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Summary Since sensor nodes use limited battery power, sensor nodes power is considered to be a challenge and fundamental issue in wireless networks. Wireless sensor networks (WSNs) have recently used clustering‐based methods and different routing protocols in clusters which are full of sensor nodes. In this way, cluster heads (CHs), which are regarded as the selected nodes among other nodes within a given cluster, accumulate the information transmitted from their own cluster and send it to the sink. In this method, they try to control power consumption in a balanced way. In clustering method and routing among network nodes, network lifetime is enhanced which leads to achieving the best efficiency and productivity. In this paper, the optimized black hole algorithm is employed to select an optimal CH from sensor nodes. The CH selection is optimized by the free buffer of nodes, residual energy, and distance. The path between source CH and sink is identified by using ant colony optimization (ACO) algorithm. The combination of black hole algorithm and ant colony algorithm, with respect to clustering and routing, leads to the optimization of the proposed method in terms of operational criteria such as power consumption and eventually lifetime enhancement. The results obtained from simulating the proposed method in Matlab environment indicated that it has better results in comparison with other methods. The outputs of the obtained results from the proposed method indicated that it outperformed the other four comparative methods in terms of packet delivery rate and the number of transmitted packets to the CH and to the sink.

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The rapid development of Cloud Computing (CC) has led to the release of many services in the cloud environment. Service composition awareness of Quality of Service (QoS) is a significant challenge in CC. A single service in the cloud environment cannot respond to the complex requests and diverse requirements of the real world. In some cases, one service cannot fulfill the user’s needs, so it is necessary to combine different services to meet these requirements. Many available services provide an enormous QoS and selecting or composing those combined services is called an Np-hard optimization problem. One of the significant challenges in CC is integrating existing services to meet the intricate necessities of different types of users. Due to NP-hard complexity of service composition, many metaheuristic algorithms have been used so far. This article presents the Artificial Bee Colony and Genetic Algorithm (ABCGA) as a metaheuristic algorithm to achieve the desired goals. If the fitness function of the services selected by the Genetic Algorithm (GA) is suitable, a set of services is further introduced for the Artificial Bee Colony (ABC) algorithm to choose the appropriate service from, according to each user’s needs. The proposed solution is evaluated through experiments using Cloud SIM simulation, and the numerical results prove the efficiency of the proposed method with respect to reliability, availability, and cost.

There is a way to prolong the life of sensor networks according to which a hierarchical routing algorithm is used intelligently, which employs all network elements in data transmission. Clustering the nodes is one of the best methods that can significantly increase the network life. Making a cluster, selecting a Cluster Head (CH) and data transmission in Wireless Sensor Network (WSN) are the issues that affect energy consumption. Software-Defined Networks (SDN) are a modern network architecture that distinguishes the network control panel from the data plate also this architecture cause the network utilizing is increased, and the operational cost is reduced. This method also causes creativity and perfection in the network area. Moreover, the possibility of implementing management protocols, including traffic management, which is an inevitable part of networks, can be implemented in SDN with a higher level of flexibility. In this paper, the CH has selected trough game theory, which sends data with the help of game theory rewards and calculating the geographical location of other nodes. Then, high-priority data is sent according to the proposed algorithm with the help of game theory. The simulation results in NS3 software show that the proposed method has obtained acceptable results compared with Artificial Bee Colony algorithm (ABC), Genetic Algorithm (GA), Cuckoo Search algorithm (CS), Firefly algorithm (FA) and Grey Wolf Optimization Algorithm (GWO).

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