Internet of Things in food safety: Literature review and a bibliometric analysis
Monday, June 29, 2020
Internet of Things in food safety: Literature review and a bibliometric analysis
A B S T R A C T
Background: Internet of Things (IoT) is growing exponentially and can become an enormous source of information. IoT has provided new opportunities in different domains but also challenges are apparent that must be addressed. Little attention has been paid to the potential use of IoT in the food safety domain and therefore the aim of this study was to fill this gap.
Scope and approach: This paper reviews the use of IoT technology in food safety. A literature review was conducted using academic documents written in English language and published in peer-reviewed scientific journals. The relevant articles were analysed using the bibliometric networks to investigate the relationships between authors, countries, and content.
Key findings and conclusions: IoT in food safety is a relatively new approach; the first article appeared in 2011 and has increased since then. Majority of these studies were performed by Chinese universities and the main IoT applications reported were on food supply chains to trace food products, followed by monitoring of food safety and quality. The vast majority of publications were related to food, meat, cold chain products and agricultural products. These studies used sensors to monitor mainly temperature, humidity, and location. The most frequently used communication technologies were Internet, radio frequency identifications (RFID) and wireless sensor networks (WSN). This article identifies knowledge gaps to inform the community, industry, government authorities about research directions for IoT in food safety.
Internet of Things (IoT) has changed the way data are used to be collected and extended the vision of Internet further, from computers interconnected by internet, to every object that is able to receive or transfer digital data interconnected and therefore IoT can become an enormous source of information. A clear indicator of the growth of IoT is the phenomenal growth in machine to machine (M2M) connections from just under a billion of M2M connections in 2017 to 3.9 billion by 2022, which is bringing together people, processes, data, and things to make networked connections more relevant and valuable (Cisco, 2019, pp. 1–33).
The European Commission Information Society (Saint-Exupery, 2009, pp. 1–50) defines IoT as "Things having identities and virtual personalities operating in smart spaces using intelligent interfaces to connect and communicate within social, environmental, and user contexts" or "Interconnected objects having an active role in what might be called the Future Internet". The 'things' in IoT can include anything from a smart-watch to a cruise control system that contains some type of sensors (e.g. temperature, light, motion, location, etc.). The other component of the IoT ecosystem is the communication medium (Bluetooth, RFID, 4G, etc.), which facilitates communication with other machines or human and the computing resources.
IoT architecture includes several layers as shown in Fig. 1: i) device layer, ii) network layer, iii) service support layer, iv) application layer, v) and management and security. Device layer includes all devices implemented in the environment and communication gateways, which means sensors (e.g. temperature, light, motion and location, etc.), devices that transmit and receive information over the communication network directly or via gateways (e.g. receptors and transmitters), energy supply devices (e.g. batteries, solar panels), devices that are able to manage functionalities, and gateways. Device layer also includes all relevant communication technologies, wired and wireless, such as CAN bus, Wi-Fi, Bluetooth, Zigbee, etc. Network layer provides the essential features of device data and related protocol conversion to network layer protocols. It includes functionality for the network (i.e. connectivity, mobility, authentication, authorization, and accounting) and transport layers in the open systems interconnection (OSI) protocol reference model. Service support layer represents services that enable IoT applications and services. It contains functionalities such as data processing and storage, as well as specialized functionality, per application and service, since emerging services have different requirements. The application layer includes the IoT applications and services. Finally, management and security refers to the typical management and security of configuration, topology, resource, performance, fault, and account.
In the strategic research roadmap of the European Commission Information Society (Saint-Exupery, 2009, pp. 1–50), the use of IoT in food supply chains was mentioned as one of the promising new area with applications in precision agriculture, food production, processing, storage, distribution, consumption, traceability, visibility, and controllability challenges. The new technologies that are based on IoT are expected to bring safer, more efficient, and sustainable food chains in the near future. Recently, Talavera et al. (Talavera et al., 2017) conducted a review on IoT in agriculture and observed that the main applications occurred in monitoring, control, logistics and prediction. It seems that the majority of the IoT studies in agriculture deals with applications in the supply chain, followed by arable farming, and the applications are mostly on monitoring and sensing (Verdouw, Wolfert, Beulens, & Rialland, 2016). Because of these limited areas of application, their conclusion was that IoT in agriculture and food is still in the early development. Thibaud and colleagues (Thibaud, Chi, Zhou, & Piramuthu, 2018) also published a literature review about the use of IoT in a wider range of domains including healthcare, mining and energy, connected vehicle, building and infrastructure management, and food supply chain. For each of these domains, the motivations of research and implementation of IoT systems was outlined including the specific IoT characteristics (e.g. system architecture, sensor levels, communication and business aspects), as well as challenges and solutions in these fields.
In all available studies, little attention has been given to the potential use of IoT in the food safety domain and therefore the aim of this study was to fill this gap. To this end, scientific articles reporting the use of IoT technology in food safety published in the scientific literature until 2018 were collected, summarized and assessed. The collected articles were analysed using bibliometric networks to determine relationships between authors, institutes, countries, and content. Finally, knowledge gaps were identified that require further exploration, and opportunities for future research in this area.
To obtain an overview of the potential use of IoT in food safety, a literature search was conducted in several databases containing scientific publications and conference proceedings (e.g. IEEE, Science Direct, Scopus, Google Scholar). This search delivered 533 potentially relevant papers of which after further assessment resulted in 48 relevant publications, which were used for this overview. These relevant publications were analysed using bibliometric networks to investigate the relationship between authors, countries and content.
2. Literature search and bibliometric networks
2.1. Literature search
A literature review on IoT related to food safety was conducted to answer the following research question. What are the current (applied or proposed) IoT technological solutions in the food sector related to food safety? To answer this question, the following scientific databases and search engines were explored: IEEE, Science Direct, Scopus, and Google Scholar. The search terms used consisted of two groups, group 1 with the term "Internet of Things" only and group 2 with food safety related topics. No time limitation was applied. The search terms used in group 2 were: food safety; food supply chain; precision farming; food monitoring; food preparation; food handling; food processing; food storage; food origin; food label; food traceability; food additive; pesticide; pesticide residue; safe water; clean water; food contamination; physical contamination; physical hazard; chemical contamination; chemical hazard; biological contamination; biological hazard; animal husbandry; meat; fish; vegetable; fruit; milk; dairy; cereal; egg.
The aim was to use both search terms queries in the title, abstract and keywords of each database, but this was not possible in all databases. In IEEE Xplore a search in the keywords was not possible and therefore, metadata was chosen, which searches in the abstract and title text. The entire search string could only be used in Scopus. In Science Direct the search query of group 2 had to be split up into 4 groups, as only 8 Boolean operators can be used at once. For IEEE each term of group 2 had to be search individually with the term "Internet of Things", otherwise the outcome resulted into too many irrelevant papers. For Google Scholar, the search query of group 2 also had to be split up into groups, because when the search string contained more than 12 search terms no hits were obtained. The search queries for each database are available in supplement including explanations. In total, 927 articles were found with the search queries in the four databases and search engines (Table 1). These records were transferred to End-Note and the duplicates were removed, after which 533 articles remained. The distribution of the selected records over the four databases is presented in Table 1.
These 533 remaining references were firstly checked for relevance (relevant, not relevant) according to the title in a four-eye-principle. The following topics were evaluated as not relevant for food safety: studies in other languages; sustainability; dairy growth; plant growth; milk distribution system; crop productivity; medical application; nutrition; proceedings from conferences; chemical engineering; obesity; animal behaviour; optimal crop cultivation; irrigation; healthy food; commerce; energy management; tomato classification; municipal wastewater, water quality monitoring; NFC payment; gas concentrations. This initial screening gave 190 relevant articles, which were further assessed for relevance by reading the full article (i.e. abstract, method, results and conclusion) applying the selection criteria as:
- Is the article accessible with the available subscription or is the article freely available?
- Is the topic of the article food safety related? Is the article addressing the search terms?
- Does the study present a (comprehensive) specific solution for IoT in food safety?
Articles incidentally mentioning IoT as a new technology in food safety or current state of art of IoT or the evolution towards IoT such as literature reviews were not considered relevant. After applying all these steps, 48 articles remained relevant and are used for this study. As shown in Table 1, the majority of the remaining articles came from SCOPUS (N=41), followed by Google Scholar (N=6) and Science Direct (N= 1). The large reduction of relevant articles in the last exclusion step (e.g. from 190 to 48) was because 92 articles were not accessible and articles that were not related to food safety (e.g. agricultural field management).
2.2. Bibliometric networks
The selected relevant articles were analysed using the bibliometric networks methods such as co-authorship analysis, citation analysis and co-citation analysis (Garfield, 2009; Pilkington & Meredith, 2009; Van Eck & Waltman, 2017). In this study, network and overlay visualizations were built to visualise bibliometric networks using VOSviewer, 1 which is an open access tool that contains the basic functionality needed for this analysis. Network visualisation has shown to be a powerful approach to analyse a large variety of bibliometric networks, ranging from networks of citation relations between publications to networks of co-authorship relations between researchers or networks of co-occurrence relations between keywords (Tran et al., 2019). In an overlay visualisation, the colour of a node indicates a certain property of the node. For instance, nodes may represent authors and the size of a node may indicate the number of times an author has been cited (Van Eck & Waltman, 2010, Van Eck & Waltman, 2014).
3. IoT in food safety literature search
3.1. Publication year
As shown in Fig. 2, the first publication on IoT in food safety appeared in 2011 and the number of articles rapidly increased with a peak in 2016 (11 articles). The number of papers published in 2018 was 9 articles.
3.2. Food commodities and countries of publication
The vast majority of the IoT publications were related to food (N= 16), followed by meat (N= 7), cold chain products (N=5) and agricultural products (N= 4). The publications on food and agriculture did not specify a food commodity but the publications on meat included beef, pork, sheep, and sausages production. The food commodities studied in the other papers were dairy, fish, crop-field cucumber, fresh food products, pre-packaged food, seafood and wine. Analysing the residence countries of the research institute (mainly universities) of the authors published about IoT in food safety. Striking finding is the large dominance of Chinese universities (N =23) followed by Taiwan (N= 3), India (N =3) Italy (N=3), a number of countries with 2 papers (e.g. United Kingdom, Sweden, Spain, France, and Austria), and a large number (i.e. N=17) of countries with one publication.
4. IoT in food safety bibliometric networks
4.1. Network and overlay visualisation for authors
Fig. 2. Number of publications in IoT in food safety per year.
Fig. 3. Network visualisation of the authors.
Fig. 3 shows the network visualisation of authors' co-occurrence network of all papers collected on IoT in this study. Each circle in the figure represents one author name. The size of the circle is related to the number of papers each author has published on this topic. In general, the closer the authors are located in the visualisation, the more strongly they are related to each other based on bibliographic coupling. The network visualisation of the authors shows six clusters, which consist of researchers in information science and technology from Fudan University in Shanghai (yellow cluster), researchers in software development from the same university (purple and bleu clusters), and researchers in technology and sensors from the same university (red and green). The overlay visualisation for authors is available in Figure A1 in supplement.
4.2. Network and overlay visualisation for content
Fig. 4 shows the co-occurrence network visualisation of content. In the network presented in Fig. 4, each circle represents a keyword. The size of a circle indicates the number of publications that have the corresponding term in their key words. Terms that co-occur a lot tend to be located close to each other in the visualisation. In this case, the keywords were grouped into eight clusters, of which four are of significant size.
The green cluster covers terms related to IoT technology, food safety, and food supply chain. The red cluster consists of RFID, IoT, and traceability terms. The yellow cluster is more related to cold chain, wireless sensing and food waste while the blue cluster is more focused on HACCP, supply chains and blockchain. The overlay visualisation for content is available in Figure A2 in supplement.
5. Application areas of IoT in food safety
5.1. Sensing variables
Table 2 shows for each relevant paper collected the variables that are measured in food safety. The majority of the studies measure temperature (60%), humidity (40%) and geographical location (GPS) (40%).
5.2. Communication technologies
The most frequent used technologies in communication between IoT devices in food safety (Table 3) were Internet/Ethernet, radio frequency identifications (RFID) and wireless sensor networks (WSN). In food safety, RFID has been used in several IoT projects in tracking and tracing food and product authenticity measures. For instance, Chen (Chen, 2015) proposed to use RFID tags and readers to trace products along the supply chain from farm to retail. In Lui et al. (Liu et al., 2016), the RFID tags were used in a pilot IoT project for tracking and tracing food in the supply chain. For the transmission of these data WSN, WiFi and Ethernet were used. Mededjel et al. (2017) developed an IoT traceability system, where the proposed architecture uses fog and cloud platform, RFID tags and electronic product codes (EPC). Yan et al. (2012) developed a traceability platform for tilapia produced in aquaculture, which uses RFID, EPC technologies as well as ONS (Object Name Service) and EPCIS (EPC Information Service) server and a database server. Using IoT for traceability purposes also enables the option to control and mitigate counterfeiting as demonstrated by (Zou, 2016). This author designed a architecture for pork anti-counterfeiting and traceability, which uses EPC, RFID and NFC (near field communication) technologies and ZigBee in the Network layer.
5.3. Application topics
The main application areas of IoT in food safety were: (i) supply chain, (ii) monitoring, and (iii) supply chain and monitoring (see Table 4). The first topic contains articles about IoT solutions designed to trace food products along the supply chain. The second topic contains studies on monitoring of food safety and food quality and the third topic groups articles about supply chain and monitoring.
The majority of articles within the category monitoring studied control of food quality (N=10). Quality control in this regard had several meanings: controlling critical control points (HACCP), monitoring food adulterants in food, monitoring contamination, monitoring degradation, monitoring of resource consumption and disease prevention. Articles discussing the topic of shelf life (N=4) were dealing with preserving the freshness of products in the cold chains. Another possibility of applying IoT in food safety monitoring is related to detecting food safety risks in general or more specifically pesticide residues on the field or in agricultural products. More details about the main food safety topics within the category monitoring are available in Table A1 in supplement.
188.8.131.52. Food safety and quality monitoring. A food fraud IoT system was designed by Gupta and Rakesh (Gupta & Rakesh, 2018) to monitor adulterants in food products. The proposed system was described as simple and effective, which can be used by several actors in the food supply chain (e.g. farmers, consumers and authorities) to detect food adulteration. The system contains various sensors for temperature, oil, humidity, salt, metal, colour, pH, and viscosity. Another food fraud IoT system was developed by Rajakumar et al. (Rajakumar et al., 2018), which focused on the detection of adulterants in milk using several sensors such as gas, temperature, viscosity, salinity and a RFID readers.
Fig. 4. Network visualisation of the content.
Nirenjena and collegues (Nirenjena et al., 2018) developed an IoT system to prevent contamination and degradation of food, which can be used to monitor the quality of food in general, but can also be adapted to specific food commodities. They used several sensors to detect food degradation such as temperature, moisture, and GPS locations.
Smiljkovikj and Gavrilovska (Smiljkovikj & Gavrilovska, 2014) developed a cloud based system called SmartWine for monitoring the wine supply chain. The objective of the system was to manage resource consumption (e.g. water and pesticides), disease prevention and quality improvement. They integrated several sensors to collect data on air temperature, air humidity, atmospheric pressure, solar radiation, ultraviolet radiation, wind speed, wind direction, leaf wetness, soil temperature and soil water tension. Another proposed IoT system which already has a more practical use, was developed by Shih and Wang (Shih & Wang, 2016). They developed an architecture for a real-time remote monitoring system. Wireless sensors measure the temperature along a cold chain in a food processing system, which includes preparation and transportation of food.
184.108.40.206. Shelf life monitoring. Chen and colleagues (Chen et al., 2014) proposed a smart cold chain system to monitor food freshness using many sensors recording temperature, humidity and pressure in cold chain products and a new type of RFID. The system is able to monitor food products with a mobile code, which ensures that the system can continue its services even when the backend system is disconnected. This also reduces the load of the system (storage, processing, and communication) as the backend system does not need to be accessed all the time.
220.127.116.11. Pesticide residue monitoring. Jin and his colleagues (Jin et al., 2017) described a new mobile sensitivity absorptiometer to monitor pesticide residues on the field. The system used a photo detection frontend sensor and an on-board microcontroller that communicates with 4G technology between the smartphone, the cloud, and the food safety specialist. Bluetooth technology was used for the communication between the on-field detector and the interface. The visualisation and the reporting of the results were displayed on a smartphone. Another system for pesticide residue-detection in agricultural products was designed by Zhao and colleagues (Zhao et al., 2015). The system uses biosensors, wireless transmission and a detection device (single-chip microcomputer) and information-sharing platform. The system was tested on real samples and the results were positive.
5.3.2. Supply chain
Articles in supply chain category uses IoT technology mainly for tracing and tracking different food products along the food chain (N= 16) and some minor applications (traceability and anti-counterfeiting (N=1), food packaging (N= 1) and risks (N=1)). More details are available in Table A2 in the supplement. Traceability can help to obtain information about the safety of the products along the chain as well as to improve the logistics behind the food products. Thus, the function of traceability can also be used to obtain other relevant information on food safety, food quality, and logistics. Another application of traceability is to use it as an anti-counterfeiting system in the supply chain, which can be important for the actors involved in the supply chain.
18.104.22.168. Traceability. Traceability systems were proposed to obtain information about the safety of the products along the food chain as well as to improve the logistics behind the food products. Chen (Chen, 2015) proposed an autonomous IoT tracing system for product usage life cycle. Products were identified from farm to retail with RFID tags and readers, WSN and EPCglobal (electronic product code). EPCglobal enables information sharing regarding product problems. The authors demonstrated in experiments that their system could effectively trace food along the supply chain. Another study performed by Liu and colleagues (Liu et al., 2016) introduced an IoT architecture for tracking and tracing food in the supply chain. They used RFID and video recording as sensors and they used WSN, WiFi, Ethernet for the transmission of data. This system also contained an application service platform for data processing, analysing, and visualisation. Applied in several real cases, the platform was evaluated as efficient and effective. Liu and colleagues (Li et al., 2017) developed a tracking and tracing system for pre-packaged food covering the entire supply chain. The platform integrated QR codes and RFID, which reduced the implementation cost of the architecture. The effectiveness of the traceability system was verified in a case study and proved to ensure safety and quality of pre-packaged food. Traceability system can be used to share relevant information on food safety, food quality, and logistics. For example, Mededjel and al (Mededjel et al., 2017) developed a traceability system to improve cooperation among stakeholders in the food supply chain. The proposed architecture allows to collect, transfer, store and share information in the food supply chain and uses RFID tags, electronic product codes (EPC) and a cloud platform. Yan et al. (Yan et al., 2012) developed a IoT traceability platform for aquatic foods (i.e. Tilapia), where several actors (i.e. consumers, businesses and governments) can retrieve traceability information about the products. Besides providing a tracing system along the chain (breeding up to sales), this IoT platform contains management systems for aquaculture, processing, distribution, sales, and monitoring. The platform uses RFID, EPC technologies as well as ONS (Object Name Service) and EPCIS (EPC Information Service) server and a database server.
Table 2 Sensing variables discussed in each relevant article.
Table 3 Communication technologies used in food safety IoT applications.
Table 4 Authors per topics studied in IoT in food safety and number of articles.
22.214.171.124. Traceability and anti-counterfeiting. Using IoT for traceability purposes also allows controlling and mitigating food fraud. Zou (X. Zou, 2016) designed an IoT solution that can improve traceability and limit food fraud in the pork supply chain. This traceability system enables upward anti-counterfeiting and traceability for customers to retrieve information on logistic, inspection and quarantine, cultivation and farms and slaughterhouses, as well as downward traceability to the distribution points and point of sale.
126.96.36.199. Food packaging. Beker and colleagues (Beker et al., 2016) outlined an IoT solution that can be applied in food supply chain to improve food safety and quality. In this solution, smartphones can be used by consumers to gather information from the packaging, which are already indicated on the package (e.g. ingredients, allergies, and nutrition values) and other information such as product quality, freshness, origin and applied pesticides. Besides consumers, also retailers can profit from this system by predicting the shelf life of products and compare it with the real shelf life in order to improve their logistics. The authors concluded that the development of smart packages is just a beginning but the potentials of IoT in the food chain are considered enormous.
5.3.3. Monitoring and supply chain
In several articles, we observed an overlap between supply chain and monitoring of food products in the food chain. For instance, Liu and colleagues (Liu et al., 2014) developed a new architecture for supply chain, which monitors food products along the food supply chain from farm to market. The new architecture enabled efficient storage and sharing of information between actors and resulted in benefits for all actors in the supply chain. Musa and Vidyansankar (Musa & Vidyasankar, 2017) proposed an architecture which introduces fog computing as an important tool to improve the efficiency in tracing food products in the supply chain. They introduced along the blackberry supply chain (i.e. harvesting, transportation, and packinghouse) sensors to monitor temperature, relative humidity, carbon dioxide, and light. In the Netherlands, Verdouw and collegeus (Verdouw et al., 2016) proposed an IoT architecture for a virtual food supply chain called FIspace. 2 The system is a cloud-based platform that allows actors in the supply chain to visualise, monitor, control, plan and optimise business processes in real-time. The platform functions between the four layers of virtualisation (i.e. sensing and actuating, data exchange, information integration, application services). The platform supports several Apps such as production information App, shipment status App, logistics planning App, market place operations service App and cargo search App. FIspace was validated in a fish case study and is expected to have several benefits for companies and industries.
6. Discussions and future challenges
In most studies, the proposed IoT architecture is mainly a theoretical structure without any real application, which means that an implementation of the IoT in practice in food safety is rare. Few examples of real implementation of IoT in food safety have been reported by Shih and Wang (Shih & Wang, 2016) in which they developed a system for monitoring a cold chain. The system was successfully implemented and the authors reported that this system resulted in increased annual sales, higher turnover, new jobs, as well as a reduction of energy consumption. Similarly, Yan et al. (Yan et al., 2012) designed an IoT traceability platform for fish supply chains, which also resulted in a positive effect on the turnover and market satisfaction for companies. Musa and Vidyasankar (Musa & Vidyasankar, 2017) monitored the temperature of blackberries along the chain using sensors and the nodes compute the information to estimate the shelf life of the blackberries. When the calculated shelf life deviates from a threshold level, the system autonomously actuates an alarm by sending an email or SMS to involved actors in the supply chain. The last example is the SmartWine monitoring system, established by (Smiljkovikj & Gavrilovska, 2014), which can be connected to an automation system that reacts autonomously to climate conditions in order to prevent diseases and safeguard the quality in the winemaking process.
In Europe, the implementation of the IoT solutions in food safety was rare. Although, several projects in IoT related to food chain have been started, food safety was studied only in few projects such as Musetech, 3 IQ-FRESHLABEL 4 and ebbits. 5 In Musetech project, three sensing technologies (i.e. photoacoustic spectroscopy, quasi-imaging spectrometry and temperature) were integrated in a multi sensor device for real-time monitoring of multiple parameters associated with the quality and the chemical safety of food. IQ-FRESHLABEL project aims to develop a novel smart label to monitor temperature and oxygen content in frozen food and packaged products. The ebbits project aims to develop architecture, technologies and processes, which allow businesses to semantically integrate the IoTs into mainstream enterprise systems and support interoperable real-world, on-line end-to-end business applications. The main IoT projects in food chain in Europe are given in the Appendix Table A3.
This study has shown many promising applications of IoT in the food safety domain, however, only a few have led to real implementations. Assessment of the collected studies has identified some constraints that may have caused this limited uptake of the technology and will be discussed per topic below.
6.1. Food safety and traceability
Traditionally, tracking of food products using RFID or NFC tags has been problematic due to the high cost associated with this technology relative to the product price. Therefore, new technologies that focus on monitoring chemical and microbiological hazards in the food chain should be: i) based on small cheap sensors, ii) suitable for real time detection at the levels of interest and iii) easy to implement and use. RFID technology has been widely used in production, warehouse management, logistics tracing and product authenticity measures, etc. The RFID standard of the EU is following the American EPC global standard, and in the application area they are at the same developmental stage. Methodologies used for food traceability should be valid for every type of product, independently from their inherent characteristics. IoT technologies applied in food safety need to be validated using referenced, official methodologies and protocols. Since IoT will be used in future food business, it may influence consumer's preferences when purchasing food products online, hence an appropriate legislation regarding this issue is needed (Kim Dang et al., 2018; Nguyen et al., 2018).
The data produced today by IoT devices can be difficult to be interpreted, communicated, and shared because of lack standardize communication protocols. In future, more and more IoT devices will follow FAIR guiding principles. This will enable both Internet of Data and services helping data and algorithms to find, talk, and remain available for data sharing and reuse. Recent developments in this area point toward development of metadata standards for machines (Wittenburg et al., 2019).
6.2. Communication technology and security challenges
Some of the wireless networks used in today's IoT devices were designed for other purposes, such as multimedia and telecommunication. As a result, they are not optimized for IoT communication and consume significant amounts of computing power in the communication protocols. Several different protocols have been proposed and used for IoT systems. The users have not yet converged on a single standard for IoT communication services. Protocols that rely on a specific physical layer do not use the Internet Protocol, while protocols that are physical layer agnostic do use IP.
Security is an essential requirement for all IoT projects in food safety. However, several IoT systems are much less secure than classical systems for computers such as Mac and Linux. Several issues associated with IoT security are: inadequate security features in hardware, poorly designed software, default passwords, and other security design errors. Insecure IoT nodes create problems for the security of the entire IoT system and for the rest of the internet.
This study conducted a literature review on IoT solutions in the food sector related to food safety. A literature review was performed using academic documents written in English and published in peer-reviewed journals. The relevant articles were analysed using the bibliometric networks to investigate the relationships between authors, institutes, countries, and content. This study showed that IoT in food safety was first reported in 2011 and the number of articles published has increased since then with a dominance of Chinese universities in the number of articles published in this topic. IoT in food safety publications were mainly focused on proposing and designing IoT architectures in supply chain, as well as outlining challenges and possibilities. The solutions were related to the architecture or implemented only in pilots, but not applied in practice. The research has also shown that the focus of IoT in food safety was food supply chains to trace food products (e.g. food, meat, cold chain products and agricultural products), followed by food safety and quality monitoring. The majority of the studies used sensors to measure temperature, humidity, and location. The investigation of the most communication technologies used has shown that Internet, RFID and WSN were the most used.
The present study has been one of the first attempt to examine the literature of the use of IoT technology in food safety. Overall, the results showed great potentials of this technology and some successful implementations were reported, it is clear that further research and innovation is required to capture the full potential of IoT. For this overview, only articles in the English were considered. Since, the majority of the developments appear to be in China, it would be preferable to widening this overview with articles published in Chinese.
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Article made possible through the contribution of Y. Bouzembrak et al. and Wageningen Food Safety Research