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With the help of wireless power transfer (WPT) technology, the mobile charger (MC) can transfer energy to the sensor nodes. This technology provides a new solution to prolong the lifetime of wireless rechargeable sensor networks (WRSNs). However, most of the existing studies focused on improving the charging efficiency or minimizing the latency, while very few studies improved the data accuracy of the network. This article proposes an efficient-energy recharging schedule for MC, aiming to maximize the data accuracy of the given network. Initially, the data accuracy of each sensor is measured by considering the spatial and temporal qualities. The proposed recharging schedule considers the spatial quality contribution of each recharging requested sensor. In addition, an energy management strategy is proposed for each requested sensor to locally adjust the sensing time sequence, aiming to improve the temporal quality. Each sensor might have a different energy consumption rate; therefore, this study also formulates an adaptive recharging request threshold for the sensor nodes, which is suitable for real applications. The experimental study shows that the proposed algorithm outperforms the literature in terms of data accuracy and recharged sensor’s spatial quality contributions. [ABSTRACT FROM AUTHOR]

Wireless recharging using a mobile charger has been widely discussed in recent years. Most of them considered that all sensors were equally important and aimed to maximize the number of recharged sensors. The purpose of energy recharging is to extend the lifetime of sensors whose major work is to maximize the surveillance quality. In a randomly deployed wireless rechargeable sensor network, the surveillance quality highly depends on the contributions of coverage and network connectivity of each sensor. Instead of considering maximizing the number of recharged sensors, this article further takes into consideration the contributions of coverage and network connectivity of each sensor when making the decision of recharging schedule, aiming to maximize the surveillance quality and improve the number of data collected from sensors to the sink node. This article proposes an energy recharging mechanism, called an energy recharging mechanism for maximizing the surveillance quality of a given WRSNs (ERSQ), which partitions the monitoring region into several equal-sized grids and considers the important factors, including coverage contribution, network connectivity contribution, the remaining energy as well as the path length cost of each grid, aiming to maximize surveillance quality for a given wireless sensor network. Performance studies reveal that the proposed ERSQ outperforms existing recharging mechanisms in terms of the coverage, the number of working sensors as well as the effectiveness index of working sensors. [ABSTRACT FROM AUTHOR]

In wireless sensor networks (WSNs), area coverage is an important issue that aims to schedule a minimal number of sensors to cover the given monitoring area without any hole. Many of the existing strategies investigated that sensors are battery powered and considered Boolean Sensing Model (BSM). However, BSM affects the actual sensing quality since it did not consider the physical characteristics of the sensor. This study proposed an area coverage algorithm called $\textit {MSQAC}$ , which considers the rechargeable sensors and utilizes solar-powered energy. The proposed mechanism aims to maximize the surveillance quality for a given monitoring area by applying the Probabilistic Sensing Model (PSM). Two issues have been investigated while designing the $\textit {MSQAC}$ algorithm. The first one is the energy discharging rate of each sensor is larger than the energy recharging rate. Thus, how to better utilize the recharged energy by scheduling the right sensor in the right working time is a big challenge. The second challenge is the contribution of each sensor with or without cooperation is different by considering the PSM. If the neighboring sensors are scheduled at work in the same time slot, they can be treated as the combination set of neighboring sensors. It is another big challenge to find the best combination set of sensors for each space point at each time slot such that the minimal monitoring quality can be maximized. Through extensive simulations, it is shown that $\textit {MSQAC}$ yields the best performance in terms of monitoring quality ($\textit {QoM}$), coverage ratio and fairness index. [ABSTRACT FROM AUTHOR]

In wireless rechargeable sensor networks (WRSNs), target coverage is an important issue. However, most studies assumed that the sensors were powered by battery with fixed sensing radiuses. Furthermore, most existing studies applied the Boolean Sensing Model (BSM) which cannot reflect the physical features of sensing. As a result, the detection accuracy was decreased in a real application. This paper proposes a target coverage mechanism, called TCSAR, which aims to maximize the surveillance quality of the point of interests (POIs) under the constraint of perpetual network lifetime. The proposed TCSAR applies the Probabilistic Sensing Model (PSM) and assumes that the sensing radius of each sensor is adjustable. The proposed mechanism consists of two phases. In the scheduling phase, the TCSAR initially evaluates the surveillance contribution of each sensor and then schedules the sensor with the maximal contribution to the bottleneck POIs. In the space time transformation phase, the surveillance quality for the POIs can be further improved by adjusting the sensing radius of some sensors from space dimension to time dimension. The experimental results show that the proposed mechanism outperforms the existing mechanisms in terms of the worst surveillance quality and the quality of monitoring. [ABSTRACT FROM AUTHOR]

Wireless charging is one of the most important issues in wireless sensor networks (WSNs), which aims to cope with the energy limitation problem of the sensors. Many of the existing researches applied the mobile charger to recharge the sensors for maintaining the perpetual lifetime of sensor networks. However, when choosing sensors to be recharged, the coverage contribution of sensors was ignored. This paper proposes a recharging scheme, called CAERM, which considers the coverage contribution of each requested sensor and constructs the recharging path to maximize the coverage of the whole networks. Two algorithms, including Simple Recharging Coverage Benefit (S-RCB) and Chain-Effect Recharging Coverage Benefit (CE-RCB) algorithms, are proposed to evaluate the coverage contribution of the requested sensors effectively. Through extensive simulations, experimental study shows that the proposed algorithm improves the recharging efficiency, while maximizing the coverage contribution and monitoring quality of the given sensor networks. [ABSTRACT FROM AUTHOR]

Data collection using mobile sink has received much attention in the past years. In the literature, most studies assumed that the mobile sink was controllable and the proposed algorithm made decisions to control the path, speed, and stop locations. However, there still exists some challenges in using the controllable mobile sink, including limited energy and the adaptive capability for overcoming the obstacles. This article considers the bus as the uncontrollable mobile sink to collect data from a given set of deployed static sensors. A distributed data collection mechanism, called energy balanced multihop data collection (EBMDC), is proposed that aims at maximizing the transmitted data volume from sensors to the bus while prolonging the network lifetime of the wireless sensor networks (WSNs). To prolong the network lifetime, each sensor node distributes its data to its multiple parents in trees according to their remaining energies. Then, each anchor node locally schedules its transmission slots based on its and its neighbor's data volumes and remaining energies such that all data can be forwarded to the bus without collision and the lifetime of each anchor can be balanced. Experimental studies reveal that the proposed EBMDC algorithm outperforms related studies in terms of throughput, network lifetime, slot utilization, data loss ratio, and fairness. [ABSTRACT FROM AUTHOR]

Data collection is one of the most important issues in Wireless Sensor Networks (WSNs). Many data collection algorithms have been proposed for collecting data using multiple mobile sinks. However, the anchor selection strategy, which significantly impacts the network lifetime, still can be improved. In addition, the collaboration between multiple mobile sinks is still rare, resulting in the long latency of each round of data collection. This paper proposes an Exploring Cooperative Opportunities, Reducing Latency and Prolong Network Lifetime algorithm for Data Collection, called CORL2, aiming to improve the network lifetime and reduce the latency of data collection. Initially, the proposed CORL2 partitions the monitoring region into several equal-sized subregions and assigns each mobile sink to each subregion. Then CORL2 further partitions each subregion into several equal-sized grids and constructs a path passing through all grids in that subregion. To reduce the energy consumption for packet forwarding, the proposed CORL2 further partitions each grid into several small grids and adjusts the path such that it can pass through high-density small grids, aiming to maximize the number of anchors and prolong the network lifetime. Then the cooperative strategy is applied to cooperatively reduce the maximal latency of data collection in all subregions and balance the data collection workload between neighboring mobile sinks. Performance evaluations reveal that the proposed CORL2 outperforms existing data collection algorithms in terms of latency, energy consumptions, the workload balancing improving index(WBII), and network lifetime for data collection in the given WSNs. [ABSTRACT FROM AUTHOR]

Traditional methods of Human Activity Recognition (HAR) do not take into consideration physical attributes of human subjects such as height, weight, gender etc. Thus, a particular recognition model does not perform consistently for different subjects having diverse physical attributes. In this paper, we propose a novel Physique-based HAR method that takes care of this problem and provides better accuracy in comparatively less time. Raw sensor data are acquired from inbuilt accelerometer and gyroscope sensor modules of smartphones. After pre-processing of the collected data physique-based datasets were prepared, based on the similarities of subjects’ physique. Analysis is done on both of these physique-based datasets and normal traditional dataset with the help of various machine learning algorithms. The work not only identifies the suitable learning algorithms for HAR but also shows that the proposed physique-based method outperforms the traditional HAR approach for both publicly available dataset and our own generated dataset, with an accuracy of 99.88%. Individual activity-wise accuracy results are also compared with several recent benchmarks to show the efficiency of the proposed Physique-based HAR method. [ABSTRACT FROM AUTHOR]

Barrier coverage problem is an important issue in wireless sensor networks (WSNs). Many solutions have been proposed for constructing a defense barrier aiming to maximize the surveillance quality while prolonging the network lifetime. However, most of them assumed that the sensor nodes are battery powered without considering the rechargeable sensors. Though the energy conservation issue has been taken into account in most studies, the perpetual network lifetime is still impossible. In addition, most of existing works considered Boolean Sensing Model (BSM) which cannot reflect the physical features of sensing. This paper proposes a barrier coverage algorithm, called MCDP, which considers the rechargeable solar-powered sensors and applies the Probability Sensing Model (PSM) aiming to maximize the surveillance quality while the perpetual network lifetime of WSNs can be achieved. The MCDP algorithm first partitions the time line and monitoring region into several space time points and calculates the detection probability of each sensor to each space time point. Then the proposed MCDP algorithm schedules sensors staying in sensing state and recharging state in each time slot such that the weakest cooperative surveillance quality of the time-space point can be maximized. Experimental study shows that the proposed MCDP algorithm achieves better performance than existing work in terms of surveillance quality, stability of cooperative detection probability as well as fault tolerance. [ABSTRACT FROM AUTHOR]

Data collection is one of the most fundamental and important operations in applications of wireless sensor networks (WSNs). Recent research has shown that using mobile sink to collect data brings many advantages to WSNs, such as improving energy efficiency of sensor nodes and prolonging network lifetime. However, the limited speed of mobile sink leads to larger latency, which cause that the data is not fresh or the buffer overflow problem of static sensor nodes. Based on the adaptive rate control, this paper proposes a data collection algorithm which aims to reduce the length of path along which mobile sink collects data from static sensor nodes. The proposed algorithm not only guarantees the completeness of data collection from each static node but also reduces path length by adopting appreciate transmission rate. Through extensive simulations, experimental results show that our approach can notably reduce the path length of mobile sink, while the data of every sensor can be collected completely and efficiently. [ABSTRACT FROM AUTHOR]