Message trust‐based secure multipath routing protocol for opportunistic networks

Opportunistic networks (OppNets) are composed of wireless nodes opportunistically communicating with each other. These networks are designed to operate in a challenging environment characterized by high delay, intermittent connectivity, and no guarantee of fixed path between the sender and the destination nodes. One of the most vital issues in designing and maintaining practical networks over a time period is the security of the messages flowing in OppNets. This paper proposes a new method called message trust‐based secure multipath routing protocol (MT‐SMRP) for opportunistic networks. Various routing protocols such as ProPHet, Epidemic, and HiBOp, to name a few, have been proposed for OppNets, but none of these have applied a secure multipath routing technique. The proposed MT‐SMRP scheme relays the message to the destination through the disjoint paths, each applying a soft‐encryption technique to prevent message fabrication attacks. Simulations are conducted using the Haggle Infocom'06 real mobility data traces, showing that when time‐to‐live is varied, (1) the proposed MT‐SMRP scheme outperforms D‐MUST by 18.10%, 7.55%, 3.275%, respectively, in terms of delivery probability, messages dropped, and average latency; (2) it also outperforms SHBPR by 21.30%, 7.44%, and 4.85%, respectively, in terms of delivery probability, messages dropped, and average latency. Under similar performance metrics, the performance of MT‐SMRP is also shown to be better than that of D‐MUST and SHBPR when the buffer size (respondents. the message generation interval) is varied.     

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

Opportunistic 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.     

Routing protocol based on social characteristics for opportunistic networks简

Opportunistic networks are derived from delay tolerant networks, where mobile nodes have no end-to-end connections. Nodes are represented by people, which means that opportunistic networks can be regarded as social networks. Human mobility plays an important role in affecting the performance of forwarding protocols in social networks, furthermore, the trajectory of people＇s movements are driven by social characteristics. However, current routing protocols rely on simple mobility models, and rarely consider social characteristics. Considering two heterogeneous network models, an social opportunistic networks routing（SONR） was proposed which brings an adapted discrete Markov chain into nodes＇ mobility model and calculates the transition probability between successive status. Comparison was made between Spray, Wait and Epidemic protocol. Simulation show that SONR can improve performance on delivery ratio, delivery latency and network overhead, meanwhile. SONR approaches the performance of Epidemic routing.     

带有编码冗余控制的机会网络数据转发机制

网络编码方法能有效提高数据传输效率,但采用该方法的机会网络数据转发机制均未考虑传输中的编码数据迭代冗余副本控制问题。为了充分降低译码复杂度并减少迭代冗余副本,提出了相遇持续时间估计及编码机会感知方法,进而设计了带有传输容量估计的编码冗余控制数据转发机制。节点以分布式的方式感知数据编码机会,为不同扩散程度的数据确定相应的优先级,并根据传输容量估计结果完成转发控制,以充分利用节点间的相遇持续时间。结果表明,所提出机制有效地减少了网络中的冗余副本,使译码所需的数据量趋于理论上的最小值,改善了网络资源利用率。     

Neural network‐based routing protocol for opportunistic networks with intelligent water drop optimization

Opportunistic Networks (OppNets) is a system of wirelessly connected nodes in a varying network topology. Routing in OppNets is a challenge. To overcome the problem of routing, an intelligent dynamic strategy to select next best node for forwarding a message is required. This paper proposes an intelligent routing mechanism based on Intelligent Water Drop (IWD) Algorithm which is used in tandem with Neural Networks (NNs) as an optimization technique to solve the problem of routing in such networks. The nature–inspired IWD algorithm provides robustness, whereas the neural network base of the algorithm helps it to make intelligent routing decisions. The weights in the Neural Network model are calculated by IWD Algorithm using training data consisting of inputs that are characteristic parameters of nodes, such as buffer space, number of successful deliveries and energy levels along with transitive parameters such as delivery probabilities. The proposed protocol Intelligent Water Drop Neural Network (IWDNN) is compared with other protocols that use similar ideologies such as MLProph, K‐nearest neighbour classification based routing protocol (KNNR), Cognitive Routing Protocol for Opportunistic Network (CRPO), and Inheritance Inspired Context Aware Routing Protocol (IICAR), as well as the standard protocol Prophet. IWDNN is shown to outperform all other protocols with an average message delivery ratio of 60%, which is a significant improvement of over 10% in comparison to other similarly conceived algorithms. It has one of the lowest latency among the protocols studied, in a range of 3000 to 4000 s, and incurs comparably low overhead costs in the range of 15 to 30. The drop ratios are one of the lowest, staying near six and approaching zero as buffer size is increased. Average amount of time a message stayed in the buffer was the lowest, with a mean of 1600 s.     

An efficient weighted trust‐based malicious node detection scheme for wireless sensor networks

The aim of wireless sensor networks (WSNs) is to gather sensor data from a monitored environment. However, the collected or reported information might be falsified by faults or malicious nodes. Hence, identifying malicious nodes in an effective and timely manner is essential for the network to function properly and reliably. Maliciously behaving nodes are usually detected and isolated by reputation and trust‐based schemes before they can damage the network. In this paper, we propose an efficient weighted trust‐based malicious node detection (WT‐MND) scheme that can detect malicious nodes in a clustered WSN. The node behaviors are realistically treated by accounting for false‐positive and false‐negative instances. The simulation results confirm the timely identification and isolation of maliciously behaving nodes by the WT‐MND scheme. The effectiveness of the proposed scheme is afforded by the adaptive trust‐update process, which implicitly performs trust recovery of temporarily malfunctioning nodes and computes a different trust‐update factor for each node depending on its behavior. The proposed scheme is more effective and scalable than the related schemes in the literature, as evidenced by its higher detection ratio (DR) and lower misdetection ratio (MDR), which only slightly vary with the network's size. Moreover, the scheme sustains its efficient characteristics without significant power consumption overheads.     

Multiattribute‐based routing for lifetime maximization in opportunistic mobile social networks

With the advancement in wireless technology and portable devices, smart phones have become one of the belongings of human being. People share their data online, but it has limitations due to transmission range and mobility. Yet opportunistic mobile social network enables users to share data online even if there is no connected path between source and destination. The widespread use of mobile phones equipped with WiFi, Bluetooth, and several other components and contact opportunities among humans bridge the gap between internet available and nonavailable area. In this paper, we have proposed a new routing approach which utilizes both spatial and temporal attributes of user such as probability to meet a particular location and remaining intercontact time between two nodes to select better relay nodes. Generally, users visit different locations such as sports stadium and mall, with varying probability. Users with similar interest form group, and each user has different intercontact time with other users according to their point of interest and visiting pattern. By utilizing multiattributes, different forwarding strategies have been devised for both inter and intragroup routing. The proposed work “point of interest (PoI)‐based routing” is implemented in Opportunistic Network Environment simulator, and the performance is analysed in terms of delivery rate, latency, overhead, goodput, and energy consumed. The simulation results show that PoI diminishes 23% of overhead and yields 24% improvement in goodput over the state‐of‐the‐art protocol. Thus, the simulation results reveal that our proposed work provides the balance between routing performance and resource consumption.     

An evolutionary bargaining‐based approach for incentivized cooperation in opportunistic networks

Opportunistic networking enables users to communicate in an environment where connectivity is intermittent or unstable. However, such networking scheme assumes that mobile nodes voluntary cooperate, which cannot be guaranteed. Some nodes can simply exhibit selfish behavior and thus diminish the effectiveness of the approach. In this paper, a game scenario is formulated in which the nodes try to convince each other to participate in packets forwarding. Each node is considered as a player in this game. When a node comes in the communication range of another, a bargaining game starts between them as part of the message forwarding process. Both players try to have a mutual agreement on a price for message forwarding. We present a new incentive mechanism called evolutionary bargaining‐based incentive scheme (EBIS) to motivate selfish nodes to cooperate in data forwarding. In EBIS, a node negotiates with other nodes to obtain an agreeable amount of credit for its forwarding service. Nodes apply a sequential bargaining game and then adapt their strategies using an evolutionary model to maximize the probability of reaching an agreement. Unlike classical bargaining games, nodes in our model are boundedly rational. In addition, we use the evolutionary stable strategy (ESS) concept to determine the adaptive strategies for the nodes. The comparison of EBIS with a benchmarked model demonstrates that EBIS performs better in terms of packet delivery ratio and average latency.     

机会传感器网络中基于优先级的查询分发和处理算法

提出了机会无线传感器网络中一种基于查询优先级的查询分发和处理(PBQ)算法。算法根据用户定义的查询优先级合理选取消息转发的中间节点,并控制查询消息的转发和拷贝量;同时,动态地调整查询消息的优先级,使得查询结果能快速回传到查询发起者并清理网络中查询的残留消息,避免了不必要的消息传输并提高查询处理的整体效用。模拟实验表明PBQ算法能有效提高机会网络中查询处理的成功率,降低查询的开销和时间延迟。     

基于社会特征周期演化的机会移动网络路由转发策略

针对分布式k团社区检测引起的超大社区问题,提出了具有节点退出机制的?-window社区检测方法,相应提出了?-window中心性估计。通过实验发现?-window社区和?-window中心性具有周期演化特性,利用该特性,提出TTL(time to live)社区检测和TTL中心性估计,以更准确预测消息生存期上节点的相遇。随后,利用TTL社区和TTL中心性作为转发测度,设计了新的机会移动网络路由算法PerEvo。实验结果表明,与现有的基于社会特征的路由算法比较,PerEvo在保持基本不变的传输开销的同时,有效提高了机会移动网络消息投递的成功率。 关键词：机会移动网络;社区;中心性;周期演化;消息转发     

基于节点社会特征的机会网络最优发送策略

建立了基于节点社会特征的机会网络信息传输模型,使用庞特里亚金极大值定理求得最优发送策略,该策略服从阈值形式,设停止时间为h,当t < h时,节点以最大概率发送信息,当t > h时,节点停止发送信息。实验表明,该策略优于最优静态策略。进一步分析发现,节点的平均朋友数目越多,最优发送策略的停止时间越小,同时,其性能也越好。     

EFFORT: energy‐efficient opportunistic routing technology in wireless sensor networks

The utilization of limited energy in wireless sensor networks (WSNs) is the critical concern, whereas the effectiveness of routing mechanisms substantially influence energy usage. We notice that two common issues in existing specific routing schemes for WSNs are that (i) a path may traverse through a specific set of sensors, draining out their energy quickly and (ii) packet retransmissions over unreliable links may consume energy significantly. In this paper, we develop an energy‐efficient routing scheme (called EFFORT) to maximize the amount of data gathered in WSNs before the end of network lifetime. By exploiting two natural advantages of opportunistic routing, that is, the path diversity and the improvement of transmission reliability, we propose a new metric that enables each sensor to determine a suitable set of forwarders as well as their relay priorities. We then present EFFORT, a routing protocol that utilizes energy efficiently and prolongs network lifetime based on the proposed routing metric. Simulation results show that EFFORT significantly outperforms other routing protocols. Copyright © 2011 John Wiley & Sons, Ltd.     

Random forest classifier‐based safe and reliable routing for opportunistic IoT networks

Designing a safe and reliable way for communicating the messages among the devices and humans forming the Opportunistic Internet of Things network (OppIoT) has been a challenge since the broadcast mode of message sharing is used. To contribute toward addressing such challenge, this paper proposes a Random Forest Classifier (RFC)‐based safe and reliable routing protocol for OppIoT (called RFCSec) which ensures space efficiency, hash‐based message integrity, and high packet delivery, simultaneously protecting the network against safety threats viz. packet collusion, hypernova, supernova, and wormhole attacks. The proposed RFCSec scheme is composed of two phases. In the first one, the RFC is trained on real data trace, and based on the output of this training, the second phase consists in classifying the encountered nodes of a given node as belonging to one of the output classes of nodes based on their past behavior in the network. This helps in proactively isolating the malicious nodes from participating in the routing process and encourages the participation of the ones with good message forwarding behavior, low packet dropping rate, high buffer availability, and a higher probability of delivering the messages in the past. Simulation results using the ONE simulator show that the proposed RFCSec secure routing scheme is superior to the MLProph, RLProph, and CAML routing protocols, chosen as benchmarks, in terms of legitimate packet delivery, probability of message delivery, count of dropped messages, and latency in packet delivery. The out‐of‐bag error obtained is also minimal     

Near‐optimal online routing in opportunistic networks

In opportunistic networks, nodes communicate intermittently based on store‐carry‐forward paradigm while exploiting node mobility. The challenge is to determine the ideal nodes to deliver the messages since there is no end‐to‐end connectivity. The nodes might make this decision based on the data sensed from the network. This technique is not ideal in scenarios where the speed of changes in the network topology is greater than the speed at which the nodes can collect info on the network, which might, in turn, be restricted due to usage constraints and uncertainty of knowledge about future contacts. To tackle the problems raised by the non‐deterministic environments, in this paper, a stochastic optimization model and corresponding algorithm are developed to find the optimal routes by considering the short and long‐term impact of choices, ie, the next hop. Herein, we first propose a stochastic model to resolve the routing problem by identifying the shortest path. In the second step, we show that the optimal solution of the proposed model can be determined in polynomial time. An online algorithm is then proposed and analyzed. The algorithm is O( lognρ ) competitive considering the number of nodes and their associated energy. This model can take advantage of the unexpected meets to make the routing more elastic in a short time of contact and with less of a burden on the buffer. The simulation results, against the prominent algorithms, demonstrate significant improvement of the proposed approach in delivery and average delay ratio.     

A green trust‐distortion resistant trust management scheme on mobile ad hoc networks

Trust management is an emerging security approach used to conduct nodes' relationships in mobile ad hoc networks. It relates to assigning a trust level to each network component based on its cooperative behavior with respect to system goals. Because of its infrastructure‐less nature, frequent network dynamics, and severe resource constraints, it is complex to establish trust in such a network. Mainly, trust systems are vulnerable to attacks that make use of inherent properties of the trust model to alter the accuracy of estimated trust levels, referred to as trust‐distortion attacks. Because of the contradictory nature of such attacks, their detection can be confusing, complex, and energy‐demanding, especially in multiattack environments. To handle such threats, we propose a Green Trust‐distortion Resistant Trust Management Scheme, called GTRTMS, which handles different trust‐distortion attacks in multiattack environments. The proposed solution self‐adapts its trust knowledge monitoring according to the network context to conserve the energy of mobile nodes and reduce the produced CO₂ emissions. Simulation results prove that GTRTMS exhibits significantly better performance than the other counterpart in presence of simultaneous and contradictory different trust‐distortion attacks.     

机会网络中基于社会属性的按需密钥管理方案

针对机会网络的间歇性连通、快速移动、自组织管理等特征,提出了基于社会属性的按需密钥管理方案。首先利用基于身份的门限签名方案,实现了节点社会属性的自认证。随后结合机会网络的路由特性,节点之间根据社会属性匹配度有选择地颁发身份证书,并建立可度量的信任网。算法在优化证书图的同时,避免了恶意节点可能导致的无效证书链路的生成。实验仿真表明,该方案可提供较高的证书链重构成功率与节点认证可达率,并有效地降低了密钥管理所需的网络开销。     

节点连接态势感知的低开销机会网络消息传输策略

利用节点保存的相遇历史信息,提出一种节点连接态势感知的消息传输策略,通过建立时序图模型估计节点间的连接态势,以时延、投递率以及负载率为联合优化目标参数,采用均衡和量化方法,为消息分布式地选择最佳中继节点。仿真结果表明,所提出的消息传输策略能够有效降低网络负载48%以上,并改善消息投递率和平均时延性能。     

Lightweight opportunistic routing forwarding strategy based on Markov chain

A lightweight opportunistic routing forwarding strategy (MOR) was proposed based on Markov chain.In the scheme,the execute process of network was divided into a plurality of equal time period,and the random encounter state of node in each time period was represented by activity degree.The state sequence of a plurality of continuous time period constitutes a discrete Markov chain.The activity degree of encounter node was estimated by Markov model to predict its state of future time period,which can enhance the accuracy of activity degree estimation.Then,the method of comprehensive evaluating forwarding utility was designed based on the activity degree of node and the average encounter interval.MOR used the utility of node for making a routing forwarding decision.Each node only maintained a state of last time period and a state transition probability matrix,and a vector recording the average encounter interval of nodes.So,the routing forwarding decision algorithm was simple and efficient,low time and space complexity.Furthermore,the method was proposed to set optimal number of the message copy based on multiple factors,which can effectively balance the utilization of network resources.Results show that compared with existing algorithms,MOR algorithm can effectively increase the delivery ratio and reduce the delivery delay,and lower routing overhead ratio.     

An opportunistic routing mechanism for real‐time voice service in mobile ad hoc networks

The opportunistic routing mechanism can use several lossy broadcast links to support reliable transmission. In this paper, a simple opportunistic routing mechanism for real‐time multimedia services is proposed. This mechanism is based on the dynamic source routing protocol with some modifications, multiple route request, and route reply messages are used to construct the forwarder list, and the nodes within the forwarder list forward the packets which they overhear. The forwarder list is placed on the packet header in the form of a Bloom filter, which will restrict the size of the forwarder list to a constant value. There are no strict scheduling mechanisms for the forwarding order of the forwarder nodes, thus our opportunistic routing mechanism can be scalable for multiple simultaneous flows. Simulations show that our mechanism can effectively decrease the transmission times and the amount of the control messages for each packet and reduce the end‐to‐end delay for real‐time voice service, the quality of service for these services can be supported well over the unstable wireless channel. Copyright © 2009 John Wiley & Sons, Ltd.