4. The Swarm Behaviour of the Augmented and Quantified Human—The Next of the Internet of Everything?
After some decades of hard work on symbolic processing, AI experts tend to consider other reliable and effective ways of dealing with information and decision-making processes. Social insects—especially ant colonies—emerged as a superb model of what is called “swarmintelligence” [ 32 ]. This decentralized way of achieving collective decisions (even for human scenarios, see [ 33 ]) and collaborative workflows has demonstrated its value for robotic applications in some special domains [ 1 ]. We consider that this bioinspired method, combined with the Internet of Things that is emerging at this moment, can provide new ways of dealing with the collective experience of being an individual as well as being a member of a broad collective. Our augmented devices can, for example, collaborate between them with or without our conscious decision; for example, allowing certain permissions of collaboration under special circumstances. A network of informational devices can provide new ways of managing social interaction, beyond classic structures of power or control. For example, recall the Hong Kong protests against Chinese censorship and how thousands of citizens avoided governmental censorship of networks using a phone-to-phone connection app (Firechat) to avoid the loss of connectivity [ 34 ]. At the same time there is another crucial aspect: how these new technological mechanisms can enhance, upgrade, or radically modify feeling and sensing of the world: creating augmented worlds, expanding the scope and thresholds of our natural senses, or creating new synesthesic ways of experiencing the world. Imagine, for example, a military vet which could provide sensory pressure experience of a radar evaluation of close targets into a 360 o perimeter: combining the position of pressure and its intensity, a soldier could experience the proximity of non-visual targets and increase their response accuracy. Thus, not only one of the generally accepted twenty-one human senses can be utilized in order to expand human perception abilities, but radically new senses can be introduced in humans by directly activating certain areas of human brain. This enhancement can affect not only individual minds, but thanks to dozens of gadgets and sensors, can connect us to others’ experiences and feel other kinds of informational values in a more complex way. Our smart clothing could combine its regulation properties according to the proximity of other persons, creating a smarter and efficient management of collective temperatures (inside a plane, a tank, the metro facilities, or a bus, for example).
Abstract. The Internet of Things (IoT) represents the global network which in- terconnects digital and physical entities. It aims at providing objects with intel- ligence that allows them to perceive, decide and cooperate with other objects, machines, systems and even humans to enable a whole new class of applications and services. Agent-Based Computing paradigm has been exploited to deal with the IoT system development. Many research works focus on making objects able to think by themselves thus imitating human brain. SwarmIntelligence studies the collective behavior of systems composed of many individuals who interact locally with each other and with their environment using decentralized and self- organized control to achieve complex tasks. Swarmintelligence-based systems provide decentralized, self-organized and robust systems with consideration of coordination frameworks. We explore in this paper the exploitation of swarmintelligence-based features in IoT-based systems. Therefore, we present a refer- ence swarm-based architectural model that enables cooperation among devices in IoT systems.
Figure 4 shows a screenshot from an online Sherlock setting called Sherlock+sam (‘Sherlock + Sensor Assignment to Missions’) designed to support HCC experiments in an ISR context. The game world here involves a number of locations, six of which — the Amber, Gold, Emerald, Ruby, Sapphire and Silver Rooms — can be viewed via simulated cameras. Each room can be viewed via feeds from a colour (RGB) or black and white (B&W) camera. There are six POIs, each represented by a cartoon animal: a key to identifying them is at the bottom of the screen. Each POI eats one unique type of fruit, plays one unique sport, and occasionally wears a hat of a unique colour. The centre of the screen provides the user — playing the role of a tactical intelligence operator — with four panels in which they can access a camera feed. Bandwidth limitations mean that there is a limit on how many RGB and B&W camera feeds that can be accessed simultaneously in a game turn. On the right of the screenshot is the ‘chat’ interface to the conversational agent, via which the user can report what they see in the feeds during each game turn.
We all know about the importance of security in the digital world which has now become a non-ignorable aspect. Most of the security issues which are concerned with the IOT services be it their advantages, interest or popularity under society are discussed in detail later in this paper. IOT faces issues that are mainly related to security of its “path of delivery” to the services to end user. This is described in this paper with the help of a layered model . There are many solutions to conquer these problems but in this paper we have proposed AI view to solve them out in a modern fashion. Basically AI activates the process in which the machine’s decision turn out to be like humans, which makes it useful in various domains of computation specially in security where it is to be decided which parameter of services have to be limited up and to what extent to secure the system with intelligence. Integration of IOT and AI seems to play a key role in security as it makes the infrastructure of IOT more robust . This paper provides an overall perspective to these techniques so as to give rise to an effective security system based on AI for IOT. This paper will emphasize on the security issues and techniques to enable resilience in IOT systems augmented by human- machine interactions through AI concepts deeply . The human in the loop of human-machine interaction has been recognized as a common point of weakness for the IOT systems security. AI, in particular, has been used to model human behaviour and hence it provides a huge domain for security considerations as well. Addressing the challenges related to IOT services requires collaboration between several different research and development techniques which are highlighted in this paper.
Cloud computing is not a new technology or technique, but a concept. The cloud represents the Internet. The Internet now provides substitutes for the software installed on computers and acts as an external hard disk space; the cloud in turn uses Internet services to perform various tasks and stores file data in a massive virtual space. According to the National Institute of Standards and Technology sp800-145 definition, cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, apps, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. Furthermore, the roles of cloud computing, cloud computing activities, and cloud computing components, as well as the relationships between them, were clearly described. Cloud computing is provided as a service to customers through the Internet.
The Particle Swarm Algorithm, an evolutionary intelligence algorithm, was first proposed by Kennedy and Eberhart in 1995 . Comparing to other algorithms, it has many advantages. For instance, it is not easy to indulge in local optimum but is easy to implement, has high robustness and low requirement for computer hardware.
In contrast, in the proposal of Bagchi , Fuzzy Logic was used to maintain streaming playback quality and achieve improvements in energy consumption. However, there are also proposals for the improvement of vehicle systems, such as the cases of –. In the first, the authors proposed improvements in the exchange of information between vehicles and servers in order to save energy. Meanwhile, in the second, the authors proposed a system to create applications through the use of voice. Meanwhile, others focus on energy saving in homes, as in , where they use Fuzzy Logic to make decisions based on outdoor temperature and humidity and the interior temperature of the house using two IoT networks and to know if it is necessary to turn on or to turn off the heating and/or air conditioning.
It is not new or unwarranted to have such confidentiality and security issues regarding a coming technology. We felt the same when we first made Internet-based email systems available or when we accessed our data in cloud-based infrastructures. It will be relevant how the industry which promotes IOT addresses such issues. If they can regularly demonstrate the safe use of IOT, this will really open up opportunities for a safer, healthier and more prosperous existence for us all .
aggregated values being passed to the central node and totals for all data being calculated. Warnings or alerts are issued (to users or applications) when the rule is triggered.
Aside from its ability to federate, ParStream fits this IOT environment well because of its speed and its small footprint at each node (the executable is about 50 megabytes). This means that the remote nodes need few resources (CPU, memory and storage) even when resilience is configured in. For example, a typical mobile device would be more than adequate as a server. This in turn provides a high level of flexibility on how an IOT system may be designed and deployed.
entities or virtual objects that will act in full interoperability
— Auto-organize on context, circumstances or environments — Ambient intelligence: host built upon Ubiquitous computing . — Share: to make their own « objectives » converging.
In recent years, as the important part of energy industry, “Smart Grid” has attracted great 217
218 attention of researchers. The smart grid represents a vision of future electricity grid, and it is radically 219 different from current electricity grids that have been deployed. It is an electricity grid that uses 220 analog or digital communication technology to collect information and take action for automatically 221 improving the efficiency, reliability, economic benefit and sustainability of the production and 222 distribution of electricity, . In the literature , Ramchurn et al. have presented: delivering 223 the decentralized, autonomous and intelligent system, smart grid, is a grand challenge for computer 224 science and artificial intelligence research. As a typical case that is tightly related to the CSI framework 225 in the smart grid, optimizing the electricity usage of electric vehicles is worth studying. For example, 226 with analyzing the spatio-temporal trajectory data from an intelligent transportation system, the 227 routing pattern of electric vehicles can be acquired, and then a national electric supply company 228 can make time- and area-divisiory electricity prices to control the usage of electricity and therefore to 229 improve the efficiency of smart grid.
The onboard sensing required for a proof of concept system must meet three requirements: it must supply sufficient information to enable autonomous flight; it must supply sufficient information to enable flocking; and (ideally) it must supply information to be used in the distributed computation task to be undertaken by the swarm as a whole. (The last consideration derives from the kind of task most likely to be undertaken by a deployed system, which will take advantage of the system's capacity for distributed sensing.) After reviewing a range of possibilities, we have reached the rather surprising conclusion that an onboard vision system offers the most immediate prospect of progress on all three fronts. We are fitting each helicopter with a downward-looking colour miniature video camera (a spycam); the deciding factor, which is intended purely as a temporary measure to speed up development, is that we are using wireless cameras which allow fast off-board vision processing, the results of which can then be passed back to the helicopters via the main communications network. The cameras, which weigh 6.7g but can easily be reduced to around 5.5g, can be adjusted to broadcast on different frequencies, and the available bandwidth allows up to four to operate in the same environment.
In One Network’s future vision for the “Internet of Things”, each item you may manufacture or sell is tagged. You know exactly how many you have, and where each one may be in the world at any given time. The same applies to your employees, trading partners, vendors, and peers. It’s time to deploy a platform that will create competitive differentiation for your company, your employees, your trading partners, and your shareholders.
Several factors are contributing to the rapid expansion of the IoT. Broadband Internet continues to be more widely available and the cost of being connected is decreasing. More devices are Wi-Fi enabled and have built-in sensors that are smaller, less costly, and more powerful. Technology costs, including the costs to analyze terabytes of data, are coming down, and smart phone penetration is skyrocketing. All of these things are creating a perfect storm for the IoT – cheap sensors, cheap connectivity, cheap storage, and cheap visualization.
the Information Age, underpin many of the world’s various privacy regimes. In particular, the FIPs’ principle of “notice and consent” has become the dominant means for authorizing data collection and processing. “Notice and consent” generally requires that the individual whose personal data is being processed has been informed of the reason, context, and purpose of the collection and processing (e.g., by posting of privacy policies) and has given consent (e.g., via click-through consent mechanisms). As the IoT develops and we move fully beyond the era of desktop computing, notice and consent may become unworkable, 15 and pressure is likely to mount to establish default rules and systems that minimize the costs and
Your game console talks to Netflix and other sites on the Internet. Your TV, DVR, smart-phone and tablet PC are all capable of accessing the Internet. You may very likely have a smart electric, gas or water meter that is connected to your house and able to talk to your heating and cooling systems. You can buy a refrigerator that can inventory itself and message the grocery store with an order for pickup or delivery. Your tires talk wirelessly to your car’s main computer system. Your car accesses the Internet for GPS and other services. In the factory, embedded diagnostics use ubiquitous networking, both wired and wireless, to message maintenance computers and generate work orders. You wear an RFID tag in the plant so that in case of emergency, safety personnel can find you. All of this happens without you pressing one key. All of this is happening now.
The ordinary PSO issues are those whose arrangements can be spoken to as an arrangement of focuses in a n- dimensional Cartesian direction framework, as it would be simple, in such issues, to decide the past and next positions for every point (i.e., molecule). Then again, PSO neglects to work if the issue representation does not offer an unmistakable approach to remarkably characterize what the following and past molecule positions are to look in the arrangement space . For instance, Li proposed a species-based PSO (SPSO), which partitions the swarm into numerous species (gatherings of particles having comparable attributes) and empowers them to simultaneously hunt down various optima .
In  the author has done a survey of vehicular traffic increase in India. In the survey he has noted down the day by day the population is increasing, the number of vehicular use is also increasing. Thus he has inferred that as the rate at which vehicular use is increasing is double the rate at which population is increasing. In  author proposed to address situations where optimu m optimizat ion strategies change with traffic conditions . It carried an important advantage that ma kes it robust under communication difficulties.In  authors ma ke the use of Sensor Networks along with Embedded Technology. The current scenario of the traffic is given and the solutions of the traffic proble ms are also mentioned. Auto routing feature is one of the solutions. In  authorspresented an idea that can be imple mented in traffic safety by the application of Robotics & Co mputer Vision through SwarmIntelligence.
behaviour of insects how they work collaboratively to accomplish a task. Same concept we are using here to make the logistic management in warehouse efficient. Methodology involved is communication between the subordinates, Communication between the swarm bots and the master station having a central control over each bot, task allocation to each individual and to find best possible way to complete the task. They organize themselves according to priority of task and each individual of swarm can take its own decision with respect to the current status of other members to complete the given task. This system consists of swarm of 'n' robots that works in a centralized way to perform an allotted task to the swarm. Hence each robot in our system will have a trolley based lift and place mechanism. The robot will lift item and take it to the required destination and also can arrange it in required manner keeping the space constrains into consideration. As there is no human intervention in our system eventually the labour effort gets minimized. The whole system leads to the minimization of error and hence increases the reliability.
In , the authors look at the interaction between IoT and the environment. This is caused by the environment creating events, rather than receiving input from the user. This architecture does allow discovery and search-ability of Things using an IoT Browser but does not have a way to rank them. The browser also allows users to view statistics and certain context of sensors and devices based on current events. The events themselves are viewable and dynamic in nature. This approach is formal, and does show some coding and algorithms. However, this research does not consider scalability.