Impact of Input Data on Intelligence Partitioning Decisions for IoT Smart Camera Nodes

Image processing systems exploit image information for a purpose determined by the application at hand. The implementation of image processing systems in an Internet of Things (IoT) context is a challenge due to the amount of data in an image processing system, which affects the three main node constraints: memory, latency and energy. One method to address these challenges is the partitioning of tasks between the IoT node and a server. In this work, we present an in-depth analysis of how the input image size and its content within the conventional image processing systems affect the decision on where tasks should be implemented, with respect to node energy and latency. We focus on explaining how the characteristics of the image are transferred through the system until finally influencing partition decisions. Our results show that the image size affects significantly the efficiency of the node offloading configurations. This is mainly due to the dominant cost of communication over processing as the image size increases. Furthermore, we observed that image content has limited effects in the node offloading analysis.

Energy-Aware Activity Control for Wireless Sensing Infrastructure Using Periodic Communication and Mixed-Integer Programming

This paper addresses the energy conservation problem in wireless sensor networks, in which sensor data are aggregated into packages and then transmitted periodically to the base station according to an established schedule. We formulate two mixed integer programming problems, first for minimum total energy usage and second for min-max per node energy usage. We present two algorithms for slot allocation that exploit the periodic nature of the data collection process by allocating a subset of nodes to subsequent frames. A mixed-integer solver solves the resulting mathematical programming task. The performed numerical experiments show that the proposed approach is appropriate for relatively small networks when minimizing the total energy consumption problem. Hopefully, the problem may be solved for medium size networks when a maximum node energy consumption performance index is used.

Optimized Cluster Establishment and Cluster-Head Selection Approach in WSN

In recent years, limited resources of user products and energy-saving are recognized as the major challenges of Wireless Sensor Networks (WSNs). Clustering is a practical technique that can reduce all energy consumption and provide stability of workload that causes a larger difference in energy depletion among other nodes and cluster heads (CHs). In addition, clustering is the solution of energy-efficient for maximizing the network longevity and improvising energy efficiency. In this paper, a novel OCE-CHS (Optimized Cluster Establishment and Cluster-Head Selection) approach for sensor nodes is represented to improvise the packet success ratio and reduce the average energy-dissipation. The main contribution of this paper is categorized into two processes, first, the clustering algorithm is improvised that periodically chooses the optimal set of the CHs according to the speed of the average node and average-node energy. This is considerably distinguished from node-based clustering that utilizes a distributed clustering algorithm to choose CHs based on the speed of the current node and remaining node energy. Second, more than one factor is assumed for the detached node to join the optimal cluster. In the result section, we discuss our clustering protocols implementation of optimal CH-selection to evade the death of SNs, maximizing throughput, and further improvise the network lifetime by minimizing energy consumption.

A Chaotic Elite Niche Evolutionary Algorithm for Low-Power Clustering in Environment Monitoring Wireless Sensor Networks

In recent years, as people’s demand for environmental quality has increased, it has become inevitable to monitor sensitive parameters such as temperature and oxygen content. Environmental monitoring wireless sensor networks (EMWSNs) have become a research hotspot because of their flexibility and high monitoring accuracy. This paper proposes a chaotic elite niche evolutionary algorithm (CENEA) for low-power clustering in EMWSNs. To verify the performance of CENEA, simulation experiments are carried out in this paper. Through simulation experiments, CENEA was compared with shuffled frog leaping algorithm (SFLA), differential evolution algorithm (DE), and genetic algorithm (GA) in the same conditional parameters. The results show that CENEA balances node energy and improved node energy usage efficiency. CENEA’s network energy consumption is reduced by 8.3% compared to SFLA, 3.9% lower than DE, and 4.6% lower than GA. Moreover, CENEA improves the precision and minimizes the computation time.

Performance Evaluation and Discrimination of AODV and AOMDV VANET Routing Protocols Based on RRSE Technique

The routing protocol is an applied standard to determine the communication scheme of different entities with each other to transfer and process the desired data in considerable time via the best routes from the source to the destination. This paper presents the performance evaluation and discrimination for various parameters of two different routing protocols using the Root Relative Squared Error (RRSE). The two protocols under study are Ad-hoc On-demand Distance Vector (AODV), and Ad-hoc On-demand Multipath Distance Vector (AOMDV). The literature reviews reveals the simulation results of number of nodes that varies approximately between 5 to 100 nodes. Therefore, that he simulation results will be analyzed for two different experiment as follows the first, the effect of initial node energy variation between 50 to 100 Joules at a fixed network size. Whereas in the second, reveals the impact of the network size, which varies between 50 to 450 nodes at constant initial node energy that it is tested between 50 to 100 Joules. The obtained results of the selected parameters prove that the AOMDV protocol is more efficient, robust, and reliable than the AODV protocol for the first experiment, while the RRSE values of AODV are better for the second. Moreover, the proposed technique based on the RRSE algorithm advantageous to compare the two routing protocols.

Wireless Sensor Network (WSN) Configuration Method to Increase Node Energy Efficiency through Clustering and Location Information

Wireless Sensor Network (WSN) technology, for services that are difficult to access or which need to be continuously monitored regardless of location, needs further research and development due to an expansion of fields where it can be applied and due to increases in efficiency. In particular, in the field of defense, research on the latest IT technologies including sensor networks is being actively conducted as an alternative to the risky use of personnel in areas such as surveillance and surveillance reconnaissance. This paper experimented with analyzing the conditions necessary for increasing the energy efficiency of the nodes constituting a sensor network using a clustering routing technique and a location-based routing technique. The derived factors include a method for selecting a cluster head (CH), a method for establishing a path from each channel to a base station (BS), and a method for transmitting collected data. We experimented with the derived factors and proposed a WSN configuration method that increases the energy efficiency of each node by applying optimal results and methods that were verified experimentally.