CC-CAT: CONGESTION CONTROL FOR CACHE-AWARE TRANSPORT PROTOCOL IN WIRELESS SENSOR NETWORKS

Congestion control mechanism is vital component of an effective and efficient transport protocol both for wired and wireless networks. It is one of the primary functions of the transport layer together with a reliable data delivery. Wireless sensor networks (WSNs) are distinctive group of wireless ad hoc networks with unique characteristics and imperative restraints. It was proven that caching in the intermediate nodes reduces end-to-end retransmission that makes it a better option for an energy efficient transport protocol. However, none of the congestion control protocols developed for wireless sensor networks have considered the use of intermediate caching. Thus, is it not yet known which congestion control technique is appropriate for caching-aware data transport. This paper presents a new congestion control mechanism called Congestion Control for Cache-Aware Transport (CC-CAT). It was implemented in a cache-based transport protocol such as in an enhanced Distributed Transport Sensor Networks (DTSN+). The main idea of the congestion control algorithm is to adjust the transmission window AW of the sender based on the cache size in the intermediate nodes and congestion state. The movement of the window is based on two instances: the optimum energy efficiency and optimum goodput, which are both function of cache size. The simulation results indicate that the Congestion Control for Caching-Aware Transport was able to improve the DTSN+ protocol in terms of end-to-end packet delay and throughput on the average. The CC-CAT achieved remarkable packet end-to-end delay gain of 2.31%, 19.43% and 18.90% at condition where high congestions and packet error rate are being manifested in the network at cache sizes of 10, 20 and 30 packets, respectively. Although the CC-CAT obtained slightly notable throughput gain, the mechanism delivered better response in avoiding further occurrence of congestion as seen in the behavior of the transmission window AW shown in Figure 1. With this novel approach, CC-CAT is more compatible with WSN applications where strictly minimal end-to-end packet delay is required but may compromise the amount of data to be transmitted. Such applications can be in Wireless Multimedia Sensor Networks (WMSN) that implements interactive voice and video

In-store customer traffic and path monitoring in small-scale supermarket using UWB-based localization and SSD-based detection

Nowadays, retailers are embracing the Internet of Things as the latest technology to drive superior customer experience. Leverage data sources from sensors, beacons and mobile devices to identify and analyze in-store customer shopping behavior. With this motivation, this study implemented an in-store customer traffic and path monitoring system for supermarket using image processing and object detection. the system utilized the ultra-wideband indoor positioning technique to monitor the customer shopping path and the single shot multibox detection technique to monitor the real-time customer traffic. The customer monitoring system was implemented and evaluated in an actual small-scale supermarket. Results showed that the detection model prediction score and the traffic counting both obtained an accuracy score of 99%. In addition, the localization system achieved the minimum error difference of 9.73% for x coordinate and 3.86% for y coordinate between pre-determined positions and the actual anchor position readings. Furthermore, the system successfully generated the most frequent path and the total customer traffic of the day. In the future, this work can aid retail owners make better choices, run businesses more efficiently, and deliver improved customer service. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature

Value-based utility implementation in software-defined testbed for sensor data traffic management

Data transmitted among wireless sensor nodes are generated from sensing physical events with diverse traffic types. These data have to reach their destinations with a specific Quality of Service (QoS) requirement. With the increasing usage of sensors in various Internet of Things applications, there is a need to address the traffic management for handling these critical data while satisfying their respective QoS requirements. In this work, we implemented a traffic management mechanism using Value-based Utility (VBU) model that utilizes network packet statistics to cope with QoS requirement of each sensor data. The mechanism ensures that the demands of each sensor data are satisfied by allocating queue resources based on a utility function. The function defines an expectation range and the state where minimal requirements are met for each sensor. We evaluated the performance of the mechanism over a simple low-cost software-defined testbed with different sensor data type having different QoS requirements. Results show that each sensor node achieved the level of satisfaction based on their required utility function both in an ideal testbed scenario and in an actual indoor deployment. © 2019 Elsevier B.V

On the design of nutrient film technique hydroponics farm for smart agriculture

Smart farming is seen to be the future of agriculture as it produces higher quality of crops by making farms more intelligent in sensing its controlling parameters. Analyzing massive amount of data can be done by accessing and connecting various devices with the help of Internet of Things (IoT). However, it is not enough to have an Internet support and self-updating readings from the sensors but also to have a self-sustainable agricultural production with the use of data analytics for the data to become useful. In this work, we designed and implemented a smart hydroponics system that automates the growing process of the crops using Bayesian Network model. Sensors and actuators are installed to monitor and control the parameters of the farm such as light intensity, pH, electrical conductivity, water temperature, and relative humidity. The sensor values gathered are used in the building the Bayesian Network, which classifies and predicts the optimum value in each actuator to autonomously control the hydroponics farm. Results show that the fluctuations in terms of the sensor values were minimized in the automatic control using BN as compared to the manual control. The prediction model obtained 84.53% accuracy after model validation and the yielded crops on the automatic control was 66.67% higher than the manual control. © 2019 Asian Agricultural and Biological Engineering Associatio