This article introduces the concept of context-aware attribute learning with cipher policy-attribute-based encryption (CP-ABE) to preserve the privacy of users’ information in IoT-enabled Society 5.0. The concept of Society 5.0 pioneers an abstract system unifying different smart environments (SEs) to provide seamless services to the citizens. While serving different applications, these SEs store users’ information in the cloud engendering users’ privacy. CP-ABE is one of the conventional security systems that preserves privacy with group data accessibility. Contemporary CP-ABE solutions enforce users to manually provide their contextual information, namely, attributes, to encrypt/decrypt data. From these solutions it can be conjectured that incorrect attribute selection by a user raises the issue of unauthenticated access to information. To address these issues, we propose a scheme, named the context-aware attribute learning scheme (CASE), which autonomously learns users’ contextual information, exploiting edge intelligence, generates attributes, and reduces the post-encryption data size using the learned attributes. We examine the performance of CASE with the help of a case study on CP-ABE over smart healthcare systems (SHSs). Extensive experimental results show that CASE outperforms the existing CP-ABE-based security schemes by reducing 32%–33% average network delay, 33%–35% average energy consumption, and 31%–36% average packet loss. Additionally, we analyze the performance of attribute learning schemes using the support vector machine (SVM), decision tree (DT), and naive Bayes (NB) learning models. We observe that DT reports better performance over SVM and NB in prediction accuracy, prediction time, and clock cycles required for execution.

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.

In this article, we present the design of an Internet-of-Things (IoT)-based dynamic irrigation scheduling system (AgriSens) for efficient water management of irrigated crop fields. The AgriSens provides real time, automatic, dynamic as well as remote manual irrigation treatment for different growth phases of a crop's life cycle using IoT. A low-cost water-level sensor is designed to measure the level of water present in a field. We propose an algorithm for automatic dynamic-cum-manual irrigation based on farmer requirements. The AgriSens has a farmer-friendly user interface, which provides field information to the farmers in a multimodal manner - visual display, cell phone, and Web portal. It achieves significant results with respect to different performance metrics, such as data validation, packet delivery ratio, energy consumption, and failure rate in various climatic conditions and with dynamic irrigation treatments. Experimental results show that the AgriSens helps improve the crop productivity by at most 10.21% over the traditional manual irrigation method, expands the network's lifetime 2.5 times more than the existing system yet achieving a reliability of 94% even after 500 h of operation.

This paper explores the concept of AI-based Communication-as-a-Service (ACUTE) to reduce transmission delay and energy consumption, while transmitting data from end-devices to the cloud in the context of Society 5.0. Society 5.0 revolutionizes connected living with the help of a unified system that provides fully automated and end-to-end services, while addressing the demands of all the citizens or users in a society. On the other hand, 6G is one of the promising communication platforms that offers the communication requirements of Society 5.0 by provisioning dense network deployment and fast data delivery. Building Society 5.0 founded on the 6G architecture enables serialized data transmission in the connected living fabric by allowing a user to connect with an access point and transmit data over a single path. Without concurrent and intelligent data transmission, the communication framework of Society 5.0 increases network delay and overall energy consumption and affects the Quality-of-Service (QoS). To address these issues, we propose a solution founded on the concept of Communication-as-a-Service (CaaS), which offers an architecture to facilitate intelligent access point virtualization for enabling concurrency in data transmission across individual users in a 6G-enabled Society 5.0. In ACUTE, a virtual module (VM) employed at each edge device performs concurrent data transmissions by associating with a virtual access point (VAP), which is a set of access points optimally selected using Fuzzy C-Means. Thereafter, the VM forms a virtual path (VP), which maps to a set of paths between physical access points and VAPs. ACUTE distributes the data through the VAP and associated VP and randomizes data sequence for transmission across VP. Experimental results show that ACUTE outperforms the state-of-the-art while reducing the network delay by 27%, energy consumption by 95%, packet loss by 95%, and service cost by 26%.

In this article, we propose an Energy-aware Collaborative Routing (ECoR) scheme for optimally handling task offloading between source and destination UAVs in a grid-locked UAV swarm. We divide the proposed scheme into two parts - routing path discovery and routing path selection. The scheme selects the most optimal path between a source and destination from a massive set of all possible paths, based on the maximization of residual energy of UAVs along a selected path. This routing path selection ensures balanced energy utilization between members of the UAV swarm and enhances the overall path lifetime without incurring additional delays in doing so. Actual readings from our small-scale UAV swarm testbed are utilized to emulate a large-scale scenario and analyze the behavior of our proposed scheme. Upon comparison of the ECoR scheme with broadcast-based routing and the shortest path based routing, we observe better sustainability regarding the longevity of the UAV lifetimes in the swarm, optimized individual UAV, as well as reduced collective path-based energy consumption, all the while having comparable transmission delays to the shortest path based scheme.

Integration of various technologies to an Internet of Things (IoT) framework share the common goals of a consistent and structured data format that can be applied to any device, given the vast application scope of IoT. Additional goals include minimizing channel traffic and system energy consumption. In this paper, we propose to dismiss the requirement of certain seemingly crucial identifier fields from packets arriving through various sensor nodes in an agricultural IoT deployment. The proposed approach reduces packet size, thereby reducing channel traffic and energy consumption, as well as retaining the capability of identifying these originating nodes. We propose a method of a blind agricultural IoT node and sensor identification, which can be sourced and operated from a master node as well as a remote server. Additionally, this scheme has the capability of detecting the radio link quality between the master and slave nodes in a rudimentary form, as well as identifying the sensor nodes. We successfully trained and tested various multilayer perceptron-based models for blind identification, in real-time, using our implemented agricultural IoT implementation. The effect of changes in learning rate and momentum of the optimizer on the accuracy of classification is also studied. The projected cumulative energy savings across the network architecture, of our scheme, in conjunction with TCP/IP header compression techniques, are substantial. For a 100 node deployment using a combination of the proposed blind identification reduced sampling strategies over regular IPv4-based TCP/IP connection, an estimated annual saving of ≈99% is projected.

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 this paper, we propose the use of a Long Short-Term Memory (LSTM) based server-side sequence prediction algorithm to ease network data-load caused by rapid polling of multiple sensors onboard aerial robotic platforms, which are wirelessly tethered to a remote server for control and coordination. Our scheme reduces the network access time latencies between these platforms and the remote server hosting the control and scheduling mechanisms. Reduction in the TDMA-based access time is achieved by reducing the actual amount of data transmitted over the network, using partial transmission of actual sensor data over the network and server-side sequence prediction of the voluntarily missed sensor values. Our scheme allows the TDMA control of an increased number of networked platforms without change of infrastructure or the network characteristics.

In many developing countries, agriculture is one of the primary livelihoods of common people. Agriculture requires various types of technologies for improving crop yields. The attack of animals in the agricultural land and the theft of crops by humans cause heavy loss in cultivation. In this work, we propose a hardware prototype using Wireless Sensor Network (WSN) for intruder detection in an agricultural field. The proposed system is named Agricultural Intrusion Detection (AID). AID helps to generate alarms in the farmer's house and at the same time transmits a text message to the farmer's cell phone when an intruder enters into the field. In order to implement the proposed scheme, we design and deploy Advanced Virtual RISC (AVR) micro-controller-based wireless sensor boards over an outdoor environment and evaluate the performance.

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.