A comparative node evaluation model for highly heterogeneous massive‐scale Internet of Things‐Mist networks

Internet of Things (IoT) is a new technology that is driving the connection of billions of devices around the world. Because these devices are often resource‐constrained and very heterogeneous, this presents unique challenges. To address some of these challenges, new paradigms of Edge and Fog are emerging to bring computational resources of the IoT networks from remote devices like cloud closer to the end‐devices. Mist computing is a new paradigm that attempts to make use of the more resource‐rich nodes that are closer than Edge nodes to end‐users. Since these nodes might have enough resources to host services, execute tasks or even run containers, the utilization of network resources might be improved, and delay reduced by utilizing these nodes. The nodes must, therefore, be assessed to determine which nodes should offer resources to other nodes based on their situation. In this article, a new comparative assessment model for ranking Mist nodes in highly heterogeneous massive‐scale IoT networks in order to discover nodes that can offer their resources is proposed. The Mist nodes are evaluated based on parameters like resources, connections, applications, and environmental parameters to heuristically compare the neighbors with a novel learning‐to‐rank method to predict a suitability score for each node. The most suitable neighbor is then selected based on the score, with load balancing accomplished by a second chance method. When evaluating the performance, the results show that the proposed method succeeds in identifying resource‐rich nodes, while considering the selection of other nodes.publishedVersio

Enhanced Resource Discovery Mechanisms for Unstructured Peer-to-Peer Network Environments

This study explores novel methods for resource discovery in unstructured peerto-peer (P2P) networks. The objective of this study is to develop a lightweight resource discovery mechanism suitable to be used in unstructured P2P networks. Resource discovery techniques are examined and implemented in a simulator with high scalability in order to imitate real-life P2P environments. Simulated topology generator models are reviewed and compared, the most suitable topology generator model is then chosen to test the novel resource discovery techniques. Resource discovery techniques in unstructured P2P networks usually rely on forwarding as many query messages as possible onto the network. Even though this approach was able to return many resources, the flooding of the network with query messages have an adverse effect on the network. Flooding the network has undesirable consequences such as degenerative performance of the network, waste of network resources, and network downtime. This study has developed alpha multipliers, a method of controlling query message forwarding to deal with the flooding effect of most resource discovery techniques in unstructured P2P networks. The combination of alpha multipliers and breadth-first search (BFS), ↵-BFS, was able to avoid the flooding effect that usually occurs with BFS. The ↵-BFS technique also increases the combined query efficiency compared to the original BFS. Aside from improving a uninformed search technique such as the BFS, this study also examines the network communication cost of several informed resource discovery techniques. Several issues that arise in informed resource discovery techniques, such as false positive errors, and high network communication costs for queries to update search results are discussed. This detailed analysis forms the basis of a lightweight resource discovery mechanism (LBRDM) that reduces the network communication cost by reducing the number of backward updates inside the network when utilising the blackboard resource discovery mechanism (BRDM). Simulations of BRDM and LBRDM show that the lightweight version can also return an almost identical combined query efficiency than the BRDM. The solution to control query message forwarding in ↵-BFS, and the removal of unnecessary exchange of information in LBRDM open a new perspective on simplifying resource discovery techniques. These approaches can be implemented on other techniques to improve the performance of resource discovery