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%.
Internet of things (IoT) is a complex and massive wireless network, where millions of devices are connected together. These devices gather different types of data from different systems that transform human daily lives by modernizing home appliances, business, medicine, traveling, research, and so on. Security is a critical challenge for a stable IoT network, for instance, routing attacks, especially sinkhole attack is a severe attack which has the capability to direct network data toward the intruder, and it can also disrupt and disconnect the devices from their network. The IoT needs multi-facet security solutions where network communication is protected with integrity, confidentiality, and authentication verification services. Therefore, the IoT network should be secured against intrusions and disruptions; the data exchanged throughout the network should be an encrypted form. In this article, an intrusion detection system for the prevention of an active sinkhole routing attack (PASR) in IoT is presented. The proposed PASR solves the problem of the sinkhole attack; for this purpose, the whole network is divided into the clusters of IoT. All the IoT devices are connected to their respective gateways. The gateway devices are equipped with an intrusion detection system. The intrusion detection system activates intrusion analyzer to detect anomalies in the context of ad hoc on-demand distance vector protocol. The base station is the main device that is responsible to receive data from all devices. Therefore, it detects and prevents sinkhole attacks; the base station keeps the record of all active devices and their possible links. The PASR is implemented and compared with the existing intrusion detection techniques ad hoc on-demand distance vector, and dual attack detection for black and gray hole attack. It was observed from the simulation results that the PASR outperforms in terms of data packet delivery, energy consumption, the detection rate of sinkhole attack, and routing overhead.
The scheduling algorithm is a fundamental design problem to allocate resources amongst different entities in distributive wireless sensor networks (WSNs). These sensor nodes have limited power and non-replenishable energy resources. In WSNs, the duty cycling mechanism is commonly used to save energy due to idle listening. On the other hand, a fixed duty cycling mechanism increases transmission latency in WSNs. Therefore, in order to ensure the prolonged network-life of WSNs, the medium access control (MAC) protocol should be tackled in an efficient manner to improve energy efficiency by minimizing idle listening, maximizing sleep duration, and eliminating data collision. This paper proposes a practical Adaptive Sleep Efficient Hybrid Medium Access Control (AEH-MAC) algorithm in which the key idea is to dynamically adjust nodes' sleep time to improve the scheduling in WSNs. The AEH-MAC allows nodes to adjust sleep time dynamically according to the traffic load and coordinate wakeup time with neighbour nodes. A series of short taken packets are transmitted to wake the receiver, and a prediction field is introduced in the ACK packets (GRANT/RELEASE) to reduce the waiting time of the source node. In the proposed algorithm, each node maps a conflict-free time slot for itself up to two-hop neighbouring nodes. The simulation results show that the AEH-MAC algorithm achieves high performance in terms of runtime, number of rounds, energy consumption, and slot reservation.
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