A smart city is a metropolis technology that employs information technology with several internet of things (IoT) devices to enhance the quality of services for citizens, such as the traffic system, energy consumption, and waste collection. In fact, the quality of service (QoS) of these daily routine services are based on an assistive observation system. Wireless sensor networks (WSNs), as the key component of IoT, are used here to gather data into surveillance subsystems for supporting the decision making. To enhance the collected data management of the surveillance subsystems, many clustering techniques are introduced. The low-energy adaptive clustering hierarchy protocol (LEACH) is a key clustering technique of WSN. However, this protocol has deterring limitations, especially in the cluster formation step, which negatively impacts the residual power of many nodes. In fact, a limited number of efforts that try to optimize the clustering formation step represent the main motivation of this work. Considering this problem, the current research proposes an optimized approach to enhance the cluster formation phase of LEACH. The proposed approach depends on the suitability of the residual energy in the nodes to cover the communication energy, with CHs (cluster heads) as a key factor when allocating the node clusters in the first competition. The remaining power and the density of CHs are employed to weigh the accepted CHs and adjust the optimized size of the clusters in the secondary competition. The third competition helps each cluster to select the optimal members from the candidate members according to the impact of each. The advantages and efficiency of the ICSI (intelligent cluster selection approach for IoT) are observed via the ratio of surviving nodes increasing by 21%, residual energy increasing in 32% of the nodes, and a 34% higher network lifetime.

An IoT data system is a time constraint in which some data needs to be serviced on or before its deadline. Distributed processing is one of the most latent sources in such systems and is considered a vital design concern. Many sources of delay in the IoT can affect the availability of data from different resources, which may cause missing data deadlines, resulting in a catastrophic effect. In fact, such systems are inherently distributed in nature and use distributed processing. The distributed processing permits different nodes to obtain the information from remote sites, which may take a long time to access the required data. Therefore, it is considered one of the most latent sources in such systems, which is considered a vital design concern. The typical recommended solution for this problem is to commit distributed transactions locally. Therefore, replication techniques are used to enhance the availability of data and consequently avoid the resulting latency. However, the use of local processing raises inconsistent periods. Therefore, this study proposes a new synchronization framework to minimize periods of temporal inconsistency. It permits several connected nodes to synchronize the shared data on demand concurrently without any need to use distributed synchronization, which consumes the system resource and raises its delay cost. The proposed framework aims to enhance the timely response of IoT real-time systems by minimizing the temporal inconsistency periods. The results indicate that the synchronization framework can be completed within a reasonable time period. They also depict improved consistency by minimizing the temporal inconsistency duration and increasing the chance of meeting critical time requirements.

Discovering Gene Regulatory Network (GRN) gives some idea about gene pathways and helps many potential applications in medicine. The essential source of data for this task is the gene expression data. High complexity and poor quality of gene expression data acquired by high throughput methods like microarray provide many difficulties in the context of the current issue. A promising method for evaluating gene expression noisy data to characterize processes made up of locally interacting components is Bayesian Network. In fact, because of the intricacy of the inputs and results of the cellular mechanism, inferring GRN from expression data presents numerous difficulties. This work proposes a new approach for inferring GRNs from time series gene expression data. The present work extends the existing Bayesian Network methods to include the regulation properties of genes to improve the process of capturing natural classes during inferring the relations between genes. The proposed approach is evaluated in comparing to the corresponding techniques of the related works, and the results show the ability of the present approach is efficient to some level to deal with such high dimensional data even without dimension reduction, but in the presence of regulatory information.

A smart city uses Internet of Things (IoT) to enhance the management of many daily routine tasks such as traffic system, energy consumption, and waste collection. The Quality of Service (QoS) of these daily routine tasks are based on an assistive observation system. Wireless Sensors Networks (WSNs) as the key component of IoT are used here to gather data into surveillance subsystems for supporting the decision making. To enhance the collected data management of surveillance subsystems, many clustering techniques are introduced. The low-power adaptive clustering protocol is a key technique of the Internet of Things (IoT). However, this protocol has deterring limitations, especially in the cluster formation step, which negatively affect many nodes. Considering this problem, the current research proposes an Opt-LEACH system that attempts to optimize the low-energy adaptive clustering hierarchy. The proposed system depends on the suitability of residual energy in nodes to cover the communication energy with CHs as a key factor when allocating the node clusters in the first competition. The remaining power and the density of CHs are employed to weight the accepted CHs and adjust the optimized size of clusters in the secondary competition. The impact factor of each candidate member node is applied in the third competition. The simulation results clarify the ability of Opt-LEACH to improve the cluster formation and to enhance communication within clusters. The advantages and efficiency of Opt-LEACH are observed via the increased number of surviving nodes, increased residual energy of nodes and higher network lifetime.