This paper describes Election Algorithm (EA), an optimization and search technique, inspired by presidential election. The EA is an iterative population based algorithm, which works with a set of solutions known as population. Each individual of the

Nowadays, the efficiency of multiagent systems in wireless sensor networks prompts the researchers to use these emerging mobile software packets in different simulated approaches or real-world applications. Heterogeneous and distributed wireless sensor networks could be integrated with the multiagent systems to map the real-world challenges into the artificial intelligence world. The multiagent systems have been applied from simulated approaches like object detection/tracking, healthcare, control/assistant, and security systems to real-world applications, including medical/human-care systems and unmanned aerial vehicles. Furthermore, the integration of wireless sensor networks with multiagent systems have emerged novel application, which is known as mobile robots. However, the extensive use of mobile agents in wireless sensor networks has posed different challenges for researchers, including security, resource, and timing limitation. In this work, we review recent simulated approaches and real-world applications of multiagent systems in wireless sensor networks, in which a set of common factors about the things that have been studied are extracted and compared to analyze the performance of mobile agent–based systems in the wireless sensor networks, as well. This analysis provides new research directions about multiagent systems in wireless sensor networks for interested researchers. Finally, a novel framework for dealing with the challenges of multiagent-based applications in the wireless sensor networks which have been mentioned is suggested.

Driven by the vision of cloud and edge computing as the 5th utility, a major challenge is the efficient resource management and pricing taking into account incentives and preferences of cloud/edge providers, users, and the system. In recent years, auction mechanism design has gained wide attention as a tool for addressing this challenge. Here existing surveys lack detailed studies in this area, hence this work presents a comprehensive survey on the state-of-the-art auction-based mechanisms in the field of cloud/edge computing. First, the problem statements and background are presented for cloud/edge computing and auction theory. This is followed by a collaboration model on the benefits of auction mechanism designs to cloud/edge computing. Next, various auction mechanisms in cloud computing are reviewed based upon the following aspects: (1) direction of bids-forward, reverse, and two-sided auctions, (2) the heterogeneity of resources-combinatorial auctions, (3) the number and types of adopted attributes in winner determination-multi-attribute auctions, (4) the setting of auction-offline, online, or sequential auctions, and (5) the interoperability among cloud providers-inter-clouds. For each aspect, existing studies are summarized along with their saliencies and drawbacks. Moreover, existing auction mechanisms in the context of edge computing are also presented. Our comprehensive survey shows that although significant progress has been achieved in this field, there still exist key challenges that need to be investigated, as discussed here to provide future research directions.

Edge computing is a new computing paradigm, aims at enhancing user experience by bringing computing resources closer to where data is produced by Internet of Things (IoT). Edge services are provided by small data centers located at the edge of network, called cloudlets. However, IoT users often face strict Quality of Service (QoS) constraints for a proper remote execution of their applications. Each user has specific resource requirements and budget limitations, while each cloudlet offers a limited number and types of resources each with a specific cost. Therefore, a key challenge is how to efficiently match cloudlets to IoT applications and enable a convenient any-time access to edge computing services considering the preferences and incentives of the users and the cloudlets. In this paper, we address this problem by proposing a novel two-sided matching solution for edge services considering QoS requirements in terms of service response time. In addition, we determine dynamic pricing of the edge services based on the preferences and incentives of cloudlets, IoT users, and the system. The proposed matching is incentive compatible, individually rational, weakly budget balanced, asymptotically allocative efficient, and computationally efficient. We perform a comprehensive assessment through extensive performance analysis experiments for evaluation.

Edge computing is a new paradigm of computing, which aims at enhancing user experience by bringing computing resources closer to where data is produced by Internet of Things (IoT). Cloudlets, additional infrastructure components nearby users, facilitate edge services to decrease latency and network traffic. IoT users require edge services for their applications meeting a strict quality of service (QoS). A key challenge is how to efficiently match cloudlets to IoT applications to enable a convenient any-time access to edge computing services. In this paper, we address this problem by proposing novel two-sided matching solutions for edge services considering QoS requirements in terms of service response time. The matching mechanisms enhance the quality of experience of the users. In addition, we determine dynamic pricing of the edge services based on preferences and incentives of cloudlets, IoT users, and the system. The proposed matchings are Pareto-efficient, incentive compatible, weakly budget balanced, and computationally efficient. We perform a comprehensive assessment through extensive performance analysis experiments to evaluate our proposed matching and pricing solutions.