Unmanned Aerial Vehicles (UAVs) are integral in various sectors like agriculture, surveillance, and logistics, driven by advancements in 5G. However, existing research lacks a comprehensive approach addressing both data freshness and security concerns. In this paper, we address the intricate challenges of data freshness, and security, especially in the context of eavesdrop-ping and jamming in modern UAV networks. Our framework incorporates exponential AoI metrics and emphasizes secrecy rate to tackle eavesdropping and jamming threats. We introduce a transformer-enhanced Deep Reinforcement Learning (DRL) approach to optimize task offloading processes. Comparative analysis with existing algorithms showcases the superiority of our scheme, indicating its promising advancements in UAV network management.

Unmanned Aerial Vehicles (UAVs) play a vital role in modern Internet of Things (IoT) ecosystems by providing services like task offloading. Although simultaneous execution can be done through resource optimization and offloading, it is crucial to choose which task to be executed where considering a trade-off between energy consumption and execution delay. Considering these constraints, in this work, we propose a Federated Learning (FL) aided framework for multi-device task offloading in UAV-enabled edge networks while reducing energy consumption and task execution delay. The proposed approach involves a two-step process to execute tasks on various computing devices. In the first step, a task's priority is determined, considering factors like delay deadlines (maximum allowed delay) and resource requirements which include memory, storage and CPU instructions. In the second step, we leverage FL to dynamically calculate energy and delay of the task for different paradigms. This approach aims to maintain a balance between minimizing energy consumption by the UAV and reducing task execution delay. The effectiveness of the proposed framework is evaluated through experiments in terms of energy efficiency and end-to-end execution delay of the UAVs.

The technological advancements in Unmanned Aerial Vehicles (UAVs) have brought significant changes in various domains including surveillance, agriculture and disaster rescue. The convergence of Machine Learning (ML) and UAV networks contributes significantly to their automation and decision-making capabilities. Traditional ML techniques are centralized, i.e., they face many issues such as privacy due to data sharing, scalability and single-point failure. In this work, we propose a hierarchical decentralized framework for Federated Learning (FL) that addresses all the aforementioned issues. The proposed framework, Cluster Based Pseudo Hierarchical Decentralized Federated Learning (PHDFL), is tailored to UAV networks for learning where the learning and aggregation tasks are distributed among different UAVs in the network. This also introduces the concept of pseudo-hierarchy as all the UAVs are at the same level due to Decentralized Federated Learning (DFL) but the learning happens in a hierarchical manner where the network is divided into clusters and each cluster has a cluster head which in then communicates with other cluster heads. The effectiveness of the proposed framework is evaluated through experiments in terms of learning time, energy consumed and convergence of the model.

Over the last few decades, Internet of Things (IoT) has become the spotlight area of research within the Industries and Academics. Primarily, IoT devices are characterized by small and nonscalable resources, including low processing capabilities, less internal memory, and short battery life. However, IoT applications demand extensive storage and faster response to ensure seamless and interoperable communication. Hence, Edge Computing and data/task offloading among the edge or cloud servers become promising, while also posing critical research challenges for edge-enabled large-scale IoT ecosystems. Several research activities have addressed the difficulties of determining an efficient and scalable data offloading strategy utilizing edge and cloud computing-supported technologies. This article focuses on the state-of-the-art edge IoT data offloading techniques, and optimization models in the heterogeneous IoT environment. We examine how edge and cloud-supported technologies can handle delay-sensitive IoT applications efficiently. Moreover, we introduce an IoT-based healthcare use case scenario to explain edge data execution and resource provisioning in IoT networks. Finally, we discuss several challenging issues and possible solutions to establish interoperable communication and computation for IoT applications.

Industry 5.0 aims to elevate industrial operations, businesses, and revolution to new heights by promoting sustainable, resilient, and human-centric practices. The popularity of Industry 5.0 is reflected in the increasing demand for real-time and near-edge processing in most latency-critical Industrial Internet of Things (IIoT) applications. However, designing an efficient task priority assignment strategy and accordingly executing tasks within the stipulated deadline is complex and challenging. Therefore, in this work, we design a novel Multi-device Edge Service Provisioning (MESP) framework for optimizing delay in Industry 5.0. At first, the MESP strategy classifies edge executable tasks using multi-nomial probability theory. Then, we prove that multi-device service demand at the edge devices is an NP-Hard problem, which requires approximate algorithms for finding near-optimal solutions. To follow this, we propose a game-theoretic approach where multiple IIoT devices request various services simultaneously while maximizing their mutual satisfaction. We also examine the structural property of the proposed game and show how this property helps in achieving the equilibrium point of the proposed game with finite improvement steps. Experimental analysis shows that MESP reduces computational overhead and end-to-end execution delay by 20-30% compared to standard algorithms.

The IPv6 over time-slotted channel hopping mode of IEEE 802.15.4e (6TiSCH) wireless protocol stack is released to offer high-throughput, low and bounded latency, energy efficient, and reliable communication in industrial Internet of Things (IoT). However, scheduling communicationcellamong the nodes for exchanging sensory data is not trivial in 6TiSCH networks when the network traffic is highly dynamic and unpredictable. The existing autonomous scheduling schemes suffer from static allocation, high end-to-end latency, and high energy consumption. To address the abovementioned problems, in this work, we proposeon-the-fly autonomous slot allocation(OASA)scheme to schedule slots for adaptive traffic in 6TiSCH networks autonomously and immediately. OASA also enables a lowradio-duty-cycleof the nodes when network traffic is less, which is not considered by any existing adaptive autonomous schedulers. To validate the effectiveness of OASA, we implemented it on Contiki-NG and performed testbed experiments on FIT IoT-LAB. The testbed experiment results demonstrate the effectiveness of OASA in terms of latency, packet delivery ratio, and energy consumption compared to the existing autonomous scheduling schemes.

IEEE has standardized the 802.15.4e Time Slotted Channel Hopping (TSCH) mode to provide stringent latency, higher reliability, and low duty-cycle in various Internet of Things (IoT) applications. TSCH eliminates interference and multi-path fading on channels, but its channel hopping feature severely affects the 6TiSCH (IPv6 over IEEE 802.15.4e TSCH mode) network formation. Further, 6TiSCH Minimal Configuration standard does not provide sufficient bandwidth (i.e., minimal cell) for quick transmission of control packets required by the new nodes (i.e., pledges) during their network association. Many works have been proposed on 6TiSCH network formation as it has high impact on network performance and lifetime. However, the existing works either did not use all the available physical channels while allocating minimal cell(s) or are not stable with topology changes. Therefore, this work proposes aTime-VariantRGB(TRGB) model for minimal cell allocation and scheduling, which results in faster association of pledges and maintains network stability. We evaluate the TRGB using Markov Chain model and also on a real 60-node testbed in FIT IoT-LAB. Testbed results show that TRGB achieves 51% and 23% improvement over the state-of-the-art scheme in terms of joining time and energy consumption, respectively, while maintaining stability of the network.

The 6TiSCH communication architecture provides delay-bounded packet delivery, energy efficient, and reliable data-delivering communication in mission-critical Internet of Things (IoT) applications. It uses IETF’s 6TiSCH minimal configuration (6TiSCH-MC) standard for resource allocation during network formation and routing using a routing protocol for low power and lossy network (RPL) as routing protocol. In RPL, the DODAG information solicitation (DIS) control packet is used to solicit routing information from the existing networks. However, it is observed that malicious transmission of this DIS packet can severely affect the 6TiSCH networks in terms of nodes’ network joining time and energy consumption. Therefore, designing countermeasures of DIS attack in 6TiSCH network has become critically important. Additionally, the existing works neither considered all the possible parameters together for detecting DIS attack nor energy efficient, and create control packet overhead. In this work, we model noncooperative gaming to determine the optimal probability of responding to a DIS packet. Subsequently, we design a trust model to detect malicious DIS transmission in 6TiSCH networks. Finally, we merge both the proposed gaming model and trust model to propose a scheme—gaming and trust-based countermeasure (GTCM) to reduce the effect of DIS attack in 6TiSCH networks. We implement the GTCM on Contiki-NG and validate it using open source FIT IoT-LAB testbed. Our experimental testbed results show that GTCM reduces the effect of DIS attack in terms of pledges’ (new nodes) joining time and energy consumption significantly.

The 6TiSCH communication architecture is widely used in Industrial Internet of Things (IIoT) to provide reliable, delay-bounded, and energy-efficient communication in multihop scenarios. However, the channel hopping feature and the resource allocation strategy of 6TiSCH minimal configuration (6TiSCH-MC) standard negatively impact the 6TiSCH network by increasing network formation time. 6TiSCH-MC allows only one cell (known as minimal cell) per slotframe to transmit control packets. When the number of joined nodes increases in the network, the formation time also increases because of the increasing congestion in the minimal cell. Furthermore, the existing works did not study the effect of transmission rates of all control packets together during 6TiSCH network formation. Therefore, in this work, a noncooperative game is formulated, for optimal transmission of control packets by the joined nodes. The obtained solution of the proposed game, using the Lagrange multiplier and Karush–Kuhn–Tucker (KKT) conditions, is used in the proposed congestion control scheme—game theory-based congestion control (GTCC). GTCC calculates the slotframe window (SW) size for every node to control the congestion in minimal cell without any signaling overhead. GTCC is validated using the analytical model as well as the FIT IoT-LAB testbed. The findings of both the analytical and testbed experiments show that GTCC significantly reduces the joining time and energy consumption of new nodes (i.e., pledges) as compared to previous benchmark schemes.

The IPv6 over IEEE 802.15.4e TSCH mode (6TiSCH) network is intended to provide reliable and delay bounded communication in multi-hop and scalable Industrial Internet of Things (IIoT). The IEEE 802.15.4e Time Slotted Channel Hopping (TSCH) link layer protocol allows the nodes to change their physical channel after each transmission to eliminate interference and multi-path fading on the channels. However, due to this feature, new nodes (aka pledges) take more time to join the 6TiSCH network, resulting in significant energy consumption and inefficient data transmission, which makes the communication unreliable. Therefore, the formation of 6TiSCH network has gained immense interest among the researchers. To date, numerous solutions have been offered by various researchers in order to speed up the formation of 6TiSCH networks. This article briefly discusses about the 6TiSCH network and its formation process, followed by a detailed survey on the works that considered 6TiSCH network formation. We also perform theoretical analysis and real testbed experiments for a better understanding of the existing works related to 6TiSCH network formation. This article is concluded after summarizing the research challenges in 6TiSCH network formation and providing a few open issues in this domain of work.

6TiSCH standardization helps the Industrial Internet of Things (IIoT) to achieve reliable and timed data delivery. It also deals with minimal resource allocation during network formation. However, faster network formation using minimum resources remains an active research issue. 6TiSCH minimal configuration standard (6TiSCH-MC) recommends to use only one cell per slotframe known as the minimal cell for all the nodes to transmit their network bootstrapping traffic. It is observed that, including 6TiSCH-MC, the existing schemes did not use all the available cells, and so, all the physical channels at the timeslot where this minimal cell resides, i.e., at timeslot zero. It results in underutilization of channel resources, and thus, higher network formation time. To leverage the available cells at timeslot zero of each slotframe, and thus to improve the network formation performance, an autonomous allocation and scheduling of minimal cell (TACTILE) is proposed. The main challenge is to utilize the available cells at timeslot zero as there is a minimal cell already scheduled for network bootstrapping. To address this issue, TACTILE distributes the location of the minimal cell as per nodes' EUI64 addresses along the different physical channels followed by scheduling them intelligently to avoid de-synchronization among nodes. Combined with Markov chain-based theoretical analysis, evaluation of TACTILE is done on the FIT IoT-LAB real testbed. The testbed results show that TACTILE can achieve 87% and 42% improvements in terms of joining time and energy consumption, respectively, compared to 6TiSCH-MC.

The IPv6 over the TSCH mode of IEEE 802.15.4e (6TiSCH) Working Group released 6TiSCH minimal configuration (6TiSCH-MC) standard for 6TiSCH network bootstrapping. 6TiSCH-MC allocates only one shared cell per slotframe, known as a minimal cell, to transmit all types of control packets. Our Markov Chain-based probabilistic analysis reveals that few network parameters have a high impact on the performance of 6TiSCH network formation due to increasing congestion in the minimal cell with the increased number of nodes. This work aims to improve the 6TiSCH network formation process by reducing congestion in the minimal cell. For this, the Trickle algorithm, which is used for controlling the rate of routing information-carrying packet transmission is modified so that sufficient routing information can be provided without congesting the minimal cell. To reduce the congestion further, a slotframe window (SW)-based adaptive scheme is proposed by which nodes are restricted to transmit their control packets frequently. The proposed dynamic Trickle algorithm and SW-based scheme are implemented on Contiki-NG and evaluated using FIT IoT-LAB testbed. The experimental results show that both the proposed schemes together improve the joining time and energy consumption of the pledges compared to the state-of-the-art schemes. Additionally, both the proposed schemes provide fair control packet transmission opportunities among the nodes in a network.

Industrial applications of Internet of Things (IoT) demand high reliability, deterministic latency, and high scalability with energy efficiency to the communication and networking protocols. 6TiSCH is a time slotted channel hopping (TSCH) medium access control (MAC) protocol running under the IPv6 enabled higher layer protocols for industrial IoT (IIoT). In this paper, we theoretically analyze the network formation protocol in 6TiSCH network. Analysis reveals that the performance of the 6TiSCH network degrades when a pledge (new node) joins as it increases channel congestion by allowing to transmit beacon message. On the other hand, beacon transmission is essential to expand or reorganize the present network topology. To overcome this performance tradeoff, a channel condition based dynamic beacon interval (C2DBI) scheme is proposed in which beacon transmission interval varies with channel congestion status during network formation. Channel congestion status is estimated by each joined node in distributed manner, and subsequently changes its beacon generation interval to best fit with present condition. Finally the performance of C2DBI is compared with the minimal configuration standard and few other benchmark protocols. Analytical, simulation and real testbed results show that the proposed scheme outperforms the state of the art protocols in terms of joining time and energy consumption during network formation.

Network bootstrapping is one of the initial tasks executed in any wireless network such as Industrial Internet of Things (IIoT). Fast formation of IIoT network helps in resource conservation and efficient data collection. Our probabilistic analysis reveals that the performance of 6TiSCH based IIoT network formation degrades with time because of the following reasons: (i) IETF 6TiSCH Minimal Configuration (6TiSCH-MC) standard considered that beacon frame has the highest priority over all other control packets, (ii) 6TiSCH-MC provides minimal routing information during network formation, and (iii) sometimes, joined node can not transmit control packets due to high congestion in shared slots. To deal with these problems, this article proposes two schemes—opportunistic priority alternation and rate control (OPR) and opportunistic channel access (OCA). OPR dynamically adjusts the priority of control packets and provides sufficient routing information during network bootstrapping, whereas OCA allows the nodes having urgent packet to transmit it in less time. Along with the theoretical analysis of the proposed schemes, we also provide comparison-based simulation and real testbed experiment results to validate the proposed schemes together. The received results show significant performance improvements in terms of joining time and energy consumption.