As the Metaverse evolves, robust authentication is essential to protect digital avatar privacy from identity threats such as theft, unauthorized access, and avatar spoofing. A user’s gait, serving as an intrinsic biometric signature of their avatar, offers a seamless and continuous authentication mechanism, enhancing security. Traditional authentication methods, including passwords, biometrics, and facial or fingerprint recognition, face challenges in virtual environments due to occlusions, spoofing risks, and hardware dependencies. To address these limitations, we introduce AutoGaitAnalyzer, a novel gait authentication framework that uses 16 gait features from a large-scale simulation of 5,000 users. Benchmarked against over 10 state-of-the-art models, AutoGaitAnalyzer outperforms all, establishing a new standard for avatar security in the Metaverse.

The IEEE 802.15.4 standard is one of the widely adopted networking specifications for realizing different applications of the Internet of Things (IoT), One of its Medium Access Control (MAC) protocols, the Deterministic Synchronous Multi-channel Extension (DSME), enhances stringent QoS by allocating DSME-Guaranteed Time Slots (GTSs) between pairs of devices. However, the standard does not specify a mechanism for scheduling these DSME-GTSs, presenting numerous research opportunities in this area. In this paper, we propose a novel Dynamic Hierarchical Slot-Channel Allocation with Recursive Weighting (DyHSARW) scheme aimed at improving the scheduling of DSME-GTS in large-scale IEEE 802.15.4-based IoT networks. The proposed approach dynamically allocates non-overlapping time slots (using the HCF technique) across multiple channels (based on a device's association order), optimizing resource utilization while adapting to the hierarchical structure of the network. Specifically, the HCF condition checks if the transmission weights of a child-parent pair and the previous time slot's value are compatible, i.e., if their HCF is equal to 1. This condition indicates that the parameters are co-prime, which minimizes the likelihood of collision in the time slot allocation process. This method seeks to overcome the limitations of existing GTS scheduling algorithms, which face challenges in efficient resource allocation, increased latency, and higher energy consumption under dynamic traffic conditions.

Wireless power transfer (WPT) techniques are emerging as a fundamental component of next-generation energy management in mobile networks. In this context, the use of UAVs opens many possibilities, either using them as mobile energy storage devices to recharge IoT nodes, or to prolong their operation time via smart charging themselves at ground stations. This paper surveys the recent literature on WPT as it applies to UAVs and identifies several open research challenges for the future. As a first step, we tessellate the related research corpus in four fundamental categories (architectures, power and communications enabling technologies, optimization with respect to spatial concepts, optimization of operational aspects). Second, for each category, we provide a critical review of the recent WPT UAV approaches with respect to the way they specialize the general concept of WPT and the extent of their applicability. The survey presents the latest advances in WPT UAV methodologies and related energy-centric services, spanning all the way from the communications aspects deep in the small- and large-scale deployments, up to the operational and applications aspects. Finally, motivated by the rich conclusions of this critical analysis, we identify open challenges for future research. Our approach is horizontal, as the selected publications were drawn from across all vertical areas of research on UAVs. This paper can help the readers to deeply understand how WPT is currently applied to UAVs, and select interesting open research opportunities to pursue.

The recent advances in wireless energy transfer (WET) provide an alternate and reliable option for replenishing the battery of pervasive and portable devices, such as smart-phones. The peer-to-peer (P2P) mode of WET brings improved flexibility to the charging process among the devices as they can maintain their mobility while replenishing their battery. Few existing works in P2P-WET unrealistically assume the nodes to be exchanging energy at every opportunity with any other node. Also, energy exchange between the nodes is not bounded by the energy transfer limit in that inter-node meeting duration. In this regard, the parametric heterogeneity (in terms of device's battery capacity and WET hardware) among the nodes also affects the energy transfer bound in each P2P interaction, and thus, may lead to unbalanced network energy distributions. This inherent heterogeneity aspect has not been adequately covered in the P2P-WET literature so far, especially from the point of view of maintaining a balanced energy distribution in the networked population. In this work, we present a Heterogeneity-aware Wireless Energy Transfer (HetWET) method. In contrast to the existing literature, we devise a fine-grained model of wireless energy transfer while considering the parametric heterogeneity of the participating devices. Thereafter, we enable the nodes to explore and dynamically decide the peers for energy exchange. The performance of HetWET is evaluated using extensive simulations with varying heterogeneity settings. The evaluation results demonstrate that HetWET can maintain lower energy losses and achieve more balanced energy variation distance compared to three different state-of-the-art methods.

Battery aging is one of the major concerns for the pervasive devices such as smartphones, wearables and laptops. Current battery aging mitigation approaches only partially leverage the available options to prolong battery lifetime. In this regard, we claim that wireless crowd charging via network-wide smart charging protocols can provide a useful setting for applying battery aging mitigation. In this paper, for the first time in the state-of-the-art, we couple the two concepts and we design a fine-grained battery aging model in the context of wireless crowd charging, and two network-wide protocols to mitigate battery aging. Our approach directly challenges the related contemporary research paradigms by (i) taking into account important characteristic phenomena in the algorithmic modeling process related to fine-grained battery aging properties, (ii) deploying ubiquitous computing and network-wide protocols for battery aging mitigation, and (iii) fulfilling the user QoE expectations with respect to the enjoyment of a longer battery lifetime. Simulation-based results indicate that the proposed protocols are able to mitigate battery aging quickly in terms of nearly 46.74-60.87 % less reduction of battery capacity among the crowd, and partially outperform state-of-the-art protocols in terms of energy balance quality.

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

Peer-to-peer wireless power transfer (P2P-WPT) enables portable devices to mutually exchange energy. In opportunistic mobile networks, P2P-WPT can be uncertain due to the varying user inter-meeting duration. Existing P2P-WPT methods (unrealistically) assume the users to be exchanging energy at each opportunity, to be able to interact with all users, or the inter-node meeting duration to be unaffected by users' mobility. In this paper, in contrast to the state-of-the-art, not only we constitute more fine-grained, realistic assumptions for P2P-WPT, but also we design MobiWeb, a mobility-aware energy balancing method, which employs (for the first time) a predictor for estimating the mobility information of users. MobiWEB selects the different pairs of peers for energy exchange, such that the network energy distribution is balanced while minimizing the loss and energy difference between the peers. MobiWEB, when compared to the state-of-the-art, achieves different performance trade-offs between energy balance quality, convergence time, and energy-efficiency.

The advent of the Internet of Things (IoT) inspired various new and enhanced sets of applications in multiple domains including agriculture. The recent drive in the adoption of IoT technologies offers a major enhancement for the agricultural sectors in terms of efficiency and scalability. In this article, we investigate the specific issues and challenges associated with IoT, and review various IoT architectures, communication, middleware, and information processing technologies. We, then, discuss few IoT applications for agriculture-presenting various case studies to thoroughly analyze the solutions along with their design and implementation related parameters. Consequently, we provide a comprehensive review of the available simulation tools, data sets, and testbeds which provisions experimentation with IoT in agriculture. We enumerate open issues and challenges present in enabling IoT for agriculture. Finally, this article concludes while giving directions for future research.

Node localization is a fundamental requirement in underwater sensor networks (UWSNs) due to the ineptness of GPS and other terrestrial localization techniques in the underwater environment. In any UWSN monitoring application, the sensed information produces a better result when it is tagged with location information. However, the deployed nodes in UWSNs are vulnerable to many attacks, and hence, can be compromised by interested parties to generate incorrect location information. Consequently, using the existing localization schemes, the deployed nodes are unable to autonomously estimate the precise location information. In this regard, similar existing schemes for terrestrial wireless sensor networks are not applicable to UWSNs due to its inherent mobility, limited bandwidth availability, strict energy constraints, and high bit-error rates. In this article, we propose SecRET , a Secure Range-based localization scheme empowered by Evidence Theory for UWSNs. With trust-based computations, the proposed scheme, SecRET , enables the unlocalized nodes to select the most reliable set of anchors with low resource consumption. Thus, the proposed scheme is adaptive to many attacks in UWSN environment. NS-3 based performance evaluation indicates that SecRET maintains energy-efficiency of the deployed nodes while ensuring efficient and secure localization, despite the presence of compromised nodes under various attacks.

Virtual sensor provisioning is an essential process in sensor-cloud-based Internet of Things (IoT), and it is responsible for the efficient utilization of physical resources in the system. However, the existing schemes for virtual sensor provisioning do not provide an optimal solution while considering overall demand of multiple users/services. As a result, redundant sensor nodes are provisioned, which leads to increased energy consumption and reduced network lifetime. In this paper, we present a dynamic virtual sensor provisioning scheme for sensor-cloud-based IoT applications to maintain the energy efficiency of the deployed physical sensor nodes while maintaining the quality of service (QoS) of the service requests. We model the interaction between the cloud service provider and the sensor owners using the single-leader multifollower Stackelberg game. The players of the game exploit the spatial correlation among the on-field sensor nodes, and consequently, the oligopoly created between the players is dynamically updated. We show the existence of a Stackelberg-Nash-Cournot equilibrium in the game. We evaluated the performance of the proposed scheme through extensive simulations. The results depict improvement in the energy efficiency of the nodes as well as increase in the lifetime of the deployed on-fields sensors in the proposed scheme compared to benchmark schemes. We also plot the average number of QoS violations in each iteration for the user requests.

In sensor-cloud framework, the concept of virtual sensor provisioning is applied to serve the end- users, who requests sensing information from the deployed sensor network. In a multi-hop sensor- cloud framework, the information collection from the physical sensors to the virtual sensor needs to activate additional nodes for information forwarding to the Cloud Service Provider (CSP). The existing works mainly consider the activation of these nodes from the same sensor owner (SO) and exhibit higher energy consumption. Although, in a sensor-cloud framework, multiple SOs co-exist naturally, and consequently, the service area of these SOs overlap. In this paper, contrasting to the existing works, we argue that the collaboration between the CSP and SOs can improve dynamic virtual sensor provisioning. We propose a scheme named Intelligent Dynamic Virtual Sensor Provisioning (iDVSP) to enable optimal selection of nodes in a multi-hop path with different SOs. We employ multi-unit single-item combinatorial reverse auction to model the interaction between the CSP and SOs. The auction based scheme facilitates the CSP to dynamically negotiate with the SOs, and ensure cost-effective node selection for virtual sensor provisioning. Simulation based results indicate that the proposed scheme is 46.51% energy-efficient compared to existing literature. Furthermore, we observe that the proposed scheme employ fair policy for node selection from different SOs. Therefore, we can argue that the proposed scheme enforces cooperation between the SOs in the sensor-cloud framework.

In a smart grid, micro-grids can exchange energy among themselves in order to provide reliable energy service to customers. Therefore, the micro-grids need to exchange their real-time energy status with other micro-grids, which, in turn, maximizes the energy consumption and CO2emissions to them. In this paper, we propose a cloud-based energy forecasting scheme to minimize the energy consumption and CO2emission towards enabling a green smart grid communication technology. Additionally, we device an optimal strategy for the proposed cloud-based energy forecasting scheme to minimize the energy consumption furthermore. Numerical results show the effectiveness of the proposed scheme over without cloud-based approach in terms of message overhead, energy consumption, and CO2emissions of the micro-grids. We see that the proposed scheme can minimize the energy consumption and the CO2emissions involved in the forecasting process significantly, which supports the green architecture of the smart grid communication technology. Additionally, the message overhead for energy forecasting can also be minimized.

In this paper, energy trading for the distributed smart grid architecture is projected as an incomplete information game —a viewpoint that contrasts from all the existing pieces of literature available on the broader issue of energy management in smart grid. The incomplete information is considered as the real-time demand and price to grid and customers, respectively, due to the packet loss in the communication network. Therefore, the paper addresses a realistic scenario, in which real-time information to the destination may not be guaranteed to be received adequately, due to the packet loss. In the proposed scheme, we introduce two types of intelligent agents— customer-agents and grid-agent . The customer-agents are deployed at the customers׳ end, and are capable of estimating adequately the real-time price decided by the grid. On the contrary, the grid-agent is deployed at the service provider׳s end, and are also capable of estimating adequate real-time energy demand from the customers. Therefore, one of the key advantage of the proposed agent-based scheme is that the customers and the grid are not involved in complex calculations in order to take real-time decisions for cost-effective energy management, while there is information loss in the communication networks. In the proposed game model, the grid-agent and the customers agents are the players, and estimate real-time demand and price based on the probability of belief to each other. We show the existence of Bayesian Nash Equilibrium in the proposed model, where the utility of the players is maximized. We compare the real-time price with and without packet loss as the price with incomplete and complete information, respectively. We observe that the proposed model is beneficial for the grid, as its utility is maximized. The simulation results show that the utility of the grid increases approximately 40% over that of the existing ones under the scenario of information incompleteness.

In this paper, we propose a localization scheme named Opportunistic Localization by Topology Control (OLTC), specifically for sparse Underwater Sensor Networks (UWSNs). In a UWSN, an unlocalized sensor node finds its location by utilizing the spatio-temporal relation with the reference nodes. Generally, UWSNs are sparsely deployed because of the high implementation cost, and unfortunately, the network topology experiences partitioning due to the effect of passive node mobility. Consequently, most of the underwater sensor nodes lack the required number of reference nodes for localization in underwater environments. The existing literature is deficient in addressing the problem of node localization in the above mentioned scenario. Antagonistically, however, we promote that even in such sparse UWSN context, it is possible to localize the nodes by exploiting their available opportunities. We formulate a game-theoretic model based on theSingle-Leader-Multi-Follower Stackelberg gamefor topology control of the unlocalized and localized nodes. We also prove that both the players choose strategies to reach asocially optimal Stackelberg-Nash-Cournot Equilibrium. NS-3 based simulation results indicate that the localization coverage of the network increases upto 1.5 times compared to the existing state-of-the-art. The energy-efficiency of OLTC has also been established.

Future large-scale deployment of plug-in hybrid electric vehicles (PHEVs) will render massive energy demand on the electric grid during peak-hours. We propose an intelligent distributed dynamic pricing (D2P) mechanism for the charging of PHEVs in a smart grid architecture-an effort towards optimizing the energy consumption profile of PHEVs users. Each micro-grid decides realtime dynamic price as home-price and roaming-price, depending on the supply-demand curve, to optimize its revenue. Consequently, two types of energy services are considered-home micro-grid energy, and foreign micro-grid energy. After designing the PHEVs' mobility and battery models, the pricing policies for the home-price and the roaming-price are presented. A decision making process to implement a cost-effective charging and discharging method for PHEVs is also demonstrated based on the real-time price decided by the micro-grids. We evaluate and compare the results of distributed pricing policy with other existing centralized/distributed ones. Simulation results show that using the proposed architecture, the utility corresponding to the PHEVs increases by approximately 34 percent over that of the existing ones for optimal charging of PHEVs.

In this paper, we propose a dynamic duty scheduling scheme for minimizing the energy consumption of the on-field sensor networks in a sensor-cloud application framework. The conjugation of cloud framework with Wireless Sensor Networks (WSNs) adds enhanced processing and storage capacity to the on-field WSN applications. However, the WSN applications performing periodic information update to the cloud exhibit low network lifetime, low resource utilization, and high cost. In this regard, the advent of the sensor-cloud technology facilitates dynamic duty scheduling of the on-field WSNs. As a result, the on-field WSNs attain improved energy-efficiency and cost-effectiveness. The simulation results show the effectiveness of the proposed scheme over the traditional scenarios.