Resource management of IoT edge devices: Challenges, techniques, and solutions

With the growth in the Internet of things (IoT) paradigm, there has been a tremendous makeshift in how the distributed devices work to achieve a common goal. However, it remains essential that all these devices work in a coherent manner to perform a collective action. This makes the task of resource provisioning extremely important in such a paradigm. The end-user level in IoT mostly comprises of low computation and communication powered devices. Improper utilization of the available resources in such a scenario burdens the complete system and degrades the quality of service. In such a scenario, the use of cloud computing techniques can help to manage the resources effectively. More so, with the emergence of relatively newer cloud-based technologies such as edge and fog computing, resource management in the IoT has become far more effective. These technologies bring the computation and communication capabilities closer to the IoT devices where some of the services can be offloaded to the edge devices. These devices are called IoT edge devices and they provide a unique opportunity to tackle some of the existing and pertinent issues for resource management in IoT paradigms; yet at the same time, they face their own set of challenges. However, the use of IoT edge devices in a traditional IoT paradigm results in better utilization of the available resources as well as improving the overall quality of service. Keeping this in mind, this special issue addressed some of the aspects related to resource management in IoT edge devices with the focus on various challenges faced, and potential techniques and solutions to address such challenges by leveraging IoT edge devices. We received numerous submissions in the issue, and we accepted 13 high-quality submissions for publication as a result after following a rigorous review process. Each of the accepted papers is summarized as follows. In the first paper, Khan et al.1 presented “A cache-based approach toward improved scheduling in fog computing” for efficient resource allocation in the fog computing environment, while maintaining the quality of service. The authors use first-in first-out scheme to place the jobs in queue and cache the job type, fog server, arrival time, time to leave, and internal processing time. The jobs are then moved from the queue by the fog broker which selects fog server having sufficient required power and resources to execute the job. The authors’ proposed cache-based scheme showed promising results in terms of reducing the execution time, latency, processing delays and power consumption as compared to the conventional first-come-first-serve and shortest job first policies. The second paper on “Extensive review of cloud resource management techniques in industry 4.0: Issue and challenges” by Dewangan et al.2 sheds light on various types of resource provisioning schemes and classified those into different categories (to help understand them better) on the basis of the underlying technique and their overall objective. This survey helps to understand the optimal schemes for catering to different performance metrics such as time, cost, energy, service level of agreement rate, power consumption, resource utilization, etc. Moreover, the authors also highlighted some of the current research challenges in the domain of resource management. The next paper, “An energy efficient and low overhead fault mitigation technique for internet of thing edge devices reliable on-chip communication” by Ibrahim et al.3 presents a coding scheme to make the network-on-chip fault-tolerant. The network-on-chip provides communication backbone in the underlying network for which the proposed scheme handled both single and multibit adjacent bit errors. The next paper in this issue is on “Design and data analytics of electronic human resource management activities through Internet of Things in an organization” by Nasar et al.4 The authors focus on designing a data analytical human resource management system for IoT devices in an organization for ensuring the policies, strategies, and practices within the organization. The activities covered under this improved system include e-recruitment, e-Selection, e-performance management, e-learning, and e-compensation and the performance of the system was validated on four Kaggle databases. In the fifth paper on “A Mobile Data Offloading Framework based on a Combination of Blockchain and Virtual Voting”, Hassija et al.5 enable mobile users to offload computation tasks to resource-rich mobile-devices in order to reduce energy consumption and enhance performance. The authors used directed acyclic graphs (DAGs) for mobile offloading algorithm where the users can securely submit a transaction (powered by blockchain) request for task offloading a