On-demand charging of sensor nodes (SNs) in wireless rechargeable sensor networks has garnered immense attention. Existing works with multiple mobile chargers (MCs) overlooked the benefits of partial charging and distributed control in large-scale wireless rechargeable sensor networks. In addition, most of them have not considered the complete heterogeneity of SNs’ energy profiles and idleness of the MCs. In this article, we address the aforesaid issues and present a game theory based distributed mobile charging protocol, called DMCP. We formulate the mobile charging problem as a series of repeated games played among the MCs and represent a game as a 0-1 integer linear programming to maximize the total profit of the MCs. In each game, DMCP dynamically determines the factor of charging of the energy-deficit SNs based on their relative criticality and selects the next-to-be-recharged SNs using an efficient bi-objective payoff function. In addition, it utilizes an innovative strategy and a self-enforcing agreement to promote cooperation among the MCs. Through extensive simulations and hypothesis testing, we show that DMCP reduces the charging delay up to 44.04% and enhances the charging coverage and survival rate up to 45.12% and 49.97%, respectively.

In the edge-based Internet of Things (IoT) era, wireless sensor networks (WSNs) are the prime source for data collection. In such WSNs, mobile edge nodes such as mobile sinks (MSs) are the superior means to collect sensed data by visiting rendezvous points (RPs). However, WSNs are often obstacle-ridden, which creates hurdles to the movement of the MSs. Most of the existing path planning works dealing with obstacles do not address optimal and smooth path construction. In other words, they have not considered a) optimizing the number of RPs and constructing a feasible path and b) smoothing the constructed path by considering sharp edges and convexity of the obstacle perimeter. In this paper, we address all such issues and develop an efficient scheme for determining an optimal number of RPs using a greedy approach to the set-cover problem and optimized path construction, both in polynomial time. Then, we apply the modified BUG2 algorithm to construct an obstacle-free path, which is then smoothed using the concept of the Bezier curve. Extensive simulations show the superiority of our proposed scheme over the existing algorithms in terms of energy consumption, latency, and so on.

Delay and energy efficient task offloading from device to fog nodes involves decision making challenges wherein an integrated optimal scheme for preserving sustainability of the terminal nodes (TNs) and fog nodes (FNs) is extremely important. In this paper, we propose a novel clustering based delay aware energy efficient task offloading scheme in a Software-Defined Networking (SDN) based fog architecture. A bi-objective problem is formulated for optimum clustering of TNs with respect to FNs, selection of offloading parameters and, joint delay and energy minimization. It is then tranformed to a scalarized single objective problem which has a nested structure with the two problems: 1) optimal clustering and 2) optimal offloading for a given set of clusters. Based on this, Optimal Clustering and Offloading Parameters (OCOP) algorithm is designed which has lesser time complexity than the usual quadratic case. Through extensive simulations, we have shown that the use of explicit clustering in the proposed algorithm improves FN participation and reduces activity time and energy levels thereby increasing sustainability of the FNs and TNs as compared with the random case and a similar task offloading algorithm. Moreover, even cluster size distribution lowers our algorithm’s running time than the quadratic case.

Information theory-based feature selection (ITFS) methods select a single subset of features for all classes based on the following criteria: 1) minimizing redundancy between the selected features and 2) maximizing classification information of the selected features with the classes. A critical issue with selecting a single subset of features is that they may not represent the feature space in which individual class labels can be separated exclusively. Existing methods fail to provide a way to select the feature space specific to the individual class label. To this end, we propose a novel feature selection method called class-label specific mutual information (CSMI) that selects a specific set of features for each class label. The proposed method maximizes the information shared among the selected features and target class label but minimizes the same with all classes. We also consider the dynamic change of information between selected features and the target class label when a candidate feature is added. Finally, we provide a general framework for the CSMI to make it classifier-independent. We perform experiments on sixteen benchmark data sets using four classifiers and found that the CSMI outperforms five traditional, two state-of-the-art ITFS (multi-class classification), and one multi-label classification methods.

With the ever-increasing demand for Internet of Things (IoT) applications, wireless sensor networks (WSNs) have become the central means to disseminate data for analysis in the era of mobile edge computing. Mobile sinks (MSs) as edge nodes have emerged as an efficient solution to the performance enhancement of WSNs. One important task of the MSs is to collect data in a sustainable and optimized manner by visiting certain rendezvous points (RPs) inside the WSN. However, most existing works focus only on connected WSNs, while disjoint networks are the reality in many IoT applications. Moreover, none of them have considered a realistic propagation model. They have also ignored optimizing both the number of RPs and MSs. This paper proposes a novel data collection scheme while paying attention to all these issues. The scheme is specially designed for delay-harsh applications. First, we propose a convex hull-based algorithm to determine RPs for constructing an optimal tour of a MS. Then using the resulting set of RPs, we present another algorithm based on the Jaya metaheuristic to determine an optimal number of MSs and their balanced tours. Rigorous simulations show that our scheme outperforms existing algorithms in terms of various performance metrics.

Mobile chargers have greatly promoted the wireless rechargeable sensor networks (WRSNs). While most recent works have focused on recharging the WRSNs in an on-demand fashion, little attention has been paid on joint consideration of multiple mobile chargers (MCs) and multi-node energy transfer for determining the charging schedule of energy-hungry nodes. Moreover, most of the schemes leave out the contemplation of multiple network attributes while making scheduling decisions and even they overlook the issue of ill-timed charging response to the nodes with uneven energy consumption rates. In this paper, we address the aforesaid issues together and propose a novel scheduling scheme for on-demand charging in WRSNs. We first present an efficient network partitioning method for distributing the MCs so as to evenly balance their workload. We next adopt the fuzzy logic which blends various network attributes for determining the charging schedule of the MCs. We also formulate an expression to determine the charging threshold for the nodes that vary depending on their energy consumption rate. Extensive simulations are conducted to demonstrate the effectiveness and competitiveness of our scheme. The comparison results reveal that the proposed scheme improves charging performance compared to the state-of-the-art schemes with respect to various performance metrics.

Exploiting mobile sink (MS) for data gathering in the wireless sensor networks has been extensively studied in the recent researches to address energy‐hole issues, thereby facilitating balanced energy consumption among nodes and so prolonging network lifetime. However, such approaches suffer from an extended data collection delay causing buffer overflow problem. In this regard, finding the optimal number of locations (i.e. rendezvous points (RPs) where the MS sojourns for data collection), is not only of utmost importance, but also a challenging task. A novel scheme for trajectory design of MS for data collection is presented in this study. The authors' primary goal is to optimise the number of RPs and their locations to minimise the travelling length of the MS. First, they reduced the problem size by using a combination of breadth‐first search and Tarjan's algorithm and then applied spectral clustering to find the optimal set of RPs to plan the tour for the MS. They have performed extensive simulations, and the results are compared with relevant existing schemes. The comparative results confirm the effectiveness of their approach in terms of the number of RPs, path length, the variance of RPs, and energy consumption per round.