Routing Algorithm Based on Trajectory Prediction in Opportunistic Networks

Due to dynamic changes in network topology and constant changes in links between satellites, the routing paths calculated by the traditional shortest multipath routing algorithm are not updated in time, resulting in that the problematic paths are still transmitting data, so a large number of problems such as packet loss and service failure exist in the network. This paper designs a multipath routing algorithm for the satellite network topology which changes frequently, constrained multipath routing algorithm (CMRA). By calculating all feasible path of the entire network topology, CMRA will choose the multiple paths which satisfy the constraint conditions. After that, it will choose the lowest cost value of these paths. Repeatedly performing these operations, select multiple high-quality paths which satisfy the constraint conditions for traffic. Simulation results show that compared with the traditional shortest multipath routing algorithm and single path routing algorithm, the proposed routing algorithm is better in packet loss rate and average time delay.

In certain extreme environments where infrastructure such as base stations is absent, connections between nodes can be intermittent. Traditional networks struggle to function in these conditions. Delay Tolerant Network (DTN) are a network architecture designed for such scenarios. Utilizing Unmanned Aerial Vehicle (UAV) as nodes within DTN can enhance the network’s transmission capabilities and compensate for the deficiencies of ground nodes. However, previous routing methods have shortcomings. Given the high speed and greater freedom of movement of UAVs compared to ground nodes, the network topology is subject to constant and significant change. Traditional routing methods, which typically rely on predetermined rules for selecting forwarding nodes, struggle to adapt to such variations. This paper introduces Graph Neural Network (GNN) into routing decisions for DTN to learn network structural information and assist with routing decisions. In this paper, we first model the routing problem within DTN, accounting for the dynamic changes in network topology and limited resources. Subsequently, we propose a GNN-based dynamic routing method. This method utilizes a graph neural network model that takes as input the feature s of nodes at the current moment and the network’s connectivity information to generate optimal forwarding nodes for each data packet. The proposed method has been implemented in the Opportunistic Networking Environment (ONE) simulator. Experimental results indicate that our method achieves higher delivery ratio, lower latency, and reduced overhead compared to traditional methods.

The maximum flow problem is a fundamental problem in graph theory and combinatorial optimization with a variety of important applications. Known distributed algorithms for this problem do not tolerate faults or adjust to dynamic changes in network topology. This paper presents the first distributed self-stabilizing algorithm for the maximum flow problem. Starting from an arbitrary state, the algorithm computes the maximum flow in a acyclic network in finitely many steps. Since the algorithm is self-stabilizing, it is inherently tolerant to transient faults and can automatically adjust to topology changes and to changes in other parameters of the problem. A slight modification of the original algorithm is also presented and it is conjectured that the new algorithm computes a maximum flow in arbitrary networks. >

Adaptive and low-cost resource synchronization based on data distribution service in high dynamic networks

Mobile ad hoc wireless network is a challenging system since all nodes operate on limited battery resource. Besides, the routing protocol has to be adapted to dynamic communication channels through multi-hop path selection due to mobile nodes and relays. Hence, it is necessary to make the multicast routing tree energy efficient and fault tolerance in a changing network topology. In the research, an approach is proposed to make reliable routing decisions with minimal energy consumption. In order to obtain the optimal solutions, a mathematical formulation and an optimization-based method with heuristics for routing strategy adjustment is proposed for jointly considering energy-efficient and dynamic changes in network topology. Simulation results show that the routing decision algorithm has more significant improvement than other traditional algorithms.

A-GR: A novel geographical routing protocol for AANETs

To solve the problem of low ratio success delivery, long latency caused by the rapid dynamic change of network topology, limited node buffer, large amount of data transmission and large user population density in delay-tolerant network (DTN). This paper propose a routing algorithm in DTN based on nodes’ sociality. In this paper, contact history was used to determine the node link quality; Markov model was used to predict the node position based on the nodes’ movement track; according to nodes’ social similarity to divide community, then calculated node betweenness centrality. Finally, the best forwarding nodes for message transmission were determined. Besides, to increase the ratio of successful delivery, message delivery probability in the relay node’s buffer was calculated. Experimental results show that compared with traditional routing algorithm, the proposed algorithm performs better on the delivery ratio and average end-to-end delay.

Due to many challenging issues in vehicular ad-hoc networks (VANETs), such as high mobility and network instability, this has led to insecurity and vulnerability to attacks. Due to dynamic network topology changes and frequent network re-configuration, security is a major target in VANET research domains. VANETs have gained significant attention in the current wireless network scenario, due to their exclusive characteristics which are different from other wireless networks such as rapid link failure and high vehicle mobility. In this are, the authors present a Secured and Safety Protocol for VANET (STVAN), as an intelligent Ad-Hoc On Demand Distance Vector (AODV)-based routing mechanism that prevents the Denial of Service attack (DoS) and improves the quality of service for secured communications in a VANET. In order to build a STVAN, the authors have considered a smart traffic environment in a smart city and introduced the concept of load balancing over VANET vehicles in a best effort manner. Simulation results reveal that the proposed STVAN accomplishes enhanced performance when compared with other similar protocols in terms of reduced delay, better packet delivery ratio, reasonable energy efficiency, increased network throughput and decreased data drop compared to other similar approach.

이동 애드-혹 네트워크는 물리적 위치 이동이 가능한 노트들에 의해 자체적으로 형성되는 시스템으로 동적 위상을 고려한 라우팅 프로토콜을 필요로 한다 본 논문에서는 IETT의 라우팅 프로토콜 DSR을 확장한 R-DSR이라 불리는 온-디멘드 다중경로 프로토콜을 제안한다. 제안된 프로토콜에서는 DSR에 의해 구축되는 단인 경로 상의 2-홉 간격의 모든 노드들에 대해 별도의 노드론 경유하는 추가 경로가 연결된다. 수학적 분석을 통하여 DSR 관련 연구 중 가장 대표적인 방법 중의 하나인 Das의 다중경로 프로토콜보다 더 높은 메시지 전달 확률을 제공함을 보였다. A mobile ad-hoc network is an autonomous system of mobile nodes which are free to move around randomly and organize themselves arbitrarily, hence a routing protocol that handles the dynamic network topology changes is required for the network. In this paper we present a multi -Path on-demand routing protocol called R-DSR (Robust Dynamic Source Routing), an extension to the existing IETF protocol DSR. The proposed protocol has it that every pair of 2-hop away nodes on the single route is additionally connected via an alternative node. Throughout mathematical analysis we show the proposed protocol reveals higher message delivery rate than that of the Das's multi-path protocol, currently known as one of the most typical approaches related to DSR.

Group team communication serving for collaboration-work of special members, is an important application of MANET. In which, multicast protocol is a key technique to the group team application, which must guarantee the performance requirements: adaptable to the dynamic change of network topology, timeliness, minimizing routing overhead etc. This paper focuses on the improvement of MAODV applied to group team communication. Considering the existence of special nodes and determining the optimal repair node, GT-MAODV is proposed. After discussing the problems of link repair process of MAODV, a link repair mechanism--GTR-MAODV which improves the successful repair rate effectively through distinguishing different multicast tree branches is proposed on the basis of GT-MAODV. Through analysis and simulation, it is showed that the GTR-MAODV provides better performance than MAODV and GT-MAODV on successful repair rate, packet delivery rate and end to end delay.

To solve the problem of the existing self-organizing network which mainly adopts unicast routing protocol and its narrow application, this paper proposes an improved load balancing-based non-coherent routing protocol - LIRP. This method can effectively avoid congestion node and path, make the network load equilibrium, and reduce the packet transmission end-to-end delay. It has a good adaptability with the dynamic change of network topology, which can shorten the routing reconstruction time caused by broken chains and improve the robustness of network and reliability of data transmission.

The paper proposed a wireless access authentication mechanism for unmanned systems, which can effectively solve the problem of secure access authentication under the dynamic change of network topology. The authentication process of cooperative authentication and the realization of secure access were designed to meet the application requirements of random access and anti-attack capabilities. The simulation results showed that the cooperative authentication mechanism not only ensures the security of the unmanned system, but also reduces the transmission overhead and the number of interactive authentications. The network access time is less than 30 milliseconds, and the success ratio of network access is close to 100%.

Robust PMU Placement in Branch Switching Power Networks for Minimum Variance State Estimation

Wireless Ad Hoc network routing algorithm is one of the core technologies of wireless Ad Hoc networks. Ad Hoc network is dynamic topology on characteristic, mainly reflected on the network node number and node movement, which lead to network topology changes, in order to study the Ad Hoc network size and topology changes influence on packet delivery, time delay and routing control, through experiment it is concluded that, the dynamic source routing protocol is an ideal choice.

Coding-Aware routing algorithm improves the performance of Network Coding (NC) by selecting the paths with more coding opportunities, thus increasing the throughput of network transmission. However, the Unmanned Aerial Vehicle (UAV) flight in Flying Ad-hoc Network (FANET) causes network topology changes and calculating coding opportunities is NP-hard complex problem, so the performance of traditional coding-aware routing is limited. We try to solve this problem using reinforcement learning and propose a deep reinforcement learning-based coding-aware routing (RLCAR) that maximizes throughput and coding opportunities. In RLCAR, we define new concepts of coding benefits, throughput and energy distribution, and we put these parameters into the routing metric (Reward function). We evaluate the performance of RLCAR and compare the throughput, packet delivery ratio (PDR), coding/decoding rate with other coding-aware routings. The results show that RLCAR acquires more coding opportunities and improves the throughput of FANET. And, RLCAR can cope with the dynamic changes of UAV topology.