A Novel Location-Free Greedy Forward Algorithm for Wireless Sensor Networks

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1 This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the ICC 8 proceedings. A Novel Location-Free Greedy Forward Algorithm for Wireless Sensor Networks Azzedine Boukerche, Horacio A.B.F. Oliveira, Eduardo F. Nakamura, and Antonio A.F. Loureiro PARADISE Research Laboratory, SITE University of Ottawa, Canada Department of Computer Science Federal University of Minas Gerais, Brazil Department of Computer Science Federal University of Amazonas, Brazil FUCAPI Analysis, Research and Technological Innovation Center, Brazil {boukerch,horacio}@site.uottawa.ca, {nakamura,loureiro}@dcc.ufmg.br Abstract In this work, we propose a new Greedy Forward algorithm to be used in Wireless Sensor Networks (WSNs) routing protocols. Differently from current Greedy Forward algorithms, which depend on the position information of nodes, our approach uses only the Received Signal Strength Indicator (RSSI) of exchanged packets. Since nodes position are usually computed based on three or more distance estimations (commonly by means of RSSI measurements) and the positions of beacon nodes (which have inaccuracies by themselves), by using only RSSI measurements, we should achieve better results. Based on this observation, we propose the RSSR (Received Signal Strength Routing) algorithm with two variants: and RSSR Selection. In the, the next hop is dynamically elected at each step and no packets are required for the routing task. In the, neighbors exchange packets with RSSI information and the next hop of the packet is then selected from a routing table at each step. Our results indicate clearly that the proposed algorithms have all the benefits of a Greedy Forward algorithm but with better performance and without requiring positions information. I. INTRODUCTION Wireless sensor networks (WSNs) [1] [5] are composed of a large number of sensor nodes used to monitor an area of interest. This type of network has become popular due to its applicability that includes several areas, such as environmental, health, industrial, domestic, agricultural, meteorological, spacial, and military. Several physical properties can be monitored, including temperature, humidity, pressure, ambient light, and movement. Usually, the gathered information needs to be sent hop-by-hop to a central node, called sink, that is able to process the data and send the results to a Network Management and Monitoring facility using a more powerful communication equipment. As a matter of fact, data routing towards the sink node is an important task to make viable most of the WSN applications. Therefore, different routing algorithms for WSNs have been proposed [6] [13]. In particular, geographic routing algorithms [7] [13] are closely related to the current work and have a number of advantages especially important for WSNs, such as scalability, energy-efficiency, low route discovery overhead, and low memory requirements (nodes need to store only information about their neighbors). For these reasons, geographic routing is the protocol of choice for many emerging applications in sensor networks [1]. A well-known technique used by most geographic routing algorithms is the Greedy Forward, which uses location information of neighbor nodes to forward the packet to the node that is geographically closer to the destination node. Geographic routing solutions have shown their importance in terms of scalability and energy efficiency, but in order to work they require the previous execution of a Localization Discovery System which is not always available [14], [15] and, in most cases, the Localization System must provide very precise position information since even small localization errors can lead to loops and low routing performance [1], [15] [17]. In this work, we propose a novel Greedy Forward algorithm, which we refer to as the RSSR (Received Signal Strength Routing) algorithm. The main idea of RSSR is to take advantage of the greater capability of the sink node and equip this special node with a more powerful communication device so it can send a query packet to all nodes of the WSN in a single hop. Then, regular nodes can reply the sink query by using a multihop communication and our proposed RSSR algorithm. The same query reaches all nodes with different Received Signal Strength Indicators (RSSI) in such a way that the most distant nodes experiments the lower signal strengths due to propagation issues. The basic principle of RSSR is to forward the packet to the neighbor that received the query with greater signal strength which is, in theory, the neighbor that is closest to the sink node. We then propose two versions of the proposed approach: the and the RSSR Selection. In the, a leader election algorithm that requires no packet exchange is proposed to choose the next hop. In the, neighbor nodes exchange RSSI information and the neighbor with the greatest query packet RSSI is selected at each step. The proposed algorithms allows the execution of a Greedy Forward strategy that requires neither location information, nor virtual coordinates (as done in [14]). Also, since the positions of the nodes in a WSN are usually computed based on three or more RSSIs plus the positions of beacon nodes (which have inaccuracies by themselves), to use only RSSI information instead of positions /8/$25. 8 IEEE 96

2 This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the ICC 8 proceedings. results in better performance, as we show latter on. The remainder of this paper is organized as follows. In the next Section, we describe the related work. Section III describes both proposed RSSR algorithms, the and the, which are evaluated in Section IV. In section V we briefly discuss the applicability, scope, advantages, and limitations of the proposed solution. Finally, Section VI presents our conclusions and future directions. II. RELATED WORK Greedy Forward (GF) has been used by a number of geographic routing algorithms [7] [13] applied to Ad Hoc [8], [11], Sensor [9], and Vehicular Networks [12]. Basically we can identify three classes of protocols and algorithms that use the GF algorithm. In the first class, the GF algorithm is combined with a perimeter/face routing algorithm to deal with the cases of local maxima (when the packet reaches a node that has no neighbor closer to the destination caused by holes in the network and by localization inaccuracies). For instance, GPSR (Greedy Perimeter Stateless Routing) [8] uses the GF to forward packets and, in the presence of a local maximum, it switches to a perimeter mode that uses the right hand rule to bypass the hole until reaching a node closer to the destination, where the GF resumes. A similar approach is used by GFG (Greedy-Face-Greedy) [11] and GOAFR+ [18]. In the second class, the metric that is used in the neighbor selection is changed. The GEAR (Geographical and Energy Aware Routing) algorithm [9] uses the Greedy Forward associated with a cost for each node to allow the packet to be forwarded around holes and also to distribute the routing work among the nodes (energy-aware). Advanced Greedy Forwarding (AGF) [12] significantly improves GPSR (and other GF based protocols) performance in VANETs (Vehicular Ad Hoc Networks) by incorporating movement directions and speeds into the packets. NADV (Normalized Advance) [13] proposes a new link metric that selects neighbors with the optimal trade-off between proximity and link cost. A similar approach is the GF-RSSI (Greedy Forward with Received Signal Strength Indication) [19] that uses RSSI information to forward the packet towards a more reliable neighbor. At last, in the third class, the algorithms use the GF technique without actual location information by using virtual coordinates. In [14] and [15], virtual coordinates are assigned to nodes in order to apply the standard geographic routing over these coordinates. This virtual coordination assignment greatly increases the complexity of the algorithm and reduces its scalability. Our RSSR algorithms differ from the algorithms of the first class since we are not dealing with perimeter/face routing yet (to overcome voids or holes). We can say that RSSR fits in the second class since we are proposing the use of a new metric, the RSSI, to forward packets, but our proposed algorithm differs from current algorithms because it does not rely on the position information of neighbor (or any other) nodes. The proposed RSSR algorithms can also fit in the third class since it is a location-free Greedy Forward. But, differently from the current solutions, RSSR does not rely on any virtual coordination assignment or any other costly coordination system. III. RSSR A NOVEL LOCATION-FREE GREEDY FORWARD ALGORITHM In this section, we propose a new Greedy Forward algorithm: the RSSR (Received Signal Strength Routing). The main idea is to take advantage of the greater capability of the sink node and equip this special node with a more powerful communication device so it can send a query packet to all nodes of the WSN in a single hop. This is a reasonable assumption for some scenarios, as discussed in Section V. The key aspect of the RSSR algorithm is to take advantage of the fact that the same sink query will reach all regular nodes with different Received Signal Strength Indicators (RSSI) in such a way that the more distant the node, the lower the signal strength (due to propagation issues), as depicted in Figures 1(a) and 1(b). Thus, the basic working principle of RSSR is to forward the packet to the neighbor that receives the query with greater signal strength which is, in theory, the neighbor that is closest to the sink node. In fact, these received signal strengths can be easily translated into real estimated distances (and vice versa). Thus, from now on, for the sake of simplification and without any loss of generality, we will use distance estimations instead of received signal strengths. In the next sections, we will show the two versions of the proposed approach: the, explained in Section III-A and the, explained in Section III-B. A. The complete algorithm of the is shown in Algorithm 1 and it starts when the sink node sends a query to all nodes in an one hop communication (lines 4-8). Every node that receives the query estimates its distance to the sink node and stores it in a forwarding table (lines 9-1). Then, the node checks if the query was sent to him and, if that is the case, it broadcasts a reply and save it in a table of forwarded packets (to avoid loops lines 1-16). Then, we need to choose the next node to forward the packet towards the sink. In the, neighbor nodes from a node that sent a reply towards the sink (line 17) elect themselves as the next hop if nobody does that after a given time. This time is proportional to the distance (RSSI) of the neighbor nodes to the sink node. The idea is that nodes closer to the sink (that received a greater RSSI) will wait for a very small time before electing themselves as a leader and the nodes farthest from the sink node that received the query will elect themselves as the next hop only after a (relative) longer time (line 27). When a node elects itself as the next hop, it immediately forwards the reply packet via broadcast sending the packet towards the sink node (lines 32-34). Such a reply serves as a notification to the other nodes (waiting to be the next hop) that they should cancel their timers in order to avoid duplicated packets (lines 18-). 97

3 This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the ICC 8 proceedings. Fig. 1. Fig. 2. Signal strength decreasing with distance. Limiting the leader participants. But a leader election of this type requires that all participant nodes can communicate with each other so the leader can notify all the other participants that they lost the election, which is not the case of the nodes that received the reply packet. Thus, we need to limit the nodes that will participate on the election (line 25). First, we can exclude all neighbor nodes that receives the sink query packet with smaller signal strength than the node that just forward the reply, i.e., we are excluding the nodes that are more distant from the sink than the node that just sent the reply, as shown in Figure 2(a). Then, we need to limit the participants to the nodes inside an equilateral triangle with sides lengths of half the communication range (Figure 2(b)). By doing this, we guarantee that all nodes inside this triangle can communicate with each other, allowing the leader election. To decide if it is inside or outside this equilateral triangle, a node only needs to compute the two points of intersection between the circles formed by its distance to the sink node and its distance to the node that sent the reply (estimated also using RSSI). As shown in Figure 2(c), this computation is quite easy and does not require any position information (only distances). If the distance between the two points of intersection is smaller than the distance of the current node to the node that sent the reply, than the current node is inside the triangle and can participate on the leader election, otherwise, it is outside and simply ignore the reply packet. All nodes inside the triangle save the query as forwarded (line 26) in order to stop eventual loops earlier. When the sink receives the reply packet, it also sends a packet informing his neighbors that the packet arrived at the destination (lines 28-3). B. The is more simple and more similar to the original Greedy Forward technique. The algorithm is shown in Algorithm 2 and it starts when the sink sends the query (lines 6-1) and regular nodes estimate their distances (lines 15-16). But in this case, when the sink sends the first query, all nodes exchange their estimated distances with their Algorithm 1 Algorithm Variables: 1: queries i = ; {Set of received queries} 2: fwddt able i = ; {Set of forwarded replies} 3: waittimer i ; {Timer to wait to forward a packet} 4: Sink Node receiving from the Monitoring Facility a QueryRequest(dest k,qryid k,qry k ). 5: src i = n i ; 6: dist i =; 7: queries i = queries i (src i,dist i,dest k,qryid k,qry k ); 8: Send query(src i,dest k,qryid k,qry k ) to All Nodes. 9: msg i = query(src k,dest k,qryid k,qry k ) such that dist i = distanceestimation(msg i ). 1: queries i := queries i (src k,dist i,dest k,qryid k,qry k ); 11: if dest k == n i then {Is the query for me?} 12: dest i = src k ; 13: rpl i = getreplyf orquery(qry k ); 14: fwddt able i := fwddt able i (dest i,qryid k ); 15: Send reply(dest i,qryid k,rpl i,dist i ) n j neig i. 16: end if 17: msg i = reply(dest k,qryid k,rpl k,dist k ) such that rpldist i = distanceestimation(msg i ). 18: if waittimer i.isactive() then 19: waittimer i.cancel() {Our packet were forwarded} : end if 21: if (dest k,qryid k ) fwddt able i then 22: return; {Packet already forwarded} 23: end if 24: dist i = getmydistt osink(queries i,dest k,qryid k ); 25: if insidef wt riangle(dist i,dist k,rpldist i ) then 26: fwddt able i := fwddt able i (dest k,qryid k ); 27: waittimer i.start(timet ow ait(dist i ),msg i ); 28: if dest k == n i then {Is the reply for to me?} 29: Send reply to Monitoring Facility. 3: end if 31: end if 32: waittimer i.timeout(msg k ). 33: (dest k,qryid k,rpl k,dist i )=msg k ; 34: Send reply(dest k,qryid k,rpl k,dist i ) n j neig i ; neighbors by broadcasting advertisements (lines 17-). A node receives one advertisement for each neighbor and stores their information in a routing table (lines 29-3). Then, the node checks if the query is for him and, if that is the case, it waits for a while (so the advertisements are exchanged), sends a reply to the neighbor closest to the sink (selected from the routing table), and saves it in the table of forwarded packets (lines 21-28). Next, a node receiving a reply chooses the next closest hop from the routing table and forwards the reply directly to this selected node (lines 35-39) until the reply reaches the sink node (lines 32-33). In this last step, the sink node, in order to include itself in the forward table of its neighbors, must have sent an advertisement packet by using the same communication range of the regular nodes (as shown in lines 11-14). 98

4 This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the ICC 8 proceedings. Algorithm 2 Algorithm Variables: 1: queries i = ; {Set of received queries} 2: fwddt able i = ; {Set of forwarded replies} 3: sentadvs i = ; {Set of sent advertisements} 4: neigt able i = ; {Neighbors distances to Sink} 5: waittimer i ; {Timer to wait to setup GF} 6: Sink Node receiving from the Monitoring Facility a QueryRequest(dest k,qryid k,qry k ). 7: src i = n i ; 8: dist i =; 9: queries i = queries i (src i,dist i,dest k,qryid k,qry k ); 1: Send query(src i,dest k,qryid k,qry k ) to All Nodes. 11: Decrease communication range; 12: (dest i,dist i ):=(n i,.); 13: Send adv(dest i,dist i ) n j neig i. 14: Increase communication range. 15: msg i = query(src k,dest k,qryid k,qry k ) such that dist i = distanceestimation(msg i ). 16: queries i := queries i (src k,dist i,dest k,qryid k,qry k ); 17: if src i / sentadvs i then 18: dest i = src k ; 19: Send adv(dest i,dist i ) n j neig i. : end if 21: if dest k == n i then {Is the query for me?} 22: wait(.5); {Wait to receive advertisements.} 23: dest i = src k ; 24: rpl i = getreplyf orquery(qry k ); 25: fwddt able := fwddt able (dest i,qryid k ); 26: nexthop i = closestnode(neigt able i ); 27: Send reply(dest i,qryid k,rpl i ) to nexthop i ; 28: end if 29: msg i = adv(dest k,dist k ). 3: neigt able i = neigt able i (dest k,dist k ); 31: msg i = reply(dest k,qryid k,rpl k ). 32: if dest k == n i then {Is the reply for to me?} 33: Send reply to Monitoring Facility. 34: else 35: if (dest k,qryid k ) / fwddt able i then 36: fwddt able := fwddt able (dest k,qryid k ); 37: nexthop i = closestnode(neigt able i ); 38: Send reply(dest k,qryid k,rpl k ) to nexthop i ; 39: end if : end if IV. EVALUATION In this section, we evaluate the proposed RSSR algorithms and compare their performance with the original Greedy Forward technique. A. Methodology The evaluation is performed through simulations by using the NS-2 simulator. In all graphics, curves represent average values, while error bars represent confidence intervals for 95% of confidence from 33 different instances (seeds). The simulation parameters are based on the MicaZ sensor node and the default values shown in Table 1. Regarding the network topology, we assume that the node deployment Parameter Value Sensor field m 2 Number of nodes 512 nodes (disturbed grid) Density.3 nodes/m 2 Communication range m Short RSSI inaccuracy 15% of real dist. [], [21] Long RSSI inaccuracy 1% of real dist. [22], [23] Position inaccuracies 2 x RSSI in. x Comm. range TABLE I DEFAULT SCENARIO CONFIGURATION. obeys a disturbed grid, in which the location of each node is disturbed by a random zero-mean Gaussian error. Therefore, nodes will tend to uniformly occupy the sensor field without forming a regular grid. At last, to simulate RSSI inaccuracy, each range sample is disturbed by a Gaussian distribution with the actual distance as the mean and a percentage of this distance as the standard deviation [], [21]. Then, we compare the RSSR algorithms to the original GF algorithm. To simulate the position computation inaccuracies, we disturbed the advertised position of the nodes by twice the short distant RSSI inaccuracy times the communication range, as shown by a number of works that experiments the effect of RSSI inaccuracy on the position computation [16], []. B. The Impact of the Network Scale Scalability is evaluated by increasing the network size from 256 to 124 nodes with a constant density of.3 nodes/m 2. Thus, the sensor field is resized according to the number of sensor nodes. In all cases the packet delivery success rate were above 9%. We can see that the performances of all three experimented algorithms are not highly affected by the number of nodes, but from the graph depicted in Figure 3(a) we can see that the RSSR algorithms are able to make better greedy decisions at each step while the GF algorithm has a greater percentage of wrong decisions of the next hop (due to localization errors) which results in larger paths from nodes to the sink, as shown in Figure 3(b). C. The Impact of the Communication Range Shorter communication ranges lead to fewer neighbors, which reduces the number of possibilities for the next hops, especially for the algorithm. We evaluated this impact by increasing the communication range of the nodes from 15m to m. Figure 3(c) shows that the number of successfully delivered packets for the algorithm decreases to 7% when the communication range is in 15m. In this case, nodes have an average of 14 neighbors which increases the chances of no nodes being inside the limited equilateral triangle, in which case the algorithm stops. The and GF are not highly affected by the communication range, as shown in Figures 3(c) and 3(d). D. Impact of Short Range RSSI Inaccuracy The impact of short range RSSI inaccuracies (distance estimations among regular nodes) are evaluated by increasing this 99

5 This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the ICC 8 proceedings Number of Nodes Increase over Shortest Path (%) Number of Nodes Packet Delivery Success Rate Communication Range (m) Communication Range (m) (a) (b) (c) (d) Packet Delivery Success Rate RSSI Inaccuracy (% of distance) RSSI Inaccuracy (% of distance) Packet Delivery Success Rate Long RSSI Inaccuracy (% of distance) Long RSSI Inaccuracy (% of distance) (e) (f) Fig. 3. Simulation results. (g) (h) inaccuracy from to 3% of the real distances among nodes. As shown in Figures 3(e) and 3(f), both RSSR algorithms are not highly influenced by this inaccuracy, especially the RSSR Selection since it does not use this information. On the other hand, the GF algorithm is highly affected, as confirmed by other works [1], [15] [17]. E. Impact of Long Range RSSI Inaccuracy We also evaluated the impact of long range RSSI inaccuracies, since the long range signal strength is the main metric used by our RSSR algorithms. We evaluated this impact by increasing the inaccuracy from to 1% of the real distances (1% can result in m RSSI errors for the experimented scenarios). Figures 3(g) and 3(h) show that the successfully delivered packets in the algorithm decreases as the long range RSSI inaccuracy increases, because nodes inside the triangle may conclude they are outside (and viceversa). The only effect on the occurs in nexthop decisions, because nodes closer to the sink can advertise that they are farther in the presence of errors. But because in these cases near nodes will probably have correlated RSSI inaccuracy, 1% of inaccuracy for a long range RSSI is a critical scenario, as we discuss in next section. V. APPLICABILITY OF THE PROPOSED SOLUTION In this work, we consider a sink node equipped with a powerful communication device in such a way that it can send a query packet to all nodes in a single hop. This is a reasonable assumption in some scenarios of WSNs a number of proposed protocols have the assumption that one or even all nodes (e.g., LEACH [24]) can communicate in a single hop to other nodes when necessary. In cases where it is not possible for a single sink to reach all nodes (e.g., a 1Km 2 WSN) we can think of a clustered multiple-sink solution that would still be able to use the RSSR algorithms. We also assume that RSSI is more accurate and stable for long-distance links, as shown in [22] and [23]. However, accurate distance estimation is not a major concern for the RSSR algorithms (especially for the ). The important thing is that near nodes have consistent RSSIs. For instance, if there is an obstacle in the middle of the network, the RSSI rule still applies to all nodes behind the obstacle, since near nodes will still receive packets with greater signal strength. From this perspective, an accurate long-distance RSSI makes even more sense. At last, other more accurate and expensive distance estimation techniques could also be used such as: (a) the RSSI average of several queries sent by the sink; (b) ToA (Time of Arrival); and (c) TDoA (Time Difference of Arrival). Since the proposed RSSR algorithms are basically a Greedy Forward algorithm, they still need to be combined with a perimeter/face routing to overcome holes, voids, and greater RSSI inaccuracies. Also, since the RSSR algorithms do not use positions information of the nodes, new RSSI-only based perimeter/face routing need to be proposed. The algorithm has the best performance in all experimented scenarios. Being influenced only by long distant RSSI inaccuracy, this algorithm performs better than the GF algorithm even on high RSSI inaccuracies of 1% of the long distances (which could reach errors of about m on our experimented scenarios). The drawback of this algorithm is that for each new query source, advertisements packets must be exchanged. This is not a problem for WSNs composed of a few sink nodes (up to 3). The is affected by both short and long-distant RSSI inaccuracies, but under Gaussian inaccuracies the algorithm performed quite well, especially for a routing algorithm that does not require any control packet exchange. This last algorithm can also be applied to mobile sensor and ad hoc wireless networks since there will be no need for information updates. This issue will be addressed in future works. 21

6 This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the ICC 8 proceedings. VI. CONCLUSIONS In this paper, we have proposed two new location-free RSSI-based Greedy Forward algorithms, which we referred to as the RSSR (Received Signal Strength Routing) algorithms. The main idea of RSSR is to reply a query, sent to all nodes by the sink in a single hop, by greedily forwarding it to the neighbor node that received the query with greatest signal strength which is, in theory, the closest to the sink. In the, nearby nodes perform a leader election and distributedly decide the next hop of the packets without exchanging any control packet. In the, nodes exchange query RSSI information with their neighbors and, at each step, the neighbor with greatest RSSI (received the query packet with the greatest signal strength) is selected by the node that currently holds the packet. We also presented an extensive set of experiments that shows that our algorithms outperforms the GF algorithm by not using position information. The results are very promising, but some advantages and limitations still need to be further exploited in future works, for instance, combining our solution with a perimeter/face routing algorithm and evaluate them in mobile networks. REFERENCES [1] I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cyirci. Wireless sensor networks: A survey. Computer Networks, 38(4): , March 2. [2] D. Estrin, L. Girod, G. Pottie, and M. Srivastava. Instrumenting the world with wireless sensor networks. In ICASSP 1, Salt Lake City, Utah, June 1. [3] Mohammad Ilyas and Imad Mahgoub. Handbook of Sensor Networks: Compact Wireless and Wired Sensing Systems, chapter. CRC Press LLC, 4. [4] G. J. Pottie and W. J. Kaiser. Wireless integrated network sensors. Communications of the ACM, 43(5):51 58, May. [5] Azzedine Boukerche. Handbook of Algorithms for Wireless Networking and Mobile Computing. Chapman & Hall/CRC, 5. [6] Chalermek Intanagonwiwat, Ramesh Govindan, and Deborah Estrin. Directed diffusion: A scalable and robust communication paradigm for sensor networks. In MobiCom, pages 56 67, Boston, MA, USA, August. ACM Press. [7] M. Mauve, A. Widmer, and H. Hartenstein. A survey on position-based routing in mobile ad hoc networks. Network, IEEE, 15(6):3 39, 1. [8] Brad Karp and H. T. Kung. Gpsr: Greedy perimeter stateless routing for wireless networks. In 6th International Conference on Mobile Computing and Networking, pages , Boston, MA, USA,. [9] Y. Yu, R. Govindan, and D. Estrin. Geographical and energy aware routing: A recursive data dissemination protocol for wireless sensor networks. Technical Report CSD-TR-1-23, UCLA Computer Science Department, 1. [1] Karim Seada, Ahmed Helmy, and Ramesh Govindan. On the effect of localization errors on geographic face routing in sensor networks. 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Oliveira, Eduardo F. Nakamura, Antonio A.F. Loureiro, and Azzedine Boukerche. Error analysis of localization systems in sensor networks. In GIS 5, pages 71 78, Bremen, Germany, November 5. [17] Tian He, Chengdu Huang, Brian M. Blum, John A. Stankovic, and Tarek Abdelzaher. Range-free localization schemes for large scale sensor networks. In MobiCom 3, pages 81 95, New York, NY, USA, 3. ACM Press. [18] Fabian Kuhn, Roger Wattenhofer, Yan Zhang, and Aaron Zollinger. Geometric ad-hoc routing: of theory and practice. In PODC 3, pages 63 72, New York, NY, USA, 3. ACM Press. [19] Nam N. Pham, J. Youn, and Chulho Won. A comparison of wireless sensor network routing protocols on an experimental testbed. In Sensor Networks, Ubiquitous, and Trustworthy Computing, 6. IEEE International Conference on, volume 2, pages , 6. [] Koen Langendoen and Niels Reijers. Distributed localization in wireless sensor networks: a quantitative comparison. volume 43, pages , New York, NY, USA, 3. 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