Today, information NETWORKs play an important role in supply chain management. Therefore, in this article, clustering-based routing protocols, which are one of the most important ways to reduce energy consumption in wireless sensor NETWORKs, are used to optimize the supply chain informational cloud NETWORK. Accordingly, first, a clustering protocol is presented using self-organizing map neural NETWORK, SOM. Second, we cluster the NETWORK nodes based on two criteria of neighborhood and energy level using K-means clustering pattern. Third, we survey the efficiency and inefficiency of the clusters to balance the energy properly among the clusters. Then, to increase the NETWORK LIFETIME and to maintain the NETWORK DEA method is used. Finally, the model is tested for the information NETWORK of oil supply chain.

Recent advances in wireless electronic and communications provide us the ability to build small, economical sensors with low power consumption and many diverse applications. Limited energy capacity of sensors is a huge challenge that will affect these NETWORKs. Clustering has been used as a well-known method to handle this challenge. To find appropriate location of clusters' heads, imperialist competitive algorithm as an emerging topic in computational intelligence has been used. Clusters' heads are connected in a three-level model so that cluster heads with low energy capacity and far from station are considered as level three indirectly communicating with base station. This eventually increases LIFETIME of wireless sensor NETWORKs.

The tiny and low-cost sensors cannot simultaneously sense more than one channel since they do not have high-speed Analog-to-Digital-Convertors (ADCs) and high-power batteries. It is a critical problem when they are used for multi-channel sensing in cognitive sensor NETWORKs (CSNs). One solution for this problem is that the sensors sense various channels at different sensing periods. Due to the energy limitation in these scenarios, the LIFETIME maximization will become an important issue. In this paper, maximizing the LIFETIME of a CSN is investigated by selecting both the cooperative sensors and their detector threshold, such that the desired detection performance constraints are satisfied. This is a NP-complete problem, and obtaining the optimum solution needs exhaustive search with exponential complexity order. Here we have proposed two convex-based optimization algorithms with low order of complexity. First algorithm applies the known instantaneous Signal-to-Noise-Ratio (SNR) and obtains the proper detector thresholds by solving an equation for every channel. Investigation the effect of detector thresholds on the energy consumption, the false alarm probability and the detection probability shows that we can minimize the detector thresholds such that the detection constraints are met. In the second algorithm in order to reduce the needed time for obtaining answers, the Bisection method is proposed for determining detector thresholds. Because knowing the instantaneous SNR is difficult, we have investigated the performance of the second algorithm by average value of SNR. Simulation results show that the proposed algorithms improve the performance of the NETWORK in case of LIFETIME and energy consumption.

Wireless sensor NETWORKs contain of many sensors that can serve as powerful tools for data collection in environments. A key challenge in these NETWORKs is the limited LIFETIME of sensor batteries. Ideally, all nodes would exhaust their energy simultaneously or through regular scheduling, maximizing the LIFETIME. Consequently, the primary concern is achieving optimal energy utilization to extend the NETWORK's LIFETIME over a logical duration. Depleting the batteries of the sensors means stopping the operation of the NETWORK, because it is practically impossible to replace the batteries of thousands of nodes. To address this issue, the low energy adaptive clustering hierarchical (LEACH) protocol has been widely recognized as one of the prominent solutions for clustering WSNs. However, the random selection of cluster heads in each round under the LEACH protocol fails to guarantee proper convergence. To overcome this limitation, this paper proposes a refined approach by utilizing a genetic algorithm and a novel objective function that incorporates various factors, including energy level and distance. The algorithm employs chromosomes to represent CHs and facilitates the selection of cluster nodes. Notably, the proposed algorithm dynamically performs clustering, meaning that clustering is conducted iteratively, considering identifying dead nodes. By leveraging this approach, the algorithm significantly enhances the clustering quality, ultimately leading to an increased NETWORK LIFETIME. To validate its effectiveness, it is compared with LEACH, LEACH_E and LEACH_EX algorithms, demonstrating its superior capabilities. On average, the proposed algorithm has more alive nodes in the NETWORK, and the remaining energy is at least 11% higher than the best other algorithms.

Abstract: Wireless Sensor NETWORKs (WSNs) comprising of tiny, power-constrained nodes are getting very popular due to their potential uses in wide applications like monitoring of environmental conditions, various military and civilian applications. The critical issue in the node is energy consumption since it is operated using battery, therefore its LIFETIME should be maximized for effective utilization in various applications. In this paper, a Game theory based Hybrid MAC protocol (GH-MAC) is proposed to reduce the energy consumption of the nodes. GH-MAC is combined with the game based energy efficient TDMA (G-ETDMA) for intra-cluster communication between the cluster members to head nodes and Game theory based nanoMAC (G-nanoMAC) protocol used for inter-cluster communication between head nodes. Performance of GH-MAC protocol is evaluated in terms of energy consumption, delay and compared with conventional MAC schemes. The results obtained using GH-MAC protocol shows that the energy consumption is enormously reduced and thereby the LIFETIME of the sensor NETWORK is enhanced.

This research proposes a model that calculates the customer LIFETIME value and simultaneously considers the NETWORK effects. An oligopoly market is also considered in which companies compete with each other. Each company has a number of buyers and sellers and offers its services to the buyers free while receiving the sellers' membership fees in return. Interestingly, the customers interact with each other and change the companies' clients. This interaction between them is known as word-of-mouth marketing, and it also exists among the sellers. It is noteworthy that the existence of both buyers and sellers is only meaningful. Indeed, the increase of buyers leads to a rise in the number of sellers and also makes the company more profitable. In fact, the NETWORK effects are categorized into four forms that are as follows: (1) buyers’ effects on each other, (2) sellers’ effects on each other, (3) buyers’ effects on sellers, and (4) sellers’ effects on their buyers. These effects are the main factors that the companies tend to take into consideration when determining the optimal marketing and pricing policies. Applying differential game theory makes it possible to receive the companies' market share, advertising, and pricing strategy.

In many applications, wireless mesh NETWORKs work by battery as a power source. In this scenario, routing method has a great impact on the NETWORK LIFETIME.In this research a new backbone based wireless mesh NETWORK routing method for maximizing LIFETIME has been proposed. This approach is compatible with the features provided by IEEE standard for wireless mesh NETWORKs. In this method, backbone routers are selected based on the maximum remaining energy.The proposed algorithm is compared with optimum and shortest path routing methods. Simulation results show acceptable increase in NETWORK LIFETIME in the proposed approach.

The area of wireless sensor NETWORK is shrouded with significant ranges of problems where majority of the prior research attempts were carried out to address energy conservation issues. The present manuscript introduces a novel technique called as ENLPL or Enhancing NETWORK LIFETIME Using Probabilistic Logic where the prime focus is laid on conserving the energy for large scale wireless sensor NETWORK. ENLPL presents design of inter and intra clustering approach using probability theory. It also laid emphasis on ensuring maximum participation of the sensor nodes in selection process of cluster-leader with an aid of maximum number of parameters. The outcome of the study was compared with conventional and standard hierarchical clustering algorithm (LEACH) to find ENLPL ensures better energy efficiency for large number of the sensor. With the adoption of the probabilistic reasoning technique, the simulation results shows that the proposed system maximizes the cumulative LIFETIME of the NETWORK, along with an efficient selection process of the cluster leader in every iteration.