DTN(Delay Tolerant Networks)是一種缺乏網路基礎設備的網路架構，在這類的網路架構下，無線節點之間的通訊連線並非同時存在，而是間歇式建立的。因為節點的移動、或是裝置省電模式運作與環境因素的影響，造成連線可能不定時的失效。有不保證連線特性的網路，在DTN網路中節點間相遇的機會很少，節點間利用 Store-Carry-Forward 的方式傳遞訊息，且有相當長的傳遞延遲(Propagation Delay)。在這樣的網路特性下，傳統的文獻中，都是假設所有節點都會幫忙傳遞。但不幸的，在現實生活中有自私節點(Selfish Nodes)的存在，因自己本身設備資源有限，如電力資源、網路資源...等，節點不願意幫忙傳送訊息，這些自私節點的存在，會對DTN網路架構造成破壞，導致無法傳送訊息到目的地。為了解決自私節點的問題，我們提出了MTBIS(Mobile Trusted Bank of Incentive Strategies)，當發送節點(Source Node)要求傳送訊息時，給予回饋給幫忙轉傳訊息的節點，來鼓勵節點間互相幫忙傳送訊息，我們稱這些回饋為Incentive Credit。而節點也可利用Incentive Credit來要求別的節點幫忙轉傳訊息。另外也加入SI(Social Incentive)機制，與DGT(Dynamic Grudger Threshold) ，吸引自私節點願意幫忙轉傳訊息，改進了 MTBIS 在自私節點環境下的不足， 利用經濟學的角度來解決節點運用Credit的問題，借此提高訊息的傳遞率(Delivery Ratio)。 本篇研究也注重於自私節點的模擬，利用四種不同特性的節點： Sucker(傻瓜)、Cheater (騙子)、Grudger(小氣鬼)、Ecci(投機者)， 這些自私節點會照成DTN在設計上無法使用，甚至降低訊息的傳達率，因此我們模擬了這些自私節點的行為，並且使用我們所提出的獎勵機制，來解決這些自私節點的問題，實驗結果也證明，Grudger可以有效的抑制自私節點對效能大幅降低的問題，與傳統的演算法相較，效能高出34%。 / DTN (Delay Tolerant Networks) is a network structure without need to use any infrastructure. In DTNs, wireless connections between nodes do not always exist, i.e., nodes are connected intermittently. Due to the mobility, power issues, or surrounding environment of nodes, connections between nodes may be disruptive occasionally or randomly. In a DTN, nodes usually transfer the message to the encountering nodes. By this way, the messages are stored, carried, and forwarded to the next nodes, possibly the destination. However, in reality, nodes may not be willing to help each other for the message forwarding. More specifically, there are “Selfish nodes” which refuse to forward messages due to issues such as energy and network bandwidth. Thus it will prevent messages from being forwarded to its destination. In order to solve the problem of message forwarding failure caused by selfish nodes, we proposed an Incentive Strategy called “MTBIS (Mobile Trusted Bank and Incentive Strategies)”. We construct a rewarding scheme called “Incentive Credit” for nodes who forward the messages for the source node. In addition, to increase the intention of the selfish nodes for forwarding messages, we add two more mechanism called SI(Social Incentive) and DGT(Dynamic Grudger Threshold). The DGT uses credits to solve selfish node problems from an economics point of view to enhance MTBIS to work with existence of selfish nodes. In this thesis, we emphasis on the simulation of the behaviors of selfish nodes, using four different types of nodes: the “Sucker”, the “Cheater”, the “Grudger”, and the “Ecci”. These selfish nodes will decrease the success rate of message forwarding, and even make the DTN unusable. We simulated the behaviors of these selfish nodes, using the rewarding scheme we proposed. From the results of our experiment, we see that the Grudger can effectively deal with performance issues caused by selfish nodes, and the system can gain 34% in performance compared to the traditional algorithms.

獎勵系統是使用獎勵去激勵顧客購買或忠誠度行為的一種手法。隨著電子平台服務的演進，具動態與成本效益的電子獎勵機制已發展成增加重複拜訪率以及留住顧客的工具策略，例如:Facebook發行虛擬貨幣去提高使用者的平台參與度; Dropbox使用免費的儲存空間去吸引和保留新舊顧客; Myspace使用虛擬勳章去鼓勵使用者達成平台設定的參與目標等等。並且電子平台上的獎勵的形式可從實體的現金獎品到虛擬的勳章以滿足個人勝任感，有別於傳統獎勵方式，然而在電子平台不斷的創新下，目前仍少有研究針對電子獎勵進行探討。本研究提出電子獎勵分類法，並透過獎勵的動機形式與獎勵的互動程度為分析準則，針對1000網站排名進行內容分析，進而劃分出四種類型的電子獎勵機制: 物質、名聲、個人滿足和社交類型，後續並針對每一種類型的電子獎勵提出平台管理上的洞見。本研究期望能夠提供給後續研究電子獎勵者研究者的參考基礎和平台經營者實作電子獎勵的建議。 / A reward system is a diverse and popular program implemented by many firms in various circumstances to motivate customers’ behavior for frequent and loyal patronage. With the evolving diversity of electronic platforms (e-platforms), more dynamic, cost-effective, and interactive electronic rewards (e-rewards) have been developed as a tool or strategy to retain customers and encourage frequent visits to the platform. For instance, Facebook uses virtual currency to gain user participation. Dropbox uses bonus storage to increase membership and prolong service usage. Myspace uses virtual badges to encourage achievement of specific goals in order to encourage frequent visits. The rewards on e-platforms can range from real items such as cash and discounts, to virtual recognition that enhances self-esteem. These strategies differ from those used by enterprises dealing with face-to-face customers. As variant services on e-platforms have emerged, little research has been done to understand the novel concept and effects of reward systems on e-platforms. Therefore, we propose a taxonomy of e-rewards based on forms of motivation and degree of interactivity. Using content analysis, we examined the world’s 1,000 most accessed Web sites, then, we conducted expert interviews to classifying the 228 e-rewards approaches, discovered from content reviews, into four types of e-rewards: material, prestige, gratification, and affinity. Finally, we propose practical recommendations with insights on the implementation of each type of e-reward systems on e-platform services. We hope that the findings will provide a basis for further study on various impacts of reward systems, and provide a guide for managers in designing and implementing more effective reward systems on e-platforms.