The Artificial Intelligence of Things (AIoT), a combination of the Internet of Things (IoT) and Artificial Intelligence (AI), plays an increasingly important role in smart agriculture (SA). AIoT has been adopted in many applications including agriculture, such as crop yield estimation, soil and water conservation, pest and disease detection and supply chain management. While there are plenty of studies on AIoT applications in healthcare, smart cities, manufacturing, and transportation, SA still has a small share of the reported research. This paper presents a comprehensive review of the existing literature in AIoT and Federated Learning (FL) for SA. It identifies current and potential challenges and provides research direction for the future investment in both academia and industry.

In-network content caching has been a natural trend in emerging network architectures to handle the exponential growth of video traffic. However, due to the potentially wide attacking surfaces, caching video content in the increasingly untrusted networked environment inevitably raises new concerns on user privacy exposure and unauthorized video access. Existing encrypted protocols like HTTPs either fall short of fully leveraging in-network caching or require decrypting the traffic in the middle without guaranteeing the end-to-end security. In this paper, we present a new networked system for efficient encrypted video delivery while preserving the benefits of in-network caching. As video chunks are encrypted before distribution, we first design a compact, efficient, yet encrypted video fingerprint index to empower the network with a fully controlled capability of locating the cached encrypted chunks for given encrypted requests. We then explain how to deploy the encrypted design in our proposed architecture and present a secure redundancy elimination protocol to enable fast video delivery via leveraging cached encrypted chunks. We further discuss the full support of cache management, adaptive video delivery, and video access control. Rigorous analysis and prototype evaluations demonstrate the security, efficiency, and effectiveness of the design.

The emergence of social TV has transformed TV experiences, providing a unified media experience across different devices. In response to this trend, we have implemented a multi-screen social TV system, offering video teleportation as an attractive feature. The enabling technology is instantiating a cloud clone to support all media outlets of each user. As the user shifts his attention from one device to the other, the cloud clone might migrate to a better location to reduce its operational cost. This paper investigates this cloud clone migration problem, aiming to minimize the monetary cost on operating video teleportation. Specifically, we formulate it into a Markov Decision Problem, to balance the trade-off between the migration cost and the content transmission cost. Under this framework, four algorithms are proposed to solve this optimization problem. We first characterize an upper and a lower bound for the optimal cost, by considering a random fixed placement and an offline algorithm. We then present a semi-online and a more practical Q-learning approach to make online decisions. Their performances are evaluated based on both simulated and real user traces. The results show that the Q-learning method achieves up to 25% cost compared to random fixed placement in typical scenarios. The savings are affected by the delivery path length, the migration size, and the user behavior pattern. Moreover, our investigations reveal the optimal cloud clone location is either at the nearest or the furthest node to the user along the content delivery path for a single user scenario.

MatchMaker is an automated collaborative filtering system that recommends friends to people on Facebook by analyzing and matching people's online profile with the profiles of TV characters. The goal of MatchMaker is to produce friend recommendations with rich contextual information through collaborative filtering in the existing social network. Using relationships in TV programs as a parallel comparison matrix, MatchMaker projects these relationships into reality to help people find friends whose personality and characteristics have been voted to suit them well by their social network. MatchMaker also encourages more TV content viewing by using the social network context and connections to provoke people's curiosity of TV characters whom they have been matched with in their social network.

CommenTV enables users to share their comments in parallel with time-based content. CommenTV utilizes existing web technologies to enhance current TV or video player solutions with a new type of comment system. CommenTV is able to take and display texts, images, and related videos as social comments. In this way, the CommenTV users can experience audiovisual content that blends with their social networks by sharing comments. Ultimately, CommenTV can be a social bookmarking system for audiovisual content that informs content creators about how viewers perceive their content and lets viewers participate in creating a more social content.

WorldTV is a proposed TV app for the discovery of user-generated video from around the world. We describe a two-screen solution (a TV app and accompanying mobile app to control what appears on the television screen) would let viewers browse and play geotagged videos plotted on a 3D model of the earth. The content would include video from viewers' own social networks as well as user-generated news and event video filtered according to viewers' interests, preferences, and social connections. In a world in which user-generated online video content is being uploaded and consumed at an exponential rate, the idea of using a globe-based navigation metaphor has powerful appeal. It is instantly approachable and easy to use, especially for people with global networks of friends, family, and colleagues living in other countries. We also argue that the widespread availability of geotagged video provides an additional impetus for using a geo-focused UI over more traditional browsing and discovery mechanisms.

In this paper, we review recent networking developments that will help create the next-generation social television experiences. These include revisiting the way networks are created and using social connectivity to drive physical connectivity and network virtualization. Multipath dissemination and reduction of interruptions will provide better quality of experience. Content protection and privacy are also essential to enable social commentary and metadata applications and will be briefly introduced. Examples of potential applications and results of field trials are also included.

The seven papers in this special section focus om recent advances in the growing research field of television.

Making a service social means providing a way for users to engage with a product or service as a group. We are specifically interested in groups based on connections that are established using the emerging online social networking tools like Facebook, MySpace, etc. This paper explores the value chain dynamics resulting from the integration of social networks with a given service. The example we will be using in this paper involves social TV applications, with a focus on social discovery. We hypothesize that these mashup social services introduce new control points in a given service's value chain, which complement or compete with existing control points. The paper focuses on the North American experience.

Video content has become available on an increasingly diverse set of devices and from an ever growing number of sources, creating a vast amount of choice for viewers. At the same time, the varying methods of viewing, interacting with, and sharing content have diverged. This paper introduces neXtream, a new approach to delivering video by integrating multiple devices, content sources, and social networks. This concept is developed following research in social television and converged applications, providing both personalization features and social interaction. NeXtream delivers video by dynamically generating streams of video customized to a viewer, while facilitating a common dialog between users around the content, creating both a user- and community-centric viewing experience. NeXtream integrates smartphones, PCs, and TVs to deliver video content to viewers. The paper presents the system concept, theory, and architecture, and describes the developed prototype.

The impact of Internet content and IP based television on networks is growing. Video is now ubiquitous in the home and on the street. It demands new approaches to video transmission to meet the growing traffic volume. This paper presents a novel strategy for network coding in video transmission. It uses a variety of coding approaches and adds feedback and device discovery to tailor the coding to the receiver ecosystems and can achieve better overall performance than the non coded versions.

While IPTV architecture and user interfaces have received a lot of interest in recent years much less focus has been given to the issues of channel surfing in the IPTV era. IPTV is rapidly transitioning from a Telco solution into a generic service over any broadband access network. Traffic issues that relate to channel change are moving to the mainstream and are the talk of a number of standards organizations. This paper proposes a new approach to channel change. It uses peering and viewing statistics to reduce channel change delays and improve the user quality of experience.

A branching process analysis in a random environment is presented for bounding the average number of computations of sequential decoding over a finite state channel. Closed-form expressions applicable to specific cases are derived and evaluated. These unique bounds substantially reduce the need for lengthy simulations.>