1. Introduction

This paper is an extension of work originally presented in 14th International Conference on Computer Engineering and Systems (ICCES) [1]. The increase in world population that reaches 8 billion increases the demand for livestock production. As a result, it is necessary to avoid livestock losses and protect it from diseases as this could threaten the countries’ economy and food security. The process of observing livestock was difficult as it took a lot of time to detect if they had medical issues or not. Accordingly, researchers illustrated nowadays different advanced monitoring systems for livestock, these systems depend on remote sensors. Different types of sensors were used as: accelerometer, rumination, body temperature and humidity, and sound sensors. They were placed on the cattle to collect data and send it to the processing unit to take the appropriate action [2].

Today, Different technologies play an important role to increase livestock production and to save their losses as well as improving the operation efficiency in large farms. One of the recent technologies that offers the measure, actuate, sense and infer of physical and physiological parameters is the Internet of Things (IoT) technology. Livestock performance monitoring systems using IoT technology require special type of networks. Adhoc network is one of these special networks as it is energy efficient network and elongates the devices’ lifetime. In the Adhoc network, different sensors are used to monitor behavior, stress, diseases and temperature. These sensors provide the IoT network with the collected data which allow the early detection of diseases. As a result, early treatment is offered which saves money and time.

The main problem in IoT technology is the energy source as it needs to work for a long time to reduce the need for replacement of sources or human collaboration. This paper concentrates on the study of suitable network topology that provides sensor network operation using energy efficiently. Accordingly, different techniques against farms’ environment are illustrated and simulated. Additionally, a Mobile Adhoc network (MANET) algorithm is introduced to decrease the source energy drainage and assure energy sustainability.

2. Literature Review

      Several literatures are presented in previous work that proved the significance of IoT and sensor technologies to improve the livestock farm performance especially the large animal farming. In [3], the author presented a group of micro-sensors which were non-invasive sensors. They were mounted on the livestock using a collar that sent data via the internet wirelessly. As a result, a continuous connection between animals and veterinarian was established.

In [2], the author proposed a system using various sensors which were mounted on the cattle body. These sensors were used to monitor different parameters which allowed to detect the cattle body issues constantly. In [4], the author developed a system using misting systems for heat stress reduction. It was also developed to monitor humidity and temperature ranges. The system assured the avoidance of cattle death and the increase of producing high quality milk by preserving the ranges of the monitored parameters constant.

Although the literature above highlighted the use of rechargeable batteries as a main power source of the sensor nodes used in their studies, the researchers did not consider the battery recharging methodology especially for Adhoc wireless sensor networks. On the other side, the following literature developed techniques that enhance wireless sensor networks’ performance.

In [5], the author focused on wireless sensor networks and proposed a gateway based Geographical Energy Aware Routing (M-Gear) protocol. In the system, the sensor nodes were distributed according to their location into four regions. Outside the sensing area, the base station was placed and at the center of the sensing area, the gateway node was located. Their developed protocol performance was compared to the performance of Low Energy Adaptive Clustering Hierarchy (LEACH) protocol. The compared performance was in terms of energy consumption and network lifetime. The comparison proved that M-Gear protocol was better than LEACH protocol.

In [6], the author developed a Modified Sensor Protocol for Information via Negotiation which is called M-SPIN. They made a comparison between the performance of M-SPIN and the traditional SPIN protocol using the broadcast communication. In the developed protocol, the hop distance between nodes and sink node was measured using a distance discovery phase. Additionally, the total number of transmitted packets was reduced as a result of transmitting data only to the sink node. As a result the consumption of total energy was minimized.

In [7], the author proposed an algorithm to modify the Dynamic Source Routing (DSR) protocol. The number of Route Reply (RREP) packets was decreased using the proposed algorithm and it also decreased the size of the header for the data packets which resulted in decreasing the overhead. They also developed an algorithm to reduce the energy consumed in data transmission.

In [8], the author presented a Dynamic MANET On-demand (DYMO) protocol and ant colony optimization algorithm which was capable of managing environmental changes. Graphs were used in ant colony optimization algorithm which was based on probabilities’ method to find routes. Two factors were measured to evaluate the discovered routes. The measured factors were the route reliability and delay time. The software used to implement the algorithm was NS-2.

Accordingly, the proposed modified AODV technique capitalize on extending the enhancements in the literature to develop IoT operation aware Adhoc wireless sensor network that is suitable for use in livestock farm applications.

3. Overview on Energy-Efficient Routing Protocols

Mobile Adhoc network (MANET) is a group of self -configuring mobile nodes. These nodes can be used at the same time as router and end node, therefore a fail in the operation of some nodes affects the performance of the network. Energy deficiency of nodes is one of the main problems which affects the performance of network in MANET. As a result, it is important to use energy efficiently in MANET in order to increase the lifetime of node’s battery [9]. There are several routing protocols in MANET such as Destination Sequenced Vector (DSDV), Dynamic Source Routing (DSR) and Adhoc On-demand Distance Vector (AODV). These protocols are used to select the optimal route between source and destination in order to send data through it.

3.1. Destination Sequenced Distance Vector

DSDV is one of the proactive routing protocols in MANET. It is a table driven routing protocol and it is based on Bellman-Ford algorithm. In this protocol, a routing table is used at each node to store all available destinations. Additionally, advertisements are made as a severe change may exist in the table’s data. In this protocol, a sequence number is also sent to the destination in order to discriminate the old routes from the new ones which avoids the formation of loops. It isn’t recommended for highly dynamic networks because it needs regular updates in its routing tables which lead to battery drainage [10].

3.2. Dynamic Source Routing

DSR protocol creates routes only when requested, therefore it is one of the reactive routing protocols. It doesn’t use routing tables at the intermediate nodes as it uses source routing. In this protocol, the intermediate nodes send a Route Request (RREQ) packet with its own address identifier. The available routes in the DSR protocol are being tracked by a route cache. Two main mechanisms are used in DSR: route discovery and route maintenance. They are used to discover routes and to notice if any change occurs in the network topology [11].

3.3. Adhoc On-demand Distance Vector

AODV is one of the most known reactive routing protocols in MANET. It creates only a route when a source node needs to send data to a destination node therefore it is better than DSDV as it minimizes the number of broadcasts. It has a good performance in dealing with large number of mobile nodes as it creates dynamically at each intermediate node a routing table. It also has a sequence number counter which prevents the formation of loops in its routing tables. This sequence number also keeps the nodes updated by comparing their routing tables to check how updated information they have about other nodes [12].

4. Performance Evaluation of Adhoc networks

There are different parameters measured to assess the performance of MANET protocols as summarized in Table 1.

4.1. Packet Delivery Fraction

The ratio between the number of broadcasted packets by the source node and the number of received packets at the destination node is known as PDF. In case of low number of nodes, DSR performs better in this metric but the performance declines with the increase of the number of nodes. On the contrary, DSDV performs better in case of large number of nodes but AODV showed that its performance is uniform in both cases [13].

4.2. Average end to end delay

It is the time it takes from the packet being created at the source node to the packet being delivered at the destination node. In this metric, DSDV performance declines with the increase of the number of nodes. However, the DSR and AODV performance is uniform [13].

4.3. Number of Packets Dropped

It is the failed data packets that couldn’t reach the destination node. In case of large number of nodes, DSR and AODV have a good performance. On the other hand, DSDV has the worst performance as it declines with the increase of the number of nodes. The performance of AODV in this metric is the best as it exchanges information periodically between nodes which ensures connection [13].

5. Proposed Architecture for Livestock monitoring IoT networks

Figure 1 shows the architecture of a group of mobile nodes connected in MANET network. Each node in this network has different sensors such as accelerometer, skin temperature, humidity and ambient temperature, and respiratory rate. The data sensed from these sensors will be transmitted to a central processing server (CPS) to perform the proper action. In such a network, Power should be used efficiently to elongate the battery lifetime of each node. Accordingly, AODV protocol which is an energy efficient routing protocol is used to select the optimal route to send the collected data to the destination node. As a result, the total transmitted power of source node is reduced.

Figure 1: System Architecture for Monitoring livestock Performance

Figure 2 shows the flowchart of how AODV protocol is implemented. When a source node starts to search for a route to send its data through it to the destination node, it searches its routing table. If there is a route in its routing table to destination, it sends its data packets through this route. If there is no route to the destination node, it broadcasts a Route Request (RREQ) packet to all the neighbor nodes. If the node that received the RREQ packet is the destination node or it has in its routing table a route to the destination node, it sends a Route Reply (RREP) packet to the source node. If the receiving node isn’t the destination node or there is no route for the destination in its routing table, it broadcasts a RREQ packet to all its neighbors. When the source node receives a RREP packet, it transmits its data packets through the same route where it received the RREP packet. If several RREP packets are received by the source node, it selects the RREP packet received from the shortest route and discard other packets. As a result, the power for transmitting the data packets is reduced which increases the source node battery lifetime.

A modified AODV protocol is proposed to enhance the overall network power performance. The optimal route in the modified protocol is selected depending on the index weight of the path rather than depending only on the shortest path. The index weight is a new factor measured to indicate the operation time of each node in a certain path. Each node is given a certain index which represents its operation time. The index weight of the path is measured as the total index of nodes in a certain path divided by the number of nodes in this path. The path with the highest index weight has the lowest priority to be selected while the path with the lowest index weight has the highest priority to be selected.

Figure 2: AODV Flowchart

Figure 3: Modified AODV Flowchart

      The flowchart in Figure 3 shows how the modified AODV protocol is executed. When a source node starts to search for a route to send its data through it to the destination node, it searches its routing table. If there is a route in its routing table to destination, it checks its index weight. If it is low, it sends its data packets through this route. If there is no route in its routing table or the index weight of the available route is high, it broadcasts a Route Request (RREQ) packet to all the neighbor nodes.

If the node that received the RREQ packet is the destination node or it has in its routing table a route to the destination node, it sends a Route Reply (RREP) packet to the source node. If the receiving node isn’t the destination node or there is no route for the destination in its routing table, it broadcasts a RREQ packet to all its neighbors. When a RREP packet reaches the source node, it checks the index weight of the route. If it is high, it sends RREQ packet to all its neighbors searching for other routes with lower index weight. If it is low, it starts to send data packets to the destination through the same route where it received the RREP packet. If the source node receives more than one RREP packet. It selects the route with the lowest index weight to send data packets. As a result, it increases the lifetime of the serving nodes which enhances the overall network power performance.

6. Simulation Model Results

In this simulation, MATLAB software is used to simulate AODV protocol. In Figure 4, it is shown how the source node, destination node and the number of nodes are entered in this model. It is assumed in this simulation that the network has 20 mobile nodes randomly distributed in a farm size of 100 m² and a 20 m transmission range between nodes. The source node and the destination node are assumed to be node 2 and node 10 respectively. AODV protocol is implemented in this model to enable the source node to request different available paths to the destination node. The source node will receive RREP packet from the intermediate nodes if they have an available route to the destination node. In Figure 4, the available routes are shown.

Figure 4: Simulation Model

In our work, two main performance indicators are used to measure the impact of the proposed advanced AODV protocol on the IoT network performance mainly the energy consumption and network lifetime.

1. Energy consumption is based on MATLAB model that is governed by the following equation

Energy Consumption (Joule) = Energy Consumption Rate (Joule/bit) * Transmission rate (packets per sec) * Packet size (bits)                                                                                     (1)

The multipath fading is also accounted in the simulator model given that the amount of Energy consumption rate considered in the model for the transmitter or receiver circuitry is 50 x 10^-9 and amount of energy consumption for multipath fading is 0.0015 x 10^-12.

7. Network lifetime that is defined as the ratio between its residual energy and the energy consumed to transmit data packets (i.e. the time spent until the node run out of energy). It is the third parameter calculated at each route as shown in Figure 7. In this case, the route with the longest network lifetime is the optimal route as it has the lowest energy consumption and the shortest distance. As a result, route 12 is the optimal route in terms of distance, energy consumption, and network lifetime.

      The simulation results for applying AODV protocol in cows’ performance monitoring system are shown in this section. The first parameter measured as shown in Figure 5 is the total distance between the source node and the destination node at each route. The optimal route is found based on this parameter as the optimal route is the shortest route which is selected to transmit data through it. In this case, the route with the shortest distance is route 12 so it is the optimal route.

Figure 5: Total Distance versus The Number of Each Route Found During Simulation

Figure 6: Energy Consumption versus The Number of Each Route Found During Simulation

Figure 7: Network Life Time versus The Number of Each Route Found During Simulation

In this simulation, the energy consumption is the second parameter calculated at each route as shown in Figure 6. Equation 1 represents the energy consumption model. The route with the shortest distance has the lowest energy consumption. In this case, route 12 is the optimal route in terms of energy consumption and distance.

Figure 8:  Transmitted Power versus Distance between Source and Destination During Simulation

Figure 9:  Power Saving versus Node Density in Mobile Adhoc Network

The fourth parameter calculated in this simulation as shown in Figure 8 is the transmitted power at each route. The data are transmitted with minimum power when transmitted through the shortest optimal route to the destination node. In this case, route 12 is the optimal route in terms of distance, energy consumption, network lifetime, and transmitted power.

The fifth parameter measured is the power saving as shown in Figure 9. It indicates when selecting the shortest optimal route in MANET how much transmitted power is saved. It is measured at different number of nodes as shown in Figure 9 to indicate that when the node density increases the power saving decreases. Figure 10 shows the power saving which indicates how much transmitted power is saved when selecting the shortest route in MANET compared to the transmitted power in fixed network. It is measured at different number of nodes to indicate that when the node density increases the power saving decreases as shown in Figure 10.

Table 2: Simulation Results

Figure 11 shows different paths between source and destination which are path1 (P1), path2 (P2), path3 (P3), path4 (P4) and path5 (P5). It is assumed for each path that the distance between nodes is equal and the total transmitted power of the source node is 30 dBm. It also shows the transmitted power of each intermediate node in each path. There are some nodes which are common in more than one path such as nodes: 3, 4, 7 and 9.  These nodes are called busy intermediate nodes as they could be used several times in the network which will lead to the drainage of their batteries. As a result, as mentioned earlier in section 5 a modified AODV protocol is proposed to save the battery of these busy nodes. There are different scenarios for node process shown in Table 3 as each intermediate node will have an index to indicate how busy this node in order to select the optimal path in terms of its index. In Table 4, it shows the transmitted power of each node and its index of processing. It also shows their index weight which is the total index of nodes divided by the number of nodes in each path. The selection of the optimal route will be based on its index weight, the route with lowest index weight is the optimal route to send data through it.

Figure 10:  Power Saving versus Node Density in Fixed Network

Figure 11: Different Paths Available to Send Data Packets from Source Node (S) to Destination Node (D)

Table 3: Different Scenarios for Node Process and Its Index

Table 4: Parameters of Intermediate Nodes

Figure 12 shows the index weight of each path which indicates the status of the intermediate nodes. If the index weight of a path is high, it indicates that its intermediate nodes were processing for long time therefore this path can’t be selected to transmit data through it. As a result, this will save the lifetime of their batteries.

Figure 12: Index Weight versus Path Number

      Figure 13 shows the power gain of each path which indicates how much power is saved by selecting a certain path. The optimal path shown in Figure 13 is path 4 as it has the highest power gain.

      Figure 14 shows the power gain versus the index weight to indicate how much power is saved when selecting the path with the lowest index weight. As shown in Figure 14, the path with the lowest index weight has the highest power gain.

Figure 13: Power Gain versus Path Number

Figure 14: Power Gain versus Index Weight

7. Discussion and Conclusion

The results from applying the AODV routing protocol to select the shortest route showed that when the total distance between source and destination decreases, the energy consumption decreases which increases the network lifetime. It also showed that a decrease in the transmitted power occurs when the total distance decreases. For MANET and classical fixed networks, the power saving is calculated for each route found at different node densities. It showed that when the node density increases, the power saving decreases.

Table 5: Power Gain for Different Path Selection Scenarios

Table 6: Traditional AODV versus Proposed Modified AODV Protocol

The traditional AODV protocol focused only on how to enhance the battery lifetime of the source node depending on selecting shortest optimal route. It didn’t highlight the importance of the intermediate nodes which are serving nodes and their batteries could be drained quickly due to their operation time in the network. As a result a modified AODV protocol is proposed, this protocol added a new feature to select the optimal route. The selection of the optimal route in the modified AODV protocol is depending on the operation time of each node in the path. Each node is given an index which specifies its operation time as shown in Table 3. An index weight which is the total index of the nodes in a certain path divided by the number of nodes in this path is measured for each path. This factor indicates if the available path could be used or not. The path with a high index weight has the lowest priority to be selected because it indicates that the operation time of the intermediate nodes in this path is very high and thus the probability to have lower battery state of charge is higher.

Accordingly, the optimal path is the path that has the lowest index weight.

By applying the modified AODV protocol to select the optimal route depending on the index weight of the route as a priority to selection of the shortest route (i.e. if two routes have the same index, then the shortest one is selected). The protocol supports to give the intermediate node with longer operation time the opportunity to focus on sensed data processing. The networking processing function is then off loaded from this intermediate node to other nodes with lower operational time and accordingly higher probability of battery charge lifetime.

As observed in Table 5 the optimal path in terms of power gain is path2 and path4. It is also observed in Table 4 that the best path in terms of index weight is path 4. By comparing the power gain when selecting the shortest route (measured in hop counts) which is path 3 in Figure 11 to the power gain when selecting path4 which has the lowest index weight, it proved that the power is enhanced by 33.3% as shown in Table 6.

As a summary, a convenient network topology is studied in this paper to provide a network of sensors which has an energy efficient operation. Accordingly, in order to decrease the source energy drainage which assures energy sustainability, MANET algorithm is proposed. In MANET, AODV is the most common known routing protocol as its performance is the best compared to other MANET protocols. In this simulation, AODV was applied to allow the selection of the optimal route between source node and destination node. The optimal route is selected as it has the shortest distance, the lowest energy consumption and the longest lifetime.

A modified AODV algorithm is proposed in order to enhance the overall network power performance. The protocol depends on giving intermediate nodes with lower operation time a higher priority to share in the network routing even if the path could be longer which could lead to a power saving that reaches 33.3%. As a result, the paths in the modified AODV are selected based on the index weight of the path. The selected path is the path with the lowest index weight.

1. S.S. Abou Emira, K.Y. Youssef, M. Abouelatta, “Power Efficient Routing Protocol (AODV) for IoT Based Livestock Applications,” in 2019 14th International Conference on Computer Engineering and Systems (ICCES), 407–412, 2019, doi:10.1109/ICCES48960.2019.9068149.
2. M. Meenakshi, S.S. Kharde, “Advance Cattle Health Monitoring System Using Arduino and IOT,” 5(4), 3365–3370, 2016, doi:10.15662/IJAREEIE.2016.0504162.
3. J. Edwards, D. Gibson, “Novel Technology for the Remote Monitoring of Animals,” Companion Animal Society Newsletter, 23(2), 56–59, 2012.
4. M. M, S. A, “IOT-BASED MEASURING TEMPERATURE AND HUMIDITY IN THE CATTLE DIARY,” International Journal of Sustainable Development, 7(11), 7358–7360, 2015, doi:10.24327/IJRSR.
5. Q. Nadeem, M.B. Rasheed, N. Javaid, Z.A. Khan, Y. Maqsood, A. Din, “M-GEAR: Gateway-based energy-aware multi-hop routing protocol for WSNs,” in Proceedings – 2013 8th International Conference on Broadband, Wireless Computing, Communication and Applications, BWCCA 2013, 164–169, 2013, doi:10.1109/BWCCA.2013.35.
6. Z. Rehena, S. Roy, N. Mukherjee, “A modified SPIN for wireless sensor networks,” in 2011 3rd International Conference on Communication Systems and Networks, COMSNETS 2011, 2011, doi:10.1109/COMSNETS.2011.5716469.
7. M. Tamilarasi, S. Chandramathi, T. Palanivelu, “Efficient energy management for mobile ad hoc networks,” Ubiquitous Computing and Communication Journal, 3(5), 12–19, 2001.
8. A.M. Oranj, R.M. Alguliev, F. Yusifov, S. Jamali, “Routing Algorithm for Vehicular Ad Hoc Network Based on Dynamic Ant Colony Optimization,” International Journal of Electronics and Electrical Engineering, 4(1), 79–83, 2016, doi:10.18178/ijeee.4.1.79-83.
9. M. Tarique, K.E. Tepe, M. Naserian, “Energy saving dynamic source routing for ad hoc wireless networks,” in Proceedings – WiOpt 2005: Third International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, 305–310, 2005, doi:10.1109/WIOPT.2005.21.
10. P. Manickam, T. Guru Baskar, M. Girija, D. Manimegalai, “Performance Comparisons of Routing Protocols in Mobile Ad Hoc Networks,” International Journal of Wireless & Mobile Networks, 3(1), 98–106, 2011, doi:10.5121/ijwmn.2011.3109.
11. V. Denkovski, B. Stojcevska, T. Dovenski, V. Pachovski, A. Bozinovski, “Performance Evaluation of Routing Protocols in a Wireless Sensor Network for Targeted Environment,” JITA – Journal of Information Technology and Applications (Banja Luka) – APEIRON, 10(2), 2016, doi:10.7251/jit1502110d.
12. I.D. Chakeres, E.M. Belding-Royer, “AODV routing protocol implementation design,” in Proceedings – International Conference on Distributed Computing Systems, 698–703, 2004, doi:10.1109/icdcsw.2004.1284108.
13. S. Ade, P.. Tijare, “Performance Comparison of Routing Protocols in Mobile Ad Hoc Networks,” International Journal of Information Technology and Knowledge Management, 2(2), 545–548, 2010, doi:10.1007/978-981-10-6890-4_4.

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1. Mya Soe Soe Moe, Win Mar Oo, "Hybrid Feature Selection for Anomaly Detection in IoT Network Intrusion Detection Systems", Advances in Science, Technology and Engineering Systems Journal, vol. 11, no. 2, pp. 17–29, 2026. doi: 10.25046/aj110203
2. Nnamdi Chimaobi Ezenwegbu, Austin Ikechukwu Gbasouzor, Augustine Azabaze Akaho, Ogochukwu Clementina Okeke, Chebet Evaline Langat, "Economic Replacement of Plants and Equipment: A Decision-Making Framework in Engineering", Advances in Science, Technology and Engineering Systems Journal, vol. 10, no. 5, pp. 20–32, 2025. doi: 10.25046/aj100503
3. Amalia Rodriguez Espinoza de los Monteros, Maximo Giovani Tandazo Espinoza, Byron Ivan Punina Cordova, Ronald Eduardo Tandazo Vanegas, "IoT and Business Intelligence Based Model Design for Liquefied Petroleum Gas (LPG) Distribution Monitoring", Advances in Science, Technology and Engineering Systems Journal, vol. 9, no. 4, pp. 79–92, 2024. doi: 10.25046/aj090409
4. Stefania Nanni, Massimo Carboni, Gianluca Mazzini, "From Sensors to Data: Model and Architecture of an IoT Public Network", Advances in Science, Technology and Engineering Systems Journal, vol. 9, no. 4, pp. 14–20, 2024. doi: 10.25046/aj090403
5. Sheikh Tareq Ahmed, Annamalai Annamalai, Mohamed Chouikha, "Strengthening LoRaWAN Servers: A Comprehensive Update with AES Encryption and Grafana Mapping Solutions", Advances in Science, Technology and Engineering Systems Journal, vol. 9, no. 1, pp. 33–41, 2024. doi: 10.25046/aj090104
6. Nizar Sakli, Chokri Baccouch, Hedia Bellali, Ahmed Zouinkhi, Mustapha Najjari, "IoT System and Deep Learning Model to Predict Cardiovascular Disease Based on ECG Signal", Advances in Science, Technology and Engineering Systems Journal, vol. 8, no. 6, pp. 08–18, 2023. doi: 10.25046/aj080602
7. Abdulwahid Mohammed, Mohamed S. Elbakry, Hassan Mostafa, Abdelhady Abdelazim Ammar, "Doubling the Number of Connected Devices in Narrow-band Internet of Things while Maintaining System Performance: An STC-based Approach", Advances in Science, Technology and Engineering Systems Journal, vol. 8, no. 4, pp. 01–10, 2023. doi: 10.25046/aj080401
8. Chanuka Bandara, Yehan Kodithuwakku, Ashan Sandanayake, R. A. R. Wijesinghe, Velmanickam Logeeshan, "Design and Implementation of an Automated Medicinal-Pill Dispenser with Wireless and Cellular Connectivity", Advances in Science, Technology and Engineering Systems Journal, vol. 8, no. 3, pp. 161–169, 2023. doi: 10.25046/aj080318
9. Emily Holt, Casey Corrado, "Emerging Trends in Green Best Practices and the Impact on Government Policy", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 6, pp. 212–221, 2022. doi: 10.25046/aj070623
10. Marzouk Hassan, Abdelmajid Badri, Aicha Sahel, "A Self-Adaptive Routing Algorithm for Real-Time Video Transmission in VANETs", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 5, pp. 91–101, 2022. doi: 10.25046/aj070512
11. Jérémy Quignon, Anthony Tornambe, Thibaut Deleruyelle, Philippe Pannier, "Antenna System Design To Increase Power Transfer Efficiency with NFC Wireless Charging Technology", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 3, pp. 123–128, 2022. doi: 10.25046/aj070314
12. Ming Fong Sie, Jingze Wu, Seth Austin Harding, Chien-Lung Lin, San-Tai Wang, Shih-wei Liao, "Secured Multi-Layer Blockchain Framework for IoT Aggregate Verification", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 3, pp. 106–115, 2022. doi: 10.25046/aj070312
13. Afsah Sharmin, Farhat Anwar, S M A Motakabber, Aisha Hassan Abdalla Hashim, "A Secure Trust Aware ACO-Based WSN Routing Protocol for IoT", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 3, pp. 95–105, 2022. doi: 10.25046/aj070311
14. Kartit Zaid, Diouri Ouafaa, "Taxonomy of Security Techniques for Routing Protocols in Mobile Ad-hoc Networks", Advances in Science, Technology and Engineering Systems Journal, vol. 7, no. 2, pp. 25–31, 2022. doi: 10.25046/aj070203
15. Osaretin Eboya, Julia Binti Juremi, "iDRP Framework: An Intelligent Malware Exploration Framework for Big Data and Internet of Things (IoT) Ecosystem", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 5, pp. 185–202, 2021. doi: 10.25046/aj060521
16. Piwai Chikasha, Kemlall Ramdass, Ndivhuwo Ndou, Rendani Maladzhi, Kgabo Mokgohloa, "Industrial Engineers of the Future – A Concept for a Profession that is Evolving", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 4, pp. 72–79, 2021. doi: 10.25046/aj060409
17. Antonio Casquero Jiménez, Jorge Pérez Martínez, "Remote Patient Monitoring Systems with 5G Networks", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 4, pp. 44–51, 2021. doi: 10.25046/aj060406
18. Niranjan Ravi, Mohamed El-Sharkawy, "Enhanced Data Transportation in Remote Locations Using UAV Aided Edge Computing", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 1091–1100, 2021. doi: 10.25046/aj0602124
19. Kakoma Chilala Bowa, Mabvuto Mwanza, Mbuyu Sumbwanyambe, Kolay Ulgen, Jan-Harm Pretorius, "Assessment of Electricity Industries in SADC Region Energy Diversification and Sustainability", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 894–906, 2021. doi: 10.25046/aj0602102
20. Murtadha Arif Bin Sahbudin, Chakib Chaouch, Salvatore Serrano, Marco Scarpa, "Application-Programming Interface (API) for Song Recognition Systems", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 846–859, 2021. doi: 10.25046/aj060298
21. Athanasios Tziouvaras, Georgios Dimitriou, Michael Dossis, Georgios Stamoulis, "Frequency Scaling for High Performance of Low-End Pipelined Processors", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 763–775, 2021. doi: 10.25046/aj060288
22. Mochammad Haldi Widianto, Ari Purno Wahyu, Dadan Gusna, "Prototype Design Internet of Things Based Waste Management Using Image Processing", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 709–715, 2021. doi: 10.25046/aj060282
23. Bismark Tei Asare, Kester Quist-Aphetsi, Laurent Nana, "Node-Node Data Exchange in IoT Devices Using Twofish and DHE", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 622–628, 2021. doi: 10.25046/aj060271
24. Kuzichkin Oleg R., Vasilyev Gleb S., Surzhik Dmitry I., Kurilov Igor A., "Application of Piecewise Linear Approximation of the UAV Trajectory for Adaptive Routing in FANET", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 559–565, 2021. doi: 10.25046/aj060263
25. Elgaali Elgaali, Majid Akram, "Recycling and Reuse of Wastewater Generated in Car-Washing Facilities", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 521–525, 2021. doi: 10.25046/aj060259
26. Khadija Alaoui, Mohamed Bahaj, "Categorization of RDF Data Management Systems", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 221–233, 2021. doi: 10.25046/aj060225
27. Elgaali Elgaali, Adel Al Wazeer, "Green Blocks Made of Recycled Construction Waste using Recycled Wastewater", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 2, pp. 51–57, 2021. doi: 10.25046/aj060207
28. Shahenaz S. Abou Emira, Khaled Y. Youssef, Mohamed Abouelatta, "Simulated IoT Based Sustainable Power System for Smart Agriculture Environments", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 1030–1039, 2021. doi: 10.25046/aj0601114
29. Amin S. Ibrahim, Khaled Y Youssef, Mohamed Abouelatta, "Traffic Aggregation Techniques for Optimizing IoT Networks", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 509–518, 2021. doi: 10.25046/aj060156
30. Fang-Lin Chao, Wei Zhong Feng, Kaiquan Shi, "Smart Collar and Chest Strap Design for Rescue Dog through Multidisciplinary Approach", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 386–392, 2021. doi: 10.25046/aj060144
31. Vítor Viegas, J. M. Dias Pereira, Pedro Girão, Octavian Postolache, "Study of latencies in ThingSpeak", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 342–348, 2021. doi: 10.25046/aj060139
32. Zainatul Yushaniza Mohamed Yusoff, Mohamad Khairi Ishak, Kamal Ali Alezabi, "The Role of RFID in Green IoT: A Survey on Technologies, Challenges and a Way Forward", Advances in Science, Technology and Engineering Systems Journal, vol. 6, no. 1, pp. 17–35, 2021. doi: 10.25046/aj060103
33. Afsah Sharmin, Farhat Anwar, S M A Motakabber, "Efficient and Scalable Ant Colony Optimization based WSN Routing Protocol for IoT", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 1710–1718, 2020. doi: 10.25046/aj0506204
34. Hasan Tariq, Abderrazak Abdaoui, Farid Touati, Mohammad Abdullah Al Hitmi, Damiano Crescini, Adel Ben Mnaouer, "Real-time Gradient-Aware Indigenous AQI Estimation IoT Platform", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 1666–1673, 2020. doi: 10.25046/aj0506198
35. Nesma N. Gomaa, Khaled Y. Youssef, Mohamed Abouelatta, "On Design of IoT-based Power Quality Oriented Grids for Industrial Sector", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 1634–1642, 2020. doi: 10.25046/aj0506194
36. Sule Adeniyi Olaniyan, "Impact of Changing Microstructural Compositions of Lime Based Mortar on Flexibility: Case Study of Sustainable Lime-Cement Composites", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 1488–1498, 2020. doi: 10.25046/aj0506179
37. Nour Najeeb Abdalkareem Qubbaj, Anas Abu Taleb, Walid Salameh, "LEACH Based Protocols: A Survey", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 1258–1266, 2020. doi: 10.25046/aj0506150
38. Clara Silveira, Leonilde Reis, Vitor Santos, Henrique S. Mamede, "Creativity in Prototypes Design and Sustainability – The case of Social Organizations", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 1237–1243, 2020. doi: 10.25046/aj0506147
39. Marouane EL Midaoui, Mohammed Qbadou, Khalifa Mansouri, "A Novel Approach of Smart Logistics for the Health-Care Sector Using Genetic Algorithm", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 1143–1152, 2020. doi: 10.25046/aj0506138
40. Mohammed Hadwan, Rehan Uallah Khan, Khalil Ibrahim Mohammad Abuzanouneh, "Towards a Smart Campus for Qassim University: An Investigation of Indoor Navigation System", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 831–837, 2020. doi: 10.25046/aj050699
41. Naeem Ahmed Haq Nawaz, Hamid Raza Malik, Ahmed Jaber Alshaor, Kamran Abid, "A Simulation Based Proactive Approach for Smart Capacity Estimation in the Context of Dynamic Positions and Events", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 423–438, 2020. doi: 10.25046/aj050651
42. Yevhen Fediv, Olha Sivakova, Mykhailo Korchak, "Multi Operated Virtual Power Plant in Smart Grid", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 6, pp. 256–260, 2020. doi: 10.25046/aj050630
43. Muhammad Usman Ali Khan, Raad Raad, Javad Foroughi, "Transient Response & Electromagnetic Behaviour of Flexible Bow-Tie Shaped Chip-less RFID Tag for General IoT Applications", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 757–764, 2020. doi: 10.25046/aj050592
44. Ines Kechiche, Ines Bousnina, Abdelaziz Samet, "A Review of RPL Objective Function based Enhancement Approaches", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 201–211, 2020. doi: 10.25046/aj050525
45. Liana Khamis Qabajeh, Mohammad Moustafa Qabajeh, "Detailed Security Evaluation of ARANz, ARAN and AODV Protocols", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 5, pp. 176–192, 2020. doi: 10.25046/aj050523
46. Deepti Sehrawat, Nasib Singh Gill, "IoT Based Human Activity Recognition System Using Smart Sensors", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 516–522, 2020. doi: 10.25046/aj050461
47. Pratik Kumar Singh, Fadillah Binti Ismail, Chan Shiau Wei, Muhammad Imran, Syed Ashfaq Ahmed, "A Framework of E-Procurement Technology for Sustainable Procurement in ISO 14001 Certified Firms in Malaysia", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 424–431, 2020. doi: 10.25046/aj050450
48. Sarun Duangsuwan, Chakree Teekapakvisit, Myo Myint Maw, "Development of Soil Moisture Monitoring by using IoT and UAV-SC for Smart Farming Application", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 381–387, 2020. doi: 10.25046/aj050444
49. Erika Barojas-Payán, Diana Sánchez-Partida, Miguel-Josué Heredia-Roldan, Victorino Juárez-Rivera, Jesús Medina-Cervantes, "Logistics Solutions in the Preparation Phase for the Appearance of Disasters", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 323–330, 2020. doi: 10.25046/aj050438
50. Bambang Leo Handoko, Ang Swat Lin Lindawati, "The Importance of Sustainability Audit Report in Go Public Companies Sector, in Indonesia", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 217–222, 2020. doi: 10.25046/aj050427
51. Olayan Alharbi, "Industry 4.0 Operators: Core Knowledge and Skills", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 177–183, 2020. doi: 10.25046/aj050421
52. Kanishk Rai, Keshav Kumar Thakur, Preethi K Mane, Narayan Panigrahi, "Design of an EEG Acquisition System for Embedded Edge Computing", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 119–129, 2020. doi: 10.25046/aj050416
53. Maximo Giovani Tanzado Espinoza, Joseline Roxana Neira Melendrez, Luis Antonio Neira Clemente, "A Survey and an IoT Cybersecurity Recommendation for Public and Private Hospitals in Ecuador", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 3, pp. 518–528, 2020. doi: 10.25046/aj050364
54. Noraziah Adzhar, Yuhani Yusof, Muhammad Azrin Ahmad, "A Review on Autonomous Mobile Robot Path Planning Algorithms", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 3, pp. 236–240, 2020. doi: 10.25046/aj050330
55. Mutuma Ichaba, Felix Musau, Simon Nyaga Mwendia, "Performance Effects of Algorithmic Elements in Selected MANETs Routing Protocols", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 3, pp. 62–71, 2020. doi: 10.25046/aj050309
56. Rajesh Kannan Megalingam, Santosh Tantravahi, Hemanth Sai Surya Kumar Tammana, Nagasai Thokala, Hari Sudarshan Rahul Puram, Naveen Samudrala, "ROS Based Multimode Control of Wheeled Robot", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 2, pp. 688–696, 2020. doi: 10.25046/aj050285
57. Lylia Alouache, Mohamed Maachaoui, Rachid Chelouah, "Securing Hybrid SDN-based Geographic Routing Protocol using a Distributed Trust Model", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 2, pp. 567–577, 2020. doi: 10.25046/aj050271
58. Ricardo Simões Santos, António João Pina da Costa Feliciano Abreu, Joaquim José Rodrigues Monteiro, "Using Metaheuristics-Based Methods to Provide Sustainable Market Solutions, Suitable to Consumer Needs", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 2, pp. 399–410, 2020. doi: 10.25046/aj050252
59. ?ahin Aydin, Mehmet Nafiz Aydin, "A Sustainable Multi-layered Open Data Processing Model for Agriculture: IoT Based Case Study Using Semantic Web for Hazelnut Fields", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 2, pp. 309–319, 2020. doi: 10.25046/aj050241
60. Mihaela Balanescu, George Suciu, Marius-Alexandru Dobrea, Cristina Balaceanu, Radu-Ioan Ciobanu, Ciprian Dobre, Andrei-Cristian Birdici, Andreea Badicu, Iulia Oprea, Adrian Pasat, "An Algorithm to Improve Data Accuracy of PMs Concentration Measured with IoT Devices", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 2, pp. 180–187, 2020. doi: 10.25046/aj050223
61. Nour Mostafa, "Resource Selection Service Based on Neural Network in Fog Environment", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 1, pp. 408–417, 2020. doi: 10.25046/aj050152
62. Amina Bengag, Asmae Bengag, Mohamed Elboukhari, "Routing Protocols for VANETs: A Taxonomy, Evaluation and Analysis", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 1, pp. 77–85, 2020. doi: 10.25046/aj050110
63. Aref Hassan Kurd Ali, Halikul Lenando, Mohamad Alrfaay, Slim Chaoui, Haithem Ben Chikha, Akram Ajouli, "Performance Analysis of Routing Protocols in Resource-Constrained Opportunistic Networks", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 6, pp. 402–413, 2019. doi: 10.25046/aj040651
64. Amine Kardi, Rachid Zagrouba, "Attacks classification and security mechanisms in Wireless Sensor Networks", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 6, pp. 229–243, 2019. doi: 10.25046/aj040630
65. Samruan Wiangsamut, Phatthanaphong Chomphuwiset, Suchart Khummanee, "Chatting with Plants (Orchids) in Automated Smart Farming using IoT, Fuzzy Logic and Chatbot", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 5, pp. 163–173, 2019. doi: 10.25046/aj040522
66. Alghamdi Abdullah, Mohammed Thanoon, Anwar Alsulami, "Toward a Smart Campus Using IoT: Framework for Safety and Security System on a University Campus", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 5, pp. 97–103, 2019. doi: 10.25046/aj040512
67. Anang Hudaya Muhamad Amin, Nazrul Muhaimin Ahmad, Subarmaniam Kannan, "Event Monitoring using Distributed Pattern Recognition Approach on Integrated IoT-Blockchain Network", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 4, pp. 256–264, 2019. doi: 10.25046/aj040432
68. Tlija Amira, Istrate Dan, Badii Atta, Gattoufi Said, Bennani Az-eddine, Wegrzyn-Wolska Katarzyna, "Stress Level Classification Using Heart Rate Variability", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 3, pp. 38–46, 2019. doi: 10.25046/aj040306
69. Amir Rizaan Rahiman, Md. Ashikul Islam, Md. Noor Derahman, "Resourceful Residual Energy Consumption in TDMA Scheduling for IoT-based Wireless Sensor Network", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 3, pp. 31–37, 2019. doi: 10.25046/aj040305
70. James Robert Pogge, Stephen lucien Scott, "Enabling the Edge – A method for dynamic virtualizable connections for 5G deployments", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 2, pp. 270–279, 2019. doi: 10.25046/aj040235
71. Halikul Lenando, Mohamad Alrfaay, Haithem Ben Chikha, "Multiple Social Metrics Based Routing Protocol in Opportunistic Mobile Social Networks", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 2, pp. 176–182, 2019. doi: 10.25046/aj040223
72. Imtiaz Parvez, Arif I. Sarwat, "A Spectrum Sharing based Metering Infrastructure for Smart Grid Utilizing LTE and WiFi", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 2, pp. 70–77, 2019. doi: 10.25046/aj040209
73. Shruthi Narayanaswamy, Anitha Vijaya Kumar, "Application Layer Security Authentication Protocols for the Internet of Things: A Survey", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 1, pp. 317–328, 2019. doi: 10.25046/aj040131
74. Rufus Ogbuka Chime, Odo Fidelis O, "Analysis of Garri Frying Machine Manufacturing in Nigeria: Design Innovation", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 6, pp. 403–411, 2018. doi: 10.25046/aj030648
75. Lin Dong, Akira Rinoshika, "Analysis and Methods on The Framework and Security Issues for Connected Vehicle Cloud", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 6, pp. 105–110, 2018. doi: 10.25046/aj030611
76. Francesco Nucci, "Fuzzy Uncertainty Management in Multi-Shift Single-Vehicle Routing Problem", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 6, pp. 33–45, 2018. doi: 10.25046/aj030603
77. Maysoon Abdullah Mansor, "Economic and Social Sustainability for Iraqi Middle Provinces", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 5, pp. 447–453, 2018. doi: 10.25046/aj030551
78. Christina Wirawan, Gatot Yudoko, Yuliani Dwi Lestari, "Developing A Conceptual Framework of Product-Service System Management Toward Firms’ Sustainability for Indonesian Industrial Estate Firms", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 5, pp. 128–139, 2018. doi: 10.25046/aj030517
79. Helen Hasenfuss, Muftah Fraifer, Sameer Kharel, Asma Elmangoush, Alan Ryan, Walid Elgenaidi, "It Takes Two to Tango: Merging Science and Creativity to Support Continued Innovation in the IoT Domain", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 5, pp. 82–91, 2018. doi: 10.25046/aj030511
80. Michael J. Hirsch, Azar Sadeghnejad, "Minimizing Time Delay of Information Routed Across Dynamic Temporal Sensor Networks", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 4, pp. 327–340, 2018. doi: 10.25046/aj030433
81. Kun Zhang, Liu Liu, Cheng Tao, Ke Zhang, Ze Yuan, Jianhua Zhang, "Wireless Channel Measurement and Modeling in Industrial Environments", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 4, pp. 254–259, 2018. doi: 10.25046/aj030425
82. Vittorio Miori, Dario Russo, Luca Ferrucci, "Supporting Active Aging Through A Home Automation Infrastructure for Social Internet of Things", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 4, pp. 173–186, 2018. doi: 10.25046/aj030415
83. Akbor Aziz Susom, "Effectiveness of Routing Protocols for Different Networking Scenarios", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 4, pp. 112–121, 2018. doi: 10.25046/aj030412
84. Moises Levy, Daniel Raviv, "An Overview of Data Center Metrics and a Novel Approach for a New Family of Metrics", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 2, pp. 238–251, 2018. doi: 10.25046/aj030228
85. Tanveer Ahmed, Muhammad Kaleem, Khurram Saleem Alimgeer, Mustafa Shakir, Sajid Nazir, "Optimization of Depth-Based Routing for Underwater Wireless Sensor Networks through Intelligent Assignment of Initial Energy", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 3, pp. 1799–1803, 2017. doi: 10.25046/aj0203219
86. Davar Pishva, "IoT: Their Conveniences, Security Challenges and Possible Solutions", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 3, pp. 1211–1217, 2017. doi: 10.25046/aj0203153
87. Stefania Nanni, Elisa Benetti, Gianluca Mazzini, "Indoor monitoring in Public Buildings: workplace wellbeing and energy consumptions. An example of IoT for smart cities application", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 3, pp. 884–890, 2017. doi: 10.25046/aj0203110
88. Muftah Fraifer, Mikael Fernström, "Designing a Smart Car Parking System (PoC) Prototype Utilizing CCTV Nodes: A vision of an IoT parking system via UCD process", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 3, pp. 755–764, 2017. doi: 10.25046/aj020396
89. Zineb Squalli Houssaini, Imane Zaimi, Mohammed Oumsis, Saïd El Alaoui Ouatik, "GPSR+Predict: An Enhancement for GPSR to Make Smart Routing Decision by Anticipating Movement of Vehicles in VANETs", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 3, pp. 137–146, 2017. doi: 10.25046/aj020318
90. Jorge Oliveira e Sá, João Cacho Sá, José Luís Pereira, Francisco Pimenta, Manuel Monteiro, "Internet of Things: An Evolution of Development and Research area topics", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 1, pp. 240–247, 2017. doi: 10.25046/aj020129
91. Omina Mezghani, Pr. MAHMOUD ABDELLAOUI, "Mobi-Sim: An Emulation and Prototyping Platform for Protocols Validation of Mobile Wireless Sensors Networks", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 1, pp. 108–120, 2017. doi: 10.25046/aj020113