Green routing in a two-level network of cross docks given the shipping price (Case study: Pegah Co.)

In this study, a green routing model is provided in a two-level network of cross docking given the shipping price. Three objectives were proposed in this model including: total cost reduction, shipping costs reduction and carbon emissions reduction. The overall objective of the model, is getting the best route in the distribution network which will impose the least cost and also minimizes emissions of environmental pollutants. For the model to approach the real situation, Pegah corporation’s warehouses and distribution network are considered as a case study. Solving the developed model was carried out by GAMS. By the size of the problem increasing, the running time of the program is notably increased and this means the problem is np-hard. So, in order to solve the model in medium and large dimensions, we used meta-heuristic MOGWO and NSGA II algorithm. The results of investigating various problems with meta-heuristics, indicates the high performance of the proposed algorithm in terms of the time needed and the response achieved. Results indicated that the proposed model reduced the emission of environmental pollutants along with total cost and shipping cost reduction. Also given the time window, the products were shipped to customers in a timely manner.

Blockchain-Based Key Management and Green Routing Scheme for Vehicular Named Data Networking

Due to the distributed and dynamic characteristics of the Internet of Vehicles (IoV) and the continuous growth in the number of devices, content-centric decentralized vehicular named data networking (VNDN) has become more suitable for content-oriented applications in IoV. However, the existing centralized architecture is prone to the failure of single points, which results in trust problems in key verification between cross-domain nodes and consuming more power and reducing the lifetime. Focusing on secure key management and power-efficient routing, this article proposes a blockchain-based key management and green routing scheme for VNDN. A blockchain-based key management scheme is presented to achieve secure and efficient distribution and verification of keys. Specifically, all trusted agencies (TAs) form a consortium blockchain for storing public key hashes to ensure the authenticity of users’ public keys. A green global routing scheme based on node relaying pressure (GGNRP) is proposed to save power consumption and reduce the forwarding delay. A new node relay pressure metric is introduced to assist with routing decisions. Detailed experiments and analysis show that, compared with the existing scheme, the proposed scheme can achieve secure key management and GGNRP can decrease the power consumption and average delay by 15.8% and 63.2%, respectively.

An Overview of Machine Learning-Based Energy-Efficient Routing Algorithms in Wireless Sensor Networks

Machine learning (ML) technology has shown its unique advantages in many fields and has excellent performance in many applications, such as image recognition, speech recognition, recommendation systems, and natural language processing. Recently, the applicability of ML in wireless sensor networks (WSNs) has attracted much attention. As resources are limited in WSNs, identifying how to improve resource utilization and achieve power-efficient load balancing is becoming a critical issue in WSNs. Traditional green routing algorithms aim to achieve this by reducing energy consumption and prolonging network lifetime through optimized routing schemes in WSNs. However, there are usually problems such as poor flexibility, a single consideration factor, and a reliance on accurate mathematical models. ML techniques can quickly adapt to environmental changes and integrate multiple factors for routing decisions, which provides new ideas for intelligent energy-efficient routing algorithms in WSNs. In this paper, we survey and propose a theoretical hypothetic model formulation of ML as an effective method for creating a power-efficient green routing model that can overcome the limitations of traditional green routing methods. In addition, the study also provides an overview of past, present, and future progress in green routing schemes in WSNs. The contents of this paper will appeal to a wide range of audiences interested in ML-based WSNs.