Optimizing RPL performance based on the selection of best route between child and root node using E-MHOF method

IETF has proposed the routing protocol for low power and lossy networks (RPL) for IOT as view as light weight routing protocol. In RPL, the objective function (OF) is used to select the best route between child and root node. Several researches have been conducted in order to, enhance OF according to number parameters such as number of hops, remaining energy and expected number of transmissions (ETX), without a consideration to other challenges such as congestion node problem and latency. So, to overcome these challenges a new technique called “Enhance-Minimum Rank with Hysteresis Objective Function (MHOF)” is proposed in this paper, to select the ideal path between the child and root node. The technique is consisted of three layers: parent selection layer in which parent is selected based on three parameters (ETX, RSSI and nodes’ residual energy), path selection layer in which the best route is chosen according to the minimum of (average ETX value) and maximum of (average remaining energy value) of all nodes in the selected route. The last layer is child node minimization, which utilized to solve the congestion node energy problem by using two parameters (RSSI reference and threshold value). The proposed method has been implemented and evaluated by using Cooja simulator software. The simulation results have shown that selected path with E-MHOF is increased the network lifetime and reduced latency in comparison with MHOF

Evaluation of routing protocol with multi-mobile sinks in WSNs using QoS and energy consumption parameters

An efficient networks’ energy consumption and Quality of Services (QoS) are considered the most important issues, to evaluate the route quality of the designed routing protocol in Wireless Sensor Networks (WSNs). This study is presented an evaluation performance technique to evaluate two routing protocols: Secure for Mobile Sink Node location using Dynamic Routing Protocol (SMSNDRP) and routing protocol that used K-means algorithm to form Data Gathered Path (KM-DGP), on small and large network with Group of Mobile Sinks (GMSs). The propose technique is based on QoS and sensor nodes’ energy consumption parameters to assess route quality and networks’ energy usage. The evaluation technique is conducted on two routing protocols in two phases: The first phase is used to evaluate the route quality and networks’ energy consumption on small WSN with one mobile Sink Node (SN) and GMSs. The second phase, is used to evaluate the route quality and networks’ energy consumption on large network (four WSNs) with GMSs. The two phases are implementated by creating five sceneries via using NS2.3 simulator software. The implementation results of the proposed performance evaluation technique have demonstrated that SMSNDRP gives better networks’ energy consumption on small single network in comparison with KM-DGP. Also, it gives high quality route in large network that used four mobile SN, in contrast to KM-DGP that used sixteen mobile SNs. While in large network, it found that KM-DGP with sixteen mobile SNs gives better networks’ energy consumption in comparison with SMSNDRP with four mobile SNs

Enhancing IoT performance via using Mobility Aware for dynamic RPL routing protocol technique (MA-RPL)

Nodes' aware-mobility in the Internet of Things (IoTs) stills open defy for researchers, due to the dynamic changing of routing path and networks’ resource limitations. Therefore, in this study a new method is proposed called Mobility Aware - “Routing Protocol for Low power and Lossy Networks” (MA-RPL), that consists of two phases: in the first phase splitting the entire network into sub areas based on reference nodes with “Time Difference of Arrival” (TDoA) technique. While, the second phase, is about managing mobile nodes (MNs) in RPL according to the sub areas' ID. The Cooja simulator software has been used to implement and assess MA-RPL method performance, according to the data packet metrics (lost packet, packet delivery ratio PDR), latency and nodes' power usage in comparison with two methods: Corona (Co-RPL) and Mobility Enhanced (ME-RPL). The simulation results have been shown that the MA-RPL method consumes less nodes' energy usage, gives less latency with minimum data packet loss in comparison with Co-RPL and ME-RP

Enhancing IoT Performance via Using Mobility Aware for Dynamic RPL Routing Protocol Technique (MA-RPL)

Nodes' aware-mobility in the Internet of Things (IoTs) stills open defy for researchers, due to the dynamic changing of routing path and networks’ resource limitations. Therefore, in this study a new method is proposed called Mobility Aware - “Routing Protocol for Low power and Lossy Networks” (MARPL), that consists of two phases: in the first phase splitting the entire network into sub areas based on reference nodes with “Time Difference of Arrival” (TDoA) technique. While, the second phase, is about managing mobile nodes (MNs) in RPL according to the sub areas' ID. The Cooja simulator software has been used to implement and assess MA-RPL method performance, according to the data packet metrics (lost packet, packet delivery ratio PDR), latency and nodes' power usage in comparison with two methods: Corona (Co-RPL) and Mobility Enhanced (ME-RPL). The simulation results have been shown that the MA-RPL method consumes less nodes' energy usage, gives less latency with minimum data packet loss in comparison with Co-RPL and MERPL

Intrusion detection method for Internet of things based on the spiking neural network and decision tree method

The prevalence of using the applications for the Internet of things (IoT) in many human life fields such as economy, social life, and healthcare made IoT devices targets for many cyber-attacks. Besides, the resource limitation of IoT devices such as tiny battery power, small storage capacity, and low calculation speed made its security a big challenge for the researchers. Therefore, in this study, a new technique is proposed called intrusion detection system based on spike neural network and decision tree (IDS-SNNDT). In this method, the DT is used to select the optimal samples that will be hired as input to the SNN, while SNN utilized the non-leaky integrate neurons fire (NLIF) model in order to reduce latency and minimize devices’ power usage. Also, a rand order code (ROC) technique is used with SNN to detect cyber-attacks. The proposed method is evaluated by comparing its performance with two other methods: IDS-DNN and IDS-SNNTLF by using three performance metrics: detection accuracy, latency, and energy usage. The simulation results have shown that IDS-SNNDT attained low power usage and less latency in comparison with IDS-DNN and IDS-SNNTLF methods. Also, IDS-SNNDT has achieved high detection accuracy for cyber-attacks in contrast with IDS-SNNTLF