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This study proposes targeted modernization of the Department of Defense (DoD's) Joint Forces Ammunition Logistics information system by implementing the optimized innovative information technology open architecture design and integrating Radio Frequency Identification Device data technologies and real-time optimization and control mechanisms as the critical technology components of the solution. The innovative information technology, which pursues the focused logistics, will be deployed in 36 months at the estimated cost of $568 million in constant dollars. We estimate that the Systems, Applications, Products (SAP)-based enterprise integration solution that the Army currently pursues will cost another $1.5 billion through the year 2014; however, it is unlikely to deliver the intended technical capabilities.

In this chapter, different security-related examples are shown in SGL dealing with discovery, tracing and analysis of multiple mobile objects, technical or human, in distributed environments. Starting from how overall command and control of a hypothetical missile defence can be automatically managed in SGL by following and supervising the movement of multiple ballistic missiles on their full path from discovery to elimination. Other case is dealing with fully distributed tracing of cruise missiles with complex and tricky routes, which can be effectively chased, analysed and controlled by mobile spatial intelligence spreading through intelligent sensor network. Another one is providing high-level simulation and tracing of multiple objects in outer space to avoid collisions for new vehicles launched, with engagement of scattered space observation sensors integrated under SGT. The chapter also shows how to organize distributed simulation, assistance and control of flow of refugees through international borders.

The Internet of Things (IoT) is becoming increasingly popular in agribusiness to help increase food production capacity for the ever-expanding global population. This chapter provides a holistic overview of the latest trends around the applications of IoT in agriculture. We begin by giving an overview of IoT and its capabilities, followed by a deep dive into the practical and realistic aspects of leveraging IoT into the agroecosystem. IoT is already being used for many intelligent agriculture applications, such as open-field agriculture, controlled environment agriculture (greenhouse), livestock breeding, agricultural machinery, and more. This chapter examines those applications and ventures beyond the farm into several other aspects of the ecosystem, including storage, warehouse ambiance control, agri-data analytics and decision control, logistics, environmental safety, etc. The contents of the chapter would be based on extensive studies and empirical analysis of the latest research papers on this subject from around the globe, accurately interpreted and transformed by the authors in light of their academic background and professional experience in the digital transformation arena.

Supply of food from the early stages of societies was one of the most important needs of humans. Sustainable supply of food is a demand in modern societies. The agri-food market grows as the population rises every year. The increase in the demand side of the market is more than the growth of the supply side. The rate of using technology in the supply side is increasing rapidly. By using technology in some parts, the efficiency of production improved and caused more production while resources are the same. Availability of resources in different areas causes different ways of production and nurturing innovative technologies to maintain food security. Water, soil, climate change, and growth of population are drivers of using technology in food security. To depict the role of different technologies in the food industry, the authors have reviewed the role of the most important technologies in this field. Knowing the trends of changes in the industry will help to focus on the most important questions and solutions. Having a share in the global food market requires the major use of technology in production processes. In this chapter, the authors will review the most important trends of technology absorption in the food industry.

Purpose: With this study, the authors aim to highlight the application of machine learning in smart appliances used in our day-to-day activities. This chapter focuses on analysing intelligent devices used in our daily lives to examine various machine learning models that can be applied to make an appliance ‘intelligent’ and discuss the different pros and cons of the implementation.

Methodology: Most smart appliances need machine learning models to decrypt the meaning and functioning behind the sensor’s data to execute accurate predictions and come to appropriate conclusions.

Findings: The future holds endless possibilities for devices to be connected in different ways, and these devices will be in our homes, offices, industries and even vehicles that can connect each other. The massive number of connected devices could congest the network; hence there is necessary to incorporate intelligence on end devices using machine learning algorithms. The connected devices that allow automatic control appliance driven by the user’s preference would avail itself to use the Network to communicate with devices close to its proximity or use other channels to liaise with external utility systems. Data processing is facilitated through edge devices, and machine learning algorithms can be applied.

Significance: This chapter overviews smart appliances that use machine learning at the edge. It highlights the effects of using these appliances and how they raise the overall living standards when smarter cities are introduced by integrating such devices.