Toward Interference Aware IoT Framework: Energy and Geo-Location-Based-Modeling

Abstract

In multi-hop wireless communication, a sensor node should consume its energy efficiently for relaying of data packets. However, most IoT-devices are equipped with limited battery power and computing resources for wireless communications, and thus energy optimization becomes one of the major concerns in wireless sensors routing design. The wireless technologies usually use unlicensed frequency bands of 2.4 GHz to transmit the data. Due to the broadcasting medium, the wireless transmission interferes with the reception of surrounding radios. As a result, data transmission failure increases resulting in low-communication quality. Therefore, one of the best solutions to this problem is to select the hop distance node that has a few neighbor nodes to disseminate packets until it reaches the ultimate receiver. The proposed routing selects the node that has few neighboring nodes and thus less interference. In another word, the scheme finds a better load balancing, and thus minimizes the probability of overload on a sensor node. It also introduces a new clustering algorithm around a single base station to shorten the transmission distances. This approach periodically selects the cluster heads (CHs) according to its location based distance from the final destination. The extensive simulation studies reveal that the proposed algorithm finds the best routing node and clustering formation to forward the traffic and thereby minimizes the interference ratio. In addition, the proposed protocol achieves low-energy consumption and longer network lifetime than other popular protocols. INDEX TERMS Internet of Things (IoT), routing protocol, path selection, link quality, green computing, link reliability, interference, wireless sensor network (WSN).

Physical Layer Security in Vehicular Communication Networks in the Presence of Interference

“…drEdgE. (27) Comparing (27) and (14), shows that both expressions are similar with maximum distance R = r max and source power P s . Thus, using (20), M χE (z) = M ψ (zP s |r max ) and we obtain from (20) and (26) the expression…”

“…The significance of investigating the physical layer security in a vehicular network is crucial due to rapid advancements towards autonomous vehicles and smart/cognitive transportation networks to minimise the risk from compromise. It has however been observed that the effect of interference on the physical layer security has received much less attention, even though interference is inherent within shared networks [14] and has been shown to affect the secrecy performance. For example, in [15] the effect of interference on the secrecy capacity of a cognitive radio (CR) network was examined, while in [16], the effect of interference on secrecy outage probability was considered in a Rayleigh faded channel.…”

Towards Improved Clustering and Routing Protocol for Wireless Sensor Networks

“…This protocol is based on the neuro-fuzzy inference system to decide the CH and neural network for data routing decisions. Another load balancing and routing based on energy and geolocation-based modeling (EGBM) protocol is proposed in [45]. This protocol attempts to handle interference issues that result in a drop in the number of packets to the BS.…”

W-GUN: Whale Optimization for Energy and Delay-Centric Green Underwater Networks

“…The widely growing application domains for underwater sensor networks (UWSNs) have attracted potential attention in R&D from the Internet of Things (IoT)-oriented industries and academia [1][2][3]. The growing domains include border security-centric military applications [4,5], energy and cost-centric…”