Security and trust management in opportunistic networks: a survey

The construction of social networks over mobile devices in the events or at locations has emerged as a new network paradigm. These new mobile social networks enable people to communicate and share their experiences without the need to have Internet access and with minimum required infrastructure. However, the functionality and security of such networks would be potentially undermined without an effective trust management scheme. Although many trust management systems have been proposed, few of them can be applied to these new mobile social networks because of the unique network and communication characteristics. This paper presents a novel trust management system, termed MobileTrust, to establish decentralized, secure and reliable trust relationships between mobile ad hoc social network participants. Specifically, the construction of trust models encompasses both scenarios that users are experienced with the network and users are unacquainted with the environment. The trust models cover various important factors of trust relationships in social networks, including the similarity of user profile, reputation, and history of friendship. A set of simulations is conducted to evaluate our system deployed in a mobile social network in the presence of dishonest users.

Background: Routing in opportunistic networks is subject to attacks such as blackhole and grayhole attacks. A blackhole attack is a type of attack where the malicious node called blackhole node attracts all the data packets by using a forged route to falsely claim that it has a shortest route to the destination, then drops all the data packets that it receives. On the other hand, a greyhole attack is a kind of denial of service attack where a malicious node in the network acts as a normal node, causing eaves dropping and selective forwarding. Blackhole and greyhole attacks in opportunistic networks have been extensively studied in the literature and several defence mechanisms against those attacks have been proposed. This paper adds a novel contribution to this problem by proposing a novel secured protocol (so-called SHBPR) against such attacks. Keywords: Blackhole attack, delay tolerant networks (DTNs), greyhole attack, opportunistic network simulator (ONE), opportunistic networks (Oppnets), secure routing.

The present work describes a remote environment monitoring system based on the paradigms of mesh networks and opportunistic networks, whereby a sensor node can explore “con-nectivity windows” to transmit information that will eventually reach another network participants. We discuss the threats to the system's security and propose security mechanisms for the system ensuring the integrity and availability of monitoring information, something identified as critical to its proper operation.

In this paper, we present a forwarding algorithm for mobile social opportunistic networks which is both centrality-based and energy-aware. In opportunistic networks, the nodes usually exploit a contact opportunity to perform hop-by-hop routing, since an end-to-end path between the source node and destination node may not exist. Centrality-based forwarding schemes use the structurally important nodes (i.e. nodes having many contacts with neighbors) as message relays and have been shown to be good candidates for social forwarding in opportunistic network environments. However, the most central nodes which are structurally more important than other network nodes, have the disadvantage to be used very frequently as message relays, thus consuming their energy very quickly. Here, we exploit the advantage of using a centrality-based scheme which is distributed and needs only the history of contacts as social information. In addition, we modulate the computed node centrality value using the node energy level in order to avoid transmissions in nodes more central but with a limited energy budget. Simulating three real-world mobility traces, we show that our scheme is able to achieve better delivery ratio and overhead ratio while sensibly reducing the energy consumption of nodes.

The rapid development and increasing popularity of mobile devices have facilitated the development of computation‐intensive applications and services, but their constrained compute, storage and battery capabilities have also created the requirements for optimized resource provision and service orchestration. Emerging mobile edge computing (MEC) and next‐generation networks (6G) can meet these requirements of mobile devices by migrating complex computing tasks and large volumes of data to edge servers with acceptable latency, thus effectively enhancing the quality of experience (QoE) of mobile users. Intelligent Vehicular Collaboration, as an intelligent mobile application or service in intelligent connected vehicle (ICV) applications, enables precise and effective driving decisions based on a large amount of real‐time traffic information statistics and intelligent analysis of vehicle status to improve the user driving experience and the efficiency of intelligent transportation systems(ITS). However, the openness and dynamics of wireless communications and the high frequency of information interaction of MEC‐enabled intelligent vehicular collaboration services in the 6G era make them highly vulnerable to cyber attacks. How to achieve dynamically trustworthy, secure, and intelligent vehicular collaboration services in the presence of malicious node access and cyber attacks in the 6G MEC‐enabled vehicular network is a significant challenge. In this paper, we propose a novel blockchain‐enabled trust management mechanism to address this challenge. First, we design a spatiotemporal‐correlated message credibility assessment method and a dynamic mutual trust evaluation method to obtain accurate ratings for messages and mobile nodes respectively. Based on the above methods, we propose an improved blockchain‐based intelligent trust management mechanism that enables transparent, tamper‐evident, and trustworthy secure intelligent vehicular collaboration services while maintaining stable and efficient system performance. Simulation results show that our suggested solutions can efficiently evaluate the trust level of ICVs and achieve efficient and secure intelligent vehicular collaboration with acceptable communication and computation overheads.

The lack of pre-existing infrastructure or dynamic topology makes it impossible to establish end-to-end connections in opportunistic networks (OppNets). Instead, a store-and-forward strategy can be employed. However, such loosely knit routing paths depend heavily on the cooperation among participating nodes. Selfish or malicious behaviors of nodes impact greatly on the network performance. In this paper, we design and validate a dynamic trust management model for secure routing optimization. We propose the concept of incorporating social trust into the routing decision process and design a trust routing based on social similarity (TRSS) scheme. TRSS is based on the observation that nodes move around and contact each other according to their common interests or social similarities. A node sharing more social features in social history record with the destination is more likely to travel close to the latter in the near future and should be chosen as the next-hop forwarder. Furthermore, social trust can be established based on an observed node's trustworthiness and its encounter history. Based on direct and recommended trust, those untrustworthy nodes will be detected and purged from the trusted list. Since only trusted nodes' packets will be forwarded, the selfish nodes have the incentives to behave well again. Simulation evaluation demonstrates that TRSS is very effective in detecting selfish or even malicious nodes and achieving better performance.

Opportunistic Mobile Social Networks (MSNs) are a type of delay-tolerant networks that are composed of mobile nodes with social characteristics. Most of current research on forwarding algorithms for MSNs leverages the properties of social networks, such as community and centrality. However, the fundamental property, interaction, has not received sufficient attention. The effects of interactions consist of two parts: direct effects and indirect effects. Most existing work based on interactions takes only direct effects into consideration. We propose a distributed Interaction Based Routing (IBR) algorithm. Our main contribution is a distributed method to compute the popularity of nodes by using the direct and indirect effects of interactions. We compare IBR with Epidemic, Prophet, and BubbleRap under three different scenarios. The results show that IBR improves the forwarding efficiency significantly and independent of the network scenarios.

Trust management is fundamental to identify malicious, selfish, and compromised nodes which have been authenticated. Many trust management mechanisms are proposed to provide effective security solution for wire and wireless networks, even some of them have become one of the most known in this field. But in fact, trust management in wireless sensor networks still remains a challenging field. Traditional trust models developed for wireless sensor networks are not well suited for sensor networks because the trust evaluation process isn’t related to certain application. In this paper, we propose a self-adaptive trust management scheme, which can adaptively adjust the trust evaluation process to cope with application requirement changes of trust management. The simulation results show that our scheme has less rate of inauthentic downloads of nodes, energy consumption and message overheads as compared to the current state-of-the-art trust management schemes, and it is more suitable for large-scale sensor networks.

Mobile social networks (MSNs) are a kind of delay tolerant network that consists of lots of mobile nodes with social characteristics. Recently, many social-aware algorithms have been proposed to address routing problems in MSNs. However, it also brings more security and privacy concerns. In this paper, we discuss a specific type of Sybil attack in MSNs, which few researches has been focused on. We proposed a security mechanism to detect Sybil nodes and eliminate them to ensure the routing security while routing forwarding. It needs to cooperate by two sides, which is, respectively measuring distance in client side and eliminating Sybil nodes in server side. We also demonstrate the solution is correct and analyze its energy costs.

Distributed control and optimization form the backbone of many critical control systems, with typical examples including sensor networks, power grids, and intelligent transportation systems. As these systems grow in scale and complexity, ensuring the privacy of individual agents becomes increasingly essential. Privacy protection is no longer an optional add-on; it is a crucial element in the design of these systems. Sensitive data, such as states, measurements, and decisions, must be shielded from unauthorized access or malicious attacks, while still allowing the system to function effectively. This special issue aims to explore novel techniques and strategies that protect privacy in distributed control and optimization, showcasing how these approaches can be seamlessly integrated into real-world applications. The diverse contributions in this issue demonstrate the growing importance of privacy in the evolving field of distributed control systems. RNC7747 introduces a privacy-preserving method for coverage control in multi-agent systems with limited communication ranges, ensuring efficient operation without compromising agents' privacy. RNC7798 proposes a privacy-preserving consensus mechanism based on pulse-coupled oscillators, helping maintain privacy while agents coordinate in distributed networks. RNC7778 presents privacy preservation techniques for cloud-based cooperative LQG control systems, enabling privacy protection even when leveraging cloud resources. RNC8051 discusses achieving privacy-preserving average consensus in unreliable network environments, ensuring privacy despite communication challenges. RNC7929 introduces a novel approach to open privacy-preserving consensus via state decomposition, allowing agents to reach consensus while maintaining privacy. RNC7966 investigates leader-follower consensus in multi-agent systems with distributed event-triggered observations, ensuring privacy during agent coordination in real-time systems. RNC7789 discusses the tradeoff between privacy protection strength and control performance in control systems. RNC8029 proposes a new privacy mechanism for distributed average consensus on time-varying directed graphs. RNC7776 investigates event-triggered distributed optimization with differential privacy, reducing communication costs while safeguarding sensitive data. RNC7885 develops an event-triggered privacy-preserving optimization method for directed communication networks, emphasizing privacy during optimization tasks. RNC7791 focuses on robust optimization for virtual power plant scheduling under uncertainty, incorporating privacy preservation into the optimization process. RNC7730 explores prescribed-time distributed optimization with set constraints, offering time guarantees while maintaining privacy in dynamic optimization scenarios. RNC7926 provides a privacy-preserving algorithm for constrained resource allocation with communication delays, crucial for real-time decision-making in distributed systems. RNC7848 presents an adaptive event-triggered control strategy for cyber-physical systems under DoS and deception attacks, ensuring privacy and system security. RNC8068 addresses initial state privacy in nonlinear systems on Riemannian manifolds, offering methods to protect privacy in complex, high-dimensional control systems. RNC7906 focuses on dynamic consensus-based formation control for multi-robot systems, preserving privacy through output masking techniques while achieving coordination. RNC7688 introduces a periodic dynamic encoding mechanism for stealthy attack detection in distributed state estimation, focusing on security and privacy under attack scenarios. RNC70033 tackles robust consensus Kalman filtering for distributed state-saturated systems, incorporating dynamic-disturbed saturation levels and censored measurements to ensure privacy under uncertain conditions. The contributions in this special issue span a broad spectrum of topics related to privacy in distributed control and optimization. As distributed systems continue to evolve, these papers contribute to the ongoing conversation about balancing privacy protection with efficient system operation, highlighting the importance of privacy as a key consideration in the design and optimization of future distributed control systems. The authors declare no conflicts of interest.

Exploring the trust management mechanism in self-organizing complex network based on game theory

The development of Internet of Things (IoT) has paved way for the Internet of Vehicles (IoV) and intelligent transportation systems (ITS). Vehicular ad-hoc networks (VANETs) are indispensable for ITS with intelligent vehicles (IV) as their key players. To ensure proper and reliable VANET operation, IVs need secure inter- and intra-network communication with trust and reliability of data (provenance). This paper aims to provide trust management in VANETs by proposing a trustless system model using blockchain and a certificate authority (CA) for registering IVs as well as revoking their registration if need be. Furthermore, to preserve data reliability, this paper uses physical unclonable functions (PUFs). Implementation of DrivMan shows that it is able to establish distributed trust management and enables secure data sharing while preserving the privacy of IVs.

The existing peer-to-peer networks have several problems such as fake content distribution, free riding, white-washing and poor search scalability, lack of a robust trust model and absence of user privacy protection mechanism. Although, several trust management and semantic community-based mechanisms for combating free riding and distribution of malicious contents have been proposed by some researchers, most of these schemes lack scalability due to their high computational, communication and storage overhead. This paper presents a robust trust management scheme for P2P networks that utilizes topology adaptation by constructing an overlay of trusted peers where the neighbors are selected based on their trust ratings and content similarities. While increasing the search efficiency by intelligently exploiting the formation of semantic community structures by topology adaptation among the trustworthy peers, the scheme provides the users a very high level of privacy protection of their usage and consumption patterns of network resources. Simulation results demonstrate that the proposed scheme provides efficient searching to good peers while penalizing the malicious peers by increasing their search times as the network topology stabilizes.

Close proximity of mobile devices can be utilized to create ad hoc and\ndynamic networks. These mobile Proximity Based Networks (PBNs) are\nOpportunistic Networks that enable devices to identify and communicate with\neach other without relying on any communication infrastructure. In addition,\nthese networks are self organizing, highly dynamic and facilitate effective\nreal-time communication. These characteristics render them very useful in a\nwide variety of complex scenarios such as vehicular communication, e-health,\ndisaster networks, mobile social networks etc. In this work we employ the\nAllJoyn framework from Qualcomm which facilitates smooth discovery, attachment\nand data sharing between devices in close proximity. We develop\n\\textit{Min-O-Mee}, a Minutes-of-Meeting app prototype in the Android platform,\nutilizing the AllJoyn framework. Min-O-Mee allows one of the participants to\ncreate a minutes-of-meeting document which can be shared with and edited by the\nother participants in the meeting. The app harnesses the spatial proximity of\nparticipants in a meeting and enables seamless data exchange between them. This\ncharacteristic allows Min-O-Mee to share not just minutes-of-meeting, but any\ndata that needs to be exchanged among the participants, making it a versatile\napp. Further, we extend the basic AllJoyn framework to enable multi-hop\ncommunication among the devices in the PBN. We devise a novel routing mechanism\nthat is suited to a proximity centric wireless network as it facilitates data\nrouting and delivery over several hops to devices that are at the fringe of the\nPBN.\n

With the explosive growth of mobile devices at the edge of mobile social networks (MSNs), the amount of the content that needs to be transmitted is exploded. Traditional content delivery mechanisms leverage only local information to make routing decisions, which results in both high latency and low delivery rate. Software-defined networking (SDN) is a novel network paradigm, the design philosophy of which could be applied to MSN for improving the content delivery performance. In this article, the centralized control thought of SDN is introduced into MSN to efficiently process social information. The classical routing algorithm of BubbleRap is improved from the perspective of network density, which is the basis of designing the sparse and dense routing mechanisms for MSNs. In addition, flexibly switching between these two routing mechanisms is implemented by a discriminating scheme, achieving efficient yet adaptive routing. The experimental results show that the delivery ratio of sparse routing is up to 83%, and the dense routing could reach up to 93%.