Adaptive-Gossiping for An Energy-Aware Routing Protocol in Wireless Sensor Networks Ahyoung Lee
Ilkyeun Ra
Department of Computer Science and Engineering University of Colorado Denver Denver, CO. U.S.A.
Department of Computer Science and Engineering University of Colorado Denver Denver, CO. U.S.A.
[email protected]
[email protected] including routing protocols and algorithms to provide seamless and efficient services to users. Many articles in the literature [1-3] address the typical challenges of sensor networks: (1) resource constraints such as limited processing speed, communication bandwidth and supply of energy, and (2) routing challenges due to the dense deployment of sensor nodes either deterministically or randomly distributed. The densely deployed sensors may require a high degree of interaction between sensor nodes that generally use a conventional ad hoc routing method based on a flooding algorithm.
ABSTRACT Energy efficiency is the essential consideration in advances of wireless sensor networks (WSNs) usefully designed for low data rate, low power consumption, and low-cost networking. Mindful of the above constraints, selecting an appropriate routing protocol can significantly improve overall performance with limited sensor network resources, especially energy awareness in WSNs. We propose an energy-aware routing protocol improved by our adaptive-gossip algorithm that will reduce the redundant routing messages so that it can minimize the overall energy consumption of a WSN. To analyze the energy efficiency, we introduce two energy performance metrics: Delay*Energy and NormalizedRoutingLoad*Energy. These metrics suggest that energy consumption heavily depends on both packet losses and packet delivery successes that affect delay and routing overhead respectively. We present both analytical and experimental results thoroughly to evaluate our adaptive-gossip proposal, and demonstrate its advantages over flooding and static-gossiping based protocols for densely deployed networks and different types of network such as peer-to-peer and multi-to-one.
In this paper, we focused on routing challenges to conserve the power of sensor nodes as well as to increase communication efficiency. There are some critical problems with flooding overhead causing broadcast storms [5] because many routing messages are propagated unnecessarily. Furthermore, there exist high packet collisions due to rebroadcasting sensor nodes that are close to each other and periodical rebroadcasts. Therefore, routing protocols based on the use of flooding are not efficient and significantly affect energy conservation that is the basic consideration in WSNs. To solve the problem, we propose an energy-aware routing protocol improved by our adaptive-gossip algorithm that is a probabilistic broadcast mechanism based on the percolation theory [15]. The concept of the adaptive-gossiping is simple. The p is assigned by depending on the number of neighbor nodes when routing packets are broadcasting with a gossip probability p. It is scalable because it can significantly reduce the communication overhead compared to flooding and other gossiping approaches for dens networks as WSNs. In literatures [4,6,16,17], most of the gossip-based routing protocols are static that all nodes have the same gossip probability p for all gossip packets during executions of the whole network, which is unnecessary. Some other adaptive gossip approaches [7,9] are not scalable effectively because a gossip probability p depended on a node of the reception of a gossip message within a time interval, which raises an overhead of the duplicate messages.
Categories and Subject Descriptors C.2.2 [Computer-Communication Protocols – Routing protocols.
Networks]:
Network
General Terms Algorithms, Performance, Measurement.
Keywords Wireless sensor networks, Adaptive-gossiping, Energy-aware routing, Performance evaluations.
1. INTRODUCTION Recent advances in wireless technologies and communications have provided wireless sensor networks (WSNs) to enable the development of low-cost networking, low power consumption, and very small size devices, usually for low data rate transmissions. WSNs require self-organizing capabilities to be capable of random deployment in inaccessible or ubiquitous environments [1]. Thus, various types of sensor network applications in WSNs need wireless ad hoc network techniques
In addition, we observe the performance impact of Node Traversal Time (NTT) that should be enough to allow neighboring nodes to gossip for reliable communications, and suggest an optimal NTT value [4,8]. Therefore, our approach is not only to choose an optimal value of gossip probability p, but also to find an optimal NTT so that it can support higher reliability and throughput, and save energy in WSNs. We evaluate our proposed approach with two new energy performance metrics: Delay*Energy and NormalizedRoutingLoad*Energy. These metrics suggest the energy consumption associated with both packet losses and packet delivery successes that affect delay and
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neighbors with probability 1 for the first k hops and with probability p for the rest; if a node with n neighbors receives a message and does not broadcast it, but then does not receive the message from at least m neighbors within a reasonable timeout period, it broadcasts the message to all its neighbors with probability 1.
routing overhead respectively. Our simulation results indicate that the adaptive-gossip based approach outperforms and uses less energy compared to flooding and (static)gossiping based protocols for low data rate, densely deployed sensor nodes in a network with both static and mobile base stations, and different types of network topologies such as ‘peer-to-peer’ and ‘multi-to-one’ (sink type) that is the important topology in WSNs, where communications are typically between multiple sensor nodes and a sink node or base station.
However, we discovered some critical issues for a broadcasting message with probability p on the relation of n neighbors in GOSSIP3(p,k,m). For example, given GOSSIP3(0.65,1,1) is defined as
2. ENERGY-AWARE ROUTING AND ENERGY ANALYSIS
if n 0, a broadcasting message with p 1 if n 1, a broadcasting message with p 0.65
In this section, we (1) briefly discuss the key features of the energy-aware routing protocol based on the adaptive-gossip algorithm called A_GSPaodv, and then (2) mathematically analyze the energy consumption of the broadcast mechanisms.
(1)
, which means this algorithm doesn’t care about a node has how many number of neighbor nodes to broadcast a message with p to its neighbors. For example, a node with too many neighbors could yield high overhead and collisions, while too few neighbors could result in unreliability even with a good heuristic gossip probability p. Thus, this static value of the gossip probability p might not improve overall performances in a sensor network where sensor nodes are densely deployed with random distribution in a static or a mobile base station WSN. Therefore, our adaptive-gossip algorithm, called GOSSIPadapt(p/n,k,m), differs from the GOSSIP3 for determining a gossip probability p that is an essential element of gossiping. But our adaptive-gossip algorithm is designed for dense sensor networks for a large-scale network. From the equation (1), let the adaptive-gossip probability called padapt be defined as
We choose AODV [8] routing protocol to implement A_GSPaodv for analyzing the energy consumption and evaluating performances of the adaptive-gossip algorithm. In our previous study [18], reactive ad hoc routing protocols as AODV are better able to reduce routing overheads than proactive protocols, and the simulation results shown AODV outperforms others at high mobility in the large network. Thus, to reduce the energy consumption, AODV may be preferable compared with the different broadcast algorithms.
2.1 Ad Hoc On-Demand Distance Vector Routing (AODV) AODV [8] is designed to use bandwidth efficiently and to be capable of supporting large populations of nodes in dynamically changing networks. It uses an on-demand approach, which means that a route is established only when a source node needs to send packets to some destinations. To find a route from a source node to the destination, the basic operation of AODV is a route discovery procedure comprising three messages: a route request (RREQ) used to discover routes, a route reply (RREP) sent as an answer to a RREQ, and a route error (RERR) reporting the new unreachable destinations. In route maintenance, AODV uses both a RREQ message and a HELLO message. If the source node does not receive a RREP before its route request expiration timer, then the source node rebroadcasts the RREQ message. If the source node moves, then it can reinitiate a new route to the destination. If an intermediate node moves, then the neighbors of the moved node can detect the link failure by a HELLO message broadcasted periodically to maintain the local connectivity of a node, and sends a special RREP to its upstream neighbors until it reaches the source node that can reinitiate route discovery if still needed. AODV has great knowledge of network connectivity by its use of the HELLO message. However, the HELLO message leads to unnecessary bandwidth consumption. Also, flooding RREP messages in response to a single RREQ message may lead to high routing overheads and packet collisions.
if n 0, a broadcasting message with padapt 1 if n 1, a broadcasting message with padapt p n
(2)
, which means our gossip probability padapt is an adaptive gossiping dependent on the number of neighbor nodes n to broadcast messages as illustrated in Figure 1. For rest of k n1 For k hops k=1
p=1
p=1 S
padapt= p/n p=1
p=1
n2
N
D
m n3
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Figure 1. Adaptive-Gossip algorithm GOSSIPadapt(p/n,k,m) We prove our adaptive-gossiping, A_GSPaodv routing protocol, can improve energy efficiency significantly relative to routing overhead and end-to-end delay by both analytical and empirical results in the following sections.
2.2 Energy-Aware Routing based on Adaptive-Gossiping
2.3 Energy Analysis In this paper, to analyze the energy consumption of three broadcast mechanisms: flooding, gossiping (static), and adaptivegossiping. At each node we consider only the power consumed for both transmitting and receiving packets. The idle listening power is ignored in our computation, because it is the same for all nodes with small amount power charged. We denote by N the number of
A_GSPaodv is designed based on the adaptive-gossip algorithm to reduce the redundant routing packets, which can save energy consumption by reducing network overall overhead. Our adaptivegossip algorithm has been developed based on GOSSIP3(p,k,m) developed by Hass et al. [4]. This algorithm function stipulates that at the beginning, a node broadcasts the request message to its
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nodes and k the average number of neighbors of a node within the same radio transmission range in the sensor network. We assume that the power required Pw for both transmitting and receiving during a packet broadcasting process, and for a packet the transmit power α required is about at least three times more than the receive power [14,19] to reach the next hop. The total number of packets sent per node SENT and the average number of packets received per node REVD are defined in the following subsections.
3. SIMULATIONS Our simulations were performed using the ns-2 network simulator with version ns-2.32 [10] over IEEE 802.15.4 [11]. In simulations, we mainly concentrate on energy efficiency, and compare the energy performance of A_GSPaodv routing protocol with the original AODV.
3.1 Simulation Setup We simulate two scenarios to evaluate the performance of A_GSPaodv routing protocol over IEEE 802.15.4. The two scenarios are peer-to-peer and multi-to-one (sink type) communication networks that are deployed basically in a combination of beacon enabled mode and non-beacon enabled mode followed by the experimental setup [12] which shows a very high successful association rate (more than 99%). The first scenario is for a peer-to-peer application traffic in a WSN with statically fixed nodes; the second scenario is for a sink-type application traffic, which is the important application for WSNs since traffic is typically between multiple sensor nodes and a sink node or base station, in a mobile WSN with randomly distributed nodes. Both scenarios run in a multi-hop environment and the performances are evaluated with respect to the following parameters: (1) Beacon enabled mode: There are 101 nodes in an 80 x 80 m2 area with a 10 meter transmission range, where node 0 is the PAN coordinator as a sink node, and all the other nodes as sensor nodes. For the beacon enabled mode, we set the same value of beacon order (BO) and superframe order (SO) as BO=SO=6; performance was a high successful rate 100% [12] with a low packet collision rate [13]. (2) Traffic models: The application traffic that is CBR (constant bit rate) was used for two traffic modes: peer-to-peer communications over the static sensor network, and multi-to-one communications over the mobile sensor network. The data packet size is 90 bytes with a sending rate of 0.1, 0.2, 1, and 2 packets per second, respectively. (3) Mobility models: The mobility model uses the Random Waypoint model for the mobile sensor network. Each sensor node starts its journey from a random location to a random destination as a sink node with the speeds of nodes randomly distributed between 0 to 0.02 m/s and 0.2 m/s for 525 seconds simulated time. (4) AdaptiveGossiping parameters: We set the heuristic values of p=0.65, k=2, . m=1 for our GOSSIPadapt( ,2,1), where the value p is based on the experiments of Hass et al. [5]. We also agree that the gossip threshold of about 0.65 is an optimal gossip probability value in our study simulations associated with NTT = i * Node Traversal Time for i = 1, 2, 3, and 4 and given Node Traversal Time = 30ms.
2.3.1 Flooding When each node receives a requested packet, it simply rebroadcasts the packet once to all its neighbors k. The average number of packets sent/received per node during flooding is defined by
SENT flooding 1
(3)
REVD flooding k
(4)
The average energy consumption ENG is obtained by the equations (3) and (4) that N kN Pw k Pw ENG flooding (5) N
2.3.2 Gossiping When each node receives a requested packet, it broadcasts the packet with probability p to its neighbors, and discards the request packet with probability 1–p. The average number of packets sent/received per node during gossiping is defined by SENTgossiping
1 N N i N i p i p 1 p i 0 i N
k k k i REVDgossiping i 0 i p i 1 p kp i The average ENG is given by N p kp N Pw k p Pw ENGgossiping N
(6) (7)
(8)
2.3.3 Adaptive-Gossiping When each node receives a requested packet, it broadcasts the packet to its neighbors with probability p/n where n is its neighbor nodes k, and discards the request packet with probability 1–p/n.
SENTadapt _ gossiping
p p p k1 k2 kn k1 k2 kn Np p N N i
(9)
k i
k p p p k REVDadapt _ gossiping i 0 i 1 k p (10) i k k k The average ENG is given by N p p N Pw p Pw ENGadapt _ gossiping (11) N
3.2 Performance Metrics For performance evaluations, our two means of performance metrics, Delay*Energy and NormalizedRoutingLoad*Energy, are presented. Also we consider other important performance metrics for routing protocol evaluation. (1) Delay*Energy (DE): The energy consumption associated with packet loss ratio that impacts on delay; the equation is defined as:
Clearly, we observe that our adaptive-gossip algorithm has less energy consumed than the other broadcast techniques from the equations (5), (8) and (11) for k >1 and α ≥3 as following
k Pw k p Pw p Pw
PDF DE 1 E 2 Edelayavg NEU avg 100
(12)
(13)
where PDF is the packet delivery fraction, which is the ratio of data packets delivered to the destinations to those generated by the CBR sources; E2Edelayavg, which is the average end-to-end delay that includes all possible delays caused by buffering during route
To prove that the A_GSPaodv outperforms AODV, we have experimented with A_GSPaodv in two aspects of simulation testbeds: one for the static base station WSN, and the other one for the mobile base station WSN.
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NRL*Energy analyzed by different packet sent rates per second (pps) are shown in Figures 3 (a), (b) and (c). The throughputs of A_GSPaodv are slightly better at most packet sent rates. In addition, A_GSPaodv outperforms AODV in the Delay*Energy performance measured (b); AODV consumes much more energy (associated with end-to-end delay caused by packet loss), about 92% at 0.1 pps and about 54% at 2 pps. Moreover, as shown in (c) for the NRL*Energy performance measured, A_GSPaodv has less energy consumed (between 22% to 51%) than AODV. This happens because AODV creates a route with higher routing overhead to keep flooding packets due to exchanging RREQs and RREPs that also cause higher end-to-end delay in a network.
discovery latency, queuing at the interface queue, retransmission delays at the MAC layer, propagation time, and transfer time; and NEUavg, which is the average node energy used, in Joules, from a given initial amount of energy to each node. (2) NormalizedRoutingLoad*Energy (NRLE): The energy consumption associated with packet delivery successes that impact on routing overhead; it only concerns routing packets as forwarded packets and received packets at each intermediate node in a given sensor network. For analyzing energy efficiency of a routing protocol, it is a very interesting performance metric, which equation is defined as: NRLE NRL E 2 Edelayavg
(14)
where NRL is the normalized routing load for the number of routing packets transmitted per data packet delivered at the destination. Each hop-wise packet transmission is counted at the RTR layer as one transmission. Thus, NRL is an important factor to evaluate the efficiency of the routing protocol. The following simulation results have been averaged over 10 times run with different seeds.
In the multi-to-one (sink type) communication network that has mobile sensor nodes in a WSN, its performances are measured by differing the speeds of mobile nodes between 0 to 0.02m/s and 0.2m/s for all packet traffics through sensor nodes to a sink node. A comparison of performance of the two protocols based on the speeds of mobile nodes have been analyzed by differing the packet sent rates per second as shown in Figure 4.According to the throughput data in Figure 4 (a), the two protocols perform similarly at low packet sent rates of 0.1 pps and 0.2 pps. A_GSPaodv outperforms AODV at a high packet sent rate of 2.0 pps with a high speed of mobile nodes as 0.2m/s. As presented in Figure 4 (b) Delay*Energy and (c) NRL*Energy, performance results show concrete evidence of the relationship between end-toend delay for packet loss and energy consumption. A_GSPaodv consumes much less energy than AODV, especially for 2 pps; about 40% less energy is consumed at low speed 0.02m/s and about 66% less energy is consumed at high speed 0.2m/s. It also suggests substantial evidence of the relationship between overall routing overhead for packet delivery successes and energy consumption. AODV has a greater routing overhead for most all packet sent rates and speeds of mobile nodes, significantly for 2 pps (about 72% at low speed 0.02m/s and about 76% at high speed 0.2m/s). Hence, we can expect that A_GSPaodv may be the most efficient routing protocol in high traffic WSNs. Through the observation of the performance results, it is clear that lower endto-end delay and overall routing overhead are significant factors in terms of energy efficiency.
4. EVALUATIONS To compare the performance of two protocols, A_GSPaodv and AODV, with respect to the above performance parameters, we present important graphs of the performance results in the sections that follow.
4.1 Node Traversal Time (NTT) For a high efficiency of energy consumption, node traversal time for one hop is a significantly important factor in the adaptivegossiping parameters. If one chooses a short NTT that causes a new route discovery even if a valid route is still available, resending packets with p=1 is like flooding to neighboring nodes; if one chooses a too-long NTT, it might send packets on an invalid route, hence packet delay, eventually increasing end-to-end delay. Thus, these two cases of NTT seriously diminish energy efficiency and also overall performance in a dense deployment of sensor networks. Our experimental results enabled us to find an optimal NTT value. As shown in Figure 2, the performance results are based on a packet sent rate of 1 per second by different NTT values between 30ms and 120ms in our test-bed of a static ad hoc WSN. Figures 2, we have the results of the packet delivery fraction and the throughput (inset the graphs omitted for saving pages) show that A_GSPaodv outperforms AODV; in particular, A_GSPaodv has a 56% higher packet delivery and a 55% higher throughput rate than AODV at 120ms of NTT. As shown in Figures 2 (b) and (c), our adaptive-gossip algorithm consumes much less energy than the AODV flooding-based method, whereas A_GSPaodv has significantly less E2E delay, between 87% to 91% at each NTT compared to AODV, as shown in (b) Delay*Energy performance. In addition, Figure 2 (c) shows that A_GSPaodv demonstrates significantly lower routing overheads than AODV, 86% lower at 120ms NTT, even though Figure 2 (a) shows that the average node energy used in A_GSPaodv and AODV is close to each other for most NTT values. The reason is that AODV has to keep flooding packets to broadcast to its neighbors for exchanging RREQs and RREPs until it discovers a route.
5. CONCLUSIONS We propose the adaptive-gossip routing implemented by the A_GSPaodv protocol as an energy-aware routing for densely distributed WSNs. We defined the gossip probability padapt as an adaptive gossiping by controlling a number of neighbor nodes n to broadcast messages associated with NTT parameters. To evaluate the energy-aware performance of A_GSPaodv routing protocol based on adaptive-gossiping compared with the original AODV based on flooding, we introduced two new performance metrics: Delay*Energy and NormalizedRoutingLoad*Energy. Our analysis of energy efficiency performances proved that our A_GSPaodv approach greatly reduced end-to-end delay and overall routing overhead against the flooding problems, which significantly reduced energy consumption and prolonged network lifetime in WSNs.
6. ACKNOWLEDGMENT The authors would like to thank Anatolii A. Phualskii who is an associate professor of mathematics at the University of Colorado Denver for explaining the relevant analysis of adaptive-gossiping.
4.2 Communication Network Traffics In the peer-to-peer communication network that has stationary sensor nodes in a WSN, comparison results between the two routing protocols with respect to Throughput, Delay*Energy, and
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Figure 4. Multi-to-one application traffic by increasing speeds of mobile nodes (all sensors and a sink) in a mobile base station WSN. [5] S.-Y. Ni, Y-C. Tseng, Y.-S. Chen, and J.-P. Sheu. The Broadcast
Figure 2. Packet sent rate 1 per second by increasing NTT values.
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Figure 3. Peer-to-peer application traffic by increasing packet sent rates in a static base station WSN.
[15] [16]
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