Probabilistic Routing Protocol Using History Of Encounters And Transitivity Research Articles

Poisson's probability‐based Q‐Routing techniques for message forwarding in opportunistic networks

SummaryOpportunistic Networks (OppNets) are intermittently connected infrastructure less wireless networks. There is no continuous end‐to‐end connection between the sender and the receiver, and hence nodes follow a store‐carry‐forward mechanism. The routing algorithm is required to be adaptive to the changing topology of the network. In this work, Q‐Routing technique has been used with forwarding probability and incorporated using Poisson's probability for decision making and controlling transmission energy. The algorithm refines the forwarding decision of finding the next suitable hop by exploiting the characteristics of nodes such as daily routines, mobility pattern, etc. In simulations, the performance of PBQ‐Routing is compared with Q‐Routing, Epidemic Routing, PRoPHET (Probabilistic Routing Protocol using History of Encounters and Transitivity), and HBPR (History Based Prediction Routing) for OppNets. The use of Poisson's distribution improves the effectiveness of the probabilistic forwarding decision. The findings show that the delivery probability of PBQ‐Routing almost gets doubled and overhead ratio reduces to half in comparison with that of Q‐Routing when used in OppNets. PBQ‐Routing outperforms other Q‐Routing based algorithms over Poisson's distribution. As there is less replication in case of PBQ‐Routing, it also saves the transmission energy.

Drop Policies for DTN Routing Protocols with Delivery Probability Estimation

Delay Tolerant Networks (DTN) are characterized by a lack of end-to-end connectivity. As such, messages (called bundles) can be stored in buffers for a long time. Network congestion can result in poor delivery rates, as bundles are dropped before having a chance of reaching their destination. Some routing protocols, such as MaxProp and Probabilistic Routing Protocol using History of Encounters and Transitivity (PRoPHET), maintain estimations of delivery probabilities for each destination. In this paper, a new drop policy called Largest Bundle’s Hosts Deliverability (LBHD) is proposed that considers all the hosts that received a replica of the same bundle, and their respective delivery probability as estimated by a routing protocol. LBHD uses this additional information to better manage congestion. Simulation results show that LBHD consistently achieves the best delivery probability when paired with PRoPHET and compared with other drop policies proposed in the literature. Also, when paired with MaxProp, LBHD shows the most efficient performance among all the other state of the art policies considering performance metrics such as average delay, overhead ratio and bundle delivery rate. In addition, another drop policy called One Hop Delivery Estimation Drop (OHDED) is proposed. OHDED takes advantage of the encounter predictions of every node in the network stored in every node when using MaxProp. By accurately predicting the bundles that have the highest probability of being delivered directly or in two hops, the results show the best performance in delivery rate and overhead ratio in high congestion scenarios.

An intelligent approach in delay tolerant network routing

Delay tolerant networks (DTNs) are wireless intermittent networks. DTNs have different applications such as wildlife tracking, military, and space searching. Conventional mobile ad hoc network (MANET) routing protocols are not efficient in these networks because of intermittency. DTNs use store-carry-forward (SCF) for data transferring. In SCF, nodes store the messages and carry them until finding appropriate nodes for forwarding. Message replication greatly helps to improve the delivery ratio while increasing overhead. This paper examines the use of intelligent routing to choose nodes that have more probability to reach their destination. This will help to increase the message delivery ratio while reducing overhead. The proposed method, SADTN, uses simulated annealing (SA), which has shown successful results in finding global minimal, to find the next hop. Comparison of the proposed method to previously implemented methods such as epidemic routing (ER) and Probabilistic ROuting Protocol using History of Encounters and Transitivity (PROPHET), which are usually used for evaluating other methods, shows increasing message delivery ratio and decreasing overhead in SADTN. Overhead in SADTN has on average fallen to 0.01484 of ER and 0.02325 of PROPHET. This is a great advantage of SADTN.

효율적인 상황 인지 기회적 라우팅 프로토콜

Opportunistic routing is designed for an environment where there is no stable end-to-end routing path between source node and destination node, and messages are forwarded via intermittent contacts between nodes and routed using a store-carry-forward mechanism. In this paper, we consider PRoPHET(Probabilistic Routing Protocol using History of Encounters and Transitivity) protocol as a base opportunistic routing protocol and propose an efficient context-aware opportunistic routing protocol by using the context information of delivery predictability and node type, e.g., pedestrian, car, and tram. In the proposed protocol, the node types of sending node and receiving node are checked. Then, if either sending node or receiving node is tram, messages are forwarded by comparing the delivery predictability of receiving node with predefined delivery predictability thresholds depending on the combination of sending node and receiving node types. Otherwise, messages are forwarded if the delivery predictability of receiving node is higher than that of sending node, as defined in PRoPHET protocol. Finally, we analyze the performance of the proposed protocol from the aspect of delivery ratio, overhead ratio, and delivery latency. Simulation results show that the proposed protocol has better delivery ratio, overhead ratio, and delivery latency than PRoPHET protocol in most of the considered simulation environments.

Probabilistic Routing Based on Two-Hop Information in Delay/Disruption Tolerant Networks

We investigate an opportunistic routing protocol in delay/disruption tolerant networks (DTNs) where the end-to-end path between source and destination nodes may not exist for most of the time. Probabilistic routing protocol using history of encounters and transitivity (PRoPHET) is an efficient history-based routing protocol specifically proposed for DTNs, which only utilizes the delivery predictability of one-hop neighbors to make a decision for message forwarding. In order to further improve the message delivery rate and to reduce the average overhead of PRoPHET, in this paper we propose an improved probabilistic routing algorithm (IPRA), where the history information of contacts for the immediate encounter and two-hop neighbors has been jointly used to make an informed decision for message forwarding. Based on the Opportunistic Networking Environment (ONE) simulator, the performance of IPRA has been evaluated via extensive simulations. The results show that IPRA can significantly improve the average delivery rate while achieving a better or comparable performance with respect to average overhead, average delay, and total energy consumption compared with the existing algorithms.

Particle swarm optimization in intelligent routing of delay-tolerant network routing

Delay-tolerant networks (DTNs) are wireless partitioned networks. Because of intermittency, mobile ad hoc network (MANET) routing protocols are not efficient in DTNs. Wildlife tracking, vehicular networks, interplanetary networks, etc. are different applications of DTN. Regarding DTN applications, different parameters should be considered while designing DTN routing protocols. Message delivery ratio, message delivery delay, overhead, message drop, etc. are some important factors that are usually considered in routing algorithms. This paper proposes a method which tries to reduce overhead and message drop while increasing message delivery ratio. Choosing the appropriate number of message copies to distribute in the network is important. Few numbers of copies can lead to message drop. So, the message cannot be delivered to the destination. On the other hand, increasing the number of copies causes overhead increase in the network. The proposed algorithm uses particle swarm optimization (PSO) in intelligent choosing of number of message copies. Regarding message delivery ratio and network overhead, PSO greatly helps in finding the suitable number of copies. In order to evaluate our method, which is called PSODTN, we compared it with epidemic routing (ER) and probabilistic routing protocol using history of encounters and transitivity (PROPHET). PSODTN helps to reduce overhead, on average, 95.6% compared to ER and PROPHET. While reducing overhead, PSODTN message delivery ratio is on average 98%.

전장 환경에서 접촉 횟수 정보를 고려한 확률적 라우팅 기법

The network nodes in a tactical network moves continuously and due to the physical and electronic obstacles, the connections are not always available. Due to the frequent disconnections, it is hard to discover the path among the nodes in a DTN. According to PROPHET(Probabilistic Routing Protocol using History of Encounters and Transitivity), one of the most well-known DTN routing protocols, a DTN node determines whom to forward a packet according to the packet delivery probability. From the viewpoint of a node, the packet delivery probability of another node is degraded while the nodes are disconnected whereas it is improved when they encounter. In this paper, we enhance the algorithm estimating the packet probability by considering the encounter count as an additional parameter. Our algorithm prefers the node that encounters the destination more frequently in selecting the next hop toward the destination. We evaluated the performance of our algorithm by simulating military operations using a DTN-dedicated simulator. Through the simulations, we show that our proposed algorithm achieve higher packet delivery ratio with similar overhead compared with PROPHET.