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Sensor networks, as a special subtype of wireless networks, consist of sets of wirelessly connected sensor nodes often placed in hard-to-reach environments. Therefore, it is expected that sensor nodes will not be powered from the power grid. Instead, sensor nodes have their own power sources, the replacement of which is often impractical and requires additional costs, so it is necessary to ensure minimum energy consumption. For that reason, the energy efficiency of wireless sensor networks used for monitoring environmental parameters is essential, especially in remote networking scenarios. In this paper, an overview of the latest research progress on wireless sensor networks based on LoRa was provided. Furthermore, analyses of energy consumption of sensor nodes used in agriculture to observe environmental parameters were conducted using the results of real measurements in the field, as well as simulations carried out based on collected data about real equipment. Optimization methods of energy consumption, in terms of choosing the appropriate data collection processes from the conducted field measurements, as well as the settings of network radio parameters imitating real conditions used in conducted simulations were highlighted. In the analyses, special emphasis was placed on choosing the optimal packet size. Unlike in other papers analyzing energy efficiency of LoRa communication, in this paper, it was proven that the adjustment of the transmission speed to the actual size of the packet is important for better energy efficiency of communication and that it can reduce energy consumption considerably. Moreover, in the paper, the contents of a packet that can be used in precision agriculture is suggested in order to prove that the 6-bit packet is sufficient for energy-efficient collection of parameters from the environment, in contrast to the 11-bit packets used in standard commercially available equipment. [ABSTRACT FROM AUTHOR]

Blockchain technology is an information security solution that operates on a distributed ledger system. Blockchain technology has considerable potential for securing Internet of Things (IoT) low-powered devices. However, the integration of IoT and blockchain technologies raises a number of research issues. One of the most important is the energy consumption of different blockchain algorithms. Because IoT devices are typically low-powered battery-powered devices, the energy consumption of any blockchain node must be kept low. IoT end nodes are typically low-powered devices expected to survive for extended periods without battery replacement. Energy consumption of blockchain algorithms is an important consideration in any application that combines both technologies, as some blockchain algorithms are infeasible because they consume large amounts of energy, causing the IoT device to reach high temperatures and potentially damaging the hardware; they are also a possible fire hazard. In this paper, we examine the temperatures reached in devices used to process blockchain algorithms, and the energy consumption of three commonly used blockchain algorithms running on low-powered microcontrollers communicating in a wireless sensor network. We found temperatures of IoT devices and energy consumption were highly correlated with the temperatures reached. The results indicate that device temperatures reached 80 °C. This work will contribute to developing energy-efficient blockchain-based IoT sensor networks. [ABSTRACT FROM AUTHOR]

Underwater acoustic sensor networks (UASNs) are critical to a range of applications from oceanographic data collection to submarine surveillance. In these networks, efficient energy management is critical due to the limited power resources of underwater sensors. The LEACH protocol, a popular cluster-based protocol, has been widely used in UASNs to minimize energy consumption. Despite its widespread use, the conventional LEACH protocol faces challenges such as an unoptimized cluster number and low transmission efficiency, which hinder its performance. This paper proposes an improved LEACH protocol for cluster-based UASNs, where the cluster number is optimized with an underwater energy propagation model to reduce energy consumption, and a transmission scheduling algorithm is also employed to achieve conflict-free parallel data transmission. Replication computing is introduced to the LEACH protocol to reduce the signaling in the clustering and data transmission phases. The simulation results show that the proposed protocol outperforms several conventional methods in terms of normalized average residual energy, average number of surviving nodes, average round when the first death node occurs, and the number of packets received by the base station. [ABSTRACT FROM AUTHOR]

Sensor nodes are widely distributed in the Internet of Things and communicate with each other to form a wireless sensor network (WSN), which plays a vital role in people's productivity and life. However, the energy of WSN nodes is limited, so this paper proposes a two-layer WSN system based on edge computing to solve the problems of high energy consumption and short life cycle of WSN data transmission and establishes wireless energy consumption and distance optimization models for sensor networks. Specifically, we propose the optimization objective of balancing load and distance factors. We adopt an improved sparrow search algorithm to evenly distribute sensor nodes in the system to reduce resource consumption, consumption, and network life. Through the simulation experiment, our method is illustrated, effectively reducing the network's energy consumption by 26.8% and prolonging the network's life cycle. [ABSTRACT FROM AUTHOR]

Energy-efficient and reliable underwater acoustic communication attracts a lot of research due to special marine communication conditions with limited resources in underwater acoustic sensor networks (UASNs). In their final analysis, the existing studies focus on controlling redundant communication and route void that greatly influence UASNs' comprehensive performances. Most of them consider directional or omnidirectional transmission for partial optimization aspects, which still have many extra data loads and performance losses. This paper analyzes the main issue sources causing redundant communication in UASNs, and proposes a lightweight differentiated transmission to suppress extra communication to the greatest extent as well as balance energy consumption. First, the layered model employs layer ID to limit the scale of the data packet header, which does not need depth or location information. Second, the layered model, fuzzy-based model, random modeling and directional-omnidirectional differentiated transmission mode comb out the forwarders step by step to decrease needless duplicated forwarding. Third, forwarders are decided by local computation in nodes, which avoids exchanging controlling information among nodes. Simulation results show that our method can efficiently reduce the network load and improve the performance in terms of energy consumption balance, network lifetime, data conflict and network congestion, and data packet delivery ratio. [ABSTRACT FROM AUTHOR]

Security is one of the major concerns while designing robust protocols for underwater sensor networks (UWSNs). The underwater sensor node (USN) is an example of medium access control (MAC) that should control underwater UWSN, and underwater vehicles (UV) combined. Therefore, our proposed method, in this research, investigates UWSN combined with UV optimized as an underwater vehicular wireless network (UVWSN) that can completely detect malicious node attacks (MNA) from the network. Thus, MNA that engages the USN channel and launches MNA is resolved by our proposed protocol through SDAA (secure data aggregation and authentication) protocol deployed in UVWSN. SDAA protocol plays a significant role in secure data communication, as the cluster-based network design (CBND) network organization creates a concise, stable, and energy-efficient network. This paper introduces SDAA optimized network known as UVWSN. In this proposed SDAA protocol, the cluster head (CH) is authenticated through the gateway (GW) and the base station (BS) to guarantee that a legitimate USN oversees all clusters deployed in the UVWSN are securely established for providing trustworthiness/privacy. Furthermore, the communicated data in the UVWSN network guarantee that data transmission is secure due to the optimized SDAA models in the network. Thus, the USNs deployed in the UVWSN are securely confirmed to maintain secure data communication in CBND for energy efficiency. The proposed method is implemented and validated on the UVWSN for measuring reliability, delay, and energy efficiency in the network. The proposed method is utilized for monitoring scenarios for inspecting vehicles or ship structures in the ocean. Based on the testing results, the proposed SDAA protocol methods improve energy efficiency and reduce network delay compared to other standard secure MAC methods. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) can be used to relay sensing information and computational workloads from ground users (GUs) to a remote base station (RBS) for further processing. In this paper, we employ multiple UAVs to assist with the collection of sensing information in a terrestrial wireless sensor network. All of the information collected by the UAVs can be forwarded to the RBS. We aim to improve the energy efficiency for sensing-data collection and transmission by optimizing UAV trajectory, scheduling, and access-control strategies. Considering a time-slotted frame structure, UAV flight, sensing, and information-forwarding sub-slots are confined to each time slot. This motivates the trade-off study between UAV access-control and trajectory planning. More sensing data in one time slot will take up more UAV buffer space and require a longer transmission time for information forwarding. We solve this problem by a multi-agent deep reinforcement learning approach that takes into consideration a dynamic network environment with uncertain information about the GU spatial distribution and traffic demands. We further devise a hierarchical learning framework with reduced action and state spaces to improve the learning efficiency by exploiting the distributed structure of the UAV-assisted wireless sensor network. Simulation results show that UAV trajectory planning with access control can significantly improve UAV energy efficiency. The hierarchical learning method is more stable in learning and can also achieve higher sensing performance. [ABSTRACT FROM AUTHOR]

Accuracy is the vital indicator in location estimation used in many scenarios, such as warehousing, tracking, monitoring, security surveillance, etc., in a wireless sensor network (WSN). The conventional range-free DV-Hop algorithm uses hop distance to estimate sensor node positions but has limitations in terms of accuracy. To address the issues of low accuracy and high energy consumption of DV-Hop-based localization in static WSNs, this paper proposes an enhanced DV-Hop algorithm for efficient and accurate localization with reduced energy consumption. The proposed method consists of three steps: first, the single-hop distance is corrected using the RSSI value for a specific radius; second, the average hop distance between unknown nodes and anchors is modified based on the difference between actual and estimated distances; and finally, the least-squares approach is used to estimate the location of each unknown node. The proposed algorithm, named Hop-correction and energy-efficient DV-Hop (HCEDV-Hop), is executed and evaluated in MATLAB to compare its performance with benchmark schemes. The results show that HCEDV-Hop improves localization accuracy by an average of 81.36%, 77.99%, 39.72%, and 9.96% compared to basic DV-Hop, WCL, improved DV-maxHop, and improved DV-Hop, respectively. In terms of message communication, the proposed algorithm reduces energy usage by 28% compared to DV-Hop and 17% compared to WCL. [ABSTRACT FROM AUTHOR]

With the advances in sensing technologies, sensor networks became the core of several different networks, including the Internet of Things (IoT) and drone networks. This led to the use of sensor networks in many critical applications including military, health care, and commercial applications. In addition, sensors might be mobile or stationary. Stationary sensors, once deployed, will not move; however, mobile nodes can move from one place to another. In most current applications, mobile sensors are used to collect data from stationary sensors. This raises many energy consumption challenges, including sensor networks' energy consumption, urgent messages transfer for real-time analysis, and path planning. Moreover, sensors in sensor networks are usually exposed to environmental parameters and left unattended. These issues, up to our knowledge, are not deeply covered in the current research. This paper develops a complete framework to solve these challenges. It introduces novel path planning techniques considering areas' priority, environmental parameters, and urgent messages. Consequently, a novel energy-efficient and reliable clustering algorithm is proposed considering the residual energy of the sensor nodes, the quality of wireless links, and the distance parameter representing the average intra-cluster distance. Moreover, it proposes a real-time, energy-efficient, reliable and environment-aware routing, taking into account the environmental data, link quality, delay, hop count, nodes' residual energy, and load balancing. Furthermore, for the benefit of the sensor networks research community, all proposed algorithms are formed in integer linear programming (ILP) for optimal solutions. All proposed techniques are evaluated and compared to six recent algorithms. The results showed that the proposed framework outperforms the recent algorithms. [ABSTRACT FROM AUTHOR]

In modern trends, wireless sensor networks (WSNs) are interesting, and distributed in the environment to evaluate received data. The sensor nodes have a higher capacity to sense and transmit the information. A WSN contains low-cost, low-power, multi-function sensor nodes, with limited computational capabilities, used for observing environmental constraints. In previous research, many energy-efficient routing methods were suggested to improve the time of the network by minimizing energy consumption; sometimes, the sensor nodes run out of power quickly. The majority of recent articles present various methods aimed at reducing energy usage in sensor networks. In this paper, an energy-efficient clustering/routing technique, called the energy and distance based multi-objective red fox optimization algorithm (ED-MORFO), was proposed to reduce energy consumption. In each communication round of transmission, this technique selects the cluster head (CH) with the most residual energy, and finds the optimal routing to the base station. The simulation clearly shows that the proposed ED-MORFO achieves better performance in terms of energy consumption (0.46 J), packet delivery ratio (99.4%), packet loss rate (0.6%), end-to-end delay (11 s), routing overhead (0.11), throughput (0.99 Mbps), and network lifetime (3719 s), when compared with existing MCH-EOR and RDSAOA-EECP methods. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) form an important part of industrial application. There has been growing interest in the potential use of WSNs in applications such as environment monitoring, disaster management, health care monitoring, intelligence surveillance and defence reconnaissance. In these applications, the sensor nodes (SNs) are envisaged to be deployed in sizeable numbers in an outlying area, and it is quite difficult to replace these SNs after complete deployment in many scenarios. Therefore, as SNs are predominantly battery powered devices, the energy consumption of the nodes must be properly managed in order to prolong the network lifetime and functionality to a rational time. Different energy-efficient and energy-balanced routing protocols have been proposed in literature over the years. The energy-efficient routing protocols strive to increase the network lifetime by minimizing the energy consumption in each SN. On the other hand, the energy-balanced routing protocols protract the network lifetime by uniformly balancing the energy consumption among the nodes in the network. There have been various survey papers put forward by researchers to review the performance and classify the different energy-efficient routing protocols for WSNs. However, there seems to be no clear survey emphasizing the importance, concepts, and principles of load-balanced energy routing protocols for WSNs. In this paper, we provide a clear picture of both the energy-efficient and energy-balanced routing protocols for WSNs. More importantly, this paper presents an extensive survey of the different state-of-the-art energy-efficient and energy-balanced routing protocols. A taxonomy is introduced in this paper to classify the surveyed energy-efficient and energy-balanced routing protocols based on their proposed mode of communication towards the base station (BS). In addition, we classified these routing protocols based on the solution types or algorithms, and the input decision variables defined in the routing algorithm. The strengths and weaknesses of the choice of the decision variables used in the design of these energy-efficient and energy-balanced routing protocols are emphasised. Finally, we suggest possible research directions in order to optimize the energy consumption in sensor networks. [ABSTRACT FROM AUTHOR]

Wireless sensor network-based (WSN-based) applications need an efficient and reliable data dissemination service to facilitate maintenance, management and data distribution tasks. As WSNs nowadays are becoming pervasive and data intensive, bulk data dissemination protocols have been extensively studied recently. This paper provides a comprehensive survey of the state-of-the-art bulk data dissemination protocols. The large number of papers available in the literature propose various techniques to optimize the dissemination protocols. Different from the existing survey works which separately explores the building blocks of dissemination, our work categorizes the literature according to the optimization purposes: Reliability, Scalability and Transmission/Energy efficiency. By summarizing and reviewing the key insights and techniques, we further discuss on the future directions for each category. Our survey helps unveil three key findings for future direction: (1) The recent advances in wireless communications (e.g., study on cross-technology interference, error estimating codes, constructive interference, capture effect) can be potentially exploited to support further optimization on the reliability and energy efficiency of dissemination protocols; (2) Dissemination in multi-channel, multi-task and opportunistic networks requires more efforts to fully exploit the spatial-temporal network resources to enhance the data propagation; (3) Since many designs incur changes on MAC layer protocols, the co-existence of dissemination with other network protocols is another problem left to be addressed. [ABSTRACT FROM AUTHOR]

Underwater acoustic sensor networks (UASNs) are fundamental assets to enable discovery and utilization of sub-sea environments and have attracted both academia and industry to execute long-term underwater missions. Given the heightened significance of battery dependency in underwater wireless sensor networks, our objective is to maximize the amount of harvested energy underwater by adopting the TDMA time slot scheduling approach to prolong the operational lifetime of the sensors. In this study, we considered the spatial uncertainty of underwater ambient resources to improve the utilization of available energy and examine a stochastic model for piezoelectric energy harvesting. Considering a realistic channel and environment condition, a novel multi-agent reinforcement learning algorithm is proposed. Nodes observe and learn from their choice of transmission slots based on the available energy in the underwater medium and autonomously adapt their communication slots to their energy harvesting conditions instead of relying on the cluster head. In the numerical results, we present the impact of piezoelectric energy harvesting and harvesting awareness on three lifetime metrics. We observe that energy harvesting contributes to 4% improvement in first node dead (FND), 14% improvement in half node dead (HND), and 22% improvement in last node dead (LND). Additionally, the harvesting-aware TDMA-RL method further increases HND by 17% and LND by 38%. Our results show that the proposed method improves in-cluster communication time interval utilization and outperforms traditional time slot allocation methods in terms of throughput and energy harvesting efficiency. [ABSTRACT FROM AUTHOR]

As an alternative to flat architectures, clustering architectures are designed to minimize the total energy consumption of sensor networks. Nonetheless, sensor nodes experience increased energy consumption during data transmission, leading to a rapid depletion of energy levels as data are routed towards the base station. Although numerous strategies have been developed to address these challenges and enhance the energy efficiency of networks, the formulation of a clustering-based routing algorithm that achieves both high energy efficiency and increased packet transmission rate for large-scale sensor networks remains an NP-hard problem. Accordingly, the proposed work formulated an energy-efficient clustering mechanism using a chaotic genetic algorithm, and subsequently developed an energy-saving routing system using a bio-inspired grey wolf optimizer algorithm. The proposed chaotic genetic algorithm–grey wolf optimization (CGA-GWO) method is designed to minimize overall energy consumption by selecting energy-aware cluster heads and creating an optimal routing path to reach the base station. The simulation results demonstrate the enhanced functionality of the proposed system when associated with three more relevant systems, considering metrics such as the number of live nodes, average remaining energy level, packet delivery ratio, and overhead associated with cluster formation and routing. [ABSTRACT FROM AUTHOR]

Recent advances in wireless networking technology and the proliferation of industrial wireless sensors have led to an increasing interest in using wireless networks for closed loop control. The main advantages of Wireless Networked Control Systems (WNCSs) are the reconfigurability, easy commissioning and the possibility of installation in places where cabling is impossible. Despite these advantages, there are two main problems which must be considered for practical implementations of WNCSs. One problem is the sampling period constraint of industrial wireless sensors. This problem is related to the energy cost of the wireless transmission, since the power supply is limited, which precludes the use of these sensors in several closed-loop controls. The other technological concern in WNCS is the energy efficiency of the devices. As the sensors are powered by batteries, the lowest possible consumption is required to extend battery lifetime. As a result, there is a compromise between the sensor sampling period, the sensor battery lifetime and the required control performance for the WNCS. This paper develops a model-based soft sensor to overcome these problems and enable practical implementations of WNCSs. The goal of the soft sensor is generating virtual data allowing an actuation on the process faster than the maximum sampling period available for the wireless sensor. Experimental results have shown the soft sensor is a solution to the sampling period constraint problem of wireless sensors in control applications, enabling the application of industrial wireless sensors in WNCSs. Additionally, our results demonstrated the soft sensor potential for implementing energy efficient WNCS through the battery saving of industrial wireless sensors. [ABSTRACT FROM AUTHOR]

In wireless sensor networks (WSNs), sensor nodes are deployed for collecting and analyzing data. These nodes use limited energy batteries for easy deployment and low cost. The use of limited energy batteries is closely related to the lifetime of the sensor nodes when using wireless sensor networks. Efficient-energy management is important to extending the lifetime of the sensor nodes. Most effort for improving power efficiency in tiny sensor nodes has focused mainly on reducing the power consumed during data transmission. However, recent emergence of sensor nodes equipped with multi-cores strongly requires attention to be given to the problem of reducing power consumption in multi-cores. In this paper, we propose an energy efficient scheduling method for sensor nodes supporting a uniform multi-cores. We extend the proposed T-Ler plane based scheduling for global optimal scheduling of a uniform multi-cores and multi-processors to enable power management using dynamic power management. In the proposed approach, processor selection for a scheduling and mapping method between the tasks and processors is proposed to efficiently utilize dynamic power management. Experiments show the effectiveness of the proposed approach compared to other existing methods. [ABSTRACT FROM AUTHOR]

Compressive sensing (CS) provides an energy-efficient paradigm for data gathering in wireless sensor networks (WSNs). However, the existing work on spatial-temporal data gathering using compressive sensing only considers either multi-hop relaying based or multiple random walks based approaches. In this paper, we exploit the mobility pattern for spatial-temporal data collection and propose a novel mobile data gathering scheme by employing the Metropolis-Hastings algorithm with delayed acceptance, an improved random walk algorithm for a mobile collector to collect data from a sensing field. The proposed scheme exploits Kronecker compressive sensing (KCS) for spatial-temporal correlation of sensory data by allowing the mobile collector to gather temporal compressive measurements from a small subset of randomly selected nodes along a random routing path. More importantly, from the theoretical perspective we prove that the equivalent sensing matrix constructed from the proposed scheme for spatial-temporal compressible signal can satisfy the property of KCS models. The simulation results demonstrate that the proposed scheme can not only significantly reduce communication cost but also improve recovery accuracy for mobile data gathering compared to the other existing schemes. In particular, we also show that the proposed scheme is robust in unreliable wireless environment under various packet losses. All this indicates that the proposed scheme can be an efficient alternative for data gathering application in WSNs. [ABSTRACT FROM AUTHOR]

Underwater wireless sensor networks (UWSNs) have attracted increasing attention in recent years because of their numerous applications in ocean monitoring, resource discovery and tactical surveillance. However, the design of reliable and efficient transmission and routing protocols is a challenge due to the low acoustic propagation speed and complex channel environment in UWSNs. In this paper, we propose a novel cross-layer routing protocol based on network coding (NCRP) for UWSNs, which utilizes network coding and cross-layer design to greedily forward data packets to sink nodes efficiently. The proposed NCRP takes full advantages of multicast transmission and decode packets jointly with encoded packets received from multiple potential nodes in the entire network. The transmission power is optimized in our design to extend the life cycle of the network. Moreover, we design a real-time routing maintenance protocol to update the route when detecting inefficient relay nodes. Substantial simulations in underwater environment by Network Simulator 3 (NS-3) show that NCRP significantly improves the network performance in terms of energy consumption, end-to-end delay and packet delivery ratio compared with other routing protocols for UWSNs. [ABSTRACT FROM AUTHOR]

Due to the recent development in the field of Wireless Sensor Networks (WSNs), the Wireless Body Area Networks (WBANs) have become a major area of interest for the developers and researchers. Human body exhibits postural mobility due to which distance variation occurs and the status of connections amongst sensors change time to time. One of the major requirements of WBAN is to prolong the network lifetime without compromising on other performance measures, i.e., delay, throughput and bandwidth efficiency. Node prioritization is one of the possible solutions to obtain optimum performance in WBAN. IEEE 802.15.6 CSMA/CA standard splits the nodes with different user priorities based on Contention Window (CW) size. Smaller CW size is assigned to higher priority nodes. This standard helps to reduce delay, however, it is not energy efficient. In this paper, we propose a hybrid node prioritization scheme based on IEEE 802.15.6 CSMA/CA to reduce energy consumption and maximize network lifetime. In this scheme, optimum performance is achieved by node prioritization based on CW size as well as power in respective user priority. Our proposed scheme reduces the average back off time for channel access due to CW based prioritization. Additionally, power based prioritization for a respective user priority helps to minimize required number of retransmissions. Furthermore, we also compare our scheme with IEEE 802.15.6 CSMA/CA standard (CW assisted node prioritization) and power assisted node prioritization under postural mobility in WBAN. Mathematical expressions are derived to determine the accurate analytical model for throughput, delay, bandwidth efficiency, energy consumption and life time for each node prioritization scheme. With the intention of analytical model validation, we have performed the simulations in OMNET++/MIXIM framework. Analytical and simulation results show that our proposed hybrid node prioritization scheme outperforms other node prioritization schemes in terms of average network delay, average throughput, average bandwidth efficiency and network lifetime. [ABSTRACT FROM AUTHOR]

This paper presents cooperative routing scheme to improve data reliability. The proposed protocol achieves its objective, however, at the cost of surplus energy consumption. Thus sink mobility is introduced to minimize the energy consumption cost of nodes as it directly collects data from the network nodes at minimized communication distance. We also present delay and energy optimized versions of our proposed RE-AEDG to further enhance its performance. Simulation results prove the effectiveness of our proposed RE-AEDG in terms of the selected performance matrics. [ABSTRACT FROM AUTHOR]

To balance energy consumption and reduce latency on data transmission inWireless Sensor Networks (WSNs), a type of low-latency data gathering method with multi-Sink (LDGM for short) is proposed in this paper. The network is divided into several virtual regions consisting of three or less data gathering units and the leader of each region is selected according to its residual energy as well as distance to all of the other nodes. Only the leaders in each region need to communicate with the mobile Sinks which have effectively reduced energy consumption and the end-to-end delay. Moreover, with the help of the sleep scheduling and the sensing radius adjustment strategies, redundancy in network coverage could also be effectively reduced. Simulation results show that LDGM is energy efficient in comparison with MST as well as MWST and its time efficiency on data collection is higher than one Sink based data gathering methods. [ABSTRACT FROM AUTHOR]

Due to their special environment, Underwater Wireless Sensor Networks (UWSNs) are usually deployed over a large sea area and the nodes are usually floating. This results in a lower beacon node distribution density, a longer time for localization, and more energy consumption. Currently most of the localization algorithms in this field do not pay enough consideration on the mobility of the nodes. In this paper, by analyzing the mobility patterns of water near the seashore, a localization method for UWSNs based on a Mobility Prediction and a Particle Swarm Optimization algorithm (MP-PSO) is proposed. In this method, the range-based PSO algorithm is used to locate the beacon nodes, and their velocities can be calculated. The velocity of an unknown node is calculated by using the spatial correlation of underwater object's mobility, and then their locations can be predicted. The range-based PSO algorithm may cause considerable energy consumption and its computation complexity is a little bit high, nevertheless the number of beacon nodes is relatively smaller, so the calculation for the large number of unknown nodes is succinct, and this method can obviously decrease the energy consumption and time cost of localizing these mobile nodes. The simulation results indicate that this method has higher localization accuracy and better localization coverage rate compared with some other widely used localization methods in this field. [ABSTRACT FROM AUTHOR]

User behaviour and choice is a significant parameter in the consumption patterns of energy in the built environment. This paper introduces a behavior-based approach for developing smart energy applications. With the rapid development of wireless sensor networks and the Internet of Things (IoT), human-computer interfaces can be created through the mapping of user experiences. These applications can provide users with dynamic feedback on their energy consumption patterns in their built environment. The paper describes a "Sensible Energy System" (SENS) that is based on user experience design methods with sensor network technology. Through SENS, solar energy simulation is combined with device consumption data in order to achieve an IoT network to facilitate the interaction between user behaviors and electricity consumption. The interaction between users and devices through SENS can not only optimize power consumption, but also provide consumers with additional choice and dynamic decision making regarding their consumption. This article provides an (1) understanding and analysis of users' spatial interaction, explains the (2) planning of the new smart environment design and user experiences, discusses (3) designing a suitable Wireless sensor network (WSN) agent and energy connection, describes (4) the information that has been collected, and (5) incorporates a rooftop solar potential simulation for predicting energy outputs into the sensor network model. [ABSTRACT FROM AUTHOR]

Routing protocols based on trust mechanisms have been widely investigated for wireless sensor networks, and the works have achieved good results, while there are few works on trusted routing for underwater acoustic networks (UANs). However, trust-aware routing is the key to improving the packet delivery rate and the energy efficiency of UANs. Therefore, inspired by the theory of trust evaluation, a trust-aware and fuzzy logic-based reliable layering routing protocol (TAFLRLR) is proposed. In the TAFLRLR protocol, to avoid the problem of the void area and improve the transmission reliability, the candidate nodes of the next-hop forwarding nodes are determined according to the layers of neighbor nodes. Moreover, a fuzzy logic-based trust evaluation mechanism (FLTEM) is provided, which employs the fuzzy comprehensive evaluation decision model to calculate the comprehensive trust value for underwater sensor nodes. Further, the node density of a candidate node and its comprehensive trust value are taken as the input of a fuzzy control system and the forwarding probability (FP) of the node is taken as the output, and the candidate node with the highest FP is selected as the best forwarding node. Simulation results illustrate the superiority and effectiveness of the TAFLRLR protocol in terms of energy efficiency, routing reliability, and transmission reliability. [ABSTRACT FROM AUTHOR]

This research delves into the aspects of communication and connectivity problems within random Wireless Sensor Networks (WSNs). It takes into account the distinctive role of the sink node, its placement, and application-specific requirements for effective communication while conserving valuable network resources. Through mathematical modeling, theoretical analysis, and simulation evaluations, we derive, compare, and contrast the probabilities of partial and full connectivity within a random WSN, factoring in network parameters and the maximum allowable hop distance/count h m a x . h m a x captures the diverse range of delay-sensitive requirements encountered in practical scenarios. Our research underscores the significant impact of the sink node and its placement on network connectivity and the sensor connection rate. The results exemplify a noteworthy decline in the sensor connection rate, dropping from 98.8 % to 72.5 % , upon relocating the sink node from the network center to the periphery. Moreover, as compared with full connectivity, partial connectivity and the sensor connection rate are more suitable metrics for assessing the communication capability of random WSNs. The results illustrate that 1.367 times more energy is required to connect less than 4 % of the remote sensors, based on the examined network settings. Additionally, to increase the sensor connection rate slightly from 96 % to 100 % , an additional 538 % more energy is required in multipath fading based on the widely adopted energy consumption model. This research and its outcomes contribute to establishing appropriate performance metrics and determining critical network parameters for the practical design and implementation of real-world wireless sensor networks. [ABSTRACT FROM AUTHOR]

Charging schemes utilizing mobile wireless chargers can be applied to prolong the lifespan of a wireless sensor network. In considering charging schemes with mobile chargers, most current studies focus on charging each sensor from a single position, then optimizing the moving paths of the chargers. However, in reality, a wireless charger may charge the same sensor from several positions in its path. In this paper we consider this fact and seek to minimize both the number of charging locations and the total required charging time. Two charging plans are developed. The first plan considers the charging time required by each sensor and greedily selects the charging service positions. The second one is a two-phase plan, where the number of charging positions is first minimized, then minimum charging times are assigned to every position according to the charging requirements of the nearby sensors. This paper also corrects a problem neglected by some studies in minimizing the number of charging service positions and further provides a corresponding solution. Empirical studies show that compared with other minimal clique partition (MCP)-based methods, the proposed charging plan may save up to 60% in terms of both the number of charging positions and the total required charging time. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) are one of the fundamental infrastructures for Internet of Things (IoTs) technology. Efficient energy consumption is one of the greatest challenges in WSNs because of its resource-constrained sensor nodes (SNs). Clustering techniques can significantly help resolve this issue and extend the network's lifespan. In clustering, WSN is divided into various clusters, and a cluster head (CH) is selected in each cluster. The selection of appropriate CHs highly influences the clustering technique, and poor cluster structures lead toward the early death of WSNs. In this paper, we propose an energy-efficient clustering and cluster head selection technique for next-generation wireless sensor networks (NG-WSNs). The proposed clustering approach is based on the midpoint technique, considering residual energy and distance among nodes. It distributes the sensors uniformly creating balanced clusters, and uses multihop communication for distant CHs to the base station (BS). We consider a four-layer hierarchical network composed of SNs, CHs, unmanned aerial vehicle (UAV), and BS. The UAV brings the advantage of flexibility and mobility; it shortens the communication range of sensors, which leads to an extended lifetime. Finally, a simulated annealing algorithm is applied for the optimal trajectory of the UAV according to the ground sensor network. The experimental results show that the proposed approach outperforms with respect to energy efficiency and network lifetime when compared with state-of-the-art techniques from recent literature. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks are subjected to some design constraints (e.g., processing capability, storage memory, energy consumption, fixed deployment, etc.) and to outdoor harsh conditions that deeply affect the network reliability. The aim of this work is to provide a deeper understanding about the way redundancy and node deployment affect the network reliability. In more detail, the paper analyzes the design and implementation of a wireless sensor network for low-power and low-cost applications and calculates its reliability considering the real environmental conditions and the real arrangement of the nodes deployed in the field. The reliability of the system has been evaluated by looking for both hardware failures and communication errors. A reliability prediction based on different handbooks has been carried out to estimate the failure rate of the nodes self-designed and self-developed to be used under harsh environments. Then, using the Fault Tree Analysis the real deployment of the nodes is taken into account considering the Wi-Fi coverage area and the possible communication link between nearby nodes. The findings show how different node arrangements provide significantly different reliability. The positioning is therefore essential in order to obtain maximum performance from a Wireless sensor network. [ABSTRACT FROM AUTHOR]

In this paper, we examine routing protocols with the shortest path in sensor networks. In doing this, we propose a genetic algorithm (GA)-based Ad Hoc On-Demand Multipath Distance Vector routing protocol (GA-AOMDV). We utilize a fitness function that optimizes routes based on the energy consumption in their nodes. We compare this algorithm with other existing ad hoc routing protocols including LEACH-GA, GA-AODV, AODV, DSR, EPAR, EBAR_BFS. Results prove that our protocol enhances the network performance in terms of packet delivery ratio, throughput, round trip time and energy consumption. GA-AOMDV protocol achieves average gain that is 7 to 22% over other protocols. Therefore, our protocol extends the network lifetime for data communications. [ABSTRACT FROM AUTHOR]

Topology control is one of the most essential technologies in wireless sensor networks (WSNs); it constructs networks with certain characteristics through the usage of some approaches, such as power control and channel assignment, thereby reducing the inter-nodes interference and the energy consumption of the network. It is closely related to the efficiency of upper layer protocols, especially MAC and routing protocols, which are the same as underwater acoustic sensor networks (UASNs). Directional antenna technology (directional transducer in UASNs) has great advantages in minimizing interference and conserving energy by restraining the beamforming range. It enables nodes to communicate with only intended neighbors; nevertheless, additional problems emerge, such as how to guarantee the connectivity of the network. This paper focuses on the connectivity problem of UASNs equipped with tri-modal directional transducers, where the orientation of a transducer is stabilized after the network is set up. To efficiently minimize the total network energy consumption under constraint of connectivity, the problem is formulated to a minimum network cost transducer orientation (MNCTO) problem and is provided a reduction from the Hamiltonian path problem in hexagonal grid graphs (HPHGG), which is proved to be NP-complete. Furthermore, a heuristic greedy algorithm is proposed for MNCTO. The simulation evaluation results in a contrast with its omni-mode peer, showing that the proposed algorithm greatly reduces the network energy consumption by up to nearly half on the premise of satisfying connectivity. [ABSTRACT FROM AUTHOR]

Full-duplex (FD) with simultaneous wireless information and power transfer (SWIPT) in wireless ad hoc networks has received increased attention as a technology for improving spectrum and energy efficiency. This paper studies the outage performance for a SWIPT-based decode-and-forward (DF) FD relaying network consisting of a single-antenna source S, a two-antenna relay R, and a multi-antenna destination D. Specifically, we propose four protocols, namely static time-switching factor with selection combining (STSF-SC), static time-switching factor with maximal ratio combining (STSF-MRC), optimal dynamic time-switching factor with selection combining (ODTSF-SC), and optimal dynamic time-switching factor with maximal ratio combining (ODTSF-MRC) to fully investigate the outage performance of the proposed system. In particular, the optimal time-switching factor from the ODTSF-SC and ODTSF-MRC methods is designed to maximize the total received data at the destination. In this context, we derive exact closed-formed expressions for all schemes in terms of the outage probability (OP). Finally, the Monte Carlo simulations are conducted to corroborate the theoretical analysis's correctness and the proposed schemes' effectiveness. [ABSTRACT FROM AUTHOR]

In unmanned aerial vehicle (UAV)-aided wireless sensor networks (UWSNs), a UAV is employed as a mobile sink to gather data from sensor nodes. Incorporating UAV helps prolong the network lifetime and avoid the energy-hole problem faced by sensor networks. In emergency applications, timely data collection from sensor nodes and transferal of the data to the base station (BS) is a prime requisite. The timely and safe path of UAV is one of the fundamental premises for effective UWSN operations. It is essential and challenging to identify a suitable path in an environment comprising various obstacles and to ensure that the path can efficiently reach the target point. This paper proposes a hybrid path planning (HPP) algorithm for efficient data collection by assuring the shortest collision-free path for UAV in emergency environments. In the proposed HPP scheme, the probabilistic roadmap (PRM) algorithm is used to design the shortest trajectory map and the optimized artificial bee colony (ABC) algorithm to improve different path constraints in a three-dimensional environment. Our simulation results show that the proposed HPP outperforms the PRM and conventional ABC schemes significantly in terms of flight time, energy consumption, convergence time, and flight path. [ABSTRACT FROM AUTHOR]

Underwater sensor nodes are usually deployed by ships, aircraft, etc., in random drops, and there is current movement in the underwater environment, which results in an uneven distribution of sensor nodes and thus, different energy consumption in each area of the network. In addition, the underwater sensor network also has a "hot zone" problem. To address the uneven energy consumption of the network caused by the above problem, the non-uniform clustering algorithm for energy equalization is put forward. Considering the residual energy, density and coverage redundancy of nodes, this algorithm selects the cluster heads and makes them more reasonably distributed. Additionally, according to the selected cluster heads, the size of each cluster is designed to equalize the energy consumption of the network during multi-hop routing. In this process, the residual energy of cluster heads and the mobility of nodes are considered, and real-time maintenance is performed for each cluster. The simulation results demonstrate that the proposed algorithm is effective in prolonging the network lifetime and balancing the network energy consumption; moreover, the network coverage maintenance is better than that of other algorithms. [ABSTRACT FROM AUTHOR]

The recent development of wireless wearable sensor networks offers a spectrum of new applications in fields of healthcare, medicine, activity monitoring, sport, safety, human-machine interfacing, and beyond. Successful use of this technology depends on lifetime of the battery-powered sensor nodes. This paper presents a new method for extending the lifetime of the wearable sensor networks by avoiding unnecessary data transmissions. The introduced method is based on embedded classifiers that allow sensor nodes to decide if current sensor readings have to be transmitted to cluster head or not. In order to train the classifiers, a procedure was elaborated, which takes into account the impact of data selection on accuracy of a recognition system. This approach was implemented in a prototype of wearable sensor network for human activity monitoring. Real-world experiments were conducted to evaluate the new method in terms of network lifetime, energy consumption, and accuracy of human activity recognition. Results of the experimental evaluation have confirmed that, the proposed method enables significant prolongation of the network lifetime, while preserving high accuracy of the activity recognition. The experiments have also revealed advantages of the method in comparison with state-of-the-art algorithms for data transmission reduction. [ABSTRACT FROM AUTHOR]

Existing duty-cycling and pipelined-forwarding (DCPF) protocols applied in battery-powered wireless sensor networks can significantly alleviate the sleep latency issue and save the energy of networks. However, when a DCPF protocol applies to a linear sensor network (LSN), it lacks the ability to handle the bottleneck issue called the energy-hole problem, which is mainly manifested due to the excessive energy consumption of nodes near the sink node. Without overcoming this issue, the lifespan of the network could be greatly reduced. To that end, this paper proposes a method of deploying redundant nodes in LSN, and a corresponding enhanced DCPF protocol called redundancy-based DCPF (RDCPF) to support the new topology of LSN. In RDCPF, the distribution of energy consumption of the whole network becomes much more even. RDCPF also brings improvements to the network in terms of network survival time, packet delivery latency, and energy efficiency, which have been shown through the extensive simulations in comparison with existing DCPF protocols. [ABSTRACT FROM AUTHOR]

Today's critical goals in sensor network research are extending the lifetime of wireless sensor networks (WSNs) and lowering power consumption. A WSN necessitates the use of energy-efficient communication networks. Clustering, storage, communication capacity, high configuration complexity, low communication speed, and limited computation are also some of the energy limitations of WSNs. Moreover, cluster head selection remains problematic for WSN energy minimization. Sensor nodes (SNs) are clustered in this work using the Adaptive Sailfish Optimization (ASFO) algorithm with K-medoids. The primary purpose of research is to optimize the selection of cluster heads through energy stabilization, distance reduction, and latency minimization between nodes. Because of these constraints, achieving optimal energy resource utilization is an essential problem in WSNs. An energy-efficient cross-layer-based expedient routing protocol (E-CERP) is used to determine the shortest route, dynamically minimizing network overhead. The proposed method is used to evaluate the packet delivery ratio (PDR), packet delay, throughput, power consumption, network lifetime, packet loss rate, and error estimation, and the results were superior to existing methods. PDR (100%), packet delay (0.05 s), throughput (0.99 Mbps), power consumption (1.97 mJ), network lifespan (5908 rounds), and PLR (0.5%) for 100 nodes are the performance results for quality-of-service parameters. [ABSTRACT FROM AUTHOR]

The rapid development of AIOT-related technologies has revolutionized various industries. The advantage of such real-time sensing, low costs, small sizes, and easy deployment makes extensive use of wireless sensor networks in various fields. However, due to the wireless transmission of data, and limited built-in power supply, controlling energy consumption and making the application of the sensor network more efficient is still an urgent problem to be solved in practice. In this study, we construct this problem as a tree structure wireless sensor network mathematical model, which mainly considers the QoS and fairness requirements. This study determines the probability of sensor activity, transmission distance, and transmission of the packet size, and thereby minimizes energy consumption. The Lagrangian Relaxation method is used to find the optimal solution with the lowest energy consumption while maintaining the network's transmission efficiency. The experimental results confirm that the decision-making speed and energy consumption can be effectively improved. [ABSTRACT FROM AUTHOR]

Time synchronization is an essential issue in industrial wireless sensor networks (IWSNs). It assists perfect coordinated communications among the sensor nodes to preserve battery power. Generally, time synchronization in IWSNs has two major aspects of energy consumption and accuracy. In the literature, the energy consumption has not received much attention in contrast to the accuracy. In this paper, focusing on the energy consumption aspect, we introduce an energy-efficient reference node selection (EERS) algorithm for time synchronization in IWSNs. It selects and schedules a minimal sequence of connected reference nodes that are responsible for spreading timing messages. EERS achieves energy consumption synchronization by reducing the number of transmitted messages among the sensor nodes. To evaluate the performance of EERS, we conducted extensive experiments with Arduino Nano RF sensors and revealed that EERS achieves considerably fewer messages than previous techniques, robust time synchronization (R-Sync), fast scheduling and accurate drift compensation for time synchronization (FADS), and low power scheduling for time synchronization protocols (LPSS). In addition, simulation results for a large sensor network of 450 nodes demonstrate that EERS reduces the whole number of transmitted messages by 52%, 30%, and 13% compared to R-Sync, FADS, and LPSS, respectively. [ABSTRACT FROM AUTHOR]

Underwater Wireless Sensor Networks (UWSNs) have aroused increasing interest of many researchers in industry, military, commerce and academe recently. Due to the harsh underwater environment, energy efficiency is a significant theme should be considered for routing in UWSNs. Underwater positioning is also a particularly tricky task since the high attenuation of radio-frequency signals in UWSNs. In this paper, we propose an energy-efficient depth-based opportunistic routing algorithm with Q-learning (EDORQ) for UWSNs to guarantee the energy-saving and reliable data transmission. It combines the respective advantages of Q-learning technique and opportunistic routing (OR) algorithm without the full-dimensional location information to improve the network performance in terms of energy consumption, average network overhead and packet delivery ratio. In EDORQ, the void detection factor, residual energy and depth information of candidate nodes are jointly considered when defining the Q-value function, which contributes to proactively detecting void nodes in advance, meanwhile, reducing energy consumption. In addition, a simple and scalable void node recovery mode is proposed for the selection of candidate set so as to rescue packets that are stuck in void nodes unfortunately. Furthermore, we design a novel method to set the holding time for the schedule of packet forwarding base on Q-value so as to alleviate the packet collision and redundant transmission. We conduct extensive simulations to evaluate the performance of our proposed algorithm and compare it with other three routing algorithms on Aqua-sim platform (NS2). The results show that the proposed algorithm significantly improve the performance in terms of energy efficiency, packet delivery ratio and average network overhead without sacrificing too much average packet delay. [ABSTRACT FROM AUTHOR]

Wireless sensor networks have been widely adopted, and neighbor discovery is an essential step to construct the networks. Most existing studies on neighbor discovery are designed on the assumption that either all nodes are fully connected or only two nodes compose the network. However, networks are partially connected in reality: some nodes are within radio range of each other, while others are not. Low latency and energy efficiency are two common goals, which become even more challenging to achieve at the same time in partially connected networks. We find that the collision caused by simultaneous transmissions is the main obstruction of achieving the two goals. In this paper, we present an efficient algorithm called Panacea to address these challenges by alleviating collisions. To begin with, we design Panacea-NCD (Panacea no collision detection) for nodes that do not have a collision detection mechanism. When n is large, we show the discovery latency is bounded by O (n · ln n) for any duty cycle (the percentage time to turn on the radio), where each node has n neighbors on average. For nodes that can detect collisions, we then present Panacea-WCD which also bounds the latency within O (n · ln n) slots. Finally, we conduct extensive evaluations and the results also corroborate our analyses. [ABSTRACT FROM AUTHOR]

Sensor nodes perform missions based on the effectual invariable coverage of events, and it is commonly guaranteed by the determinate deployment for sensor nodes who deviate from the optimum site frequently. To reach the optimal coverage effect with the lowest costs is a primary goal of wireless sensor networks. In this paper, by splicing the sensing area optimally with cellular grids, the best deployment location for sensors and the required minimum number of them are revealed. The optimization problem of coverage rate and energy consumption is converted into a task assignment problem, and a dynamic partition algorithm for cellular grids is also proposed to improve the coverage effect when the number of sensors is variable. Furthermore, on the basis of solving the multi-objective problem of reducing and balancing the energy cost of sensors, the vampire bat optimizer is improved by introducing virtual bats and virtual preys, and finally solves the asymmetric assignment problem once the number of cellular grids is not equal to that of sensors. Simulation results indicate that the residual energy of sensors during redeployment is balanced notably by our strategy when compared to three other popular coverage-enhancement algorithms. Additionally, the total energy cost of sensor nodes and coverage rate can be optimized, and it also has a superior robustness when the number of nodes changes. [ABSTRACT FROM AUTHOR]

Smart sensor nodes can process data collected from sensors, make decisions, and recognize relevant events based on the sensed information before sharing it with other nodes. In wireless sensor networks, the smart sensor nodes are usually grouped in clusters for effective cooperation. One sensor node in each cluster must act as a cluster head. The cluster head depletes its energy resources faster than the other nodes. Thus, the cluster-head role must be periodically reassigned (rotated) to different sensor nodes to achieve a long lifetime of wireless sensor network. This paper introduces a method for extending the lifetime of the wireless sensor networks with smart nodes. The proposed method combines a new algorithm for rotating the cluster-head role among sensor nodes with suppression of unnecessary data transmissions. It enables effective control of the cluster-head rotation based on expected energy consumption of sensor nodes. The energy consumption is estimated using a lightweight model, which takes into account transmission probabilities. This method was implemented in a prototype of wireless sensor network. During experimental evaluation of the new method, detailed measurements of lifetime and energy consumption were conducted for a real wireless sensor network. Results of these realistic experiments have revealed that the lifetime of the sensor network is extended when using the proposed method in comparison with state-of-the-art cluster-head rotation algorithms. [ABSTRACT FROM AUTHOR]

In wireless sensor networks, energy efficiency is important because sensor nodes have limited energy. 3-dimensional group management medium access control (3-D GM-MAC) is an attractive MAC protocol for application to the Internet of Things (IoT) environment with various sensors. 3-D GM-MAC outperforms the existing MAC schemes in terms of energy efficiency, but has some stability issues. In this paper, methods that improve the stability and transmission performance of 3-D GM-MAC are proposed. A buffer management scheme for sensor nodes is newly proposed. Fixed sensor nodes that have a higher priority than the mobile sensor nodes in determining the group numbers that were added, and an advanced group number management scheme was introduced. The proposed methods were simulated and analyzed. The newly derived buffer threshold had a similar energy efficiency to the original 3-D GM-MAC, but improved performance in the aspects of data loss rate and data collection rate. Data delay was not included in the comparison factors as 3-D GM-MAC targets non-real-time applications. When using fixed sensor nodes, the number of group number resets is reduced by about 43.4% and energy efficiency increased by about 10%. Advanced group number management improved energy efficiency by about 23.4%. In addition, the advanced group number management with periodical group number resets of the entire sensor nodes showed about a 48.9% improvement in energy efficiency. [ABSTRACT FROM AUTHOR]

Underwater Acoustic Sensor Networks (UASN) have two important limitations: a very aggressive (marine) environment, and the use of acoustic signals. This means that the techniques for terrestrial wireless sensor networks (WSN) are not applicable. This paper proposes a routing protocol called "Self-Organizing and Scalable Routing Protocol" (SOSRP) which is decentralized and based on tables residing in each node. A combination of the hop value to the collector node and the distance is used as a criterion to create routes leading to the sink node. The expected functions of the protocol include self-organization of the routes, tolerance to failures and detection of isolated nodes. Through the implementation of SOSRP in Matlab and a model of propagation and energy being appropriate for marine environment, performance results are obtained in different scenarios (varying both nodes and transmission range) that include parameters such as end-to-end packet delay, consumption of energy or length of the created routes (with and without failure). The results obtained show a stable, reliable and suitable operation for the deployment and operation of nodes in UASN networks. [ABSTRACT FROM AUTHOR]

Virtual-force algorithms (VFAs) have been widely studied for accurate node deployment in wireless-sensor-network (WSN) applications. Their main purpose is to achieve the maximum coverage area with the minimum number of sensor nodes in the target area. Recently, we reported a new VFA based on virtual spring force (VFA-SF) and discussed in detail the corresponding efficiency via statistical analysis. The optimized strategy by adding an external central force (VFA-SF-OPT) was presented, which effectively eliminates the coverage hole or twisted structure in the final network distribution. In this paper, the parameter effects on VFA-SF and the VFA-SF-OPT were further investigated: (1) Node velocity dramatically affects the convergence rate of the node-deployment process. (2) A suitable external central force improves equilibrium distance and reduces energy consumption. (3) The effects of VFA-SF and VFA-SF-OPT for different types of obstacles are discussed. Generally, by choosing suitable parameters, both VFA-SF and VFA-SF-OPT can effectively improve node deployment and energy consumption for the whole sensor network. The results give important insight in parameter selection and information fusion in the application of a large-scale WSN. [ABSTRACT FROM AUTHOR]

Wireless Power Transfer (WPT) is a promising technology to replenish energy of sensors in Rechargeable Wireless Sensor Networks (RWSN). In this paper, we investigate the mobile directional charging optimization problem in RWSN. Our problem is how to plan the moving path and charging direction of the Directional Charging Vehicle (DCV) in the 2D plane to replenish energy for RWSN. The objective is to optimize energy charging efficiency of the DCV while maintaining the sensor network working continuously. To the best of our knowledge, this is the first work to study the mobile directional charging problem in RWSN. We prove that the problem is NP-hard. Firstly, the coverage utility of the DCV's directional charging is proposed. Then we design an approximation algorithm to determine the docking spots and their charging orientations while minimizing the number of the DCV's docking spots and maximizing the charging coverage utility. Finally, we propose a moving path planning algorithm for the DCV's mobile charging to optimize the DCV's energy charging efficiency while ensuring the networks working continuously. We theoretically analyze the DCV's charging service capability, and perform the comprehensive simulation experiments. The experiment results show the energy efficiency of the DCV is higher than the omnidirectional charging model in the sparse networks. [ABSTRACT FROM AUTHOR]

Underwater Acoustic Sensor Networks (UASNs) have become one of the promising technologies for exploring underwater natural resources and collecting scientific data from the aquatic environment. As obstacles hinder the communications among sensor nodes in UASNs, designing an effective bypass routing protocol to avoid obstacles is an urgent need. Moreover, the sensor nodes are typically powered by batteries, which are difficult to replace, restricting the network lifetime of UASNs. In this paper, an Energy-efficient and Obstacle-Avoiding Routing protocol (EOAR) is proposed not only to address the issue of marine animals acting as obstacles that interfere with communications, but also to balance the network energy according to the residual energy. In the EOAR protocol, when the current node perceives the existence of marine animals, the interference area of the animal-nodes is first calculated using the underwater acoustic channel model, and then the candidate forwarding relay set of the current node is obtained according to the constraint conditions. The optimal candidate forwarding relay is determined by a fuzzy logic-based forwarding relay selection scheme based on considering the three parameters of the candidate forwarding relay, which includes the propagation delay, the included angle between two neighbor nodes, and the residual energy. Furthermore, in order to solve the problem of energy waste caused by packet collision, we use a priority-based forwarding method to schedule the packet transmission from the candidate forwarding relay to the destination node. The proposed EOAR protocol is simulated on the Aqua-sim platform and the simulation results show that proposed protocol can increase the packet delivery ratio by 28.4% and 11.8% and can reduce the energy consumption by 53.4% and 32.7% and, respectively, comparing with the hop-by-hop vector-based forwarding routing protocol (HHVBF) and void handling using geo-opportunistic routing protocol (VHGOR). [ABSTRACT FROM AUTHOR]

Clustered-based wireless sensor networks have been extensively used in the literature in order to achieve considerable energy consumption reductions. However, two aspects of such systems have been largely overlooked. Namely, the transmission probability used during the cluster formation phase and the way in which cluster heads are selected. Both of these issues have an important impact on the performance of the system. For the former, it is common to consider that sensor nodes in a clustered-based Wireless Sensor Network (WSN) use a fixed transmission probability to send control data in order to build the clusters. However, due to the highly variable conditions experienced by these networks, a fixed transmission probability may lead to extra energy consumption. In view of this, three different transmission probability strategies are studied: optimal, fixed and adaptive. In this context, we also investigate cluster head selection schemes, specifically, we consider two intelligent schemes based on the fuzzy C-means and k-medoids algorithms and a random selection with no intelligence. We show that the use of intelligent schemes greatly improves the performance of the system, but their use entails higher complexity and selection delay. The main performance metrics considered in this work are energy consumption, successful transmission probability and cluster formation latency. As an additional feature of this work, we study the effect of errors in the wireless channel and the impact on the performance of the system under the different transmission probability schemes. [ABSTRACT FROM AUTHOR]

Clustering is an effective technique used to reduce energy consumption and extend the lifetime of wireless sensor network (WSN). The characteristic of energy heterogeneity of WSNs should be considered when designing clustering protocols. We propose and evaluate a novel distributed energy-efficient clustering protocol called DCE for heterogeneous wireless sensor networks, based on a Double-phase Cluster-head Election scheme. In DCE, the procedure of cluster head election is divided into two phases. In the first phase, tentative cluster heads are elected with the probabilities which are decided by the relative levels of initial and residual energy. Then, in the second phase, the tentative cluster heads are replaced by their cluster members to form the final set of cluster heads if any member in their cluster has more residual energy. Employing two phases for cluster-head election ensures that the nodes with more energy have a higher chance to be cluster heads. Energy consumption is well-distributed in the proposed protocol, and the simulation results show that DCE achieves longer stability periods than other typical clustering protocols in heterogeneous scenarios. [ABSTRACT FROM AUTHOR]

Target tracking is a critical wireless sensor application, which involves signal and information processing technologies. In conventional target position estimation methods, an estimate is usually demonstrated by an average target position. In contrast, this work proposes a distributed information compression method to describe the measurement uncertainty of tracking problems in cluster-based wireless sensor networks. The leader-based information processing scheme is applied to perform target positioning and energy conservation. A two-level hierarchical network topology is adopted for energy-efficient target tracking with information compression. A Level 1 network architecture is a cluster-based network topology for managing network operations. A Level 2 network architecture is an event-based and leader-based topology, utilizing the concept of information compression to process the estimates of sensor nodes. The simulation results show that compared to conventional schemes, the proposed data processing scheme has a balanced system performance in terms of tracking accuracy, data size for transmission and energy consumption. [ABSTRACT FROM AUTHOR]