need local clocks of sensor nodes to be synchronized to achieve the basic function of data fusion, i.e. combining data from multiple sensors into high level data. Synchronization can help in power saving thus extending network lifetime. The sensed data need to be accompanied by the coordinates of the sensor position and time stamp to render it to be useful [20]. Although the wired networks suffer from link failures, the topology remains the same relatively over long periods. The global behaviors of the WSN, as a networked sensor nodes can be quite complex though individual sensor nodes have only limited functionality. Traditional synchronization methods do not suit WSN as some intrinsic properties of sensor networks such as the constrained resources of energy, storage, bandwidth, and computation power [21]. 2.2.1 Synchronization algorithms The early synchronization protocol used in the internet domain is the network time protocol (NTP). The NTP clients synchronize their clocks to the NTP time servers. The time servers are synchronized by external time sources using GPS. Elson [22] describe reference broadcast synchronization a method in which uncertainty of the sender is eliminated by ensuring that the critical path is devoid of sender. Many of the time synchronization protocols use a sender to receiver synchronization methods. The synchronization is achieved
by sending the timestamp information from sender side and the receiver synchronizes. Timing-sync protocol for sensor networks (TPSN) is a traditional sender-receiver based synchronization protocol that uses a tree structure to organize the network [23]. RBS and TPSN both achieve accurate clock synchronization [24]. Yet another protocol that improves up on the disadvantages of TPSN is flooded time sync protocol (FTSP). A completely distributed time synchronization protocol known as the gradient time synchronization protocol (GTSP) is discussed in [25]. Villas work [26] exhibits that solution for localization and synchronization in single protocol for unmanned aerial vehicle application without external device. They have achieved energy, scalability and synchronization.
2.3 QoS routing The term quality of service is an umbrella term for specifying Service requirements, which are a set of measurable service parameters supported by a set of technologies that allow network aware applications to request and receive predictable service levels. Due to the versatile usages and unique characteristics of WSNs, they are increasingly finding critical, multimedia and real-time applications that require guaranteed performances. Hence, WSNs should be application tailored, scalable, secure, energy efficient [27] and of longer lifetime. Owing to the above requirements, the quality of service is defined one from the user / application perspective and another from the network perspective [28]. User perspective is concerned with the end results delivered by the application, while the network perspective is determined by efficient utilization of network resources. Thus, Quality of Service is an overused term; there is no common or formal definition of this term. In the past that the quality of service can be enforced in traditional networks, like Internet through the network over-provisioning, traffic engineering, and differential packet treatment inside routers [29]. The emphasis, across the network, is on maximizing throughput and minimizing delay. In most WSN applications, traffic mainly flows from a large number of sensor nodes to a small subset of sink nodes. WSNs are characterized by high redundancy in the sensor data. The severe resource constraints, unpredictable nature of the wireless links, and dynamic topology due to node failure or link failure and mobility of nodes in WSNs, routing and maintaining the quality of service requirements become a challenge. 2.3.1 Quality of service based routing algorithms Historically, many routing solutions have been proposed for WSNs. We present a set of protocols in brief. SAR protocol, sequential assignment routing (SAR) protocol—one of the routing protocols that were proposed early on, which is
123
Author's personal copy Cluster Computing
a multi-path, table driven routing protocol provides some Quality of Service and tries to achieve both energy efficiency and fault tolerance. Initially, to guarantee the reliable delivery, the Multipath based routing Multipath Routing Protocol was proposed in [30]. SPEED is designed with the objective of maintaining the desired delivery speed and minimizing the end-to-end delay with support for soft real-time communication across the network. DAST [31], directed alternative spanning tree protocol considers three important QoS parameters namely energy efficiency, network communication traffic and failure tolerance/reliability. An extension to SPEED with multipath routing is MMSPEED protocol [32], EQSR protocol is energy efficient and QoS aware multipath based routing, gives real-time traffic absolute preferential treatment over the non-real-time traffic [33]. QuEst protocol (energy-efficient sensor routing) [26,34], a protocol based on the multi-objective genetic algorithm that optimizes application specific QoS parameters such as end-to-delay, energy and bandwidth requirements. ReInForM [35] (Reliable Information Forwarding Protocol) is a priority based Multipath routing protocol which provides desired reliability in data delivery based on packet priority. Mamta [36] proposes a protocol for scalability issues namely MMQARP. It performs moderate in terms large number of nodes. Regarding throughput it will be moderate only The CLS_AODV protocol adopts cross-layer design approach [37], an enhancement of AODV that provides low-layer information for implementing adaptive routing. The low-layer information essentially means received signal strength, localization information and neighbors’ information; the combination can reveal link state and topology. One of the objectives being the stable route discovery which extends AOMDV that avoids unstable routes by preferring most consistent route over the shortest route. The routing updates obtained via RREQs or RREPs are known as ‘route advertisements’ as in the case of AOMDV. End to end complete path information is maintained by RREQ packet. The notion of source routing is used only in multiple routes discovery. During the route discovery procedure, unstable routes discovery is implemented first, to ensure the admitted links are stable in the near future. However, in mobile networks, as the number of links along a path increases the reliability degrades. While sending RREQ it have three information, Grey zone detection, hop count detection, loop free detection. Grey zone detection took more processing time and it degrades quality of service It is evident from the study of related work described in the Sect. 2, that there is a need for study that propose an approach on the routing in wireless sensor network by considering the combined effect of localization, synchronization and quality of service. Which stands the motivation for design of contemporary natured wireless sensor network protocol.
123
Protocol will be better in terms of energy, throughput, network life time, routing overhead, routing delay.
3 Proposed LTSQR model 3.1 Finding path based on best neighbor The proposed localization time synchronization based quality of service routing (LTSQR) is an enhancement of CLS_AODV which is also a stable routing mechanism.it have been applied in wide band networks as well as MANET. It can produce promising results for wireless sensor networks also. The best neighbor can be found using the following process: (a) Identification of best neighbors, (b) Loop detection, (c) Update root information obtained from RREQ with link quality value (d) Computation of rout stability value (e) Selection of the most suitable path.
3.2 Generation of multiple paths and route stability metric The proposed LTSQR also takes care of quality of service issues in the process c). The route stability metric algorithm presented below: Step 1: The signal strength of route request packet (RREQ) packet is compared with the reference value Pr e f . If the Signal strength of RREQ is greater than Pr e f then it proceeds further else the packet is discarded. This help in identifying the possible set of neighbors. Step 2: The elimination of loop by the node by inspecting the received RREQ packet for its own address. If found, the RREQ packet is discarded else moves on to the next step. Step 3: Updates backward route table with the RREQ message. Insertion of the link quality value (LQV) of this specific link to backward_LQV_list with QoS values which is used to update the LQV value of every link along the backward path such as bandwidth and energy. The LQV is computed as in Eq. (1)
LQVri j =
AVG_RSSI_PKTrij − Pref Pref
(1)
Where LQVrij is Link Quality Value of rout between nodes i and j and Pr e f is the reference value to obtain best RSSI nodes.
Author's personal copy Cluster Computing
QOSri,j =
Wts (d) ∗ Required QoS distance Measured QoS distance Wts (l) ∗ Required QoS loss + Measured QoS loss Wt (delay) ∗ Required QoSdelay + s MeasuredQoSdelay Wts (band) ∗ Required QoSband + MeasuredQoSband
3.3 Pseudo code Pseudo code: LTSQR stability routing Mechanism Data: sensor Nodes, CBR Flows Result: Optimal path
(2)
where QOSri j is the QoS based metric for routing between nodes i and j, Wts is weight for QoS factors (d—distance, l— loss, delay and bandwidth) for traffic (like TCP, CBR etc.). CBR is considered here. ij is the link between two nodes i & j and r indicates routing. Step 4: If the current node happens to be the destination node, RREP message is generated and transmitted to the source through the backward path. Otherwise, node address of it is appended to the RREQ message before broadcasting takes place. For the route request and route reply we consider two lists namely forward quality list and backward quality list. In forward quality list the QoS value, distance and LQV will be calculated for each link and it will updated inside the forward list and it reaches the destination and the destination node will reply along the backward path. Step 5: In the algorithm, after receiving the route reply it checks the values of QoS, energy, distance and LQV and computes the average AVGSr and standard deviation S DSr .
1. For each src-BS, do 2. Establish local broadcast route discovery process. 3. Select the list of next hop node from Source S, 4. for each Neighbor Node of the Current Node do 5. If (Neighbor Node! = destination) then check If (TX range of received RREQ packet is < Pthresh) Accept RREQ; 6. Update route table with the RREQ message. Insert the QOS LQV factor of this link to QOS LQV_list, which is used to record the QOS value of each link along the path with backward and forward list. Else 7. Discard RREQ. 8. Repeat the step 6 and 7 until reaches BS node D 9. Select the path with best Qos Lqv path. 10. If (Residual energy of the selected node < Energy threshold) Change the path to alternate disjoint path Else Select the path of current flow
The flow chart of the above algorithm is depicted in the following two flowcharts (a) RouteREQ computation and (b) route selection in Figs. 2 and 3 respectively.
4 Performance evaluation The simulation is carried out using network simulator (NS2.34) and analysis is presented below. This simulation is to evaluate the performance and to validate the effectiveness of proposed LTSQR through. The simulation environment,
FORWARD_LQV + BACKWARD_QoS + FORWARD_QoS AVGS = 2 ∗ hop_count B AC K W A R D_L I ST ( L QVirj − AV G Sr )2 + F O R E W A R D_L I ST (L QVirj − AV G Sr )2 r S DS = 2 ∗ hop_count r
BACKWARD_LQV +
Step 6: from that computed values from (3) & (4) above, the Route efficiency Value REVr is obtained using equation given in (5) below. The path which has the largest REVr is selected as the most suitable path and data transmission takes place through that path. REVr = AV G Sr /S DSr
(5)
If any failure happens during the communication either due to energy loss or overflow etc., it selects the alternate route efficient value route for transmission.
(3)
(4)
performance metrics and simulation results are presented in this section. A comparative study on the metrics, with two protocols namely EQSR, LEDMPR are also presented in the graphs below. The simulation is performed for the network size varying from 50 nodes to 250 nodes [wpc]. Table 2 indicates the network is defined with properties according to the table. Effect of Quality of service against number of nodes The PDR decreases with increase in number of transmitting nodes. LTSQR is observed to be stable and the decrease in PDR is very marginal with a variation of less than 4%. EQSR has nearly 10% reduction and LEDMPR has
123
Author's personal copy Cluster Computing
Fig. 3 Flow chart for RREP computation in LTQSR Fig. 2 Flow chart for RREQ computation in LTQSR
just over 12%. This is significant improvement by the proposed LTQSR. Figure 4 depicts packet delivery ratio (PDR) for given number of nodes involved in transmission. Packet delivery ratio is better due to the loop detection and identification neighbors will provides the complete information of Link quality value, based on this best route will be selected by the LTSQR also performed for multipath. Where as in EQSR and LEDMPR are single path. Effect of routing over head with respect to number of nodes Figure 5 present the overhead with respect to the number of nodes. Overhead increases with increase in the number of nodes. As the number of nodes increase, the number of transmissions increases. LTSQR has less overhead compared to LEDMPR and EQSR. When the network size crosses the 200 nodes routing overhead of EQSR, LEDMPR will three times that of the LTSQR. Where 14 and 18 s. LTQSR keeps 6
123
Table 2 Network properties for simulation NS-2.34 -Variables
Property
Values
set val(chan)
Channel type
Channel/wireless channel
set val(prop)
Propagation
Propagation/ TwoRayGround
set val(netif)
Network interface type
Phy/WirelessPhy
set val(mac)
Mac type
Mac/802_11
set val(ifq)
Interface queue type
Queue/DropTail/PriQueue
set val(ll)
Link layer type
LL
set val(ant)
Antenna type
Antenna/OmniAntenna
set val(ifqlen)
Max packet in ifq
100
set val(nn)
Number of nodes
50,100,150,200,250
set val(rp)
Comparable protocols
LTSQR,EQSR,LEDMPR
set val(x)
Network field length
1000
set val(y)
Network field breadth
1000
set val(stop)
Simulation time
200 s
Number of Sources
Transmitting nodes
20
Author's personal copy Cluster Computing
Fig. 6 Effect of delay performances analysis on varying Network size
Fig. 4 Effect of packet delivery ratio on number of nodes
Fig. 7 Effect of packet loss ratio analysis on varying network size
Fig. 5 Effect of routing overhead analysis on varying network size
as claimed, LEDMPR uses multipath for critical information to increase reliability still generates lots of control messages leading to huge overheads. LTSQR has the optimum efficiency achieved by reduced routing overhead due to minimal number of control messages. Effect of network delay with respect to number of nodes Figure 6 show that the average delay, defined as the time taken to transmit the packet from source to destination. The LTSQR shows the least delay compared to the EQSR and LEDMPR. The delay in the case of LEDMPR is found to be proportional to the number of nodes as the network size is scaled up. In this case at maximum number of nodes LEDMPR having the 1 s delay .EQSR shows half second proposed LTSQR shows 0.2 s. Because there is no need computing all the times for the routing procedure, link quality value. Once the values obtained the best route assigned transmission goes on without break up. In the aspect of routing delay LTSQR shows one third of the compared protocols. EQSR and LEDMPR uses single path. It leads to network delay.
Fig. 8 Effect of throughput analysis on varying network size
Figure 7 shows packet loss ratio achieved by proposed and existing protocol. In the beginning where network size 50 nodes LTSQR and EQSR having same packet loss of 3%. On the other hand LEDMPR have 7%. This will be maintained till the network size will be 100 nodes .after 100 nodes EQSR and LEDMPR have packet loss ratio above 10%. But LTSQR maintains around 5% of packet loss till the maximum network size of 250 nodes. Due to the link quality value calculation and dynamic route stability makes LTSQR consistent.
123
Author's personal copy Cluster Computing
network. The results of the simulation depict the scenario that the proposed LTSQR has a better lifetime of approximately 150 s out of the total simulation time of 200 s. EQSR at the second position with approximately 172 s and followed by LEDMPR with 120 s. LEDMPR maintains the network lifetime close to 100 as the network size scales up; whereas the other two methods maintain almost linear reduction. This is depicted in Fig. 10.
5 Conclusion Fig. 9 Effect of average energy consumption analysis on varying network size
Fig. 10 Effect of network life time analysis on varying network size
Figure 8 Exhibits throughput shown by the LTSQR, EQSR and LEDMPR. When network size will be 50 nodes all the three protocols have similar throughput. The throughput of the LTSQR 2% more than compared EQSR and LEDMPR. After increasing of the network size EQSR and LEDMPR throughput became less than 50%. At the same time throughput of the LTQSR maintains the 55%. This because of the route stability metric, link quality value obtained from the list (forward and backward) of LTQSR. Figure 9 shows the results for the energy consumption under node failures. LTSQR protocol consumes 15 joules when number of nodes 50, parallel the EQSR protocol and LEDMPR consumes 20, 28 joules. When number of nodes reaches 250 the proposed protocol maintains the marginal consumption 22 joules. In the case of EQSR and LEDMPR consumes 28, 35. LTSQR consumes less energy due to link quality estimation at the time route request phase minimize the energy utilization. Existing protocol spends energy more than the proposed protocol because every time it will process the link quality of the nodes. This leads to more consumption of energy than LTSQR. Consistent with the general definition of the Network lifetime, we consider the first death of any of the nodes of the
123
In this work proposed LTSQR, an efficient and inclusive protocol designed specifically for wireless sensor networks that combines three considerations namely localization, time synchronisation and quality of service aware multi-path routing has been presented. LEDMPR protocol uses agents for finding route and considers only the partial topology information. Route failure is also reactive in nature. EQSR uses modified queuing model, where it splits the message into different segments. Each segment have single parameter .route failure is also reactive in nature. Simulation results show that the proposed protocol achieves lower average delay of close to 0.25 s, Packet loss ratio at the minimum approximately 4%, Routing Overhead is around 6 packets, average energy consumption below 21 Jules and higher packet delivery ratio near 98%, throughput around 55 kbps, Network Lifetime up to 190 ms the number of nodes being 250 which are better than the two protocols considered for comparative study. Based on the simulation results obtained, on the simulation conducted using NS-2.34 simulator for the compared protocols, the proposed LTSQR performs well on the measured parameters over EQSR and LEDMPR. Future research in this area can produce optimized results. It can be applied for multimedia e sensor networks and also in heterogeneous category.
References 1. Park, J., Seong, D., Kim, H., et al.: A data-centric storage scheme for high storage utilization in wireless sensor networks. Clust. Comput. 18(1), 247–257 (2015) 2. Chitraz, V., Nithya, A., Prasannavenkatesan, G.K.D.: An energy efficient data collection for mobile user with cluster points. IOSR J. VLSI Signal Process. (IOSR-JVSP) 4(3), 44–47 (2014) 3. Rajaram, P., Prakasam, P.: Two dimensional Gaussian distribution for dynamic node deployment in wireless sensor network. Asian J. Inf. Technol. 15(22), 4608–4616 (2016) 4. Rajaram, P., Prakasam, P.: Shape and area based coverage connectivity using robots in wireless sensor network. J. Signal Process. Wirel. Netw. 1(1), 29–34 (2016) 5. Dargie, D., Poellabauer, C.: Time synchronization chapter 9. In: Dargie, D., Poellabauer, C. (eds.) Fundamentals of Wireless Sensor Networks, Theory and Practice, pp. 229–246. Wiley, Chichester (2010)
Author's personal copy Cluster Computing 6. Karl, H., Willig, A. (eds.): Need for time synchronization: chapter 8.1.1 page 202, Quality of service chapter 13.1.1 pp. 360. In: Protocols and architectures for wireless sensor networks. Wiley, Chichester (2007) 7. Palaniappan, S., Vaithiyashankar, J., Sayeed, S. Optimal design of wireless sensor network for providing Qos. In: 2015 IEEE International Conference on Kuala Lumpur on Smart Sensors and Application (ICSSA), pp. 65–70 (2015) 8. Santi, P.: Topology Control in Wireless ad hoc and Sensor Networks, Chapter 2.4, p. 22. Wiley, Chichester (2005) 9. Khan, S., Pathan, A.S.K., Alrajeh, N.A.: Wireless Sensor Networks: Current Status and Future Trends, Chapter 8, p. 182. CRC Press, New York (2012) 10. Palaniappan, S., Vaithiyashankar, J., Sayeed, S.: Life time maximization of wireless sensor networks using group characteristic based dynamic wake up scheduling. J. Signal Process. Wirel. Netw. 1(1), 13–18 (2016) 11. Kim, H.Y.: An energy-efficient load balancing scheme to extend lifetime in wireless sensor networks. Clust. Comput. 19(1), 279– 283 (2016) 12. Rajasekar, R., Prakasam, P.: Proposed coordinating multiple sampling tasks in sensor field using geometric progression algorithm for efficient data collection in wireless sensor networks. Wirel. Pers. Commun. 82, 1809–1824 (2015) 13. Dargie, W., Poellabauer, C.: Fundamentals of Wireless Sensor Networks, Theory and Practice, Chapter 10, pp. 249–264. Wiley, Chichester (2010) 14. Pal, A.: Localization algorithms in wireless sensor networks: current approaches and future challenges. Netw. Protoc. Algorithms 2(1), 45–73 (2010) 15. Zhang, Q., Huang, J., Wang, J., Jin, C. e t al.: A new centralized localization algorithm for wireless sensor network. In: International Conference on Communications and Networking in China (2008) 16. Naraghi-Pour, M., Rojas, G.C.: A novel algorithm for distributed localization in wireless sensor networks. ACM Trans. Sensor Netw. 11(1), 1 (2014) 17. Mardini, W., Khamayseh, Y., Almodawar, A.A., Elmallah, A.: Adaptive RSSI-based localization scheme for wireless sensor networks. Peer-to-Peer Netw. Appl. 9(6), 991–1004 (2015) 18. Alippi, C., Vanini, G.: A RSSI-based and calibrated centralized localization technique for wireless sensor networks. In: Proceedings of Fourth IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOMW’06), Pisa, Italy, pp. 301–305 (2006) 19. Sutagundar, A.V., Manvi, S.S.: Location aware event driven multipath routing in Wireless Sensor Networks: agent based approach. Egypt. Inf. J. 14, 55–65 (2013) 20. Rhee, K., Lee, J., Kim, J., Serpedin, E.: Clock synchronization in wireless sensor networks an over view. Sensors 9, 56–85 (2009) 21. Ranganathan, P., Nygard, K.: Time synchronization in wireless sensor networks: a survey. Int. J. UbiComp (IJU) 1(2), 92–102 (2010) 22. Elson, J., Girod, L., Estrin, D.: Fine-grained network time synchronization using reference broadcasts. In: ACM SIGOPS Operating Systems Review—OSDI ’02: Proceedings of the 5th symposium on Operating systems design and implementation, vol. 36, pp. 147– 163. Boston (2002) 23. Ganeriwal, S., Kumar, R., Srivastava, M.: Timing Sync Protocol for Sensor Networks. In: ACM SenSys ’03 (2003) 24. Youn, S.: A comparison of clock synchronization in wireless sensor networks. Int. J. Distrib. Sens. Netw. 9(12), 532986 (2013) 25. Sommer, P., Wattenhofer, R.: Gradient clock synchronization in wireless sensor networks. In: Proceedings of the International Conference on Information Processing in Sensor Networks (2009)
26. Villas, L.A., Guidoni, D.L., Maia, G., Pazzi, R.W., Ueyama, J., Loureiro, A.F.: An energy efficient joint localization and synchronization solution for wireless sensor networks using unmanned aerial vehicle. Wirel. Netw. 21, 485–498 (2015) 27. Palaniappan, S., Periyasamy, P.: WISEN: mote as an innovative approach in precision agriculture monitoring using wireless sensor network. Int. J. Print. Pack. Allied Sci. 4(5), 3475–3487 (2016) 28. Sumathi, R., Srinivas, M.G.: A Survey of QoS based routing protocols for wireless sensor networks. J. Inf. Process. Syst. 8(4), 589–602 (2012) 29. Palaniappan, S., Periasamy, P.: Wireless Personal Communicatiion. https://doi.org/10.1007/s11277-017-4821-z (2017) 30. He, T., Stankovic, J., lu, C., Abdelzaher, T.: SPEED: a stateless protocol for real-time communication in sensor networks. In: The Proceedings of the International Conference on Distributed Computing Systems, Providence, RI, USA, May, 19–22, pp. 46–55 (2003) 31. Ji, P., et al.: DAST: a QoS-aware routing protocol for wireless sensor networks. In: Proceeding of International Conferences on Embedded Software and Systems Symposia, Sichuan, vol. 29–31, pp. 259–264 (2008) 32. Felemban, E., Lee, C.G., Ekici, E.: MMSPEED: multipath multispeed protocol for QoS guarantee of reliability and timelines in wireless sensor networks. IEEE Trans. Mobile Comput. 5(6), 738– 754 (2006) 33. Ben-Othman, J., Yahya, B.: Energy efficient and QoS based routing protocol for wireless sensor networks. J. Parallel Distrib. Comput. 70, P-849-857 (2010) 34. Saxena, N., Roy, A., Shin, J.: QuESt: a QoS-based energy efficient sensor routing protocol. J. Wirel. Commun. Mobile Comput. 9(3), 417–426 (2009) 35. Deb, B., Bhatnagar, S., Nath, B.: ReInForm: reliable information forwarding using multiple paths in sensor networks. In: Proceedings of IEEE International Conference on Local Computer Networks, Germany, pp. 406–415 (2003) 36. Balachandra, M., Prema, K.V., Makkithaya, K.: Multiconstrained and multipath QoS aware routing protocol for MANETs. Wirel. Netw. 20, 2395–2408 (2014) 37. Weng, L.-N., Yang, J.: A cross-layer stability-based routing mechanism for ultra-wideband networks. Comput. Commun. 33(18), 2185–219 (2010) Sathyaprakash Palaniappan is a Ph.D. scholar in Information and Communication Engineering, Anna University, Chennai, India. He is currently an assistant professor at Mahabarathi Engineering College. He received his B.E. in the Department of Computer Science engineering from Anna University, Chennai, India 2008. He received M.Tech. degree in Information technology, Anna University of technology Coimbatore, India 2010. His research interest lies in wireless sensor networks, Particular design to improve Quality of service. He has authored five research publication in international and national journals and conferences. He is an IEEE member for past 4 years.
123
Author's personal copy Cluster Computing Prakasam Periasamy has obtained his B.E. degree in Electronics and Communication Engineering from Madras University in 1994. He received his M.Tech. degree in Advanced Communication Systems from Sastra University, Tanjore, India in 2002. He obtained his Ph.D. from Anna University Chennai, India in 2010 in the field of Signal Processing in Communication Systems. At present he is a Professor/ECE of SNS College of Engineering, Coimbatore, India and also he is holding the position as Director/CLT, SNS Group of Institutions, Coimbatore, India. He has authored more than Ninety Five research publications in inter-
123
national and national journals and conferences. His special areas of interest are Signal Processing, Wireless Networks, Communication Systems and Applications of signal processing in Mobile Communication Systems. He is an editorial board member of IRED, USA journals. He is also editor-in-chief for Journal of Signal Processing and Wireless Net-works. P. Prakasam is a member of IEEE (USA), life member of ISTE, IACSIT, IAENG and VSI (India). He is named in Marquis Who’s who in the world in the year 2009.
