Dear Colleague,

The term ‘Internet of Things’ (IoT) was coined by Kevin Ashton in 1999. The central idea was to empower everyday objects with internet connectivity, thus enabling pervasive and autonomous communication. The foundation of IoT is based on Weiser’s vision of profound software/hardware technologies that weave themselves into the fabric of everyday life such that they become indistinguishable. The functionality and modalities of these technologies is distributed across a variety of interconnected objects. This interconnectivity of these objects is pivotal as the collective intelligence of the IoT network emerges from simple object level interactions. In turn, such a collective intelligence can be credited with driving significant innovations in the context of various applications under the umbrella of smart homes and cities. What will make IoT a $7.1 trillion market is the fact that connecting many cheap and low power sensors to the internet opens a wide range of possibilities for rapid development and deployment of city-scale application solutions. These solutions intend to exploit the power of cloud-hosted application programming interfaces and data sets to derive collective fine-grain intelligence. While cloud computing is geared toward alleviating requirements for onboard processing and storage, this comes at a cost of increased latency and in some cases with increased cybersecurity challenges. Moreover, the high density of devices often deployed in hard-to-reach areas also renders power consumption a major problem. The purpose of this Special Issue is to address this challenging research area and propose novel solutions for improving energy efficiency performance. We solicit original manuscripts presenting recent advances in this area with special preference to the following topics:

• Novel network architecutures for energy-efficient IoT;
• Optimisation and scheduling policies for energy-efficienct IoT;
• Physical layer (PHY)-related aspects for energy-efficienct IoT;
• Medium access control (MAC)-related aspects for energy-efficienct IoT;
• Machine learning and artificial intelligence for energy-efficienct IoT;
• Federated learning for energy-efficienct IoT;
• Ambient energy harvesing techniques for IoT;
• Backscattering-based IoT systems;
• Combination of fixed and mobile nodes for energy optimization;
• Orchestration and management techniques for energy-efficienct IoT.

Dr. Adnan Shahid
Dr. Syed Ali Raza Zaidi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• energy efficiency
• optimization
• Internet of Things
• cloud computing
• machine learning
• artificial intelligence
• federated learning