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Abstract. In a multihop Wireless Sensor Network (WSN), a salient point among routing protocols that do not depend on network topology and existence of neighboring nodes is the need to know sensor node's geographical location with respect to the sink node. This is obtained by some means like Global Positioning System (GPS) and localization techniques. In a prior work, we have proposed RSSI-based Forwarding (RBF) protocol that works without knowledge of node's location by using a Received Signal Strength Indicator (RSSI) level of beacon signals transmitted by the sink. Through contention, a next-hop node is determined among the forwarding candidates using a timer-based suppression scheme. We propose an improvement of the suppression scheme in which a contender closer to the sink is favored with a higher probability for being selected as a next-hop node. By means of simulation, it is shown that the performance of RBF is significantly improved using the enhanced mechanism.

Fog computing (FC) is an evolving computing technology that operates in a distributed environment. FC aims to bring cloud computing features close to edge devices. The approach is expected to fulfill the minimum latency requirement for healthcare Internet-of-Things (IoT) devices. Healthcare IoT devices generate various volumes of healthcare data. This large volume of data results in high data traffic that causes network congestion and high latency. An increase in round-trip time delay owing to large data transmission and large hop counts between IoTs and cloud servers render healthcare data meaningless and inadequate for end-users. Time-sensitive healthcare applications require real-time data. Traditional cloud servers cannot fulfill the minimum latency demands of healthcare IoT devices and end-users. Therefore, communication latency, computation latency, and network latency must be reduced for IoT data transmission. FC affords the storage, processing, and analysis of data from cloud computing to a network edge to reduce high latency. A novel solution for the abovementioned problem is proposed herein. It includes an analytical model and a hybrid fuzzy-based reinforcement learning algorithm in an FC environment. The aim is to reduce high latency among healthcare IoTs, end-users, and cloud servers. The proposed intelligent FC analytical model and algorithm use a fuzzy inference system combined with reinforcement learning and neural network evolution strategies for data packet allocation and selection in an IoT–FC environment. The approach is tested on simulators iFogSim (Net-Beans) and Spyder (Python). The obtained results indicated the better performance of the proposed approach compared with existing methods.