EDARD: efficient data access based on rumour dissemination in wireless sensor networks. Kheroua Leila*, Moussaoui Samira and Mansour Louiza

Transcription

1 Int. J. Trust Management in Computing and Communications, Vol. 1, No. 1, EDARD: efficient data access based on rumour dissemination in wireless sensor networks Kheroua Leila*, Moussaoui Samira and Mansour Louiza Faculty of Electronic and Computing Science, Laboratory of Information System, USTHB, BP 32 Al Alia, B. Ezzouar, Algeria *Corresponding author Abstract: In this paper, we present a rumour dissemination protocol named: efficient data access based on rumour dissemination (EDARD). EDARD ensures an information uniform distribution to enhance data accessibility in wireless sensor networks. In EDARD, rumours (event occurrences) and queries (requests of information) are disseminated through trajectories as straight as possible preventing backward paths. The straitening trajectory based on a selective next hop choice and the forking procedure introduced in EDARD protocol have the objective to find data and establish paths to the source event in short delays with fewer transmissions. Simulation and comparison results show that EDARD protocol achieves improvements in terms of query delivery rate, data access time and network traffic. Keywords: data dissemination; data access; rumour agents; forking agents; routing; wireless sensor networks. Reference to this paper should be made as follows: Leila, K., Samira, M. and Louiza, M. (2013) EDARD: efficient data access based on rumour dissemination in wireless sensor networks, Int. J. Trust Management in Computing and Communications, Vol. 1, No. 1, pp Biographical notes: Kheroua Leila is an Assistant Professor in the Computing Department, University of Science and Technology, USTHB. She received her BS and MS degrees from the Computing Department of the USTHB University, in 2002 and 2006 respectively. She is a PhD candidate attached to the Computing System Laboratory (LSI) from 2008 up to now. Her current research interests are focused on the use of rumour agents to improve data dissemination and routing in wireless sensor networks. Moussaoui Samira received her doctor degree in Computer Science from the University of Sciences and Technologies Houari Boumediene (USTHB), Algiers, Algeria in She is a Lecturer Researcher at the Department of Computer Science of USTHB, since Her research interests include mobile computing systems, mobile networks, and peer-to-peer systems. Copyright 2013 Inderscience Enterprises Ltd.

2 74 K. Leila et al. Louiza Mansour received her Bachelor and Master degrees from the Computing Department of the USTHB University, in 2008 and 2010 respectively. She is a PhD candidate attached to the Computing System Laboratory (LSI-USTHB) from 2010 up to now. Her current research interests are focused on data dissemination in wireless sensor networks and data harvesting in vehicular sensor networks. 1 Introduction A wireless sensor network (WSN) is a collection of sensor nodes (small sensing devices) and sink node (base station) connected through wireless channels. These small devices can be used for building distributed systems for data collection and processing (Wang et al., 2008). WSNs suffer from several restrictions, e.g., limited energy supply, limited computing power, and limited bandwidth. One of the main design goals of WSNs is to carry out data communication while trying to save network lifetime. Data access in WSNs is a challenging task. In fact, for routing purposes and for many other applications, maximum data has to be disseminated over the WSN. In rumour dissemination protocols, once an event occurred in the network, the sensor node that witnesses the event (for example, increased temperature in an area being monitored) creates a rumour. A rumour is a packet that is responsible for spreading event identifier in the network. Each rumour is associated with the time to live (TTL) that determines the number of hops the rumour can reach. A rumour maintains an event list in which the events names and distance to the event s node source are stored. We note that rumours also called agents are considered as active messages with a small code of list synchronisations as used in Braginsky and Estrin (2002) and Banka et al. (2005). When the sink node is looking for a certain event, it creates a query. A query is a packet which only contains a call for specific information. It travels through the network blindly and searches each visited node s event table for requested information. Each query is associated with a TTL that determines the number of hops the query can make. Unless it matches the requested event before the expiration of its TTL, a query is considered undelivered. The neighbouring information in the network is kept by node s neighbour list. Rumour dissemination protocols were developed for environments without any localisation systems. The main problem of these rumour random walk protocols is the information non-uniform distribution. Rumour information may be concentrated in a restricted network region which makes data access delays very important. To fix that problem, efficient data access based on rumour dissemination (EDARD) protocol introduces a straitening trajectory for both events and queries paths. EDARD maintains neighbouring information at two hops in lightweight packets, frequently reinitialised. For larger data dissemination, EDARD protocol implements a forking procedure that leads to more agents creation distributing information to different part of the network. We believe that for important and critique data in real time applications, EDARD protocol can build paths leading to source events in very short delays.

3 EDARD: efficient data access based on rumour dissemination 75 The rest of this paper is organised as follows: Section 2 discusses related researches on rumour dissemination protocols in WSNs. The proposed EDARD protocol is introduced in Section 3. Section 4 presents simulation and comparison results of the proposed EDARD protocol, while Section 5 concludes the paper. 2 Related work Rumour routing (RR) (Braginsky and Estrin, 2002) protocol attempts to solve the problem of network overhead by introducing a logical compromise between query and event flooding. When a node in the network witnesses an event, an agent is created and the event is inserted into an EventList. For its next hop, the agent chooses an unvisited node among neighbouring nodes. If all the neighbouring nodes were already visited, the agent will choose one of them randomly. The agent progresses till the expiration of its TTL creating a history list that contains the identity (IDs) of the previously visited nodes. When a query is sent by a sink node, it travels through the network blindly until it meets the concerned event. If not, the same query will be flooded. The drawback of RR is that it produces a highly non-uniform distribution of information due to the random choice of the next hop when forwarding agents and queries. The agent path may be a spiral route around a restricted region in the network. To deal with these later, zonal rumour routing (ZRR) was proposed. In cases where sensors are uniformly distributed, ZRR defines a zone optimal number formula to maximise the query delivery rate. In ZRR, the topology of the network is reconsidered, since it is partitioned into virtual zones where each node is exactly member of one zone. Agents and queries are forwarded as far as possible in the network moving from a zone to another. ZRR seems to be a good approach to disperse agents and queries further in the network. Even though they cross long distances, chances are that they may loop back toward their native zones considering their partial historic information and the data limit storage. Forking agents (Haenselmann and Effelsberg, 2005) proposes to distribute agents more equally in the network. For that purpose, it introduces master agents that are sent immediately after detecting an event in a cell or a region of the network. Master agents have the property to fork themselves while travelling through the network for a predefined number of times. The forking process leads to child agent s creation. The idea behind forking agents is to propagate the event information very far from the owner cell reducing by the same time the number of transmissions. Forking agents successes to ensure relatively a uniform events information distribution in the network. However, it impossible to predict child agents directions since the system loose all control when creating them. Along and across routing algorithm (AL&AC) (Chim, 2005) uses a tree structure hop organised on levels starting from a random root node. It consists of three major steps: building the hop tree, distribution of event attributes and propagation of queries. Events are routed along hop levels and queries are routed across hop levels. They may go on upper or lower directions or both to seek for an answer. In AL&AC the hop tree building process consumes precious battery power and paths between events and queries are usually not the shortest one.

4 76 K. Leila et al. Straight line routing (SLR) (Chou et al., 2005) focused first, on the problem of winding paths. It proposed to transmit agents and queries on straight line trajectories. For SLR, at each next hop selection, computation is needed and may fail in some worst cases. Directed rumour routing (DRR) (Shokrzadeh et al., 2009) was proposed first for environments with available positioning systems. DRR was improved by eliminating the localisation devices. In this case, cheaper and more available equipments like: angle of arrival (AoA) antennas, electronic compasses and signal strength detectors are used to obtain direction of the received signal, absolute directions and distances of different neighbours. The neighbour with the least absolute total deviation is then selected as the next hop. The drawback of DRR is the partial historic information of the visited nodes. Its preventing loops strategy is not clear, since its stores only the previous node that transmits the agent. Fast rumour agents (FRA) (Kheroua and Moussaoui, 2011) was designed to fix the spiral path routing problem using neighbouring information at each hop. Although, FRA reduces time of path establishment, it slightly decreases the query delivery rate since the rumour is disseminated along a thin path. 3 EDARD protocol 3.1 Model environment We consider a stationary WSN without any localisation systems and with reliable communication links. We suppose a network with uniform distribution of sensor nodes partitioned into virtual zones or clusters and every node is a part of one zone only. Once an event is detected in the network, a rumour agent is created. It will be propagated from one zone to another according to a straitening trajectory. A query is a request of information, it will be propagated exactly as an agent until it crossroad the desired agent path. Both the agent and the query have their respective TTLs that determines the number of hops they can make. 3.2 Solution overview EDARD main objective is to disseminate rumours uniformly, as far as possible in the network. The purpose is to increase the chances of crossroad between agents and queries paths to optimise time and query delivery rate using fewer transmissions. Rumours are data descriptions embedded in agent s packets. Their role is to inform about the existence of the data using efficiently the limited bandwidth and space of sensor nodes. Once the sink node decided that the data is too important, it will be propagated using the constructed path. EDARD protocol principal s procedures are: Agents and queries dissemination Agents and queries dissemination is based on path construction following a straitening trajectory. The path construction is based on a selective next hop scheme. Neighbouring

5 EDARD: efficient data access based on rumour dissemination 77 information is maintained using beacons. To store the historical path information, we use the history zone array (HistZA). The HistZA is an array of bits with a size equal to the total number of zones in the network; all its entries are initialised to zero. At each time the agent (respectively. the query) visits a zone, the corresponding entry on this array is updated to one. The HistZA offers to agents and queries, a global historic view of their respective visited zones and lightweight packets since it contains bits instead of zone identifiers as in Banka et al. (2005). For intra zones hops, we use temporary node list (TempNL). It contains the identifier of the first node that transmits the agent (respectively. the query) and its neighbouring nodes identifiers also. It is updated each time the agent (respectively. the query) moves from a node to another inside the same zone. It prevents agents and queries to have a spiral path inside a zone and it is reinitialised each time the agent (respectively. the query) moves to another zone. For extra zones hops, we use temporary zone list (TempZL). It contains the neighbouring zones identifiers of the last visited zone. It allows the agent (respectively. the query) to progress faster in the network, as far as possible from the last visited zone. It is updated each time the agent (respectively. the query) moves to a different zone. Figure 1 details the respective agent and query packets; NodeId is the identifier of the node that transmits the agent (respectively. the query); NextHop is the identifier of the node that receives the agent (respectively. the query) and Evt is the event the query looks for. Figure 1 (a) Agents and (b) query packet structure NodeId NextHop TTL EventList HistZA TempNL TempZL (a) NodeId NextHop TTL Event HistZA TempNL TempZL (b) At the agent creation, the event list is initialised and the agent creates the HistZA changing the value of the entry corresponding to its zone from 0 to 1. For its first next hop, the agent looks for an external neighbour (belonging to a different zone). If it is found, an agent packet is forwarded with the HistZA. If the external neighbour cannot be found, the agent is randomly forwarded with its TempNL to an internal neighbour (belonging to the same zone). For all next hops, the agent will try to find a neighbour that satisfies the two following conditions: 1 belong to an unvisited zone 2 belong to a zone different from ones in the TempZL to maintain the trajectory as straight as possible. We note that the priority is given to the first condition. In the worst case, the next hop may belong to a zone of the TempZL. If the two conditions are not satisfied, the agent will be sent to an internal neighbor. For all its internal hops, the agent transports its

6 78 K. Leila et al. TempZL and its TempNL. But, when it finds a way to another zone, it keeps only its TempZL and reinitialised the TempNL since the oldest one becomes not useful. The agent continues its way until the expiration of its TTL. Figure 2 explains an example of the agent next hop zone selection during the path construction. The agent from zone 1 is at zone 4. For its next zone hop, the candidate zones are zone 5, 6 and 7. The best next agent zone hop choice is a node from zone 7. But, even if it chooses node from zone 5 or 6, the agent path will slightly deviate from the straitening trajectory since the only candidate zones at zone 5 and 6 are zone 7 and another zones that are not so far from the straitening path (z1-z4-z7-.). Query dissemination is quite similar to the agent one, except that the query searches the desired event at each intermediate node. When a path is found, the query is directly routed to the event. The query routing terminates if a path to the event is found or if its TTL expires. We note that data access starts immediately ones the path is constructed. Figure 2 EDARD next hop zone selection Agent s forking procedure To improve query delivery rate success and take in consideration the scalability feature, we have implemented a forking process during the agent dissemination. For that purpose, we have defined time to fork (TTF) as a number of hops the agent will cross in order to start forking procedure. It is suitable that TTF value is half or more of the initial agent TTL. Thus, the rumour may progress deeper in the network. At a forking step, child agents are created and routed exactly as the initial ones. The number of child agents may be fixed or dynamically determined according to the number of neighbour zones.

7 EDARD: efficient data access based on rumour dissemination 79 Figure 3 EDARD pseudo algorithm 1. Node X detects event E. 2. Node X creates a Rumour Agent (RA). Node X and RA saves E in their respective Event List with (E.distance == 0) 3. RA saves the X zone number and maintains its Neighbor Zone List. 4. IF TTL =! TTF then, RA looks f or an external neighbor Y (selective next hop scheme) a. If found: i. RA is transferred and Event List synchronization is done. ii. Back to 4. b. If not: i. An internal node is chosen. ii. Back to If not: Forking process starts with child agent s creation according to the number of entries in the Candidate Zone List. 6. Each child agents is routed according to a selective next hop scheme until the expiration of its TTL. 4 Performances evaluations 4.1 Simulation parameters We have implemented scenarios for the creation of a 1024 nodes network distributed uniformly in a grid of 100 m 100 m. The maximum transmission range is fixed to 5 m for each node (sensing, sending and receiving), we fixed the number of zones to 127 and agents TTL to 100 according to Banka et al. (2005). Queries TTL are fixed to 250 to allow query dissemination up to the quarter of the network size (Haenselmann and Effelsberg, 2005). All tests were conducted using the network simulator, NS Table 1 details the simulation configuration parameters. Table 1 Simulation configuration parameters Parameters configuration Network size 100 m 100 m Number of nodes 1,024 Max transmission range 5 m Node distribution Uniform Number of zones 127 Agent s TTL (hop) 100 Child agent s TTL 50 Query TTL 125 TTF 50, 80 Number of query per event 10, 20, 30, 40, 50, 60,70 Number of events 2 Number of agents per event 1, 2

8 80 K. Leila et al. Following is the list of the used performance metrics: Traffic: the total traffic generated in the network is calculated according to the total number of agents and queries transmissions and receptions in the network. This metric can be used to inform about the total consumed energy in the network. Query delivery rate: the ratio of queries correctly routed to the source over all generated queries for a certain event. Data access time: is the time of path establishment. It is the difference between the time where the query reaches the source event and the time it was generated. 4.2 Simulation results To evaluate the best time of forking process, we have tested the following EDARD variants. TTF mentioned number of hops agent crosses before child agent s creation starts. EDARD1: two child agents at TTF = 50 EDARD2: two child agents at TTF = 80 EDARD3: two child agents at TTF = 50 and two another child agents at TTF = 80 EDARD4: one child agents at TTF = 50 and another one at TTF = 80. Figure 4 shows the query delivery rate for two events for which we vary the number of queries from 10 to 70. The results show that the EDARD variants have a satisfactory query delivery rate since it is between 97 and 100%. But, fewer than 40 queries per event, EDARD1 and EDARD4 rates are lower than EDARD3 and EDARD2. Concerning EDARD2, the delivery rate decreased after 30 queries per event which makes EDARD3 as the best variant probably because it generates more child agents with a double forking process. Figure 4 Query delivery rate

9 EDARD: efficient data access based on rumour dissemination 81 Figure 5 Traffic (number of transmissions) Figure 5 shows the total traffic generated by the EDARD variants. Results show that for the four variants, the traffic increased with the number of queries. But for EDARD 2, the traffic increased unexpectedly between 30 and 60 queries. This may be explained as follow: since the forking process in EDARD2 is postponed to 80%, the number of undelivered queries increased which increases queries transmissions in the network. Figure 6 shows the data time access of the EDARD variants. From Figures 3, 4 and 5, we note that EDARD3 has the best results in terms of query delivery rate, traffic and data access time. This is due to the number of rumour agents and the twice forking process associated. Figure 6 Data access time

10 82 K. Leila et al. Figure 7 Query delivery rate We have compared the best performance of EDARD protocol (EDARD3) with FRA (Kheroua and Moussaoui, 2011) and ZRR (Banka et al., 2005) algorithms. Figure 7 shows that unlike ZRR and FRA, the number of queries does not affect the performance of EDARD protocols. EDARD protocol improves the query delivery rate for about 7.75%.compared to the FRA and ZRR ones. This is certainly due to the use of child agents and the straitening routing strategy that allow a better rumour dissemination. Figure 8 Traffic (number of transmissions)

11 EDARD: efficient data access based on rumour dissemination 83 Figure 9 Data access time Figure 8 shows that the number of queries does not affect the performances of both EDARD and FRA protocols. However, the ZRR traffic increased after 60 queries per event probably because queries do not much the desired rumour agent paths and are finally lost in the network. The results proof the efficiency of EDARD protocol because even if it creates more agents, traffic results are satisfying since this process decreased query traffic. EDARD protocol improves the network generated traffic by saving 12,000 messages transmissions compared to the FRA one. Figure 9 shows that EDARD protocol improves data access time for about 35% compared to the ZRR protocol and for about 12% compared to the FRA one. This is principally due to the EDARD rumour dissemination strategy. 5 Conclusions For WSNs, collecting and gathering data in reasonable delays is a very challenging task especially in environments without positioning systems. To achieve this purpose, efficient data dissemination techniques have to be implemented. For critical data gathering, this paper proposes a data dissemination protocol named: EDARD. EDARD disseminate data in straight paths according to historic and neighbouring information embedded in lightweight packets frequently reinitialised. Also, EDARD propose an adaptive forking process to cover larger parts of the network. Performances analyses show that even if EDARD protocol creates more transmissions, query delivery rate and traffic results are satisfying. For real time applications with critical data, one interesting issue for EDARD protocol is to implement data replication. Each rumour agent will carry the data and copy it at each 50% and 80% of its TLL. Proceeding this way, we first enable the data to be available far from its native region and secondly we may overcome the problem of path deficiency.

12 84 K. Leila et al. References Banka, T., Tandon, G. and Jayasumana, A. (2005) Zonal rumor routing for wireless sensor networks, Proceedings of the International Conference on Information Technology: Coding and Computing, Vol. 2, pp Braginsky, D. and Estrin, D. (2002) Rumor routing algorithm for sensor networks, Proceedings of the 1st ACM International Workshop on Wireless Sensor Networks and Applications, pp Chim, T.W. (2005) Along & across algorithm for routing events and queries in wireless sensor networks, Proceedings of International Symposium on Intelligent Signal Processing and Communication Systems, Hong Kong. Chou, C.F., Su, J.J. and Chen, C.Y. (2005) Straight line routing for wireless sensor networks, Proceedings of the 10th IEEE Symposium on Computers and Communications, pp Haenselmann, T. and Effelsberg, W. (2005) Forking agents in sensor networks, 3rd (German) Workshop on Mobile Ad-Hoc Networks (WMAN), GI Jahrestagung, No. 2, pp Kheroua, L. and Moussaoui, S. (2011) Efficient agent based rumor propagation for wireless sensor networks, International Journal of Measurement Technologies and Instrumentation Engineering, April-June, Vol. 1, No. 2, pp.61 72, IGI Publishing. Shokrzadeh, H., Haghighat, A.T. and Nayebi, A. (2009) New routing framework base on rumor routing in wireless sensor networks, Journal of Computer Communications, Vol. 32, No. 1, pp Wang, M.M., Cao, J.N., Li, J. and Dasi, S.K. (2008) Middleware for wireless sensor networks: a survey, Journal of Computer Science and Technology, May, Vol. 23, No. 3, pp