Mobile Opportunistic Networks Research Articles

A dual incentive mechanism based on graph attention neural network and contract in mobile opportunistic networks

In mobile opportunistic networks, messages are transmitted through opportunistic contacts between nodes. Hence, the successful delivery of messages heavily relies on the mutual cooperation among nodes in the network. However, due to limited network resources such as node energy and cache space, nodes tend to be selfish, and they are unwilling to actively participate in message forwarding. In response to this challenge, lots of incentive mechanisms have been proposed. However, most of them rely on single incentives, there are issues such as inadequate handling of selfish nodes and vulnerability to malicious attacks, which ultimately lead to poor incentive effects. Therefore, in this paper, a Dual Incentive mechanism based on Graph attention neural network and Contract (DIGC) is introduced to encourage active participation of network nodes in data transmission. This incentive mechanism is divided into two steps. In the first step, the graph attention neural network is used to evaluate the reputation of nodes to achieve the goal of reputation-based incentive, and blockchain is employed to store and manage node reputation to ensure security and transparency. In the second step, an incentive based on contract theory is introduced, where personalized contracts were designed based on the different resources owned by nodes, thereby establishing a reward mechanism to encourage collaborative transmission. Extensive simulations based on two real-life mobility traces have been done to evaluate the performance of our DIGC compared with other existing incentive mechanisms. The results show that, our proposed mechanism can greatly improve throughput and reduce average delay while ensuring the overall delivery performance of the network.

JamholeHunter: On detecting new wormhole attack in Opportunistic Mobile Networks

This paper first shows that Prophet, Spray and Wait, Epidemic, and First Contact routing protocols in Opportunistic Mobile Networks (OMNs) are vulnerable to the Jamhole attack. In Jamhole attack, an attacker, Eve, compromises two different locations in OMNs by (i) jamming the GPS signal of victim nodes in these locations and (ii) by establishing a pair-wise hidden wormhole tunnel among these locations to route packets and to achieve high packet delivery ratio. The Jamhole attack enables Eve to disrupt the routing, obtain more packets of victim nodes, and possibly launch more severe attacks like packet modification, packet dropping, and packet injection attacks.In this paper, the impact of Jamhole attack on OMNs routing protocols is investigated using different attack parameters (i.e., area of compromised locations, attack frequency, and attack duration). To identify the Jamhole attack, this paper proposes JamholeHunter, a detection protocol that employs nodes’ wireless ranges, velocities, and last available GPS locations. The paper measured the impact of Jamhole attack and evaluated the JamholeHunter technique through extensive simulation experiments using synthetic and real-world mobility traces. The results demonstrate that (i) the Jamhole attack can cause a serious impact on the OMNs routing protocols, (ii) the effectiveness of JamholeHunter in identifying Jamhole attack with Detection Rate (75% to 100%) depending on various attack parameters with ∼95% Accuracy, and low False Positive Rate (≤ 3.7%), and (iii) the reliability of JamholeHunter in real-world scenarios of OMNs under different attack parameters, mobility models, and nodes velocity.

Dynamic Co-Operative Energy-Efficient Routing Algorithm Based on Geographic Information Perception in Opportunistic Mobile Networks

Opportunistic mobile networks, as an important supplement to the traditional communication methods in unique environments, are composed of mobile communication devices. It is a network form that realizes message transmission by using the opportune encounter of these mobile communication devices. Consequently, mobile communication devices necessitate periodic contact detection in order to identify potential communication opportunities, thereby leading to a substantial reduction in the already limited battery life of such devices. Previous studies on opportunistic networks have often utilized geographic information in routing design to enhance message delivery rate. However, the significance of geographic information in energy conservation has been overlooked. Furthermore, previous research on energy-efficient routing has lacked diversification in terms of the methods employed. Therefore, this paper proposes a dynamic co-operative energy-efficient routing algorithm based on geographic information perception (DCEE-GIP) to leverage geographic information to facilitate dynamic co-operation among nodes and optimize node sleep time through probabilistic analysis. The DCEE-GIP routing and other existing algorithms were simulated using opportunistic network environment (ONE) simulation. The results demonstrate that DCEE-GIP effectively extends network service time and successfully delivers the most messages. The service time of DCEE-GIP increased by 8.05∼31.11%, and more messages were delivered by 14.82∼115.9%.

Replication control strategy based on a simple game of life in opportunistic networks

Although flooding-based message dissemination in opportunistic mobile networks yields high delivery performance, the nodes' resources, such as energy and buffer, are rapidly depleted due to the enormous quantity of message replicas dispersed over the networks. This study aims to reduce the number of message replicas in the networks while maintaining an acceptable delivery rate. Inspired by Conway's Game of Life, which uses cellular automaton-based neighbor conditions to regulate the total population, we propose RiGoL, a replication control-based technique for determining how many neighbors hold the same messages. We utilize a counter to keep track of the number of neighbors and two thresholds, namely forward and drop thresholds. Our strategy works as follows: when a pair of nodes meet, the node checks to see if the peer has the same message. The counter is increased by one if the peer does not hold the same message. On the other hand, if the peer already has the message, the counter is decremented by one. Only when the counter exceeds the forward threshold, then the message forwarded to another node. In contrast, the message is deleted from the node's buffer if the counter value is less than the drop threshold. We conduct extensive simulations using ONE Simulator to evaluate our proposed strategy. The simulation results show that although RiGoL yields higher latency than Epidemic, RiGoL successfully reduces the message replication in the network and prolongs the hub node's lifetime.

Retracted: Adaptive Clustering Algorithm for IIoT Based Mobile Opportunistic Networks

Retracted: Adaptive Clustering Algorithm for IIoT Based Mobile Opportunistic Networks

Social mobility and geo-context aware macroscopic routing scheme for mobile opportunistic network

Mobile opportunistic networks (MONs) are deliberated as important aspect to proliferate the wireless communications. These networks pose several challenges such as network lifetime, storage capacity, and forwarding capacity. End-to-End routing schemes are considered as promising technique solution to deal with these issues. In this domain, opportunistic routing has gained huge attention because it follows the broadcasting nature of wireless communication and focus on selection of relay node for packet transmission to ensure the better quality of service (QoS) and energy efficiency. This work focuses on optimizing the next hop selection process and introduced reinforcement learning approach which considers distance, energy and link connectivity to assign the reward for different actions to identify the suitable relay node. Moreover, geo-context and social behaviour based opportunistic routing models are used to increase the reliability of next hop selection. Similarly, social model considers social profiling, social connectivity, and social interaction model to identify the relay node. The outcome of proposed approach is compared with several existing approaches such as prophet, spray and wait, and epidemic routing in terms of packet delivery, and network overhead. The relative study shows that the proposed approach achieves the average packet delivery as 47.22% and minimizes the network overhead.

Joint Content Caching and Recommendation in Opportunistic Mobile Networks Through Deep Reinforcement Learning and Broad Learning

Edge caching has been a research hotspot of the Mobile Edge Computing (MEC) in recent years, which is an effective way to ease the burden of traffic in cellular networks. It places contents to the edge of the networks and assists contents transmission via Device-to-Device (D2D) links. Traditional caching strategies strictly depend on the personal preferences of users, and they are scarcely possible to reduce the transmission cost while ensuring the high effectiveness. Fortunately, recent studies have found that the combination of caching and recommendation can effectively improve the efficiency of edge caching and reduce the transmission cost. In this paper, we jointly consider the content caching and recommendation through Opportunistic Mobile Networks (OMNs) to reduce the cost of Content Service Center (CSC). In order to obtain the optimal caching and recommendation solutions with sparse rating matrix, we propose a Joint Content Caching and Recommender System (JCCRS). In JCCRS, a Broad Incremental Learning based Collaborative Filtering algorithm, named BILCF, is first proposed to predict the missing ratings. Afterwards, we quantify the relationship between each pair of Mobile Users (MUs) according to their mobility, similarity and preference. The content caching and recommendation problem is then modeled as a Non-Linear Integer Programming (NLIP) problem and we prove that it belongs to NP-hard. To solve this problem, a Deep Deterministic Policy Gradient (DDPG) based Content Caching and Recommendation method, named DCRM, is further proposed to obtain the approximate optimal solutions. Extensive experiments on both a realistic dataset and a synthetic dataset validated by the realistic data show that the proposed algorithms outperform other baseline methods under different scenarios.

Location-aware hybrid microscopic routing scheme for mobile opportunistic network

<p class="Abstract"><span lang="EN-US">Mobile opportunistic networks (MON) has been used for provisioning delay-tolerant applications. In MON the device communicates with each other with no assured end-to-end paths from source and destination because of frequent topology changes, node mobility, low density, and intermittent connectivity. In MON the device battery drains very fast for performing activities such as scanning, transceiver, and other computational processes, impacting the overall performance thus, designing energy-efficient routing is a challenging task. The routing employs a store-carry-and-forward mechanism for packet communication, where the packet is composed of time-to-live (TTL) and is kept in buffer till the opportunity arises. In improving delivery ratio message replication has been adopted; however, induces high network congestion. Here we present a location-aware hybrid microscopic routing (LAHMR) scheme for MON. The LAHMR provides an effective packet transmission scheme with location awareness and high reliability by limiting unnecessary packets being circulated in the network. Experiment outcome shows the LAHMR scheme achieves a much better delivery ratio with less delay, and also reduces the number of a forwarder for transmitting a packet, aiding in the reduction of network overhead concerning recent routing method namely the social-aware reliable forwarding (SCARF) technique.</span></p>

On Detecting Route Hijacking Attack in Opportunistic Mobile Networks

In this paper, we show that Hybrid Routing and Prophet protocols in Opportunistic Mobile Networks (OMNs) are vulnerable to the <i>CollusiveHijack</i> attack, in which a malicious attacker, Eve, compromises a set of nodes and lies about their Inter-Contact-Times (ICTs). Eve claims that her nodes meet more frequently than in reality to hijack the routes of legitimate nodes in OMNs. The CollusiveHijack attack enables Eve to launch more severe attacks like packet modification, traffic analysis, and incentive seeking attacks. To identify the CollusiveHijack attack, we propose the Kolmogorov-Smirnov two-sample test to determine whether the statistical distribution of the packets&#x2019; delays follows the derived distribution from the ICTs among the nodes. We propose three techniques to detect the CollusiveHijack attack, the Path Detection Technique (PDT), the Hop Detection Technique (HDT), and the Early Hop Detection Technique (EHDT), which trade off compatibility with the Bundle Security Protocol, the detection rate, and the detection latency. We evaluated our techniques through extensive trace-driven simulations and a proof-of-concept system implementation and show that they can detect CollusiveHijack attacks with 80.0&#x0025; to 99.4&#x0025; detection rates (when Eve hijacks more than 60 packets) while maintaining a low false positive rate (<inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula>3.6&#x0025;) and a short detection latency (7-14 hours) for EHDT (75&#x0025;-85&#x0025; enhancement compared to PDT and HDT).

Opportunistic Mobile Networks Content Delivery for Important but Non-Urgent Traffic

Opportunistic Mobile Networks Content Delivery for Important but Non-Urgent Traffic

Optimizing Bulk Transfer Size and Scheduling for Efficient Buffer Management in Mobile Opportunistic Networks

Mobile Opportunistic Networks (MONs) are characterized by intermittent connectivity with long isolation period, and nodes following redundant transmissions for reliable message delivery. This often leads to unnecessary buffer occupancy, preventing new messages from getting replicated due to small contact duration and low bandwidth, or leading to packet drop under constrained buffer. Although attempts have been made to mitigate buffer congestion, the existing schemes are localized, are slow to react, or are specific to a routing scheme. Moreover, they rely on message exchanges to obtain buffer state/occupancy, thereby incurring additional overhead. In this paper, we first develop a generalized probabilistic forwarding model where the forwarding probability denotes the likelihood of a message to get forwarded to the encountered node. Based on the forwarding probability, we develop a congestion indicator and predict the point of congestion using the Kalman filter. Using this, a node can decide the optimal number and the exact set of messages to replicate, which leads to an optimal performance with minimal packet drop and overhead. Simulation results using a synthetic mobility model and a real-life mobility trace show that the proposed scheme outperforms the existing schemes.

Research on the digital economy promoting the high-quality development of trade in the central and western regions under the background of big data technology

In order to improve the effect of the digital economy in promoting the development of trade in the central and western regions, this paper combines the intelligent digital economy identification algorithm to construct a trade analysis model, and proposes a collaborative cache strategy in the hybrid mobile opportunistic network scenario. By placing the AP access point at the center of the network, the disadvantage of relying only on node mobility for opportunistic data access is effectively compensated. For different communication scenarios, this paper divides the data transmission stage into D2D communication stage and D2A communication stage according to different relay choices in the data transmission process. Moreover, this paper conducts modeling analysis on them respectively, and discusses the access delay of nodes to data in the network. Finally, this paper verifies that the algorithm in this paper has a certain effect through experimental research, and puts forward suggestions for promoting the high-quality development of trade in the central and western regions.

Robust load-balanced backbone-based multicast routing in mobile opportunistic networks

Robust load-balanced backbone-based multicast routing in mobile opportunistic networks

RAIM: A Reverse Auction-Based Incentive Mechanism for Mobile Data Offloading Through Opportunistic Mobile Networks

Offloading cellular traffic through Opportunistic Mobile Networks (OMNs) has been an effective method to ease the traffic burden of cellular networks. However, providing data offloading services consumes a lot of resources. Since nodes in OMNs are rational and selfish, they will not be willing to provide data offloading services if they are not properly rewarded. Therefore, it is important to exploit incentive mechanisms to motivate nodes to provide data offloading services. This paper proposes a Reverse Auction-based Incentive Mechanism, named RAIM. In RAIM, reverse auction is used as the incentive mechanism, and the incentive-driven data offloading process is modeled as Non-Linear Integer Programming (NLIP) from the business point of view, aiming to minimize the cost of the Content Service Provider (CSP). Then, a heuristic method named Decay-based Helper Selection Method (DBHSM) is proposed to resolve the problem. Moreover, a payment rule based on the standard Vickrey-Clarke-Groves scheme is proposed to ensure the individual rationality and truthfulness properties of DBHSM. Finally, real mobility trace-driven simulation results show that DBHSM outperforms other baseline methods in terms of the CSP's cost and the offloading rate under different scenarios.

Selfish node management in opportunistic mobile networks with improved social based watchdog and dynamic power AODV protocol

Selfish node management in opportunistic mobile networks with improved social based watchdog and dynamic power AODV protocol

Improving Delivery Probability in Mobile Opportunistic Networks with Social-Based Routing

There are contexts where TCP/IP is not suitable for performing data transmission due to long delays, timeouts, network partitioning, and interruptions. In these scenarios, mobile opportunistic networks (MONs) are a valid option, providing asynchronous transmissions in dynamic topologies. These architectures exploit physical encounters and persistent storage to communicate nodes that lack a continuous end-to-end path. In recent years, many routing algorithms have been based on social interactions. Smartphones and wearables are in vogue, applying social information to optimize paths between nodes. This work proposes Refine Social Broadcast (RSB), a social routing algorithm. RSB uses social behavior and node interests to refine the message broadcast in the network, improving the delivery probability while reducing redundant data duplication. The proposal combines the identification of the most influential nodes to carry the information toward the destination with interest-based routing. To evaluate the performance, RSB is applied to a simulated case of use based on a realistic loneliness detection methodology in elderly adults. The obtained delivery probability, latency, overhead, and hops are compared with the most popular social-based routers, namely, EpSoc, SimBet, and BubbleRap. RSB manifests a successful delivery probability, exceeding the second-best result (SimBet) by 17% and reducing the highest overhead (EpSoc) by 97%.

Estimating Buffer Occupancy sans Message Exchange in Mobile Opportunistic Networks

The multi-copy routing schemes in a mobile opportunistic network (MON) lead to buffer congestion, thereby affecting the network performance. This calls for a proactive buffer management policy for congestion mitigation. Existing works on buffer management in MONs require additional message exchange and are reactive in nature. In this letter, we devise a generalized probabilistic routing model to analyze and estimate the average buffer occupancy, without message exchange. We then estimate the buffer overflow probability using the Chernoff’s bound. The accuracy of the theoretical model is validated through simulation using a synthetic mobility model and a real-life mobility trace.

Adaptive Clustering Algorithm for IIoT Based Mobile Opportunistic Networks

The clustering algorithms play a crucial role for energy saving solutions in mobile opportunistic networks. If the selection of cluster head is made appropriately, then the energy can be consumed optimally. The existing clustering algorithms do not consider the optimal selection of the cluster head resulting in low survival rates and high energy consumption rates in nodes. The adaptive clustering is required in Industrial Internet of Things (IIoT) based sophisticated networks where seamless connectivity is imperative for rapid communication. In order to meet this research gap, an adaptive clustering algorithm for mobile opportunistic networks is proposed within military bases that uses a heuristic algorithm (GA) for adaptive clustering. An analysis of the opportunity network at the connected military base is carried out and the mobile opportunity model is constructed using the adaptive clustering for the similar traffic. In a mobile machine network, the next hop node is determined by the node clustering principle. A LEACH clustering protocol enables communication between cluster heads and base stations based on single-hop and multihop cluster nodes. In order to perform adaptive clustering of mobile network nodes based on network partitioning and scheduling of clusters, genetic algorithms are used. The proposed approach can be applied to the IIoT systems in places where adaptive clustering is required to optimize energy consumption, to reduce latency rates, and to enhance the throughput of mobile networks. The experimental findings suggest that the proposed adaptive strategy is capable of optimizing energy consumption rates, reducing network latency, and boosting efficiency by increasing throughput in mobile opportunistic networks.

Balanced wireless crowd charging with mobility prediction and social awareness

The advancements in peer-to-peer wireless power transfer (P2P-WPT) have empowered the portable and mobile devices to wirelessly replenish their battery by directly interacting with other nearby devices. The existing works unrealistically assume the users to exchange energy with any of the users and at every such opportunity. However, due to the users’ mobility, the inter-node meetings in such opportunistic mobile networks vary, and P2P energy exchange in such scenarios remains uncertain. Additionally, the social interests and interactions of the users influence their mobility as well as the energy exchange between them. The existing P2P-WPT methods did not consider the joint problem for energy exchange due to user’s inevitable mobility, and the influence of sociality on the latter. As a result of computing with imprecise information, the energy balance achieved by these works at a slower rate as well as impaired by energy loss for the crowd. Motivated by this problem scenario, in this work, we present a wireless crowd charging method, namely MoSaBa, which leverages mobility prediction and social information for improved energy balancing. MoSaBa incorporates two dimensions of social information, namely social context and social relationships, as additional features for predicting contact opportunities. In this method, we explore the different pairs of peers such that the energy balancing is achieved at a faster rate as well as the energy balance quality improves in terms of maintaining low energy loss for the crowd. We justify the peer selection method in MoSaBa by detailed performance evaluation. Compared to the existing state-of-the-art, the proposed method achieves better performance trade-offs between energy-efficiency, energy balance quality and convergence time.

Construction of Optimal Membership Functions for a Fuzzy Routing Scheme in Opportunistic Mobile Networks

This article proposes FRIMF, a fuzzy routing scheme for opportunistic mobile networks (OMNs). In FRIMF, we exploit the pairwise intercontact times to evaluate the connection strength between nodes. Instead of assuming a random movement model, in the present case we consider node contact processes in OMNs as bursty events. Consequently, we introduce a burstiness parameter to characterize the variability in the dynamics of pairwise interactions. This variance metric, along with the statistical mean of pairwise intercontact times, is used to define a single FRIMF routing metric called closeness through a fuzzy inference system. This reflects the tie strength of the pair nodes. To improve the transmission environment, we further propose a method to develop optimal membership functions for the FRIMF’s fuzzy parameters based on the contact information. Particularly, we leverage the membership function elicitation techniques commonly used in collective opinion aggregations based on a direct rating process to establish the relevancy between vagueness estimates of the routing parameters and statistical distributions of the pairwise intercontact times in a way that eventually presents asymmetric triangular fuzzy numbers. In turn, these TFNs are used to properly define the fuzzy sets of the FRIMF’s routing parameters. Through simulations in the real human mobility environments, we show that FRIMF utilizing the enhanced asymmetric TFNs can outperform that using the typical symmetric TFNs developed based on our subjective preferences. Lastly, comparing with several algorithm benchmarks, we confirm the efficiency of FRIMF in transmission cost and delay.