Recent development in sensor technologies encourages the adoption of mobile data collectors (MDCs), that is, vehicles or robots, to collect data in the Internet of Things (IoT) environment. However, the slow travel speed of the MDCs leads to significant delays in the transmission of data. This work proposes an optimal region location point based route planning (ORLP-RP) scheme to construct a shorter tour with minimized data delivery latency. Primary goal of the work is to distinguish the overlapped wireless sensor network (WSN) clusters and specify the overlapped regions to find optimal location points within each identified region to ensure one-hop data gathering from each cluster. Thereafter, MDC trajectory is formed by using the nearest-neighbor (NN) heuristic approach that generates a near optimal solution. In performance evaluation, the ORLP-RP scheme has been compared with the existing algorithms such as EAPC, RDP, MOPSO, and NDCMC for data collection in terms of the number of optimal location points, total path length, data gathering latency, network lifespan, energy consumption, and data gathering ratio. Simulation results demonstrate the effectiveness of the proposed approach by improving the number of optimal location points with an average up to 58%, total path length improvement by 17%, energy consumption and network lifespan improvement on average by 18% and 12%, data gathering ratio by the MDC up to 9%, and reducing the delay of the MDC path by 21% compared with considered approaches. Thus, simulation results indicate better performance of the proposed scheme at all the indices listed above.

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Wireless sensor networks (WSNs) have become increasingly important in the informative development of communication technology. The growth of Internet of Things (IoT) has increased the use of WSNs in association with large scale industrial applications. The integration of WSNs with IoT is the pillar for the creation of an inescapable smart environment. A huge volume of data is being generated every day by the deployment of WSNs in smart infrastructure. The collaboration is applicable to environmental surveillance, health surveillance, transportation surveillance and many more other fields. A huge quantity of data which is obtained in various formats from varied applications is called big data. The Energy efficient big data collection requires new techniques to gather sensor-based data which is widely and densely distributed in WSNs and spread over wider geographical areas. In view of the limited range of communication and low powered sensor nodes, data gathering in WSN is a tedious task. The energy hole is another considerable issue that requires attention for efficient handling in WSN. The concept of mobile sink has been widely accepted and exploited, since it is able to effectively alleviate the energy hole problem. Scheduling a mobile sink with energy efficiency is still a challenge in WSNs time constraint implementation due to the slow speed of the mobile sink. The paper addresses the above issues and the proposal contains four-phase data collection model; the first phase is the identification of network subgroups, which are formed due to a restricted range of communication in sensor nodes in a wide network, second is clustering which is addressed on each identified subgroup for reducing energy consumption, third is efficient route planning and fourth is based on data collection. The two time-sensitive route planning schemes are presented to build a set of trajectories which satisfy the deadline constraint and minimize the overall delay. We have evaluated the performance of our schemes through simulation and compared them with the generic enhanced expectation-maximization (EEM) mobility based scenario of data collection. Simulation results reveal that our proposed schemes give much better results as compared to the generic EEM mobility approach in terms of selected performance metrics such as energy consumption, delay, network lifetime and packet delivery ratio.

Summary Mobile data collectors (MDCs) are very efficient for data collection in internet of things (IoT) sensor networks. These data collectors collect data at rendezvous points to reduce data collection latency. It is paramount to determine these points in an IoT network to collect data in real time. It is important to consider IoT network characteristics to collect data on a specific deadline. First, the disconnected IoT sensor network is a real challenge in IoT applications. Second, it is essential to determine optimal data collection points (DCPs) and MDCs simultaneously to collect data in real time. In this study, Deadline‐based Data Collection using Optimal Mobile Data Collectors (DDC‐OMDC) scheme is proposed that aims to collect data in a disconnected network with the optimal number of mobile data collectors in a specific deadline for delay‐intolerant applications. DDC‐OMDC works in two phases. In the first phase, the optimal number of MDCs is determined to collect data at the optimal data collection points to guarantee one‐hop data collection from each cluster. The optimal mobile data collectors are determined using optimal DCPs, data collection stopping time, and a specific deadline. In the second phase, the optimal data collection trajectory is determined for each MDC using the nearest neighbor heuristic algorithm to collect data in real time. The simulation results show that the proposed scheme outperforms in collecting data in real time and determines optimal mobile data collectors and optimal data collection trajectory to collect data in a specific deadline for delay‐intolerant applications.