Many-core smartphones and other mobile devices can run more complex tasks than they used to do, but the main issue of mobile devices remains: resources are limited and phones, for example, cannot use a massive amount of energy to solve high computation tasks. Offloading tasks to remote servers can be beneficial if the execution time is reduced and battery energy saved. Up to now, offloading strategies did not consider the individual device use of cores (e.g., average load) and their unused potential for performance. In this paper, experimental results of three applications executed on five phones are presented and analysed to determine the specific device characteristics that can inform the decision to run an application on the mobile device or remotely by offloading.

Cars, environment sensors, or home-deployed appliances can be connected to the Internet or are accessible via the Internet which becomes the Internet of Things (IoT). Implementing an IoT network presents challenges in terms of security and privacy, as well as limitations related to communications and management. Cloud technology is considered as an effective solution for managing IoT systems by moving all complex processes to the cloud and making them accessible to users. Adopting cloud technology can improve the reliability and scalability of IoT systems. However, introducing the cloud paradigm is not a straightforward task. Edge computing was proposed to avoid some of cloud limitations such as latency and security concerns, but it has its own constraints in terms of storage, computing and mobility. This paper discusses the possibility of managing an IoT system by a cloud-edge system. It introduces the cloud and fog computing technologies for addressing IoT limitations. In addition, critical metrics are identified for the purpose of assessing the advantages and difficulties of these technologies. Finally, this paper is proposing a framework able to handle the mentioned limitations, balance the services among the cloud and the edge servers, and boost the overall performance.

Cloud computing has attracted researchers and organizations in the last decade due to the powerful and elastic computation capabilities provided on-demand to users. Mobile cloud computing is a way of enriching users of mobile devices with the computational resources and services of clouds. The recent developments of mobile devices and their sensors introduced the crowd sensing paradigm that uses powerful cloud computing to analyze, manage and store data produced by mobile sensors. However, crowd sensing in the context of using the cloud is posing new challenges that increase the importance of adopting new approaches to overcome them. This chapter introduces a middleware solution that provides a set of services for cost-effective management of crowd sensing data.

Crowdsensing for smart city applications utilizes cloud computing to send to, store and publish data. The amount of data stored in the cloud is large and this introduces cost and storage challenges, since the cloud has a pay-as-you-use model. In this paper, we propose, as part of the data management architecture, a partitioning method that helps the user examine the flexibility of data in order to decide which of the reduction services can be applied on data. The results of the proposed partitioning method and its associated services show a large amount of storage savings in the cloud, from 24% to more than 30% in our analysis.

Drones are now being widely used in different civilian applications, such as delivering shipments to consumers, as proposed by Amazon, and providing internet access to users, as offered by Facebook and Google. Drones can also contribute in emergencies by helping to find victims in places that are not reachable by rescuers, as well as assisting emergency centers to better manage a reported emergency. However, drones have a short flying time due to limited battery life. Therefore, a reliable strategy that minimizes energy consumption and uses collaborative working is required in order to increase drones' ability to operate for longer periods in emergency situations. This paper presents an adaptive task scheduler that allows tasks to be shared/transferred among the drones in a cloud of drones, in order to extend the operational time, achieve faster task execution and, at the same time, reduce the usage of each drone's resources. The ultimate result is an extension of battery life that leads to longer flying and service time for individual drones.

Many emergency cases require the swiftest possible response from an appropriate medical service if they are not to become life-threatening. In the medical emergency field, response times to emergency cases are a major concern and gain a high degree of attention. Many of the systems proposed in the literature are either intended to replace the existing emergency system with a fully automated one, or build on unreliable or less efficient frameworks that are based on some sort of social media application, such as redirecting emergency requests to Facebook friends. We have designed a mobile cloud service that works side by side with the existing emergency system and is aimed at reducing the time spent waiting for emergency help to arrive, as well as making the best use of medical professionals who may be located in close proximity to the medical case. The experimental results show that the amount of time needed to find a medical professional and establish communication was between 4 and 25 seconds, depending on the communication method used. This result means that no extra time is added to the total medical response time and could enhance the chance of a better outcome.

A wide range of crowdsensing smart city applications utilize cloud computing to send, store and publish data. The amount of data sent to the cloud is relatively large and this introduces bandwidth, network and storage challenges. In this paper, we offer a smart city architecture that includes a data reduction service located in the proximity of the crowd. In this data reduction service, we propose a lossless compression step for single-precision floating-point data received from the crowd, such as accelerometer readings and GPS coordinates. Floating-point compression has proved to reduce the cost of transmitting a large amount of data. Our compression method was evaluated and achieved a good compression ratio.

In a traditional mobile ad-hoc network (MANET), if two nodes are engaged in a session and one of them departs suddenly, their communication is aborted. The session is not active any more, work is lost and, consequently, the energy of the batteries has been wasted. This paper proposes a model that uses a cloud service to register, save, pause and resume sessions between MANET member nodes so that both work in progress and energy are saved. A checkpoint technique is introduced to capture the progress of a session and allow it to be resumed. This is an additional service to our cloud management of the MANET. The model proposed in this paper was tested on Android-based devices and an Amazon cloud instance. Experimental results show that the model is feasible, robust, saves time and, more importantly, energy if session breaks occur frequently.

This paper proposes the concept of a multiservice cloud of drones that provides services required by people in crowded open air places, such as Internet connectivity or access to emergency services. The cloud of drones has the benefit of resource replication and tackling scalability. At the same time, their number and operation can be controlled by the cloud to enhance reliability and provide greater performance. This paper presents an initial design of a model called a cloud of drones that aims to serve users in emergencies, as well as routing requests between drones. Some significant aspects related to the use of drones, such as battery usage and the flying management of drones, are discussed in the paper. The first of a set of services proposed in the paper was tested and evaluated. The experimental results indicated that accessing the Internet through a Raspberry Pi that is attached to a flying drone is possible and will not impact the user experience in terms of delay and battery usage. Furthermore, the results show that accessing the Internet through a drone can not only be similar to using a mobile device cellular network, but also costs less in terms of time and power.

SummaryEmergency events require a fast response and decisions based on first‐hand information. This paper presents a mobile cloud service that makes the best use of social media applications, such as Twitter, in emergency and risk management. Here, risk and emergency teams can receive data that can inform their decisions in a matter of seconds when an emergency has affected areas under their management. The proposed service allows users to provide on‐the‐ground information regarding such an event, as well as early notification to people who are in the vicinity of an emergency situation. The service matches users' requests to a set of predefined labels that will help rescuers to understand the situation more clearly. The service was implemented and tested with Android devices and a cloud‐computing instance hosted on an Amazon platform. A cloud‐based tool is also provided for risk and emergency management teams to interact with users' requests. The experimental results show that the proposed mobile cloud service enhanced the early detection of emergencies. This paper also provides a review of research papers on the use of social media in risk and emergency management and the benefits of integrating social media with mobile cloud computing to provide a better healthcare service in emergencies.

Existing research on implementing the mobile cloud computing paradigm is typically based on offloading demanding computation from mobile devices to cloud-based servers. A continuous, high quality connection to the cloud infrastructure is normally required, with frequent high-volume data transfer, which can have a detrimental impact on the user experience of the application or service. In this paper, the Context Aware Mobile Cloud Services (CAMCS) middleware is presented as a solution that can deliver an integrated user experience of the mobile cloud to users. Such an experience respects the resource limitations of the mobile device. This is achieved by the Cloud Personal Assistant (CPA), the user's trusted representative within CAMCS, which completes user-assigned tasks using existing cloud-based services, with an asynchronous, disconnected approach. A thin client mobile application, the CAMCS Client, allows the mobile user to send descriptions of tasks to his/her CPA, and view task results saved at the CPA, when convenient. The design and implementation of the middleware is presented, along with results of experimental evaluation on Amazon EC2. The resource usage of the CAMCS client is also studied. Analysis shows that CAMCS delivers an integrated user experience of mobile cloud applications and services.

Mobile phones and their sensing capabilities might be the main source of data for smart city applications. However, before sending a large amount of data to the cloud, it is essential to make sure that the data collected are useful. In this paper, we present a cloud architecture for smart city applications that provides, as a main service, a scheduler for controlling the transmission of data to the cloud. This scheduler will run as close to the crowd data sources as possible (i.e., public local servers). Data with a high priority value are sent to the cloud first. We designed an application for the Android platform to carry out experiments with the scheduling process. The simulation results are included in this paper

All emergency events require fast response and decisions based on first-hand information. This paper presents a mobile cloud service that makes the best use of social media applications, such as Twitter, in emergency and risk management. Risk and emergency teams can receive in matter of seconds data that can inform their decisions when an emergency has affected areas under their management. The proposed system allows users to provide on-the-ground information regarding such an event, as well as early notification to people who are in the vicinity of the location of an emergency situation. The system matches users' requests to a set of pre-defined labels that will help rescuers to understand the situation more clearly. The service is implemented and tested with Android devices and a cloud-computing instance hosted on an Amazon platform. A cloud-based tool is also provided for risk and emergency management teams to interact with users' requests. The experimental results show that the system enhances the early detection of emergencies.

The Internet of Things (IoT) aims at connecting all communicating "things" that surround us. The acts of addressing billions of heterogeneous devices, interconnecting them and running cost-effective applications on top of them require new architectures, services and protocols. This position paper presents our thoughts on IoT and discusses a distributed cloud model for supporting IoT and its applications.

This paper presents a novel system that allows mobile users to have access to healthcare services in the case of emergencies. The cloud hosts a directory service containing a list of pre-registered professionals, such as doctors and nurses. Once a registered user sends a help message to the first responder cloud service, a look-up operation is started to search for the most suitable medical professional, taking into account the location of both users. Then, the cloud service will establish/monitor a communication link between the person requesting aid and the selected professional whereby these two users can start a chat session to exchange messages. For the purpose of enhancing system availability and dealing with connection issues, SMS is provided as an alternative communication method. The paper includes screenshots of the application and experimental data regarding setting up the connection between someone in an emergency situation and the first responder. The experimental results show that the amount of time needed to set up communication between users and medical professionals is around five seconds, which means no extra time will be added to the total medical response time. This will increase the chance of a better outcome.

The mobile cloud computing paradigm can offer relevant and useful services to the users of smart mobile devices. Such public services already exist on the web and in cloud deployments, by implementing common web service standards. However, these services are described by mark-up languages, such as XML, that cannot be comprehended by non-specialists. Furthermore, the lack of common interfaces for related services makes discovery and consumption difficult for both users and software. The problem of service description, discovery, and consumption for the mobile cloud must be addressed to allow users to benefit from these services on mobile devices. This paper introduces our work on a mobile cloud service discovery solution, which is utilised by our mobile cloud middleware, Context Aware Mobile Cloud Services (CAMCS). The aim of our approach is to remove complex mark-up languages from the description and discovery process. By means of the Cloud Personal Assistant (CPA) assigned to each user of CAMCS, relevant mobile cloud services can be discovered and consumed easily by the end user from the mobile device. We present the discovery process, the architecture of our own service registry, and service description structure. CAMCS allows services to be used from the mobile device through a user's CPA, by means of user defined tasks. We present the task model of the CPA enabled by our solution, including automatic tasks, which can perform work for the user without an explicit request.

The mobile cloud computing model promises to address the resource limitations of mobile devices, but effectively implementing this model is difficult. Previous work on mobile cloud computing has required the user to have a continuous, high-quality connection to the cloud infrastructure. This is undesirable and possibly infeasible, as the energy required on the mobile device to maintain a connection, and transfer sizeable amounts of data is large; the bandwidth tends to be quite variable, and low on cellular networks. The cloud deployment itself needs to efficiently allocate scalable resources to the user as well. In this paper, we formulate the best practices for efficiently managing the resources required for the mobile cloud model, namely energy, bandwidth and cloud computing resources. These practices can be realised with our mobile cloud middleware project, featuring the Cloud Personal Assistant (CPA). We compare this with the other approaches in the area, to highlight the importance of minimising the usage of these resources, and therefore ensure successful adoption of the model by end users. Based on results from experiments performed with mobile devices, we develop a no-overhead decision model for task and data offloading to the CPA of a user, which provides efficient management of mobile cloud resources.

This paper describes implementations of two mobile cloud applications, file synchronisation and intensive data processing, using the Context Aware Mobile Cloud Services middleware, and the Cloud Personal Assistant. Both are part of the same mobile cloud project, actively developed and currently at the second version. We describe recent changes to the middleware, along with our experimental results of the two application models. We discuss challenges faced during the development of the middleware and their implications. The paper includes performance analysis of the CPA support for the two applications in respect to existing solutions.

Mobile devices and their sensors facilitate the development of a large range of environment-sensing applications and systems. Crowd sensing is used to feed smart city applications with anonymous but still relevant data. The quality and success of smart city applications depend on several aspects of user involvement, such as data trust and information about data origin. However, with the anonymity and openness of crowd sensing, smart city applications are exposed to untrustworthy and malicious data that can lead to poor decisions. In this paper, we propose a cloud architecture for smart city applications that includes, as a core service, a reputation system for evaluating the trustworthiness of crowd sensing data. This service will run locally, as close to the crowd as possible, for example, on wireless local area network (WLAN) access points (AP). Additionally, data stored in the cloud is traceable by its origin information.

One benefit of creating a mobile ad-hoc network (MANET) is that members with no direct access to the Internet can avail themselves of peers' connections to the Internet and use public cloud services. Guaranteed access to cloud services is important in the case of emergency or disaster recovery situations. As managing a MANET is a difficult task, this paper introduces a new model for MANET management that uses cloud services. While preserving the ad-hoc nature of a mobile network, its management by the cloud provides reliability and robustness. The design and implementation of the system are presented, as well as a method for allowing ad-hoc network creation on Android devices. To evaluate the feasibility of the proposed prototype, an Android application was designed and tested. It offers communication with the cloud and supports file sharing among peers.