Design and development of intelligent waste bin system with advertisement solution

Abstract

In cities where a large geographical area of the city is densely populated, the process of waste collection is cumbersome, tiresome and expensive. Often, the burden of manually tracking and collecting of waste causes waste management companies enormous wasted effort and get them involved in tasks that are not necessary. No doubt, a digital interaction between waste management companies and targeted waste collection areas could ensure the process becomes fast, efficient and traceable as they become aware of the states of the wastes, aptly. It will considerably reduce any discrepancies that may occur due to the lack of information available during a particular time. Accordingly, this paper proposes a novel approach towards waste management combined with the internet of things to reduce the problems that would occur due to the accumulation of wastes and hence improvise waste collection/management process. Additionally, an innovative feature which generates revenue and creates business opportunities for waste management companies is introduced via advertisement solution based on network-attached storage technology.

Smart Collection of Waste Bread in Algeria Using the Internet of Things

“…The proposed system could assist Algerian SMEs in collecting waste bread by monitoring the fullness status of outdoor bins with a list of requirements. Several studies examined the domain of smart waste management (household/solid) using different materials and techniques [8][9][10][11][12][13][14][15][16][17]. In this context, this study adopted an appropriate approach to the specificity of the product, stale bread, the area, and the country.…”

“…As there are no similar studies on using IoT for smart collection of stale bread, the proposed system was compared with studies on recycling other products. This study utilized the same communication technologies with [5,6,17] for solid and [10][11][12][13][14][15][16]18] for household waste. The architecture design adopted a model that responds to:  The specificity of the product: To avoid the high cost of the system as the main objective was to recycle at a lower cost.…”

Advancements in Machine Learning-Based Face Mask Detection: A Review of Methods and Challenges

“…Deep learning (DL) is a subset of ML that can learn without the need for human intervention, using both labeled and unlabeled data. Deep learning techniques, on the other hand, may be excessive for simpler tasks because they require access to a large amount of data to be effective [3] 1, has many convolution layers where filtering is performed to input images to extract image features [11] [12] [13]. Following the convolution layer, a non-linear activation function is applied.…”