There is a large number of connected devices in the Internet of Things (IoT) networks that are typically several orders of magnitude bigger than enterprise networks and campus networks. The exponential increase in the number of interconnected smart devices is expected to exceed 60 billion smart objects shortly. The requirements needed for IoT networks are scalability, low power consumption, and simplified routing and security protocols. IoT networks are also heterogeneous, composing different types of networks together. Legacy network protocols like IPv4 has deemed to be inefficient for IoT networks. As the number of IPv4 addresses is almost consumed with regular network devices, we propose the use of IPv6 addressing for IoT-connected devices as IPv4 cannot accommodate the scalability requirement of IoT. IPv6 provides extended address space and enhanced mobility which are very essential for IoT networks. In this research, we apply IPv6 to IoT networks to avoid the scalability bottleneck of the IPv4 subnet. IPv6 accommodates a large number of connected devices and solves issues resulting from the heterogeneous nature and access methods of IoT devices. However, IPv6 is a large protocol that does not suit itself well in the IoT world. The Maximum Transmission Unit (MTU) permitted for IEEE 802.15.4 MAC data frames with their encapsulated IPv6 packet is limited to 127 bytes. We need 40 bytes for the uncompressed IPv6 header and 8 bytes are needed for the uncompressed UDP header. As a result, there are either 54 bytes left for the payload when security is not considered or 33 bytes when security is considered. We investigate throughput improvement for IoT networks by applying adaptation to IPv6 through header compression with UDP header compression. We also apply fragmentation for MAC frames that exceed the 127 bytes MTU limit. Simulation results showed that IPv6 compression with or without fragmentation serves toward adapting IPv6 packets to IoT networks. In the case of applying fragmentation, the technique of fragment forwarding greatly enhances the performance. Network traffic within the same network can be compressed to 2 bytes. On leaving the 802.15.4 network, the header increases to 12 bytes if the network prefix is known or to 20 bytes if the network prefix is unknown. Two evaluation metrics, namely, Compression Gain and Packet Delivery Ratio were applied to our proposed implementation method to prove the validity and the efficiency of our proposal.

In today’s digital age, there is an increasing demand for integrated wireless and wired technologies; however, there is a difficulty in achieving secure and reliable communications within buildings and facilities. This paper presents a proposal for maintaining the infrastructure while expanding it to implement communication technologies with high transmission and reception speeds and high levels of data confidentiality to enhance the operational efficiency of organizations. Three main technologies have emerged as promising solutions for this purpose: Wi-Fi, Li-Fi, and BPL. Despite the advantages that each technology offers, some drawbacks appear in these technologies that affect data transmission. Wi-Fi, Li-Fi, and BPL can be combined to achieve maximum security and reduce noise and interference via the ESP8266 module. This combination could be an important step toward achieving an integrated and secure indoor communication system. The paper provides a comprehensive overview of the three techniques and how they are applied in practice. In addition, OSE, ABPF, and ASBF filters are used to detect and eliminate interference and attack secure internal networks.

Wireless sensor networks (WSNs) are an advanced solution for data collection in Internet of Things (IoT) applications and remote and harsh environments. These networks rely on a collection of distributed sensors equipped with wireless communication capabilities to collect low-cost and small-scale data. WSNs face numerous challenges, including network congestion, slow speeds, high energy consumption, and a short network lifetime due to their need for a constant and stable power supply. Therefore, improving the energy efficiency of sensor nodes through solar energy harvesting (SEH) would be the best option for charging batteries to avoid excessive energy consumption and battery replacement. In this context, modern wireless communication technologies, such as Wi-Fi and Li-Fi, emerge as promising solutions. Wi-Fi provides internet connectivity via radio frequencies (RF), making it suitable for use in open environments. Li-Fi, on the other hand, relies on data transmission via light, offering higher speeds and better energy efficiency, making it ideal for indoor applications requiring fast and reliable data transmission. This paper aims to integrate Wi-Fi and Li-Fi technologies into the SEH-WSN architecture to improve performance and efficiency when used in all applications. To achieve reliable, efficient, and high-speed bidirectional communication for multiple devices, the paper utilizes a Markov model, sleep scheduling, and smart switching algorithms to reduce power consumption, increase signal-to-noise ratio (SNR) and throughput, and reduce bit error rate (BER) and latency by controlling the technology and power supply used appropriately for the mode, sleep, and active states of nodes.

JCM is an open access journal on the science and engineering of communication.

Many countries are working to install small solar power plants on residential and local buildings' roofs. In Egypt, numerous public buildings, such as schools, have empty and unutilized roofs that are mostly free from shadows or obstacles. The research suggests the installation of small-scale solar stations on the building’s rooftops. These stations would make use of large portions of the roofs to produce clean electricity, powering the institutions and connecting them to the public electrical grid. The research explores ways to enhance the efficiency of these small-scale independent solar stations by examining various factors such as building dimensions, site coordinates, panel size, roof area available for installation, Sun angles each day, and roof heights. Key factors that affect photovoltaic panels include regional climatic conditions, panel inclination angles, distances between panel rows, solar radiation, and panel temperature. To analyze these variables, the research utilized numerous programs involving artificial intelligence, including PVSYST, Skelion, and Google Earth. These tools helped determine optimal distances between rows and optimal inclination angles for solar panel installation. They also enabled the assessment of climatic factors' impact on panel performance, calculations of the produced electrical energy, and estimations of annual energy production losses. The findings indicated that the optimal inclination angle for solar module ranges from 18 to 22º to minimize shadows’ effects on energy production. Furthermore, the results emphasized the significance of solar radiation, demonstrating a direct relationship between the average monthly sun radiation and the power produced by the panels.