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With the evolutionary development of the latency-sensitive Industrial Internet of Things (IIoT) applications, delay restriction becomes a critical challenge, which can be resolved by distributing IIoT applications on nearby fog devices. Besides that, efficient service provisioning and energy optimization are confronting serious challenges with the ongoing expansion of large-scale IIoT applications. However, due to insufficient resource availability, a single fog device cannot execute large-scale applications completely. In such a scenario, a partial service provisioning strategy provides a promising outcome to enable the services on multiple fog devices or collaboration with cloud servers. By motivating this scenario, in this paper, we introduce a new Deep Reinforcement Learning (DRL)-enabled partial service provisioning strategy in the green industrial fog networks. With this strategy, multiple fog devices share the excessive workload of an application among themselves. To reflect this, a task partitioning policy is introduced to partition the requested applications into a set of independent or interdependent tasks. Further, we develop an intelligent partial service provisioning strategy to utilize maximum fog resources in the network. Experimental results express the significance of the proposed strategy over the traditional baseline algorithms in terms of energy consumption and latency up to 25% and 16%, respectively.

Wireless sensor networks (WSNs) consist of nodes distributed in the region of interest (ROI) that forward collected data to the sink. The node’s location plays a vital role in data forwarding to enhance network efficiency by reducing the packet drop rate and energy consumption. WSN scenarios, such as tracking, smart cities, and agriculture applications, require location details to accomplish the objective. Assuming a 3D application space, a combination of received signal strength (RSS) and time of arrival (TOA) can be helpful for reliable range estimation of nodes. Notably, the anchor node can minimize localization error for non-line-of-sight (NLOS) signals. We proposed an error minimization protocol for localization of the sensor node, assuming that the anchor node’s location is known prior and can limit the receiving signal in LOS, single, or twice reflection. We start to exploit the sensor node’s geometrical relationship and the anchor node for LOS and NLOS signals and address misclassification. We started initially from the erroneous node position, bound its volume in 3D space, and reduced volume with each iteration following the constraint. Our simulation result outperforms the traditional methods on many occasions, such as boundary volume and computational complexity.

In wireless sensor networks (WSNs), simulation practices, system models, algorithms, and protocols have been published worldwide based on the assumption of randomness. The applied statistics used for randomness in WSNs are broad, e.g., random deployment, activity tracking, packet generation, etc. Even though authors’ adequate formal and informal information and pledge validation of the proposal became challenging, the minuscule information alteration in implementation and validation can reflect the enormous effect on eventual results. In this proposal, we show how the results are affected by the generalized assumption made on randomness. In sensor node deployment, ambiguity arises due to node error-value ( e ), and its upper bound in the relative position is estimated to understand the delicacy of diminutives changes. Besides, the effect of uniformity in the traffic and participation of scheduling position of nodes is also generalized. We propose an algorithm to generate the unified dataset for the general and some specific applications system models in WSNs. The results produced by our algorithm reflect the pseudo-randomness and can efficiently regenerate through seed value for validation.

Wireless sensor networks are a group of spatially distributed nodes deployed to sense, gather, and transmit data to the sink for further analytics. Due to continuous operations, the battery-equipped sensor nodes (SNs) drain energy rapidly, and replacing them is a hectic task. Wireless energy transfer (WET) is evolved as a promising innovation to recharge the SNs battery wirelessly to address the challenges. A WET is embedded in a vehicle called a mobile charger (MC) and traveled in the network to recharge the SNs. However, scheduling the mobile charger over the network before a sensor node dies is challenging. In this work, we introduced a partial charging strategy to avoid the long waiting time for MC because full recharging of a single node takes a long time. The partial charging strategy preempts the current charging node and moves to the newly requested node to minimize the network’s dead nodes. However, it will increase the traveling distance. Hence, adequate charging time and MC traveling path are required. In this context, this paper proposes a deep reinforcement learning-based mobile charger scheduling strategy called dynamic partial mobile charger scheduling using deep-Q-networks (DPMCS). The proposed DPMCS learns from the environment and decides each sensor’s charging duration in an identified tour. Experimental results reveal that the proposed DPMCS outperforms well compared to the existing studies, enhance the lifetime and diminish the dead nodes count.

Fog computing has emerged as a promising paradigm that borrows the user-oriented cloud services to the proximity of the Internet-of-Things (IoT) users in sixth-generation (6G) networks. Currently, service providers establish a proprietary fog architecture to prolong a specific group of IoT users by offering resources and services to the edge level. However, this sort of activity creates a service barrier and limits the development of fog services to the IoT-users. Keeping this in mind, we develop a 6G-aware fog federation model for utilizing maximum fog resources and providing demand specific services across the network while maximizing the revenue of fog service providers and guaranteeing the minimum service delay and price for IoT-users. To achieve this goal, we formulate our objective function into a mixed-integer nonlinear problem. By jointly optimizing the dynamic services cost and user demands, a noncooperative Stackelberg game interaction algorithm is formulated to schedule the fog and cloud resources distributively. Further maximizing the profit for the service providers and the seamless resource provisioning, a resource controller is initiated to manage the available fog resources. Extensive simulation analysis over 6G-aware Quality-of-Service parameters demonstrates the superiority of the proposed fog federation model and it reduces up to 15%–20% service delay and 20%–25% of service cost over the standalone fog and cloud frameworks.

In wireless sensor networks (WSNs), the sensor nodes collect data from the target area and send it to the base station through a multi-hop connection. As a result, high energy dissipation of the sensor node may occur. Some times failure of nodes on large scale create partitions in the network. Also, the presence of obstacles is the reason for the coverage gap in networks. The introduction of mobile nodes has been measured to be a worthy approach to significantly decrease the energy dissipation of the static sensor nodes. This job is realized by considering the route through which the mobile collectors move to collect data from the sensor nodes. Another challenging job is to find the path of the mobile collector in an area with obstacles. In this work, an obstacle aware connectivity establishment (OACE) using mobile nodes in wireless sensor networks is proposed. The mobile robot collects data from each partition and carries forward to other partitions so data finally reaches to sink. To achieve this, a Delaunay triangulation based obstacle avoidance clustering has been performed. The performance of the proposed algorithm has been shown by varying the sensor nodes to a single mobile node and multiple mobile nodes. The number of partitions in the network also plays an important role in network performance. Simulation results show that the proposed method with multiple mobile collectors is more energy-efficient with less latency as compared to the single mobile collector for data collection.

Wireless Sensor Networks (WSNs) are emerging as they demands for various applications, for example, military surveillance, home automation, vehicle tracking, environmental monitoring, wildlife tracking, health monitoring, and scientific exploration. Usually, sensor nodes operate with limited battery capacity. Using conventional batteries, it is not always efficient to design long-lasting sensor networks. Moreover, the replacement of the batteries is too challenging to operate in harsh environmental conditions. Therefore, to overcome, one such technique is to recharge the battery of sensor nodes using an energy harvesting system. On the other hand, some of the existing energy harvesting WSNs still lacking the intelligent strategy for judiciously utilizing both the energy management and harvesting system. The review work we present is categorized into energy management and renewable energy harvesting techniques. In energy management techniques, we discuss various methods to save energy consumption of the energy harvesting sensor networks. Notably, we study their protocol design strategies for energy-saving and essential strategies such as prediction for maximizing the energy harvesting of the sensor nodes. We also summarize their shortcomings and ability to deal with the energy harvesting system. In renewable energy harvesting schemes, we present various energy harvesting mechanisms such as solar, wind and others. We also discuss the different energy harvesting mechanisms, especially their protocol design strategies for maximizing energy harvesting, and summarize their merits and demerits. The work also discusses various challenging issues for energy harvesting WSNs followed by future research directions, and some recent applications.

In wireless sensor networks (WSNs), a mobile sink accumulate the data instead of routing directly to the sink to avoid the hotspot problem. In this process, it traverses a predetermined path by visiting a set of nodes called the rendezvous point (RP), and all the non-rendezvous points can transmit their data to the closest RP. Identifying the best collection of RPs and determining the mobile sink traveling path will decrease data loss and improve network performance. However, choosing a set of RPs and the route between them is a challenging task. It is more complicated in the event-driven applications due to the uneven data rate of SNs. In this context, we propose an extended ant colony optimization (ACO)-based mobile sink path construction for event-driven WSNs. In this, the best set of the RPs and the efficient mobile sink traveling path between them is determined. In addition to this, the RPs re-selection mechanism also adopted for balancing the energy between the nodes. After that, the virtual RPs are introduced to minimize the data transmissions between the sensor nodes and RPs. This process will improve WSNs’ performance in terms of reducing data losses while increasing network lifetime. The improved performance of the extended ACO-MSPD over existing is confirmed through simulation tests.

In recent times, fog computing becomes an emerging technology that can exhilarate the cloud services towards the network edge for increasing the speeds up of various Internet-of-Things (IoT) applications. In this context, integrating priority-aware scheduling and data offloading allow the service providers to efficiently handle a large number of real-time IoT applications and enhance the capability of the fog networks. But the energy consumption has become skyrocketing, and it gravely affects the performance of the fog networks. To address this issue, in this paper, we introduce an Energy-Efficient Task Offloading ( EETO ) policy combined with a hierarchical fog network for handling energy-performance trade-off by jointly scheduling and offloading the real-time IoT applications. To achieve this objective, we formulate a heuristic technique for assigning a priority on each incoming task and formulate a stochastic-aware data offloading issue with an efficient virtual queue stability approach, namely the Lyapunov optimization technique. The proposed technique utilizes the current state information for minimizing the queue waiting time and overall energy consumption while meeting drift-plus-penalty . Furthermore, a constraint restricted progressive online task offloading policy is incurred to mitigate the backlog tasks of the queues. Extensive simulation with various Quality-of-Service (QoS) parameters signifies that the proposed EETO mechanism performs better and saves about 23.79% of the energy usage as compared to the existing ones.

The utilization of wireless sensor networks (WSNs) is proliferating in our daily life. It depends on the environmental monitoring such as weather tracking, battled field, etc. However, the main challenge of WSNs is energy management. Generally, traditional nodes of the sensor network were powered by non-rechargeable batteries with limited energy capacity. The alternative solution has been discovered to resolve the limitation of the sensor battery called energy harvesting (EH). EH offers effective and an alternative solution to recharge the battery of the sensor node. It assists with the environment, and EH technique can be possible different kinds such as solar, wind, thermal and mechanical. In this paper, we propose a novel energy harvesting clustering protocol (NEHCP). The NEHCP is based on the hierarchical clustering routing algorithm, and this protocol uses solar EH. The collection of information from the sensor nodes is sent to the base station through the cluster head. The NEHCP algorithm is categorized into three sections, such as the initial phase, set-up phase and data transmission phase. Moreover, the unique feature of EH-WSNs is giving more efficient result in term of network lifetime. The simulation part of this method shows a strong ability to balance energy consumption and increases the network efficiency for the EH-WSNs.

Nowadays, billions of Internet-of-Things devices generate various types of delay-sensitive tasks to process within a limited time frame. By processing the tasks at the network edge using distributed fog devices can efficiently overcome the deficiency of the centralized cloud data center (CDC), i.e., long latency and network congestion. Moreover, to overcome the inefficiency of the local fog devices, i.e., limited processing and storage capabilities, we investigate the collaboration between distributed fog devices and centralized CDC, where the delay-sensitive tasks can preferably be offloaded on the local fog devices, whereas the resource-intensive tasks are offloaded on the resource-rich CDC. However, one of the challenging tasks in the fog-cloud environment is to find a suitable computing device for each real-time task by considering tradeoff between the latency and cost. To meet the above-mentioned challenge, in this article, we introduce an optimal application offloading strategy in the hierarchical fog-cloud environment using the accelerated particle swarm optimization (APSO) technique. The proposed APSO-based strategy finds an optimal computing device (i.e., fog device or cloud server) for each real-time task using multiple quality-of-service parameters, namely, cost and resource utilization (RU). The performance of the proposed algorithm is evaluated using four different real-time data sets with various performance matrices. The experimental results indicate that the proposed strategy outperforms the existing schemes in terms of average delay, computation time, RU, and average cost by 18%, 21%, 27%, and 23%, respectively.

Wireless sensor networks (WSNs) are composed with a set of sensor nodes, and its primary task is to sense and relay the data to the base station or sink. Sensor nodes are equipped with a limited battery, and more energy is consumed due to the relay of the data. While relaying on more data, nodes near to the base station will die soon and result in energy-hole or hotspot problem. A mobile sink has been introduced to collect the data from the nodes to avoid the hotspot problem. Several data collection algorithms have been proposed for 2D WSNs; however, these algorithms do not meet the requirements of the 3D WSNs due to its variations in topology, connectivity, coverage, scalability, etc. Therefore, the selection of mobile sink visiting points called rendezvous points (RPs), and path determination between then in 3D WSNs is a challenging task. In this paper, we determine the optimal RPs in 3D WSNs using unsupervised learning-based hierarchical agglomerative clustering. Further, we propose an algorithm for path determination between the RPs with minimal computational overhead. The simulation runs show that the proposed method finds the near-optimal set of RPs and path of the mobile sink while enhancing the network lifetime.

Data packet reaches from the end node to sink in a multihop fashion in the internet of things (IoTs) and sensor networks. Usually, a head node (among neighboring or special purpose nodes) can collect data packets from the nodes and forward them further to sink or other head nodes. In Time-division multiple access (TDMA) driven scheduling, nodes often own slots in a time frame and are scheduled for data forwarding in the allotted time slot (owner node) in each time frame. A time frame in which the owner node does not have data to forward goes into sleep mode. Though the supposed owner node is in sleep mode, the corresponding head node is active throughout the time frame. This active period of a head node can cause an increase in energy consumption. Besides, because the head node in an active state does not receive a data packet, it is causing significantly to the throughput, ultimately leading to low channel utilization. We propose the Markov design policy (MDP) for such head nodes to reduce the number of time slots wasted in the time frame in our work. The proposal is the first such kind of MDP-based modeling for node scheduling in TDMA. The simulation results show that the proposed method outperforms existing adaptive scheduling algorithms for channel utilization, end-to-end delay, system utilization, and balance factor. WOS:000859413200011 Q1 - Q2

In emerging business markets, the data is becoming new gold for the industries. There are several places such as marketing, manufacturing, and analysis of future trends becoming data-intensive to achieve growth. The tiny sensor placements in the field, cities, industry, buildings and sea are helping to collect data and process it either for information retrieval or decision making. The periodic scheduling of radio transceivers assists in accomplishing efficient energy utilization in sensors often uses Time-division multiple access (TDMA) protocols for node scheduling. Our objective is to reduce the amount of time; the receiver node is in the wake-up state. Besides, the slot which potentially not being used for an extended period can utilize by other nodes. The efficient utilization of slots can help to achieve low power duty cycles with low latency. To accomplish that, we propose the emulation of the classroom learning environment with the Q-learning grading for node scheduling. We proposed an analytical mapping of WSNs to classroom learning. The initial benchmark of the performance has compared to the IEEE.802.11 (TDMA-scheduling). Further, the TDMA driven protocols such as Z-MAC, learning-driven protocols (E-MAC or aloha-Q), i-Queue are compared to evaluate the parameters such as energy consumption, throughput, and latency. Q2

In an environment, one of the natural geological hazards is land surface subsidence. Underground mining and subsurface coal fires are primarily responsible for subsidence of land. Activities, such as, over-exploitation of coal, minerals, groundwater and petroleum resources, depillaring of the existing galleries and water logging of the relinquished galleries are the major factors resulting in subsidence. The deformation is primarily measured in terms of change in ground elevation values (Z-dimension) at different time intervals at identified ground locations. All the conventional and exiting techniques have certain limitations in monitoring and predicting land surface subsidence. In this work, we predict the land subsidence in Jharia Coalfield, Dhanbad, India for one year in the interval of twelve days on the datasets collected through a monitoring technique called Modified PSInSAR. The sample datasets contains 14 locations and 67 previous land subsidence value calculated from each location. We train and test predictive models and perform the prediction of the land subsidence using Vanilla and Stacked long short-term memories. Finally, we demonstrate the predicted deformation values of the 14 locations for one year. The prediction model shows the subsidence rate in Nai-dunia basti near Jharia, Dhanbad is alarming as 93.8 mm/year where as Digwadih and Godhar showed the critical rate as 82 mm/year.

Wireless sensor networks are formed with very small sensor devices with limited energy and short transmission range. Sensors are randomly deployed in remote areas with harsh conditions. Due to this, their distribution are non-uniform. In a hostile environment, the occurring of large scale failure is very often and it creates partitions of different sizes in the network. Restoring the network connectivity is very crucial for network functioning. Typically sensors are static and communication is bi-directional and we need to reconnect the network with the additional relay sensor nodes. After reconnecting the network, overhead on relays is very high and thus it consumes more energy and high computational power. Optimal number of relay nodes required for reconnecting the network is an NP-hard problem. Here, we proposed a new heuristic approach called adaptive Cluster-based Relay Placement Algorithm. Our approach is to reconnecting the networks based on finding the nearest partitions using their centroids. Using the proposed algorithm, relays are placed on the denser part of the partitions and number of required relays are also minimum. It reduces the network recovery time and increases the network lifetime. Through simulation, we show that the proposed algorithm performs better than the existing approaches.

Wireless sensor network (WSN) is one of the most promising technologies for some real-time applications because of its size, cost-effective and easily deployable nature. Due to some external or internal factors, WSN may change dynamically and therefore it requires depreciating dispensable redesign of the network. The traditional WSN approaches have been explicitly programmed which make the networks hard to respond dynamically. To overcome such scenarios, machine learning (ML) techniques can be applied to react accordingly. ML is the process of self-learning from the experiences and acts without human intervention or re-program. The survey of the ML techniques for WSNs is presented in [1], covering period of 2002–2013. In this survey, we present various ML-based algorithms for WSNs with their advantages, drawbacks, and parameters effecting the network lifetime, covering the period from 2014–March 2018. In addition, we also discuss ML algorithms for synchronization, congestion control, mobile sink scheduling and energy harvesting. Finally, we present a statistical analysis of the survey, the reasons for selection of a particular ML techniques to address an issue in WSNs followed by some discussion on the open issues.

Workflow scheduling is one of the challenging issues in emerging trends of the distributed environment that focuses on satisfying various quality of service (QoS) constraints. The cloud receives the applications as a form of a workflow, consisting of a set of interdependent tasks, to solve the large-scale scientific or enterprise problems. Workflow scheduling in the cloud environment has been studied extensively over the years, and this article provides a comprehensive review of the approaches. This article analyses the characteristics of various workflow scheduling techniques and classifies them based on their objectives and execution model. In addition, the recent technological developments and paradigms such as serverless computing and Fog computing are creating new requirements/opportunities for workflow scheduling in a distributed environment. The serverless infrastructures are mainly designed for processing background tasks such as Internet-of-Things (IoT), web applications, or event-driven applications. To address the ever-increasing demands of resources and to overcome the drawbacks of the cloud-centric IoT, the Fog computing paradigm has been developed. This article also discusses workflow scheduling in the context of these emerging trends of cloud computing.

An Infrastructure-as-a-service (IaaS) cloud is a new paradigm which offers computing and storage services in form of virtual machines to the cloud users. Due to increasing demand and diversity of the applications, most of the servers are become overloaded or imbalanced and affects the performance of the cloud system. Most of the existing strategies have developed a series of algorithms to pick out the optimal target server to achieve the immediate load balancing. However, it is proved in the literature that immediate load balancing does not meet the high execution efficiency and resource utilization of the servers. In this paper, we propose a new load balancing mechanism for a long-term process referred as LB-RC (load balancing resource clustering). The meta-heuristic Bat-algorithm is applied to obtain optimal resource clustering and their cluster centers for faster convergence. We also propose a new dynamic task assignment policy to achieve the minimum makespan and execution cost within the given constraints. The proposed algorithm is tested and compared with the existing algorithms over various synthetic datasets and performance matrices to prove its superiority.

Clustering is one of the imperative solutions to the problem of energy unbalancing prevailing in the network. The major issues of wireless sensor network with unequal clustering and multi-hop communication are the energy stability and network longevity. In this paper, we consider the improved Bat optimization algorithm to improve the unequal clustering techniques by forming groups before selecting Cluster Heads. In terms of saving energy, knowing the positions of sink nodes in WSNs plays a vital role. Bat algorithm (BA), optimization of particle swarm, differential evolution, and whale optimization algorithm is now becoming efficient clustering methods as per the metaheuristic approach. Evaluate the life span of the entire network; this paper proposes an improved BA. The BA’s primary goal of unequal clustering is to reduce energy consumption and extend the lives of the WSNs. An objective function has been formulated to reduce energy consumption and increase the network’s lifespan to achieve these goals. Compared with three existing optimization methods, the experimental results showed that the proposed improved BA achieved better efficiency in reducing total power consumption: whale optimization for topology control, and improved particle swarm optimization algorithm.