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Many IoT applications require a mechanism to disseminate commands and collect responses over a wireless network in order to control and collect data from multiple embedded devices. However, severe collisions may occur if a large number of nodes attempt to respond simultaneously and promptly, not only among the responses, but also with the dissemina...
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... is based on the widely accepted intuition that maximum interference range of a wireless transmission is within twice the maximum communication distance [47]. For example, suppose a routing tree is built as shown in Figure 4. Node G requires 4 slots for its response, but it knows that its packet will reach 1-hop node A after 3 timeslots, and node A's transmission is unlikely to interfere with node G or H's transmission. ...
Citations
... There are also numerous prior work on improving the performance of RPL under various scenarios (e.g., load balancing [24], TX power control [25], P2P [47], dissemination and collection [48]), but none of them have handled the midterm link fluctuations. To the best of our knowledge, there has been no work that discusses the impact of midterm link quality fluctuations in indoor environments, nor has anyone proposed solutions to the problem. ...
Link dynamics due to environmental change is a critical challenge for wireless networking, and handling it is essential for wireless protocols, especially for low-power and lossy multihop wireless networks (LLNs). Prior work have only studied methods to detect and cope with short-term and long-term link dynamics in LLN. In this work, we tackle the midterm dynamics due to the opening and closing of doors and windows that can happen in a time scale of a few minutes to hours in an indoor LLN. Specifically, we study how midterm link fluctuations impact the performance of standard IPv6 routing protocol for LLN (RPL), and design ‘RPLIE,’ a novel and backward-compatible lightweight enhancement to RPL that detects and overcomes the negative impact of such dynamics. We define ‘opportunistic links’ and propose a novel routing metric, expected breakage cost (EBC), which represents the expected number of link transmissions required from a link breakage to a routing parent change. We implement RPLIE on real embedded devices, and evaluate its performance against the standard RPL via experiments on a 31-node testbed to show that RPLIE achieves significantly better packet delivery performance while using less overhead.
... themselves and conflict with each other, focusing on one side is not enough to provide adequate performance for actual IoT applications. As a result, they must be considered jointly [12]. Hao and Zhang [26] analyzed media access control (MAC) performance for reliable power-line communication networks with automatic repeat request (ARQ) schemes. ...
Today, the world’s electricity consumption is growing rapidly, and therefore energy demands are also increasing. In the past few decades, various measures have been taken to improve equipment and system design to increase production and transmission efficiency and reduce power consumption. This article proposes a novel Internet of Things (IoT)-based temperature control power extender with two working modes of cooling and heating to solve power shortages. The power is turned on or off accurately and in a timely manner through a temperature-sensing element, thereby avoiding unnecessary power consumption to achieve the goal of energy-saving. This can directly power on or off the power extender through the Internet. It can also use a 2.4G Wi-Fi wireless transmission to transmit, for example, real-time temperature information, the switch status and the master–slave mode. Related data can be controlled, collected and uploaded to the cloud. Each proposed power extender’s temperature setting in a large-scale field can be set uniformly, and no staff is wasted to set the temperature separately. Taking a general industrial electric fan as an example, if it is changed to drive with this temperature control extension cable, and assuming that the industrial electric fan is activated for 900 s per hour, its power-saving rate is 74.75%
In this paper, an improved routing protocol for multipath network load balancing is proposed for defects in the traditional AOMDV (Ad hoc On-demand Multipath Distance Vector) protocol. This research work analyzes problems in traditional routing protocols and estimates the available path load according to network transmission in Wireless Mesh Networks (WMN). Moreover, we design a load distribution scheme according to a given load and improve multi-path load balancing by using the MCMR method. We also control path discovery and the number of paths while also establishing routing paths and probability balancing. Lastly, improvements are made to the AOMDV protocol and efficient data transmission is acheived. The performance results of the modified routing protocol show that the designed protocol can improve successful delivery rate and prolong network survival time.
Due to the properties of ad-hoc networks, it appears that designing sophisticated defence schemes with more computing capital is impossible in most situations. Recently, an inconsistency in the ad-hoc design of intrusion detection in the network has gotten a lot of coverage, with these intrusion detection techniques operating in either cluster-based or host-based configurations. The host and cluster-based systems have advantages and disadvantages, such as the network preserve security in case of delay in replacing a cluster head. Many detection systems in these networks use a supervised learning method to learn from shared routing knowledge. Deep learning is the trending supervised learning method which is been suggested for many applications, due to its deep feature extraction and classification capability. The deep learning method is best suitable to resolve the problems of the ad hoc network. But due to its limitation of supervised learning nature, more research finds are needed before implementation. These intelligence methods need a massive labeled dataset to self-train and take a decision in real-time. Also, these methods will be vulnerable to new attacks. To address the issues posed, the deep learning approach requires a technique of incorporating unsupervised learning behaviours. This paper proposes and highlights the methodology - Deep Embedded Median Clustering (DEMC), which performs two-phase operations (1) Organization of latent feature space (2) K-median clustering to cluster the Z with Kullback–Leibler divergence as the objective function. Many researchers suggested various methodologies for better anomaly detection in the network, but the knowledge gap and the possibilities for a better solution still exist. This study explores the new possibility and potential of an unsupervised learning technique that works with the nature of deep learning for analyzing and detecting anomalies and intrusion in ad hoc networks. The test to check the DEMC ability has been organized, and the findings are tabulated for analysis.
