Loop closure detection, a critical component of Simultaneous Localization and Mapping (SLAM) systems, can be computationally intensive, particularly in swarm robotics where coordination among multiple agents is essential. Traditional SLAM methods often involve comparing each frame with all previous frames, leading to performance bottlenecks, especially on battery-operated or resource-constrained devices. This leaves little room for other critical tasks, such as continuous coordination and information sharing among swarm robots, which require swift execution to perform effectively. This paper introduces a novel cone-based approach to streamline loop closure detection, significantly reducing the computational burden and improving system efficiency. By limiting frame comparisons to a predefined region, our method accelerates SLAM algorithms, enabling more efficient coordination and exploration among swarm robots. The approach is particularly advantageous for resource-constrained devices and battery-powered platforms operating in dynamic environments. The need for millisecond-level response times in swarm robotic tasks require the integration of 6G networks for seamless communication and coordination. Experimental results demonstrate the effectiveness of the cone-based method in enhancing loop closure accuracy while minimizing computational overhead. This makes it a valuable tool for advancing swarm robotic applications, particularly in 6G-enabled environments where real-time coordination and efficiency are paramount.

With the development of intelligent transportation systems, vehicular edge computing (VEC) has played a pivotal role by integrating computation, storage, and analytics closer to the vehicles. VEC represents a paradigm shift towards real-time data processing and intelligent decision-making, overcoming challenges associated with latency and resource constraints. In VEC scenarios, the efficient scheduling and allocation of computing resources are fundamental research areas, enabling real-time processing of vehicular tasks and intelligent decision-making. This paper provides a comprehensive review of the latest research in Deep Reinforcement Learning (DRL)-based task scheduling and resource allocation in VEC environments. Firstly, the paper outlines the development of VEC and introduces the core concepts of DRL, shedding light on their growing importance in the dynamic VEC landscape. Secondly, the state-of-the-art research in DRL-based task scheduling and resource allocation is categorized, reviewed, and discussed. Finally, the paper discusses current challenges in the field, offering insights into the promising future of VEC applications within the realm of intelligent transportation systems.

Disaster-altered environments present a unique challenge for unmanned ground vehicles (UGVs), navigating dynamic environments for rescue operations. Existing loop closure detection methods already face significant challenges due to environmental variations, such as changing lighting conditions and the presence of moving objects. Disaster-stricken areas present an additional layer of complexity, as previously static landmarks can also undergo significant changes, further hindering accurate loop closure detection. Inspired by humans that recognizes already visited places by prioritizing semantic information like landmarks and text rather than just visual features, this article introduces DisView, a novel approach that leverages this insight for loop closure detection in disaster-altered environments. DisView utilizes a mixed data feature extractor to analyze visual, semantic, and textual data. This combined approach goes beyond traditional methods that rely solely on visual IoT data, leading to more robust loop closure detection even in disaster-altered environments and with viewpoint changes, varying illumination conditions, and the presence of moving rescue infrastructure. DisView’s effectiveness is validated on six publicly available datasets, demonstrating superior performance in diverse lighting conditions and viewing angles. By enabling reliable loop closure detection, DisView significantly improves UGV navigation in disaster-altered environments, ultimately supporting faster and more efficient rescue operations.

An enormous volume of data is generated in the Internet of Things (IoT), which needs to be anonymized before sharing with public or third parties to minimize reidentification risk and protect sensitive information. Data anonymization techniques can remove information capable of identifying individuals. However, inappropriate data anonymization can increase the risk of reidentification. This work focuses on potential attack risks of anonymized data by evaluating the potential attack risks. Specifically, we analyzed the attack risks over anonymized data with both randomization and generalization techniques. We also analyzed the risk of reidentification for five commonly used data anonymization techniques. The experimental results demonstrate that the proposed solution can well evaluate the potential attack risks.

Drone-enabled vehicular ad-hoc network (VANET) is a promising solution for safe driving as it improves traffic efficiency and reliability by timely sharing road events and traffic information. However, there is an urgent need to tackle security, privacy, and computational delay related issues. In this paper, we propose a blockchain-based privacy preservation scheme using steganography to overcome the aforementioned issues in drone-enabled VANETs. The proposed scheme is based on a decentralized key management mechanism that combines lightweight authentication and key agreement. Data redundancy is avoided with the help of interplanetary file system (IPFS) which stores traffic event related data in blockchain through the smart contract. We further modified consensus algorithm using the practical byzantine fault tolerates algorithm and steganography to achieve better efficiency. Moreover, trust management is achieved by combining blockchain, IPFS, and steganography, which also enables the distributed storage and quick access to data for drone-enabled VANETs. We evaluate the performance of the proposed scheme in terms of response time, and computational time against incentive-based scheme.

Internet of Things (IoTs) is envisaged to widely capture the realm of logistics and transportation services in future. The applications of ubiquitous IoTs have been extended to Maritime Transportation Systems (MTS) that spawned increasing security threats; posing serious fiscal concerns to stakeholders involved. Among these threats, Distributed Denial of Service Attack (DDoS) is ranked very high that can wreak havoc on IoT artifacts of the MTS networks. Timely and effective detection of such attacks is imperative for necessary mitigation. Conventional approaches exploit entropy of attributes in network traffic for detecting DDoS attacks. However, the majority of these approaches are static in nature and consider only a few network traffic parameters, limiting the number of DDoS attack detection to a few types and intensities. In current research, a novel framework named “Dual Stack Machine Learning (S2ML)” has been proposed to calculate distinct entropy-based varying 10-Tuple (T) features from network traffic features, three window sizes and associated Rate of Exponent Separation (RES). These features have been exploited for developing an intelligent model over MTS-IoT datasets to successfully detect multiple types of DDoS attacks in MTS. S2ML is an efficient framework that overcomes the shortcomings of prevalent DDoS detection approaches, as evident from the comparison with Multi-layer Perceptron (MLP), Alternating Decision Tree (ADT) and Simple Logistic Regression (SLR) over different evaluation metrics (Confusion metrics, ROCs). The proposed S2ML technique outperforms prevalent ones with 1.5% better results compared to asserted approaches on distribution of normal/attack traffic. We look forward to enhancing the model performance through dynamic windowing, measuring packet drop rates and infrastructure of Software Defined Networks (SDNs).

6G is the future of intelligent connectivity artefacts with Artificial Intelligence (AI) at its backbone. The Multi-Access Edge Computing (MEC) based 6G enabled infrastructure helps in achieving the required zero latency for autonomous Intelligent Transport Systems (ITS) with features of low power consumption, lower end-to-end latency, minimal processing/transmission overheads, higher throughput and reliability. MEC are prone to a deadlock due to the limited amount of available computational resources, resulting in fatal delays in Vehicle-to-Vehicle (V2V), Vehicles to Road Side Unit (RSU) and RSU to ITS communication. The unresolved deadlock may entail higher energy consumption that can adversely affect the Quality of Service (QoS) in terms of safety and reliability with potential threats of causing fatal accidents. Therefore, it is almost imperative to resolve the deadlocks from MEC to comply with the QoS parameters of MEC based autonomous vehicles. The asserted goals can be achieved by employing an intelligent and adaptive deadlock resolution strategy. In this paper, a deadlock-aware, and collaborative edge decision algorithm has been proposed for facilitating the seamless communication of autonomous vehicles over MEC. Additionally, deadlock avoidance and prevention schemes have been evaluated using Bankers resource request avoidance algorithm, wound wait algorithm and wait-die algorithms for resource provisioning in collaborative MEC. Furthermore, the effectiveness of deadlock avoidance and prevention algorithms in real-time scenarios has been analyzed in MEC systems. The metrics used for a comparative analysis in this research include Round-trip time, Queue wait-time and CPU utilization. The proposed algorithm shows promising results when compared with prevalent techniques.

Blockchain-enabled Internet of Things (IoT) envisions a world with rapid development and implementations to change our everyday lives based on smart devices. These devices are attached to the internet that can communicate with each other without human interference. A well-known wireless network in blockchain-enabled IoT frameworks is the Low Power and Lossy Network (LLN) that uses a novel protocol known as Routing protocol for low power and lossy networks (RPL) to provide effective and energy-efficient routing. LLNs that run on RPL are inherently prone to multiple Denial of Service (DoS) attacks due to the low cost, shared medium, and resource-constrained nature of blockchain-enabled IoT devices. A Spam DODAG Information Solicitation (DIS) attack is one of the novel attacks that drains the energy source of legitimate nodes and ends up causing the legitimate nodes to suffer from DoS. To address this problem, a mitigation scheme named DIS Spam Attack Mitigation (DISAM) is proposed. The proposed scheme effectively mitigates the effects of the Spam DIS attack on the network’s performance. The experimental results show that DISAM detects and mitigates the attack quickly and efficiently.

Abstract Due to recent advancements in smart city traffic and transport monitoring industry 4.0 applications. Flying Ad‐Hoc Networks (FANETs) ability to cover geographically large areas, makes it a suitable technology to address the challenges faced during remote areas traffic monitoring. The implementation of drone based FANETs have several advantages in remote traffic monitoring, including free air‐to‐air drone assisted communication zone and smart surveillance and security. The drone‐based FANETs can be deployed within minutes without requiring physical infrastructure, making it suitable for mission critical applications in several areas of interests. Here a drone‐based FANETs application for smart city remote traffic monitoring is presented while addressing several challenges including coverage of larger geographical area and data communication links between FANETs nodes. A FANET‐inspired enhanced ACO algorithm that easily coped with drone assisted technology of FANETs is proposed to cover the large areas. Simulation results are presented to compare the proposed technique against different network lifetime and number of received packets. The presented results show that the proposed technique perform better compared to other state‐of‐the‐art techniques.

Industry 5.0 requires intelligent self-organi- zed, self-managed, and self-monitoring applications with ability to analyze and predict the human as well as machine behaviors across interconnected devices. Tackling dynamic network behavior is a unique challenge for Internet of Things applications in Industry 5.0. Knowledge-defined networks (KDN) bridge this gap by extending software-defined networking architecture with knowledge plane, which learns the network dynamics to avoid suboptimal decisions. Cognitive routing leverages the sixth-generation (6G) self-organized networks with self-learning feature. This article presents a self-organized cognitive routing framework for a KDN which uses link-reliability as a routing metric. It reduces end-to-end latency by choosing the most reliable path with minimal probability of route-flapping. The proposed framework precalculates all possible paths between every pair of nodes and ensures self-healing with a constant-time convergence. An experimental test-bed has been developed to benchmark the proposed framework against the industry-stranded link-state and distance-vector routing algorithms SPF and DUAL, respectively.

Existing navigation services provide route options based on a single metric without considering user’s preference. This results in the planned route not meeting the actual needs of users. In this paper, a personalized route planning algorithm is proposed, which can provide users with a route that meets their requirements. Based on the multiple properties of the road, the Polychromatic Sets (PS) theory is introduced into route planning. Firstly, a road properties description scheme based on the PS theory was proposed. By using this scheme, users’ travel preferences can be quantified, and then personalized property combination schemes can be constructed according to these properties. Secondly, the idea of setting priority for road segments was utilized. Based on a user’s travel preference, all the property combination schemes can be prioritized at relevant levels. Finally, based on the priority level, an efficient path planning scheme was proposed, in which priority is given to the highest road segments in the target direction. In addition, the system can constantly obtain real-time road information through mobile terminals, update road properties, and provide other users with more accurate road information and navigation services, so as to avoid crowded road segments without excessively increasing time consumption. Experiment results show that our algorithm can realize personalized route planning services without significantly increasing the travel time and distance. In addition, source code of the algorithm has been uploaded on GitHub for this algorithm to be used by other researchers.

In this article, we investigate the wireless power transfer and energy-efficiency (EE) optimization problem for nano-empowered vehicular networks operating over the terahertz band. The nano-sensors in air can harvest energy from a power station and then can transmit the trace information to the micro-device under reconnaissance vehicular scenarios. Hence, by considering the properties of the terahertz band, we develop a long-term EE optimization problem. Furthermore, with the help of the equivalent transformation method, we converted the EE optimization problem into a series of energy-efficient resource allocation problems over the time slots. Each reformulated optimization problem becomes a mixed integer nonlinear programming (MINLP) over a time slot. Hence, to obtain the sub-optimal solution of the reformulated optimization problem, we developed a Quantum-behaved Particle swarm-based EE Optimization (QPEEO) algorithm. Furthermore, by exploiting the special structure of the reformulated problem, we propose an Improved Discrete Particle swarm-based EE Optimization (IDPEEO) algorithm. The proposed IDPEEO algorithm handles the problem's constraints effectively, and greatly reduces the search space and the convergence time. Our simulation results validate the theoretical analysis of the proposed scheme.

The new developments in mobile edge computing (MEC) and vehicle-to-everything (V2X) communications has positioned 5G and beyond in a strong position to answer the market need towards future emerging intelligent transportation systems and smart city applications. The major attractive features of V2X communication is the inherent ability to adapt to any type of network, device, or data, and to ensure robustness, resilience and reliability of the network, which is challenging to realize. In this work, we propose to drive further these features by proposing a novel robust, resilient and reliable architecture for V2X communication based on harnessing MEC and blockchain technology. A three stage computing service is proposed. Firstly, a hierarchcial computing architecture is deployed spanning over the vehicular network that constitutes cloud computing (CC), edge computing (EC), fog computing (FC) nodes. The resources and data bases can migrate from the high capacity cloud services (furthest away from the individual node of the network) to the edge (medium) and low level fog node, according to computing service requirements. Secondly, the resource allocation filters the data according to its significance, and rank the nodes according to their usability, and selects the network technology according to their physical channel characteristics. Thirdly, we propose a blockchain-based transaction service that ensures reliability. We discussed two use cases for experimental analysis, plug-in electric vehicles in smart grid scenarios, and massive IoT data services for autonomous cars. The results show that car connectivity prediction is accurate 98% of the times, where 92% more data blocks are added using micro-blockchain solution compared to the public blockchain, where it is able to reduce the time to sign and compute the proof-of-work (PoW), and deliver a low-overhead Proof-of-Stake (PoS) consensus mechanism. This approach can be considered a strong candidate architecture for future V2X, and with more general application for everything-to-everything (X2X) communications.

Mobile Ad-hoc Cloud (MAC) is the constellation of nearby mobile devices to serve the heavy computational needs of the resource-constrained edge devices. One of the major challenges of MAC is to convince the mobile devices to offer their limited resources for the shared computational pool. Credit-based rewarding system is considered as an effective way of incentivizing the arbitrary mobile devices for joining the MAC network and to earn the credits through computational crowdsourcing. The next challenge is to get the reliable computation as incentives attract the malicious devices to submit fake computational results for claiming their reward and we have used the blockchain based reputation system for identifying the malicious participants of MAC. This paper presents a malicious node identification algorithm integrated within the Iroha based permissioned blockchain. Iroha is a project of hyperledger which is focused on mobile devices and thus light-weight in nature. It is used for keeping the track of rewarding and reputation system driven by the malicious node detection algorithm. Experiments are conducted for evaluating the implemented test-bed and results show the effectiveness of algorithm in identifying the malicious devices and conducting reliable data analysis through the blockchain based computational crowdsourcing in MAC.

Degree verification is the process of verifying the academic credentials of successfully graduated students. It is a time-consuming and costly process as universities annually spend millions of dollars on handling the degree verification requests. Hence, there is a dire need to improve the degree verification process, and the Massachusetts Institute of Technology, Cambridge, MA, USA, has introduced the blockcerts, a blockchain-based solution for freely handling the degree verification requests. Although blockcerts eliminates the cost of the degree verification process, it also alters the existing workflow of degree issuance. This is because blockcerts are primarily focused on facilitating the students, and there is room for improvement from the perspective of educational institutes. In this article, we have introduced the docschain to tackle the three mentioned limitations of the blockcerts. Docschain seamlessly incorporates within the existing workflow of degree issuance by operating over the hard copies of the degree documents. This is achieved through optical character recognition (OCR), and the record of each degree document is stored along with the details of the corresponding OCR template to understand the semantics of the data stored at different sections of the degree document. In contrast to blockcerts, docschain also supports the bulk submission of degree details for both the previously and newly graduated students.

A Tactile Internet-based nanonetwork is an emerging field that promises a new range of e-health applications, in which human operators can efficiently operate and control devices at the nanoscale for remote-patient treatment. A haptic feedback is inevitable for establishing a link between the operator and unknown in-body environment. However, haptic communications over the terahertz band may incur significant path loss due to molecular absorption. In this paper, we propose an optimization framework for haptic communications over nanonetworks, in which in-body nanodevices transmit haptic information to an operator via the terahertz band. By considering the properties of the terahertz band, we employ Brownian motion to describe the mobility of the nanodevices and develop a time-variant terahertz channel model. Furthermore, based on the developed channel model, we construct a stochastic optimization problem for improving haptic communications under the constraints of system stability, energy consumption, and latency. To solve the formulated nonconvex stochastic problem, an improved time-varying particle swarm optimization algorithm is presented, which can deal with the constraints of the problem efficiently by reducing the convergence time significantly. The simulation results validate the theoretical analysis of the proposed system.

The routing protocols play a vital role in saving energy, especially by minimizing the time a packet takes to travel from source to destination. The aim of energy-aware routing protocols is to select a route that engages routers in such a way that the overall energy consumption is minimized. In this paper, a relationship between resource utilization and energy consumption is stated, further, a resource-aware dynamic routing algorithm for SDN is proposed. The contribution of this paper is a queuing theory-based approach that measures the average waiting time of nodes and links based on their utilization and finds a path that costs the least time. The paper also proposes a framework for implementing routing algorithm over an SDN. Performance of the algorithm is verified using a GNS3 based implementation with an Opendaylight controller.

A quality of service (QoS) scheme for multimedia multicast communications in wireless mesh networks (WMNs) is proposed in this study. It uses a new bandwidth calculation scheme to provide rate-adaptive admission control. It relies on information it receives from the network and application layers to calculate the network bandwidth consumption and operates independently of the media access control (MAC) layer. Using the proposed QoS scheme, the network layer provides feedback on network congestion to the application layer. The multimedia multicast sender adapts the real-time data transmission rate based on the network congestion feedback it receives. In this study, the authors describe the detailed architecture of the proposed QoS scheme. Furthermore, the authors have implemented the QoS scheme in our previously developed uni-directional link aware multicast extension to AODV (UDL-MAODV) routing protocol. The authors present validation tests to ensure the correct functionality of the QoS algorithm using our SwanMesh WMN testbed. The authors have also performed simulation tests to evaluate the performance of the proposed scheme. The simulation results show the effectiveness of the proposed QoS scheme.

Wireless mesh networks (WMNs) facilitate both data transfer and real-time applications over wireless medium. Owing to the shared nature of wireless frequencies, bandwidth limitation is a major challenge facing WMNs. If real-time multimedia applications, such as live video streaming, are shared among multiple clients using unicast communications, it could result in network resources starvation. Multicast transmission saves network resources by replicating live multimedia transmitted data from one source to multiple destinations using the same stream. The authors have developed a novel implementation of a multicast extension to ad hoc on-demand distance vector (MAODV) routing protocol in Linux kernel 2.6 user space, which is referred to as unidirectional link-aware MAODV (UDL-MAODV). Multicast video transmissions use user datagram protocol, which does not use implicit handshaking dialogues for guaranteeing reliability of data. Therefore the authors propose and have implemented modifications to the MAODV route discovery process to improve the reliability of multicast video transmissions. These modifications enable UDL-MAODV to ensure reliable route establishment for multimedia multicast communications over WMNs in the presence of UDLs. The authors describe in this study the software architecture of the UDL-MAODV implementation in the Linux kernel 2.6 user space, and also present multicast validation and results of performance tests using the SwanMesh WMN testbed. Furthermore, UDL-MAODV has been cross-compiled and tests are presented to compare the performance of the implementation using X86 and ARM architecture-based SwanMesh nodes. The test results show that the proposed algorithm is reliable and efficient.