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In Recent studies, mobile element acts as a mechanical carrier equipped with a powerful transceiver and battery. It directly collects the data from the sensors in the sensing environment via single-hop communication when traversing its transmission range and eventually delivers the collected data to the remote central. As a mobile element collects the data from every sensor node, the length of the mobile element tour will be increased. It results in increased data gathering latency. To solve this problem, several algorithms have been proposed. One of them called Toward Energy Efficient Big Data Gathering (TEEBD). Even it simplifies the mobile element data gathering by calculating the optimum number of clusters. Mobile element should wait until all of its cluster members uploads its data. It gives increased data gathering latency, and Packet loss due to buffer flow. In this paper, we propose two novel approaches called Energy Efficient Big Data Gathering using Local data Collector (EEBDG-LC) and Energy Efficient Big Data Gathering using Local data Collector with Threshold (EEBDG-LCWT). First approach concentrates on placing a local data collector in every centroid of the region. In which mobile element collects the information only from local data collector instead of all of its sensor nodes. It increases the speed of mobile element data gathering. The main goal of the second approach is to reduce the traffic in the local sensing region of EEBDG-LC based on the threshold value. In which node reaches the threshold value are only allowed to transmit data to the local data collector. Others go to the sleep mode immediately. Thus, increases the lifetime of the sensor network, and packet delivery ratio. Various data gathering mechanisms such as mobile element data gathering and data gathering using UAV have been used and comparison between these two has been done. The effectiveness of our approach is validated through extensive simulations.

In this paper genetic algorithm based energy efficient data gathering approach is proposed to maximize the network lifetime in terms of rounds. The proposed approach has two phases, namely a setup phase and steady state phase. In the setup phase, the cluster formation is done based on the query sent by the base station in a dynamic fashion. The nodes, which satisfy the query are only allowed to participate in the clustering process others go to the sleep mode immediately. In which relay nodes are used as routing element, it collects the aggregated information from the cluster head and transmits to the base station via other relay node in a multihop fashion. It balances the network load among the relay nodes. It also reduces the packet loss because of data traffic. From the simulation results, we show that the proposed approach outperforms than the existing protocol in terms of increased network lifetime and decreased energy consumption.

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We Propose Balanced, Localized, Robust, Dynamic state changing and energy efficient spanning tree approaches for Wireless sensor networks which we call Balanced energy Efficient Spanning Tree with Sleep scheduling(BEESP-SS). In this paper first we construct the spanning tree based on RNG(Relative Neighborhood Graph) after that we find the minimum spanning tree it considers the different parent selection strategies and select the best among them, Our major concern is to balance the load with in the spanning tree, actual spanning tree is constructed based on the transmitter and receiver residual energy. It is done by measuring the energy level of the nodes in the spanning tree, because energy is drain out when large number of messages travelling through the particular node by limiting the messages travelled through the sensor nodes we can improve the lifetime of the spanning tree and reduce the frequency of spanning tree, it is done by partitioning the messages based on the energy level of the node in the spanning tree and redirect the messages to the some other nodes in the spanning tree. The proposed solution also adapted the Sleep Scheduling Algorithm, it wake up the sensor nodes in the network when it is needed. This paper also handles the route maintenance it includes node insertion and node deletion.