SUSTAINABLE FOOD INDUSTRY VIA INTERNET OF THE THINGS

The food sector is one of the most important manufacturing industries and a big economic contributor (FoodDrinkEurope, 2017). On the other hand, consumed resources and food waste (FW) during the consumption and manufacturing process in the industry is becoming a growing cause of worry for ecological sustainability (Garcia-Garcia et al. , 2019). All of this makes the food sector inefficient and unsustainable which is a major concern globally [DiSalvo et al. 2010]. In this case, The Internet of Things (IoT) can let the food business monitor FEW (Food waste generation and Energy and Water consumption) in real time and determining which processes are sustainable. The framework via IoT systems can help to FEW reductions. This framework has 3 important stage to follow: 1. Defining required datasets 2. Designing IoT monitoring system 3. Designing IoT-based FEW tools STAGE 1 First stage of FEW frameworks is collecting sufficient data. In this stage, FW is need to categorized depends on waste, water, and energy type. Three major categories for waste are avoidable which is edible and consumable e. g. bread, possibly avoidable which is edible and partly consumable e. g. apple skins and unavoidable which is inedible e. g. eggshells (WRAP, 2009). Two main categorises for energy are direct and indirect energy. Direct energy is required for some processes like cleaning and washing, meantime indirect energy is utilized during process like storing and transporting like heating and lightning(Seow, 2011). Lastly, water can be categorised into two major categories: production water which is used directly during process and non-production water which is used by utilities to support production (Sachidananda et al. , 2016). After type of FEW data has been established, the proper hardware such as sensors and smart meters capture the data from the equipment and stored in the cloud or local server. The data analysis software results are displayed to all stakeholders, and if there is an odd pattern of production, management can be warned. STAGE 2 IoT system structure have several layers and devices are identified by the IoT system. For the FEW systems, an Azure based provisioning service registers and configurates devices across several hubs through Application programming Interface (API). Since there are a great deal of devices, IoT structure needs compatible with high volume of data which is processing in the real time. Meantime, wireless Internet protocol (IP) needs to secure connections with disabling open ports in IoT devices or avoid devices that does not support asymmetric encryption also to secure confidential data. IoT sensing devices such as cameras communicates with the local server or cloud platform via direct (e. g. Arduino, Wi-Fi) or indirect (e. g. Bluetooth) Internet communication during sensing/perception layer (Jagtap and Rahimifard, 2019). The most appropriate sensor node to collect FEW data is the gateway, which can acquire data and transfer it via the internet rather than clustering. Wireless data transfer of this devices is possible by technologies like Zigbee, Wi-Fi, and Bluetooth. However, in the food sector, machines have less capacity to send data wirelessly due to long distance environment and short distance communication technologies like Near Field Communication (NFC) or Radio Frequency Identification (RFID) can be used instead. For better result using both Wi-Fi and Bluetooth technologies are suggested. Not to mention that the sensor nodes are developed on the factory floor to ensure that production is not corrupted. Network/communication layer gathers data in special IoT format. Afterwards, wired or GSM-based Internet connection send information to related local servers or cloud platform for service layer. This connection between layers is established through various protocols such as Hyper Text Transfer Protocol/Hyper Text Transfer Protocol Secure (HTTP/HTTPS), message queuing telemetry transport (MQTT) or Constrained application protocol (CoAP) (Yokotani and Sasaki, 2016). HTTP is easiest protocol on text base which provides combined and uniform connectivity. Despite this, MQTT which is based on broker model with small overhead (2 bytes/messages) and CoAp which has binary approach and provides Representational State Transfer (REST) application are more appropriate. The service layer analyses and brokers communications with MQTT broker to communicate with all devices and combines data from them. This layer can even serve and warn based on the FEW data. To monitor data, service layer has ability to process complex transactions, supports huge data storages and send alerts to take action in real time based on analysis. Online monitoring of FEW information requires cloud or local servers. However, continuous data could be too much for local servers and, therefore, cloud resources are preferable. Clouds reduce the amount of energy consumed for running the servers, process both continuous and batch data and provide efficient Big Data frameworks such as Spark and MapReduce. IoT systems can analyse gathering data in application layer to determine reasons and amount of FEW consumptions. For the FEW monitoring systems, there is a need to build a web-based front-end portal that allows User Interfaces (UIs) to design and collect data from large number of sensors. Like Java and Python platforms, this layer support lots of different server-side platforms as well. STAGE 3 The FEW monitoring systems consist of the three sections: a scale and image processing unit for FW monitoring (Jagtap and Rahimifard (2019), Jagtap et al. (2019a)), commercial sensors/smart meters for energy monitoring, and ultrasonic meters for water monitoring. The IoT-based energy monitoring records the energy data data such as energy consumption, peak consumption periods, cost of energy consumption, timings and CO2 emissions of energy consumed as a part of environmental reporting. The IoT-based water monitoring identifies leaks and water waste. Finally, The collected data on FW, energy and water are combined to build the live, shared and interactive FEW dashboards in mobile app, desktop app or web service that powered by ASP, NET, HTML5 to provide access of all needed data, analysation of results and notification of unexpected situation. In conclusion, all these process of real time FEW data from a particular food manufacturing allows the user to identify the most efficient solution and reduce FW, energy, water generation consumption with improving their environmental performance as well as reducing economic costs. However, these technologies have some challenges such as setting-up costs, concerns regarding data sharing and security, burden shifting which would occur if the efficiency of some processes is improved via the utilisation of the IoT-based FEW framework, but the overall efficiency is decreased due to use of the new devices installed. Clearly, the use of such devices should not increase waste; however, these devices have electricity requirements that could exceed energy savings and producing heat at the same time.