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Source location privacy (SLP) is a serious issue in wireless sensor networks (WSN) since Eavesdroppers tries to determine the source location. Hunting Animals in Forest is considered as an example for SLP. Many conventional schemes have been proposed for SLP in WSN, namely, Random Walk Routing, and Fake Messages Transmission, which cause critical issues (less safety period, packet delivery latency, and high energy consumption). Furthermore, the security analysis is not properly investigated in any previous work. In this paper, we propose a new model called the circular chessboard-based secure source location privacy model (C2S2-LOOP) with the following tasks: key generation, network topology management (clustering), intercluster routing (travel plan), and data packets encryption. All sensor nodes are deployed in a circular chessboard (Circular Field) and the key generation ( P U K , S E K ) is invoked using elliptic curve cryptography (ECC) with Ant Lion Optimization algorithm, which mitigate the issues of conventional ECC. Then, the network topology is managed using clustering where residual energy of the nodes is used for Cluster Head (CH) selection. Intercluster routing is implemented using packet traversing using clockwise and anticlockwise directions, which are mainly concerned with establishing a secure route between the source to the destination node. To ensure data security, we present the Chaotic Artificial Neural Network (C-ANN) in which encryption is executed. Assume that CH near to the source node has a high trust value, then it traverses (clock-wise) real packets towards sink node and similarly in the left side region (anticlockwise), fake packets are transmitted. Network simulations (OMNeT++) are evaluated and compared with the previous approaches, and finally, our proposed scheme concludes that it maintains not only source node location privacy (large safety period) and also reduces energy consumption by more than 40% and latency by more than 35%. [ABSTRACT FROM AUTHOR]

In order to increase the reliability, accuracy, and efficiency in the eHealth, Internet of Medical Things is playing a vital role. Current development in telemedicine and the Internet of Things have delivered efficient and low-cost medical devices. The Internet of Medical Things architectures being developed do not completely recognize the potential of Internet of Things. The Internet of Medical Things sensor devices have limited computation power; in case if a patient is using implanted medical devices, it is not easy to recharge or replace the devices immediately. Biosensors are small devices with limited energy if these devices do not wisely utilize the energy may drain sharply and devices become inactive. The current medical solutions place the bulk of data on cloud-based systems that ultimately creates a bottleneck. In this article, an energy-efficient fog-to-cloud Internet of Medical Things architecture is proposed to optimize energy consumption. In the proposed architecture, Bluetooth enabled biosensors are used, because Bluetooth technology is an energy efficient and also helps to enable the sleep and awake modes. The proposed fog-to-cloud Internet of Medical Things works in three different modes periodic, sleep–awake, and continue to optimize the energy consumption. The proposed technique enabled the sensing modes that gathers the patients' data efficiently based on their health conditions. The sensed data are transmitted to the relevant fog and cloud devices for further processing. The performance of fog-to-cloud Internet of Medical Things is evaluated through simulation; the results are compared with the results of existing techniques in terms of an end-to-end delay, throughput, and energy consumption. It is analyzed that the proposed technique reduces the energy consumption between 30% and 40%. [ABSTRACT FROM AUTHOR]