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The Internet of Things (IoT) has become increasingly popular, and its communication requirements have grown beyond what traditional ground networks can handle. The space–air–ground (SAG) integrated network has been proposed as a potential solution, where unmanned aerial vehicles (UAVs) collect data from IoT devices and transmit them to satellites. However, the limited energy of UAVs is one of the key factors restricting communication performance, so it is necessary to consider communication energy efficiency (EE). In addition, the improvement of EE will bring about the decline of spectral efficiency (SE). Therefore we consider the tradeoff between EE and SE. Considering a SAG-IoT network, the focus of the paper is to optimize sub-channel selection, power control, and UAV position deployment to maximize EE and SE of the network, which is a multi-objective optimization (MOO) problem. On this premise, we try to improve the data throughput between IoT devices and UAVs. To solve this MOO problem, we use the ϵ -constraint to convert it into a single-objective optimization problem. We then employ various optimization algorithms such as the Dinkelbach algorithm, successive convex approximation algorithm, Lagrange dual algorithm, and block coordinate descent algorithm to solve the mixed integer non-convex problem. Simulation results show that the proposed algorithm converges to at least one sub-optimal solution. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) achieving environmental sensing are fundamental communication layer technologies in the Internet of Things. Battery-powered sensor nodes may face many problems, such as battery drain and software problems. Therefore, the utilization of self-stabilization, which is one of the fault-tolerance techniques, brings the network back to its legitimate state when the topology is changed due to node leaves. In this technique, a scheduler decides on which nodes could execute their rules regarding spatial and temporal properties. A useful graph theoretical structure is the vertex cover that can be utilized in various WSN applications such as routing, clustering, replica placement and link monitoring. A capacitated vertex cover is the generalized version of the problem which restricts the number of edges covered by a vertex by applying a capacity constraint to limit the covered edge count. In this paper, we propose two self-stabilizing capacitated vertex cover algorithms for WSNs. To the best of our knowledge, these algorithms are the first attempts in this manner. The first algorithm is stabilized under an unfair distributed scheduler (that is, the scheduler which does not grant all enabled nodes to make their moves but guarantees the global progress of the system) at most O (n 2) step, where n is the count of nodes. The second algorithm assumes 2-hop (degree 2) knowledge about the network and runs under the unfair scheduler, which subsumes the synchronous and distributed fair scheduler and stabilizes itself after O (n) moves in O (n) step, which is acceptable for most WSN setups. We theoretically analyze the algorithms to provide proof of correctness and their step complexities. Moreover, we provide simulation setups by applying IRIS sensor node parameters and compare our algorithms with their counterparts. The gathered measurements from the simulations revealed that the proposed algorithms are faster than their competitors, use less energy and offer better vertex cover solutions. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) and the Wireless Sensor Network (WSN) concepts are currently combined to improve data transmission based on sensors in near future applications. Since IoT devices exist in WSN with built-in batteries, power efficiency is a challenge that must be resolved. Clustering and routing are effectively treated as methods for reducing the dissipation of energy and maximising WSN IoT support life. This paper presents the new Energy Aware Adaptive Fuzzy neuro clustering with the WSN assisted IoT algorithm EAANFC-MR. EAANFC-MR is proposed for two main stages, clustering and multihop routing on the basis of EAANFCs. For selecting CHs with Residual Energy (RE) and Distance and Node degrees the EAANFC based cluster technique is used. The QOBFO algorithm, which is used as a Multihop Route Technique, is then used to select optimised roads to the destination. MATLAB is used to simulate the proposed EAANFC-MR model. The goodness of the EAANFC-MR model interspersed with various aspects was demonstrated through a series of experiments. [ABSTRACT FROM AUTHOR]

The fast proliferation of Internet of Things (IoT) exacerbates the current spectrum shortage. As a promising solution, licensed spectrum sharing has received great attentions recently. However, due to the unpredictable activities of incumbent users, the availability of the shared spectrum is uncertain, making the quality of service (QoS) provisioning based on it a difficult task, especially under multi-hop scenarios. In this paper, considering the energy-efficiency issue, we propose an end-to-end (E2E) QoS guaranteed green traffic off-loading scheme over the uncertain shared spectrums in multi-hop networks. Specifically, we formulate it into a two-step problem, an off-loaded traffic maximization (OTM) problem and an energy-efficiency improvement (EEI) problem. The former one is to determine the admitted sessions. To achieve the E2E QoS guarantee under the uncertain spectrum supply, we develop a concept of equivalent link capacity, and construct a risk-averse joint chance constraint. Since the formulated problem is intractable, we first develop a data-driven conservative transformation to make the joint chance constraint a set of deterministic individual ones. Then, we design a convex-hull-based and a substitution-based linearization approach to make all the constraints tractable. For the second step, the EEI problem with the same constraints as the OTM problem is designed to refine the resource scheduling for the admitted sessions to optimize the energy-efficiency. To deal with the fractional-form energy-efficiency objective, a Dinkelbach method based iterative algorithm is further developed. Numerical results have shown the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

LoRa has been recognized as one of the most promising low-power wide-area (LPWA) techniques. Since LoRa devices are usually powered by batteries, energy efficiency (EE) is an essential consideration. In this paper, we investigate the energy efficient resource allocation in LoRa networks to maximize the system EE (SEE) and the minimal EE (MEE) of LoRa users, respectively. Specifically, our objective is to maximize the corresponding EE by jointly exploiting user scheduling, spreading factor (SF) assignment, and transmit power allocations. To solve them efficiently, we first propose a suboptimal algorithm, including the low-complexity user scheduling scheme based on matching theory and the heuristic SF assignment approach for LoRa users scheduled on the same channel. Then, to deal with the power allocation, an optimal algorithm is proposed to maximize the SEE. To maximize the MEE of LoRa users assigned to the same channel, an iterative power allocation algorithm based on the generalized fractional programming and sequential convex programming is proposed. Numerical results show that the proposed user scheduling algorithm achieves near-optimal EE performance, and the proposed power allocation algorithms outperform the benchmarks. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) networks have become the infrastructure to enable the detection and reaction of anomalies in various domains, where an efficient sensory data gathering mechanism is fundamental since IoT nodes are typically constrained in their energy and computational capacities. Besides, anomalies may occur occasionally in most applications, while the majority of time durations may correspond to a healthy situation. In this setting, the range, rather than an accurate value of sensory data, should be more interesting to domain applications, and the range is represented as the category of sensory data in this paper. To decrease the energy consumption of IoT networks, this paper proposes an energy-efficient sensory data gathering mechanism, where the category of sensory data is processed by adopting the compressed sensing algorithm. The sensory data are forecasted through a data prediction model in the cloud, and sensory data of an IoT node is necessary to be routed to the cloud for the synchronization purpose, only when the category provided by this IoT node is different from the category of the forecasted value in the cloud. Experimental results demonstrate that our approach performs better than state-of-the-art techniques, in terms of the network traffic and energy consumption. [ABSTRACT FROM AUTHOR]

This paper presents an internet of things (IoTs) enabled smart meter with energy-efficient simultaneous wireless information and power transfer (SWIPT) for the wireless powered smart grid communication network. The SWIPT technique with energy harvesting (EH) is an attractive solution for prolonging the battery life of ultra-low power devices. The motivation for energy efficiency (EE) maximization is to increase the efficient use of energy and improve the battery life of the IoT devices embedded in smart meter. In the system model, the smart meter is equipped with an IoT device, which implements the SWIPT technique in power splitting (PS) mode. This paper aims at the EE maximization and considers the orthogonal frequency division multiplexing distributed antenna system (OFDM-DAS) for the smart meters in the downlink with IoT enabled PS-SWIPT system. The EE maximization is a nonlinear and non-convex optimization problem. We propose an optimal power allocation algorithm for the non-convex EE maximization problem by the Lagrange method and proportional fairness to optimal power allocation among smart meters. The proposed algorithm shows a clear advantage, where total power consumption is considered in the EE maximization with energy constraints. Furthermore, EE vs. spectral efficiency (SE) tradeoff is investigated. The results of our algorithm reveal that EE improves with EH requirements. [ABSTRACT FROM AUTHOR]

The IoT has connected a vast number of devices on a massive internet scale. With the rapid increase in devices and data, offloading tasks from IoT devices to remote Cloud data centers becomes unproductive and costly. Optimizing energy consumption in IoT devices while meeting deadlines and data constraints is challenging. Fog Computing aids efficient IoT task processing with proximity to nodes and lower service delay. Cloud task offloading occurs frequently due to Fog Computing's limited resources compared to remote Cloud, necessitating improved techniques for accurate categorization and distribution of IoT device task offloading in a hybrid IoT, Fog, and Cloud paradigm. This article explores relevant offloading strategies in Fog Computing and proposes MCEETO, an intelligent energy-aware allocation strategy, utilizing a multi-classifier-based algorithm for efficient task offloading by selecting optimal Fog Devices (FDs) for module placement. MCEETO decision parameters include task attributes, Fog node characteristics, network latency, and bandwidth. The method is evaluated using the iFogSim simulator and compared with edge-ward and Cloud-only strategies. The proposed solution is more energy-efficient, saving around 11.36% compared to Cloud-only and approximately 9.30% compared to the edge-ward strategy. Additionally, the MCEETO algorithm achieved a 67% and 96% reduction in network usage compared to both strategies. [ABSTRACT FROM AUTHOR]

The application of the Internet of Things (IoT) in wireless sensor networks (WSNs) poses serious challenges in preserving network longevity since the IoT necessitates a considerable amount of energy usage for sensing, processing, and data communication. As a result, there are several conventional algorithms that aim to enhance the performance of WSN networks by incorporating various optimization strategies. These algorithms primarily focus on the network layer by developing routing protocols to perform reliable communication in an energy-efficient manner, thus leading to an enhanced network life. For increasing the network lifetime in WSNs, clustering has been widely accepted as an important method that groups sensor nodes (SNs) into clusters. Additionally, numerous researchers have been focusing on devising various methods to increase the network lifetime. The prime factor that helps to maximize the network lifetime is the minimization of energy consumption. The authors of this paper propose a multi-objective optimization approach. It selects the optimal route for transmitting packets from source to sink or the base station (BS). The proposed model employs a two-step approach. The first step employs a trust model to select the cluster heads (CHs) that manage the data communication between the BS and nodes in the cluster. Further, a novel hybrid algorithm, combining a particle swarm optimization (PSO) algorithm and a genetic algorithm (GA), is proposed to determine the routes for data transmission. To validate the efficacy of the proposed hybrid algorithm, named PSOGA, simulations were conducted and the results were compared with the existing LEACH method and PSO, with a random route selection for five different cases. The obtained results establish the efficiency of the proposed approach, as it outperforms existing methods with increased energy efficiency, increased network throughput, high packet delivery rate, and high residual energy throughout the entire iterations. [ABSTRACT FROM AUTHOR]

Low-power wide-area networks (LPWANs) constitute a variety of modern-day Internet of Things (IoT) applications. Long range (LoRa) is a promising LPWAN technology with its long-range and low-power benefits. Performance enhancement of LoRa networks is one of the crucial challenges to meet application requirements, and it primarily depends on the optimal selection of transmission parameters. Reinforcement learning-based multi-armed bandit (MAB) is a prominent approach for optimizing the LoRa parameters and network performance. In this work, we propose a new discounted upper confidence bound (DUCB) MAB to maximize energy efficiency and improve the overall performance of the LoRa network. We designed novel discount and exploration bonus functions to maximize the policy rewards to increase the number of successful transmissions. The results show that the proposed discount and exploration functions give better mean rewards irrespective of the number of trials, which has significant importance for LoRa networks. The designed policy outperforms other policies reported in the literature and has a lesser time complexity, a comparable mean rewards, and improves the mean rewards by a minimum of 8%. [ABSTRACT FROM AUTHOR]

Energy-efficient routing protocols in Internet of Things (IoT) applications are always of colossal importance as they improve the network's longevity. The smart grid (SG) application of the IoT uses advanced metring infrastructure (AMI) to read and record power consumption periodically or on demand. The AMI sensor nodes in a smart grid network sense, process, and transmit information, which require energy, which is a limited resource and is an important parameter required to maintain the network for a longer duration. The present work discusses a novel energy-efficient routing criterion in an SG environment realised using LoRa nodes. Firstly, a modified LEACH protocol–cumulative low-energy adaptive clustering hierarchy (Cum_LEACH) is proposed for cluster head selection among the nodes. It uses the cumulative energy distribution of the nodes to select the cluster head. Furthermore, for test packet transmission, multiple optimal paths are created using the quadratic kernelised African-buffalo-optimisation-based LOADng (qAB_LOADng) algorithm. The best optimal path is selected from these multiple paths using a modified version of the MAX algorithm called the SMAx algorithm. This routing criterion showed an improved energy consumption profile of the nodes and the number of active nodes after running for 5000 iterations compared to standard routing protocols such as LEACH, SEP, and DEEC. [ABSTRACT FROM AUTHOR]

The deployment of wireless sensors in the hostile environment makes them susceptible to malicious attacks. One of the most harmful attacks is the clone attack in which a malicious node illegitimately claims the identity of a genuine node in the network and eventually tries to capture the entire network. This attack is also termed as node replication attack. The mobile nature of wireless sensor network (WSN) in smart health environment increases the vulnerability of node replication attack. Since the data involved in smart health system are highly sensitive data, preserving the system from the attack by malicious nodes is a crucial task. In this research, we proposed a new scheme called Routing Protocol for Energy Efficient Networks (RPEEN) for the detection of clone attack in IoT-based smart health application. The main advantage of this scheme is the increase in the energy efficiency as the energy efficiency is the most important constraint in WSN systems. The performance of the proposed scheme is highlighted using parameters like time delay, residual energy, throughput, energy efficiency and error rate. Further, to portray the efficacy of the proposed algorithm, this algorithm is compared with the existing Hybrid Multi-Level Clustering (HMLC) algorithm. It has been found that the proposed RPEEN scheme achieves a time delay of 0.63 and 0.6 ms with 0 dead nodes and by avoiding clone attack respectively. Furthermore, the proposed scheme attains the highest residual energy of 49.5 J for the 2500 rounds. In addition, the proposed algorithm attains the highest throughput of 99.2% for the 50 nodes. [ABSTRACT FROM AUTHOR]

Recently, green computing has received significant attention for Internet of Things (IoT) environments due to the growing computing demands under tiny sensor enabled smart services. The related literature on green computing majorly focuses on a cover set approach that works efficiently for target coverage, but it is not applicable in case of area coverage. In this paper, we present a new variant of a cover set approach called a grouping and sponsoring aware IoT framework (GS-IoT) that is suitable for area coverage. We achieve non-overlapping coverage for an entire sensing region employing sectorial sensing. Non-overlapping coverage not only guarantees a sufficiently good coverage in case of large number of sensors deployed randomly, but also maximizes the life span of the whole network with appropriate scheduling of sensors. A deployment model for distribution of sensors is developed to ensure a minimum threshold density of sensors in the sensing region. In particular, a fast converging grouping (FCG) algorithm is developed to group sensors in order to ensure minimal overlapping. A sponsoring aware sectorial coverage (SSC) algorithm is developed to set off redundant sensors and to balance the overall network energy consumption. GS-IoT framework effectively combines both the algorithms for smart services. The simulation experimental results attest to the benefit of the proposed framework as compared to the state-of-the-art techniques in terms of various metrics for smart IoT environments including rate of overlapping, response time, coverage, active sensors, and life span of the overall network. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) is a new age technology that connects virtually every IP-enabled things in the world. Because of the constrained nature of the resources, energy-efficient clustering plays a very important role in successful implementation of these networks and also in increasing its lifetime. The proposed methodology provides an adaptive cluster head selection algorithm based on glow-worm swarm optimization algorithm. Most of the existing approaches groups nodes into clusters containing a fixed number of nodes. This is not applicable in certain cases, such as, when many of the nodes are dead. In the proposed approach, the number of nodes in each cluster is not fixed, and it automatically changes according to the number of alive nodes in the network, which increases the lifetime of the network. The proposed approach also ensures the minimum overlapping of the clusters by selecting geographically distributed cluster heads which increases the energy efficiency of the network by minimising the communication overhead. Repeated selection of cluster head also helps in load balancing in the network. We have implemented the proposed algorithm in OMNET++ simulator and demonstrated how it behaves with different densities of sensor deployment and communication ranges. The proposed algorithm is found to work notably well as compared to state-of-the-art IoT clustering protocol even when more than 20% nodes run out of energy. As time progresses, the improvement is even more apt. [ABSTRACT FROM AUTHOR]

The Narrowband Internet of Things (NB-IoT) is a very promising licensed Internet of things (IoT) technology for accommodating massive device connections in 5G and beyond. To enable network scalability, this study proposes a two-layers novel mixed approach that aims not only to create an efficient spectrum sharing among the many NB-IoT devices but also provides an energy-efficient network. On one layer, the approach uses an Adaptive Frequency Hopping Spread Spectrum (AFHSS) technique that uses a lightweight and secure pseudo-random sequence to exploit the channel diversity, to mitigate inter-link and cross-technology interference. On the second layer, the approach consists of a clustering and network coding (data aggregation) approach based on an energy-signal strength mixed gradient. The second layer contributes to offload the BS, allows for energy-efficient network scalability, helps balance the energy consumption of the network, and enhances the overall network lifetime. The proposed mixed strategy algorithm is modelled and simulated using the Matrix Laboratory (MATLAB) Long Term Evolution (LTE) toolbox. The obtained results reveal that the proposed mixed approach enhances network scalability while improving energy efficiency, transmission reliability, and network lifetime when compared to the existing spread spectrum only, nodes clustering only, and mixed approach with no network coding approaches. [ABSTRACT FROM AUTHOR]

The usage of wireless sensor devices in many applications, such as in the Internet of Things and monitoring in dangerous geographical spaces, has increased in recent years. However, sensor nodes have limited power, and battery replacement is not viable in most cases. Thus, energy savings in Wireless Sensor Networks (WSNs) is the primary concern in the design of efficient communication protocols. Therefore, a novel energy-efficient clustering routing protocol for WSNs based on Yellow Saddle Goatfish Algorithm (YSGA) is proposed. The protocol is intended to intensify the network lifetime by reducing energy consumption. The network considers a base station and a set of cluster heads in its cluster structure. The number of cluster heads and the selection of optimal cluster heads is determined by the YSGA algorithm, while sensor nodes are assigned to its nearest cluster head. The cluster structure of the network is reconfigured by YSGA to ensure an optimal distribution of cluster heads and reduce the transmission distance. Experiments show competitive results and demonstrate that the proposed routing protocol minimizes the energy consumption, improves the lifetime, and prolongs the stability period of the network in comparison with the stated of the art clustering routing protocols. [ABSTRACT FROM AUTHOR]