EURASIP Journal on Wireless Communications and Networking This Provisional PDF corresponds to the article as it appeared upon acceptance Fully formatted PDF and full text (HTML) versions will be made available soon A virtual infrastructure based on honeycomb tessellation for data dissemination in multi-sink mobile wireless sensor networks EURASIP Journal on Wireless Communications and Networking 2012, 2012:17 doi:10.1186/1687-1499-2012-17 Aysegul Tuysuz Erman (tuysuza@cs.utwente.nl) Arta Dilo (A.Dilo@utwente.nl) Paul Havinga (P.J.M.Havinga@utwente.nl) ISSN Article type 1687-1499 Research Submission date April 2011 Acceptance date 16 January 2012 Publication date 16 January 2012 Article URL http://jwcn.eurasipjournals.com/content/2012/1/17 This peer-reviewed article was published immediately upon acceptance It can be downloaded, printed and distributed freely for any purposes (see copyright notice below) For information about publishing your research in EURASIP WCN go to http://jwcn.eurasipjournals.com/authors/instructions/ For information about other SpringerOpen publications go to http://www.springeropen.com © 2012 Tuysuz Erman et al ; licensee Springer This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited A virtual infrastructure based on honeycomb tessellation for data dissemination in multisink mobile wireless sensor networks Ayáegăl Tăysăz Erman , Arta Dilo and Paul Havinga s u u u Pervasive Systems Research Group, Department of Computer Science, University of Twente, Enschede, The Netherlands ∗ Corresponding author: a.tuysuz@utwente.nl Email address: AD: a.dilo@utwente.nl PH: p.j.m.havinga@utwente.nl Abstract A new category of intelligent sensor network applications emerges where motion is a fundamental characteristic of the system under consideration In such applications, sensors are attached to vehicles, or people that move around large geographic areas For instance, in mission critical applications of wireless sensor networks (WSNs), sinks can be associated to first responders In such scenarios, reliable data dissemination of events is very important, as well as the efficiency in handling the mobility of both sinks and event sources For this kind of applications, reliability means real-time data delivery with a high data delivery ratio In this article, we propose a virtual infrastructure and a data dissemination protocol exploiting this infrastructure, which considers dynamic conditions of multiple sinks and sources The architecture consists of ‘highways’ in a honeycomb tessellation, which are the three main diagonals of the honeycomb where the data flow is directed and event data is cached The highways act as rendezvous regions of the events and queries Our protocol, namely hexagonal cell-based data dissemination (HexDD), is faulttolerant, meaning it can bypass routing holes created by imperfect conditions of wireless communication in the network We analytically evaluate the communication cost and hot region traffic cost of HexDD and compare it with other approaches Additionally, with extensive simulations, we evaluate the performance of HexDD in terms of data delivery ratio, latency, and energy consumption We also analyze the hot spot zones of HexDD and other virtual infrastructure based protocols To overcome the hot region problem in HexDD, we propose to resize the hot regions and evaluate the performance of this method Simulation results show that our study significantly reduces overall energy consumption while maintaining comparably high data delivery ratio and low latency Introduction Based on recent technological advances in wireless communication, low-power microelectronics integration and miniaturization, the manufacturing of a large number of low cost wireless sensors became technically and economically feasible Wireless sensors are constrained devices with relatively small memory resource, restricted computation capability, short range wireless transmitterreceiver and limited built-in battery Hundreds or thousands of these devices can potentially be networked as a wireless sensor network (WSN) for many applications that require unattended, long-term operations Consequently, WSNs have emerged as a promising technology with various applications, such as activity recognition [1], intrusion detection [2], structural health monitoring [3], disaster management, etc In all these applications, the primary goal of a WSN is to collect useful information by monitoring phenomena in the surrounding environment Common sensing tasks are heat, pressure, light, sound, vibration, presence of objects, etc In WSNs, each sensor individually senses the local environment, but collaboratively achieves complex information gathering and dissemination tasks Typically a WSN follows the communication pattern of convergecast, where sensors -source nodes- generate data about a phenomenon and relay streams of data to a more resource rich device called sink This procedure is called data dissemination, which is a preplanned way of distributing data and queries of sinks among the sensors Traditional static WSN systems use a n-to-1 communication paradigm in which sensors forward their data towards a common static sink However, deploying one static sink limits the network lifetime as the close neighbors of the sink can become the bottlenecks of the network Multiple sinks deployment helps to spread load over the network, while mobility of sinks reduces the bottleneck problem of static sinks Exploiting multiple, mobile sinks in a WSN, instead of static ones, is thus an interesting concept to enhance the network lifetime by avoiding excessive transmission at the nodes that are close to the location of the static sink The study presented in this article is motivated by disaster management scenarios where we have a mobile multi-sink WSN in which the deployment of sensors is performed in a random fashion, e.g., dropping sensors from helicopters flying above the field [4] As shown in Figure 1, in this mobile multi-sink WSN, unmanned aerial vehicles (UAVs), emergency responders, e.g., firefighters, or vehicles, e.g., firetrucks, carry sink nodes on-board These mobile sinks are used to collect more reliable data about the event in the dangerous/inaccessible regions In this scenario, both the number of sources and that of mobile sinks may vary over time The speed of sources and sinks also vary from a typical pedestrian to a flying UAV Sink mobility brings new challenges to data dissemination in WSNs Since the location of the sink changes in time, the difficulty for sensor nodes is to efficiently track the location of the mobile sink to report the collected measurements about the event Although several data dissemination protocols have been proposed for sensor networks, e.g., Directed Diffusion [5], they all suggest that each mobile sink needs to periodically flood its location information through the sensor field, so that each sensor is aware of the sink location for sending future events and measurements However, such a strategy leads to increased congestion and collisions in the wireless transmission and is thus mainly suited for (semi) static setups Flat networks, where each node typically plays the same role, and floodingbased protocols not scale due to frequent location updates from multiple sinks Therefore, overlaying a virtual infrastructure over the physical network has been investigated as an efficient strategy for data dissemination towards mobile sinks [6] In this article, we investigate the use of virtual infrastructures to support mobile sinks in WSNs Once a virtual infrastructure is overlaid onto the physical network, it acts as a rendezvous region for storing and retrieving collected event data Sensor nodes in the rendezvous region store the generated data during the absence of the sink When the mobile sink crosses the network, the sensors in the rendezvous region are queried to notify of the event data We first present the advantages and challenges of using mobile sinks in WSNs Next, we introduce our virtual infrastructure based on honeycomb tessellation and the protocol based on it, hexagonal cell-based data dissemination (HexDD) HexDD is a geographical routing protocol based on this virtual infrastructure concept, proposing rendezvous regions for events (data caching) and queries (look-up) It is designed to improve network performance in terms of data delivery ratio and latency, besides meeting the traditional requirements of WSNs, such as energy efficiency In contrast to the rich literature on virtual infrastructure based data dissemination, especially those using greedy forwarding (GF) to send data from sources to rendezvous region, in our previous study [7] we proposed to forward data generated by sources along predefined regions called highways, which are the rendezvous regions in HexDD The main contribution of this article is to improve our data dissemination protocol, HexDD with a fault-tolerance mechanism that does not require additional networking overhead, such as extra messaging to find alternative paths The following are the key highlights of this study: (i) We discuss the advantages and challenges of mobile sinks and present a review of existing virtual infrastructure based data dissemination protocols for mobile multi-sink WSNs (ii) We present our previously proposed HexDD protocol that accommodates the dynamics of the WSN such as stimulus and sink mobility, in such a way that it avoids excessive updates caused by frequently changing environment (iii) We enhance the HexDD protocolby proposing a complete fault-tolerance algorithm that detects routing holes, and calculates and establishes alternative forwarding paths (iv) We evaluate analytically the communication cost and hot region traffic cost of HexDD and compare it with other approaches (v) We evaluate the performance of HexDD with extensive simulations in NS2, and present a large study of comparisons with two other virtual infrastructure based protocols The protocols with different virtual infrastructures allow us to study the effects of the virtual infrastructure shape and the data dissemination strategy on the networking performance (vi) We show the “hot spot” regions (i.e., heavily loaded nodes around rendezvous areas) that are created by different virtual infrastructure based protocols We present a method for resizing of rendezvous region in HexDD to alleviate hot spot problem in the network The highlights (i), (iii), (iv), and (vi) are extensions to our previous studies [7, 8] while the treatment of all (i)–(vi) in this article provides a comprehensive discussion of the protocol The rest of this article is organized as follows: The related studies are introduced with their strengths and weaknesses in Section Section motivates the use of mobile sinks in WSNs Section introduces the honeycomb tessellation and HexDD protocol Section provides analytical studies of communication cost and hot spot traffic cost of HexDD Section presents the simulation results to evaluate the performance of the proposed protocol in comparison with existing protocols Finally, Section draws the conclusions 2.1 Related work Mobility patterns and data collection strategies Sink mobility can be classified as uncontrollable or controllable in general The former is obtained by attaching a sink node on a mobile entity such as an animal or a shuttle bus, which already exists in the deployment environment and is out of control of the network The latter is achieved by intentionally adding a mobile entity e.g., a mobile robot, into the network to carry the sink node In this case, the mobile entity is an integral part of the network itself and thus can be fully controlled [9] Different sink mobility patterns provide different data gathering mechanisms ranging from single hop passive communication (i.e., direct-contact data collection), which may require controllable sink mobility, to multi-hop source to sink solutions, which can be achieved by uncontrollable or controllable sink mobility Direct-contact data collection has great advantage for energy savings That is, sinks visit (possibly at slow speed) all data sources one by one and obtain data directly from them This data collection strategy needs intelligent sink movement computed as the best sink trajectory that covers all data sources and minimizes data collection delay [10] With this approach, maximum energy efficiency and longest network lifetime is achieved at the expense of long delays This mobility scheme is feasible for delay tolerant applications Rendezvous-based data collection is proposed to achieve a good trade off between energy consumption and time delay Sensors send their measurement to a subset of sensors called rendezvous points (RPs) by multi-hop communication; a sink moves around the network and retrieves data from encountered RPs The use of RPs enables the sink to collect a large volume of data with an energy cost of multi-hop data communication, and at a time without traveling a long distance Thus, the use of RPs greatly decreases data collection delay If the virtual infrastructure of rendezvous-based protocol is well designed, one can achieve scalability and energy efficiency Rendezvous-based data collection can be used when we have uncontrollable (e.g., random) sink movement in a WSN 2.2 Data dissemination protocols Several data dissemination protocols have been proposed for WSNs with mobile sinks The proposed protocols fall in two major categories: (i) Flooding-based and (ii) Virtual infrastructure-based In general, virtual infrastructure-based protocols can be divided into (i) backbone-based approaches (e.g., [11]), and (ii) rendezvous-based approaches (e.g., [12]) depending on how the virtual infrastructure is formed by the set of potential storing nodes All protocols discussed in this section assume uncontrolled mobility in the network Directed diffusion [5] is a flooding-based approach introducing data-centric routing for sensor networks In this approach, each sink must periodically flood its location information through the sensor field This procedure sets up a gradient from sensor node to the sink node, so that each sensor becomes aware of the sink’s location for sending future data Although directed diffusion solves the problem of energy-efficiency by using several heuristics to achieve optimized paths, its flooding-based approach does not scale with the network size and increases the network congestion Pursuit-evasion games (PEG) [13] is a sensor network system that detects an uncooperative mobile agent, evader, and assists an autonomous mobile robot called the pursuer in capturing the evader The routing mechanism used in PEG, namely landmark routing, uses the node at the center of the network as landmark (i.e., only one RP) to route packets from many sources to a few sinks It constructs a spanning tree having the landmark node as the root of the tree For a node in the spanning tree to route an event to a pursuer, it first sends the data up to the root, the landmark The landmark, then, forwards the data to the pursuer The pursuer periodically informs the network of its position by picking a node in its proximity to route a query to the landmark Since data dissemination used in PEG is a combination of directed diffusion [5] towards the landmark and central re-dissemination, in order to build the gradients from sensors to landmark node (i.e., spanning tree), it uses flooding-based approach (i.e., each node sends a beacon packet which is further re-broadcasted by all the neighbors of the node) which results in broadcast storm problem increasing the congestion As the flat networks and flooding-based protocols not scale, overlaying a virtual infrastructure over the physical network often has been investigated as an efficient strategy for data dissemination in mobile WSNs [6] This strategy uses the concept of virtual infrastructure, which acts as a rendezvous area for storing and retrieving the collected measurements The sensor nodes belonging to the rendezvous area are designated to store the generated measurements during the absence of the sink After the mobile sink crosses the network, the designated nodes are queried to report the sensory input The concept of overlaying a virtual infrastructure over the physical network has several advantages The infrastructure acts as a rendezvous region for the queries and the generated data Therefore, it enables the gathering of all of the generated data in the network and permits the performing of certain data optimizations (e.g., data aggregation) before sending the data to the destination sink [6] Second, in WSNs deployed in harsh environments, source nodes can be affected by several environmental conditions (e.g., wildfire, etc.), and therefore, the risk of losing important data is high To ensure the persistence of the generated data, the source node can disseminate the data towards the rendezvous area instead of storing it locally Thus, the virtual infrastructure enables data persistence against node failures Main disadvantage of using a virtual infrastructure is the creation of hot spot regions in the network However, it is possible to solve this problem by adjusting the size of rendezvous regions Several protocols that implement a rendezvousbased virtual infrastructure have been proposed in the literature They vary in the way they construct the virtual infrastructure In the rest of this section, we summarize these protocols The geographic hash table (GHT) [14], which is illustrated in Figure 2a, introduces the concept of data-centric routing and storage GHT hashes keys into geographic coordinates, and stores a key-value pair at the sensor node geographically nearest the hash of its key In GHT, the data report type is hashed into geographic coordinates, and the corresponding data reports are stored in the sensor node, called home-node, which is the closest to these coordinates This home-node acts as a rendezvous node for storing the generated data reports of a given type There are as many home nodes as data types The main r B r r r/2 A 3√3r/2 Figure 5r/2 [i,j] l r II [3,6] [3,5] [3,4] [3,3] III I [3,7] [2,4] [2,3] [2,2] [3,2] [3,8] [2,5] [1,2] [1,1] [2,1] [3,1] b [3,9] [2,6] [1,3] [0,0] [1,0] [2,0] [3,0] [3,10] [2,7] [1,4] [1,5] [2,11] [3,17] [3,11] [2,8] [2,9] [2,10] [3,16] IV Figure [3,12] [3,13] [3,14] [3,15] V VI l II r k=2 [5,11] [4,8] [6,14] I III s [3,6] qu er k=3 [5,3] [4,2] [2,4] k=1 [2,6] IV VI k=4 [0,0] [1,4] [2,7] [1,0] [4,14] [5,18] da kH - I s = hops s [3,11] [4,15] k=6 [2,8] [3,12] V Figure [2,0] da [3,10] [1,3] { [3,9] ta b ta y [3,1] [1,2] k=5 (a) (b) r k=1 [5,13] [5,3] [3,3] [2,4] [4,3] [3,2] [2,2] [4,2] [5,2] [4,11] Communication range of node C [5,15] [3,8] [4,12] C [1,2] [2,6] x [1,3] [3,9] [2,7] [4,13] [5,17] [3,11] [5,18] [1,1] [3,1] [1,0] [0,0] [1,4] [1,5] [3,10] [4,14] [2,1] [2,8] [2,0] E x [2,11] [4,1] [5,1] [3,0] [3,17] [4,0] Communication range of node E [3,16] [4,22] [2,10] [3,15] [3,12] [4,15] k=4 [4,20] [5,25] k=5 Figure Hs 1) k-( s I s op p= h [4,4] [3,6] k=3 { [4,8] { [5,5] -p s H s hop k=2 s k=6 [5,0] b A da y er ta qu Sink moves! Figure Sink Rendezvous point Sink 1 Sink Query Circulation Re p ly Qu ery Query at io c = 0.25 D y 1 Reply Figure (a) Rendezvous point (b) Q No uer tif y ica tio n em ss Source Source Rendezvous point Sink Dissemination Event Notification Query Source Source (c) pl y pl ina Re Re in se m Reply Q is n ry ue tio n Rendezvous point Di 1 (d) (e) g ry din ue war Q or F Worst case communication cost Worst case communication cost 250000 HexDD LBDD TTDD GHT RailRoad 200000 150000 100000 50000 0 50 100 150 200 Number of data reports per source Figure (a) 250 200000 HexDD LBDD TTDD GHT RailRoad 180000 160000 140000 120000 100000 80000 60000 40000 20000 0 50 100 150 Number of queries per sink (b) 200 250 15 N = 10, 000 p = 2pq 0.18 ē = 50 q̄ = 50 18 0.16 EH EH HexDD / GHT EH EH HexDD / GHT 0.17 d 20 0.15 0.14 0.13 0.12 16 14 12 0.11 10 18 m 15 12 (n um be r of Figure 10 12 si nk s) 15 (a) 18 n er b um (n of c ur so ) es 50 300 100 250 Tota 200 l nu mber 150 100 of q 50 ueri es ( mq) (b) 150 200 l ta To r be um n of s nt ve e e) (n 2.0 1.2 1.8 1.6 1.0 EH EH HexDD / RR EH EH HexDD / RR 15 N = 10, 000 NR = 800 NRT = 480 NST =16 p = 2pq d ē = 50 q̄ = 50 1.4 1.2 1.0 0.8 0.6 0.4 0.8 0.6 0.4 0.2 0.2 0.0 0.0 200 18 15 m ( num ber 12 of sin 15 ks) Figure 11 s) ce ur 12 r be um n 18 (a) n ( of so 150 ts 50 100 en Tot ev 100 al of num 150 50 ber er 200 of mb 250 que nu rie l 300 s( ta mq) To (b) e) (n ē = 50 q̄ = 50 1.2 1.0 EH EH HexDD / LBDD 1.0 EH EH HexDD / LBDD 15 N = 10, 000 NL = 400 NST =16 p = 2pq d 0.8 0.6 0.4 0.2 0.8 0.6 0.4 0.2 0.0 0.0 200 18 15 m ( num ber of Figure 12 ) es rc ou 12 r be 12 15 sin ks) um 18 (a) n (n of s 150 50 100 100 Tot al of 150 50 num r ber 200 be m of 250 nu que rie al 300 t s( To mq) (b) s nt ve e e) (n Data Delivery Ratio (%) 0.95 0.9 0.85 0.8 HEXDD LBDD TTDD 0.75 0.95 0.9 0.85 0.8 0.75 HEXDD LBDD TTDD 0.7 0.7 Number of Sinks and Sources 10 10 15 20 Maximum Sink Speed (m/s) (b) (a) 1 Data Delivery Ratio (%) Data Delivery Ratio (%) Data Delivery Ratio (%) 0.95 0.9 0.85 0.8 0.75 0.7 HEXDD LBDD TTDD 0.65 Figure 13 0.9 0.85 0.8 0.75 0.7 HEXDD LBDD TTDD 0.65 0.6 0.6 0.95 Number of Holes (c) 6 Size of Hole (Number of Cells) (d) 10 Data Delivery Delay (msec) Data Delivery Delay (msec) 120 HEXDD LBDD TTDD 100 80 60 40 20 0 Number of Sinks and Sources 120 HEXDD LBDD TTDD 100 80 60 40 20 0 10 Data Delivery Delay (msec) Data Delivery Delay (msec) 40 35 30 25 20 15 HEXDD LBDD TTDD Number of Holes Figure 14 (c) 20 (b) (a) 10 10 15 Maximum Sink Speed (m/s) 35 30 25 20 15 HEXDD LBDD TTDD 10 Size of Hole (Number of Cells) (d) 10 6000 Energy Consumption (W) HEXDD LBDD TTDD 5000 4000 3000 2000 1000 HEXDD LBDD TTDD 6000 5000 4000 3000 2000 1000 0 Number of Sinks and Sources 10 10 15 20 Maximum Sink Speed (m/s) (b) (a) 600 2000 HEXDD LBDD TTDD 1800 Energy Consumption (W) Energy Consumption (W) Energy Consumption (W) 7000 7000 1600 1400 1200 1000 800 600 550 500 450 400 350 300 250 400 HEXDD LBDD TTDD 200 Number of Holes Figure 15 Size of Hole (Number of Cells) (c) (d) 10 1000 1400 1400 1000 1200 900 800 1200 800 1000 700 1000 600 800 Y (m) Y (m) 600 600 800 500 400 600 400 400 300 400 200 200 200 200 200 400 600 800 X (m) 1000 1200 1400 100 200 400 600 (a) 800 X (m) 1000 1200 1400 (b) 1400 1200 600 1400 1000 1200 1200 1000 500 1000 400 800 600 600 Y (m) Y (m) 800 800 300 600 200 400 400 400 200 200 200 Figure 16 400 600 800 X (m) (c) 1000 1200 1400 100 200 200 400 600 800 X (m) (d) 1000 1200 1400 Average Application Success Ratio HexDD with one ring HexDD with one cell (a) 0.8 0.6 0.4 0.2 HEXDD-one ring HEXDD-one cell LBDD 0 Figure 17 200 400 600 Time (sec) (b) 800 1000 .. .A virtual infrastructure based on honeycomb tessellation for data dissemination in multisink mobile wireless sensor networks Ayáegăl Tăysăz Erman , Arta Dilo and Paul Havinga s u u u Pervasive... and challenges of using mobile sinks in WSNs Next, we introduce our virtual infrastructure based on honeycomb tessellation and the protocol based on it, hexagonal cell -based data dissemination (HexDD)... the data towards the rendezvous area instead of storing it locally Thus, the virtual infrastructure enables data persistence against node failures Main disadvantage of using a virtual infrastructure