Capacitated spatial clustering, a type of unsupervised machine learning method, is often used to tackle problems in compressing data, classification, logistic optimization and infrastructure optimization. Depending on the application at hand, a multitude of extensions to the clustering problem may be necessary. In this article, we propose a number of novel extensions to PACK, a recent capacitated partitional spatial clustering method which uses an optimization algorithm that is based on linear programming tasks. These extensions relate to the relocation and location preference of cluster centers, outliers, and non-spatial attributes, and they can be considered jointly. In the context of edge server placement, these improve the spatial location of servers while considering, for example, application placement on the servers in response to spatial application usage patterns. We demonstrate the usefulness of an extended version of PACK with an example with simulated data, as well as a real world example in edge server placement for a city region with various different setups. These setups are evaluated with summary statistics about spatial proximity and attribute similarity. As a result, the similarity of the clusters was improved by 53% at best while simultaneously the proximity degraded only by 18%. The extensions provide valuable means for including non-spatial information in the cluster analysis, and to attain better overall proximity and similarity.

The deployment of edge computing infrastructure requires a careful placement of the edge servers, with an aim to improve application latencies and reduce data transfer load in opportunistic Internet of Things systems. In the edge server placement, it is important to consider computing capacity, available deployment budget, and hardware requirements for the edge servers and the underlying backbone network topology. In this paper, we thoroughly survey the existing literature in edge server placement, identify gaps and present an extensive set of parameters to be considered. We then develop a novel algorithm, called PACK, for server placement as a capacitated location–allocation problem. PACK minimizes the distances between servers and their associated access points, while taking into account capacity constraints for load balancing and enabling workload sharing between servers. Moreover, PACK considers practical issues such as prioritized locations and reliability. We evaluate the algorithm in two distinct scenarios: one with high capacity servers for edge computing in general, and one with low capacity servers for Fog computing. Evaluations are performed with a data set collected in a real-world network, consisting of both dense and sparse deployments of access points across a city area. The resulting algorithm and related tools are publicly available as open source software.

In this paper, we describe how the microservices paradigm can be used to design and implement distributed edge services for Internet of Things applications. As a case study, traditionally monolithic user mobility analysis service is developed, with distributed and extendable microservices, for the standardized ETSI MEC system reference architecture. In each of the edge system three tiers, microservices implement the service logic with components for movement trace analysis, movement prediction and visualization of the results. The distributed service is implemented with Docker containers and evaluated on real-world settings with low capacity edge servers and real user mobility data. The results show that the edge promise of low latency can be met in such as implementation. The integration of a software development technology with a standardized edge system provides solid background for further development.

In this paper, the Multi-access Edge Computing (MEC) system architecture, as defined by the ETSI standards, is modeled as a multi-agent system. MEC system management services and application execution components are designed as software agents, facilitating distributed artificial intelligence capabilities in their operation and cooperation. Further, the integration of current agent technologies into the standardized MEC system is discussed. Lastly, a case study is presented on how to integrate an existing Internet of Things agent framework and agent-based edge application seamlessly to the MEC system.

Prevalent weather prediction methods are based on sensor data, collected by satellites and a sparse grid of stationary weather stations. Various initiatives improve the prediction models by including additional data sources such as mobile weather sensors, mobile phones, and wireless sensor networks (WSN) of, for example, smart homes. The underlying computing paradigm is predominantly centralized, with all data collected and analyzed in the cloud. This solution is not scalable. When the spatial and temporal density of weather sensor data grows, the required data transmission capacities and computational resources become unfeasible. We identify the challenges posed by spatial distribution of a weather prediction model, and suggest solutions for those challenges. We propose EDISON: an edge-native interpolation approach based on AI methods, distributed horizontally on edge servers. Finally, we demonstrate EDISON with a simple, simulated setup.

Edge computing in Internet of Things enhances application execution by retrieving cloud resources to the close proximity of resource-constrained end devices at the edge and by enabling task offloading from these devices to the edge. In this paper, edge computing platforms are extended into the data producing end devices, including wireless sensor network nodes and smartphones, with mobile agents. Mobile agents operate, as a multi-agent system, on the opportunistic network of heterogeneous end devices. The benefits include autonomous, asynchronous and adaptive execution and relocation of application-specific computational tasks, while taking into account the local resource availability. In addition to the vertical edge connectivity, mobile agents enable horizontal sharing of information between these devices. Use cases are presented where mobile agents address challenges in current edge computing platforms. An edge application is evaluated where mobile agents as a multi-agent system process sensor data in a heterogeneous set of end devices, control the operation of the devices and share their tasks results in the system. The mobile agents operate atop a REST-compliant software agent framework that relies on embedded Web services for interoperability. A real-world evaluation and large-scale simulations show that energy consumption is reduced significantly, up to 60%, in the edge application execution.

The infrastructure built in the City of Oulu provides rich information about the city environment and objects moving in it. We utilize this infrastructure in building an IoT system for data-intensive smart city services; by collecting data from real city environment and developing analysis methods for these data. We are building Smart City Traffic Pilot on top of the infrastructure to provide the functionality to collect the data and perform the analysis. Based on this experience, we present in this article requirements for data-intensive smart city services. Moreover, we describe four implemented use cases for utilizing rich data sources available in the smart city: situational picture, driving coach, real time reasoning, and mobile code. A lively collaboration between a large number of different actors is essential in realizing these use cases. Finally, we discuss how the use cases fulfill the requirements and the lessons we have learnt.

We present how NFC transponders can be used for energy efficient programming of closed-loop sensor networks, to update or augment the existing functionality. Use cases include road tunnel inspection, water pipeline monitoring and maintaining safety information on behalf of mine workers. We utilize opportunistic movement of the human operator, the flow of fluid in a pipeline or material in mines, to move the NFC transponder in the system effortlessly and without external network connectivity. Transponders contain mobile agents in their memory, which are injected into the system when transponder comes to the proximity of a node with NFC reader component. Then mobile agents autonomously operate their tasks, i.e. collect and process sensor data in the devices, detect events from data, control physical components and report their results. Mobile agents can adapt to the operational conditions of the system and physical environment, e.g. to save energy or operate in isolated network segments in fault situations. Real-world evaluation shows that this method is energy efficient in comparison with communications atop similar wireless sensor network.

We introduce a novel interaction method which integrates humans into Internet of Things and advances from the existing client-server paradigm. We take advantage of the shared physical space to facilitate decentralized and seamless human-to-thing, human-to-human and thing-to-thing interactions. We build these interactions upon two core technologies: mobile agents and NFC. Mobile agents realize autonomous execution of user-specific interaction tasks among things while facilitating cooperation and interoperability. Humans initiate and control interactions through physical actions that trigger communications, e.g. mobile agent migration, between NFC devices even over disparate systems. Human social relationships are utilized to disseminate tasks further in the IoT system. We discuss the benefits of this method in comparison with the common smartphone-based control of smart spaces. Real-world evaluation shows that this interaction method is feasible for resource-constrained embedded IoT devices.

In this paper, we propose a novel architecture, called ADHT, that allows the participation of wireless and constrained-capacity M2M devices in a P2P overlay without compromising their battery life. The approach makes P2P-based networking feasible in scenarios where dedicated super-peer nodes are not available or not feasible to use, e.g. when all the participating nodes in a subnet are constrained devices. The concept is based on sharing the peer responsibilities among the nodes within the same subnet using mobile agent-based virtual peers. The mobile agent-based approach enables on-the-fly configurations to further reduce energy consumption in the subnet nodes during runtime. The presented mobile agent composition is both programming language- and platform-independent. Therefore it inherently enables heterogeneous platforms, from the low-power resource-constrained embedded networked devices to the cloud platforms, to operate as the P2P peer. We analyze the power consumption characteristics of the mobile agent-based approach by comparing it with alternative architectures with a numerical analysis.

In this paper, we propose a novel vibration-based communication system for Bluetooth-equipped smartphones. Smartphones are commonly utilized through the display; however, sometimes users would like to exchange pieces of information with nearby peers, without shifting their focus from current task. Additionally, this enables communication in cases without visual or sound contact. For this purpose, we developed a novel application called Hand-to-Hand on Bluetooth Communication (H2BCom). We use the common gestures of tapping and touching the smartphone display for sending a Morse coded message over a Bluetooth channel. On the receiving side, the Morse coded message is presented as vibration of the smartphone. We show the design, implementation and evaluation of H2BCom on Android phones. In our user study, we found that using the Morse code was difficult for beginners, but the users'skill improved after taking a tutorial of the Morse code.

We study an instant messaging system between two smartphones, without the need of visually following the screen of the smartphones. In the sending side, a Morse-code-type touchscreen input is used to encode the message and a vibration in the receiver side to perceive the message.

The Internet of Things presents the user with a novel means of communicating with the Web world through ubiquitous object-enabled networks. Cloud Computing enables a convenient, on demand and scalable network access to a shared pool of configurable computing resources. This paper mainly focuses on a common approach to integrate the Internet of Things (IoT) and Cloud Computing under the name of CloudThings architecture. We review the state of the art for integrating Cloud Computing and the Internet of Things. We examine an IoT-enabled smart home scenario to analyze the IoT application requirements. We also propose the CloudThings architecture, a Cloud-based Internet of Things platform which accommodates CloudThings IaaS, PaaS, and SaaS for accelerating IoT application, development, and management. Moreover, we present our progress in developing the CloudThings architecture, followed by a conclusion.

One challenging problem in Machine-to-Machine (M2M) applications is to efficiently discover the resources provided by a huge number of heterogeneous devices. This paper proposes distributed resource discovery architecture (DRD) for M2M applications. The DRD supports heterogeneous devices in resource description registration and discovery of resource value. It achieves interoperability among heterogeneous devices in disparate networks and enables resource access from the Internet. In the DRD, a resource registration component is designed for storing resource descriptions, a resource discovery component is designed to retrieve resource values on behalf of clients after getting address information by looking up resource descriptions. The DRD utilizes a peer-to-peer overlay to distribute workload and avoid single point of failure. A real-world prototype is implemented and verified with a simple demo. Preliminary evaluation on response time of resource discovery is provided.

We demonstrate interoperable mobile agents for language- and platform-independent wireless sensor network programming, with low-power resource-constrained embedded devices as static nodes and Android-based smartphones as mobile nodes, over disparate networks: 6LoWPAN and Wi-Fi. Representational state transfer architectural principles are applied in agent composition, control and migration and exposing the system resources to the Web: the devices, agents, sensor data, tasks and data processing results. The adaptable agent composition includes the task code in any programming language, the agent migrates according to a resource list and the state, i.e. the intermediate task result, represents the agent as a system resource. Mobile agents are then utilized in two tasks: first to collect light sensor data cooperatively in location, saving the mobile node battery whenever possible, and secondly the magnetometer sensor data with the scanned Wi-Fi access points' signal strength is used to detect groups of mobile nodes moving in the same direction in real-time.

We demonstrate interoperable mobile agents to integrate Internet of Things and wireless sensor networks with resource-constrained low-power embedded networked devices. We introduce adaptable composition for the mobile agent, complying with the Representational State Transfer principles, which are then used for agent migration, controlling the agent and exposing the data, system resources, tasks and services, to the Web. We gather requirements for the system and heterogeneous networked devices and present an application programming interface to enable mobile agents in these systems. The agents are demonstrated in a real-world prototype with smartphones and embedded networked devices, where we utilize a proxy component to expose system resources to the Web for human-machine interactions. Spanning over disparate networks and protocols, the proxy translates messages including the agent composition, between HTTP and Constrained Application Protocol. Lastly, we suggest an evaluation method for the agent communication and migration costs, considering the different types of system resources and utilization.

Machine-to-Machine (M2M) communications emerge to achieve ubiquitous communications among all the networked devices. One challenging problem in M2M applications is to discover the resources provided by the devices efficiently. This challenge arises from two perspectives: (1) a large number of heterogeneous devices co-exist and most of them are constrained devices (e.g., limited processing capability), (2) different communication protocols are utilized to get access to different types of resources provided by the devices (e.g., humidity information provided by a sensor). This paper proposes distributed resource directory architecture for M2M applications, referred to as DRD4M. The DRD4M supports heterogeneous devices using HTTP and CoAP protocols for resource registration and lookup. This enables the interoperability among heterogeneous devices and resource access to constrained devices from disparate network including the Internet. The DRD4M introduces two components: a resource registration component for registering resources and a resource lookup component with caching functionality for handling resource lookup with filtering. A peer-to-peer (P2P) overlay is introduced in DRD4M to connect resource peers to avoid single point of failure. A real-world prototype is implemented and is verified with a demo application. Preliminary performance evaluation in terms of response time of resource lookup is provided.