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Amidst an energy crisis stemming from increased energy costs and the looming threat of war, there has been a burgeoning interest in energy conservation and management worldwide. Industrial complexes constitute a significant portion of total energy consumption. Hence, reducing energy consumption in these complexes is imperative for energy preservation. Typically, factories within similar industries aggregate in industrial complexes and share similar energy utilities. However, they often fail to capitalize on this shared infrastructure efficiently. To address this issue, a network system employing a virtual utility plant has been proposed. This system enables proactive measures to counteract energy surplus or deficit through AI-based predictions, thereby maximizing energy efficiency. Nevertheless, deploying conventional server systems within factories poses considerable challenges. Therefore, leveraging edge devices, characterized by low power consumption, high efficiency, and minimal space requirements, proves highly advantageous. Consequently, this study focuses on constructing and employing data collection and AI systems to utilize edge devices as standalone systems in each factory. To optimize the AI system for low-performance edge devices, we employed the integration-learning AI modeling technique. Evaluation results demonstrate that the proposed system exhibits high stability and reliability. [ABSTRACT FROM AUTHOR]

In path-constrained multi-hop sensor networks (M-WSNs), maximizing data collection with minimal energy consumption is critical, especially for delay-sensitive applications. Because a mobile element (ME) moving at a constant speed along a constrained-path must receive data from sensor nodes (SNs) within a given time bound. This issue can be addressed by efficiently scheduling the SNs' data transmission. The shortest path data transmission (SPT) or its variations are a simple and energy-efficient technique for scheduling data transmission from SNs. Although this method considerably minimizes total energy spent, it does not enhance data collection efficiency due to uneven data forwarding load. To solve this issue, this paper proposes a novel efficient heuristic method. The proposed method first computes a set of discrete sub-paths on a given path based on the start and end distance points of nearby SNs, the speed of ME, and the given time delay. Then, for each sub-path and SN-communication model, a network flow graph is used to schedule and optimize SN data transmission to ME. Finally, a sub-path is chosen among those which return maximum data with minimum energy consumption. The network flow graphs are created by considering two distinct SN-communication models: (1) energy-unrestricted SN-model and (2) energy-restricted SN-model. Finally, the simulation reports demonstrate the proposed method's efficacy over baseline schemes in terms of data collected, energy usage efficiency, and success-delivery ratio. [ABSTRACT FROM AUTHOR]

In this study, we explore innovative strategies for enhancing energy efficiency in Wireless Sensor Networks (WSNs), with a focus on the IQRF network. Our approach integrates dynamic sleep scheduling and data collection methods to optimize battery usage and extend the network's operational lifespan. We introduce a battery life estimation model, taking into account various factors such as data collection frequency and network size. This model is instrumental in predicting battery longevity under different operational scenarios. Additionally, we develop a practical tool in the form of an API and an online calculator, aimed at assisting network designers in planning and maintaining energy-efficient WSNs. Our results, derived from a case study involving a CO2 sensor network, demonstrate the effectiveness of our methodologies in real-world applications. The study concludes that implementing dynamic data collection and sleep scheduling significantly enhances battery life, offering a valuable contribution to the sustainability and reliability of WSNs. [ABSTRACT FROM AUTHOR]

Wireless Technology is an integral part of today's applications. It encompasses commercial and human-oriented environmental monitoring, military applications, robotics, and healthcare monitoring systems, home or factory automation, and logistics. A wireless sensor network is a network system that uses wireless sensor nodes to monitor physical or environmental conditions such as voice, temperature, and spatial dispersive movements. Each node can locally sense its environment, process information and data, and send the data to one or more collection points within the WSN. The existing solution is categorized into member nodes and group/cluster heads (CH). The CH election process increases the overhead of the network and reduces the network lifetime. The processing and energy limitations of the nodes are considered for the CH election process. In this cluster formation methods aiming at the Cluster head selection process and providing trust in hierarchical WSN are proposed. The Energy Efficient Aggregation Data Convening Routing (E2ADCR) is used to estimate the routing path, and aggregate data collection to improve the network lifetime. The major advantage of this technique is to avoid the malicious or selfish node from becoming a dominant cluster in a group of clusters. Initially sink node selection forwards the Configuration Message (CM) to every node on the network to construct the performing node. In this, cluster selection is based on connection density, degree of the node angle, and residual energy (Quality Factor) that is evaluated from the link robustness, energy, and degree of the node. Multi-hop link transmission support path optimization technique is estimated in the path when the obstacle is present in the WSN. To introduce an Aggregated Support-based Data Collection to evaluate each packet flow monitor on the network to if any unrelated packet will eliminate to forward to the sink node. The proposed routing path of computational simplicity is achieved by a simple method. The new routing protocols, which were developed during this research, have better energy efficiency. [ABSTRACT FROM AUTHOR]

Today, the use of information and communication technology is very important in making the internet of things (IoT) elements distributable around the earth. With the development of IoT topics, today unmanned aerial vehicles (UAV) are utilized as a platform for gathering data from various IoT devices located worldwide. Determining the number and optimal locations of drones can minimize energy consumption in this data-collection system in the IoT. Using a promising multi-objective optimization algorithm (MOA) can achieve this goal. In this research, a bio-inspired MOA, termed the multi-objective spotted hyena optimizer (MOSHO), is employed on the data-collection platform for a group of IoT devices in a geographical area. The results of this method have been compared with other evolutionary MOAs. The analysis of the results shows that the MOSHO has a noteworthy consequence on the process of optimal energy consumption in this system, in addition to a high convergence associated with better diversity and robustness. The results of this research can be used to identify the optimization parameters in this system. [ABSTRACT FROM AUTHOR]

In recent years, unmanned aerial vehicles (UAVs) have been broadly employed as a data collection platform to assist in efficiently collecting data from IoT devices. However, the deployment optimization of UAVs has been challenged due to the need to minimize the energy consumption of UAVs and IoT devices. Several algorithms have been recently proposed for tackling this challenge, but they still have room for improvement due to their slow convergence speed and memory-wasting problems. Therefore, in this study, a new energy-aware approach has been proposed for accurately optimizing the entire deployment of UAVs, which could minimize the total energy consumption. This approach is based on presenting a new encoding mechanism, namely an optimized population size mechanism, for representing both location and number of stop points in an effective manner. In this mechanism, similar to some studies in the literature, the whole population is responsible for the entire deployment, and each individual is responsible for a stop point in this deployment. However, this mechanism presents a novel way to optimize the number of stop points based on adding an auxiliary variable to each stop point to determine whether it will be removed, inserted, or replaced in the newly generated deployment. This variable will be optimized by the optimization techniques during the optimization process to search for the optimal choice for each stop point that could achieve a better deployment. Two well-known optimization techniques, known as differential evolution (DE) and gradient-based optimizer (GBO), are adapted using this mechanism to present new variants, namely DEoPS and GBoPS, for accurately tackling the deployment optimization problem. Two energy consumption formulations are used in our work to investigate the performance of DEoPS and GBoPS. Several experiments have been conducted to compare the performance of both DEoPS and GBoPS with several algorithms on eleven instances. The experimental findings show the effectiveness of GBoPS for the first formulation and the effectiveness of DEoPS for the second formulation. [ABSTRACT FROM AUTHOR]

In recent years, underwater acoustic sensor networks (UASNs) have been widely investigated for ocean environmental monitoring, offshore exploration, and marine military. The core function of UASNs is to collect data for related operations. A number of factors make the monitoring challenging; ocean thermoclines may affect the communication of the underwater nodes and gliders, reducing their communication range at varying depth; moreover, the node movement caused by Ekman drifting effect can significantly interfere with the data transmissions. Thus, these factors are regarded essential towards characterizing the ocean environment. To address these challenges, a sleep-scheduling-based hierarchical data collection algorithm (SSHDCA) for underwater gliders is designed. The UASN is split into multiple virtual cubes, where the nodes in different virtual cubes sleep and work alternately to save energy. Then, the SSHDCA divides the network into a dynamic layer and a static layer. In the dynamic layer, a virtual-cube-based multi-hop method is leveraged to transmit data packets to the central area. In the static layer, an improved density-based clustering technique is applied to assign each node to an appropriate cluster, while the underwater gliders collect data from the cluster heads. Further, to reduce energy consumption, the SSHDCA compresses key and non-key data, reducing the size of the packets. Simulation results have shown that the proposed algorithm is effective in reducing the path length of the gliders and the average energy consumption of the nodes, while increasing the remaining operational life of the whole network. [ABSTRACT FROM AUTHOR]

In wireless sensor networks, the energy of sensors close to the sink within one-hop is prone to be exhausted since these sensors need to forward the data from other sensors to the sink and the energy of each sensor is limited. Once the energy of these sensors close to the sink within one-hop is exhausted, the data thus could not be sent to the sink. To address this issue, the general solution is to ensure that each sensor could consume energy to forward the data from other sensors to by using the mobile sink because the location of the mobile sink is dynamic. In this situation, each sensor may be close to the sink within one-hop. The energy consumption among all sensors thus could be balanced and the network lifespan also could be prolonged. To reduce more energy consumption of sensors by forwarding data to the sink, the one-hop data collection by mobile sink was proposed to claim the sink moved to the source sensor within one-hop to collect the data in this paper. Hence, only the source sensors send the data to the sink within one-hop and other sensors could not forward the data to the sink to save energy. In the one-hop data collection by mobile sink, the rendezvous point moving model (RPMM) and the minimum moving model (MMM) were common proposed to be used. However, the time and energy consumption for collecting data in RPMM and MMM may not be minimized with the large number of source sensors. While the source sensors increase, the mobile sink needs more time and energy to collect data. To address these issues, we proposed a one-hop data collection by four quadrants moving model, FQMM, to collect data in this paper. The implementation tool, such as simulator, was used by Java language with Java SDK to evaluate performance under our proposal and the comparing proposal. For the numerical results, the maximal moving hop count, MHC, in FQMM was 30% less than the maximal MHC in RPMM. The maximal MHC in FQMM was 36% less than the maximal MHC in MMM. The minimum MHC in FQMM was 22% less than the minimum MHC in RPMM. The minimum MHC in FQMM was 28% less than the minimum MHC in MMM. The average MHC in FQMM was 25% less than the average MHC in RPMM. The average MHC in FQMM was 31% less than the average MHC in MMM. The total energy consumption in FQMM was 55% less than that in RPMM. The total energy consumption in FQMM was 59% less than that in MMM. Since the number of source sensors is often large in the real condition, FQMM could be applied for the regarding applications of WSN with mobile sink. [ABSTRACT FROM AUTHOR]

To the characteristics of large number of sensor nodes, wide area and unbalanced energy consumption in farmland Wireless Sensor Networks, an efficient data collection strategy (GCMS) based on grid clustering and a mobile sink is proposed. Firstly, cluster is divided based on virtual grid, and the cluster head is selected by considering node position and residual energy. Then, an optimal mobile path and residence time allocation mechanism for mobile sink are proposed. Finally, GCMS is simulated and compared with LEACH and GRDG. Simulation results show that GCMS can significantly prolong the network lifetime and increase the amount of data collection, especially suitable for large-scale farmland Wireless Sensor Networks. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT), including wireless sensors, is one of the highly anticipated contributors to big data; therefore, avoiding misleading or forged data gathering in cases of sensitive and critical data through secure communication is vital. Wireless sensor networks are relatively simple, scalable networks with many applications in research. They can provide many benefits, including ad hoc distribution, lower costs, and higher flexibility. In a scenario where time is of the essence and dedicated base stations cannot be established, mobile sinks must be used to gather data. IoT systems are based on a collective organization in which devices collaborate to provide better and more accurate decisions. It is important to ensure that the information being shared is legitimate to avoid any significant degradation in system performance because of false or inaccurate information. Building trust—the "assurance" between two devices that the information being shared can be used with confidence that it is accurate—will create a trustworthy, secure system in which all devices are identified and no information is accepted from any unauthorized device. The key contribution of this work is a new, dynamic, trust-based clustering mechanism by which nodes can securely connect to one another and begin transmitting data to a sink while it is available. To demonstrate the utility of this mechanism, we examine two possible attacks on a trust-based network and present a heuristic solution for minimizing the negative effects of such attacks in an energy-efficient way. Our results show improved network performance through reduction of the number of cycles required to isolate or mitigate the effect of malicious nodes in the network, thus reducing the energy consumption in the network with a concomitant increase in its lifespan. Our cluster methodology also has the effect of spreading energy consumption among nodes, thereby reducing early fall-off of nodes and network holes. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks are very convenient to monitor structures or even materials, as in McBIM project (Materials communicating with the Building Information Modeling). This project aims to develop the concept of "communicating concretes," which are concrete elements embedding wireless sensor networks, for applications dedicated to Structure Health Monitoring in the construction industry. Due to applicative constraints, the topology of the wireless sensor network follows a chain-based structure. Node batteries cannot be replaced or easily recharged, it is crucial to evaluate the energy consumed by each node during the monitoring process. This area has been extensively studied leading to different energy models to evaluate energy consumption for chain-based structures. However, no simple, practical, and analytical network energy models have yet been proposed. Energy evaluation models of periodic data collection for chain-based structures are proposed. These models are compared and evaluated with an Arduino XBee–based platform. Experimental results show the mean prediction error of our models is 5%. Realizing aggregation at nodes significantly reduces energy consumption and avoids hot-spot problem with homogeneous consumptions along the chain. Models give an approximate lifetime of the wireless sensor network and communicating concretes services. They can also be used online by nodes for a self-assessment of their energy consumptions. [ABSTRACT FROM AUTHOR]

Contrary to many previous studies on population protocols using the uniformly random scheduler, we consider a more general non-uniform case. Here, pair-wise interactions between agents (moving and communicating devices) are assumed to be drawn non-uniformly at random. While such a scheduler is known to be relevant for modeling many practical networks, it is also known to make the formal analysis more difficult. This study concerns data collection , a fundamental problem in mobile sensor networks (one of the target networks of population protocols). In this problem, pieces of information given to the agents (e.g., sensor readings) should be eventually delivered to a predefined sink node without loss or duplication. Following an idea of the known deterministic protocol TTF solving this problem, we propose an adapted version of it and perform a complete formal analysis of execution times in expectation and with high probability (w.h.p.). We further investigate the important issue of energy consumption. The goal is to improve TTF in terms of energy complexity, while still keeping good time complexities (in expectation and w.h.p.). Namely, we propose a new parameterized protocol for data collection, called lazy TTF, and present a study showing that an appropriate choice of the protocol parameters can considerably improve energy performances (compared to TTF), at a slight expense of time performance. [ABSTRACT FROM AUTHOR]

This paper models and evaluates three FSA-based (Frame Slotted ALOHA) MAC (Medium Access Control) protocols, namely, FSA-ACK (FSA with ACKnowledgements), FSA-FBP (FSA with FeedBack Packets) and DFSA (Dynamic FSA). The protocols are modeled using an AMC (Absorbing Markov Chain), which allows to derive analytic expressions for the average packet delay, as well as the energy consumption of both the network coordinator and the end-devices. The results, based on computer simulations, show that the analytic model is accurate and outline the benefits of DFSA. In terms of delay, DFSA provides a reduction of 17% (FSA-FBP) and 32% (FSA-ACK), whereas in terms of energy consumption DFSA provides savings of 23% (FSA-FBP) and 28% (FSA-ACK) for the coordinator and savings of 50% (FSA-FBP) and 24% (FSA-ACK) for end-devices. Finally, the paper provides insights on how to configure each FSA variant depending on the network parameters, i.e., depending on the number of end-devices, to minimize delay and energy expenditure. This is specially interesting for massive data collection in IoT (Internet-of-Things) scenarios, which typically rely on FSA-based protocols and where the operation has to be optimized to support a large number of devices with stringent energy consumption requirements. [ABSTRACT FROM AUTHOR]

The past years have seen a rapid development of autonomous underwater vehicle-aided (AUV-aided) data-gathering schemes in underwater acoustic sensor networks (UASNs). The use of AUVs efficiently reduces energy consumption of sensor nodes. However, all AUV-aided solutions face severe problems in data collection delay, especially in a large-scale network. In this paper, to reduce data collection delay, we propose a prediction-based delay optimization data collection algorithm (PDO-DC). On the contrary to the traditional delay optimization algorithms, Kernel Ridge Regression (KRR) is utilized via cluster member nodes to obtain the corresponding prediction models. Then, the AUV can obtain all cluster data by traversing less cluster head nodes, which can effectively reduce the collection delay of the AUV. The experimental results demonstrate that the proposed method is both feasible and effective. [ABSTRACT FROM AUTHOR]

In many of the Internet of Things (IoT) applications, a network of sensors that are wirelessly connected is vital because it provides a better picture of remotely sensed environments. However, traditional network data collection consumes a high amount of energy because of data packets that need to be routed on a hop-by-hop basis to the base station. To alleviate this problem, Unmanned Aerial Vehicles (UAV) have been used to travel over the sensed environment to collect data. The time and motion constraints of UAVs mean that they require the shortest and smoothest paths to address these constraints. So, the shortest path is planned based on Travelling Sales Problem (TSP), and to smoothen the path, we used Bezier curves to convert paths that are flyable. The proposed algorithm exhibits faster data collection in comparison with other solutions, while achieving a high delivery rate of packets and a low energy usage. [ABSTRACT FROM AUTHOR]

In multihop scenarios, the sensor nodes nearer to the base station (BS) are overloaded because they handle their own data as well as the information obtained from far away nodes. This induces a higher energy depletion rate in nodes near to the BS causing early death of these nodes resulting in hot spot/energy hole problem in wireless sensor network (WSN). This paper proposes a novel strategy using unequal fixed grid-based cluster along with a mobile data mule for data collection from the cluster head (CH). A CH is selected in such a manner that the cumulative transmission distance for member nodes within the cluster is minimum. The paper has attempted to optimize the values for CH change time or round number (f) and also established a relationship between different size clusters by using a factor (r), as they are playing an important role in the overall performance improvement of the WSN. Integrating a mobile data mule in the protocol enhances its efficiency of handling hot spot problem and makes it more energy effective. Two different WSN-scenarios have been considered based on the movement pattern of the data mule. The results obtained through simulation in both scenarios prove the success of our scheme in terms of energy efficiency, load balancing and network lifetime as compared to the existing protocols. The paper also providing a balance trade-off between delay and high overheads by using a single mule with simple predefined path. It also minimizes the hot spot problem as it sustains more than 3000 rounds, which is far better than the existing methods. [ABSTRACT FROM AUTHOR]

Wireless sensor network (WSN) has become part of human life as it is used in several applications including healthcare, environment and agricultural, public safety, military, transportation as well as in the industry. In spite of its usefulness, it is challenging to maintain long-term operations due to limited battery life. Several energy efficient protocols have been designed to prolong the network lifetime. The integration of mobility technology with the conventional static sensor network, described as hybrid WSN, promises a new solution that balances energy consumption among sensor nodes and extends the network lifetime. To the best of our knowledge, there has not been as yet an evaluation of the energy-efficiency of the data collection approaches in terms of the energy conservation techniques adopted. In this paper, the architecture of data collection approaches in WSN is discussed. Then, we propose and discuss a taxonomy of types of data collection in WSN. We further present and discuss in details a thematic taxonomy of energy conservation techniques adopted in the various hybrid WSN data collection approaches. Consequently, we compare the different energy conservation approaches that minimize energy consumption in hybrid WSN, highlighting their pros and cons. In conclusion, we point out open research challenges and future directions in the field. [ABSTRACT FROM AUTHOR]

Multi-hop data collection in wireless sensor networks (WSNs) is a challenge issue due to the limited energy resource and transmission range of wireless sensors. The hybrid clustering and routing (HCR) strategy has provided an effective solution, which can generate a connected and efficient cluster-based topology for multi-hop data collection in WSNs. However, it suffers from imbalanced energy consumption, which results in the poor performance of the network lifetime. In this paper, we evaluate the energy consumption of HCR and discover an important result: the imbalanced energy consumption generally appears in gradient k = 1, i.e., the nodes that can communicate with the sink directly. Based on this observation, we propose a new protocol called HCR-1, which includes the adaptive relay selection and tunable cost functions to balance the energy consumption. The guideline of setting the parameters in HCR-1 is provided based on simulations. The analytical and numerical results prove that, with minor modification of the topology in gradient k = 1, the HCR-1 protocol effectively balances the energy consumption and prolongs the network lifetime. [ABSTRACT FROM AUTHOR]

As one of the fastest growing technologies, machine-to-machine (M2M) communications are expected to provide ubiquitous connectivity. M2M devices can be used for a wide range of emerging applications that have various communications requirements. While M2M communications have been developed for many years, major challenges still remain with their efficient implementation from the perspective of low energy consumption and wide coverage. To address these challenges, low power wide area (LPWA) technology is investigated as one of the potential candidate solutions. In this article, we first introduce some typical LPWA M2M application scenarios. Given their requirements, we highlight key techniques and standards that are explicitly designed for LPWA M2M communications. Finally, we present an LPWA prototype system to evaluate its performance and demonstrate its potential in bridging a technological gap for future Internet-of-Things (IoT) applications. [ABSTRACT FROM PUBLISHER]

Wireless energy transfer (WET) is a new technology that can be used to charge the batteries of sensor nodes without wires. Although wireless, WET does require a charging station to be brought to within reasonable range of a sensor node so that a good energy transfer efficiency can be achieved. On the other hand, it has been well recognized that data collection with a mobile base station has significant advantages over a static one. Given that a mobile platform is required for WET, a natural approach is to employ the same mobile platform to carry the base station for data collection. In this paper, we study the interesting problem of co-locating a wireless charger (for WET) and a mobile base station on the same mobile platform—the wireless charging vehicle (WCV). The WCV travels along a pre-planned path inside the sensor network. Our goal is to minimize energy consumption of the entire system while ensuring that 1) each sensor node is charged in time so that it will never run out of energy, and 2) all data collected from the sensor nodes are relayed to the mobile base station. We develop a mathematical model for this problem (OPT-t), which is time-dependent. Instead of solving OPT-t directly, we show that it is sufficient to study a special subproblem (OPT-s) which only involves space-dependent variables. Subsequently, we develop a provably near-optimal solution to OPT-s. Our results offer a solution on how to use a single mobile platform to address both WET and data collection in sensor networks. [ABSTRACT FROM AUTHOR]

Solar energy harvesting has been presented as a key solution to alleviate the lifespan problem of conventional battery-based wireless sensor networks, but it does not address the problem of uneven energy consumption across the network. In conventional sensor networks with a stationary sink node, the nodes around the sink node consume more energy than other nodes, which is known as the hotspot problem. In recent studies, mobile sink technology has been proved to be an effective solution to the problem, where a mobile sink visits sensor nodes and collects data. To reduce the energy consumption of the mobile sink, a subset of sensor nodes are designated as anchor nodes to collect data for a certain period, and the mobile sink visits only such anchor nodes. However, even with these approaches, small hotspots can still be formed near those anchor nodes, hence it is crucial to optimize the selection and management of anchor nodes. This study aims to propose a dual line-based anchor node selection scheme tailored to efficiently solve the hotspot problem in mobile sink-based solar-powered wireless sensor networks. The proposed scheme designates the areas, in which some nodes can be chosen as anchor nodes, in the form of two lines. This dual line-based anchor selection algorithm effectively operates for the best use of the solar energy harvested by each node in a balanced manner. The experimental results confirm that the proposed scheme minimizes hotspots in the network, reduces the blackout time of nodes, and improves the data collection rate. • The proposed scheme is designed for solar powered WSNs operating with a mobile sink. • The proposed scheme can maximize the utilization of harvested energy at each node. • The proposed scheme can alleviate the hotspot problem significantly. • The proposed scheme can reduce the blackout time for solar-powered sensor nodes. • The proposed scheme can improve the quantity and quality of the collected data. [ABSTRACT FROM AUTHOR]

It is difficult to collect data and evaluate the status of NC machine tools automatically because of the diversity of isomeric CNC systems. Aiming at this problem, a general method is designed. First, an energy consumption data collector is designed to collect energy consumption data of NC machine tools' spindle and the data feature is analyzed. Then the standard energy consumption data by NC machining simulation or pre-acquisition during the operation of every NC code program is collected and further a standard energy consumption database is built according to the standard data. Second, to judge the type of work piece and the status of NC machine tools, the time series theory is used to deal with the energy consumption data by compared with the data that of the standard database. Finally, the feasibility and reliability of the proposed method is verified through an example. [ABSTRACT FROM AUTHOR]

The energy hole problem is a major concern in the wireless sensor network (WSN) given the uneven energy depletion of the power-constrained sensor nodes. Recently, several studies have incorporated a mobile data collector (MDC) to collect data from sensor nodes where the MDC reaches a set of data collection points (DCPs) to collect data from nearby nodes. This strategy has resolved the underlying cause of unbalanced energy consumption by avoiding the long chain of multi-hop routing. However, in most of the existing protocols, sensor nodes information is relayed to the Base Station (BS) to nominate MDC sojourn points and travel sequence. Such a centralized approach results in a substantial exchange of messages among sensor nodes causing high energy consumption. Moreover, these approaches perceived a uniform data generation rate at sensor nodes while ignoring the limited buffer cache capacity during the MDC path design. In this context, this paper proposes a distributed MDC sojourn point nomination method that significantly improves the message complexity and energy consumption of the WSN. The selected DCPs information is relayed to the BS where a modified Ant Colony Optimization algorithm is applied to construct the MDC traversal path. The performance of the proposed protocol is extensively analysed with some related existing schemes in terms of network parameters like energy efficiency, network lifetime, packet delivery ratio, and end to end delay. Moreover, the scalability of the proposed scheme is evaluated by varying the number of MDC and sensor nodes in the network. • A distributed Mobile Data Collector (MDC) sojourn point nomination method. • MDC data gathering trajectory simultaneously optimizes the energy and cache memory. • Ant Colony Optimization based data collection traversal sequence of the MDC. • Works with uniform as well as non-uniform data generating sources. [ABSTRACT FROM AUTHOR]

We propose a stable backbone tree construction algorithm using multi-hop clusters for wireless sensor networks (WSNs). The hierarchical cluster structure has advantages in data fusion and aggregation. Energy consumption can be decreased by managing nodes with cluster heads. Backbone nodes, which are responsible for performing and managing multi-hop communication, can reduce the communication overhead such as control traffic and minimize the number of active nodes. Previous backbone construction algorithms, such as Hierarchical Cluster-based Data Dissemination (HCDD) and Multicluster, Mobile, Multimedia radio network (MMM), consume energy quickly. They are designed without regard to appropriate factors such as residual energy and degree (the number of connections or edges to other nodes) of a node for WSNs. Thus, the network is quickly disconnected or has to reconstruct a backbone. We propose a distributed algorithm to create a stable backbone by selecting the nodes with higher energy or degree as the cluster heads. This increases the overall network lifetime. Moreover, the proposed method balances energy consumption by distributing the traffic load among nodes around the cluster head. In the simulation, the proposed scheme outperforms previous clustering schemes in terms of the average and the standard deviation of residual energy or degree of backbone nodes, the average residual energy of backbone nodes after disseminating the sensed data, and the network lifetime. [ABSTRACT FROM AUTHOR]

• Review of building power consumption datasets. • Data collection platforms. • Power consumption anomaly detection dataset. • Improving datasets collection and exploitation. • Visualization of power consumption micro-moments. In the last decade, extended efforts have been poured into energy efficiency. Several energy consumption datasets were henceforth published, with each dataset varying in properties, uses and limitations. For instance, building energy consumption patterns are sourced from several sources, including ambient conditions, user occupancy, weather conditions and consumer preferences. Thus, a proper understanding of the available datasets will result in a strong basis for improving energy efficiency. Starting from the necessity of a comprehensive review of existing databases, this work is proposed to survey, study and visualize the numerical and methodological nature of building energy consumption datasets. A total of thirty-one databases are examined and compared in terms of several features, such as the geographical location, period of collection, number of monitored households, sampling rate of collected data, number of sub-metered appliances, extracted features and release date. Furthermore, data collection platforms and related modules for data transmission, data storage and privacy concerns used in different datasets are also analyzed and compared. Based on the analytical study, a novel dataset has been presented, namely Qatar university dataset, which is an annotated power consumption anomaly detection dataset. The latter will be very useful for testing and training anomaly detection algorithms, and hence reducing wasted energy. Moving forward, a set of recommendations is derived to improve datasets collection, such as the adoption of multi-modal data collection, smart Internet of things data collection, low-cost hardware platforms and privacy and security mechanisms. In addition, future directions to improve datasets exploitation and utilization are identified, including the use of novel machine learning solutions, innovative visualization tools and explainable mobile recommender systems. Accordingly, a novel visualization strategy based on using power consumption micro-moments has been presented along with an example of deploying machine learning algorithms to classify the micro-moment classes and identify anomalous power usage. [ABSTRACT FROM AUTHOR]

With the growing interest in big data technology, mobile IoT devices play an essential role in data collection. Generally, IoT sensor nodes are randomly distributed to areas where data cannot be easily collected. Subsequently, when data collection is impossible (i.e., sensing holes occurrence situation) due to improper placement of sensors or energy exhaustion of sensors, the sensors should be relocated. The cluster header in the sensing hole sends requests to neighboring cluster headers for the sensors to be relocated. However, it can be possible that sensors in the specific cluster zones near the sensing hole are continuously requested to move. With this knowledge, there can be a ping-pong problem, where the cluster headers in the neighboring sensing holes repeatedly request the movement of the sensors in the counterpart sensing hole. In this paper, we first proposed the near-uniform selection and movement scheme of the sensors to be relocated. By this scheme, the energy consumption of the sensors can be equalized, and the sensing capability can be extended. Thus the network lifetime can be extended. Next, the proposed relocation protocol resolves a ping-pong problem using queues with request scheduling. Another crucial contribution of this paper is that performance was analyzed using the fully-customed OMNeT++ simulator to reflect actual environmental conditions, not under over-simplified artificial network conditions. The proposed relocation protocol demonstrates a uniform and energy-efficient movement with ping-pong free capability. [ABSTRACT FROM AUTHOR]

In the traditional agricultural wireless sensor networks (WSNs), there is a large amount of redundant data and high latency on critical events (CEs) for data collection systems, which increases the time and energy consumption. In order to overcome these problems, an effective edge computing (EC) enabled data collection approach for CE in smart agriculture is proposed. First, the key features data types (KFDTs) are extracted from the historical dataset to keep the main information on CEs. Next, the KFDTs are selected as the collection data type of the software-defined wireless sensor network (SDWSN). Then, the event types are decided by searching the minimum average variance between the sensing data of active nodes and the average value of the key feature data obtained by EC. Furthermore, the sensing nodes are driven to sense the event-related data with a consideration of latency constraints by the SDWSN servers. A real-world testbed was set up in a smart greenhouse for experimental verification of the proposed approach. The results showed that the proposed approach could reduce the number of needed sensors, sensing time, collection data volume, communication time, and provide the low latency agricultural data collection system. Thus, the proposed approach can improve the efficiency of CE sensing in smart agriculture. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) and unmanned aerial vehicles (UAVs) have been used for monitoring animals but when their habitats have difficult access and are areas of a large expanse, remote monitoring by classic techniques becomes a difficult task. The use of traditional WSNs requires a restrictive number of hops in a multi-hoping routing scheme, traveling long distances to the sink node where data is stored by nodes and UAVs are used to collect data by visiting each node. However, the use of UAVs is not straightforward since the energy balance between the WSN and UAV has to be carefully calibrated. Building on this, we propose two data collection schemes in clustered based WSNs: (1) WSN oriented and (2) UAV oriented. In the former, nodes within each cluster member (CM), send information to their cluster head (CH) and for recollection, the UAV visits all CHs. As the UAV visits many CHs the flight time is increased. In the latter, all CHs send data from their CMs to a sink node, hence, the UAV only visits this node, reducing the flying time but with a higher system energy cost. To find the most suitable scheme for different monitoring conditions in terms of the average energy consumption and the buffer capacity of the system, we develop a mathematical model that considers both the dynamics of the WSN along with the UAV. [ABSTRACT FROM AUTHOR]

This paper proposes a charging-aware multi-mode routing protocol (CMRP) to collect data in the wireless rechargeable sensor networks. The routing mechanism in CMRP is not steady but changes according to the energy charging status of sensors. Sensors that cannot replenish their energy efficiency use the routing protocol with less energy consumption. On the contrary, sensors that can replenish their energy use the low propagation delay routing protocol. A novel heuristic chaining mechanism based on multi-level convex hull circle (MCC) is also proposed. Simulation results show that CMRP not only has longer operation time than LEACH and PEGASIS but also has the shortest propagation delay time. The lifetime of CMRP is less than the minimum spanning tree by about 1%, but the propagation delay is shorter than MSTP about 21–28%. In addition, CMRP considers both reducing energy consumption and shortening the propagation delay at the same time. The life-delay rate of the CMRP is close to the optimal results. [ABSTRACT FROM AUTHOR]

An improved method is developed for urban bus driving cycle construction. Driving condition data are collected on the representative routes which are selected between the major residential areas and commercial or industrial areas according to the traffic flow. The micro-trip is defined as a trip between two bus stops. A driving cycle is then constructed with the randomly selected micro-trips according to the proportion of the driving condition. The representative urban bus driving cycle is selected by the variation of the parameter mean value. The improved method of urban bus driving cycle construction and the synthesized Dalian urban bus driving cycle are both verified with the case study of urban bus in Dalian. Driving cycle by the improved method provides the design references for the measurement of pollution and fuel consumption on the electric vehicles which are developed with the characteristics of urban bus. [ABSTRACT FROM AUTHOR]