In recent years, Wireless Sensor Networks (WSNs) have benefitted from their integration with Internet of Things (IoT) applications. WSN usage for monitoring and tracing applications shows massive acceleration, whether indoors or outdoors. WSN is constructed from interconnected sensors, limited resource (battery), which requires considerable importance on deployment and routing strategies, to improve the performance of Quality of Service (QoS) in WSNs. Many of the existing strategies are based on metaheuristics algorithms such as Genetic Algorithms to resolve the problem. This research proposes a new algorithm, Enhanced Non-Dominated Sorting Genetic Routing Algorithm (ENSGRA), to improve the QoS in WSNs. The proposed algorithm relies on Non-Dominated Sorting Genetic Algorithm 3 (NSGA-III), but adjusts reference points through the use of a dynamic weighted clustered scheduled vector to obtain new solutions. Moreover, ENSGRA can be used to find an integration between two parents crossover with multi-parent crossover (MPX), to produce multiple children and improve new offspring to obtain the optimal Pareto Fronts (PF). This algorithm excels when compared with the lagged multi-objective jumping particle swarm optimization, Non-dominated Sorting Genetic Algorithm-II and NSGA-III in terms of the QoS model (31% optimization percentage). Results show that the proposed ENSGRA is superior over other algorithms in evaluation measures for multi-objective algorithms.

Mobile Ad-hoc Network (MANET) is a kind of wireless network that has the most challenging network infrastructure. It is formed using the mobile nodes without any centralized administration from the security perspective and is a self-configuring fastest emerging wireless technology, each node on the MANET will act like a router which forwards the packets. Dynamic nature of this network makes routing protocols to play a prominent role in setting up efficient route among a pair of nodes. Dynamic Source Routing (DSR) and Ad-hoc On-Demand Distance Vector (ADOV) is a reactive MANET routing protocols. Most of the attacks on MANETs are routing protocol attacks. Attacks on routing protocols, especially internal attacks will cause the damage to MANETs. Sinkhole and black hole attacks are a type of internal attack which is affected by attempting to draw all network traffic to malicious nodes that fake routing update and degrade the performance of the network. The black hole nodes should be detected from the network as early as possible via detection mechanism and should also guarantee the higher detection rate and less cross-over error rate. In this paper, we studied the characteristics of black hole attack and how it will affect the performance of the distance vector routing on demand routing protocol such as (ADOV) protocol, which recognizes the presence of black hole node from packet flow information between nodes and isolates it from the network via applying AODV protocol that one of popular routing protocol. We have evaluated the performance of the system using widely used simulator NS2, results prove the effectiveness of our prevention and detection method.

<span>Multi-objective algorithms are used to achieve high performance for quality of service (QoS) in wireless sensor networks (WSNs) is an important field for researchers because these algorithms improve two or more conflicting objectives and present the best trade-off between the conflicting objectives to solve multi-objective problems (MOPs). Previous research proposed an algorithm that relies on non-dominated sorting genetic algorithm 3 (NSGA-III), namely enhanced non-dominated sorting genetic routing algorithm (ENSGRA). This algorithm is used to optimise three objectives, which include number of worked sensors, energy consuming and node covering area. The fourth objective, which is node load balancing, is added in the current research. Such an addition aims to improve node distribution around cluster heads and decrease network congestion, thus decreasing energy consumption and increasing network lifetime. The ENSGRA algorithm is compared with multi-objective multi-step particle swarm optimisation (MOMSPSO), non-dominated sorting genetic algorithm 2 (NSGA-II), and NSGA-III. The proposed algorithm ENSGRA exceed to MOMSPSO, NSGA-II, and NSGA-III in the proposed QoS model in the final outcomes, as the proposed approach achieved (38%) average combination (optimisation) percentage. Which is the highest percentage over other methods.</span>

Clock synchronization in the Mac layer plays a vital role in wireless sensor network communication that maintains time-based channel sharing and offers a uniform timeframe among different network nodes. Most wireless sensor networks are distributed where no common clock exists among them. Therefore, joint actions are realized by exchanging messages, with time stamps using local sensor clocks. These clocks can easily drift seconds and cause functional problems to the applications that depend on time synchronization. Time synchronization is a major and challenging factor in wireless sensor networks that needs to be studied and explored. In this paper, we propose integrated time synchronization protocols that serve wireless sensor network applications under normal, secured, and unreliable environments. The proposed protocols are discussed and evaluated based on their accuracy, cost, hierarchy, reliability, and security. Simulation results show that the proposed time synchronization protocols outperform the state-of-the-art techniques in achieving a minimum synchronization time.

The most important research in the world in these days, research that looking at the internet of thing's (IoT) topics and their applications. Most of these applications depend on RFID system, which includes RFID readers and tags. The important issues in RFID system or network are how we can reduce anti-collision between readers to identify and read tags data. In these paper, we suggest an Improved anti-collision protocol, which can be used to connect RFID readers with RFID tags and reduce the number of RFID tag's collisions. The simulation shows that an Improved Class-1 Generation 2 algorithm is better than Slotted Aloha, Class-1 Generation-2 (Number of Tags Known), Class-1 Generation-2 (Number of Tags Unknown) algorithms.