With the widespread expansion of telecommunication networks, the increase in the number and complexity of base stations has led to an exponential growth in the volume of alarms. Traditional alarm prediction based on expert experience or rules has posed significant challenges due to the demand for engineers’ expertise and workload. It has become imperative to enhance efficiency by employing data-driven approaches for network alarm prognosis. In this paper, a data-driven alarm prediction model is proposed to support the alarm prognosis in base stations. To improve model performance, the proposed approach utilises ensemble deep learning methods to address the heterogeneity and highly imbalanced alarm dataset. The model is trained and validated using a dataset provided by British Telecom (BT) group. The validation results demonstrate that the proposed method achieves a top-5 accuracy of up to 90% in predicting alarms across 170 categories on the validation set.

Maritime transport is an indispensable element of the global logistics network. Most maritime loading-unloading operations are supported by quay cranes, making their availability and condition critical to port operations. This work identifies discordant trends arising during the vibration-based condition monitoring of these quay cranes in one of the busiest container ports in the United Kingdom. This work proposes an unsupervised and constrained discord detection approach for irregular but near real-time time series data obtained from multi-modal IoT-based condition monitoring sensors installed on these cranes and transmitted over a 5G network. Due to the live nature of the seaport’s operations, the development of controlled anomaly signatures for a baseline reference was not possible. To address the challenges of incomplete asset health information, irregular and batched time-series sensor data, massive data volumes, and the lack of the assets’ vibration signature baselines, this paper proposes an unsupervised, robust, and fast discord detection mechanism that can rapidly highlight discordant time-series chunks in the received vibration data at a central server. A Support Vector Machine based One-Class Classifier (OCC-SVM) is used to identify the discordant vibration signatures in the time series data. During the development of this approach, the timestamped data chunks from the add-on IoT-based vibration sensors were clustered into two weight classes (loaded and unloaded) based on the crane’s default Programmable Logic Controller (PLC) sensors. The efficacy of this method is checked against the crane maintenance logs and data from a separate crane’s vibration and PLC data. Further, a method for generating synthetic noise-embedded vibration signatures to test the effectiveness of the discord detection method has been devised. Finally, the practicality of the proposed OCC-SVM approach for discord detection was inspected in a constrained environment setting.

In this article, we propose and implement i-AVR, an Internet of Things (IoT)-based critical-aware system for point-of-care recommendation during ambulatory in-transits. The delay due to ambulances stuck in traffic congestion, disruptive roadways, and far-away hospitals restrain the smooth ambulance services. Therefore, in order to assist the time-critical scenario of a hospital-bound patient, we consider a guidance system to address the necessity. Moreover, these patients require continuous vitals monitoring, which may vary with the progress of time, to reduce the response time upon reaching the destination. The implemented i-AVR comprises two units: 1) a portable healthcare unit and 2) an android navigation unit. The healthcare unit aims to compute the criticality index of the en-route patient and recommend the nearest healthcare center while the navigation unit recommends the convenient route in case of any anomaly in vitals. We show the effectiveness of i-AVR regarding network performance while highlighting the response time of the system. We observe the system response time for computation in orders of seconds and interunit communication in milliseconds. Eventually, this analysis indicates the effectiveness of i-AVR in providing quick decisions during the time-critical situations. Our implementation provides essential intervention toward IoT-based healthcare technologies.

This article presents a ubiquitous domain adaptation (UDA) and generalizability technique for vibration-based automated machine status monitoring at the edge. The method significantly reduces the effects of signal noise artifacts and device/usage-specific vibration signatures using basic time-frequency domain signal operations and a lightweight ensemble of data-driven classifiers, allowing the method to be used for reliable domain-invariant status monitoring of motorized equipment. An experimental setup using vibration data from an air-cooled electric blender motor (source domain) is used to train an automated machine state identification classifier that can identify the operating states of an eccentric rotating mass vibration motor (target domain). Initial deployment of this method on target-domain motorized devices resulted in a machine status monitoring accuracy of at least 81.6% and a maximum training accuracy of almost 99% on known data of the source domain and 91.49% for unseen data in the target domain within an acceptable time frame. The performance of the proposed method is also comparable across platforms ranging from resource-constrained edge to a resource-rich cloud. This approach facilitates the use of noisy or uncalibrated sensor data in data-driven machine status monitoring tasks, therefore allowing for the development of reusable, low-cost monitoring systems that require meagre developmental effort, resulting in accelerated deployment times.

In this work, we propose SemBox - Semantic interoperability in a Box, to enable wireless on-the-go communication between heterogeneous wearable health monitoring devices. It can connect wirelessly to the health monitoring devices and receive their data packets. It uses a Mamdani-based fuzzy inference system with data pre-processing to classify the received data packet into one of the classes of the vital parameters. It enables semantic interoperability by labelling and annotating the data packets based on the extracted packet information. We implement SemBox using three different health monitoring wearables, with different keywords used for each vital parameter representation in the data packet. SemBox shows a maximum classification accuracy of 85.71%, with a maximum PDR of 1 at the SemBox with varying device parameters. Overall, SemBox is a potential plug-and-play solution to achieve semantic interoperability and collaboration between heterogeneous health monitoring wearable devices, irrespective of their commercial and proprietary specifications. It is customizable for applications that use multiple heterogeneous devices for collaborative monitoring and decision support. SemBox enables interoperability among health monitoring devices, introduces flexibility and ease the inter-device dynamics in the domain of biomedical research.

In this work, we propose Timed Loop Gears (TLG), as a distributed method for enablingfragmented learningin Resource-Constrained networked IoT edge devices. TLG identifies atomic operations (gears), such as feed-forward and back-propagation, necessary for training Machine Learning (ML) models. Each of these gears executes on a Fog Node (FN) exclusively for each data point at a time rather than the whole dataset in its entirety. Additionally, the networked Edge Devices (EDs) offload the training data to the fog layer using the Message Queuing Telemetry Transport (MQTT) protocol such that the participating FNs subscribe to incoming training data and store them based on topics, simplifying data sharing. TLG enables the FN to then transfer the partially learned weights to the next suitable FN for further training. This looping of weights is repeated across FNs until the training is complete. Through extensive analysis, we observe that, compared to existing distributed ML training approaches, for$n$devices, TLG reduces the probability of disruption due to device failure by$n^{2}$times. Implementation results of our fragmented learning method demonstrate that, although TLG negligibly increases the memory consumption of the IoT devices by$0.8\%$, it reduces CPU usage by almost$90\%$. The proposed method proves beneficial for developing and hosting ML models, even on constrained IoT devices, in contrast to existing lightweight ML methods.

In this work, we present a solution toward facilitating dynamic encryption schemes—Constrained Encryption as a Service (CEaaS)—in fog-enabled IoT environments. CEaaS is a two-level fuzzy inference system (FIS) in the fog layer which offers customized encryption algorithm decisions to the IoT user devices based on the current configuration, data size, and network state. Fog nodes use the two-tier FIS system to determine the category of the requesting IoT device at the first level and then the encryption scheme at the second level. Existing research on encryption focuses on developing new lightweight algorithms as a global solution for all devices without considering the heterogeneity and corresponding communication links. The device and network configurations collectively add operational delays, which elevates time and security threats. Under such circumstances, a solution that considers both the conditions (varying) for determining the appropriate encryption scheme and the key is important. Through extensive implementation and deployment of heterogeneous fog nodes, we observe that CEaaS is feasible for both powerful and resource-constrained IoT user devices with CPU and memory usage as low as 0.24% and 0.9%, respectively. CEaaS also incurs delays in the range of 0.7 s and energy consumption of 0.07 Joules while securing data transmission. With the feasibility of CEaaS, the dynamic encryption schemes ensure secure communications irrespective of the device types in a fog-enabled IoT environment.

Robust and reliable communication systems, whether based on electromagnetic (EM) waves or light, fail to perform under water due to very high attenuation and changing visibility conditions. The present generation of systems designed for underwater communications relies mostly on acoustic waves, typically in the ultrasonic frequency range. In this work, we develop and evaluate a means of implementing underwater acoustic channel-based severely constrained IoT networks using low-cost, off-the-shelf, open hardware electronics and transducers, which can support direct communication between two nodes at a data rate of 2.4 kbps for over 65 m. These nodes can be deployed over much longer distances through multihop relay topologies. Furthermore, we evaluate the efficacy of our system toward supporting multimedia data transmission and even attempt multimedia streaming through our deployed underwater IoT network using video compression and reduced sampling of the video frames. We observe that the system successfully supports multihop network topologies and undertakes multimedia transmission by compromising the quality of the data. The system has a clear tradeoff between data quality, transmission range, and transmission delays.

We propose the adaptation of vibration-based condition monitoring systems and techniques, popularly used in industrial condition-based maintenance, for identifying the possibility of compromising the privacy of personal computing systems. This work exploits the automated fan-based heat dissipation features and read/write operations of disk-based storage, commonly present in personal computers, to read computing task-specific vibration signatures on the computer’s cabinet/case. These vibration signatures are then used to identify the broad classes of tasks being executed on a separate computer without ever needing to log into the monitored machine. This work builds upon the premise that heterogeneous tasks have distinct computing requirements, which translates to variations in the amount of heat generated by the computer’s processor, eventually leading to variations in the computer’s heat control fan speed. The variations in the fan’s speed and the frequency of read/write operations to disk-based storage create unique vibration signatures, which maps uniquely to the computer’s processing operations, leading to a breach of privacy of the computer. Our work’s preliminary results suggest that computer-based tasks can be mapped from their vibration signatures with an accuracy of at least$70\%$. We additionally study the task identification granularity of such an approach.

In this article, we propose SEGA, a secured edge gateway microservices architecture for industrial Internet of things-based monitoring of machines in industries. SEGA allows the secured collection, transmission, and temporary storage of data within the edge network. A k-nearest neighbors-based analytics module hosted on the edge gateway processes time-sensitive machine monitoring data on the gateway itself and identifies machines’ operational status. The system predicts the machine state and displays the monitored parameters such as current consumed, power factor, power consumption, and vibrational state of machinery. SEGA also enables secured offloading of data and advanced analytical functions from the edge gateway to the cloud. SEGA's deployment results show negligible changes in the edge gateway's performance due to the inclusion of various security and encryption mechanisms. However, the resource-constrained edge sensor nodes show an increase in wireless packet transmission latencies between them and the gateway by approximately 84.12 ms.

IoT deployments that have limited memories lack sustained computation power and have limited connectivity to the Internet due to intermittent last-mile connectivity, particularly in rural and remote locations. For maintaining congestion-free operations, most of the collected data from these networks are discarded, instead of being transmitted remotely for further processing. In this article, we propose the paradigm Timed Loop Storage to distribute the data and use the underutilized bandwidth of local network links for sequentially queuing packets of computational data that are being operated on in parts in one of the IoT nodes. While the sequenced packets are executed sequentially on the target IoT device, the remaining packets, which are currently not being operated on, distribute and keep looping over the network links until they are required for processing. A time-synchronized packet deflection mechanism on each node handles data transfer and looping of individual packets. In our implementation, although we observe that the proposed approach requires data rates of 6 Mbps, it incurs only 45 Kb usage of primary storage systems even for sizeable data, ensuring scalability of the connected IoT devices' temporary storage capabilities, thereby making it useful for real-life applications.

In this article, we present a Q-learning-enabled safe navigation system-S-Nav-that recommends routes in a road network by minimizing traveling through categorically demarcated COVID-19 hotspots. S-Nav takes the source and destination as inputs from the commuters and recommends a safe path for traveling. The S-Nav system dodges hotspots and ensures minimal passage through them in unavoidable situations. This feature of S-Nav reduces the commuter's risk of getting exposed to these contaminated zones and contracting the virus. To achieve this, we formulate the reward function for the reinforcement learning model by imposing zone-based penalties and demonstrate that S-Nav achieves convergence under all conditions. To ensure real-time results, we propose an Internet of Things (IoT)-based architecture by incorporating the cloud and fog computing paradigms. While the cloud is responsible for training on large road networks, the geographically aware fog nodes take the results from the cloud and retrain them based on smaller road networks. Through extensive implementation and experiments, we observe that S-Nav recommends reliable paths in near real time. In contrast to state-of-the-art techniques, S-Nav limits passage through red/orange zones to almost 2% and close to 100% through green zones. However, we observe 18% additional travel distances compared to precarious shortest paths.

The Internet-of-Things (IoT) environments have hard real-time tasks that need execution within fixed deadlines. As IoT devices consist of a myriad of sensors, each task is composed of multiple interdependent subtasks. Toward this, the cloud and fog computing platforms have the potential of facilitating these IoT sensor nodes (SNs) in accommodating complex operations with minimum delay. To further reduce operational latencies, we breakdown the high-level tasks into smaller subtasks and form a directed acyclic task graph (DATG). Initially, the SNs offload their tasks to a nearby fog node (FN) based on a greedy choice. The greedy formulation helps in selecting the FN in linear time while avoiding combinatorial optimizations at the SN, which saves time as well as energy. IoT environments are highly dynamic, which mandates the need for adaptive solutions. At the chosen FN, depending on the dependencies on the DATGs, its corresponding deadlines, and the varying conditions of the other FNs, we propose an ϵ-greedy nonstationary multiarmed bandit-based scheme (D2CIT) for online task allocation among them. The online learning D2CIT scheme allows the FN to autonomously select a set of FNs for distributing the subtasks among themselves and executes the subtasks in parallel with minimum latency, energy, and resource usage. Simulation results show that D2CIT offers a reduction in latency by 17% compared to traditional fog computing schemes. Additionally, upon comparison with existing online learning-based task offloading solutions in fog environments, D2CIT offers an improved speedup of 59% due to the induced parallelism.

The atmospheric buoyancy and intangible nature of fatal communicable viruses lead to rapid transmissions among individuals, resulting in global pandemics. Strategic lockdowns and mandatory social distancing are immediate solutions in such scenarios. However, this leads to operational disruptions in education, manufacturing, economy, transportation, governance, and community. Although technological assistance is beneficial in overcoming such issues, the current Internet of Things (IoT) infrastructure has limitations. In this article, we provide a comprehensive review of the possible IoT-based solutions that have the capacity of combating the COVID-19-like viruses. We highlight the societal impacts due to pandemics and identify the specific lacunae in current IoT solutions. We also provide comprehensive detail on how to overcome the challenges along with directions toward the possible technological trends for future research. Compared to existing reviews, our work offers a holistic view of the cause, effects, and the possible solutions that are existing, along with already existing solutions that can be customized to serve the special needs during the pandemic.

In this work, we propose and develop healthcare device interoperability (HeDI)—a system to enable device interoperability in IoT-enabled in-home healthcare monitoring platforms. The system consists of multiple sensors, each connected wirelessly to an edge device, acting as a wireless communication gateway to a remote server. The system initiates information handshaking between the sensor adapters and edge device at the beginning of the operation, which is later used to detect the sensor settings to process the data received from the sensor. The system is scalable and dynamically accommodates multiple sensors without any predefined ontologies at the edge device. The implementation of our system avoids dependencies on a system’s physical ports. The low form factor and wireless connectivity of the adapter make the system portable and convenient for in-home health monitoring. Additionally, the system allows multiple homogeneous sensors to operate at the same time in the same system. We implement and evaluate our system with a 3-lead ECG, pulse, and temperature sensors against two different network configurations—star and mesh. We use the data set generated from our implemented system for performance analysis. The network-level analysis of our system shows an average packet delivery ratio of 0.92 for star network configuration and 0.98 for mesh network configuration, ensuring the reliability of performance and its suitability for healthcare monitoring systems.

We propose “DROPS”, a scheme which dynamically selects radio protocols in an energy-constrained wearable IoT healthcare system. We consider the use of multiple radio protocols, which are capable of transmitting a patient's sensed physiological parameters to the server through Local Processing Units (LPUs). As the health parameters are non-stationary and temporally fluctuating, especially for critical patients, the selection of an appropriate radio protocol is essential to maintain the accuracy and timely delivery of data from the patient to the server. Additionally, the mobility of patients through various locations within the hospital mandates the selection of the best radio protocol among the multiple available ones for each location, to enable data to offload to the remote server. We use single-leader-multiple-follower Stackelberg non-cooperative game to map the strategic interactions between a patient's LPU and the hospital's server. “DROPS” dynamically selects the appropriate radio protocol, based on the criticality index of a patient, the reputation of the radio, the Euclidean distance between the radios and the LPU, and the load on the protocol. Results on real-life data and their large-scale emulation show that the data rate increases by almost 78% and throughput by approximately 7%, as compared to existing schemes.

In this article, we propose a lightweight blockchain-inspired framework-Magnum-as a magazine of transfer learning models in blocks. We propose the storage of these blocks on proximal fog nodes to simplify access to pretrained base models by industrial plants to tune them before deployment. We design Magnum for B5G-enabled scenarios to reduce the block transfer time. We formulate a demand-centric distribution scheme to further reduce the search and access time by adopting a nonlinear program model and solving it using the branch-and-bound method. Through extensive experiments and comparison with state-of-the-art solutions, we show that Magnum retains the accuracy of the models and present its feasibility with a maximum CPU and memory usage of 80% and 6%, respectively. Additionally, while Magnum requires a maximum of 10 s for writing models as large as 17 Mb on the blocks, it requires 16 ms for fetching the same.

The proliferation of IoT in various technological realms has resulted in the massive spurt of unsecured data. The use of complex security mechanisms for securing these data is highly restricted owing to the low-power and low-resource nature of most of the IoT devices, especially at the Edge. In this article, we propose to use blockchains for extending security to such IoT implementations. We deploy a Ethereum blockchain consisting of both regular and constrained devices connecting to the blockchain through wired and wireless heterogeneous networks. We additionally implement a secure and encrypted networked clock mechanism to synchronize the non-real-time IoT Edge nodes within the blockchain. Further, we experimentally study the feasibility of such a deployment and the bottlenecks associated with it by running necessary cryptographic operations for blockchains in IoT devices. We study the effects of network latency, increase in constrained blockchain nodes, data size, Ether, and blockchain node mobility during transaction and mining of data within our deployed blockchain. This study serves as a guideline for designing secured solutions for IoT implementations under various operating conditions such as those encountered for static IoT nodes and mobile IoT devices.

Due to the low data-rate, high propagation delay, floating node mobility, and high error probability, underwater multimedia communication is still challenging. In this paper, we propose an acoustic-based reliable streaming network for resource-constrained underwater communication. The proposed protocol uses a Null Data Packet (NDP)-based contention and acknowledgment mechanism to reduce control overhead and improve reliability and energy efficiency. With the use of a lightweight Traffic Indication Map (TIM) and video compression technique, our system's efficiency for multimedia transmission is further improved. The proposed schemes provide multimedia communication without compromising on the quality of the data transmitted. We experimentally demonstrate the proposed scheme in a hydrodynamic water-tank facility on our campus. The testbed we have built in this facility is capable of running real-time video streaming. The system's performance, which is evaluated using parameters such as coverage, range, latency, and energy consumption, was found to prove the proposed solution's validity.

In this paper, we develop and analyze a smart digital stethoscope - SkopEdge - to provide reliable remote e-health monitoring with a minimum delay while enhancing overall network performance. SkopEdge initially records the heart sounds from individuals and then senses the quality of the network. Depending on the network traffic, SkopEdge converts the audio clip into an appropriate format before transferring it to remote locations for estimating the number of heartbeats and storage. Towards this, we formulate the link quality along with SkopEdge's current configuration as a Markov Decision Process (MDP) with actions as conversion format selection. The remote server then returns the result, which SkopEdge displays on its screen. Real-time implementations show that SkopEdge works efficiently in all network conditions. Further, audio conversions usually degrade the quality of sound, but our proposed system does not change its primary components. Although SkopEdge exhibits an increase in energy consumption by 79% while converting to lower-quality formats, it also reduces the energy consumption by 99% while transmitting the same, which subsequently results in energy savings. Further, we provide an analysis of the estimated heartbeats in an audio clip by SkopEdge.

In this work, we propose Over-The-Air (OTA)-based reconfigurable IoT health-monitoring wearables, which tether wirelessly to a low-power and portable central processing and communication hub (CPH). This hub is responsible for the proper packetization and transmission of the physiological data received from the individual sensors connected to each wearable to a remote server. Each wearable consists of a sensor, a communication adapter, and its power module. We introduce low-power adapters with each sensor, which facilitates the sensor-CPH linkups and on-demand network parameter reconfigurations. The newly introduced adapter supports the interoperability of heterogeneous sensors by eradicating the need for sensor-specific modules through OTA-based reconfiguration. The reconfiguration feature allows for new sensors to connect to an existing adapter, without changing the hardware units or any external interface. The proposed system is scalable and enables multiple sensors to connect in a network and work in synchronization with the CPH to achieve semantic and device interoperability among the sensors. We test the implementation in real-time using three different health-monitoring sensor types - temperature, pulse oximeter, and ECG. The results of our real-time system evaluation depict that the proposed system is reliable and responsive in terms of the achieved packet delivery ratio, received signal strength, and energy consumption.

In this work, we propose an autonomous and onboard image-based agricultural land demarcation and path-planning system—IDeA ( I oT-Based Autonomous Aerial De marcation and Path Planning for Precision A griculture) with Unmanned Aerial Vehicles (UAVs)—using our advanced UAV-based aerial IoT platform. Our work successfully addresses the problem of onboard and autonomous path planning—which conventional UAV-based systems are not capable of—during stand-alone operations and without preloaded Global Positioning SYstem (GPS) markers for flight path waypoints. Our aerial system visually identifies non-electronically and singularly tagged agricultural plots and assesses the enclosing boundaries of the identified plot. Subsequently, an onboard path planning module autonomously generates GPS waypoints for traversing the identified plot with minimal overlaps and maximal coverage. Our proposed system exhibits an area coverage efficiency of 95.39%, performs pixel-to-GPS coordinate conversion with an accuracy of 90.35%, and has high agricultural potential in applications such as surveying crop health conditions and spraying pesticide/herbicides. The proposed system has massive applications in scenarios requiring aerial detection, demarcation, geographical tagging, and coverage of an area.

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.

We propose a method for a single ground camera-based visual gesture control of a quadrotor mUAV platform, making its flight more responsive and adaptive to its human controller as compared to a human controller using keypads or joysticks for controls. The proposed camera-based gesture control scheme provides an average accuracy of 100% gestures detected, as compared to accuracies obtained using expensive Kinect-based hardware, or processing intensive CNN-based pose estimation techniques with 97.5% and 83.3% average accuracies, respectively. A fog-based stabilization mechanism is additionally employed, which allows for flight-time stabilization of the mUAV, even in the presence of unbalanced payloads or unbalancing of the mUAV due to minor structural damages. This allows the use of the same mUAV without the need for frequent weight readjustments or mUAV calibration. This approach has been tested in real-time, both indoors as well as outdoors.

This paper looks into the problem of a decentralized data offloading within an edge unmanned aerial vehicle (UAV) swarm to mitigate the complexities of a single UAV continually generating and processing large application-specific data. The mobile edge UAVs considered here are multirotor types having constrained energy and processing power, which makes long-term handling of large data volumes impossible for standalone UAVs. The load mitigation is carried out by offloading data from a source UAV to other swarm members with sufficient energy and processing requirements. In this paper, we focus on selecting the most optimal multihop path through the UAVs concerning available energies and processing resources, which can survive the duration of the data offload between the source and a target UAV. We formulate a multiarmed bandit-based offload path selection scheme, which selects the most energy and processing optimized multihop path between a source and a target UAV. Upon comparison of our scheme against the naive shortest path approach, we observe that our approach results in significant savings of collective network energies, even for long operational durations.

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.

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.