Data prioritization of heterogeneous data in wireless sensor networks gives meaning to mission-critical data that are time-sensitive as this may be a matter of life and death. However, the standard IEEE 802.15.4 does not consider the prioritization of data. Prioritization schemes proffered in the literature have not adequately addressed this issue as proposed schemes either uses a single or complex backoff algorithm to estimate backoff time-slots for prioritized data. Subsequently, the carrier sense multiple access with collision avoidance scheme exhibits an exponentially increasing range of backoff times. These approaches are not only inefficient but result in high latency and increased power consumption. In this article, the concept of class of service (CS) was adopted to prioritize heterogeneous data (real-time and non-real-time), resulting in an optimized prioritized backoff MAC scheme called Class of Service Traffic Priority-based Medium Access Control (CSTP-MAC). This scheme classifies data into high priority data (HPD) and low priority data (LPD) by computing backoff times with expressions peculiar to the data priority class. The improved scheme grants nodes the opportunity to access the shared medium in a timely and power-efficient manner. Benchmarked against contemporary schemes, CSTP-MAC attained a 99% packet delivery ratio with improved power saving capability, which translates to a longer operational lifetime.

Routing Protocol for Low-power and Lossy Network (RPL) with IPv6 support is the de facto standard for routing over Low-power and Lossy Network (LLN) called Wireless Sensor Networks (WSN). Objective Functions namely Minimum Rank with Hysteresis Objective Function (MRHOF) and Objective Function Zero (OF0) together with the Internet Control Messaging Protocol (ICMP) control messages, propel RPL to constructs routing paths called Destination Oriented Directed Acyclic Graph (DODAG), for packet routing within the sensor network. To the best knowledge of the authors, no detailed investigation has been carried out to unravel the dynamics of how these control messages impact on the network with respect to DODAG formation right before convergence is attained, particularly from the perspective of MRHOF and OF0. In this paper, the authors investigated the various types of control messages (DODAG Information Solicitation, DODAG Information Object and Destination Advertisement Object) used to setup the DODAG. RPL Simulations was carried out on one 100 nodes, starting with 10 nodes and incremented by 10 until the 100th node. Each simulation was repeated five times with a confidence level of 95%, which statistically signifies a reliable and acceptable confidence interval, for a duration of thirty minutes. Results obtained showed that the DIS–OF0 generated the least packets with 125 packets, followed by DAO–MRHOF with 1357 packets and the most generated; DIO–MRHOF with 1536 packets. Subsequently, MRHOF had higher Convergence Time of 130.67 seconds as to 87.93 seconds for OF0. This information is valuable for both the academia and industry.

Wireless Sensor Network (WSN) is gradually taking the center stage as the foundation network for the Internet of Things (IoT), however, it is not without its challenges. These include lossy wireless links, limited processing capacity, limited memory and storage, and most importantly limited battery life. These limitations are inherent because of the small form-factor of a sensor device, which could be as small as a finger-tip. Of these challenges, the most pressing is limited battery life or power as this directly affects the network lifetime. Subsequently, the standard WSN architecture presents only one sink, serving as a gateway between the local sensor network, and the external network which in most cases is the Internet. The demanding pressure of routing data through a single sink makes it a Single Point of Failure (SOF), this means if the sink goes down, the entire network goes down. This practice is not only suboptimal but inefficient. In this paper, we propose a Multi-Sink Load Balancing Mechanism (MSLBM) for WSN. This mechanism adaptively and dynamically assigns network loads or traffic to sinks with less instantaneous operational tasks. It operates in such a way that no sink is overloaded with tasks which may result in early battery drainage and inadvertently untimely shutdown of the network. MSLBM was benchmark with related mechanisms with an outstanding performance of 98% Packet Delivery Ratio (PDR), least Latency of 21.7ms, and least Power Consumption of 1.6mW. This, therefore, presents MSLBM as an optimal energy-efficient load-balancing mechanism for a prolonged operational lifetime.

Routing Protocol for Low-power and Lossy Networks (RPL), the de facto standard routing protocol for the Internet of Things (IoT) administers the smooth transportation of data packets across the Wireless Sensor Network (WSN). However, the mechanism fails to address the heterogeneous nature of data packets traversing the network, as these packets may carry different classes of data with different priority statuses, some real-time (time-sensitive) while others non-real-time (delay-tolerant). The standard Objective Functions (OFs), used by RPL to create routing paths, treat all classes of data as the same, this practice is not only inefficient but results in poor network performance. In this article, the Prioritized Shortest Path Computation Mechanism (PSPCM) is proposed to resolve the data prioritization of heterogeneous data and inefficient power management issues. The mechanism prioritizes heterogeneous data streaming through the network into various priority classes, based on the priority conveyed by the data. The PSPCM mechanism routes the data through the shortest and power-efficient path from the source to the destination node. PSPCM generates routing paths that exactly meet the need of the prioritized data. It outperformed related mechanisms with an average of 91.49% PDR, and average power consumption of 1.37mW which translates to better battery saving and prolonged operational lifetime while accommodating data with varying priorities.

The IEEE 802.15.4 standard regulates the operations of low-rate wireless personal area network devices that forms the building block for the Internet of Things (IoT) and the modern-day smart world. Defined in 2003, by the IEEE 802.15 working group, it focuses on the Physical and Data Link Layers; which is further sub-layered into the Medium Access Control (MAC) and Logical Link Control (LLC) sub-layers. Of all things in the IEEE 802.15.4 domain, power is the most paramount as IEEE 802.15.4 devices are power constrained due to their small form factor and independent power nature, hence it become imperative to effectively manage the scarce power in other to prolong the operational lifetime of the Wireless Sensor Network (WSN). While managing the power of IEEE 802.15.4 devices, a poorly designed MAC protocols may lead to early power drainage and hence shortened operational lifetime. In this paper, four MAC implementations have been analysed and compare with respect to power efficiency; the ContikiMAC, CXMAC, XMAC and NullMAC. With 95% statistical confidence level, results show that ContikiMAC is the most power efficient MAC, which consumes the least of all three power-consuming operations, Low Power Mode (LPM) or sleep mode - 0.154 milliwatts, Listen - 0.4 milliwatts and Transmit - 0.36 milliwatts. Other forms of MAC exceed one milliwatts for all forms of power consuming operations. Detailed analysis on the subject matter is presented in this paper.

The quest for faster and seamless mobile communication services has given rise to the Fifth Generation (5G) mobile communication technology. 5G presents enormous opportunities for end-users, businesses, and the global economy at large. The major edge 5G has over its predecessor technologies; the 3rd and 4th Generation (3G and 4G), is ultra-low latency, which translates to ultra-fast data speed, which has the potential to peak to about 1Gbps at the frequency range of 6GHz to 100GHz. Its application of Multiple Input, Multiple Output (MIMO) antenna technology enables it to beam dedicated radio signals to 5G enabled User Equipment (UEs) using the advanced beam steering algorithm. This results in maximum data throughput with ultra-high bandwidth transmission to end-user applications resulting in new and never-seen-before business opportunities across different industries, investments, and business services. By expanding the scope of wireless technologies and making devices more autonomous, 5G has the potential to reduce carbon emission footprint and encourage greener communities. The application of 5G cuts across all spheres of our lives; industry, transportation, education, communications, health, and businesses. 5G is more inclusive, progressive, proven, and powerful than any of its predecessor generations of communications technology. With more power and improved efficiency, 5G is on its way to creating a truly connected world.