With the exponential increase in the number of IoT devices and the amount of emitted data from these devices, it is expensive and inefficient to offload all tasks to the remote data center. How to optimize the energy consumption of application requests from IoT devices meeting the deadline constraint is also a challenge. Fog computing adjacent to users has the feature of lower service delay but less resource than the remote cloud. Fog does not appear to replace cloud, they are complementary to each other, cooperation between them is worth studying. This paper proposes a general IoT-fog-cloud architecture that fully exploits the advantages of fog and cloud. Then, the energy and time efficient computation offloading and resource allocation is formulated into the energy and time cost minimization problem. We then propose an ETCORA algorithm to solve the problem, improving the energy consumption and completion time of application requests. Finally, extensive simulations are carried out to verify that the proposed method indeed outperforms the other two methods in reducing energy consumption and completion time of requests.

Energy and time efficient task offloading and resource allocation on the generic IoT-fog-cloud architecture

In today’s world, the Internet of Things (IoT) has become more relevant owing to the growth in smart grid, smart city and smart home applications. Network sustainability is considered as a significant characteristic for IoT based applications. Wireless Sensor Network (WSN) offers such network sustainability where WSN is acted as the subnets in the IoT model. However, the multi-objectives like coverage, connectivity and energy consumption are required to improve the quality of service in IoT based WSN (IWSN) model. An appropriate optimization strategy of these multi-objective facilitates the better development of IWSN. In this paper, the Adaptive Coverage and Connectivity (ACC) scheme is proposed to attain the efficient IWSN model. It employs two underlying methodologies in which the first method provides the optimal coverage to all target objects and its mathematical model guarantees the coverage rate. The second method deals with connectivity and energy consumption of the network. The experimental results manifest that, unlike existing schemes, the proposed ACC scheme can sustain the network for a prolonged time.

A novel multi-objective optimization strategy for enhancing quality of service in IoT-enabled WSN applications

Internet of Things (IoT) entities compile a massive volume of sensing data and transmit it to the cloud for processing and reasoning. Reliable and secure data aggregation and forwarding in IoT incorporates resource-constrained low-power and lossy devices that served diverse and sensitive applications with special obligations is a significant challenge for IoT. This paper proposes Reliable Data Dissemination for the Internet of Things Using Harris Hawks Optimization (RDDI) scheme, which is a secure data diffusion mechanism that accoutered a fuzzy hierarchical network model for Wireless Sensor Networks (WSN) based IoT. RDDI discloses attacks and monitors the behavior of nodes information exchange processes. Our scheme prowls to synthesize routing capabilities, energy-aware and geographic data circulation, and fuzzy clustering to provide a reliable, nature-inspired optimized routing called Harris Hawks Optimization (HHO) algorithm for IoT. The performance of RDDI, under five metrics of reliability, end-to-end delay, energy consumption, computational overhead as well as packet forwarding distance in multi-cluster scenarios, is evaluated with three comparative approaches. Findings of simulations reveal that RDDI achieves a reliable strategy and preferable achievement over the other three disposals.

Reliable data dissemination for the Internet of Things using Harris hawks optimization

In this paper, we propose a new power-aware routing protocol for Wireless Sensor Network (WSN) based on the threshold rate and fuzzy logic for improving energy efficiency. The cluster heads are elected based on the probability values of every node in WSN, which are calculated from the remaining energy of every node. Cumulative remaining node energy is used to calculate mean energy of the whole network of the current phase. The nodes with high probability will have more chances to be selected as the cluster head, which gathers packets from the cluster member via single hop communication. The cluster head forwards the gathered data to sink by using fuzzy control with multi-hop communication. Fuzzy control takes three parameters namely queue length of a node, the distance of a node from the base station, and the remaining energy of node. The evidence from experiments suggest that the proposed energy efficient cluster-based routing protocol method (called MLSEEP) gives better results than the existing protocols by the supplement of those techniques.

Probability-based cluster head selection and fuzzy multipath routing for prolonging lifetime of wireless sensor networks

Abstract Pipelines are the safest tools for transporting oil and gas. However, the environmental effects and sabotage of hostile people cause corrosion and decay of pipelines, which bring financial and environmental damages. Today, new technologies such as the Internet of Things (IoT) and wireless sensor networks (WSNs) can provide solutions to monitor and timely detect corrosion of oil pipelines. Coverage is a fundamental challenge in pipeline monitoring systems to timely detect and resolve oil leakage and pipeline corrosion. To ensure appropriate coverage on pipeline monitoring systems, one solution is to design a scheduling mechanism for nodes to reduce energy consumption. In this paper, we propose a reinforcement learning-based area coverage technique called CoWSN to intelligently monitor oil and gas pipelines. In CoWSN, the sensing range of each sensor node is converted to a digital matrix to estimate the overlap of this node with other neighboring nodes. Then, a Q-learning-based scheduling mechanism is designed to determine the activity time of sensor nodes based on their overlapping, energy, and distance to the base station. Finally, CoWSN can predict the death time of sensor nodes and replace them at the right time. This work does not allow to be disrupted the data transmission process between sensor nodes and BS. CoWSN is simulated using NS2. Then, our scheme is compared with three area coverage schemes, including the scheme of Rahmani et al., CCM-RL, and CCA according to several parameters, including the average number of active sensor nodes, coverage rate, energy consumption, and network lifetime. The simulation results show that CoWSN has a better performance than other methods. 

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An energy-aware and Q-learning-based area coverage for oil pipeline monitoring systems using sensors and Internet of Things

Due to the random deployment of Wireless Sensor Network (WSN) nodes in dangerous and inaccessible environments and their sensing radius limitations, complete coverage of the monitoring area cannot be achieved. Clustering techniques, applying the mobile nodes, and adjusting the sensing radius of sensor nodes are regarded as practical techniques to improve the coverage and reduce the energy consumption. The challenges including, how to grouping the nodes, selecting the cluster heads, managing the mobility of mobile nodes, and scheduling sleep intervals should be considered in the hybrid techniques to improve the coverage with minimum overlapping. In this paper, we propose a DEHCIC (Distributed Energy-aware Hexagon based Clustering algorithm to Improve Coverage) algorithm that considers energy and topological features such as the number of mobile neighbor nodes and number of neighbor nodes for electing the cluster heads. In addition, DEHCIC attempts to cover the holes as much as possible by the static sensor nodes so that if it is not possible, the closest mobile will cover the coverage holes. Moreover, the DEHCIC algorithm retains the sensor nodes in the active mode that cover the interest points; also, it puts others into the low-powered sleep mode. The simulation results show that the proposed algorithm reduces dependency on the movement of mobile nodes and with the minimum number of active nodes can prolong the coverage lifetime and improve the network coverage effectively.

Somaieh Zakariayi

Papers

DEHCIC: A distributed energy-aware hexagon based clustering algorithm to improve coverage in wireless sensor networks