Opportunistic Mobile Social Networks Research Articles

Enhancing data integrity in opportunistic mobile social network: Leveraging Berkle Tree and secure data routing against attacks

In Opportunistic Mobile Social Networks (OMSNs), ensuring data integrity is crucial. The anonymous and opportunistic nature of node communication makes these networks vulnerable to data integrity attacks. The existing literature identified significant shortcomings in effectively addressing data integrity attacks with high efficiency and accuracy. This paper addresses these issues by proposing the "Berkle Tree", a novel data structure designed to mitigate data integrity attacks in OMSNs. The Berkle Tree leverages the EvolvedBloom filter, which is a variant of the bloom filter with a negligible False Positive Rate (FPR). The key contributions of this study include i) an innovative application of EvolvedBloom for membership testing and Berkle Tree root validation, and ii) comparative analysis with existing data structures like Merkle and Verkle Trees. The Berkle Tree demonstrates superior performance, reducing tree generation and integrity validation times and leading to substantial computational cost reductions of 79.50 % and 90.57 %, respectively. The proposed method integrates the Berkle Tree into OMSN routing models and evaluates performance against Packet Drop, Modification, and Fake Attacks (PDA, PMA, PFA). Results show average Malicious Node Detection Accuracy of 98.2 %, 85.2 %, and 94.4 %; Malicious Path Detection Accuracy of 98.6 %, 86.6 %, and 90.2 %; Malicious Data Detection Accuracy of 98.4 %, 80.2 %, and 93.4 %; and False Negative Rates of 1.8 %, 14.8 %, and 5.6 % for PDA, PMA, and PFA, respectively. The major findings demonstrate that the proposed approach significantly improves OMSN routing models by reducing Packet Dropping, Modifying, and Faking Rates by 48.62 %, 28.99 %, and 31.2 %, respectively. Compared to existing methods, the Berkle Tree achieves a substantial reduction in filter size by approximately 25 %–40 %, while maintaining a negligible FPR. These advancements contribute to the state-of-the-art of OMSNs by providing robust solutions for data integrity with significant implications for enhancing security and trustworthiness in OMSNs.

VADIA-Verkle Tree-based Approach for Dealing Data Integrity Attacks in Opportunistic Mobile Social Networks

Opportunistic Mobile Social Networks (OMSN) are prone to data integrity attacks that jeopardize the integrity of the routing data inside the network. Among the several techniques that cope with these attacks in OMSN, tree-based approaches have proven to be the most effective due to its ease of data verification and ensurance in data integrity. This paper evaluates two tree-based data structures, Merkle tree and Verkle tree in terms of their effectiveness in detecting and preventing such attacks. The evaluation considers tree-generation time and proof-checking time, and the results demonstrate that the Verkle tree is a bandwidth-efficient solution and have lower proof-checking time, with a reduction of 98.33% than Merkle tree. This makes Verkle tree a good choice for handling data integrity attacks in OMSN. A Verkle tree-based approach, named VADIA, is proposed to handle data integrity attacks such as packet dropping, packet modification and pollution attack in OMSN. The proposed approach is implemented in the Opportunistic Network Environment (ONE) simulator and is shown to be effective in detecting malicious nodes and paths, reducing false negative rates, and improving accuracy in detecting malicious activities. The results demonstrate a 47%, 84% and 69% improvement in malicious node, malicious path and malicious activity detection over a period of time. Furthermore, the approach achieves an 80% reduction in false negative rates.

Regular routine aware routing in opportunistic mobile social networks

AbstractDue to intermittent connectivity, routing protocols in opportunistic mobile social networks (OMSN) forward messages through opportunistic contacts. Previous opportunistic routing protocols focus on the past contact history of nodes to make a forwarding decision. It was found that predicting temporally evolving contacts and communities according to different times of the day would improve the accuracy of the prediction. In this paper, a routing paradigm called RRoutine is proposed that allows message holders to exchange multiple copies of the message based on the predicted number of communities. This distribution continues until the remaining copies of the message become one. Each message holder applies either inter or intra‐community forwarding and selects a set of potential forwarders based on the prediction of future encounters in the community and an expected remaining duration between two nodes (ERIMD). The estimation of ERIMD takes into account the sequence of daily activities, the time scale of current activities, and the return time to regular meetings of the nodes. Also, unlike traditional routing, where a single forwarder is selected, a set of potential candidates is selected and prioritized according to their contact prediction, with the most likely candidate receiving the highest priority. Simulation results obtained from synthetic and real mobility data show that the proposed paper improves the performance of the system in terms of message delivery rate, throughput, and latency.

A New Contemporary Profession as Game Boosters: The Behavioural Emergence of Intellectual Opportunism

This study investigates the emergence of intellectual opportunism for game boosters in mobile social networks. It shows the supply and demand for upgrading game levels in mobile social networks. These supply and lucrative demand highlight a new temporary profession as game boosters. Game boosters facilitate framed newbie gamers who want to play at a higher game level without playing for a long time. This emergence of professionally game boosters is due to economic opportunism, promising their potential future incomes. Moreover, opportunistic mobile social networks (OMSNs) support the muscular appearance of the game booster profession. On the other hand, this study demonstrates that mobile social networks exploit hedonistic human needs by reflecting their behaviour and social relationships when building efficient and trustworthy business contracts. This study presents uniqueness supported by critical reasoning. First, it shows that the game booster phenomenon has emerged as a contemporary profession in society, promising future cash inflows above the average salaries of typical jobs. Second, this research demonstrates the sequential logic of economic opportunities, intellectual opportunism, and mobile social networks, constructing game boosters’ income generating. Nevertheless, this study acknowledges that the game booster profession has low integrity due to its members’ inconsistent and unprincipled virtues.

R-SOR: Ranked Social-based Routing Protocol in Opportunistic Mobile Social Networks

Exploiting social information to improve routing performance is an increasing trend in Opportunistic Mobile Social Networks (OMSNs). Selecting the next message’s relay node based on the user’s social behavior is a critical factor in attaining a high delivery rate. So, to ascertain the most efficient selection of the next relay, the correlation between daily social activities and the social characteristics in the user profiles can be exploited. In this paper, we consider the impact of the social characteristics on mobile user activities during certain periods of the day and then rank these characteristics based on their relative importance in order to be included in the routing protocol. These processes consolidate the proposed Ranked Social-based Routing (R-SOR) protocol to provide an effective way for data dissemination in OMSN. We use the real data set INFOCOM06 to evaluate the proposed protocol. The experimental results show that the proposed protocol has higher routing efficiency than flooding-based protocols such as Epsoc and Epidemic, prediction-based protocols such as PRoPHET, and social-based protocols such as MSM and Bubble Rap.

Activity-based routing algorithm in opportunistic mobile social networks

In opportunistic mobile social networks, nodes are clustered according to their interests or hobbies and take part in different activities regularly. We delve into the temporal and spatial mobility characteristics of network nodes and put forward an activity-based message opportunistic forwarding algorithm. The main idea of the algorithm is that we choose different message forwarding methods according to the situation of nodes participating in activities. If the source node and the destination node are both attend in the same activities, we select the best relay node which has the biggest delivery probability. While the source node and the destination node are not in the same activities at the same time, we need to find the optimal path which owns highest indirect delivery probability, and messages will be transmitted through the optimal path. The simulation results show that the proposed routing algorithm can not only improve the successful delivery ratio of messages but also reduce the network delay and the network overhead obviously, in comparison with the classical opportunistic routing algorithms, such as community-aware message opportunistic transmission algorithm, community-based message transmission scheme algorithm, PRoPHET, Epidemic algorithm, and interest characteristic probability prediction algorithm.

A survey on routing algorithms for opportunistic mobile social networks

Opportunistic Mobile Social Networks (OMSNs) are kind of Delay Tolerant Networks (DTNs) that leverage characteristics of Mobile Ad Hoc Networks (MANETs) and Social Networks, particularly the social features, to boost performance of routing algorithms. Users in OMSNs communicate to share and disseminate data to meet needs for variety of applications. Such networks have attracted tremendous attention lately due to the data transmission requirement from emerging applications such as IoT and smart city initiatives. Devices carried by human is the carrier of message transmission, so the social features of human can be used to improve the ability of data transmission. In this paper, we conduct a comparative survey on routing algorithms in OMSNs. We first analyze routing algorithms based on three social features. Since node selfishness is not really considered previously in aforementioned routing algorithms, but has significant impact on network performance, we treat node selfishness as another social feature, classify and elaborate routing algorithms based on incentive mechanism. To assess the impact of social features on routing algorithms, we conducted simulation for six routing algorithms and analyzed the simulation result. Finally, we conclude the paper with challenges on design of routing in OMSNs and point out some future research directions.

Anonymising group data sharing in opportunistic mobile social networks

The security and privacy risks of group users are major concerns in opportunistic mobile social network (OMSN) platforms, especially when the users share data publicly in a proximity area. However, existing schemes on group data sharing usually do not enforce the anonymity and/or privacy preservation properties and making them relatively unsuitable for large scale implementation. In this paper, we construct a compelling lightweight cryptographic encryption protocol scheme to anonymise and preserve the identity and data privacy of the users for a large scale OMSN. The users are authenticated to forward data-packet notification via Bluetooth-enabled smartphones, respond via 4G mobile network and can deny the notification with zero detection. The scheme’s notification delay considerably reduces with the introduction of some passers-by and hence achieves high reliability of packet notification forwarding in a 1000 m-squared proximity area. Simulation and testing for the performance requirement using Proverif software prove the scheme as easy to implement for large scale OMSN. Furthermore, the security analysis with proof based on the hardness of decisional bilinear Diffie–Hellman condition demonstrates the scheme as semantically secure under chosen-plaintext and chosen-ciphertext attacks. Nonetheless, resource constraints management has not been tackled in the present study.

TSIRP: A Temporal Social Interactions-Based Routing Protocol in Opportunistic Mobile Social Networks

With the increasing number of smart device users, data transmission between users is becoming more important, and a network architecture called opportunistic mobile social network (OMSN) is gaining attention. However, routing in OMSNs is a challenging problem due to the frequent disconnection between nodes and the absence of paths from the source to the destination. It results in a complex topology and a low packet transmission success rate. Therefore, we propose a novel routing algorithm called the temporal social interactions-based routing protocol (TSIRP) for solving the problem of low network performance due to the improper selection of message relay nodes. First, we focus on the temporal context of social interactions. Specifically, at a certain time of the day, a person has specific people with whom the person usually interacts (e.g., workers usually meet co-workers during working hours; students usually meet their classmates during class). Based on temporal social interactions between nodes, potential forwarding metrics are proposed and calculated for each time of the day to make forwarding decisions. Second, we propose a new scheme to control the message spreading rate, which allows achieving a balance between delivery latency and overhead ratio. In addition, an analytical model is also designed using an absorbing Markov chain to estimate the performance of TSIRP. Simulations were also conducted, and the results indicate that TSIRP can achieve better performance than existing routing protocols in terms of packet delivery ratio, delivery latency, network overhead ratio, and average hop count.

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

SummaryWith 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.

Opportunistic mobile social networks: architecture, privacy, security issues and future directions

Mobile Social Networks and its related applications have made a very great impact in the society. Many new technologies related to mobile social networking are booming rapidly now-a-days and yet to boom. One such upcoming technology is Opportunistic Mobile Social Networking. This technology allows mobile users to communicate and exchange data with each other without the use of Internet. This paper is about Opportunistic Mobile Social Networks, its architecture, issues and some future research directions. The architecture and issues of Opportunistic Mobile Social Networks are compared with that of traditional Mobile Social Networks. The main contribution of this paper is regarding privacy and security issues in Opportunistic Mobile Social Networks. Finally, some future research directions in Opportunistic Mobile Social Networks have been elaborated regarding the data's privacy and security.

Routing in Mobile Opportunistic Social Networks with Selfish Nodes

When the connection to Internet is not available during networking activities, an opportunistic approach exploits the encounters between mobile human-carried devices for exchanging information. When users encounter each other, their handheld devices can communicate in a cooperative way, using the encounter opportunities for forwarding their messages, in a wireless manner. But, analyzing real behaviors, most of the nodes exhibit selfish behaviors, mostly to preserve the limited resources (data buffers and residual energy). That is the reason why node selfishness should be taken into account when describing networking activities: in this paper, we first evaluate the effects of node selfishness in opportunistic networks. Then, we propose a routing mechanism for managing node selfishness in opportunistic communications, namely, SORSI (Social-based Opportunistic Routing with Selfishness detection and Incentive mechanisms). SORSI exploits the social-based nature of node mobility and other social features of nodes to optimize message dissemination together with a selfishness detection mechanism, aiming at discouraging selfish behaviors and boosting data forwarding. Simulating several percentages of selfish nodes, our results on real-world mobility traces show that SORSI is able to outperform the social-based schemes Bubble Rap and SPRINT-SELF, employing also selfishness management in terms of message delivery ratio, overhead cost, and end-to-end average latency. Moreover, SORSI achieves delivery ratios and average latencies comparable to Epidemic Routing while having a significant lower overhead cost.

FRRF: A Fuzzy Reasoning Routing-Forwarding Algorithm Using Mobile Device Similarity in Mobile Edge Computing-Based Opportunistic Mobile Social Networks

The arrival of 5G is accompanied by massive data transmission between mobile devices (MDs) and huge transmission energy consumption in the wireless networks. Therefore, how message applications select the appropriate relay MDs to complete the efficient data transmission process in the opportunistic mobile social networks (OMSNs)? At present, designing an efficient routing–forwarding algorithm is extremely challenging. Some routing–forwarding algorithms choose appropriate nodes as relay nodes based on the similarity between nodes, but most existing routing–forwarding algorithms only consider a few similar factors and even completely ignore the importance of movement similarity in data transmission of the node. In particular, existing routing–forwarding algorithms will bring extra energy consumption to the nodes in the wireless networks, and excessive energy consumption will further affect the delay and data transmission efficiency. In order to solve the problems in the existing strategies, we apply the mobile edge computing (MEC) to OMSNs, and we propose the fuzzy reasoning routing–forwarding (FRRF) algorithm that integrates the movement and social similarity in the MEC-based OMSNs. In detail, the fuzzy evaluation of the movement and social similarity is integrated to determine the transmission priority value between MDs, and finally, the transmission priority between MDs is compared to make transmission decision. Through simulation experiment and comparison with other algorithms, the correctness of the theoretical analysis and the efficiency of the FRRF algorithm in energy consumption, delay, and transmission efficiency are verified.

Multiple Social Metrics Based Routing Protocol in Opportunistic Mobile Social Networks

Multiple Social Metrics Based Routing Protocol in Opportunistic Mobile Social Networks

An Effective Data Transmission Algorithm Based on Social Relationships in Opportunistic Mobile Social Networks

With the popularization of mobile communication equipment, human activities have an increasing impact on the structure of networks, and so the social characteristics of opportunistic networks become increasingly obvious. Opportunistic networks are increasingly used in social situations. However, existing routing algorithms are not suitable for opportunistic social networks, because traditional opportunistic network routing does not consider participation in human activities, which usually causes a high ratio of transmission delay and routing overhead. Therefore, this research proposes an effective data transmission algorithm based on social relationships (ESR), which considers the community characteristics of opportunistic mobile social networks. This work uses the idea of the faction to divide the nodes in the network into communities, reduces the number of inefficient nodes in the community, and performs another contraction of the structure. Simulation results show that the ESR algorithm, through community transmission, is not only faster and safer, but also has lower transmission delay and routing overhead compared with the spray and wait algorithm, SCR algorithm and the EMIST algorithm.

A Dynamic Trust Framework for Opportunistic Mobile Social Networks

Opportunistic mobile social network (OMSN) enables users to form an instant social network for information sharing (e.g., people watching the same soccer game can share their instant comments). OMSN is ad hoc in nature, thus relies on the cooperation of members regarding message transmission. However, some uncooperative or malicious behavior from abnormal members may reduce network performance, even damage the entire network. Currently, there does not exist effective mechanisms to detect selfish and malicious nodes. To tackle this problem, we propose a dynamic trust framework to facilitate a node to derive a trust value of another node based on the behavior of the latter. The novelty of our framework includes the following: 1) we design a new metric for a trust value of a node and 2) we propose a “two–hop feedback method” that requires intermediate nodes in a forwarding path to generate ACK messages to verify a node’s honesty if they are two hops away. In most existing trust models, final ACK messages are considered as critical factors. In OMSN, nodes are not fully connected and final ACK messages cannot be reliably received. In order to avoid the problem that few final ACK messages can be received, we propose a “two–hop feedback method.” Simulation results show that our approach is able to detect a majority of abnormal nodes including malicious nodes, selfish nodes, and those nodes launching conspiracy attacks. Thus, the entire network efficiency can be improved without negative impact of abnormal nodes. Besides, our trust framework can be easily applied to the current popular routing protocols of opportunistic networks.

EpSoc: Social-Based Epidemic-Based Routing Protocol in Opportunistic Mobile Social Network

In opportunistic networks, the nature of intermittent and disruptive connections degrades the efficiency of routing. Epidemic routing protocol is used as a benchmark for most of routing protocols in opportunistic mobile social networks (OMSNs) due to its high message delivery and latency. However, Epidemic incurs high cost in terms of overhead and hop count. In this paper, we propose a hybrid routing protocol called EpSoc which utilizes the Epidemic routing forwarding strategy and exploits an important social feature, that is, degree centrality. Two techniques are used in EpSoc. Messages’ TTL is adjusted based on the degree centrality of nodes, and the message blocking mechanism is used to control replication. Simulation results show that EpSoc increases the delivery ratio and decreases the overhead ratio, the average latency, and the hop counts as compared to Epidemic and Bubble Rap.

A social‐aware probabilistic routing approach for mobile opportunistic social networks

AbstractIn mobile opportunistic social networks (MOSNs), mobile users move around and conduct message transmission by using their portable devices without the support of communication infrastructures, in which routing is a challenge due to high connection intermittency and node mobility. The current routing schemes for MOSNs only focus on the improvement of cost‐effective delivery by using the characteristics of users' interests or mobility. In this paper, we propose a novel routing approach for MOSNs in which the probabilistic and social‐aware strategies are combined and the multiple‐copy policy is adopted. We first model an MOSN into some overlapping regions and then propose 2 region‐related centrality degrees related to users' social characteristics with the consideration of users' centrality, mobility, and interactions among users. According to the definition of centrality degrees, we design a social‐aware probabilistic routing approach in which a message replica control policy is applied to allow a user with higher encounter probability to the destination and the user could obtain more message replicas. The simulation results show that the proposed approach outperforms the previous routing algorithms in terms of delivery ratio and overhead ratio.

Social-aware data dissemination in opportunistic mobile social networks

Opportunistic Mobile Social Networks (OMSNs), formed by mobile users with social relationships and characteristics, enhance spontaneous communication among users that opportunistically encounter each other. Such networks can be exploited to improve the performance of data forwarding. Discovering optimal relay nodes is one of the important issues for efficient data propagation in OMSNs. Although traditional centrality definitions to identify the nodes features in network, they cannot identify effectively the influential nodes for data dissemination in OMSNs. Existing protocols take advantage of spatial contact frequency and social characteristics to enhance transmission performance. However, existing protocols have not fully exploited the benefits of the relations and the effects between geographical information, social features and user interests. In this paper, we first evaluate these three characteristics of users and design a routing protocol called Geo-Social-Interest (GSI) protocol to select optimal relay nodes. We compare the performance of GSI using real INFOCOM06 data sets. The experiment results demonstrate that GSI overperforms the other protocols with highest data delivery ratio and low communication overhead.

FaceChange: Attaining Neighbor Node Anonymity in Mobile Opportunistic Social Networks With Fine-Grained Control

In mobile opportunistic social networks (MOSNs), mobile devices carried by people communicate with each other directly when they meet for proximity-based MOSN services (e.g., file sharing) without the support of infrastructures. In current methods, when nodes meet, they simply communicate with their real IDs, which leads to privacy and security concerns. Anonymizing real IDs among neighbor nodes solves such concerns. However, this prevents nodes from collecting real ID-based encountering information, which is needed to support MOSN services. Therefore, in this paper, we propose FaceChange that can support both anonymizing real IDs among neighbor nodes and collecting real ID-based encountering information. For node anonymity, two encountering nodes communicate anonymously. Only when the two nodes disconnect with each other, each node forwards an encrypted encountering evidence to the encountered node to enable encountering information collection. A set of novel schemes are designed to ensure the confidentiality and uniqueness of encountering evidences. FaceChange also supports fine-grained control over what information is shared with the encountered node based on attribute similarity (i.e., trust), which is calculated without disclosing attributes. Advanced extensions for sharing real IDs between mutually trusted nodes and more efficient encountering evidence collection are also proposed. Extensive analysis and experiments show the effectiveness of FaceChange on protecting node privacy and meanwhile supporting the encountering information collection in MOSNs. Implementation on smartphones also demonstrates its energy efficiency.