A Hybrid Cross-Layer Energy Efficient Protocol in Sink-Oriented Wireless Sensor Networks

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(1)國立中興大學資訊科學與工程學系博士學位論文. 應用於無線感測網路之混合式跨階層的省電協定設計 A Hybrid Cross-Layer Energy Efficient Protocol in Sink-Oriented Wireless Sensor Networks. 指導教授：張軒彬博士研究生：許順智. 中華民國 101 年 7 月.

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(3) 中文摘要對基地型無線感測網路資料傳輸而言，我們不僅要減少感測節點網路層路由控制封包的數量，以增加基地應用層資料封包的接收率，同時也要儘量減少每個感測節點能源不必要的耗損，以延長整體無線感測網路的生命週期。以往的方法，如 Funneling-MAC，利用混合式 MAC (TDMA/CSMA)以減少基地附近的壅塞現象。然而，這個方法沒有考慮減少路由控制封包數量，而且基地需要額外廣播時間同步排程才可以使用，因此所得到的效能有限。在本論文裡，我們提出應用於無線感測網路之混合式跨階層的省電協定設計 (CEEP)，在不需額外增加階層介面的情況下即可解決網路傳輸效能問題。這個協定是建構在混合式高效率路由協定(HERP)的基礎設計下，使得基地型感測網路的封包壅塞情形獲得有效疏解。同時，整體無線感測網路的生命週期也會因為每個感測節點的能源耗損降低而得以延長。再者，經由本研究論文的理論分析與網路實驗模擬驗證，使用混合式改良的主動與被動路由協定不但可以在負載強度高的無線感測網路中增加資料封包的接收率與能源有效使用率，也可以在負載強度低的無線網路中以動態調節感測節點的醒睡機制來節省不必要的能源耗損。因此，使用這種內建式通訊技術可以大幅提升基地型感測網路中每個感測節點傳送與接收資料封包的效能。. i.

(4) Abstract To provide an optimal resolution of data transmission in sink-oriented wireless sensor networks, we need to reduce the routing control packets derived from network layers of sensor nodes to increase the acceptance rate of data packets at the application layer of the sink. At the mean time, we have to avoid unnecessary energy drainage at each sensor node to extend the entire network lifetime from being partitioned in the early phase. In the past, some researchers used a funneling-MAC solution which implements a hybrid MAC scheme (TDMA/CSMA) in the intensity region of the event funnel to mitigate the congestion phenomenon near the sink. However, the network efficiency was limited because they didn’t consider any scheme to reduce the routing control packets, whereas extra beacon overhead was needed for the sink to transmit the synchronous schedule into the network. In this dissertation, we propose a hybrid cross-layer energy efficient protocol (CEEP) which results in a loosely coupled style without creating any extra interfaces for information to provide communication efficiency in sink-oriented wireless sensor networks. Congestion mitigating, as is provided through the concept of the Hybrid Efficient Routing Protocol (HERP) which classifies the network coverage into proactive region and reactive region, can serve as a basis for such an efficient routing protocol on the sensor nodes which are designated to collect and transmit information toward the sink. At the mean time, the entire network life time is extended by improving the energy consumption on each sensor node over the network. Furthermore, theoretically and empirically, the results show that CEEP can boost network fidelity by adapting proactive and reactive routing protocols under intense traffic and effectively achieve power efficiency for mobile communications under light traffic by determining the sleep-wakeup schedules of the sensor nodes. Thereafter, communications with such embedded operation have provided sensor devices an integrated technology to efficiently transceive data packets in sink-oriented wireless sensor networks.. Keywords:. CEEP;. HERP;. hybrid;. cross-layer;. routing. protocol;. mobile. communications; sensor network; sink-oriented; power efficiency; congestion; funneling. ii.

(5) Contents Chapter 1. 1.1. 1.2. 1.3. Chapter 2. 2.1. 2.2. 2.3. 2.4. 2.5. 2.6. 2.7. Chapter 3. 3.1. 3.2. 3.3. 3.4.. Introduction. .............................................................................................. 1 Overview of Sink-Oriented WSN .............................................................. 2 Research Motivation .................................................................................. 5 Research Objectives ................................................................................... 6 Related Work ............................................................................................. 8 Overview of Routing Classification........................................................... 8 Overview of Hybrid Routing Framework .................................................. 8 Overview of Funneling-MAC .................................................................. 10 Overview of Sensor MAC ....................................................................... 11 Overview of Cross-layer Optimization .................................................... 12 Overview of Network Fidelity ................................................................. 13 Overview of Power Efficiency................................................................. 14 Hybrid Routing Implementations ............................................................ 16 Overview of HERP .................................................................................. 16 Hybrid Routing Analysis.......................................................................... 17 Region Sizing Schemes............................................................................ 20 Heuristics: A Power-aware route discovery algorithm in funneling regions ...................................................................................................... 23 Chapter 4. Cross-Layer Energy Efficiency Implementations ................................... 27 4.1. Overview of CEEP ................................................................................... 27 4.2. Hybrid Routing Analysis.......................................................................... 29 4.3. Power Saving Scheme.............................................................................. 30 4.4. Impact of Network Lifetime .................................................................... 34 Chapter 5. Simulation Results................................................................................... 36 5.1. Hybrid Routing Simulation Scenes .......................................................... 36 5.2. Hybrid Routing Simulation Results ......................................................... 37 5.3. Cross-Layer Energy Efficiency Simulation Scenes ................................. 47 5.4. Cross-Layer Energy Efficiency Simulation Results ................................ 48 Chapter 6. Conclusions and Future Work ................................................................. 53 6.1. Conclusions .............................................................................................. 53 6.2. Future Work ............................................................................................. 54 References .................................................................................................................... 56. iii.

(6) List of Tables Table 3-1. The settings of environment parameters ……………………………..… 22 Table 3-2. A dynamic list of variables k and m ………………………..……...…… 22 Table 3-3. A dynamic list of variable m and CTOTAL ………….………..………..… 22 Table 3-4. A Pseudo-coded algorithm to discover optimal route …………….…..… 25 Table 4-1. Comparisons of the relative hybrid routing protocols ………………..… 29 Table 4-2. Pseudocoded algorithms to overlap different wakeup patterns on node j ………………………………………………………………...…. 33. iv.

(7) List of Figures Figure 1-1. A sink-oriented WSN topology with data transmission from allocated sources ……………...…………………………………………….…….... 2 Figure 1-2. Loss rate and cumulative distribution over varying distance from sink .... 3 Figure 1-3. Routes with distance as the third axis in a sink-oriented WSN topology cited from Funneling-MAC [2] ……………………………..…. 4 Figure 1-4. Routes with distance as the third axis in a sink-oriented WSN topology where adapted pattern MAC is implemented at sensor nodes .... 6 Figure 2-1. An illustration of the sensor testbed for Funnelig-MAC by Ahn et al. [2] …………………………………………..……………...11 Figure 2-2. An illustration of the difference of sleep-wakeup patterns between SMAC and PMAC ……………………………………………………….12 Figure 2-3. An illustration of cross-layer optimization at network layer / link layer ………………………………………………………..…..…. 13 Figure 2-4. Success rate of routing protocols with varying data rates ……….….…. 14 Figure 2-5. Success rate of routing protocols with varying data rates and mobility of 10 m/ses ………………………………...…….............……. 14 Figure 3-1. Framework of the intensity region as a sink extension with M proactive sensor nodes …………………………………….............……. 17 Figure 3-2. A network topology of n_hops x n_hops distributed sensor nodes utilizing radio range between 1.0e and 1.42e ……………………...…… 19 Figure 3-3. Min search of determining optimal intensity region size at step 2 and step 4 …………………………………………………………….…. 21 Figure 3-4. INTRA-HERP forward and backward route formation within 3 hops from sink ………………………………………………………..… 24 Figure 3-5. INTER-HERP forward and backward route formation initiated from source node ………………………………………………….….… 24 Figure 4-1. A framework of intensity region illustrated as a sink extension of M proactive sensor nodes ……………………………………………. 28 Figure 4-2. INTER-CERP implements a source routing scheme to discover the route path …………………………………………………….…..…. 31 v.

(8) Figure 4-3. Illustrations of combining different wakeup patterns initiated by RREP packets on node j ……………………………………….....…. 33 Figure 5-1. Success rate of HERP protocols with varying data rates and zero mobility …………………………………………………...…... 38 Figure 5-2. Success rate of HERP protocols with varying data rates and mobility of 10 m/s ……………………………………………..……….. 38 Figure 5-3. Energy consumption of HERP protocols with varying data rates and zero mobility ……………………………………………...……...… 39 Figure 5-4. Energy consumption of HERP protocols with varying data rates and mobility of 10 m/s ....……………………………………...……...… 40 Figure 5-5. Power efficiency of HERP protocols with varying data rates and zero mobility ……………………………………………...……...… 40 Figure 5-6. Power efficiency of HERP protocols with varying data rates and mobility of 10 m/s .....……..……………………………...……...… 41 Figure 5-7. Network lifetime of HERP protocols with simulation duration …….…. 42 Figure 5-8. Success rate of funnelling-MAC protocols with varying data rates and zero mobility …………………………………………………...…... 43 Figure 5-9. Success rate of funnelling-MAC protocols with varying data rates and mobility of 10 m/s...………………………………………..……….. 44 Figure 5-10. Comparison of success rate of routing protocols with varying data rates and zero mobility ……………………………………………...……...… 44 Figure 5-11. Comparison of success rate of routing protocols with varying data rates and mobility of 10 m/s ....……………………………………...……...… 45 Figure 5-12. Comparison of power efficiency of routing protocols with varying data rates and zero mobility ………………………………………...…... 45 Figure 5-13. Comparison of power efficiency of routing protocols with varying data rates and mobility of 10 m/s……………………………...……...… 46 Figure 5-14. Success rate of HERP/SMAC protocols with varying data rates and zero mobility when duty cycle is set to be 10..………………...…... 49 Figure 5-15. Power efficiency of HERP/SMAC protocols with varying data rates and zero mobility when duty cycle is set to be 10..………………...…... 49 Figure 5-16. Success rate of CEEP/SMAC protocols with varying data rates and zero mobility when duty cycle is set to be 10…………......……...… 50 vi.

(9) Figure 5-17. Power efficiency of CEEP/SMAC protocols with varying data rates and zero mobility when duty cycle is set to be 10…………......……...… 51 Figure 5-18. Comparison of success rate of HERP/CEEP protocols with varying data rates and zero mobility when duty cycle is set to be 10…….…...… 51 Figure 5-19. Comparison of power efficiency of HERP/CEEP protocols with varying data rates and zero mobility when duty cycle is set to be 10…….…...… 52. vii.

(10) Chapter 1 Introduction. Wireless sensor networks (WSNs) have developed numerous applications with integrated technology in various fields such as health care, intelligence life, disaster forecast, biology habitation, pollution detection; even extent to art and education [22]. The sensor devices communicate with each other in the vicinity area based on the non-infrastructure network, where routing selection plays a significant role to achieve the network fidelity, including data throughput and energy savings. It has been revealed that different network environments may need different routing protocols to effectively transceive the data packets. To reduce energy consumption in WSN environment, Elbhiri et al. [15] proposed a more equitable and stochastic technique which uses a dynamic probability to elect the cluster head to uniformly distribute the energy consumed through the network. In addition, we might face the empirical issue that identifying mobile user requirements and considering the constraints of current technologies for wireless computing are important factors to develop m-commerce services and quality models [3] [4] [5] [26]. Some researchers partitioned the network to distribute energy load among any sensor nodes to largely extent network lifetime [50], whereas some others exploit the sharing route for multiple destinations to save bandwidth and power consumption [9]. Therefore, to create and implement an efficient communication protocol in a specific WSN is a considerable topic in this paper. 1.

(11) 1.1. Overview of Sink-Oriented WSN It has been our research interest to provide an efficient communication solution in a sink-oriented WSN due to observing the congestion phenomenon of transit packets toward the sink. With a number of sensor nodes deployed in the non-infrastructure environment, this sink can receive the transit data collected from the sensors. For example, a mobile wireless network system with sink was proposed for informing pedestrians who were fleeing into a virtual urban environment of the safest and quickest exit routes [25]. It is one of the sink-oriented WSN functionalities that routing finds paths to forward data packets to a destination through distinctive multi-hop, many-to-one pattern. After the route paths have been reinforced by the sink, the transceivers of the sensor nodes in the network topology start sending the collected data toward it, as illustrated in Figure1-1.. Figure 1-1. A sink-oriented WSN topology with data transmission from allocated sources. However, it’s natural to form a choke point of the packet flows near the sink due to the intensive contention of medium access among the neighboring nodes and limited 2.

(12) buffer memory, which eventually cause severe network congestion and packet delay problems. Some researchers specified that the funneling effect due to congestion and delay as packet flows move closer to the sink could lead to significant packet collision, packet loss, and localized power drainage. Eventually, the entire sink-oriented sensor network collapses unexpectedly in the intensity region, within few hops from the sink [2] [21] [41] [44]. Even under light traffic condition in a sink-oriented WSN, the majority of packet loss occurs within the first few hops from the sink, as illustrated in. Loss Rate / Cumulative Distribution(%). Figure 1-2. 100. 4.0 pps. 80. 8.0 pps. 60. 12.0 pps. 40. CD 4.0 pps. 20. CD 8.0 pps CD 12.0 pps. 0 1. 2. 3. 4. Distance of Hops from Sink Figure 1-2. Loss rate and cumulative distribution over varying distance from sink Some research papers have addressed clustering techniques of data aggregation to solve the issue of congested load in sink-oriented sensor networks [11] [19]. In addition, substantial gains of data funneling can be obtained by suppressing certain reads and encoding from a set of sensors to a single destination in energy constrained wireless networks [21]. The combination of hop-by-hop communications and centralized data collection at a sink is illustrated in Figure 1-3, where the third axis represents the distance of a sensor node away from the sink. Figure 1-3 also shows all 3.

(13) transceivers of the sensor nodes in the network always keep ready to communicate with their neighbors, no matter they are on or off the routes toward the sink [20].. Wan and some other researchers [44] investigated that using end-to-end mechanisms in a network layer does not solve the funneling problem in wireless networks because the transmissions of concurrent radio on different links interact with each other. Besides, considering it is very difficult to get hold of power supply and battery replacement in a hostile environment, power savings is a significant issue in a sink-oriented WSN. Some researchers used a virtual sink node nearby the sources to play the role of sink node and broadcast local interest messages and gathered data toward the real destination [14]. Kathiravan et al. [24] used minimum number of forward nodes to relay the broadcast packets to solve the broadcast storm problem. Some researchers observed that exploiting the tradeoffs between energy and latency, and using hierarchical routing are effective techniques to save power consumption within the wireless networks [48]. Since mobile node’s location information has been 4.

(14) very useful for efficient routing and location-aware applications [6], it has been of interests to a few researchers for selecting a particular routing protocol on a network application which involves evaluating many inter-dependent tradeoffs and can be an overwhelming task for a network designer. However, the better the routing infrastructure gets selected, the more the significant impact of communication performance is achieved [23].. 1.2. Research Motivation Ahn et al. [2] has provided a funneling-MAC solution which implements a hybrid schedule-based and contention-based MAC scheme in the intensity region of the event funnel to mitigate the congestion phenomenon. However, it seems not quite natural for wireless sensor networks to maintain transit synchronization in the intensity region, neither efficient enough to wait for a neighboring node which has nothing to transmit at its scheduled time slot. Moreover, the more routing control packets initiated in the network, the more MAC control overhead produced in the intensity region of the event funnel, but not vice versa. Some researchers investigated the measurement of channel contention and interference levels by designing interference or load aware routing metric which relies on a probabilistic frames arrival model [1] [31] [34]. Furthermore, some researchers observed that the design of hybrid routing framework evidently finds the optimal mix of proactive route dissemination and reactive route discovery to mitigate the control overhead in ad-hoc sensor networks [40]. In order to mitigate the funnel effect in the intensity region of the sink-oriented sensor network specified in the previous section, we have been naturally encouraged to design a hybrid routing framework in this research. However, power efficiency is another challenge while we focus on pursuing the network fidelity. 5.

(15) Recently, to solve resource allocation problems in the wireless communication networks, cross-layer optimization approaches have been comprehensively used [28]. Such that, it seems interesting in this research to preserve the energy consumption at each sensor node by compulsively turning off its radio power from the upper-layer command over the entire network. Regarding to secure both of network fidelity and power savings in a sink-oriented WSN, we are motivated to design mechanisms as illustrated in Figure 1-4, where sensor nodes are allowed to completely turn off their transceivers from time to time, and keep the data flows in an efficient fashion at the same time.. 1.3. Research Objectives In this paper, we propose a hybrid cross-layer energy efficient protocol (CEEP) which results in a loosely coupled style without creating any extra interfaces for information to achieve energy conservation in sink-oriented wireless sensor networks. 6.

(16) Being provided through the concept of HERP, CEEP also groups the network coverage into two topological regions, denoted as intensity region and far-site region in Figure 1-4, whereas existing flat routing protocols can be divided into two categories including proactive routing protocols (e.g., DSDV abbreviated from Destination-Sequenced Distance Vectoring, Link state), and reactive routing protocols (e.g., DSR abbreviated from Dynamic Source Routing, AODV abbreviated from Ad-hoc On-demand Distance Vector) [36]. Existing proactive routing protocols (e.g., Link state, DSDV), are also known as table-driven protocols which maintain routes between each pair of nodes in the network, and reactive routing protocols (e.g., AODV, DSR), are also known as event-driven or on-demand protocols where routes between source nodes and destination nodes are created when they are on demand [18]. Considering that the better the routing infrastructure gets selected, the more the significant impact of communication performance is achieved [23], we are interested in deploying an adapted proactive routing protocol in the intensity region and an adapted reactive routing protocol in the far-site region cross-layered with an adapted cooperative pattern MAC in the WSN. Based on this approach, the first objective of this paper is to present a hybrid cross-layer energy efficient protocol to highlight the important influence of diminishing the funneling effect in a sink-oriented WSN. The second objective is to evaluate the network performance with cross-layer schemes implemented on sensor nodes. Our last objective is to investigate the impact of network lifetime regarding some observations in the sink-oriented topological regions.. 7.

(17) Chapter 2 Related Work. 2.1. Overview of Routing Classification Choosing the optimal routing protocol for sensor nodes depends on network characteristics and may adapt dynamically to provide excellent network performance. Existing flat routing protocols are generally either proactive or reactive [20]. A proactive routing protocol (e.g., Link state, DSDV) detects the layout of the network actively, which maintains a routing table at every intermediate node to achieve a route with minimal delay. Generally, the proactive protocol can provide good reliability on the current network topology. However, it cannot update the route information instantly while a node moves frequently. In addition, if only few nodes are involved on the transitional routes, continuously broadcasting to each other node in the network generates extra traffic overhead. A reactive routing protocol (e.g., AODV, DSR), also known as an event-driven or on-demand protocol, detects the layout of the network reactively where routes between source nodes and destination nodes are created when they are on demand. 2.2. Overview of Hybrid Routing Framework In general, geographic routing solutions are likely to perform much limited control overhead and overall bandwidth consumption because extra packet transmission can easily cause neighboring interferences and collisions in the wireless. 8.

(18) networks [31] [41]. Based on the concepts of zone routing protocol which form the basis of the hybridization framework, we are interested in mitigating the routing control overhead in the entire tree-based network topology as more as possible. Moreover, the more routing control overhead initiated in the network, the more MAC control overhead produced in the intensity region of the event funnel, but not vice versa. Within a specified scope (i.e., m=3 hops), also named as an intra-zone, a proactive routing protocol is implemented at the ad hoc sensor nodes to preserve their routing information before the data transitions start. At the other portion of the network topology, also named as an inter-zone, a reactive routing protocol is implemented at the ad hoc sensor nodes to discover the optimal path toward the destination. The design of hybrid routing framework evidently finds the optimal mix of proactive route dissemination and reactive route discovery to mitigate the control overhead in ad-hoc sensor networks. Independent zone routing (denoted as IZR) is an enhancement of scalability of hybrid zone routing protocol on the per-node basis [40]. Two-zone hybrid routing protocol (denoted as TZRP) is a general MANET routing framework on the per-node basis to effectively cut off control overhead by implementing each node with two zones to provide both proactive routing information and heuristic routing information [45]. Researchers also investigated a hybrid adaptive routing model (denoted as SHARP) of trade-off between proactive and reactive routing protocols, which can effectively reduce the control overhead in the proactive zone with smaller radius [38]. Considering energy conservation on each sensor node in the remote environment is extremely restricted, none of the above routing schemes could effectively collaborate with the network channel by turning on/off the radio transceiver in a dynamic slot pattern.. 9.

(19) 2.3. Overview of Funneling-MAC In general, Carrier Sense Multiple Access (CSMA) is a contention based MAC protocol which may require multiple retransmissions of packets due to collisions. In contrast, Time Division Multiple Access (TDMA) MAC protocols eliminate collisions, guarantee fairness and provide bounds on per-hop latency by setting a synchronous schedule among the sensor nodes in the wireless network [16] [37]. To mitigate the congestion phenomenon near the sink and boost the network fidelity in the sink-oriented wireless sensor network, Ahn et al. [2] provided a funneling-MAC solution which implements a hybrid MAC scheme (TDMA/CSMA) [39] in the intensity region of the event funnel where the majority of packet loss occurs within the first few hops from the sink, even under light traffic conditions. The funneling-MAC is triggered by a beacon broadcast from the sink while all sensor nodes perform CSMA by default unless they receive a beacon and are then deemed f-nodes of TDMA, as illustrated in Figure 2-1. In order to effectively mitigate the funnel effect due to different traffic load in the sink-oriented wireless network, the sink resizes the boundary of the intensity region by adapting its transmission power to broadcast the beacon. Construction of such a spanner for ad hoc node with variable transmission range can be performed in a localized fashion [35]. Later in this research, we will investigate the network fidelity of funneling-MAC based on few intensity region cases, such as 2 hops, 3 hops and 4 hops from the sink.. 10.

(20) Figure 2-1. An illustration of the sensor testbed for Funnelig-MAC by Ahn et al. [2]. 2.4. Overview of Sensor MAC Considering most energy of a wireless sensor node is wasted by idle listing in classic MAC protocols, it is insignificant to compare to other forms of energy waste such as collisions, protocol overhead, and overhearing [10]. To achieve the optimal energy conservation on each sensor node in the remote environment, a few link layer protocols have been investigated by researchers in the past few years. The primary goal of the duty cycle of a sensor-MAC (e.g., SMAC) is to reduce energy consumption by utilizing a static combined scheduling or contention scheme to completely turn off the transceiver at its sleep time slots [42] [47]. However, Zheng et al. [49] observed that the static duty cycle (sleep-wakeup cycle) scheme tuned for high traffic loads may result in energy wastage when traffic is low before the most updated schedule is induced. In addition, the static duty cycle scheme tuned for low traffic loads may result in low throughput under high traffic loads in the wireless sensor networks. Therefore, an adaptive energy-efficient MAC protocol (e.g., PMAC) has been implemented to dynamically determine a sensor node’s pattern schedules 11.

(21) based on its own traffic and that of its neighbours. Figure 2-2 shows that SMAC has a fixed pattern of sleep-wakeup time-slotted periods, no matter of how much the traffic is, whereas PMAC is in sleep when no traffic is perceived and immediately awake when traffic is perceived. wakeup. wakeup. wakeup. SMAC sleep. sleep. sleep. wakeup. PMAC sleep. sleep PRTM. PETM. Figure 2-2. An illustration of the difference of sleepwakeup patterns between SMAC and PMAC. 2.5. Overview of Cross-layer Optimization In general, networking protocols are supposed to expose a wider range of energy-latency tradeoffs during route discovery. That is, a routing protocol to discover paths with acceptable power savings could induce end-to-end latency while reducing energy consumption. Recently, to solve resource allocation problems in the wireless sensor networks, cross-layer optimization approaches have been comprehensively used. That is, different layers of the protocol stack are coupled through a limited amount of information being passed back and forth [8] [12] [28] [43]. Lin et al. successfully utilized a simpler distributed scheduling (potentially imperfect scheduling) component in the cross-layer framework to replace the complex MAC layer, and optimized the system performance. However, it is the major objective of this research to minimize both of the latency and energy consumption in a cross-layer 12.

(22) solution which collaborates source routing information and patterned scheduling incurred in network layer and data link layer respectively, as illustrated in Figure 2-3.. p eu ak W. Network layer. ts bi. Data link layer Power save. Figure 2-3 An illustration of cross-layer optimization at network layer / link layer. 2.6. Overview of Network Fidelity In this research, we are interested in measuring throughput fidelity (data success rate) at the sink while remote sensor sources are transmitting data packets in varying rates. Figure 2-4 shows that the network fidelity is empirically investigated in a NS2 simulation environment [30] of a 90-node wireless sensor network using CSMA-based 802.11-MAC and CBR application over a 1500m x 1500m area. Such as, the overall data success rates (in percents) of AODV, DSDV, and DSR measured by increasing data rates (in packets per second) are in a trend of declining where DSR obviously shows better network fidelity than the others, as illustrated in Figure 2-4. We are also interested in observing the network fidelity when remote sensor nodes are moving randomly at a maximum speed (e.g., mobility = 10 m/s) in the far site region, as illustrated in Figure 2-5.. 13.

(23) Success Rate at Sink (%). 100 AODV. 90. DSDV DSR. 80 70 60 50 0. 20. 40. 60. 80. Data Rate [packets / second (pps)]. Success Rate at Sink (%). Figure 2-4. Success rate of routing protocols with varying data rates. 100. AODV_10. 90. DSDV_10. 80. DSR_10. 70 60 50 0. 20. 40. 60. 80. Data Rate [packets / second (pps)]. Figure 2-5. Success rate of routing protocols with varying data rates and mobility of 10 m/s. 2.7. Overview of Power Efficiency It’s natural that sensor nodes involved on the communication route consume 14.

(24) more power as they conduct more packet transmission and receiving. We are interested in defining a measuring formula of power efficiency to investigate the relative routing protocols specified in this paper. That is, power efficiency is the throughput achieved per unit of energy consumed [49], which is given as below: Power efficiency =. total throughput at base station total energy consumption within network. 15. (2.1).

(25) Chapter 3 Hybrid Routing Implementations. 3.1. Overview of HERP According to the routing measurements which we have investigated in the previous sections, the traditional proactive DSDV protocol can be adapted to be suitable as a proactive INTRA-HERP routing protocol by constraining the scope of the topology region to m hops, marked as intensity region in Figure 3-1. Hence, Each INTRA-HERP node can periodically exchange or locally update their routing information by tuning the responding route data toward the destination provided from its neighboring nodes within the scope of the intensity region. Correspondingly, the traditional reactive AODV protocol can be adapted as the INTER-HERP routing protocol on the other nodes distributed in the far-site reactive region. All the source nodes, willing to send data packets to the sink of the intensity region, are supposed to be utilized by this reactive routing protocol. Border nodes of the intensity region with exactly m hops from the sink can receive the crossing RREQ packets generated by the source nodes utilizing reactive routing protocol and reply the RREP packets back to the corresponding source nodes after looking up the routing tables to search the sink. Border nodes also can receive the crossing data packets sent by the source nodes and proactively forward them to the sink. Unlike SHARP where zone radius is adapted dynamically to decrease loss rate and overall control overhead, we are advanced interested in the funneling effect improvement and network lifetime optimization in. 16.

(26) sink-oriented sensor networks. Furthermore, Unlike TZRP where each node independently has two zones surrounding itself to decline routing control overhead and lift query success rate, HERP employ power-aware proactive protocol only on the nodes within the funneling intensity region whereas the far-site nodes also utilize heuristic reactive protocol to communicate with the border nodes of such intensity proactive region, which is likely illustrated as a sink extension with M proactive. Far-Site Region. n-m_hops. Intensity Region. m_hops. sensor nodes in Figure 3-1.. N-M nodes. M nodes. Sink Figure 3-1. Framework of the intensity region as a sink extension with M proactive sensor nodes. 3.2. Hybrid Routing Analysis Considering routing overhead is the most important metric considered in the trade-off management between proactive routing and reactive routing [38][40], we’d like to further investigate the cost model of HERP by amortizing the routing overhead generated in both the proactive intensity region and the reactive far-site region. 17.

(27) Ramasubramanian et al. [38] adapted TORA protocol to be the proactive SPR protocol on the ad hoc nodes within a routing zone. In the face of fault tolerance, additional control overhead corresponding to the link lifetime λ between any paired neighboring nodes is involved. The routing overhead of link failure for each SPR node is presented as 1 2 ( β n −1) λ. ,. where. β n = ∑i =1 1i n. where n is the number of downstream links attached to the node. On the other hand, the independent routing overhead of link lifetime for each reactive AODV node on the route is estimated as (h-r)/λ, where h is the routing length from a source to the destination, and r is the radius of the proactive zone around the destination [38]. To accurately estimate the routing overhead of control packets from the protocol schemes of HERP, we intent to calculate the sum of control packets due to proactive routes, reactive routes, and the routes between intensity proactive region and far-site reactive region. Regarding the proactive INTRA-HERP protocol, each node within the network periodically (i.e., f cp , frequency of construction) broadcasts a construction control packet during the reconstruction interval. It also periodically (i.e., f up , frequency of update) transmits an update control packet during the beacon interval. However, INTRA-HERP induces additional control overhead to tolerate the link failure with an average link lifetime of λ . Hence, the INTRA-HERP routing overhead of control packets is presented as below, C INTRA. = M × ( f cp + f up + λ1 ). ................................................................. (3.1). where M is the number of proactive sensor nodes within the intensity region illustrated as shaded area in Figure 3-2. By considering the distributed density of the sensor nodes as ρ over the entire network, the equation 3.1 can be converted as below, 18.

(28) C INTRA. = m 2 ρ × ( f cp + f up + λ1 ). ............................................................... (3.2). n_hops h_hops. Legend :. n_hops. Intermediate sensor nodes Fowarding sensor nodes Source nodes. e. m_hops. Sink/ Destination. e. m_hops. Figure 3-2. A network topology of n_hops x n_hops distributed sensor nodes utilizing radio range between 1.0e and 1.42e. Unlike. INTRA-HERP. where. periodical f cp and f up are. dominant. factors,. INTER-HERP involves ( N − M ) RREQ packets plus (m + 1) × (h − m) RREP packets during the discovery phase where N denotes the number of sensor nodes distributed all over the network, m denotes the at least hops from sink to the border of the intensity region, n denotes the at least hops from sink to the external edge of the far-site region, and h denotes the average length of (n+m)/2 hops from sink to a source node within the far-site region in Figure 3-2. However, INTER-HERP also induces additional control overhead to tolerate any link failure along the route to reach the border ring. Hence, the INTER-HERP routing overhead of control packets is totaled. 19.

(29) as below,. C INTER. =. ∑{[ N − M + (m + 1) × (h − m)] × f. cr. + (h − m) × ( f ur + λ1 )} ...... (3.3). where f cr and f ur denote the frequency of route construction and route update for the reactive INTER-HERP nodes respectively. Furthermore, by considering the distributed density of the sensor nodes as ρ over the entire network, the equation 3.3 can be converted as below,. C INTER = k × {[n 2 ρ − m 2 ρ + 12 (m + 1) × (n − m)] × f cr + 12 (n − m) × ( f ur + λ1 )} ..... (3.4) where k denotes the number of source nodes within the far-site region. Hence, the total routing overhead for HERP is presented as below,. CTOTAL = C INTRA + C INTER .................................................................................. (3.5). 3.3. Region Sizing Schemes To search the optimal size of the intensity region, researchers utilized min search scheme to reach the minima of a polynomial function. Besides, we propose another strategy of theoretical evaluation in this research. The min search scheme employs an iterative searching process, incrementing or decrementing the intensity region size m by 1 as illustrated in Figure 3-3, to reach the minima of the overall routing overhead curve generated from the previous section. On the other hand, the theoretical evaluation intents to induce a formula of determining the optimal intensity region size by minimizing the overall routing overhead whereas m is set as the varying factor. In addition to the two extremes of pure reactive network and pure proactive network where m = 0 and m = n respectively, we propose a general formula to adapt the region size based on the environment parameters of the sensor network model. 20.

(30) Routing Overhead Traffic. 0 4. 1. 3. 2 Intensity region size m. Figure 3-3. Min search of determining optimal intensity region size at step 2 and step 4. Hence, after we combine the equation 3.2 and 3.4, the equation 3.5 is then available as below,. CTOTAL = m2 ρ × ( f cp + fup + λ1 ) + kfcr [n 2 ρ − m2 ρ + 12 (m + 1)(n − m)] + 12 k (n − m)( fur + λ1 ) Apparently, the total routing overhead curve is presented as a multi-variable function of k and m variables when the others are set to be constants as the environment parameters. Considering the role effect of proactive INTRA-HERP frequency terms (i.e., f cp = 0.1 and f up = 0.1 ) is such trivial to versus 1/λ of 10 in the above formula of total routing overhead, the above equation can be merely adapted as below,. CTOTAL = m 2 ρ ( λ1 ) + kf cr [n 2 ρ − m 2 ρ + 12 (m + 1)(n − m)] + 12 k (n − m)( f ur + λ1 )  (3.6). Let ,. ∂ CTOTAL ∂m. =0. Hence, m =. ( n −1)( kλf cr + λf ur +1) ( 4 ρ + 2 ) kλf cr + 2 λf ur + 2 − 4 ρ. Being one of the objectives to approach the minima of CTOTAL , we are interested in further investigating the magnitudes of CTOTAL under some environment consideration. 21.

(31) By setting the environment parameters as below:. Table 3-1. The settings of environment parameters. f cp. f up. λ. n. ρ. f cr. fur. 0.1. 0.1. 0.1. 10. 1.0. 1.0. 1.0. parameter Value. We obtain a dynamic table of the pairs of variables k (number of sources) and m (intensity region size in hops) as below:. Table 3-2. A dynamic list of variables k and m k. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. m. 0. 0. 0. 0.5. 1. 3. n. n. n. n. Theoretically, the routing control overhead is likely to be strictly affected by the sink-oriented network size. However, once the network topology of distributed ad-hoc nodes becomes such dense, the proactive routing protocol seems helpless to diminish the routing traffic over the network. To verify that the minima of CTOTAL is not in a local terrain in Figure 3-3, we calculate the corresponding CTOTAL with varying values of m while there exists 6 source nodes in the far-site region of the ad-hoc sensor network (k=6) as below:. Table 3-3. A dynamic list of variable m and CTOTAL m. 0. 1. 2. 3. 4. 5. 6. 7. 8. 9. CTOTAL. 960. 955. 952. 951. 952. 955. 960. 967. 976. 987. By observing such a detailed illustration from the above table, it is informative that 22.

(32) the intensity region size of 3 hops from the sink provides a minimum of routing control overhead over the entire network while some certain amount of data flows are transiting toward it. Besides, we are looking forward to verifying the analytical results of these region sizing schemes later in the simulation section.. 3.4. Heuristics: A Power-aware route discovery algorithm in funneling regions Network lifetime is an important characteristic of performance for all kinds of communication wireless networks. Zytoune et al. [51] Observed that the network lifetime increases by taking into account the battery residual energy in sensor nodes and the energy required for transmission along the path toward the sink. The basic proposal of this algorithm is based on the decision to update the routing entry stored in each sensor node depends on simple metrics estimated from distance delay plus power reserve (energy residual) hold on the successor node. The considered network topology is also based on a hybrid routing acquisition framework, including both proactive INTRA-HERP protocol and reactive INTER-HERP protocol. We also recall that an INTRA-HERP node can periodically exchange or locally update its routing information by tuning the corresponding route data toward the destination provided from its neighboring nodes within the scope of the intensity region, as shown in Figure 3-4. Unlike INTRA-HERP protocol, there is no need to maintain any INTER-HERP routing table when it is not valid, neither to periodically broadcast routing table exchange [18]. We also recall that the INTER-HERP scheme is implemented in three phases: query flooding, optimal route construction, and path maintenance, as shown in Figure 3-5.. 23.

(33) B F. F. B. B : Border node F : Forwarding node D : Sink. : Forward link : Backward link. F. D. B. Figure 3-4. INTRA-HERP forward and backward route formation within 3 hops from sink. B. B : Border node F F : Forwarding node. B F. S : Source node. S. : Forward link : Backward link. B. Figure 3-5. INTER-HERP forward and backward route formation initiated from source node Although minimum spanning tree based algorithms can offer the overall minimum cost of energy power for the entire network [46], it is still avoidless to prevent the entire network from being partitioned due to path overused problem [27]. Considering the residual battery energy of each mobile sensor node is an accurate metric to describe the lifetime of itself [29], we must use each sensor node’s battery power fairly to maximize the lifetime of the entire network from being partitioned. When an INTRA-HERP node receives a new update request, it initializes a temp route to cache the upstream node ID (i.e., NH) and routing information (i.e., delay_hops, power_reserve) as illustrated in Table 3-4 for backward learning operation and thus broadcasts the update request to its neighboring INTRA-HERP nodes. However, an 24.

(34) INTER-HERP node forwards the reply message initiated by the border nodes of the intensity region to the source node only when it is first arrived during the expected time interval. Similarly, the optimal route discovery algorithm allows any other intermediate node to discard the less competitive update packets assigned with the same sequence number, which is composed of broadcast-id and source-id. But, the previously recorded routing information might be updated if the later one is superior. The optimal route discovery protocol of backward learning, implemented as a pseudo-coded algorithm in Table 3-4, meets the conditions of heuristic metrics on each intermediate node whereas a threshold (denoted as γ) represents a standard lower bound of battery power reserve for all sensor nodes. Based on these characteristics of ad-hoc sensor networks, a concept of optimal route discovery algorithm has been developed to prolong the entire network lifetime from being partitioned in the early stage.. Table 3-4. A Pseudo-coded algorithm to discover optimal route ________________________________________________________ I. Initialize a temp route to sink in backward learning 1.. NH ← nil. //next hop ot sink. 2.. DS ← nil. //delay to sink. 3.. PR ← nil. //power reserve of successor node to sink. II. Repeat for an incoming request from neighboring node (i) 1.. if (NH ≠ nil) then. //topology is in place. 2.. if (power_reserve(i) ≥ γ and PR ≥ γ) then. 3.. if (DS < delay_hops(i) + 2) then. 4. 5. 6.. drop(i) ; else {NH ← i ;. 7.. DS ← delay_hops(i) + 1 ;. 8.. PR ← power_reserve(i) ; 25.

(35) 9.. Update_Route_Entry ;}. 10. 11.. end if elseif (power_reserve(i) ≥ γ and PR < γ) then {NH ← i ;. 12. 13.. DS ← delay_hops(i) + 1 ;. 14.. PR ← power_reserve(i) ;. 15.. Update_Route_Entry ;}. 16.. elseif (power_reserve(i) < γ and PR ≥ γ) then. 17. 18.. drop(i) ; else. 19.. if (DS < delay_hops(i) + 2) then. 20.. drop(i) ;. 21.. else {NH ← i ;. 22. 23.. DS ← delay_hops(i) + 1 ;. 24.. PR ← power_reserve(i) ;. 25.. Update_Route_Entry ;}. 26. 27.. end if end if. 28. else 29.. {NH ← i ;. 30.. DS ← delay_hops(i) + 1 ;. 31.. PR ← power_reserve(i) ;. 32.. Add_Route_Entry ;}. 33. end if ________________________________________________________. 26.

(36) Chapter 4 Cross-Layer Energy Efficiency Implementations. 4.1. Overview of CEEP A hybrid cross-layer energy efficient protocol (CEEP) is proposed in this research to achieve network fidelity and energy conservation in sink-oriented wireless sensor networks. CEEP groups the network coverage into two topological regions, denoted as intensity region and far-site region, with proactive routing protocols and reactive routing protocols cross-layered cooperative pattern MACs deployed on the sensor nodes respectively. According to the hybrid routing schemes which we have investigated in the previous sections, HERP clinches the optimal mix of proactive route dissemination and reactive route discovery in a sink-oriented ad-hoc sensor network [20]. However, without deploying power savings technology at sensor nodes in a WSN, the entire network will pay for extra traffic overhead and waste energy resources. That is, the power efficiency of HERP seems not performing well enough as we expected it should than the other traditional routing protocols in Figure 6 while the funneling effect indeed extensively decreases. To highlight the important influence of diminishing the funneling. effect in a. sink-oriented WSN, DSDV can be adapted as a proactive INTRA-CEEP routing protocol by constraining the scope of the topology region to m hops, illustrated as an intensity region of M nodes in Figure 4-1. Hence, Each INTRA-CEEP node can periodically exchange or locally update their routing information by tuning the. 27.

(37) responding route data toward the destination provided from its neighboring nodes within the scope of the intensity region. In addition, a source routing protocol (e.g., AODV, DSR) cross-layered with sensor MAC can be adapted as a reactive INTER-CEEP source routing protocol implemented at sensor nodes in the far-site region where all source nodes are supposed to be in place. Border nodes utilized with adaptive cooperative SMAC of the intensity region can receive crossing RREQ packets generated by the source nodes and instantly reply RREP packets back to the corresponding source nodes. While routes have been reinforced in the route discovery phase, border nodes can receive the incoming data packets sent by the source nodes and proactively forward them to their neighboring nodes after look up the routing. far-site region. n-m_hops. intensity region. m_hops. entries designating to the sink.. awake. N-M nodes. in sleep. M nodes. sink. Figure 4-1. A framework of intensity region illustrated as a sink extension of M proactive sensor nodes Among the relative hybrid routing protocols in Table 4-1, unlike SHARP where zone radius is adapted dynamically to decrease loss rate and overall control overhead, we are advanced interested in improving both of funneling effect and power conservation in sink-oriented sensor networks. In addition, unlike IZR and TZRP 28.

(38) where each node independently has two zones surrounding itself to decline routing control overhead and lift query success rate, CEEP deploys a proactive routing protocol in the intensity region. It also employs a source routing protocol cross-layered with cooperative pattern MAC in the far-site region of the network to communicate with the border nodes of the intensity region. Table 4-1. Comparisons of the relative hybrid routing protocols. Per-node based Multi-hop pattern Funneling effect solution Power saving solution Cross-layered Network/MAC. CEEP. HERP. SHARP. IZR. TZRP. No. No. Multi sinks. Yes. Yes. Yes. Yes. Yes. Yes. Yes. Yes. Yes. Yes. No. No. Yes. Power aware. No. No. No. Yes. No. No. No. No. 4.2. Hybrid Routing Analysis The funneling effect of a sink-oriented WSN can be effectively diminished when routing overhead is limited as less as possible by the trade-off management between proactive routing and reactive routing [38][40]. The total cost model of the hybrid routing regions, including the proactive intensity region and the reactive far-site region where the duty cycle of a sensor node is estimated 100 percents awake, is presented in the equation as below. CTOTAL. = C INTRA + C INTER. (4.1). To further investigate the total cost model of CEEP by amortizing the routing overhead generated in the network regions in Figure 4-1, please see section 3.3. By observing the detailed illustration in Table 3-3, it is informative that the intensity region size of 3 hops from the sink provides a minimum of routing overhead over the 29.

(39) entire network while some certain amount of data flows are forwarded toward the sink.. 4.3. Power Saving Scheme Oh and Han [32] observed a demand-based slot assignment (DSA) algorithm to allocate time slots based on the bandwidth demand of each node in a tree based network topology during each data collection round. They proposed DSA to allocate a sequence of receiving slots and a sequence of sending slots to each sensor node, which significantly reduced the power consumption of nodes at lower depths of the network tree topology. Based on that approach, a cross-layer energy efficient scheme which results in a loosely coupled style without creating any extra interfaces could be more likely to collaborate between routing demands and frame slots to achieve energy conservation in sink-oriented wireless sensor networks. In this research, we are interested in deploying an adapted proactive routing protocol (INTRA-CEEP) in the intensity region and an adapted reactive routing protocol (INTER-CEEP) cross-layered with cooperative pattern MAC in the far-site region of the sink-oriented WSN. Considering either the static duty cycle or traditional CSMA schemes (Carrier Sense Multiple Access) may result in energy wastage under light loads, it seems so natural that a dynamic duty cycle scheme is desperately needed to avoid such situation in the power-short networking environment. The essential of power savings for CEEP is to implement a source routing protocol (e.g., AODV, DSR), which route table cache or query packets may record the IDs of the intermediate nodes it has traversed during the route discovery phase [18], at the sensor nodes in the far-site region of the network. Once the destination is reached, the reply packet (RREP) then copies in its header the entire route sequence retrieved from the request packet (RREQ) to respond 30.

(40) to the source node (i.e., DSR), as illustrated in Figure 4-2(a) (b). Destination 7. 1-2-5 1-2. 3. Source 1. 6. 1-2-5. 1-2. 1. 1-2-5-6. 5. 2. 1-2-3 4. (a) Creation of the routing request RREQ Destination 7. 1-2-5-7 1-2-5-7. 5. 2. 6. 1-2-5-7. Source 1. 3 4. (b) Creation of the routing reply RREP Figure 4-2. INTER-CERP implements a source routing scheme to discover the route path. Among the sensor MACs, pattern sensor MAC (e.g., SMAC, PMAC) has been investigated as a cooperative scheme which tries to save more power without yielding on the throughput. The sleep-wakeup pattern cyclically repeated during the main time frame is a reserved plan for a sensor node to turn off its transceiver in a time slot when the pattern bit is 0, while bit 1 indicates the node intends to stay awake during the time slot [49]. The new patterns generated at the end of each main time frame on neighbouring nodes are exchanged to each other during an amount of time, called pattern exchange time frame. Based on comparing with the receiving node’s pattern status, a sensor node can simultaneously adjust its tentative sleep-wakeup pattern onto a transmitting schedule. The observers of dynamic sensor MACs tends to increase the 31.

(41) number of 0 bits in a pattern during light traffic, whereas the pattern goes back to 1 if it has data to send at any time. In this research, we’d like to adapt this kind of cooperative pattern scheme cross-layered with the source routing information, which is retrieved from route cache or query packets in different topological cases at the network layer during route discovery. In Figure 4-3(a), node i forwards a RREP packet to node h via node j, while node k forwards another RREP packet to node g via node j. In this case, node j has to create its wakeup pattern to receive any data from node h or node g at the most appropriate time. In Figure 4-3(b), node i forwards a RREP packet to node h via node j, while node h forwards another RREP packet to node i via node j. In this case, node j also has to create its wakeup pattern to receive any data from node h or node i at the most appropriate time. In Figure 4-3(c), node i forwards multiple RREP packets from different sources at different time to node h via node j. In this case, node j also has to create its wakeup pattern to receive any data from node h at the earliest time.. 32.

(42) g. i RR. EP. k. i RR. j. EP. j RR. P RE. EP. h. R. h. (b) An opposite case. (a) A cross case. i RR. EP. j h. (c) A parallel case Figure 4-3. Illustrations of combining different wakeup patterns initiated by RREP packets on node j. By perceiving the least distance (hops_to_source * 2) to receive a data packet, an INTER_CEEP node only needs to wakeup after that time period based on a demanded slot assignment in a sink-oriented WSN. In other words, a sensor node can turn off its transceiver during the least distance of time slots without compromising on the throughput. However, by considering the transmission interferences among the adjacent nodes, a combination operation of the crossed wakeup patterns is illustrated as the pseudocoded algorithms in Table 4-2. Table 4-2. Pseudocoded algorithms to overlap different wakeup patterns on node j ------------------------------------------------------------------------------------------------------I. In process of receiving an incoming RREP packet at network layer of node j 1.. receiving PACKET from MAC layer ;. 2.. if (node j is not destination) then. 3.. if (PACKET.type = = ‘RREP’) then 33.

(43) 4.. retrieve DISTANCE to the source node ;. 5.. send DISTANCE down to MAC ;. 6.. end if. 7.. forward PACKET ;. 8.. end if. -------------------------------------------------------------------------------------------------------II. In process of forwarding an outgoing RREP packet at MAC layer of node j 1.. receiving PACKET from network layer ;. 2.. calculating the least index of slots needed to receive data packet ;. 3.. if (it appears within current pattern repeat time frame) then. 4. 5.. wakeup all slots after it ; else if (it appears within next pattern repeat time frame) then. 6.. wakeup all slots after it ;. 7.. end if. 8.. forward PACKET ;. ------------------------------------------------------------------------------------------------------Thereafter, it has been a significant objective to evaluate the impact of this networking performance with cross-layer schemes implemented at sensor nodes in the following section of simulations.. 4.4. Impact of Network Lifetime Considering that the power-short issues commonly exist in hostile areas, researchers have investigated few significant mechanisms to extent the amount of time that a wireless network can satisfy its coverage objective of interest. Having all the sensor nodes remain awake would ensure network coverage, but that would reduce the network lifetime as the nodes would discharge quickly. Thereafter, some researchers proposed an efficient method to extend the sensor network operational time by organizing the sensors into a maximal number of disjoint set covers [7]. A standard approach of maximizing the network lifetime is to densely deploy sensor 34.

(44) nodes in overlap in the intensity region of a sink-oriented network, while some others used the residual power of each node as a metric to implement a power-aware algorithm to prevent the entire network from being partitioned [20]. Considering the sensor nodes in the bottle neck area around the sink have high risk to drain out of their power, it’s so natural to investigate the increments of either energy capacity on sensor nodes or node distribution density in the funneling region of the network.. 35.

(45) Chapter 5 Simulation Results. 5.1. Hybrid Routing Simulation Scenes In this research, we are interested in resolving the funneling effect and prolonging network lifetime within the intensity region around the sink. Logically, smaller routing control traffic can leads to lower power consumption, less congestion, smaller delays, and less packet loss. Further, it makes faster access to the communication channel with less contention. The NS-2 simulation environment was used to simulate the HERP framework. DSDV-based INTRA-HERP was used as the basis for the proactive nodes within the intensity region, and an AODV-based (or DSR-based) INTER-HERP was used as the basis for the reactive nodes within the far-site region. The network consists of 90 nodes allocated evenly in an area of 1500m x 1500m (unless noted otherwise). Further, the non-zero node movement scenarios were also under estimation in this research. A node within the far-site region moved at a maximum speed v (i.e., 10 m/s) and was assigned a random destination in the specific far-site region with zero pause time, which was formulated by the CMU’s scenario generator mechanism. We used IEEE 802.11b as the MAC protocol for all interesting routing models in this research. Without packet collision, we assumed the background channel interference and receiver noise limit the transmission/reception range of packets to a physical radius of 250 meters. Although different simulation models were performed for different routing protocols, different data traffic settings and region radius values, the network environment was made to remain exactly the 36.

(46) same; i.e., the nodes move from the same locality, have same movement scenarios, and initiate same transmission sessions from the same sources at the same instants. No data was collected in the first 25 seconds of the simulations, while the initial proactive routing information could be constructed. A constant bit rate (CBR) traffic generator initiated the data packet traffic in this simulation. All source nodes were deployed with even spacing in the far sit region. The routing agent of each source node was configured to transmit data packets of 512 bytes at a specific rate. The interface queue length of each sensor node was constrained to be 50. Initially, each sensor node has 100 Joules of battery power.. 5.2. Hybrid Routing Simulation Results As we observed in Figure 5-1 and 5-2 where far-site network mobility is zero and 10 m/s respectively, the hybrid models of HERP are up to 20% superior to the original models of AODV and DSDV in success rate of receiving CBR data from the source nodes distributed in the far-site region under some intense traffic. We also illustrate the projected performance of an adaptive routing protocol for all HERP_xh models in the sink-oriented wireless sensor network, such as HERP in the following simulation figures.. 37.

(47) Success Rate at Sink (%). 100. AODV DSDV. 90. HERP_2h. 80. HERP_3h HERP_4h. 70. HERP. 60 50 0. 20. 40. 60. 80. Data Rate [packets / second (pps)] Figure 5-1. Success rate of HERP protocols with varying data rates and zero mobility. Success Rate at Sink (%). 100. AODV_10. 90. DSDV_10 HERP_2h_10. 80. HERP_3h_10 HERP_4h_10. 70. HERP_10. 60 50 0. 20. 40. 60. 80. Data Rate [packets / second (pps)] Figure 5-2. Success rate of HERP protocols with varying data rates and mobility of 10 m/s We also experimentally measured the zone size effect among four scenarios, implementing HERP_2h, HERP_3h, and HERP_4h routing protocols in the intensity region of 2 hops radius, 3 hops radius, and 4 hops radius respectively whereas HERP illustrates the radius adapting protocol. Regarding the region sizing scheme in section 38.

(48) 3.4, the theoretical size of the intensity region is estimated as 3 hops while the entire network size is in 10 hops and there exists six source sensor nodes in the far-site region. HERP_2h is merely superior to the others at lower data rate. However, when the data rate increases beyond 50 pps, either one of the HERP_xh seems useful to boost the network fidelity. In other words, HERP needs to adapt the sink’s intensity region size with the varying data rates from time to time. The other significant measurement to be made to what we’d like to observe in this research is the energy consumption with varying traffic loads in the network during the simulation. Initially, each sensor node was assigned battery capacity of 100 joules. We found an interesting case that all participants consumed almost the same energy rate at low data rate (below 35 pps) in Figure 5-3 and Figure 5-4, where the larger the network mobility, the higher the energy consumed. The original DSDV models obviously consume lower energy than the others at high data rate (beyond 35 pps). We also observed the. Energy Consumption (Joules/second). power efficiency of HERP models in Figure 5-5 and Figure 5-6. 16 AODV. 14. DSDV. 12. HERP_2h. 10. HERP_3h. 8. HERP_4h. 6 4 0. 20. 40. 60. 80. Data Rate [packets / second (pps)] Figure 5-3. Energy consumption of HERP protocols with varying data rates and zero mobility. 39.

(49) Energy Consumption (Joules/second). 16. AODV_10. 14. DSDV_10. 12. HERP_2h_10. 10. HERP_3h_10. 8. HERP_4h_10. 6 4 0. 20. 40. 60. 80. Data Rate [packets / second (pps)] Figure 5-4. Energy consumption of HERP protocols with varying data rates and mobility of 10 m/s. Power Efficiency (packets/Joule). 4. AODV DSDV. 3. HERP_2h HERP_3h. 2. HERP_4h HERP. 1 0 0. 20. 40. 60. 80. Data Rate [packets / second (pps)] Figure 5-5. Power efficiency of HERP protocols with varying data rates and zero mobility. 40.

(50) Power Efficiency (packets/Joule). 4. AODV_10 DSDV_10. 3. HERP_2h_10 HERP_3h_10. 2. HERP_4h_10 HERP_10. 1 0 0. 20. 40. 60. 80. Data Rate [packets / second (pps)] Figure 5-6. Power efficiency of HERP protocols with varying data rates and mobility of 10 m/s. The last objective of this research is to prolong the network lifetime. Considering the residual battery energy of each mobile sensor node is an accurate metric to describe the lifetime of itself, we must use each sensor node’s battery power fairly to maximize the lifetime of the entire network from being partitioned. In this simulation, we are interested in observing the number of dead sensor nodes (in zero battery power) near the sink area. Figure 5-7 illustrates the cumulative dead nodes in the simulation with zero mobility, session interval of 1.0 second, 45 source nodes, and a running duration of 900 seconds. HERP_P denotes a HERP model with power awareness algorithm in Table 3-4. Both HERP and DSDV enter into a situation as all routing models eventually will do that the entire network is partitioned at the time frame around 840 seconds because the sensor nodes within the first two hops from the sink drain out of battery power. However, HERP_P curve in the figure represents fewest dead nodes whereas the network also fails shortly after the time frame of 900 seconds.. 41.

(51) Cumulative Dead Nodes (number). 50. AODV DSDV. 40. DSR 30. HERP HERP_P. 20 10 0 500. 600. 700. 800. 900. Simulation Duration (seconds) Figure 5-7. Network lifetime of HERP protocols with simulation duration. To gather the relative performance of funneling-MAC in the lab work tested under. NS-2. simulation. environment,. we. deployed. an. adapted. hybrid. TDMA/CSMA-802.11b protocol on each node in the intensity region and pure CSMA-802.11b protocol on each node in the far-site region where AODV was configured as the only routing protocol on each node in the entire sink-oriented WSN. The objective we approached is to present the network fidelity and power efficiency of funneling-MAC (FMAC) with exactly the same NS2 simulation conditions HERP was configured. As illustrated in Figure 5-8 and Figure 5-9, the success rates at sink with zero mobility and mobility of 10m/s were presented for 3 FMAC protocols with 2, 3, and 4 hops of intensity region size respectively. As noticed in the figures, FMAC_2h performs very well when the data rate of network traffic is light (i.e., under 10 pps). Apparently, FMAC_2h is superior than the other two relative protocols both in success rate at sink and network power efficiency no matter of any network 42.

(52) mobility. Later in this section, we choose FMAC_2h as the representative protocol of FMAC in the following comparison figures, such as Figure 5-10, Figure 5-11, Figure 5-12, and Figure 5-13. As we observe in this research under the same simulation conditions, HERP is superior to the conventional MANET routing protocols (i.e., AODV and DSDV) and FMAC both in success rate at sink and network power efficiency no matter of any network mobility.. Success Rate at Sink (%). 100 AODV. 90. FMAC_2h. 80. FMAC_3h. 70. FMAC_4h. 60 50 0. 20. 40. 60. 80. Data Rate [packets / second (pps)] Figure 5-8. Success rate of funneling-MAC protocols with varying data rates and zero mobility. 43.

(53) Success Rate at Sink (%). 100 AODV_10. 90. FMAC_2h_10. 80. FMAC_3h_10. 70. FMAC_4h_10. 60 50 0. 20 40 60 Data Rate [packets / second (pps)]. 80. Figure 5-9. Success rate of funneling-MAC protocols with varying data rates and mobility of 10 m/s. Success Rate at Sink (%). 100 AODV. 90. DSDV. 80. FMAC. 70. HERP. 60 50 0. 20. 40. 60. 80. Data Rate [packets / second (pps)] Figure 5-10. Comparison of success rate of routing protocols with varying data rates and zero mobility. 44.

(54) Success Rate at Sink (%). 100 AODV_10. 90. DSDV_10. 80. FMAC_10. 70. HERP_10. 60 50 0. 20. 40. 60. 80. Data Rate [packets / second (pps)] Figure 5-11. Comparison of success rate of routing protocols with varying data rates and mobility of 10 m/s. Power Efficiency (packets/Joule). 5 AODV. 4. DSDV. 3. FMAC. 2. HERP. 1 0 0. 20. 40. 60. 80. Data Rate [packets / second (pps)] Figure 5-12. Comparison of power efficiency of routing protocols with varying data rates and zero mobility. 45.

(55) Power Efficiency (packets/Joule). 5 AODV_10. 4. DSDV_10. 3. FMAC_10. 2. HERP_10. 1 0 0. 20. 40. 60. 80. Data Rate [packets / second (pps)] Figure 5-13. Comparison of power efficiency of routing protocols with varying data rates and mobility of 10 m/s. In addition, we’d like to make a brief summary among the modern related researches of hybrid zone routing protocols for mobile ad-hoc wireless networks. The HERP protocol with power awareness algorithm is the only one to investigate the impact of the funnelling effect from multiple sensor sources in sink-oriented mobile ad-hoc networks whereas the others are adapted to support the general mobile ad-hoc networks (MANET). In other words, those routing protocols may treat each ad-hoc node as the centre of its proactive zone whereas HERP only allows the sink node to create its intensity region of proactive routing protocol. However, all of these researches discuss about the zone radius effect over the routing control packets. TZRP reveals that the original ZRP has the minimum of routing overhead when the zone radius size is 3 under high network traffic. When the network traffic goes down, the appropriate size of zone radius is 2. The TZRP which has two additional zones for each ad-hoc node could cut 50% routing overhead down regardless of zone radius size 46.

(56) [45]. Also, The IZR which has an independent zone for each ad-hoc node could always minimize the routing overhead by tuning the zone radius size between 1 and 3 [40]. The researchers of SHARP reveal that the hybrid routing overhead is obviously lower than either AODV (reactive protocol) or SPR (proactive protocol) with varying mobility fractions no matter of single destination or multi-destination is implemented in the ad-hoc wireless networks [38]. The packet loss rate of SHARP declines as the zone radius for each ad-hoc node is enlarged.. 5.3. Cross-Layer Energy Efficiency Simulation Scenes In the lab work tested under NS-2 simulation environment, we deployed an adapted proactive routing protocol (i.e., hybrid DSDV/AODV) in the intensity region and an adapted reactive routing protocol (i.e., AODV) in the far-site region cross-layered with an adaptive cooperative pattern sensor MAC (i.e., SMAC, PMAC) on each node in a sink-oriented WSN. The first objective we approached is to present a hybrid cross-layer energy efficient protocol to highlight the important influence of diminishing the funneling effect over the entire network. The second objective is to evaluate the network power efficiency with cross-layer schemes implemented on the sensor nodes. A hybrid DSDV/AODV-based INTRA-CEEP was used as the basis for the proactive nodes within the intensity region, and an AODV-based INTER-CEEP was used as the basis of the reactive nodes within the far-site region. Each of them is cross-layered with an adapted CEEP MAC (i.e., SMAC, PMAC) on a sensor node. The network consists of 90 nodes allocated evenly in an area of 1500m x 1500m (unless noted otherwise). Without packet collision, we assumed the background channel interference and receiver noise limit the transmission/reception range of packets to a physical radius of 250 meters. Although different simulation models were 47.

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