In wireless, possibly mobile networks, different tech- niques should be considered. Routing has received a consider- able amount of interest in the research ...
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Energy-Efficient Routing in Wireless Sensor Networks Using Probabilistic Strategies Mohamed Hamdi, Nejla Essaddi, and Noureddine Boudriga CN&S Research Lab., University of November 7th at Carthage, Tunisia
Abstract— Wireless Sensor Networks (WSNs) are being used in many applications in order to gather sensitive information and forward it to an analysis center. Since a WSN consists of resourceimpoverished sensor nodes, the packet forwarding process should be energy efficient. Therefore, resource limitations should be taken into consideration when designing a WSN infrastructure. This paper proposes a random routing strategy for WSNs. The approach relies on the flooding technique, which has the advantage to possess minimal routing overhead (in the sense that no routing table information is exchanged) and maximal delivery rate. We introduce an enhancement that allows reducing energy consumption and extending network lifetime by randomly forwarding packets at every node. The forward probability is a decreasing function of the number of hops made by the packet. An analytical model is developed in order to illustrate the functionalities of the proposed strategy. Finally, simulations are conducted to assess the performance of the probabilistic routing protocols.
Keywords Random flooding, energy-efficient, packet delivery rate, dynamic randomized forwarding. I. I NTRODUCTION Recent advances in Wireless Sensor Networks (WSNs) have motivated the development of specific protocols. Energy awareness is certainly the focal issue directing the development of these protocols. In fact, even though the sensor nodes are equipped with processing and communication capabilities, they are characterized by severe power and energy limitations that make energy cost-effectiveness the main constraint for the implementation of functionalities in WSNs. At the network layer, a consistent has been done in order to find ways for energy-efficient route setup and reliable relaying of data from the sensor nodes to the sink so that the lifetime of the network is maximized. In wired networks, routing protocols are usually based on state link or distance vector algorithms (Dijsktra or BellmanFord). In wireless, possibly mobile networks, different techniques should be considered. Routing has received a considerable amount of interest in the research literature especially in the context of ad hoc networks. A large number of algorithms and protocols have been developed. They are customarily categorized into (a) proactive protocols, which keep accurate information in their routing tables, and (b) on-demand protocols, which do not substantially maintain routing tables but rather construct them when a packet is being sent to a destination for which no routing information is available. Examples of
proactive protocols are Destination-Sequenced Distance Vector (DSDV) [1], Clusterhead Gateway Switch Routing (CGSR) [2], and Wireless Routing Protocol (WRP) [3]. Well-known on-demand routing protocols include Dynamic Source Routing (DSR) [4], Temporally Ordered Routing Algorithm (TORA) [5], and Ad-Hoc On-Demand Distance Vector (AODV) [6]. Routing protocols developed in the context of wireless ad hoc networks do not always conform with the requirements related to WNSs, which are discussed in the Section II. In fact, the energy, processing, and memory limitations of sensor nodes makes the application of traditional routing protocols to WNSs unfeasible. The major shortcoming of ad hoc protocols is that they rely on a flooding process to discover the topology of the network. Moreover, issues such as multi-path routing and energy conservation are often not addressed. This paper proposes a random flooding strategy that is used to perform energy-efficient routing in WSNs. The fundamental assumption is that the probability for a node to forward a received packet to its neighbors depends on the distance to the node that generated the packet. To this end, we consider an exponentially decreasing function to tune the packet elimination ratio with regard to the number of hops made by the packet. The three major advantages of this approach are: (a) the approach is very simple to implement since it relies on the intrinsic packet relaying functionality; (b) the randomized routing strategy does not induce a traffic overhead since it does not requires the exchange of specific data (as for the update of routing tables); and (c) the concavity and exponential decreasing rate of the probability function can be customized to fit the WSN density (i.e., number of nodes per surface unit). Moreover, realistic simulations are performed in order to evaluate the performance of the proposed routing technique with respect to the existing approaches. The rest of the paper is structured as follows. Section II reviews the most important routing protocols for WSNs. Section III introduces our probabilistic flooding strategy. Moreover, the properties of the proposed protocol are analytically studied. A simulation-based performance evaluation is described in SectionIV. Finally, Section V concludes the paper. II. ROUTING TECHNIQUES FOR WIRELESS SENSOR NETWORKS
This section first discusses the requirements that should be fulfilled by a routing algorithm for WSNs and highlights the differences between sensor and ad hoc networks. Then, a review of the approaches that have been proposed in the literature is performed.
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A. Routing algorithms for WSNs: fundamental requirements Sensor nodes are characterized by severe memory, CPU, and energy limitations. Moreover, the topology of a WSN is timevariant due to mobility or actuation scheduling. Hence, the design of a routing protocol for WSNs should always provide a graceful tradeoff between lifetime and efficiency. From one hand, the packet forwarding and relaying process should be energy-aware in order to extend the lifetime. From the other hand, due to the sensitive nature of most of the WSN-based applications, packet transmission from sensor nodes to the analysis center should be reliable even though the topology of the network changes. In the following, we list the most important considerations related to the development of WSN routing protocols. 1) Process distribution: Due to the limited transmission range of the elementary sensor nodes, only multi-hop routing approaches can apply to WSNs. Approaches involving a central node performing all routing computations can not be considered. Hence, source and intermediate nodes should execute specific processes in order to determine to which neighboring node an incoming packet should be passed on. 2) Overhead reduction: Since the topology of the sensor network frequently changes, new routing tables should be built adaptively. The exchange of routing information across the network should not add an important overhead to the transmitted messages. Routing protocols should therefore reduce the amount of exchanged data. 3) Energy conservation: As it has been stated above, energy efficiency is the most crucial aspect that one should concentrate on when addressing WSN-based applications. The focal question to ask is whether it is better to invest the energy resources in sending data or in performing computations? Most of the references discussing this issue agree that communication is considerably more expensive undertaking than computation. Therefore, the routing algorithm should encompass computation processes whenever possible to save energy resources. 4) Memory conservation: Due to the memory limitations of the sensor nodes, huge routing tables would be impossible to handle. This means that appropriate mechanisms should be developed to control the size of the stored routing information. It should also be noticed that mobility and energy scavenging protocols exacerbate the important need for such size control techniques. 5) Resiliency: When nodes are mobile or rely on activity scheduling for their operation, they might disappear at unforeseeable points of time until it returns to the same location or it receives an actuation message. This might affect the delivery of the forwarded packets to the analysis center. A potential solution is to use more than a single path between the sender node and the analysis center. Multi-path routing provides not only route redundancy but also the opportunity to implement load balancing mechanisms.
B. Related work Below, we describe the major routing protocols categories that have been developed for WSNs. Three classes can be distinguished: 1) Unicast forwarding: In this case, the sender node transmits packets towards another, uniquely identified, node. Moreover, routing tables are prohibitive (in order to minimize routing overhead). The less sophisticated manner to perform such task is flooding (iteratively forwarding every incoming packet to all nodes within the communication range). Nonetheless, since this approach is intrinsically energy-consuming, more advanced protocols are needed. For instance, randomized forwarding is an idea that can be implemented in this context. The key parameter of such approaches is the probability with which a node retransmits a newly incoming message. Haas et al. [8] use a constant probability and show that there exists a threshold value below which the random strategy is not efficient (in the sense that the packet reaches only a small number of nodes). Another alternative is to use random walks to model the behavior of a packet in the network. At a given sensor node ν, the packet is therefore forwarded to a randomly chosen neighbor even if multiple nodes are within the transmission range of ν. Rumor routing [9] and destination-based randomized forwarding [10] are among the most renowned protocols belonging to this category. 2) Broadcasting: When being broadcasted, a packet is sent to all nodes in the network. It should be noticed that this differs from flooding (i.e., every node forwards each new incoming message) even though flooding may be an option to implement broadcasting. Since this strategy is energy-consuming, it has been rarely used in the literature. 3) Geographic routing: This approach, consisting in sending data to arbitrary nodes in a given region, is also referred to as geocasting. In the WSN context, since nodes are considered as interchangeable and are only distinguished by external aspects, in particular their position, a location service is usually not necessary. In the literature, many geocasting approaches have been introduced, references [11], [12] provide complete surveys of these approaches. III. P ROBABILISTIC STRATEGY FOR RANDOM ROUTING The objective of our work is to develop an energy-efficient randomized packet flooding strategy. To this purpose, we propose a Dynamic Random Flooding (DRF) approach where the probability for a node to forward a packet is variable, and depends on the number of hops made by the packet. This would obviously reduce the overhead of the simple flooding approach. A. The DRF algorithm In order to control the decrease of packet forward rate, we use the function φγ,σ (.) defined as follows: φc,s :
R→
[0, 1]
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(1)
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Behavior of the function φc,s for different values of c and s.
Algorithm 1 random_flooding(si , c, s, τ ) begin ∀h ∈ {1, .., hmax } begin αhi :=0 βhi :=0 end repeat if ¬ is_empty_stack(si ) π := read_packet_from_stack(si ); stack(si ):=stack(si )\{π}; h:=read_hop_count(π); αhi := αhi + 1; αi −β i if hαi h < φc,s (h − 1) then h flood(π, Ri ); else withdraw(π); βhi := βhi + 1; end else wait(τ ); end end end
x →
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where c and s are two integers. These parameters allow controlling the concavity and the stretching of its curve, respectively. Figure 1 depicts the curve of φc,s for different values of c and s. Our routing algorithm is such that a given node forwards a given packet within its communication range with a probability equal to φc,s (h − 1), where h is the number of hops made by the packet. This evidently presupposes that the packet header includes a hop counter field, which is the case for most of the layer 3 protocols. As a result from the reasoning explained in the foregoing discussion, the implementation of our randomized flooding
Fig. 2. Choosing the values of c and s according to the network parameters.
strategy in a sensor si is done according to Algorithm 1, where: • Ri is the communication range of sensor si , • πi denotes the number of packets received by node si , • βi denotes the number of packets blocked by node si . The essence of the algorithm is that a packet is forwarded only if the ratio of the forwarded packets is less than φc,s (h − 1). In fact, the algorithm is based on two counters αhi and βhi representing the total number of packets received by node si having made h hops and the proportion, among these packets, that has been blocked; respectively. The forwarding process consists in an infinite loop where the packets are read from the packet stack. When this stack is empty, the algorithm waits for a duration τ before attempting a new read_packet_from_stack() operation. Having retrieved a packet from the stack, the randomized flooding algorithm determines, from the Time-To-Live (TTL) field of its header, the number of hops it has made. Accordingly, the packet is αi −β i forwarded only if the ratio hαi h is less than φc,s (h − 1). h This guarantees that the packet forwarding probability, for a packet having made h hops, is less than φc,s (h − 1). It is obvious that c and s represent the key parameters of the algorithm. Figure 1 shows that when c is small, the number of hops made by the packet before being dropped decreases. Moreover, when the stretching factor s is small, the number of hops before blocking decreases abruptly. Hence, the parameters c and s can be chosen according to the area of the monitored area and the coverage density of the WSN. More concretely, we consider the case where (c, s) = (25, 50) depicted in Figure 2. The reader can notice that this curve consists of three portions: • x ∈ [0, x1 ]: φc,s (.) is approximately constant and close to its maximum, which equals to 1 • x ∈ [x1 , x2 ]: φc,s (.) decreases consistently • x ∈ [x2 , 100]: φc,s (.) is approximately constant and close to its minimum, which equals to 0 From Figure 1, it comes that the parameters x1 and x2 change when c and s vary. In the following, we give a sense, 2569
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from the networking perspective, to these parameters. 1) The first portion, delimited by x1 , defines the number of hops for which a node forwards the incoming packets with a probability close to 1. In other terms, in the interval [0, x1 ], the applied strategy is passing all, which is equivalent to the traditional flooding mechanism. 2) The third portion, delimited by x2 , defines the number of hops for which the node blocks all the incoming packets. This strategy is called blocking all. 3) These portions correspond to the extreme strategies. The second portion, corresponding to the interval [x1 , x2 ], relates to the number of hops where the probability for the packet to be forwarded decreases exponentially. The strategy is called selective forwarding. The width of each of the aforementioned portions can be used to determine the efficiency of a particular function φc,s in terms of dissipated energy and packet delivery rate. This gives rise to a potential technique for selecting the pair (c, s) according to some specified needs. For instance, a possible rule to select the concavity and stretching parameters is to require that the packet delivery rate is greater than a given threshold ρ. Practically, this is expressed as a function of the topological diameter of the network, which is the maximum number of hops that can be made a packet. This requirement is mathematically expressed by φc,s < ρ. Obviously, many other requirements can be specified and solved using this reasoning. Finally, it noteworthy that the parameter τ , defining the time for which the algorithm waits before re-attempting to retrieve packets from the stack, can be also subjected to a selection problem. In fact, when τ increases, so does the risk of getting the stack overwhelmed by the incoming packets. Conversely, if τ is too small, much energy would be spent to read packets from the stack while it is effectively empty. Practically, the parameter τ can be dynamically updated in order to fit the average packet arrival rate. B. Analytical study This section provides a mathematical study of the energy consumption during a flooding process. We consider both the simple and randomized schemes in order to highlight the benefit resulting from the application of our approach. The key metric used to express the energy consumption is the Total Number of Transmission over the Monitored Area (TNToMA). In fact, we suppose that all the sensors are identical and that elementary packet forwarding operation has a constant cost (i.e., the energy spent to forward a packet is independent from the sensor and the amount of residual energy in the sensor). The probability that a packet is forwarded across h0 hops without being withdrawn equals to: φh0 =
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Conversely, the probability that a packet is withdrawn after having been forwarded across h0 hops is: wh0 = φc,s (h0 − 1)
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Supposing that hsmax is the maximum number of hops made by a packet originating from sensor s to reach all the nodes of the network, and that the number of packets generated by the node s per unit of time is ηs , the average energy consumed by s per unit of time is expressed by: hs
max E(s) = φhsmax .hsmax .ηs .E0 + Σi=1
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where E0 is the unitary packet transmission energy cost (amount of energy needed to transmit one packet). One key parameter in the expression of E(s) is hsmax . Effectively, this parameter is related to the TNToMA by the following equation. hsmax (hsmax − 1) . (5) 2 The problem of computing hsmax can be solved using the cover time problem, which has been extensively addressed in random graph theory [13]. It basically consists in determining the total number of moves that allow a random walk characterized by a probability p to entirely visit each of the nodes of the graph. In [13], it has been shown that for a graph including N nodes, TNToMA verifies: N.TNToMA ∼ log(N ). (6) E 2 TNToMA =
This means that the average flooding time would decrease to zero if N increases to infinity. This problem will be more practically discussed in the following section by addressing the influence of coverage density (obtained by dividing N on the size of the monitored region) on the efficiency of the randomized flooding protocols. IV. P ERFORMANCE EVALUATION In order to assess the efficiency of the proposed randomized routing strategy, we performed a number of experiments. The most interesting results are summarized in this section. First, we considered a set of 3000 sensor nodes deployed over a region having an area of 500×500 meters. We also suppose that the sensing radius of every sensor nodes equals 50 meters. According to [7], this configuration guarantees 1coverage of the monitored area, meaning that every point is covered by one sensor node. Furthermore, the packet loss at the MAC layer has not been considered in order to evaluate the routing techniques in an ideal context. The objective of the first set of experiments is to assess the influence of the parameters c and s on the efficiency and the energy dissipated by the dynamic randomized flooding algorithm. In fact, when the blocking probability increases, some of the nodes that are far from the packet source will not be able to receive the message. In addition, the energy consumed during the flooding process varies according to the blocking probability distribution defined by c and s. When the blocking probability is very high, the sensor nodes dissipate more energy because they will forward an important proportion of the received packets. Figure 3 shows the influence of the concavity and stretching parameters on the efficiency and dissipated energy. In Figure 3(a), the efficiency of the randomized flooding 2570
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mechanism is evaluated by depicting, for different values of c and s, the average number of nodes that have received a message. This number is determined by issuing 1000 packets from uniformly distributed locations in the monitored area. It can be remarked that the best performances are obtained for small values of the concavity and scaling parameters. The same remark applies for the consumed energy that we evaluated as a ratio with respect to the maximum dissipated energy. For the sake of clarity, we have delimited the area in Figure 3(a) containing all (c, s) pairs that provide energy dissipation lower than 50% (curve γ1 ) and another area in Figure 3(b) characterized by the pairs (c, s) for which more than 500 sensors are reached (curve γ2 ). Clearly, the domain delimited by curves γ1 and γ2 corresponds to the pairs (c, s) satisfying an efficiency-energy balance defined by 500-50. Such reasoning allows defining a strategy for the choice of the concavity and stretching parameters according to statistical data measured from the WSN. In an other set of experiments, we compared our approach to the gossip technique proposed in [8] and to the simple flooding mechanism. We varied the coverage density of the monitored region and we measured the corresponding dissipated energy, in milli-Joules (mJ), and flooding delay, in milliseconds (ms). Figure 4 shows that, by varying the coverage density from 1 to 5 and setting c and s to 5 and 10, respectively, our Dynamic Randomized Flooding (DRF) technique provides better results,
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in terms of dissipated energy, than the Simple Flooding (SF) and Randomized Flooding (RF) approaches. However, DRF presents less than 4% of additional average forwarding delay (see Figure 5). This is mainly due to the complexity of the function φc,s (.). V. C ONCLUSION In this paper, we present a random routing strategy for WSNs. Our approach enhances the existing randomized flooding mechanism by making the packet forwarding probability decrease according to the number of hops made by the packet. An experimental comparison between the proposed algorithm and two among the most renowned flooding approaches shows that our technique provides a substantial gain in terms of energy consumption at a reasonable computational cost. In fact, the additional delay of the proposed routing algorithm is comparable to those of the existing techniques. A study of the influence of the coverage density aspect has also been done. As an extension to this study, the efficiency of dynamic random 2571
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routing in a k-connex WSN can be addressed. Experiments towards this objective are being conducted. R EFERENCES [1] C. Perkins, P. Bhagwat,"Highly Dynamic Destination-Sequenced Distacne-Vector Routing (DSDV) for Mobile Computers," Proceedings of the ACM SIGCOMM, pp. 234-244, London, 1994. [2] C.-C. Chiang, H. Wu, W. Liu, M. Gerla, "Routing in Clustered Multihop, Mobile Wireless Networks," Proceedings of the IEEE Singapore International Conference on Networks, pp. 297-211, Singapore, 1997. [3] J. Raju, J.J. Garcia-Luna-Aceves, "A Comparison of On-Demand and Table Driven Routing for Ad-Hoc Wireless Networks," Proceedings of the ICC, New Orleans, USA, 2000. [4] D. Maltz, "On-Demand Routing in Multi-Hop Wireless Ad Hoc Networks," PhD thesis, Carnegie Mellon University, USA, 2001. [5] V.D. Park, M.S. Corson, "A Highly Adaptive Distributed Routing Algorithm for Mobile Wireless Networks," Proceedings of INFOCOM, pp.1405-1413, Japan, 1997. [6] C.E. Perkins, E.M. Royer, "Ad-Hoc On-Demand Distance Vector Routing," Proceedings of the 2nd IEEE workshop on Mobile Computing Systems and Applications, pp. 90-100, New Orleans, USA, 1999. [7] M. Hamdi, N. Boudriga and M. S. Obaidat "WHOMoVeS: An Optimized Broadband Sensor Network for Military Vehicle Tracking," Accepted for publication in the International Journal of Communication Systems, 2008. [8] Z.J. Haas, J.Y. Halpern, L. Li, "Gossip-based ad hoc routing," TwentyFirst Annual Joint Conference of the IEEE Computer and Communications Societies INFOCOM, Volume 3, Issue , 2002 Page(s): 1707 - 1716, NY, USA, 2002. [9] D. Braginsky, D. Estrin, "Rumour Routing Algorithm for Sensor Networks," Proceedings of the First ACM Workshop on Sensor networks and Applications, GA, USA, 2002. [10] S.D. Servetto, G. Barrenchea, "Constrained Random Walks on Random graphs: Routing Algorithms for large Scale Wireless Sensor Networks," Proceedings of the First ACM Workshop on Sensor Networks and Applications, GA, 2002. [11] C. Mailhofer, "A Survey of Geocast Routing Protocols," IEEE Communications Surveys and Tutorials, Vol. 6, No. 2, pp. 32-42, 2004. [12] M. Mauve, J. Widmer, H. Harnenstein, "A Survey of Position-Based Routing in Mobile Ad-Hoc Networks," IEEE Network, Vol 15, pp. 3039, 2001. [13] R. van der Hofstad, G. Hooghiemstra, P. Van Miegham, "The Flooding Time in Random Graphs," Extremes Springer Journal, Mathematics and Statistics, Vol. 5, No. 2, 2002.
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