Chapter 4 Routing Protocols

Chapter 4 Routing Protocols

Overview Routing in WSNs is challenging due to distinguish from other wireless networks like mobile ad hoc networks or cellular networks. First, it is not possible to build a global addressing scheme for a large number of sensor nodes. Thus, traditional IP-based protocols may not be applied to WSNs. In WSNs, sometimes getting the data is more important than knowing the IDs of which nodes sent the data. Second, in contrast to typical communication networks, almost all applications of sensor networks require the flow of sensed data from multiple sources to a particular BS. Routing in WSNs is challenging due to the inherent characteristics that distinguish these networks from other wireless networks like mobile ad hoc networks or cellular networks. First, due to the relatively large number of sensor nodes, it is not possible to build a global addressing scheme for the deployment of a large number of sensor nodes. Thus, traditional IP-based protocols may not be applied to WSNs. In WSNs, sometimes getting the data is more important than knowing the IDs of which nodes sent the data. Second, in contrast to typical communication networks, almost all applications of sensor networks require the flow of sensed data from multiple sources to a particular BS.

Overview (cont.) Third, sensor nodes are tightly constrained in terms of energy, processing, and storage capacities. Thus, they require carefully resource management. Fourth, in most application scenarios, nodes in WSNs are generally stationary after deployment except for, may be, a few mobile nodes. Fifth, sensor networks are application specific, i.e., design requirements of a sensor network change with application. Sixth, position awareness of sensor nodes is important since data collection is normally based on the location. Finally, data collected by many sensors in WSNs is typically based on common phenomena, hence there is a high probability that this data has some redundancy.

Overview (cont.) The task of finding and maintaining routes in WSNs is nontrivial since energy restrictions and sudden changes in node status (e.g., failure) cause frequent and unpredictable topological changes. To minimize energy consumption, routing techniques proposed for WSNs employ some well-known routing strategies, e.g., data aggregation and in-network processing, clustering, different node role assignment, and data-centric methods were employed.

Outline 4.1 Routing Challenges and Design Issues in WSNs
4.2 Flat Routing 4.3 Hierarchical Routing 4.4 Location Based Routing 4.5 QoS Based Routing 4.6 Data Aggregation and Convergecast 4.7 Data Centric Networking 4.8 ZigBee 4.9 Conclusions

Chapter 4.1 Routing Challenges and Design Issues in WSNs

Overview The design of routing protocols in WSNs is influenced by many challenging factors. These factors must be overcome before efficient communication can be achieved in WSNs. Node deployment Energy considerations Data delivery model Node/link heterogeneity Fault tolerance Scalability Network dynamics Transmission media Connectivity Coverage Data aggregation/convergecast Quality of service

Node Deployment Node deployment in WSNs is application dependent and affects the performance of the routing protocol. The deployment can be either deterministic or randomized. In deterministic deployment, the sensors are manually placed and data is routed through pre-determined paths. In random node deployment, the sensor nodes are scattered randomly creating an infrastructure in an ad hoc manner. Node deployment in WSNs is application dependent and affects the performance of the routing protocol. The deployment can be either deterministic or randomized. In deterministic deployment, the sensors are manually placed and data is routed through pre-determined paths. In random node deployment, the sensor nodes are scattered randomly creating an infrastructure in an ad hoc manner. If the resultant distribution of nodes is not uniform, optimal clustering becomes necessary to allow connectivity and enable energy efficient network operation.

Energy Considerations
Sensor nodes can use up their limited supply of energy performing computations and transmitting information in a wireless environment. Energy conserving forms of communication and computation are essential. In a multi-hop WSN, each node plays a dual role as data sender and data router. The malfunctioning of some sensor nodes due to power failure can cause significant topological changes and might require rerouting of packets and reorganization of the network.

Data Delivery Model Time-driven (continuous) Event-driven Query-driven
Suitable for applications that require periodic data monitoring Event-driven React immediately to sudden and drastic changes Query-driven Respond to a query generated by the BS or another node in the network Hybrid The routing protocol is highly influenced by the data reporting method 資料回報可以分成三個種類 1定時週期的回報 2.偵測到事件回報 3.由BS發出要求、再回報這三種回報的方式都會被能源消耗、和Routing的計算影響

Node/Link Heterogeneity
Depending on the application, a sensor node can have a different role or capability. The existence of a heterogeneous set of sensors raises many technical issues related to data routing. Even data reading and reporting can be generated from these sensors at different rates, subject to diverse QoS constraints, and can follow multiple data reporting models. 根據用途的不同、Sensor會有不同的性質和不同能力但異質性的感測節點，再傳輸的過程當中（Routing方面），也衍生出了一些問題，例：因為不同性質節點會有不同的QoS限制

Fault Tolerance Some sensor nodes may fail or be blocked due to lack of power, physical damage, or environmental interferences It may require actively adjusting transmission powers and signaling rates on the existing links to reduce energy consumption, or rerouting packets through regions of the network where more energy is available 有些能源會因為能源不足，或是損壞、環境的因素而失效， 1.調整傳輸功率、頻率去減少能源讓費 2.重新改變傳送的packet

Scalability The number of sensor nodes deployed in the sensing area may be on the order of hundreds or thousands, or more. Any routing scheme must be able to work with this huge number of sensor nodes. In addition, sensor network routing protocols should be scalable enough to respond to events in the environment. 因為Sensor的數量可以從幾百個～幾千個，再Routing的設計方面必須要考量到大數量的以及適應相對的環境。

Network Dynamics Routing messages from or to moving nodes is more challenging since route and topology stability become important issues Moreover, the phenomenon can be mobile (e.g., a target detection/ tracking application). 移動節點導致拓樸的動態改變，也是一個議題 Table detection Tracking 都視為可以移動的現象所以Routing對於這方面也要能夠有相對應的調整。

Transmission Media In general, the required bandwidth of sensor data will be low, on the order of kb/s. Related to the transmission media is the design of MAC. TDMA (time-division multiple access) CSMA (carrier sense multiple access) 再無線傳輸頻道的部份，也會影響到routing 所以在MAC層也有相對應的設計 TDMA CSMA

Connectivity High node density in sensor networks precludes them from being completely isolated from each other. However, may not prevent the network topology from being variable and the network size from shrinking due to sensor node failures. In addition, connectivity depends on the possibly random distribution of nodes. 為了讓連接性有一定的程度（避免某些節點孤立）會需要高密度的節點數量，但高密度的節點，會無法避免node的失效所帶來的拓樸變化，此外、連接性也會依靠這些隨機分佈的節點來建立。

Coverage In WSNs, each sensor node obtains a certain view of the environment. A given sensor’s view of the environment is limited in both range and accuracy. It can only cover a limited physical area of the environment. 每一個Sensor都有自己的偵測範圍（受到物理條件的限制）再routing的時候也會考量覆蓋率的部份

Data Aggregation/Convergecast
Since sensor nodes may generate significant redundant data, similar packets from multiple nodes can be aggregated to reduce the number of transmissions. Data aggregation is the combination of data from different sources according to a certain aggregation function. Convergecasting is collecting information “upwards” from the spanning tree after a broadcast. Data aggaregation會把多餘（重複性質的data）從不同的Sensor收集　可以減少傳送次數 Covergast利用Spanning tree把收集到的資料向上傳，所以Route的結構就非常重要

Quality of Service In many applications, conservation of energy, which is directly related to network lifetime. As energy is depleted, the network may be required to reduce the quality of results in order to reduce energy dissipation in the nodes and hence lengthen the total network lifetime. 為了讓能量的消耗降低，有時後會把通訊品質下降，同時延長整體網路壽命

Routing Protocols in WSNs: A taxonomy
Network Structure Protocol Operation Flat routing SPIN Directed Diffusion (DD) Hierarchical routing LEACH PEGASIS TTDD Location based routing GEAR GPSR Negotiation based routing SPIN Multi-path network routing DD Query based routing DD, Data centric routing QoS based routing TBP, SPEED Coherent based routing Aggregation Data Mules, CTCCAP

Reference J. N. Al-Karaki and A. E. Kamal, “Routing techniques in wireless sensor networks: a survey,” IEEE Wireless Communications, vol. 11, no. 6, pp. 6-28, Dec

Chapter 4.2 Flat Routing 定向擴散協議[是一種基於查詢的路由機製。
這個通訊協定最主要是利用封包內的參數來減少不必要的資料傳輸好節省能量的消耗，首先Sink節點每隔一段時間會Broadcast一種名為interest的封包到各個感測節點，這個封包裡面大致上包含了現在時間、持續時間、及其他的訊息感測節點在收到interest的封包之後會先把這些訊息存起來，用來和後來的interest封包作比對。另外在interest封包內會紀錄這個封包是由誰傳送過來的，利用以上的訊息就可以把WSN的拓撲結構建立起來，並且利用這些資訊去決定資料傳送的路徑。當有節點故障的時候透過此通訊協定也可以很快的選擇出另一個傳輸路徑。

Overview In flat network, each node typically plays the same role and sensor nodes collaborate together to perform the sensing task. Due to the large number of such nodes, it is not feasible to assign a global identifier to each node. This consideration has led to data centric routing, where the BS sends queries to certain regions and waits for data from the sensors located in the selected regions. Since data is being requested through queries, attribute-based naming is necessary to specify the properties of data. Prior works on data centric routing, e.g., SPIN and Directed Diffusion, were shown to save energy through data negotiation and elimination of redundant.

4.2.1 SPIN Sensor Protocols for Information via Negotiation

SPIN -Motivation Sensor Protocols for Information via Negotiation, SPIN A Negotiation-Based Protocols for Disseminating Information in Wireless Sensor Networks. Dissemination is the process of distributing individual sensor observations to the whole network, treating all sensors as sink nodes Replicate complete view of the environment Enhance fault tolerance Broadcast critical piece of information 散怖是分散式sensor各自分類的過程，能觀察整個網路，所有的sensor都當作一個sink node 主要可以達到下列這些 1.觀察環境的完整的複製 2.提高容錯 3.廣播資訊重要的部份

SPIN (cont.)- Motivation
Flooding is the classic approach for dissemination Source node sends data to all neighbors Receiving node stores and sends data to all its neighbors Disseminate data quickly Deficiencies Implosion Overlap Resource blindness 散佈典型使用的方法來源節點傳送資料給所有的鄰近節點接收節點儲存並且傳送資料給所有它自己的鄰近節點快速的達到資料散布的目的缺點: Implosion Overlap Resource blindness

SPIN (cont.)-Implosion
A C B D x Node The direction of data sending The connect between nodes Implosion就是說由A點傳送資料x給B、C兩點，而D點也是B、C兩點的鄰近節點，那麼D點會同時接收到兩筆相同由A點傳送出來的資料。

SPIN (cont.)- Overlap A B C r q s (q, r) (s, r) Node
The direction of data sending The connect between nodes A B The searching range of the node Overlap C點向A、Ｂ兩點發出請求，而a、b兩點則開始感測搜尋資料，但是a、b兩點會感測到相同的資料傳送給c (q, r) (s, r) C

SPIN (cont.)- Resource blindness
In flooding, nodes do not modify their activities based on the amount of energy available to them. A network of embedded sensors can be resource- aware and adapt its communication and computation to the state of its energy resource. 在flooding中不會考慮到剩餘能源的問題在感測網路中會被資源查覺並且適用於它的通訊並且計算到它的資源能源的狀態

SPIN (cont.) Negotiation Resource adaptation
Before transmitting data, nodes negotiate with each other to overcome implosion and overlap Only useful information will be transferred Observed data must be described by meta-data Resource adaptation Each sensor node has resource manager Applications probe manager before transmitting or processing data Sensors may reduce certain activities when energy is low Negotiation(溝通) 在傳送資料前，節點之間通訊去克服Implosion和Overlap的問題只有有用的資訊會被傳送獲得的資料會使用meta-data (description of data)做傳送資源適應每一個節點都有資源管理在傳送或是處理資料前應用探測管理 Sensor在能量少的時候會減少必要的行動

SPIN (cont.)- Meta-Data
Completely describe the data Must be smaller than the actual data for SPIN to be beneficial If you need to distinguish pieces of data, their meta-data should differ Meta-Data is application specific Sensors may use their geographic location or unique node ID Camera sensor may use coordinate and orientation ˙簡單扼要的描述完整的資料會比實際上的資料還小如果你需要識別資料的部份，他們的meta-data會不一樣 ˙應用特性 Sensor會用他們的為制或是獨自的節點id Camera sensor會利用到座標以及傾向

SPIN (cont.)- SPIN family
Protocols of the SPIN family SPIN-PP It is designed for a point to point communication, i.e., hop- by-hop routing SPIN-EC It works similar to SPIN-PP, but, with an energy heuristic added to it SPIN-BC It is designed for broadcast channels SPIN-RL When a channel is lossy, a protocol called SPIN-RL is used where adjustments are added to the SPIN-PP protocol to account for the lossy channel.

SPIN (cont.)- Three-stage handshake protocol
SPIN-PP: A three-stage handshake protocol for point- to-point media ADV – data advertisement Node that has data to share can advertise this by transmitting an ADV with meta-data attached REQ – request for data Node sends a request when it wishes to receive some actual data DATA – data message Contain actual sensor data with a meta-data header Usually much bigger than ADV or REQ messages SPIN(Sensor Protocol for Information via Negotiation)協議是一種以數據為中心的自適應路由協議。SPIN協議的目的是：通過節點之間的協商，解決Flooding協議和Gossiping協議的內爆和重疊現象。SPIN協議有3種類型的消息，即ADC、REQ和DATA。 ADV(宣傳檔案)用於數據的廣播，當某一個節點有數據可以共享時，可以用其進行數據信息廣播。 REQ(回報檔案)用於請求發送數據，當某一個節點希望接受DATA數據包時，發送REQ數據包。 DATA為傳感器采集的數據包。

SPIN (3-Step Protocol) DATA ADV A REQ DATA ADV REQ B

Notice the color of the data packets sent by node B
SPIN (3-Step Protocol) DATA A B Notice the color of the data packets sent by node B

SPIN (3-Step Protocol) A DATA B
SPIN effective when DATA sizes are large : REQ, ADV overhead gets amortized

SPIN (cont.)- SPIN-EC (Energy-Conserve)
Add simple energy-conservation heuristic to SPIN-PP SPIN-EC: SPIN-PP with a low-energy threshold Incorporate low-energy-threshold Works as SPIN-PP when energy level is high Reduce participation of nodes when approaching low-energy- threshold When node receives data, it only initiates protocol if it can participate in all three stages with all neighbor nodes When node receives advertisement, it does not request the data Node still exhausts energy below threshold by receiving ADV or REQ messages ˙增加簡單的能量管理 ˙包含低能量的門檻 ˙運作如同spin-pp當能量還很充足 ˙當能量在低門檻時減少參予的節點當節點接收資料，他會開始擬定如果他可以參予到它所有周遭鄰近節點當節點結收到宣傳，它不會要求檔案 ˙節點還是有可能會在有門檻的情況下接收adv和req封包將能源耗完

SPIN (cont.)- Conclusion
SPIN protocols hold the promise of achieving high performance at a low cost in terms of complexity, energy, computation, and communication Pros Each node only needs to know its one-hop neighbors Significantly reduce energy consumption compared to flooding Cons Data advertisement cannot guarantee the delivery of data If the node interested in the data are far from the source, data will not be delivered Not good for applications requiring reliable data delivery, e.g., intrusion detection SPIN協定在複雜度、能量、計算以及通訊上都有很低的消耗。

SPIN (cont.)- Reference
J. Kulik, W.R. Heinzelman, and H. Balakrishnan, “Negotiation- based protocols for disseminating information in wireless sensor networks,” Wireless Networks, Vol. 8, pp , 2002.

4.2.2 Directed Diffusion A Scalable and Robust Communication Paradigm for Sensor Networks

Overview Data-centric communication
Data is named by attribute-value pairs Different form IP-style communication End-to-end delivery service e.g. How many pedestrians do you observe in the geographical region X? A sensor field Sources Event Directed Diffusion Sink Node

Overview (cont.) Data-centric communication (cont.)
Human operator’s query (task) is diffused Sensors begin collecting information about query Information returns along the reverse path Intermediate nodes aggregate the data Combing reports from sensors Directed Diffusion is an important milestone in the data centric routing research of sensor networks

Directed Diffusion Typical IP based networks
Requires unique host ID addressing Application is end-to-end Directed diffusion – use publish/subscribe Inquirer expresses an interest, I, using attribute values Sensor sources that can service I, reply with data 資料中心請求興趣請求可找到滿足興趣的來源中間節點傳送資料給收集站定位維修和加強多路徑傳送介於多個來源，收集站和查詢者

Directed Diffusion (cont.)
Directed diffusion consists of Interest - Query which specifies what a user wants Data - Collected information Gradient Direction and data-rate Events start flowing towards the originators of interests Reinforcement After the sink starts receiving events, it reinforces at least one neighbor to draw down higher quality events 收集者廣播想知道的事情給他的鄰居節點鄰居節點暫存收集者感興趣的訊息建立來源梯度一旦源收到興趣消息，路線沿梯度測量「興趣訊息」定義了使用者所要詢問的事情，告訴感測網路想取得的資訊。「資料訊息」是指感測器將符合使用則需求的感測資料，經過蒐集與處理後，所回報給使用者的有用資訊，可視為對於感測到的實際現象所做的簡單描述。「梯度」是一種傳輸路徑的紀錄，當需求訊息在節點間傳送的時候，接收端會紀錄來源端所在，並在兩者間建立一條梯度連線，以做為之後訊息回傳的有效路徑。又由於需求回報訊息會沿著不同的路徑回傳給使用者，使用者必需進行路徑「更新」，經過效益評估運算後，從中挑選最佳的路徑，供來源節點繼續進行資料回傳，並中斷其它回傳傳路徑。

Data Naming Expressing an Interest
Using attribute-value pairs e.g., Type = Wheeled vehicle // detect vehicle location Interval = 20 ms // send events every 20ms Duration = 10 s // Send for next 10 s Rect = [-100,100, 200,400] // from sensors in this area Other interest-expressing schemes possible e.g., hierarchical (different problem)

Interests and Gradients
Interest propagation The sink broadcasts an interest Exploratory interest with low data-rate Neighbors update interest-cache and forwards it Flooding Geographic routing Use cached data to direct interests Gradient establishment Gradient set up to upstream neighbor Low data-rate gradient Few packets per unit time needed

Gradient Set Up Inquirer (sink) broadcasts exploratory interest, i1
Intended to discover routes between source and sink Neighbors update interest-cache and forwards i1 Gradient for i1 set up to upstream neighbor No source routes Gradient – a weighted reverse link Low gradient  Few packets per unit time needed

Exploratory Gradient Exploratory Request Gradient Event
Low Data-rate Interest Low Data-rate Interest Low Data-rate Interest

Data Propagation A sensor node that detects a target
Search its interest cache Compute the highest requested data-rate among all its outgoing gradients Data message is unicast individually A node that receives a data message Find a matching interest entry in its cache Check the data cache for loop prevention Re-send the data to neighbors

Reinforcement (1/4) Positive reinforcement
Sink selects the neighboring node Original interest message but with high data-rate Neighboring node must also reinforce at least one neighbor Low-delay path is selected Exploratory gradients still exist: useful for faults Event Reinforced gradient Reinforced gradient Source A sensor field Sink

Reinforcement (2/4) Path failure and recovery
Link failure detected by reduced rate, data loss Choose next best link (i.e., compare links based on infrequent exploratory downloads) Negatively reinforce lossy link Either send interest with base (exploratory) data rate or allow neighbor’s cache to expire over time Link A-M lossy A reinforces B B reinforces C C reinforces D or A negative reinforces M M negative reinforces D Event D M Source A C B Sink

Reinforcement (3/4) Multipath routing Using negative reinforcement B
Consider each gradient’s link quality Using negative reinforcement Path Truncation Loop removal For resource saving Ex: B gets same data from both A and D, but D always delivers late due to looping B negative reinforces D, D negative reinforces E, E negative reinforces B Loop B→E →D →B eliminated Conservative negative reinforces useful for fault resilience Event Source A Sink Multiple paths D E A B C A removable loop

Design Considerations
Design Space for Diffusion Diffusion element Design Choices Interest Propagation Flooding Constrained or directional flooding based on location Directional propagation based on previously cached data Data Propagation Reinforcement to single path delivery Multipath delivery with selective quality along different paths Multipath delivery with probabilistic forwarding Data caching and aggregation For robust data delivery in the face of node failure For coordinated sensing and data reduction For directing interests Reinforcement Rules for deciding when to reinforce Rules for how many neighbors to reinforce Negative reinforcement mechanisms and rules 該表格記錄了四種 diffusion 的運作選擇機制 Interest Propagation 有三種運作機制

Directed Diffusion: Pros & Cons
Different from SPIN in terms of on-demand data querying mechanism Sink floods interests only if necessary (lots of energy savings) In SPIN, sensors advertise the availability of data Pros Data centric: All communications are neighbor to neighbor with no need for a node addressing mechanism Each node can do aggregation & caching Cons On-demand, query-driven: Inappropriate for applications requiring continuous data delivery, e.g., environmental monitoring Attribute-based naming scheme is application dependent For each application it should be defined a priori Extra processing overhead at sensor nodes

Conclusions Directed diffusion, a paradigm proposed for event monitoring sensor networks Directed Diffusion has some novel features - data-centric dissemination, reinforcement-based adaptation to the empirically best path, and in-network data aggregation and caching. Notion of gradient (exploratory and reinforced) Energy efficiency achievable Diffusion mechanism resilient to fault tolerance Conservative negative reinforcements proves useful Directed Diffusion是一個具有省能、強健性、規模性的感應器網路繞徑方法，並有一些新的特性如以資料為中心的傳播，加強基礎經驗最佳途徑，網路中暫存資料利用資料聚集技術傳送需求回應訊息到集點上，能達到感應器網路整體傳輸上省能與強健度的提升。

References C. Intanagonwiwat, R. Govindan, and D. Estrin, “Directed Diffusion: A Scalable and Robust Communication Paradigm for Sensor Networks,” in the Proceedings of the Sixth Annual International Conference on Mobile Computing and Networks (MobiCom’00), August 2000. C. Intanagonwiwat, R. Govindan, D. Estrin, J. Heidemann, and F. Silva, “Directed Diffusion for Wireless Sensor Networking,” IEEE/ACM Transactions on Networking, Vol. 11, No. 1, Feb

Chapter 4.3 Hierarchical Routing
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Overview In a hierarchical architecture, higher energy nodes can be used to process and send the information while low energy nodes can be used to perform the sensing of the target. Hierarchical routing is mainly two-layer routing where one layer is used to select cluster heads and the other layer is used for routing. Hierarchical routing (or cluster-based routing), e.g., LEACH, PEGASIS, TTDD, is an efficient way to lower energy consumption within a cluster and by performing data aggregation and fusion in order to decrease the number of transmitted messages to the base stations.

4.3.1 LEACH Low-Energy Adaptive Clustering Hierarchy

LEACH LEACH (Low-Energy Adaptive Clustering Hierarchy), a clustering-based protocol that minimizes energy dissipation in sensor networks. LEACH outperforms classical clustering algorithms by using adaptive clusters and rotating cluster-heads, allowing the energy requirements of the system to be distributed among all the sensors. LEACH is able to perform local computation in each cluster to reduce the amount of data that must be transmitted to the base station. LEACH uses a CDMA/TDMA MAC to reduce inter-cluster and intra-cluster collisions. LEACH是一個叢集式無線感測網路的路由協定，因此可以減低sensor network的能源消耗。 LEACH的方法優於傳統cluster algorithm，是因為在LEACH裡，每一個node選擇適當的 cluster 以及 cluster-head是採隨機的方式產生，輪流擔任，因此所有節點之消耗能量較能平衡，可提升網路存活期。在LEACH裡，除了 Cluster-Head 外，其它的node不會將偵測到的資料回傳到base station上，因此能減少回傳次數，所以降低能源的消耗。而LEACH在每一個cluster裡進行local computation，所以重複的資料經過Cluster-Head過濾之後，則會避免相同的資料送到BS，因此也降低電源的消耗。 60

LEACH (cont.) Sensors elect themselves to be local cluster-heads at any given time with a certain probability. Each sensor node joins a cluster-head that requires the minimum communication energy. Once all the nodes are organized into clusters, each cluster-head creates a transmission schedule for the nodes in its cluster. In order to balance the energy consumption, the cluster-head nodes are not fixed; rather, this position is self-elected at different time intervals. 每一個sensor會根據機率，在一個特定的時間，成為 cluster head，而 cluster head 也會廣播自己的狀態給其他的sensor知道。每一個sensor選擇自己要歸屬於哪一個cluster，根據sensor本身和cluster head溝通所需要的最低能量。當所有的node都組織成多個cluster時，cluster head就會對在cluster裡的每一個node(non-cluster-head node)排定schedule，用來決定傳輸的時間，如果node 不再傳輸時，就 turn off。等待cluster-head收集完所有node的data後，則將data傳送給BS。作為cluster head會消耗很多的電源，因此，為了將電源可以均衡分部在多個node共同使用，所以cluster head是不固定的，會根據不同的時間區間而有所變化。 61

LEACH: Adaptive Clustering
Periodic independent self-election Probabilistic CSMA MAC used to advertise Nodes select advertisement with strongest signal strength Dynamic TDMA cycles All nodes marked with a given symbol belong to the same cluster, and the cluster head nodes are marked with a ●.

Algorithm Periodic process Two phases per round: Setup phase
Advertisement: Execute election algorithm Members join to cluster Cluster-head broadcasts schedule Steady-State phase Data transmission to cluster-head using TDMA Cluster-head transfers data to BS (Base Station) LEACH主要分為三個步驟： Setup phase:此階段主要是cluster的形成和cluster head 的決定。 Steady-state phase:此階段主要是在描述data傳送到Base Station。 LEACH: Advertisement Cluster head self-election Status advertised broadcast to nearby nodes Non-cluster heads must listen to the medium Choose cluster head based on signal strength RSSI 64

Algorithm (cont.) Fixed-length cycle Setup phase Steady-state phase
Time slot 1 Time slot 2 Time slot 3 每個cluster中，分成setup phase和steady-state phase 在setup phase中 Advertisement phase內，每個node會決定自己是否可成為cluster head，同時在Cluster setup phase中，要成為cluster head的node會先採用CSMA機制確定頻道沒人使用後廣播自己要成為cluster head的資訊給其他人，在此cluster中的member收到此資訊後，回報自己的id給cluster head，讓cluster head可以瞭解整個cluster內的node數量，在之後的Broadcast schedule phase中，Cluster head會Broadcast CDMA code給members 之後進入Steady-state phase，讓每個node遵守TDMA傳送資料 Advertisement phase Cluster setup phase Broadcast schedule Self-election of cluster heads Cluster heads compete with CSMA Members compete with CSMA Cluster head Broadcast CDMA code to members 65 65

Algorithm Summary Set-up phase
Node n choosing a random number m between 0 and 1 If m < T(n) for node n, the node becomes a cluster-head where where P = the desired percentage of cluster heads (e.g., P= 0.05), r=the current round, and G is the set of nodes that have not been cluster-heads in the last 1/P rounds. Using this threshold, each node will be a cluster- head at some point within 1/P rounds. During round 0 (r=0), each node has a probability P of becoming a cluster-head. 每一個sensor在0到1之間選擇一個 random number m。就node n 而言，如果 m < T(n) ，則node n 就是cluster head。 P: 代表成為cluster head所需要的百分比。 r:目前的round。 round= Setup phase+Steady-state phase G: node在最後1 / P round 沒有成為cluster head的集合。 66

Algorithm Summary (cont.)
Set-up phase Cluster heads assign a TDMA schedule for their members where each node is assigned a time slot when it can transmit. Each cluster communications using different CDMA codes to reduce interference from nodes belonging to other clusters. TDMA intra-cluster CDMA inter-cluster Spreading codes determined randomly Broadcast during advertisement phase Cluster head ，使用CDMA MAC protocol和其鄰居通訊。 Cluster head加入TDMA schedule 到他的members裡，用來決定傳輸的時間，如果node 不再傳輸時，就 turn off。 67

Algorithm Summary (cont.)
Steady-state phase All source nodes send their data to their cluster heads Cluster heads perform data aggregation/fusion through local transmission Cluster heads send aggregated data back to the BS using a single direct transmission 所有的node傳送各自的data到cluster heads。 Cluster head 透過local transmission將data結合。等待cluster-head收集完所有node的data後，則將data傳送給BS。 68

An Example of a LEACH Network
While neither of these diagrams is the optimum scenario, the second is better because the cluster-heads are spaced out and the network is more properly sectioned Bad case scenario Good case scenario Node Cluster-Head Node Node that has been cluster-head in the last 1/P rounds Cluster Border X

Conclusions Advantages Increases the lifetime of the network
Even drain of energy Distributed, no global knowledge required Energy saving due to aggregation by CHs Disadvantages LEACH assumes all nodes can transmit with enough power to reach BS if necessary (e.g., elected as CHs) Each node should support both TDMA & CDMA Need to do time synchronization Nodes use single-hop communication 優點: 增加網路的lifetime。均勻的能源消耗。缺點: Highly dynamic environments會增加過多的overhead,例如:advertisements 等 Nodes use single-hop communication,所以不適用於大型的sensor network,因為如果cluster-head離BS非常遠, 這樣將會非常耗損能源 70

Reference W. Heinzelman, A. Chandrakasan, and H. Balakrishnan, “Energy-efficient communication protocol for wireless sensor networks,” Proceedings of the 33rd Hawaii International Conference on System Sciences, January 2000. 優點: 降低資料傳輸的總距離。提高每一個節點的壽命。缺點: 單一的leader會造成高度延遲。能源不均勻的消耗。( 因為只集中在 leader，當能源用完時，leader就會死掉，所以chain再重建，再重新選擇一個 leader。 ) 71

Chapter 4.4 Location Based Routing

Overview Sensor nodes are addressed by means of their locations.
The distance between neighboring nodes can be estimated on the basis of incoming signal strengths. Relative coordinates of neighboring nodes can be obtained by exchanging such information between neighbors. To save energy, some location based schemes demand that nodes should go to sleep if there is no activity. More energy savings can be obtained by having as many sleeping nodes in the network as possible. Hereby, two important location based routing protocols, GEAR and GPSR, are introduced. Geographical and Energy Aware Routing (GEAR) Greedy Perimeter Stateless Routing (GPSR)

4.4.1 GEAR Geographical and Energy Aware Routing

Geographical and Energy Aware Routing (GEAR)
The protocol, called Geographic and Energy Aware Routing (GEAR), uses energy aware and geographically-informed neighbor selection heuristics to route a packet towards the destination region. The key idea is to restrict the number of interests in directed diffusion by only considering a certain region rather than sending the interests to the whole network. By doing this, GEAR can conserve more energy than directed diffusion. The basic concept comprises of two main parts Route packets towards a target region through geographical and energy aware neighbor selection Disseminate the packet within the region GEAR主要分成兩部份， 1.封包要傳遞到目標區域時使用GEAR 2.當封包在目標區域時的散播

Energy Aware Neighbor Computation
Each node N maintains state h(N, R) which is called learned cost to region R, where R is the target region Each node infrequently updates neighbor of its cost When a node wants to send a packet, it checks the learned cost to that region of all its neighbors If a node does not have the learned cost of a neighbor to a region, the estimated cost is computed as follows: c(Ni, R) = αd(Ni, R) + (1-α)e(Ni) where α = tunable weight, from 0 to 1. d(Ni, R) = normalized the largest distance among neighbors of N e(Ni) = normalized the largest consumed energy among neighbors of N 每個節點N都會有所謂的到目標區域的learned cost，通常來說learned很少會更新如果一個node想傳送封包，他會檢查他傳輸範圍可及的所有鄰居的learned cost

Energy Aware Neighbor Computation (cont.)
When a node wants to forward a packet to a destination, it checks to see if it has any neighbor closer to destination than itself In case of multiple choices, it aims to minimize the learned cost h(Nmin, R) It then sets its own cost to: h(N, R) = h(Nmin, R) + c(N, Nmin) c(N, Nmin) = the transmission cost from N and Nmin 如果一個節點想傳送封包，他會找尋比自身離目標區域還近的鄰居來傳所以它會選learned cost最小的鄰居來傳並更改自己的learned cost為上述的公式其中c(N,Ni)是指自己傳輸到該鄰居的能量花費

Forwarding Around Holes
K L T C – T = 2 B – T = x x x F G H I J h(C,T) = h(B,T)+c(C,B) A B C D E 假設預傳輸的的點為S，目標為T，GHI為壞的節點 S會尋找附近Learned cost最小的鄰居C， Learned cost為1，並將傳遞路線指向C， C找尋GHI為Learned cost最小的點(與自身比較)，但GIH為死點，這時它會找尋一個S可到達，自身也可到達， Learned cost最小的點例如B，同時改變自身的Learned cost為B的Learned cost+C到B的cost，以此類推直到到達目標區域為止 S α is set to 1. Initially, at time 0, at node S, among all neighbors of S, B, C, D are closer to T than S. h(B,T)=c(B,T)= , h(C,T)=c(C,T)=2, h(D,T)=c(D,T)= After that , h(C, T) = h(B, T) + 1

Recursive Geographic Forwarding
Once the target region is reached, the packets are disseminated within the region by recursive geographic forwarding Forwarding stops when a node is the only one in a sub-region 當封包在目標區域時，他會使用RGF法來通知目標區域裡的點 RGF會遞迴切出小範圍的區域做傳輸，直到每個小區域都只有一個節點為止圖中假設Ni點是在目標區域，他會切出4個小區域，並向這四個區域傳輸(論文是假設切4個，切割數可以依據情況調整) 4個區域的點再執行上述步驟直到每個子區只有一個節點為止 Ni

Recursive Geographic Forwarding (cont.)
When network density is low recursive geographic forwarding is subject to two pathologies: inefficient transmissions and non-termination RGF存在的一個問題，在節點密度很低時，他有時會比Restricted flooding還沒效率，例如這張圖他比Restricted flooding還多消耗的一個能源

Recursive Geographic Forwarding (cont.)
Inefficient Transmission Recursive geographic forwarding vs. Restricted flooding Restricted flooding 1 time for sending and 4 times for receiving = consuming 5 units of energy A Recursive Geographic Forwarding 3 times for sending and 3 times for receiving = consuming 6 units of energy C RGF存在的一個問題，在節點密度很低時，他有時會比Restricted flooding還沒效率，例如這張圖他比Restricted flooding還多消耗的一個能源 E B D F

Recursive Geographic Forwarding (cont.) Pathologies
Non-Termination In the recursive geographic forwarding protocol, packet forwarding terminates when the target subregion is empty. K E H RGF存在再另一個問題，在節點密度很低時，他會出現不會中止的情況如上圖C的傳輸範圍無法到達H，那麼他將無法判定藍色區塊是否為空的區域或是有節點，於是他將會不停止RGF計算 Non-termination In the recursive geographic forwarding protocol, packet forwarding terminates when the target subregion is empty. The following heuristic is used to determine if the target sub-region is empty: if the farthest point of the region is within its transmission range, but none of its neighbors are inside the region, the region is considered empty. However, this strategy does not work when the network density is low compared to the (sub)target region size. When density is low the probability of the target region being empty is high, and the transmission range is small compared to the target region size so that the node that is closest to the target region can not reach the other end of the target region. As a result, the nodes outside the target region have no indication that the region is empty, hence the packet still searches for routes to get into the empty region. The search will not stop until the number of hops traversed exceeds the packet’s B A C L F

Recursive Geographic Forwarding (cont
Recursive Geographic Forwarding (cont.) Proposed solution for pathologies Solution: Node degree is used as a criteria to differentiate low density networks from high density ones Choice of restricted flooding over recursive geographic forwarding if the receiver’s node degree is below a threshold value 解決RGF上述問題的方式就是設立一個界線，當節點密路很低時則使用Restricted flooding 反之則使用RGF

Conclusion GEAR strategy attempts to balance energy consumption and thereby increase network lifetime GEAR performs better in terms of connectivity after initial partition GEAR平衡了能源消耗以增強網路的生命週期 GEAR在分區初始化之後會有更好的連結性

References Y. Yu, D. Estrin, and R. Govindan, “Geographical and Energy-Aware Routing: A Recursive Data Dissemination Protocol for Wireless Sensor Networks”, UCLA Computer Science Department Technical Report, UCLA-CSD TR , May 2001. Nirupama Bulusu, John Heidemann, and Deborah Estrin. “Gps-less low cost outdoor localization for very small devices”. IEEE Personal Communications Magazine, 7(5):28-34, October 2000. L. Girod and D. Estrin. “Robust range estimation using acoustic and multimodal sensing”. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2001), Maui, Hawaii, October Nissanka B. Priyantha, Anit Chakraborty, and Hari Balakrishnan. “The cricket location-support system”. In Proc. ACM Mobicom, Boston, MA, 2000. Andreas Savvides, Chih-Chieh Han, and Mani B. Strivastava. “Dynamic fine-grained localization in adhoc networks of sensors”. In Proc. ACM Mobicom, 2001.

4.4.2 GPSR Greedy Perimeter Stateless Routing

Greedy Perimeter Stateless Routing (GPSR)
Greedy Perimeter Stateless Routing (GPSR) proposes the aggressive use of geography to achieve scalability GEAR was compared to a similar non-energy-aware routing protocol GPSR, which is one of the earlier works in geographic routing that uses planar graphs to solve the problem of holes In case of GPSR, the packets follow the perimeter of the planar graph to find their routes Although the GPSR approach reduces the number of states a node should keep, it has been designed for general mobile ad hoc networks and requires a location service to map locations and node identifiers. 本文的無線路由協議中提出主動使用地理位置資訊來達到可擴展性的貪婪邊界無狀態路由算法（GPSR）。我們以在網路中的節點和移動性不斷增加的情況下的可擴展性為目標。

Algorithm & Example The algorithm consists of two methods:
greedy forwarding + perimeter forwarding Greedy forwarding, which is used wherever possible, and perimeter forwarding, which is used in the regions greedy forwarding cannot be done. GPSR = greedy forwarding + perimeter forwarding 綜合體， GPSR會優先使用greedy forwarding，如果greedy forwarding不行則改用perimeter forwarding

Greedy Forwarding (cont.)
Under GPSR, packets are marked by their originator with their destinations’ locations As a result, a forwarding node can make a locally optimal, greedy choice in choosing a packet’s next hop Specifically, if a node knows its radio neighbors’ positions, the locally optimal choice of next hop is the neighbor geographically closest to the packet’s destination Forwarding in this scheme follows successively closer geographic hops, until the destination is reached GPSR中，封包包由它們的源來標記它們的目的節。這樣，轉發節點可以做一個局部最佳化，如果一個節點知道它的鄰居節點的位置，在選擇封包的NEXT-HOP時使用貪婪轉發。就是，如果一個節點知道它的鄰居節點的位置，局部最優選擇就是選取地理位置最接近封包目的地的節點。這種體制下的轉發在地理上不斷的接近目的節點直到到達

x 從圖中可以看出Y是X可傳輸範圍裡離D最近的節點，於是X就把資料傳給Y y

A simple beaconing algorithm provides all nodes with their neighbors’ positions: periodically, each node transmits a beacon to broadcast MAC address, containing its own identifier (e.g., IP address) and position Position is encoded as two four-byte floating point quantities, for x and y coordinate values Upon not receiving a beacon from a neighbor for longer than timeout interval T, a GPSR router assumes that the neighbor has failed or gone out-of-range, and deletes the neighbor from its neighbor table 一個簡單的beacon算法為所有節點提供它們的鄰居節點位置：每個節點週期性的傳送一個beacon到MAC廣播地址。我們將位置編碼成兩個4-byte浮點數，標記x和y的坐標。為了避免鄰居節點beacon的碰撞，我們以beacon間的時間間隔B的50%抖動發送每個beacon ，這就意味著beacon間的傳遞間隔是B，統一分佈在[0.5B,1.5B]。在超過T時間間隔沒有接收到一個鄰居的beacon後，GPSR路由節點就認為鄰居節點已失效或是超出了覆蓋範圍，並且從表中刪除該鄰居節點。我們使用T=4.5B，三倍於最大的抖動beacon間隔。

Greedy Forwarding (cont.) The Problem of Greedy Forwarding
|xD|<|wD|and|yD| x will not choose to forward to w or y using greedy forwarding v z void 貪婪路由法面臨的問題如圖假設x傳輸範圍裡能傳輸給w，y，但x發現Dw，Dy的距離都比Dx要小，因此Dx就不會傳輸但實際上只要經由w或y都能傳給目標點D 這樣我們稱為一個空洞(void) w y x x x

The Right-Hand Rule: Perimeters
In previous works, use the right-hand rule to map perimeters by sending packets on tours of them. The state accumulated in these packets is cached by nodes, which recover from local maxima in greedy forwarding by routing to a node on a cached perimeter closer to the destination. This approach requires a heuristic, the no-crossing heuristic, to force the right-hand rule to find perimeters that enclose voids in regions where edges of the graph cross 為了解決void問題我們使用右手法則在路徑上發送封包來映射邊緣，這些封包堆積的狀態被節點暫存，這樣在貪婪轉發達到本地極點時，在暫存的邊緣中找到更接近目的節點的節點。這種方法需要一種不交叉的啟發式方法，使得右手法則找到將圖中有交叉邊的空洞包括起來的區域。這種啟發式方法提高了總體的可達到性，但是仍然留下了一個嚴重的缺陷：該算法在路徑存在時並不是總能找到它。這種不交叉的啟發式方法盲目地刪除了與一對交叉邊相交的邊。然而刪除這種邊之後，可能會分隔網路，如果它這樣做，將找不到跨越分隔開區域的路徑。假設以y為出發點，使用右手法則逆時針轉一圈找到x，x可以找到z，z可以找到y x z y

Right-Hand Rule Does Not Work with Cross Edges
z u D x originates a packet to u Right-hand rule results in the tour x-u-z-w-u-x w x

Remove Crossing Edge Make the graph planar Remove (w,z) from the graph
u v Make the graph planar Remove (w,z) from the graph Right-hand rule results in the tour x-u-z-v-x w x

Make a Graph Planar A graph in which no two edges cross is known as planar. A set of nodes with radios, where all radios have identical, circular radio range r, can be seen as a graph: each node is a vertex, and edge (n, m) exists between nodes n and m if the distance between n and m, d(n, m)≦r. Convert a connectivity graph to planar non-crossing graph by removing “bad” edges Ensure the original graph will not be disconnected Two types of planar graphs: Relative Neighborhood Graph (RNG) Gabriel Graph (GG)

Planarized Graphs (cont.) Relative Neighborhood Graph (RNG)
w u v 當我們從單元圖開始從圖中移除不屬於RNG的邊時，注意我們不能破壞圖的連通性。只有在u和v的覆蓋範圍中都存在同一個節點w時才會從圖中去除邊（u,v）。因此，刪除一條邊需要存在一條通過witness節點的替代路徑。在之前描述的機制下，儘管所有節點知道它們的即時鄰居，如果u和v彼此可達，其半月型區域中有其他節點，則刪除(u,v)邊從一個包含全部鄰居的表N開始，每個節點u可以採用此方法移除非RNG鏈接：

Planarized Graphs (cont.) Gabriel Graph (GG)
w u v 頂點u和v之間存在邊(u,v)，如果有其它頂點w出現在以d(u,v)為直徑的圓中，則刪除(u.v)邊 u,v之間的中點是以d(u,v)為直徑的圓的圓心。節點u可以採用如下方法來從一個包含全部鄰居節點的表N中移除非GG鏈接：

Planarized Graphs (cont.)
An algorithm for removing edges from the graph that are not part of the RNG or GG would yield a network with no crossing links The RNG is a subset of the GG It is because RNG removes more edges Hereby, the RNG is used If the original graph is connected, RNG is also connected

Connectedness of RNG Graph
Key observation Any edge on the minimum spanning tree of the original graph is not removed Proof by contradiction: Assume (u,v) is such an edge but removed in RNG u v w

Planarized Graphs (cont.)
Relative Neighborhood Graph (RNG) Original Gabriel Graph (GG) The full graph of a radio network, 200 nodes, uniformly randomly placed on a 2000 x 2000 meter region, with a radio range of 250 m. The GG subset of the full graph The RNG subset of the full and GG graphs.

Combining Greedy and Planar Perimeters
All data packets are marked initially at their originators as greedy mode GPSR packet headers include a flag field indicating whether the packet is in greedy mode or perimeter mode Packet sources also include the geographic location of the destination in packets Only a packet’s source sets the location destination field, it is left unchanged as the packet is forwarded through the network Upon receiving a greedy-mode packet for forwarding, a node searches its neighbor table for the neighbor geographically closest to the packet’s destination When no neighbor is closer, the node marks the packet into perimeter mode 所有產生的封包初始標記為貪婪模式。 GPSR標頭包含一個標誌區域來表明該封包是在貪婪模式還是在邊緣模式下轉送。封包的來源節點還在封包中加入了目的節點的地理位置。只有封包的來源節點可以設置目的地址區域，封包在網路中轉發的時候它維持不變。

GPSR Greedy Forwarding Perimeter Forwarding greedy fails
have left local maxima greedy works

Combining Greedy and Planar Perimeters (cont.)
Lp Lf e0 D x If forwarding node to D < Lp to D, returns a packet to greedy mode 假設X是greedy forwarding轉換perimeter forwarding的點，D為終點， Lf為邊與LP-D形成的的直線交叉的點，當每移動一次會檢查下一點到D的距離是否比LP到D近，如果比較近會切回greedy mode 而perimeter forwarding則是以LF交叉的邊為起點以右手法則旋轉找到的邊為傳遞路徑以此類推直到達到D為止

Conclusion GPSR’s benefits all stem from geographic routing’s use of only immediate-neighbor information in forwarding decisions. GPSR keeps state proportional to the number of its neighbors, while both traffic sources and intermediate DSR routers cache state proportional to the product of the number of routes learned and route length in hops. GPSR生成的路由協議流量獨立於路徑穿越網路的長度，因此隨著網路移動性的增加，生成了一個固定較小流量的路由協議消息，也不會受到尋路方面的降低的健壯性的影響。

References B. Karp and H. T. Kung, “Greedy Perimeter Stateless Routing for Wireless Networks”, Proc. 6th Annual ACM/IEEE Int'l. Conf. Mobile Comp. Net., Boston, MA, pp , August G. G. Finn, “Routing and addressing problems in large metropolitan-scale internetworks”, Tech. Rep. ISI/RR , Information Sciences Institute, March 1987. S. Floyd and V. Jacoboson, “The synchronization of periodic routing messages”, IEEE/ACM Transactions on Networking, Vol. 2, pp , April 1994. B. Karp “Greedy perimeter state routing”, Invited Seminar at the USC/Information Sciences Institute, July 1998. J. Saltzer, D. P. Reed, and D. Clark, “End-to-end arguments in system design”, ACM Transactions on Computer Systems, Vol. 2, No. 4, Pages: , November 1984.

Chapter 4.5 QoS Based Routing

Overview QoS is the performance level of service offered by a network to the user. The goal of QoS is to achieve a more deterministic network behavior so that the information carried by the network can be better delivered and the resources can be better utilized. In QoS-based routing protocols, the network has to balance between energy consumption and data quality. In particular, the network has to satisfy certain QoS metrics, e.g., delay, energy, bandwidth, etc. when delivering data to the BS.

Parameters of QoS Networks
Different services require different QoS parameters Multimedia Bandwidth, delay jitter & delay Emergency services Network availability Group communications Battery life Generally the parameters that are important are: bandwidth delay jitter battery charge processing power buffer space delay jitter: packet delay variation

Challenges in QoS Routing
Dynamically varying network topology Imprecise state information Lack of central coordination Hidden node problem Limited resource Insecure medium

4.5.1 TBP (Ticket-Based Probing) QoS of Bandwidth

Ticket-Based Probing Distributed multi path QoS routing scheme
Bandwidth-constrained routing and delay-constrained routing There are numerous paths from source to destination, we shall not randomly pick several paths to search We shall not use any flooding path-discovery approaches, which may send routing messages to the entire network Multipath search is tolerant to imprecise information We want to make an intelligent hop-by-hop path selection to guide the search along the best candidate paths 從Source端到destination有很多的路徑，但隨機選擇某一條路徑再來判斷是否適合，不是很好的作法，但我們也不想要使用Flooding的方法，因為大量的廣播對網路來說只會使得負擔更加吃緊，所以換個方式，我們想要找出一種hop by hop的方式，來找出最適合的路徑。

Ticket-Based Probing (cont.)
因為MANET 的特性在於每一個mobile host 可以任意的移動，整個網路拓蹼一直在變更，所以很多研究致力於在MANET 上的繞徑協定。這些研究大多提到：如何找出一條從發源端到目的端的路徑，如何維持這段路徑，以及如何傳送封包到目的地。然而，上面的方法在找尋路徑時，只專注於這是不是最短的路徑，所以資料傳輸量最大只能依照現在找尋到的路徑，所能提供的傳輸量為準。因此，關於品質服務(QoS)的需求，如一些有時間限制及頻寬要求的應用時，比較少有著墨。

A ticket is the permission to search one path. The source node issues a number of tickets based on the available state information Utilizes tickets to limit the number of paths searched during route discovery A ticket is the permission to search a single path More tickets, more QoS constraints are required Probes (routing messages) are sent from the source toward the destination to search for a low-cost path that satisfies the QoS requirement Each probe is required to carry at least one ticket 所以在這邊就提出了Ticket-Based的Routing，最基本的想法是從來源端發出一些ticket ，每一張ticket 負責找出從來源端到目的端符合頻寬要求的路徑。每一張ticket 在遇到只走一條Link 頻寬不足時，可以被分割成sub-ticket ，分送到不同的link，用來幫助找出足夠頻寬的多重路徑(multi-path)。在來源端時就會決定發出的ticket 張數並限制ticket 張數，原因是為了避免造成flooding，因為在MANET 的環境下，可用的資源已經有限，如果再利用flooding 作繞徑會嚴重地增加網路的負擔。而發出的ticket 張數與網路狀態及品質服務要求大小有關。假如網路狀態很差可是品質服務要求很高的話，這時候來源端會決定多發出幾張ticket，才可以增加找到路徑的機率。如果網路的狀態很好，可是品質服務要求很低的話，則來源端會決定發出較少的ticket 張數，因為成功的機率本來就很大。

j 以此圖來看的話，從S到D有很多條路徑可以選擇，但我們不會隨機的去選取幾條路徑來開始作搜尋的動作，效率差。也不傾向利用Flooding的方式來作地毯式的路徑搜尋，而是利用hop-by-hop的方式，來選擇並且建立最適當的路徑（以QoS為前提的條件下) P3(3) P3(3) k

Demand = 3 3 3 S x D C 3 2 5 3 2 2 雖然CB、CE 的頻寬為2+2=4 大於頻寬要求3，但是因為ticket 的方法是uni-path，所以即使只要聯合兩條link 就能滿足頻寬所需求，但是由於只能選擇其中一條link，所以造成失敗的結果 6 E B

Demand = 4 (1.1,3) (1.1,3) 3 3 S D C 3 2 5 2 (1.2,1) (1.2,1) 2 2 來源端的頻寬需求為4，但是SA、SB 的頻寬各為3、3，在一條link 不能滿足頻寬需求的情況下，我們將ticket 切割成兩張sub-tickets，各自的頻寬需求為3、1，因為可以很成功的將頻寬需求為3 的sub-ticket 從S 經A 送達D，將頻寬需求為1 的sub-ticket 由S 經B、E 送達D。 6 E B (1.2,1)

Demand = 4 3 3 S D (1.1,3) (1.1.1,2) C 3 2 (1.1.2,1) 5 2 (1.1.2,1) (1.2,1) 2 2 當頻寬不足時，ticket 會一直不停地做分割來達到ticket 上的頻寬需求。而每一張分割後的sub-ticket 都有自己的ticket ID 來區別不同的ticket。 (1.2,1) 6 E B (1.2,1)

x T1 和T2 沒有任何關係 T1 和T2 屬於兄弟的關係 T2 是T1 的sub-ticket （會產生loop，這個時候就要把這種情況避免掉）

Demand = 4 x (1,4) 3 4 S D (2.1,3) C 3 2 x 5 2 (2.1,3) (2.1,3) 4 3 S-A-D 一開始找SA（因為頻寬最大），但下一步卻發現AD不適用，所以退回A重新找路徑。此時Ticket分成兩個（藍、紫），但是在C-B-S的時候，形成Loop，所以藍色線段只能走C-B-E-D (2.2,1) (2.2,1) 6 (2.1,3) E B (2.2,1)

Conclusion The routing overhead is controlled by the number of tickets, which allows the dynamic tradeoff between the overhead and the routing performance. Issuing more tickets means searching more paths, which results in a better chance of finding a feasible path at the cost of higher overhead. A distributed routing process is used to avoid any centralized path computation that could be very expensive for QoS routing in large networks. This approach not only increases the chance of success but also improves the ability to tolerate the information imprecision because the intermediate nodes may gradually correct a wrong decision made by the source.

Conclusion (cont.) Ticket-based probing scheme achieves a balance between the single-path routing algorithms and the flooding algorithms. It does multipath routing without flooding. The basic idea is to achieve a near-optimal performance with modest overhead by using a limited number of tickets and making intelligent hop-by- hop path selection. Ticket-based的方法在Flooding跟單一路徑的方法之中，算是取得兩者優點，同時在成本消耗方面，也不會損失太多，因為有效的利用頻寬，讓整體網路的傳輸品質提高。

References S. Chen and K. Nahrstedt, “On finding multi-constrained paths,” in Proc. IEEE ICC’98, pp R. Guerin and A. Orda, “QoS-based routing in networks with inaccurate information: Theory and algorithms,” in Proc. IEEE INFOCOM’97, Japan, pp Q. Ma and P. Steenkiste, “Quality-of-service routing with performance guarantees,” in Proc. 4th Int. IFIP Workshop Quality of Service, May 1997, pp Z. Wang and J. Crowcroft, “QoS routing for supporting resource reservation,” IEEE J. Select. Areas Commun., Sept S. Chen and K Nahrstedt, “Distributed Quality-of-Service Routing in Ad Hoc Networks,” IEEE J. Select. Areas Commun, vol.17, no. 8, pp , Aug S. Chen and K Nahrstedt, “Distributed Quality-of-Service Routing in Ad Hoc Networks,” IEEE J. Select. Areas Commun, vol.17, no. 8, pp , Aug

References T. Hea, J. A Stankovic, C. Lu, and T. Abdelzaher, “SPEED: a stateless protocol for real-time communication in sensor networks,” in Proc. IEEE International Conference on Distributed Computing Systems, pp , May 2003. G. S. Ahn, A. T. Campbell, A. Veres, and L.H. Sun. “SWAN: Service Differentiation in Stateless Wireless Ad Hoc Networks,” In Proc. IEEE INFOCOM'2002, June 2002. T. Hea, J. A Stankovic, C. Lu, and T. Abdelzaher, “SPEED: a stateless protocol for real-time communication in sensor networks,” in Proc. IEEE International Conference on Distributed Computing Systems, pp , May 2003.

Chapter 4 Routing Protocols

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